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Lei Wang

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Computer Vision cond-mat.str-el Machine Learning cond-mat.mes-hall hep-ph cond-mat.mtrl-sci cond-mat.supr-con quant-ph Distributed, Parallel, and Cluster Computing cond-mat.quant-gas cond-mat.stat-mech Systems and Control eess.SY Performance hep-ex Information Theory math.IT math.OC Data Structures and Algorithms physics.comp-ph astro-ph.CO Artificial Intelligence eess.IV eess.SP Databases physics.optics cond-mat.dis-nn Cryptography and Security Social and Information Networks physics.ins-det astro-ph.GA cond-mat.soft Emerging Technologies math.CV math.NA nlin.PS physics.app-ph physics.flu-dyn astro-ph.HE Computation and Language hep-lat math.AP math.CO math.DG math.GR Neural and Evolutionary Computing nlin.SI nucl-ex nucl-th physics.med-ph physics.soc-ph Robotics Biological Physics Computation cond-mat.other eess.AS Graphics hep-th math-ph math.MP math.PR Multimedia nlin.CD Numerical Analysis physics.chem-ph physics.class-ph physics.gen-ph Populations and Evolution Sound

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preprint2026arXiv

Delta Score Matters! Spatial Adaptive Multi Guidance in Diffusion Models

Diffusion models have achieved remarkable success in synthesizing complex static and temporal visuals, a breakthrough largely driven by Classifier-Free Guidance (CFG). However, despite its pivotal role in aligning generated content with textual prompts, standard CFG relies on a globally uniform scalar. This homogeneous amplification traps models in a well-documented "detail-artifact dilemma": low guidance scales fail to inject intricate semantics, while high scales inevitably cause structural degradation, color over-saturation, and temporal inconsistencies in videos. In this paper, we expose the physical root of this flaw through the lens of differential geometry. By analyzing Tweedie's Formula, we reveal that CFG intrinsically performs a tangential linear extrapolation. Because the natural data manifold is highly curved, this uniform linear step introduces a severe orthogonal deviation. To keep the generation trajectory safely bounded, we formulate a theoretical upper bound for spatial and adaptive guidance. Based on these geometric insights, we propose Spatial Adaptive Multi Guidance (SAMG), a training-free and virtually zero-cost sampling algorithm. SAMG dynamically computes point-wise conditional guidance energy, applying a conservative minimum scale to high-energy boundary regions to preserve delicate micro-textures, while deploying an aggressive maximum scale in low-energy regions to maximize semantic injection. Extensive experiments across diverse image (SD 1.5, SDXL, SD3.5 Medium) and video (CogVideoX, ModelScope) architectures demonstrate that SAMG effectively resolves the detail-artifact dilemma, achieving superior semantic alignment, structural integrity, and temporal smoothness without any computational overhead.

preprint2026arXiv

VulTriage: Triple-Path Context Augmentation for LLM-Based Vulnerability Detection

Automated vulnerability detection is a fundamental task in software security, yet existing learning-based methods still struggle to capture the structural dependencies, domain-specific vulnerability knowledge, and complex program semantics required for accurate detection. Recent Large Language Models (LLMs) have shown strong code understanding ability, but directly prompting them with raw source code often leads to missed vulnerabilities or false alarms, especially when vulnerable and benign functions differ only in subtle semantic details. To address this, we propose VulTriage, a triple-path context augmentation framework for LLM-based vulnerability detection. VulTriage enhances the LLM input through three complementary paths: a Control Path that extracts and verbalizes AST, CFG, and DFG information to expose control and data dependencies; a Knowledge Path that retrieves relevant CWE-derived vulnerability patterns and examples through hybrid dense--sparse retrieval; and a Semantic Path that summarizes the functional behavior of the code before the final judgment. These contexts are integrated into a unified instruction to guide the LLM toward more reliable vulnerability reasoning. Experiments on the PrimeVul pair test set show that VulTriage achieves state-of-the-art performance, outperforming existing deep learning and LLM-based baselines on key pair-wise and classification metrics. Further ablation studies verify the effectiveness of each path, and additional experiments on the Kotlin dataset demonstrate the generalization ability of VulTriage under low-resource and class-imbalanced settings. Our code is available at https://github.com/vinsontang1/VulTriage

preprint2025arXiv

Modern applications of machine learning in quantum sciences

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.

preprint2024arXiv

An Inexact Preconditioned Zeroth-order Proximal Method for Composite Optimization

In this paper, we consider the composite optimization problem, where the objective function integrates a continuously differentiable loss function with a nonsmooth regularization term. Moreover, only the function values for the differentiable part of the objective function are available. To efficiently solve this composite optimization problem, we propose a preconditioned zeroth-order proximal gradient method in which the gradients and preconditioners are estimated by finite-difference schemes based on the function values at the same trial points. We establish the global convergence and worst-case complexity for our proposed method. Numerical experiments exhibit the superiority of our developed method.

preprint2024arXiv

An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification

Deep Learning has advanced Automatic Speaker Verification (ASV) in the past few years. Although it is known that deep learning-based ASV systems are vulnerable to adversarial examples in digital access, there are few studies on adversarial attacks in the context of physical access, where a replay process (i.e., over the air) is involved. An over-the-air attack involves a loudspeaker, a microphone, and a replaying environment that impacts the movement of the sound wave. Our initial experiment confirms that the replay process impacts the effectiveness of the over-the-air attack performance. This study performs an initial investigation towards utilizing a neural replay simulator to improve over-the-air adversarial attack robustness. This is achieved by using a neural waveform synthesizer to simulate the replay process when estimating the adversarial perturbations. Experiments conducted on the ASVspoof2019 dataset confirm that the neural replay simulator can considerably increase the success rates of over-the-air adversarial attacks. This raises the concern for adversarial attacks on speaker verification in physical access applications.

preprint2024arXiv

Broadband miniaturized spectrometers with a van der Waals tunnel diode

Miniaturized spectrometers are of immense interest for various on-chip and implantable photonic and optoelectronic applications. State-of-the-art conventional spectrometer designs rely heavily on bulky dispersive components (such as gratings, photodetector arrays, and interferometric optics) to capture different input spectral components that increase their integration complexity. Here, we report a high-performance broadband spectrometer based on a simple and compact van der Waals heterostructure diode, leveraging a careful selection of active van der Waals materials -- molybdenum disulfide and black phosphorus, their electrically tunable photoresponse, and advanced computational algorithms for spectral reconstruction. We achieve remarkably high peak wavelength accuracy of ~2 nanometers, and broad operation bandwidth spanning from ~500 to 1600 nanometers in a device with a ~30x20 μm2 footprint. This diode-based spectrometer scheme with broadband operation offers an attractive pathway for various applications, such as sensing, surveillance and spectral imaging.

preprint2023arXiv

A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provides fast convergence speed as well as satisfactory accuracy of deducting optimized reallocation of the thruster forces to compensate for the power loss in different fault cases. Simulations with or without environmental perturbations under different fault cases and comparisons to other traditional FTCs are presented, thus verifying the effectiveness and robustness of the proposed GOA-based fault-tolerant trajectory tracking design.

preprint2023arXiv

Effect of temperature-dependent thermophysical properties on turbulent forced convection under constant heat flux boundary condition

In this study, we performed highly resolved large-eddy simulations (LES) to investigate the influence of variable properties on the forced turbulent convection in a channel. The constant heat flux boundary condition permits wall temperature fluctuations and thus induces variations of fluid properties. Since the effect of viscosity on the flow exhibits $Re_τ^{-1}$ scaling, we only considered $Re_τ= 180$ in the present study. Compared to the flow with constant properties, results indicate that the variable properties have trivial effects on the mean velocity and temperature profiles, Reynolds shear stress, wall-normal heat flux, as well as the small-scale turbulence characteristics. However, we also observed that the turbulence intensities, low-speed streaks, burst motions, and budgets for temperature variance and wall-normal heat flux are modified by the variable properties in a perceptible way. In addition, we showed that the classic wall scaling is a good choice for flow with small and moderate variations of fluid properties.

preprint2023arXiv

Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers

Recently, decentralized optimization over the Stiefel manifold has attacked tremendous attentions due to its wide range of applications in various fields. Existing methods rely on the gradients to update variables, which are not applicable to the objective functions with non-smooth regularizers, such as sparse PCA. In this paper, to the best of our knowledge, we propose the first decentralized algorithm for non-smooth optimization over Stiefel manifolds. Our algorithm approximates the non-smooth part of objective function by its Moreau envelope, and then existing algorithms for smooth optimization can be deployed. We establish the convergence guarantee with the iteration complexity of $\mathcal{O} (ε^{-4})$. Numerical experiments conducted under the decentralized setting demonstrate the effectiveness and efficiency of our algorithm.

preprint2022arXiv

A Communication-Efficient and Privacy-Aware Distributed Algorithm for Sparse PCA

Sparse principal component analysis (PCA) improves interpretability of the classic PCA by introducing sparsity into the dimension-reduction process. Optimization models for sparse PCA, however, are generally non-convex, non-smooth and more difficult to solve, especially on large-scale datasets requiring distributed computation over a wide network. In this paper, we develop a distributed and centralized algorithm called DSSAL1 for sparse PCA that aims to achieve low communication overheads by adapting a newly proposed subspace-splitting strategy to accelerate convergence. Theoretically, convergence to stationary points is established for DSSAL1. Extensive numerical results show that DSSAL1 requires far fewer rounds of communication than state-of-the-art peer methods. In addition, we make the case that since messages exchanged in DSSAL1 are well-masked, the possibility of private-data leakage in DSSAL1 is much lower than in some other distributed algorithms.

preprint2022arXiv

A joint explanation of W-mass and muon g-2 in 2HDM

Since both $W$-mass and muon $g-2$ can be affected by the mass splittings among extra Higgs bosons $(H,~A,~H^\pm)$ in a 2HDM, we take a model with $μ$-$τ$ LFV interactions to examine the two anomalies reported respectively by CDF II and FNAL. We obtain the following observations: (i) Combined with theoretical constraints, the CDF $W$-mass measurement disfavors $H$ or $A$ to degenerate in mass with $H^\pm$, but allows $H$ and $A$ to degenerate. The mass splitting between $H^\pm$ and $H/A$ is required to be larger than 10 GeV. The $m_{H^\pm}$ and $m_{A}$ are favored to be smaller than 650 GeV for $m_H<120$ GeV, and allowed to have more large values with increasing of $m_H$. (ii) After imposing other relevant experimental constraints, there are parameter spaces that simultaneously satisfy (at $2σ$ level) the CDF $W$-mass, the FNAL muon $g-2$ and the data of lepton universality in $τ$ decays, but the mass splittings among extra Higgs bosons are strictly constrained.

preprint2022arXiv

A Medical Semantic-Assisted Transformer for Radiographic Report Generation

Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images. Despite the recent progress in this field, there are still many challenges at least in the following aspects. First, radiographic images are very similar to each other, and thus it is difficult to capture the fine-grained visual differences using CNN as the visual feature extractor like many existing methods. Further, semantic information has been widely applied to boost the performance of generation tasks (e.g. image captioning), but existing methods often fail to provide effective medical semantic features. Toward solving those problems, in this paper, we propose a memory-augmented sparse attention block utilizing bilinear pooling to capture the higher-order interactions between the input fine-grained image features while producing sparse attention. Moreover, we introduce a novel Medical Concepts Generation Network (MCGN) to predict fine-grained semantic concepts and incorporate them into the report generation process as guidance. Our proposed method shows promising performance on the recently released largest benchmark MIMIC-CXR. It outperforms multiple state-of-the-art methods in image captioning and medical report generation.

preprint2022arXiv

A New High Energy Efficiency Scheme Based on Two-Dimension Resource Blocks in Wireless Communication Systems

Energy efficiency (EE) plays a key role in future wireless communication network and it is easily to achieve high EE performance in low SNR regime. In this paper, a new high EE scheme is proposed for a MIMO wireless communication system working in the low SNR regime by using two dimension resource allocation. First, we define the high EE area based on the relationship between the transmission power and the SNR. To meet the constraint of the high EE area, both frequency and space dimension are needed. Besides analysing them separately, we decided to consider frequency and space dimensions as a unit and proposed a two-dimension scheme. Furthermore, considering communication in the high EE area may cause decline of the communication quality, we add quality-of-service(QoS) constraint into the consideration and derive the corresponding EE performance based on the effective capacity. We also derive an approximate expression to simplify the complex EE performance. Finally, our numerical results demonstrate the effectiveness of the proposed scheme.

preprint2022arXiv

A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification

Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, termed MixNorm, which consists of domain-aware mix-normalization (DMN) and domain-ware center regularization (DCR). Different from the conventional data augmentation, the proposed domain-aware mix-normalization to enhance the diversity of features during training from the normalization view of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain. To better learn the domain-invariant model, we further develop the domain-aware center regularization to better map the produced diverse features into the same space. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.

preprint2022arXiv

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints

Decentralized optimization with orthogonality constraints is found widely in scientific computing and data science. Since the orthogonality constraints are nonconvex, it is quite challenging to design efficient algorithms. Existing approaches leverage the geometric tools from Riemannian optimization to solve this problem at the cost of high sample and communication complexities. To relieve this difficulty, based on two novel techniques that can waive the orthogonality constraints, we propose a variance-reduced stochastic gradient tracking (VRSGT) algorithm with the convergence rate of $O(1 / k)$ to a stationary point. To the best of our knowledge, VRSGT is the first algorithm for decentralized optimization with orthogonality constraints that reduces both sampling and communication complexities simultaneously. In the numerical experiments, VRSGT has a promising performance in a real-world autonomous driving application.

preprint2022arXiv

Ab-initio study of interacting fermions at finite temperature with neural canonical transformation

We present a variational density matrix approach to the thermal properties of interacting fermions in the continuum. The variational density matrix is parametrized by a permutation equivariant many-body unitary transformation together with a discrete probabilistic model. The unitary transformation is implemented as a quantum counterpart of neural canonical transformation, which incorporates correlation effects via a flow of fermion coordinates. As the first application, we study electrons in a two-dimensional quantum dot with an interaction-induced crossover from Fermi liquid to Wigner molecule. The present approach provides accurate results in the low-temperature regime, where conventional quantum Monte Carlo methods face severe difficulties due to the fermion sign problem. The approach is general and flexible for further extensions, thus holds the promise to deliver new physical results on strongly correlated fermions in the context of ultracold quantum gases, condensed matter, and warm dense matter physics.

preprint2022arXiv

Active and Passive Hybrid Detection Method for Power CPS False Data Injection Attacks with Improved AKF and GRU-CNN

Influenced by deep penetration of the new generation of information technology, power systems have gradually evolved into highly coupled cyber-physical systems (CPS). Among many possible power CPS network attacks, a false data injection attacks (FDIAs) is the most serious. Taking account of the fact that the existing knowledge-driven detection process for FDIAs has been in a passive detection state for a long time and ignores the advantages of data-driven active capture of features, an active and passive hybrid detection method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, we analyze the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed. The state estimation algorithm based on non-negative positive-definite adaptive Kalman filter (NDAKF) is improved, and a passive detection method of FDIAs is constructed, with similarity Euclidean distance detection and residual detection at its core. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature-expression ability, an active detection method of FDIAs based on a GRU-CNN hybrid neural network is proposed. Finally, the results of joint knowledge-driven and data-driven parallel detection are used to define a mixed fixed-calculation formula, and an active and passive hybrid detection method of FDIAs is established, considering the characteristic constraints of the parallel mode. A simulation system example of power CPS FDIAs verifies the effectiveness and accuracy of the method proposed in this paper.

preprint2022arXiv

An efficient thermal lattice Boltzmann method for simulating three-dimensional liquid-vapor phase change

In this paper, a multiple-relaxation-time lattice Boltzmann (LB) approach is developed for the simulation of three-dimensional (3D) liquid-vapor phase change based on the pseudopotential model. In contrast to some existing 3D thermal LB models for liquid-vapor phase change, the present approach has two advantages: for one thing, the current approach does not require calculating the gradient of volumetric heat capacity [i.e., $\nabla \left( {ρ{c_v}} \right)$], and for another, the current approach is constructed based on the seven discrete velocities in three dimensions (D3Q7), making the current thermal LB model more efficient and easy to implement. Also, based on the scheme proposed by Zhou and He [Phys Fluids 9:1591-1598, 1997], a pressure boundary condition for the D3Q19 lattice is proposed to model the multiphase flow in open systems. The current method is then validated by considering the temperature distribution in a 3D saturated liquid-vapor system, the $d^2$ law and the droplet evaporation on a heated surface. It is observed that the numerical results fit well with the analytical solutions, the results of the finite difference method and the experimental data. Our numerical results indicate that the present approach is reliable and efficient in dealing with the 3D liquid-vapor phase change.

preprint2022arXiv

Attitude estimation from vector measurements: Necessary and sufficient conditions and convergent observer design

The paper addresses the problem of attitude estimation for rigid bodies using (possibly time-varying) vector measurements, for which we provide a necessary and sufficient condition of distinguishability. Such a condition is shown to be strictly weaker than those previously used for attitude observer design. Thereafter, we show that even for the single vector case the resulting condition is sufficient to design almost globally convergent attitude observers, and two explicit designs are obtained. To overcome the weak excitation issue, the first design employs to make full use of historical information, whereas the second scheme dynamically generates a virtual reference vector, which remains non-collinear to the given vector measurement. Simulation results illustrate the accurate estimation despite noisy measurements.

preprint2022arXiv

CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN\_D and CenGCN\_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines.

preprint2022arXiv

Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification

Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc. We propose Feature Centroid Contrast Learning (FCCL), which can improve target domain classification performance by extra supervision during training with contrastive loss between instance and class centroid. Compared with current unsupervised domain adaptation and domain generalization methods, FCCL performs better while only requires labeled image data from a single source domain and no target domain. We verify through extensive experiments that FCCL can achieve superior performance on at least three imaging modalities, i.e. fundus photographs, dermatoscopic images, and H & E tissue images.

preprint2022arXiv

Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

The sensor-based human activity recognition (HAR) in mobile application scenarios is often confronted with sensor modalities variation and annotated data deficiency. Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems. Specifically, we present a multi-layer residual structure involved graph convolutional neural network (ResGCNN) toward the sensor-based HAR tasks, namely the HAR-ResGCNN approach. Experimental results on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective at capturing the characteristics of actions with comparable results compared to other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%, respectively). More importantly, the deep transfer learning experiments using the ResGCNN model show excellent transferability and few-shot learning performance. The graph-based framework shows good meta-learning ability and is supposed to be a promising solution in sensor-based HAR tasks.

preprint2022arXiv

Dissipation-enabled hydrodynamic conductivity in a tunable bandgap semiconductor

Electronic transport in the regime where carrier-carrier collisions are the dominant scattering mechanism has taken on new relevance with the advent of ultraclean two-dimensional materials. Here we present a combined theoretical and experimental study of ambipolar hydrodynamic transport in bilayer graphene demonstrating that the conductivity is given by the sum of two Drude-like terms that describe relative motion between electrons and holes, and the collective motion of the electron-hole plasma. As predicted, the measured conductivity of gapless, charge-neutral bilayer graphene is sample- and temperature-independent over a wide range. Away from neutrality, the electron-hole conductivity collapses to a single curve, and a set of just four fitting parameters provides quantitative agreement between theory and experiment at all densities, temperatures, and gaps measured. This work validates recent theories for dissipation-enabled hydrodynamic conductivity and creates a link between semiconductor physics and the emerging field of viscous electronics.

preprint2022arXiv

Factorizations of almost simple orthogonal groups of plus type

This is the fifth one in a series of papers classifying the factorizations of almost simple groups with nonsolvable factors. In this paper we deal with orthogonal groups of plus type.

preprint2022arXiv

Fast and Arbitrary Beam Pattern Design for RIS-Assisted Terahertz Wireless Communication

Reconfigurable intelligent surface (RIS) can assist terahertz wireless communication to restore the fragile line-of-sight links and facilitate beam steering. Arbitrary reflection beam patterns are desired to meet diverse requirements in different applications. This paper establishes relationship between RIS beam pattern design with two-dimensional finite impulse response filter design and proposes a fast non-iterative algorithm to solve the problem. Simulations show that the proposed method outperforms baseline method. Hence, it represents a promising solution for fast and arbitrary beam pattern design in RIS-assisted terahertz wireless communication.

preprint2022arXiv

Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition

Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance. The use of first- and second-order features, i.e., joint and bone representations, has led to high accuracy. Nonetheless, many models are still confused by actions that have similar motion trajectories. To address these issues, we propose fusing higher-order features in the form of angular encoding into modern architectures to robustly capture the relationships between joints and body parts. This simple fusion with popular spatial-temporal graph neural networks achieves new state-of-the-art accuracy in two large benchmarks, including NTU60 and NTU120, while employing fewer parameters and reduced run time. Our source code is publicly available at: https://github.com/ZhenyueQin/Angular-Skeleton-Encoding.

preprint2022arXiv

Graph Neural Network with Curriculum Learning for Imbalanced Node Classification

Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of node labels. Traditional solutions for imbalanced classification (e.g. resampling) are ineffective in node classification without considering the graph structure. Worse still, they may even bring overfitting or underfitting results due to lack of sufficient prior knowledge. To solve these problems, we propose a novel graph neural network framework with curriculum learning (GNN-CL) consisting of two modules. For one thing, we hope to acquire certain reliable interpolation nodes and edges through the novel graph-based oversampling based on smoothness and homophily. For another, we combine graph classification loss and metric learning loss which adjust the distance between different nodes associated with minority class in feature space. Inspired by curriculum learning, we dynamically adjust the weights of different modules during training process to achieve better ability of generalization and discrimination. The proposed framework is evaluated via several widely used graph datasets, showing that our proposed model consistently outperforms the existing state-of-the-art methods.

preprint2022arXiv

Hardy-Sobolev inequalities with distance to the boundary weight functions

This is the first part of our research on certain sharp Hardy-Sobolev inequalities and the related elliptic equations. In this part we shall establish some sharp weighted Hardy-Sobolev inequalities whose weights are distance functions to the boundary.

preprint2022arXiv

Indirect Adaptive Control of Nonlinearly Parameterized Nonlinear Dissipative Systems

In this note we address the problem of indirect adaptive (regulation or tracking) control of nonlinear, input affine dissipative systems. It is assumed that the supply rate, the storage and the internal dissipation functions may be expressed as nonlinearly parameterized regression equations where the mappings (depending on the unknown parameters) satisfy a monotonicity condition -- this encompasses a large class of physical systems, including passive systems. We propose to estimate the system parameters using the "power-balance" equation, which is the differential version of the classical dissipation inequality, with a new estimator that ensures global, exponential, parameter convergence under the very weak assumption of interval excitation of the power-balance equation regressor. To design the indirect adaptive controller we make the standard assumption of existence of an asymptotically stabilizing controller that depends -- possibly nonlinearly -- on the unknown plant parameters, and apply a certainty-equivalent control law. The benefits of the proposed approach, with respect to other existing solutions, are illustrated with examples.

preprint2022arXiv

Instance Image Retrieval by Learning Purely From Within the Dataset

Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune the model with a task-dependent labelled auxiliary dataset. Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained. In light of this situation, this work looks into a different approach which has not been well investigated for instance image retrieval previously: {can we learn feature representation \textit{specific to} a given retrieval task in order to achieve excellent retrieval?} Our finding is encouraging. By adding an object proposal generator to generate image regions for self-supervised learning, the investigated approach can successfully learn feature representation specific to a given dataset for retrieval. This representation can be made even more effective by boosting it with image similarity information mined from the dataset. As experimentally validated, such a simple ``self-supervised learning + self-boosting'' approach can well compete with the relevant state-of-the-art retrieval methods. Ablation study is conducted to show the appealing properties of this approach and its limitation on generalisation across datasets.

preprint2022arXiv

Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation

Recently, several weakly supervised learning methods have been devoted to utilize bounding box supervision for training deep semantic segmentation models. Most existing methods usually leverage the generic proposal generators (e.g., dense CRF and MCG) to produce enhanced segmentation masks for further training segmentation models. These proposal generators, however, are generic and not specifically designed for box-supervised semantic segmentation, thereby leaving some leeway for improving segmentation performance. In this paper, we aim at seeking for a more accurate learning-based class-agnostic pseudo mask generator tailored to box-supervised semantic segmentation. To this end, we resort to a pixel-level annotated auxiliary dataset where the class labels are non-overlapped with those of the box-annotated dataset. For learning pseudo mask generator from the auxiliary dataset, we present a bi-level optimization formulation. In particular, the lower subproblem is used to learn box-supervised semantic segmentation, while the upper subproblem is used to learn an optimal class-agnostic pseudo mask generator. The learned pseudo segmentation mask generator can then be deployed to the box-annotated dataset for improving weakly supervised semantic segmentation. Experiments on PASCAL VOC 2012 dataset show that the learned pseudo mask generator is effective in boosting segmentation performance, and our method can further close the performance gap between box-supervised and fully-supervised models. Our code will be made publicly available at https://github.com/Vious/LPG_BBox_Segmentation .

preprint2022arXiv

LibFewShot: A Comprehensive Library for Few-shot Learning

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or ``tricks'', such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, with respect to the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results confirm that such a mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to enter the area of few-shot learning but also elucidate the effects of nontrivial tricks to facilitate intrinsic research on few-shot learning. The source code is available from https://github.com/RL-VIG/LibFewShot.

preprint2022arXiv

Machine Learning assisted excess noise suppression for continuous-variable quantum key distribution

Excess noise is a major obstacle to high-performance continuous-variable quantum key distribution (CVQKD), which is mainly derived from the amplitude attenuation and phase fluctuation of quantum signals caused by channel instability. Here, an excess noise suppression scheme based on equalization is proposed. In this scheme, the distorted signals can be corrected through equalization assisted by a neural network and pilot tone, relieving the pressure on the post-processing and eliminating the hardware cost. For a free-space channel with more intense fluctuation, a classification algorithm is added to classify the received variables, and then the distinctive equalization correction for different classes is carried out. The experimental results show that the scheme can suppress the excess noise to a lower level, and has a significant performance improvement. Moreover, the scheme also enables the system to cope with strong turbulence. It breaks the bottleneck of long-distance quantum communication and lays a foundation for the large-scale application of CVQKD.

preprint2022arXiv

Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease

Background: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). Objective: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. Methods: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether or not a subject would develop DAT in the future. Results: Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy=0.857) compared to MRI (Accuracy=0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy=0.614) compared to genetics (Accuracy=0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. Conclusion: MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data in the right way can improve prediction performance.

preprint2022arXiv

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solving usually replace real numbers with symbolic placeholders to focus on logic reasoning. However, different from common symbolic reasoning tasks like program synthesis and knowledge graph reasoning, MWP solving has extra requirements in numerical reasoning. In other words, instead of the number value itself, it is the reusable numerical property that matters more in numerical reasoning. Therefore, we argue that injecting numerical properties into symbolic placeholders with contextualized representation learning schema can provide a way out of the dilemma in the number representation issue here. In this work, we introduce this idea to the popular pre-training language model (PLM) techniques and build MWP-BERT, an effective contextual number representation PLM. We demonstrate the effectiveness of our MWP-BERT on MWP solving and several MWP-specific understanding tasks on both English and Chinese benchmarks.

preprint2022arXiv

OLxPBench: Real-time, Semantically Consistent, and Domain-specific are Essential in Benchmarking, Designing, and Implementing HTAP Systems

As real-time analysis of the new data become increasingly compelling, more organizations deploy Hybrid Transactional/Analytical Processing (HTAP) systems to support real-time queries on data recently generated by online transaction processing. This paper argues that real-time queries, semantically consistent schema, and domain-specific workloads are essential in benchmarking, designing, and implementing HTAP systems. However, most state-of-the-art and state-of-the-practice benchmarks ignore those critical factors. Hence, they are incommensurable and, at worst, misleading in benchmarking, designing, and implementing HTAP systems. This paper presents OLxPBench, a composite HTAP benchmark suite. OLxPBench proposes: (1) the abstraction of a hybrid transaction, performing a real-time query in-between an online transaction, to model widely-observed behavior pattern -- making a quick decision while consulting real-time analysis; (2) a semantically consistent schema to express the relationships between OLTP and OLAP schema; (3) the combination of domain-specific and general benchmarks to characterize diverse application scenarios with varying resource demands. Our evaluations justify the three design decisions of OLxPBench and pinpoint the bottlenecks of two mainstream distributed HTAP DBMSs. International Open Benchmark Council (BenchCouncil) sets up the OLxPBench homepage at https://www.benchcouncil.org/olxpbench/. Its source code is available from https://github.com/BenchCouncil/olxpbench.git.

preprint2022arXiv

Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy

Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While MPL provides rigorous security guarantees for the computation process, the models trained by MPL are still vulnerable to attacks that solely depend on access to the models. Differential privacy could help to defend against such attacks. However, the accuracy loss brought by differential privacy and the huge communication overhead of secure multi-party computation protocols make it highly challenging to balance the 3-way trade-off between privacy, efficiency, and accuracy. In this paper, we are motivated to resolve the above issue by proposing a solution, referred to as PEA (Private, Efficient, Accurate), which consists of a secure DPSGD protocol and two optimization methods. First, we propose a secure DPSGD protocol to enforce DPSGD in secret sharing-based MPL frameworks. Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL: (1) the data-independent feature extraction method, which aims to simplify the trained model structure; (2) the local data-based global model initialization method, which aims to speed up the convergence of the model training. We implement PEA in two open-source MPL frameworks: TF-Encrypted and Queqiao. The experimental results on various datasets demonstrate the efficiency and effectiveness of PEA. E.g. when $ε$ = 2, we can train a differentially private classification model with an accuracy of 88% for CIFAR-10 within 7 minutes under the LAN setting. This result significantly outperforms the one from CryptGPU, one SOTA MPL framework: it costs more than 16 hours to train a non-private deep neural network model on CIFAR-10 with the same accuracy.

preprint2022arXiv

Progressive Hard-case Mining across Pyramid Levels for Object Detection

In object detection, multi-level prediction (e.g., FPN) and reweighting skills (e.g., focal loss) have drastically improved one-stage detector performance. However, the synergy between these two techniques is not fully explored in a unified framework. We find that, during training, the one-stage detector's optimization is not only restricted to the static hard-case mining loss (gradient drift) but also suffered from the diverse positive samples' proportions split by different pyramid levels (level discrepancy). Under this concern, we propose Hierarchical Progressive Focus (HPF) consisting of two key designs: 1) progressive focus, a more flexible hard-case mining setting calculated adaptive to the convergence progress, 2) hierarchical sampling, automatically generating a set of progressive focus for level-specific target optimization. Based on focal loss with ATSS-R50, our approach achieves 40.5 AP, surpassing the state-of-the-art QFL (Quality Focal Loss, 39.9 AP) and VFL (Varifocal Loss, 40.1 AP). Our best model achieves 55.1 AP on COCO test-dev, obtaining excellent results with only a typical training setting. Moreover, as a plug-and-play scheme, HPF can cooperate well with recent advances, providing a stable performance improvement on nine mainstream detectors.

preprint2022arXiv

Projective-truncation-approximation study of the one-dimensional $ϕ^4$ lattice model

In this paper, we first develop the projective truncation approximation (PTA) in the Green's function equation of motion (EOM) formalism for classical statistical models. To implement PTA for a given Hamiltonian, we choose a set of basis variables and projectively truncate the hierarchical EOM. We apply PTA to the one-dimensional $ϕ^4$ lattice model. Phonon dispersion and static correlation functions are studied in detail. Using one- and two-dimensional bases, we obtain results identical to and beyond the quadratic variational approximation, respectively. In particular, we analyze the power-law temperature dependence of the static averages in the low- and high-temperature limits, and we give exact exponents.

preprint2022arXiv

Pursuing the Precision Study for Color Glass Condensate in Forward Hadron Productions

With the tremendous accomplishments of RHIC and the LHC experiments and the advent of the future Electron-Ion Collider on the horizon, the quest for compelling evidence of the color glass condensate (CGC) has become one of the most aspiring goals in the high energy Quantum Chromodynamics research. Pursuing this question requires developing the precision test of the CGC formalism. By systematically implementing the threshold resummation, we significantly improve the stability of the next-to-leading-order calculation in CGC for forward rapidity hadron productions in $pp$ and $pA$ collisions, especially in the high $p_T$ region, and obtain reliable descriptions of all existing data measured at RHIC and the LHC across all $p_T$ regions. Consequently, this technique can pave the way for the precision studies of the CGC next-to-leading-order predictions by confronting them with a large amount of precise data.

preprint2022arXiv

Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data

Ridesplitting, which is a form of pooled ridesourcing service, has great potential to alleviate the negative impacts of ridesourcing on the environment. However, most existing studies only explored its theoretical environmental benefits based on optimization models and simulations. By contrast, this study aims to reveal the real-world emission reduction of ridesplitting and its determinants based on the observed data of ridesourcing in Chengdu, China. Integrating the trip data with the COPERT model, this study calculates the CO2 emissions of shared rides (ridesplitting) and their substituted single rides (regular ridesourcing) to estimate the CO2 emission reduction of each ridesplitting trip. The results show that not all ridesplitting trips reduce emissions from ridesourcing in the real world. The CO2 emission reduction rate of ridesplitting varies from trip to trip, averaging at 43.15g/km. Then, interpretable machine learning models, gradient boosting machines, are applied to explore the relationship between the CO2 emission reduction rate of ridesplitting and its determinants. Based on the SHapley Additive exPlanations (SHAP) method, the overlap rate and detour rate of shared rides are identified to be the most important factors that determine the CO2 emission reduction rate of ridesplitting. Increasing the overlap rate, the number of shared rides, average speed, and ride distance ratio while decreasing the detour rate, actual trip distance, and ride distance gap can increase the CO2 emission reduction rate of ridesplitting. In addition, nonlinear effects and interactions of the determinants are examined through the partial dependence plots. To sum up, this study provides a scientific method for the government and ridesourcing companies to better assess and optimize the environmental benefits of ridesplitting.

preprint2022arXiv

Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection

K-means is one of the most widely used clustering models in practice. Due to the problem of data isolation and the requirement for high model performance, how to jointly build practical and secure K-means for multiple parties has become an important topic for many applications in the industry. Existing work on this is mainly of two types. The first type has efficiency advantages, but information leakage raises potential privacy risks. The second type is provable secure but is inefficient and even helpless for the large-scale data sparsity scenario. In this paper, we propose a new framework for efficient sparsity-aware K-means with three characteristics. First, our framework is divided into a data-independent offline phase and a much faster online phase, and the offline phase allows to pre-compute almost all cryptographic operations. Second, we take advantage of the vectorization techniques in both online and offline phases. Third, we adopt a sparse matrix multiplication for the data sparsity scenario to improve efficiency further. We conduct comprehensive experiments on three synthetic datasets and deploy our model in a real-world fraud detection task. Our experimental results show that, compared with the state-of-the-art solution, our model achieves competitive performance in terms of both running time and communication size, especially on sparse datasets.

preprint2022arXiv

Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations

The Schrödinger equation is at the heart of modern quantum mechanics. Since exact solutions of the ground state are typically intractable, standard approaches approximate Schrödinger equation as forms of nonlinear generalized eigenvalue problems $F(V)V = SVΛ$ in which $F(V)$, the matrix to be decomposed, is a function of its own top-$k$ smallest eigenvectors $V$, leading to a "self-consistency problem". Traditional iterative methods heavily rely on high-quality initial guesses of $V$ generated via domain-specific heuristics methods based on quantum mechanics. In this work, we eliminate such a need for domain-specific heuristics by presenting a novel framework, Self-consistent Gradient-like Eigen Decomposition (SCGLED) that regards $F(V)$ as a special "online data generator", thus allows gradient-like eigendecomposition methods in streaming $k$-PCA to approach the self-consistency of the equation from scratch in an iterative way similar to online learning. With several critical numerical improvements, SCGLED is robust to initial guesses, free of quantum-mechanism-based heuristics designs, and neat in implementation. Our experiments show that it not only can simply replace traditional heuristics-based initial guess methods with large performance advantage (achieved averagely 25x more precise than the best baseline in similar wall time), but also is capable of finding highly precise solutions independently without any traditional iterative methods.

preprint2022arXiv

StyTr$^2$: Image Style Transfer with Transformers

The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convolutional neural networks (CNNs), extracting and maintaining the global information of input images is difficult. Therefore, traditional neural style transfer methods face biased content representation. To address this critical issue, we take long-range dependencies of input images into account for image style transfer by proposing a transformer-based approach called StyTr$^2$. In contrast with visual transformers for other vision tasks, StyTr$^2$ contains two different transformer encoders to generate domain-specific sequences for content and style, respectively. Following the encoders, a multi-layer transformer decoder is adopted to stylize the content sequence according to the style sequence. We also analyze the deficiency of existing positional encoding methods and propose the content-aware positional encoding (CAPE), which is scale-invariant and more suitable for image style transfer tasks. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed StyTr$^2$ compared with state-of-the-art CNN-based and flow-based approaches. Code and models are available at https://github.com/diyiiyiii/StyTR-2.

preprint2022arXiv

Testing gravitational redshift based on microwave frequency links onboard China Space Station

In 2022 China Space Station (CSS) will be equipped with atomic clocks and optical clocks with stabilities of $2 \times 10^{-16}$ and $8 \times 10^{-18}$, respectively, which provides an excellent opportunity to test gravitational redshift (GR) with higher accuracy than previous results. Based on high-precise frequency links between CSS and a ground station, we formulated a model and provided simulation experiments to test GR. Simulation results suggest that this method could test the GR at the accuracy level of $(0.27 \pm 2.15) \times10^{-7}$, more than two orders in magnitude higher than the result of the experiment of a hydrogen clock on board a flying rocket more than 40 years ago.

preprint2022arXiv

The Shigesada-Kawasaki-Teramoto cross-diffusion system beyond detailed balance

The existence of global weak solutions to the cross-diffusion model of Shigesada, Kawasaki, and Teramoto for an arbitrary number of species is proved. The model consists of strongly coupled parabolic equations for the population densities in a bounded domain with no-flux boundary conditions, and it describes the dynamics of the segregation of the population species. The diffusion matrix is neither symmetric nor positive semidefinite. A new logarithmic entropy allows for an improved condition on the coefficients of heavily nonsymmetric diffusion matrices, without imposing the detailed-balance condition that is often assumed in the literature. Furthermore, the large-time convergence of the solutions to the constant steady state is proved by using the relative entropy associated to the logarithmic entropy.

preprint2022arXiv

Three-dimensional study of double droplets impact on a wettability-patterned surface

The directional movement and rebound behaviours of two droplets simultaneously impacting a designed flat surface with wettability difference is investigated based on the three-dimensional multi-relaxation-time pseudopotential lattice Boltzmann model. The effects of several factors, such as wettability difference, Weber number and droplet spacing on the directional movement and rebound behaviours are investigated in detail. The numerical results show that the unbalanced Young 's force caused by the wetting difference will cause the droplets to rebound or migrate laterally toward to the side with lower hydrophobicity on the surface, and the contact time of the droplets is found to decrease with the increase of the wetting difference. In addition, it is noted that there exists a secondary spreading behavior in the case of a lower Weber number, which in turn leads to an increase in contact time. Further, as far as the influence of the droplet spacing is concerned, we found that the coalescence intensity of the droplets decreases with the increase of droplet spacing, and in particular, the coalescing droplets are found to divide into two sub-droplets during asymmetric contraction, and three detachment patterns are then defined to reveal the effects of the droplet spacing.

preprint2022arXiv

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear technique to reveal the dynamics of the brain neural system. Recently, the topological data analysis (TDA) scheme has been used to explore the properties of space, which provides a powerful tool to think over the phase space. In this work, we proposed a topological EEG nonlinear dynamics analysis approach using the phase space reconstruction (PSR) technique to convert EEG time series into phase space, and the persistent homology tool explores the topological properties of the phase space. We perform the topological analysis of EEG signals in different rhythm bands to build emotion feature vectors, which shows high distinguishing ability. We evaluate the approach with two well-known benchmark datasets, the DEAP and DREAMER datasets. The recognition results achieved accuracies of 99.37% and 99.35% in arousal and valence classification tasks with DEAP, and 99.96%, 99.93%, and 99.95% in arousal, valence, and dominance classifications tasks with DREAMER, respectively. The performances are supposed to be outperformed current state-of-art approaches in DREAMER (improved by 1% to 10% depends on temporal length), while comparable to other related works evaluated in DEAP. The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.

preprint2022arXiv

Two-dimensional Obstructed Atomic Insulators with Fractional Corner Charge in MA$_2$Z$_4$ Family

According to topological quantum chemistry, a class of electronic materials have been called obstructed atomic insulators (OAIs), in which a portion of valence electrons necessarily have their centers located on some empty $\textit{Wyckoff}$ positions without atoms occupation in the lattice. The obstruction of centering these electrons coinciding with their host atoms is nontrivial and results in metallic boundary states when the boundary is properly cut. Here, on basis of first-principles calculations in combination with topological quantum chemistry analysis, we propose two dimensional MA$_2$Z$_4$ (M = Cr, Mo and W; A = Si and Ge, Z = N, P and As) monolayer family are all OAIs. A typical case is the recently synthesized MoSi$_2$N$_4$. Although it is a topological trivial insulator with the occupied electronic states being integer combination of elementary band representations, it has valence electrons centering empty $\textit{Wyckoff}$ positions. It exhibits unique OAI-induced metallic edge states along the (1$\bar{1}$0) edge of MoSi$_2$N$_4$ monolayer and the in-gap corner states at three vertices of certain hexagonal nanodisk samples respecting C$_3$ rotation symmetry. The readily synthesized MoSi$_2$N$_4$ is quite stable and has a large bulk band gap of 1.94 eV, which makes the identification of these edge and corner states most possible for experimental clarification.

preprint2022arXiv

Two-stream Hierarchical Similarity Reasoning for Image-text Matching

Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods.

preprint2021arXiv

An efficient HTS electromagnetic model combining thin-strip, homogeneous and multi-scale methods by T-A formulation

This study presents an HTS electromagnetic model combining the thin-strip, homogeneous and multi-scale methods using T-A formulation. In particular, we build the thin strips as both the analyzed HTS tapes and the boundaries of the homogeneous bulks where the non-analyzed tapes are merged. Thus, the coil geometry is re-constructed with several bulks, but the bulks boundaries and domains are tackled with different electromagnetic properties, and solved by T and A formulations, respectively. Firstly, we introduce the modeling process and highlight the differences and advantages over the previous models. Then, the accuracy of the proposed model is validated by comparing the results with those from the reference model based on a 2000-turn coil. The distributions of normalized current density, magnetic flux density and hysteresis losses from the two models are highly consistent, and the error of the total loss is less than 1%. Besides, the proposed model is the most time-saving among all the advanced models. Furthermore, the model can be applied in 3D simulations, and the high accuracy and efficiency are validated by simulating a 50-turn racetrack coil. The proposed method provides a feasible approach to simulating coils with many stacked tapes, and we will continue exploring more applications in solving HTS systems with complex geometries.

preprint2021arXiv

Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://github.com/Qianyu1226/Crosslink-Net.

preprint2021arXiv

Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function

In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of $d$ agents. The objective is an average of $d$ local functions, and each function is privately held by an agent and encodes its data. The agents can only communicate with their neighbors in a collaborative effort to solve this problem. In existing methods, multiple rounds of communications are required to guarantee the convergence, giving rise to high communication costs. In contrast, this paper proposes a decentralized algorithm, called DESTINY, which only invokes a single round of communications per iteration. DESTINY combines gradient tracking techniques with a novel approximate augmented Lagrangian function. The global convergence to stationary points is rigorously established. Comprehensive numerical experiments demonstrate that DESTINY has a strong potential to deliver a cutting-edge performance in solving a variety of testing problems.

preprint2021arXiv

Differentially Private Distributed Computation via Public-Private Communication Networks

This paper studies the problem of multi-agent computation under the differential privacy requirement of the agents' local datasets against eavesdroppers having node-to-node communications. We first propose for the network equipped with public-private networks. The private network is sparse and not even necessarily connected, over which communications are encrypted and secure along with the intermediate node states; the public network is connected and may be dense, over which communications are allowed to be public. In this setting, we propose a multi-gossip PPSC mechanism over the private network, where at each step, randomly selected node pairs update their states in such a way that they are shuffled with random noise while maintaining summation consistency. We show that this mechanism can achieve any desired differential privacy level with any prescribed probability. Next, we embed this mechanism in distributed computing processes, and propose privacy-guarantee protocols for three basic computation tasks, where an adaptive mechanism adjusts the amount of noise injected in PPSC steps for privacy protection, and the number of regular computation steps for accuracy guarantee. For average consensus, we develop a PPSC-Gossip averaging consensus algorithm by utilizing the multi-gossip PPSC mechanism for privacy encryption before an averaging consensus algorithm over the public network for local computations. For network linear equations and distributed convex optimization, we develop two respective distributed computing protocols by following the PPSC-Gossip averaging consensus algorithm with an additional projection or gradient descent step within each step of computation. Given any privacy and accuracy requirements, it is shown that all three proposed protocols can compute their corresponding problems with the desired computation accuracy, while achieving the desired differential privacy.

preprint2021arXiv

Distributed Algorithms that Solve Boolean Equations with Local and Differential Privacies

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular node is a {\em private} equation known to this node only, and the nodes aim to compute the exact set of solutions to the system without exchanging their local equations. We show that each private Boolean equation can be locally lifted to a linear algebraic equation under a basis of Boolean vectors, leading to a network linear equation that is distributedly solvable using existing distributed linear equation algorithms as a subroutine. A number of exact or approximate solutions to the induced linear equation are then computed at each node from different initial values. The solutions to the original Boolean equations are eventually computed locally via a Boolean vector search algorithm. We prove that given solvable Boolean equations, when the initial values of the nodes for the distributed linear equation solving step are i.i.d selected according to a uniform distribution in a high-dimensional cube, our algorithms return the exact solution set of the Boolean equations at each node with high probability. Furthermore, we present an algorithm for distributed verification of the satisfiability of Boolean equations, and prove its correctness. Finally, we show that by utilizing linear equation solvers with differential privacy to replace the in-network computing routines, the overall distributed Boolean equation algorithms can be made differentially private. Under the standard Laplace mechanism, we prove an explicit level of noises that can be injected in the linear equation steps for ensuring a prescribed level of differential privacy.

preprint2021arXiv

Fast Evaporation Enabled Ultrathin Polymeric Coatings on Nanoporous Substrates for Highly Permeable Membranes

Membranes derived from ultrathin polymeric films are promising to meet fast separations, but currently available approaches to produce polymer films with greatly reduced thicknesses on porous supports still faces challenges. Here, defect-free ultrathin polymer covering films (UPCFs) are realized by a facile general approach of rapid solvent evaporation. By fast evaporating dilute polymer solutions, we realize ultrathin coating (~30 nm) of porous substrates exclusively on the top surface, forming UPCFs with a block copolymer of polystyrene-block-poly(2-vinyl pyridine) at room temperature or a homopolymer of poly(vinyl alcohol) (PVA) at elevated temperatures. With subsequent selective swelling to the block copolymer and crosslinking to PVA, the resulting bi-layered composite structures serve as highly permeable membranes delivering ~2-10 times higher permeability in ultrafiltration and pervaporation applications than state-of-the-art separation membranes with similar rejections and selectivities. This work opens up a new, facile avenue for the controllable fabrication of ultrathin coatings on porous substrates, which shows great potentials in membrane-based separations and other areas.

preprint2021arXiv

Giant Crystal Hall Effect in Collinear Antiferromagnetic $γ$-FeMn

The spontaneous Hall effect is usually governed by three conventional mechanisms, such as the Berry curvature, skew scattering and side jump, which widely exist in ferromagnetic or antiferromagnetic materials. However, in this work, based on first principle calculations, we predict a giant crystal Hall effect (CHE) in the antiferromagnetic $γ$-FeMn, which can not be understood by the previous three conventional mechanisms and the Hall angle therein can be as large as 18.4% at low temperature. Furthermore, with Boltzmann transport equation and a tight-binding model, we conclude that, the asymmetric group velocities on Fermi surface is the origin of this CHE in $γ$-FeMn. And with a systematic symmetry argument, we show that, this unusual effect is not dependent on specific materials but universal in any crystals with similar symmetry even without local magnetization.

preprint2021arXiv

HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking

Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC AI benchmarks accelerate the process. Unfortunately, benchmarking HPC AI systems at scale raises serious challenges. This paper presents a representative, repeatable and simple HPC AI benchmarking methodology. Among the seventeen AI workloads of AIBench Training -- by far the most comprehensive AI Training benchmarks suite -- we choose two representative and repeatable AI workloads. The selected HPC AI benchmarks include both business and scientific computing: Image Classification and Extreme Weather Analytics. To rank HPC AI systems, we present a new metric named Valid FLOPS, emphasizing both throughput performance and a target quality. The specification, source code, datasets, and HPC AI500 ranking numbers are publicly available from \url{https://www.benchcouncil.org/HPCAI500/}.

preprint2021arXiv

Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.

preprint2021arXiv

Multiscale analysis of crystal defect formation in rapid solidification of pure aluminium and aluminium-copper alloys

Rapid solidification leads to unique microstructural features, where a less studied topic is the formation of various crystalline defects, including high dislocation densities, as well as gradients and splitting of the crystalline orientation. As these defects critically affect the material's mechanical properties and performance features, it is important to understand the defect formation mechanisms, and how they depend on the solidification conditions and alloying. To illuminate the formation mechanisms of the rapid solidification induced crystalline defects, we conduct a multiscale modeling analysis consisting of bond-order potential based molecular dynamics (MD), phase field crystal based amplitude expansion (PFC-AE) simulations, and sequentially coupled phase field -- crystal plasticity (PF--CP) simulations. The resulting dislocation densities are quantified and compared to past experiments. The atomistic approaches (MD, PFC) can be used to calibrate continuum level crystal plasticity models, and the framework adds mechanistic insights arising from the multiscale analysis.

preprint2021arXiv

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field.

preprint2021arXiv

Novel Two-Dimensional Layered MSi$_2$N$_4$ (M = Mo, W): New Promising Thermal Management Materials

With the miniaturization and integration of nanoelectronic devices, efficient heat removal becomes a key factor affecting the reliable operation of the nanoelectronic device. With the high intrinsic thermal conductivity, good mechanical flexibility, and precisely controlled growth, two-dimensional (2D) materials are widely accepted as ideal candidates for thermal management materials. In this work, by solving the phonon Boltzmann transport equation (BTE) based on first-principles calculations, we comprehensively investigated the thermal conductivity of novel 2D layered MSi$_2$N$_4$ (M = Mo, W). Our results point to competitive thermal conductivities (162 W/mK) of monolayer MoSi$_2$N$_4$, which is around two times larger than that of WSi$_2$N$_4$ and seven times larger than that of silicene despite their similar non-planar structures. It is revealed that the high thermal conductivity arises mainly from its large group velocity and low anharmonicity. Our result suggests that MoSi$_2$N$_4$ could be a potential candidate for 2D thermal management materials.

preprint2021arXiv

Robust I&I Adaptive Tracking Control of Systems with Nonlinear Parameterization: An ISS Perspective

This paper studies the immersion and invariance (I&I) adaptive tracking problem for a class of nonlinear systems with nonlinear parameterization in the ISS framework. Under some mild assumptions, a novel I&I adaptive control algorithm is proposed,leading to an interconnection of an ISS estimation error subsystem and an ISS tracking error subsystem. Using an ISS small-gain condition, the desired uniform global asymptotic stability of the resulting interconnected "error" system can be achieved and a sum-type strict Lyapunov function can be explicitly constructed. Taking advantage of this ISS-based design framework,it is shown that the corresponding robustness with respect to the input perturbation can be rendered to be ISS. To remove the need to solve the immersion manifold shaping PDE, a new filter-based approach is proposed, which preserves the ISS-based design framework. Finally, we demonstrate the validness of the proposed framework on a tracking problem for series elastic actuators.

preprint2021arXiv

Robust Implementable Regulator Design of General Linear Systems

Robust implementable output regulator design approaches are studied for general linear continuous-time \mbox{systems} with periodically sampled measurements, consisting of both the regulation errors and extra measurements that are generally non-vanishing in steady state. A digital regulator is first developed via the conventional emulation-based approach, rendering the regulation errors asymptotically bounded with a small sampling period. We then develop a hybrid design framework by incorporating a generalized hold device, which transforms the original problem into the problem of designing an output feedback controller fulfilling two conditions for a discrete-time system. We show that such a controller can always be obtained by designing a discrete-time internal model, a discrete-time washout filter, and a discrete-time output feedback stabilizer. As a result, the regulation errors are shown to be globally exponentially convergent to zero, while the sampling period is fixed but can be arbitrarily large. This design framework is further developed for a multi-rate digital regulator with a large sampling period of the measurements and a small control execution period.

preprint2021arXiv

Robust Output Feedback Stabilization of MIMO Invertible Nonlinear Systems with Output-Dependent Multipliers (extended version)

This note studies the robust output feedback stabilization problem of multi-input multi-output invertible nonlinear systems with output-dependent multipliers. An "ideal" state feedback is first designed under certain mild assumptions. Then, a set of extended low-power high-gain observers is systematically designed, providing a complete estimation of the "ideal" feedback law. This yields a robust output feedback stabilizer such that the origin of the closed-loop system is semiglobally asymptotically stable, while improving the numerical implementation with the power of high-gain parameters up to 2.

preprint2021arXiv

Robust Output Feedback Stabilization of Multivariable Invertible Nonlinear Systems: A Feedback Linearization-Based Method

This note studies the robust output feedback stabilization problem of a class of multi-input multi-output invertible nonlinear systems, for which an "ideal" state feedback based on feedback linearization can be designed under certain mild assumptions. By systematically designing a set of extended low-power high-gain observers, we show that this "ideal" linearizing feedback law can be approximately estimated, which provides a robust output feedback stabilizer such that the origin of the resulting closed-loop system is semiglobally asymptotically stable.

preprint2021arXiv

Simulation of an imaging system for internal contamination of lungs using MPA-MURA coded aperture collimator

The nuclides inhaled during nuclear accidents usually cause internal contamination of the lungs with low activity. Although a parallel-hole imaging system, which is widely used in medical gamma cameras, has a high resolution and good image quality, owing to its extremely low detection efficiency, it remains difficult to obtain images of inhaled lung contamination. In this study, the Monte Carlo method was used to study the internal lung contamination imaging using the MPA-MURA coded-aperture collimator. The imaging system consisted of an adult male lung model, with a mosaicked, pattern-centered, and anti-symmetric MURA coded-aperture collimator model and a CsI(Tl) detector model. The MLEM decoding algorithm was used to reconstruct the internal contamination image, and the complementary imaging method was used to reduce the number of artifacts. The full width at half maximum of the I-131 point source image reconstructed by the mosaicked, pattern-centered, and anti-symmetric Modified uniformly redundant array (MPA-MURA) coded-aperture imaging reached 2.51 mm, and the signal-to-noise ratio of the simplified respiratory tract source (I-131) image reconstructed through MPA-MURA coded-aperture imaging was 3.98 dB. Although the spatial resolution of MPA-MURA coded aperture imaging is not as good as that of parallel-hole imaging, the detection efficiency of PMA-MURA coded-aperture imaging is two orders of magnitude higher than that of parallel hole collimator imaging. Considering the low activity level of internal lung contamination caused by nuclear accidents, PMA-MURA coded-aperture imaging has significant potential for the development of lung contamination imaging.

preprint2021arXiv

Tropical Tensor Network for Ground States of Spin Glasses

We present a unified exact tensor network approach to compute the ground state energy, identify the optimal configuration, and count the number of solutions for spin glasses. The method is based on tensor networks with the Tropical Algebra defined on the semiring. Contracting the tropical tensor network gives the ground state energy; differentiating through the tensor network contraction gives the ground state configuration; mixing the tropical algebra and the ordinary algebra counts the ground state degeneracy. The approach brings together the concepts from graphical models, tensor networks, differentiable programming, and quantum circuit simulation, and easily utilizes the computational power of graphical processing units (GPUs). For applications, we compute the exact ground state energy of Ising spin glasses on square lattice up to 1024 spins, on cubic lattice up to 216 spins, and on 3 regular random graphs up to 220 spins, on a single GPU; We obtain exact ground state energy of (+/-)J Ising spin glass on the chimera graph of D-Wave quantum annealer of 512 qubits in less than 100 seconds and investigate the exact value of the residual entropy of (+/-)J spin glasses on the chimera graph; Finally, we investigate ground-state energy and entropy of 3-state Potts glasses on square lattices up to size 18 x 18. Our approach provides baselines and benchmarks for exact algorithms for spin glasses and combinatorial optimization problems, and for evaluating heuristic algorithms and mean-field theories.

preprint2021arXiv

Unified First-Principles Study of the Anomalous Hall Effect Based on Exact Muffin-Tin Orbitals

Based on the exact muffin-tin orbitals (EMTOs), we developed a first-principles method to calculate the current operators and investigated the anomalous Hall effect in bcc Fe as an example, with which we successfully separated the skew scattering contribution from the side jump and intrinsic contributions by fitting the scaling law with the introduction of sparse impurities. By investigating the temperature dependence of the anomalous Hall effect in bulk Fe, we predicted a fluctuated anomalous Hall angle as a function of temperature when considering only phonons, which, in the future, can be measured in experiments by suppressing magnon excitation, e.g., by applying a high external magnetic field.

preprint2020arXiv

A Neural Architecture Search based Framework for Liquid State Machine Design

Liquid State Machine (LSM), also known as the recurrent version of Spiking Neural Networks (SNN), has attracted great research interests thanks to its high computational power, biological plausibility from the brain, simple structure and low training complexity. By exploring the design space in network architectures and parameters, recent works have demonstrated great potential for improving the accuracy of LSM model with low complexity. However, these works are based on manually-defined network architectures or predefined parameters. Considering the diversity and uniqueness of brain structure, the design of LSM model should be explored in the largest search space possible. In this paper, we propose a Neural Architecture Search (NAS) based framework to explore both architecture and parameter design space for automatic dataset-oriented LSM model. To handle the exponentially-increased design space, we adopt a three-step search for LSM, including multi-liquid architecture search, variation on the number of neurons and parameters search such as percentage connectivity and excitatory neuron ratio within each liquid. Besides, we propose to use Simulated Annealing (SA) algorithm to implement the three-step heuristic search. Three datasets, including image dataset of MNIST and NMNIST and speech dataset of FSDD, are used to test the effectiveness of our proposed framework. Simulation results show that our proposed framework can produce the dataset-oriented optimal LSM models with high accuracy and low complexity. The best classification accuracy on the three datasets is 93.2%, 92.5% and 84% respectively with only 1000 spiking neurons, and the network connections can be averagely reduced by 61.4% compared with a single LSM. Moreover, we find that the total quantity of neurons in optimal LSM models on three datasets can be further reduced by 20% with only about 0.5% accuracy loss.

preprint2020arXiv

A Noise Filter for Dynamic Vision Sensors using Self-adjusting Threshold

Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers. However, they are sensitive to background activity (BA) events which are unwanted. we propose a new criterion with little computation overhead for defining real events and BA events by utilizing the global space and time information rather than the local information by Gaussian convolution, which can be also used as a filter. We denote the filter as GF. We demonstrate GF on three datasets, each recorded by a different DVS with different output size. The experimental results show that our filter produces the clearest frames compared with baseline filters and run fast.

preprint2020arXiv

AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite

Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process as it provides not only relevant design inputs but also relevant metrics, and tools. Unfortunately, modern workloads like Big data, AI, and Internet services dwarf the traditional one in terms of code size, deployment scale, and execution path, and hence raise serious benchmarking challenges. This paper proposes an agile domain-specific benchmarking methodology. Together with seventeen industry partners, we identify ten important end-to-end application scenarios, among which sixteen representative AI tasks are distilled as the AI component benchmarks. We propose the permutations of essential AI and non-AI component benchmarks as end-to-end benchmarks. An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application. We design and implement a highly extensible, configurable, and flexible benchmark framework, on the basis of which, we propose the guideline for building end-to-end benchmarks, and present the first end-to-end Internet service AI benchmark. The preliminary evaluation shows the value of our benchmark suite---AIBench against MLPerf and TailBench for hardware and software designers, micro-architectural researchers, and code developers. The specifications, source code, testbed, and results are publicly available from the web site \url{http://www.benchcouncil.org/AIBench/index.html}.

preprint2020arXiv

Artificial Neural Network Approach to the Analytic Continuation Problem

Inverse problems are encountered in many domains of physics, with analytic continuation of the imaginary Green's function into the real frequency domain being a particularly important example. However, the analytic continuation problem is ill defined and currently no analytic transformation for solving it is known. We present a general framework for building an artificial neural network (ANN) that solves this task with a supervised learning approach. Application of the ANN approach to quantum Monte Carlo calculations and simulated Green's function data demonstrates its high accuracy. By comparing with the commonly used maximum entropy approach, we show that our method can reach the same level of accuracy for low-noise input data, while performing significantly better when the noise strength increases. The computational cost of the proposed neural network approach is reduced by almost three orders of magnitude compared to the maximum entropy method

preprint2020arXiv

ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease

With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis. Traditional methods usually depict the data structure using fixed and predefined similarity matrix for each modality separately, without considering the potential relationship structure across different modalities. In this paper, we propose a novel multi-modality feature selection method, which performs feature selection and local similarity learning simultaniously. Specially, a similarity matrix is learned by jointly considering different imaging modalities. And at the same time, feature selection is conducted by imposing sparse l_{2, 1} norm constraint. The effectiveness of our proposed joint learning method can be well demonstrated by the experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which outperforms existing the state-of-the-art multi-modality approaches.

preprint2020arXiv

Asymmetric Distribution Measure for Few-shot Learning

The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings. Previous work mainly focuses on image-level feature representations, which actually cannot effectively estimate a class's distribution due to the scarcity of samples. Some recent work shows that local descriptor based representations can achieve richer representations than image-level based representations. However, such works are still based on a less effective instance-level metric, especially a symmetric metric, to measure the relations between query images and support classes. Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning. To that end, we propose a novel Asymmetric Distribution Measure (ADM) network for few-shot learning by calculating a joint local and global asymmetric measure between two multivariate local distributions of queries and classes. Moreover, a task-aware Contrastive Measure Strategy (CMS) is proposed to further enhance the measure function. On popular miniImageNet and tieredImageNet, we achieve $3.02\%$ and $1.56\%$ gains over the state-of-the-art method on the $5$-way $1$-shot task, respectively, validating our innovative design of asymmetric distribution measures for few-shot learning.

preprint2020arXiv

Automatic Differentiation for Second Renormalization of Tensor Networks

Tensor renormalization group (TRG) constitutes an important methodology for accurate simulations of strongly correlated lattice models. Facilitated by the automatic differentiation technique widely used in deep learning, we propose a uniform framework of differentiable TRG ($\partial$TRG) that can be applied to improve various TRG methods, in an automatic fashion. Essentially, $\partial$TRG systematically extends the concept of second renormalization [PRL 103, 160601 (2009)] where the tensor environment is computed recursively in the backward iteration, in the sense that given the forward process of TRG, $\partial$TRG automatically finds the gradient through backpropagation, with which one can deeply "train" the tensor networks. We benchmark $\partial$TRG in solving the square-lattice Ising model, and demonstrate its power by simulating one- and two-dimensional quantum systems at finite temperature. The deep optimization as well as GPU acceleration renders $\partial$TRG manybody simulations with high efficiency and accuracy.

preprint2020arXiv

Automatic differentiation of dominant eigensolver and its applications in quantum physics

We investigate the automatic differentiation of dominant eigensolver where only a small proportion of eigenvalues and corresponding eigenvectors are obtained. Backpropagation through the dominant eigensolver involves solving certain low-rank linear systems without direct access to the full spectrum of the problem. Furthermore, the backward pass can be conveniently differentiated again, which implies that in principle one can obtain arbitrarily higher order derivatives of the dominant eigen-decomposition process. These results allow for the construction of an efficient dominant eigensolver primitive, which has wide applications in quantum physics. As a demonstration, we compute second order derivative of the ground state energy and fidelity susceptibility of 1D transverse field Ising model through the exact diagonalization approach. We also calculate the ground state energy of the same model in the thermodynamic limit by performing gradient-based optimization of uniform matrix product states. By programming these computation tasks in a fully differentiable way, one can efficiently handle the dominant eigen-decomposition of very large matrices while still sharing various advantages of differentiable programming paradigm, notably the generic nature of the implementation and free of tedious human efforts of deriving gradients analytically.

preprint2020arXiv

Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge

Motivation: The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction. Results: In this paper, we propose a novel neural network-based approach to improve CPI extraction. Specifically, the approach first employs BERT to generate high-quality contextual representations of the title sequence, instance sequence, and knowledge sequence. Then, the Gaussian probability distribution is introduced to capture the local structure of the instance. Meanwhile, the attention mechanism is applied to fuse the title information and biomedical knowledge, respectively. Finally, the related representations are concatenated and fed into the softmax function to extract CPIs. We evaluate our proposed model on the CHEMPROT corpus. Our proposed model is superior in performance as compared with other state-of-the-art models. The experimental results show that the Gaussian probability distribution and external knowledge are complementary to each other. Integrating them can effectively improve the CPI extraction performance. Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks. Availability: Data and code are available at https://github.com/CongSun-dlut/CPI_extraction. Contact: yangzh@dlut.edu.cn, wangleibihami@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

preprint2020arXiv

Class Distribution Alignment for Adversarial Domain Adaptation

Most existing unsupervised domain adaptation methods mainly focused on aligning the marginal distributions of samples between the source and target domains. This setting does not sufficiently consider the class distribution information between the two domains, which could adversely affect the reduction of domain gap. To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains. It integrates a discriminative structure-preserving loss and a joint adversarial generation loss. The former effectively prevents undesired label-flipping during the whole process of image translation, while the latter maintains the joint distribution alignment of images and labels. Furthermore, our approach enforces the classification consistence of target domain images before and after adaptation to aid the classifier training in both domains. Extensive experiments were conducted on multiple benchmark datasets including Digits, Faces, Scenes and Office31, showing that our approach achieved superior classification in the target domain when compared to the state-of-the-art methods. Also, both qualitative and quantitative results well supported our motivation that aligning the class distributions can indeed improve domain adaptation.

preprint2020arXiv

Cm2 Scale Synthesis of MoTe2 Thin Films with Large Grains and Layer Control David

Owing to the small energy differences between its polymorphs, MoTe2 can access a full spectrum of electronic states, from the 2H semiconducting state to the 1T semimetallic state, and from the Td Weyl semimetallic state to the superconducting state in the 1T and Td phase at low temperature. Thus, it is a model system for phase transformation studies as well as quantum phenomena such as the quantum spin Hall effect and topological superconductivity. Careful studies of MoTe2 and its potential applications require large area MoTe2 thin films with high crystallinity and thickness control. Here, we present cm2 scale synthesis of 2H MoTe2 thin films with layer control and large grains that span several microns. Layer control is achieved by controlling the initial thickness of the precursor MoOx thin films, which are deposited on sapphire substrates by atomic layer deposition and subsequently tellurized. Despite the van der Waals epitaxy, the precursor-substrate interface is found to critically determine the uniformity in thickness and grain size of the resulting MoTe2 films: MoTe2 grown on sapphire show uniform films while MoTe2 grown on amorphous SiO2 substrates form islands. This synthesis strategy decouples the layer control from the variabilities of growth conditions for robust growth results, and is applicable to grow other transition metal dichalcogenides with layer control.

preprint2020arXiv

Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices

Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains. The application of deep learning on the mobile and embedded devices is taken more and more attentions, benchmarking and ranking the AI abilities of mobile and embedded devices becomes an urgent problem to be solved. Considering the model diversity and framework diversity, we propose a benchmark suite, AIoTBench, which focuses on the evaluation of the inference abilities of mobile and embedded devices. AIoTBench covers three typical heavy-weight networks: ResNet50, InceptionV3, DenseNet121, as well as three light-weight networks: SqueezeNet, MobileNetV2, MnasNet. Each network is implemented by three frameworks which are designed for mobile and embedded devices: Tensorflow Lite, Caffe2, Pytorch Mobile. To compare and rank the AI capabilities of the devices, we propose two unified metrics as the AI scores: Valid Images Per Second (VIPS) and Valid FLOPs Per Second (VOPS). Currently, we have compared and ranked 5 mobile devices using our benchmark. This list will be extended and updated soon after.

preprint2020arXiv

Computation and data driven discovery of topological phononic materials

The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 materials. We have clarified 5014 TP materials and classified them into single Weyl, high degenerate Weyl, and nodal-line (ring) TPs. Among them, three representative cases of TPs have been discussed in detail. Furthermore, we suggest 322 TP materials with potential clean nontrivial surface states, which are favorable for experimental characterizations. This work significantly increases the current library of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs.

preprint2020arXiv

Dark matter, electroweak phase transition and gravitational wave in the type-II two-Higgs-doublet model with a singlet scalar field

In the framework of type-II two-Higgs-doublet model with a singlet scalar dark matter $S$, we study the dark matter observables, the electroweak phase transition, and the gravitational wave signals by such strongly first order phase transition after imposing the constraints of the LHC Higgs data. We take the heavy CP-even Higgs $H$ as the only portal between the dark matter and SM sectors, and find the LHC Higgs data and dark matter observables require $m_S$ and $m_H$ to be larger than 130 GeV and 360 GeV for $m_A=600$ GeV in the case of the 125 GeV Higgs with the SM-like coupling. Next, we carve out some parameter space where a strongly first order electroweak phase transition can be achieved, and find benchmark points for which the amplitudes of gravitational wave spectra reach the sensitivities of the future gravitational wave detectors.

preprint2020arXiv

Deep Learning based HEp-2 Image Classification: A Comprehensive Review

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, the methods are organized with a deep network usage based taxonomy. The core idea, notable achievements, and key strengths and weaknesses of each method are critically analyzed. Furthermore, a concise review of the existing HEp-2 datasets that are commonly used in the literature is given. The paper ends with a discussion on novel opportunities and future research directions in this field. It is hoped that this paper would provide readers with a thorough reference of this novel, challenging, and thriving field.

preprint2020arXiv

Effective classical correspondence of the Mott transition

We derive an effective classical model to describe the Mott transition of the half-filled one-band Hubbard model in the framework of the dynamical mean-field theory with hybridization expansion of the continuous time quantum Monte Carlo. We find a simple two-body interaction of exponential form and reveal a classical correspondence of the Mott transition driven by a logarithmically divergent interaction length. Our work provides an alternative angle to view the Mott physics and suggests a renewed possibility to extend the application of the quantum-to-classical mapping in understanding condensed matter physics

preprint2020arXiv

Enhanced Solar Water Splitting by Swift Charge Separation in Au/FeOOH Sandwiched Single Crystalline Fe$_2$O$_3$ Nanoflake Photoelectrodes

In this work, single crystalline $α$-Fe$_2$O$_3$ nanoflakes (NFs) are formed in a highly dense array by Au seeding of a Fe substrate by a thermal oxidation technique. The NFs are conformally decorated with a thin FeOOH cocatalyst layer. Photoelectrochemical (PEC) measurements show that this photoanode with the $α$-Fe$_2$O$_3$/FeOOH NFs rooted on the Au/Fe structure exhibits a significantly enhanced PEC water oxidation performance compared to the plain $α$-Fe$_2$O$_3$ nanostructure on the Fe substrate. The $α$-Fe$_2$O$_3$/FeOOH NFs on Au/Fe photoanode yields a photocurrent density of 3.1 mA cm-2 at 1.5 VRHE, and a remarkably low onset potential of 0.5-0.6 VRHE in 1 M KOH under AM 1.5G (100 mW cm-2) simulated sunlight illumination. The enhancement in PEC performance can be attributed to a synergistic effect of the FeOOH top decoration and Au under-layer. While FeOOH facilitates hole transfer at the interface of electrode/electrolyte, the Au layer provides a sink for the electron transport to the back contact: this leads overall to a drastically improved charge-separation efficiency in the single crystalline $α$-Fe$_2$O$_3$ NF photoanode.

preprint2020arXiv

Enhancing Rumor Detection in Social Media Using Dynamic Propagation Structures

Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure.

preprint2020arXiv

Exploration of Input Patterns for Enhancing the Performance of Liquid State Machines

Spiking Neural Networks (SNN) have gained increasing attention for its low power consumption. But training SNN is challenging. Liquid State Machine (LSM), as a major type of Reservoir computing, has been widely recognized for its low training cost among SNNs. The exploration of LSM topology for enhancing performance often requires hyper-parameter search, which is both resource-expensive and time-consuming. We explore the influence of input scale reduction on LSM instead. There are two main reasons for studying input reduction of LSM. One is that the input dimension of large images requires efficient processing. Another one is that input exploration is generally more economic than architecture search. To mitigate the difficulty in effectively dealing with huge input spaces of LSM, and to find that whether input reduction can enhance LSM performance, we explore several input patterns, namely fullscale, scanline, chessboard, and patch. Several datasets have been used to evaluate the performance of the proposed input patterns, including two spatio image datasets and one spatio-temporal image database. The experimental results show that the reduced input under chessboard pattern improves the accuracy by up to 5%, and reduces execution time by up to 50% with up to 75\% less input storage than the fullscale input pattern for LSM.

preprint2020arXiv

Exploration of Surgeons' Natural Skills for Robotic Catheterization

Despite having the robotic catheter systems which have recently emerged as safe way of performing cardiovascular interventions, a number of important challenges are yet to be investigated. One of them is exploration of surgeons' natural skills during vascular catheterization with robotic systems. In this study, surgeons' natural hand motions were investigated for identification of four basic movements used for intravascular catheterization. Controlled experiment was setup to acquire surface electromyography (sEMG) signals from six muscles that are innervated when a subject with catheterization skills made the four movements in open settings. k-means and k-NN models were implemented over average EMG and root means square features to uniquely identify the movements. The result shows great potentials of sEMG analysis towards designing intelligent cyborg control for safe and efficient robotic catheterization.

preprint2020arXiv

Extended Batch Normalization

Batch normalization (BN) has become a standard technique for training the modern deep networks. However, its effectiveness diminishes when the batch size becomes smaller, since the batch statistics estimation becomes inaccurate. That hinders batch normalization's usage for 1) training larger model which requires small batches constrained by memory consumption, 2) training on mobile or embedded devices of which the memory resource is limited. In this paper, we propose a simple but effective method, called extended batch normalization (EBN). For NCHW format feature maps, extended batch normalization computes the mean along the (N, H, W) dimensions, as the same as batch normalization, to maintain the advantage of batch normalization. To alleviate the problem caused by small batch size, extended batch normalization computes the standard deviation along the (N, C, H, W) dimensions, thus enlarges the number of samples from which the standard deviation is computed. We compare extended batch normalization with batch normalization and group normalization on the datasets of MNIST, CIFAR-10/100, STL-10, and ImageNet, respectively. The experiments show that extended batch normalization alleviates the problem of batch normalization with small batch size while achieving close performances to batch normalization with large batch size.

preprint2020arXiv

Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks

To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation of the weighted inputs, FBN normalizes the intermediate state of the summation. We propose a novel light-weight network based on FBN, called Finet. At training time, the convolutional layer with FBN can be seen as an inverted bottleneck mechanism. FBN can be fused into convolution at inference time. After fusion, Finet uses the standard convolution with equal channel width, thus makes the inference more efficient. On ImageNet classification dataset, Finet achieves the state-of-art performance (65.706% accuracy with 43M FLOPs, and 73.786% accuracy with 303M FLOPs), Moreover, experiments show that Finet is more efficient than other state-of-art light-weight networks.

preprint2020arXiv

Globalized distributionally robust optimization problems under the moment-based framework

This paper is devoted to reduce the conservatism of distributionally robust optimization with moments information. Since the optimal solution of distributionally robust optimization is required to be feasible for all uncertain distributions in a given ambiguity distribution set and so the conservatism of the optimal solution is inevitable. To address this issue, we introduce the globalized distributionally robust counterpart (GDRC) which allows constraint violations controlled by functional distance of the true distribution to the inner uncertainty distribution set. We obtain the deterministic equivalent forms for several GDRCs under the moment-based framework. To be specific, we show the deterministic equivalent system of inequalities for the GDRCs under second order moment information with a separable distance function and a jointly convex distance function, respectively. Moreover, the feasible set of the system is convex. We also develop the deterministic equivalent inequality for the GDRC under first order moment and support information. The computationally tractable examples are presented for these GDRCs. A numerical tests of a portfolio optimization problem is given to show the efficiency of our methods and the results demonstrate that the globalized distributionally robust solutions is non-conservative and flexible compared to the distributionally robust solutions.

preprint2020arXiv

GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification

In this paper, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this paper, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. Firstly, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Secondly, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposed method can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance.

preprint2020arXiv

HPC AI500: The Methodology, Tools, Roofline Performance Models, and Metrics for Benchmarking HPC AI Systems

The recent years witness a trend of applying large-scale distributed deep learning in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC community feels a great interest in building the HPC AI systems that are dedicated to running those workloads. The HPC AI benchmarks accelerate the process. Unfortunately, benchmarking HPC AI systems at scale raises serious challenges. None of previous HPC AI benchmarks achieve the goal of being equivalent, relevant, representative, affordable, and repeatable. This paper presents a comprehensive methodology, tools, Roofline performance models, and innovative metrics for benchmarking, optimizing, and ranking HPC AI systems, which we call HPC AI500 V2.0. We abstract the HPC AI system into nine independent layers, and present explicit benchmarking rules and procedures to assure equivalence of each layer, repeatability, and replicability. On the basis of AIBench -- by far the most comprehensive AI benchmarks suite, we present and build two HPC AI benchmarks from both business and scientific computing: Image Classification, and Extreme Weather Analytics, achieving both representativeness and affordability. To rank the performance and energy-efficiency of HPC AI systems, we propose Valid FLOPS, and Valid FLOPS per watt, which impose a penalty on failing to achieve the target quality. We propose using convolution and GEMM -- the two most intensively-used kernel functions to measure the upper bound performance of the HPC AI systems, and present HPC AI roofline models for guiding performance optimizations. The evaluations show our methodology, benchmarks, performance models, and metrics can measure, optimize, and rank the HPC AI systems in a scalable, simple, and affordable way. HPC AI500 V2.0 are publicly available from http://www.benchcouncil.org/benchhub/hpc-ai500-benchmark.

preprint2020arXiv

Human Activity Recognition based on Dynamic Spatio-Temporal Relations

Human activity, which usually consists of several actions, generally covers interactions among persons and or objects. In particular, human actions involve certain spatial and temporal relationships, are the components of more complicated activity, and evolve dynamically over time. Therefore, the description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. In this paper, we develop a method for human activity recognition that tackles these two issues. In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model. A refined comprehensive spatio temporal graph is utilized to represent a single action, which is a qualitative representation of a human action incorporating both the spatial and temporal relations of the participant objects. Next, a discrete hidden Markov model is applied to model the evolution of action sequences. Moreover, a fully automatic partition method is proposed to divide a long-term human activity video into several human actions based on variational objects and qualitative spatial relations. Finally, a hierarchical decomposition of the human body is introduced to obtain a discriminative representation for a single action. Experimental results on the Cornell Activity Dataset demonstrate the efficiency and effectiveness of the proposed approach, which will enable long videos of human activity to be better recognized.

preprint2020arXiv

Industrial Scale Privacy Preserving Deep Neural Network

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become a serious problem currently, i.e., different parties cannot share data with each other. To solve this issue, most research leverages cryptographic techniques to train secure DNN models for multi-parties without compromising their private data. Although such methods have strong security guarantee, they are difficult to scale to deep networks and large datasets due to its high communication and computation complexities. To solve the scalability of the existing secure Deep Neural Network (DNN) in data isolation scenarios, in this paper, we propose an industrial scale privacy preserving neural network learning paradigm, which is secure against semi-honest adversaries. Our main idea is to split the computation graph of DNN into two parts, i.e., the computations related to private data are performed by each party using cryptographic techniques, and the rest computations are done by a neutral server with high computation ability. We also present a defender mechanism for further privacy protection. We conduct experiments on real-world fraud detection dataset and financial distress prediction dataset, the encouraging results demonstrate the practicalness of our proposal.

preprint2020arXiv

Initial-Value Privacy of Linear Dynamical Systems

This paper studies initial-value privacy problems of linear dynamical systems. We consider a standard linear time-invariant system with random process and measurement noises. For such a system, eavesdroppers having access to system output trajectories may infer the system initial states, leading to initial-value privacy risks. When a finite number of output trajectories are eavesdropped, we consider a requirement that any guess about the initial values can be plausibly denied. When an infinite number of output trajectories are eavesdropped, we consider a requirement that the initial values should not be uniquely recoverable. In view of these two privacy requirements, we define differential initial-value privacy and intrinsic initial-value privacy, respectively, for the system as metrics of privacy risks. First of all, we prove that the intrinsic initial-value privacy is equivalent to unobservability, while the differential initial-value privacy can be achieved for a privacy budget depending on an extended observability matrix of the system and the covariance of the noises. Next, the inherent network nature of the considered linear system is explored, where each individual state corresponds to a node and the state and output matrices induce interaction and sensing graphs, leading to a network system. Under this network system perspective, we allow the initial states at some nodes to be public, and investigate the resulting intrinsic initial-value privacy of each individual node. We establish necessary and sufficient conditions for such individual node initial-value privacy, and also prove that the intrinsic initial-value privacy of individual nodes is generically determined by the network structure. These results may be extended to linear systems with time-varying dynamics under the same analysis framework.

preprint2020arXiv

Method for Extracting Patterns of Coordinated Network Attacks on Electric Power CPS based on Temporal-Topological Correlation

In the analysis of coordinated network attacks on electric power cyber-physical system (CPS), it is difficult to restore the complete attack path, and the intent of the attack cannot be identified automatically. A method is therefore proposed for the extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. First, the attack events are aggregated according to the alarm log of the cyber space, and a temporal-causal Bayesian network-based cyber attack recognition algorithm is proposed to parse out the cyber attack sequences of the same attacker. Then, according to the characteristic curves of different attack measurement data in physical space, a combination of physical attack event criteria algorithm is designed to distinguish the types of physical attack events. Finally, physical attack events and cyber attack sequences are matched via temporal-topological correlation, frequent patterns of attack sequences are extracted, and hidden multi-step attack patterns are found from scattered grid measurement data and information from alarm logs. The effectiveness and efficiency of the proposed method are verified by the testbed at Mississippi State University.

preprint2020arXiv

MODEL: Motif-based Deep Feature Learning for Link Prediction

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this paper, we propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms by 20% and the state-of-the-art embedding-based algorithms by 19%.

preprint2020arXiv

Moiré metrology of energy landscapes in van der Waals heterostructures

The emerging field of twistronics, which harnesses the twist angle between two-dimensional materials, represents a promising route for the design of quantum materials, as the twist-angle-induced superlattices offer means to control topology and strong correlations. At the small twist limit, and particularly under strain, as atomic relaxation prevails, the emergent moiré superlattice encodes elusive insights into the local interlayer interaction. Here we introduce moiré metrology as a combined experiment-theory framework to probe the stacking energy landscape of bilayer structures at the 0.1 meV/atom scale, outperforming the gold-standard of quantum chemistry. Through studying the shapes of moiré domains with numerous nano-imaging techniques, and correlating with multi-scale modelling, we assess and refine first-principle models for the interlayer interaction. We document the prowess of moiré metrology for three representative twisted systems: bilayer graphene, double bilayer graphene and H-stacked $MoSe_2/WSe_2$. Moiré metrology establishes sought after experimental benchmarks for interlayer interaction, thus enabling accurate modelling of twisted multilayers.

preprint2020arXiv

Neural Canonical Transformation with Symplectic Flows

Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. We construct flexible and powerful canonical transformations as generative models using symplectic neural networks. The model transforms physical variables towards a latent representation with an independent harmonic oscillator Hamiltonian. Correspondingly, the phase space density of the physical system flows towards a factorized Gaussian distribution in the latent space. Since the canonical transformation preserves the Hamiltonian evolution, the model captures nonlinear collective modes in the learned latent representation. We present an efficient implementation of symplectic neural coordinate transformations and two ways to train the model. The variational free energy calculation is based on the analytical form of physical Hamiltonian. While the phase space density estimation only requires samples in the coordinate space for separable Hamiltonians. We demonstrate appealing features of neural canonical transformation using toy problems including two-dimensional ring potential and harmonic chain. Finally, we apply the approach to real-world problems such as identifying slow collective modes in alanine dipeptide and conceptual compression of the MNIST dataset.

preprint2020arXiv

Nonlinear imaging with all-dielectric metasurfaces

Nonlinear metasurfaces incorporate many of the functionalities of their linear counterparts such as wavefront shaping but simultaneously they perform nonlinear optical transformations. This dual functionality leads to a rather unintuitive physical behavior which is still widely unexplored for many photonic applications. The nonlinear processes render some basic principles governing the functionality of linear metasurfaces not directly applicable, such as the superposition principle and the geometric optics approximation. On the other hand, nonlinear metasurfaces facilitate new phenomena that are not possible in the linear regime. Here, we study the imaging of objects through a dielectric nonlinear metalens. We illuminate objects by infrared light and record their generated images at the visible third-harmonic wavelengths. We revisit the classical lens theory and suggest a generalized Gaussian lens equation for nonlinear imaging, verified both experimentally and analytically. We also demonstrate experimentally higher-order spatial correlations facilitated by the nonlinear metalens, resulting in additional image features.

preprint2020arXiv

Performance of Wireless Optical Communication With Reconfigurable Intelligent Surfaces and Random Obstacles

It is difficult for free space optical communication to be applied in mobile communication due to the obstruction of obstacles in the environment, which is expected to be solved by reconfigurable intelligent surface technology. The reconfigurable intelligent surface is a new type of digital coding meta-materials, which can reflect, compute and program electromagnetic and optical waves in real time. We purpose a controllable multi-branch wireless optical communication system based on the optical reconfigurable intelligent surface technology. By setting up multiple optical reconfigurable intelligent surface in the environment, multiple artificial channels are built to improve system performance and to reduce the outage probability. Three factors affecting channel coefficients are investigated in this paper, which are beam jitter, jitter of the reconfigurable intelligent surface and the probability of obstruction. Based on the model, we derive the closed-form probability density function of channel coefficients, the asymptotic system's average bit error rate and outage probability for systems with single and multiple branches. It is revealed that the probability density function contains an impulse function, which causes irreducible error rate and outage probability floors. Numerical results indicate that compared with free-space optical communication systems with single direct path, the performance of the multi-branch system is improved and the outage probability is reduced.

preprint2020arXiv

Photoanodes Based on TiO$_2$ and $α$-Fe$_2$O$_3$ for Solar Water Splitting Superior Role of 1D Nanoarchitectures and of Combined Heterostructures

Solar driven photoelectrochemical water splitting (PEC-WS) using semiconductor photoelectrodes represents a promising approach for a sustainable and environmentally friendly production of renewable energy vectors and fuel sources, such as dihydrogen (H2). In this context, titanium dioxide (TiO$_2$) and iron oxide (hematite, $α$-Fe$_2$O$_3$) are among the most investigated candidates as photoanode materials, mainly owing to their resistance to photocorrosion, non-toxicity, natural abundance, and low production cost. Major drawbacks are, however, an inherently low electrical conductivity and a limited hole diffusion length that significantly affect the performance of TiO$_2$ and $α$-Fe$_2$O$_3$ in PEC devices. To this regard, one-dimensional (1D) nanostructuring is typically applied as it provides several superior features such as a significant enlargement of the material surface area, extended contact between the semiconductor and the electrolyte and, most remarkably, preferential electrical transport that overall suppress charge carrier recombination and improve TiO$_2$ and $α$-Fe$_2$O$_3$ photo-electrocatalytic properties. The present review describes various synthetic methods, properties and PEC applications of 1D-photoanodes (nanotubes, nanorods, nanofibers, nanowires) based on titania, hematite, and on $α$-Fe$_2$O$_3$/TiO$_2$ heterostructures. Various routes towards modification and enhancement of PEC activity of 1D photoanodes are also discussed including doping, decoration with co-catalysts and heterojunction engineering. Finally, the challenges related to the optimization of charge transfer kinetics in both oxides are highlighted.

preprint2020arXiv

Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification

In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels. In real-world applications, these intra-camera labels can be readily captured by tracking algorithms or few manual annotations, when compared with cross-camera labels. In this case, it is very difficult to explore the relationships between cross-camera persons in the training stage due to the lack of cross-camera label information. To deal with this issue, we propose a novel Progressive Cross-camera Soft-label Learning (PCSL) framework for the semi-supervised person Re-ID task, which can generate cross-camera soft-labels and utilize them to optimize the network. Concretely, we calculate an affinity matrix based on person-level features and adapt them to produce the similarities between cross-camera persons (i.e., cross-camera soft-labels). To exploit these soft-labels to train the network, we investigate the weighted cross-entropy loss and the weighted triplet loss from the classification and discrimination perspectives, respectively. Particularly, the proposed framework alternately generates progressive cross-camera soft-labels and gradually improves feature representations in the whole learning course. Extensive experiments on five large-scale benchmark datasets show that PCSL significantly outperforms the state-of-the-art unsupervised methods that employ labeled source domains or the images generated by the GAN-based models. Furthermore, the proposed method even has a competitive performance with respect to deep supervised Re-ID methods.

preprint2020arXiv

Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening

There are extensive researches focusing on automated diabetic reti-nopathy (DR) detection from fundus images. However, the accuracy drop is ob-served when applying these models in real-world DR screening, where the fun-dus camera brands are different from the ones used to capture the training im-ages. How can we train a classification model on labeled fundus images ac-quired from only one camera brand, yet still achieves good performance on im-ages taken by other brands of cameras? In this paper, we quantitatively verify the impact of fundus camera brands related domain shift on the performance of DR classification models, from an experimental perspective. Further, we pro-pose camera-oriented residual-CycleGAN to mitigate the camera brand differ-ence by domain adaptation and achieve increased classification performance on target camera images. Extensive ablation experiments on both the EyePACS da-taset and a private dataset show that the camera brand difference can signifi-cantly impact the classification performance and prove that our proposed meth-od can effectively improve the model performance on the target domain. We have inferred and labeled the camera brand for each image in the EyePACS da-taset and will publicize the camera brand labels for further research on domain adaptation.

preprint2020arXiv

SABRE and the Stawell Underground Physics Laboratory: Dark Matter Research at the Australian National University

The direct detection of dark matter is a key problem in astroparticle physics that generally requires the use of deep-underground laboratories for a low-background environment where the rare signals from dark matter interactions can be observed. This work reports on the Stawell Underground Physics Laboratory - currently under construction and the first such laboratory in the Southern Hemisphere - and the associated research program. A particular focus will be given to ANU's contribution to SABRE, a NaI:Tl dark matter direct detection experiment that aims to confirm or refute the long-standing DAMA result. Preliminary measurements of the NaI:Tl quenching factor and characterisation of the SABRE liquid scintillator veto are reported.

preprint2020arXiv

SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images

This study aims to automatically diagnose thoracic diseases depicted on the chest x-ray (CXR) images using deep convolutional neural networks. The existing methods generally used the entire CXR images for training purposes, but this strategy may suffer from two drawbacks. First, potential misalignment or the existence of irrelevant objects in the entire CXR images may cause unnecessary noise and thus limit the network performance. Second, the relatively low image resolution caused by the resizing operation, which is a common preprocessing procedure for training neural networks, may lead to the loss of image details, making it difficult to detect pathologies with small lesion regions. To address these issues, we present a novel method termed as segmentation-based deep fusion network (SDFN), which leverages the domain knowledge and the higherresolution information of local lung regions. Specifically, the local lung regions were identified and cropped by the Lung Region Generator (LRG). Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images. Lastly, the obtained features were fused by the feature fusion module for disease classification. Evaluated by the NIH benchmark split on the Chest X-ray 14 Dataset, our experimental result demonstrated that the developed method achieved more accurate disease classification compared with the available approaches via the receiver operating characteristic (ROC) analyses. It was also found that the SDFN could localize the lesion regions more precisely as compared to the traditional method.

preprint2020arXiv

Secret Sharing based Secure Regressions with Applications

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potential gains are highly anticipated if different organizations could somehow collaboratively share their data for technological improvements. On the other hand, data security concerns may arise for both data holders and data providers due to commercial or sociological concerns. To make a balance between technical improvements and security limitations, we implement secure and scalable protocols for multiple data holders to train linear regression and logistic regression models. We build our protocols based on the secret sharing scheme, which is scalable and efficient in applications. Moreover, our proposed paradigm can be generalized to any secure multiparty training scenarios where only matrix summation and matrix multiplications are used. We demonstrate our approach by experiments which shows the scalability and efficiency of our proposed protocols, and finally present its real-world applications.

preprint2020arXiv

SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors

Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imaging sensors. However, they are sensitive to background activity (BA) events that are unwanted. There are some filters for tackling this problem based on spatio-temporal correlation. However, they are either memory-intensive or computing-intensive. We propose \emph{SeqXFilter}, a spatio-temporal correlation filter with only a past event window that has an O(1) space complexity and has simple computations. We explore the spatial correlation of an event with its past few events by analyzing the distribution of the events when applying different functions on the spatial distances. We find the best function to check the spatio-temporal correlation for an event for \emph{SeqXFilter}, best separating real events and noise events. We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's performance. Four neuromorphic event-based datasets, recorded from four DVS with different output sizes, are used for validation of our method. The experimental results show that \emph{SeqXFilter} achieves similar performance as baseline NNb filters, but with extremely small memory cost and simple computation logic.

preprint2020arXiv

Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.

preprint2020arXiv

SNEAP: A Fast and Efficient Toolchain for Mapping Large-Scale Spiking Neural Network onto NoC-based Neuromorphic Platform

Spiking neural network (SNN), as the third generation of artificial neural networks, has been widely adopted in vision and audio tasks. Nowadays, many neuromorphic platforms support SNN simulation and adopt Network-on-Chips (NoC) architecture for multi-cores interconnection. However, interconnection brings huge area overhead to the platform. Moreover, run-time communication on the interconnection has a significant effect on the total power consumption and performance of the platform. In this paper, we propose a toolchain called SNEAP for mapping SNNs to neuromorphic platforms with multi-cores, which aims to reduce the energy and latency brought by spike communication on the interconnection. SNEAP includes two key steps: partitioning the SNN to reduce the spikes communicated between partitions, and mapping the partitions of SNN to the NoC to reduce average hop of spikes under the constraint of hardware resources. SNEAP can reduce more spikes communicated on the interconnection of NoC and spend less time than other toolchains in the partitioning phase. Moreover, the average hop of spikes is reduced more by SNEAP within a time period, which effectively reduces the energy and latency on the NoC-based neuromorphic platform. The experimental results show that SNEAP can achieve 418x reduction in end-to-end execution time, and reduce energy consumption and spike latency, on average, by 23% and 51% respectively, compared with SpiNeMap.

preprint2020arXiv

Structure-driven intercalated architecture of septuple-atomic-layer $MA_2Z_4$ family with diverse properties from semiconductor to topological insulator to Ising superconductor

Motivated by the fact that septuple-atomic-layer MnBi$_2$Te$_4$ can be structurally viewed as the combination of double-atomic-layer MnTe intercalating into quintuple-atomic-layer Bi$_2$Te$_3$, we present a general approach of constructing twelve septuple-atomic-layer $α_i$- and $β_i$-$MA_2Z_4$ monolayer family (\emph{i} = 1 to 6) by intercalating MoS$_2$-type $MZ$$_2$ monolayer into InSe-type A$_2$Z$_2$ monolayer. Besides reproducing the experimentally synthesized $α_1$-MoSi$_2$N$_4$, $α_1$-WSi$_2$N$_4$ and $β_5$-MnBi$_2$Te$_4$ monolayer materials, another 66 thermodynamically and dynamically stable $MA_2Z_4$ were predicted, which span a wide range of properties upon the number of valence electrons (VEC). $MA_2Z_4$ with the rules of 32 or 34 VEC are mostly semiconductors with direct or indirect band gap and, however, with 33 VEC are generally metal, half-metal ferromagnetism, or spin-gapless semiconductor upon whether or not an unpaired electron is spin polarized. Moreover, we propose $α_2$-WSi$_2$P$_4$ for the spin-valley polarization, $α_1$-TaSi$_2$N$_4$ for Ising superconductor and $β_2$-SrGa$_2$Se$_4$ for topological insulator.

preprint2020arXiv

Tensor network representations of parton wave functions

Tensor network states and parton wave functions are two pivotal methods for studying quantum many-body systems. This work connects these two subjects as we demonstrate that a variety of parton wave functions, such as projected Fermi sea and projected fermionic or bosonic paired states, can be represented exactly as tensor networks. The results can be compressed into matrix product states with moderate bond dimensions so various physical quantities can be computed efficiently. For the projected Fermi sea, we develop an excellent compression scheme with high fidelity using maximally localized Wannier orbitals. Numerical calculations on two parton wave functions demonstrate that our method exceeds commonly adopted Monte Carlo methods in some aspects. It produces energy and correlation function with very high accuracy that is difficult to achieve using Monte Carlo method. The entanglement measures that were almost impossible to compute before can also be obtained easily using our method.

preprint2020arXiv

The Collectivity of Heavy Mesons in Proton-Nucleus Collisions

Using a model based on the Color Glass Condensate framework and the dilute-dense factorization, we systematically study the azimuthal angular correlations between a heavy flavor meson and a light reference particle in proton-nucleus collisions. The obtained second harmonic coefficients (also known as the elliptic flows) for $J/ψ$ and $D^0$ agree with recent experimental data from the LHC. We also provide predictions for the elliptic flows of $Υ$ and $B$ meson, which can be measured in the near future at the LHC. This work can shed light on the physics origin of the collectivity phenomenon in the collisions of small systems.

preprint2020arXiv

The three-level coupled Maxwell-Bloch equations: rogue waves, semirational rogue waves and W-shaped solitons

In this paper the coupled Maxwell-Bloch equations which describe the propagation of two optical pulses in an optical medium with coherent three-level atoms are studied by Darboux transformation. The general nth-order rogue wave solution involving two different choices of multiple roots for the spectral characteristic equation and the multiparametric nth-order semirational solution are both obtained in terms of Schur polynomials. The explicit rogue wave solutions and semirational solutions from first to second order are provided. In contrast to the known Peregrine soliton, dark and four-petaled structures, some unusual patterns such as triple-hole, twisted-pair, composite four-petaled and composite dark rogue waves are put forward. Moreover, the interaction between dark-bright soliton and dark rogue wave and interaction between breather and dark rogue wave are shown. Further, the higher-order nonlinear superposition modes which feature triple and quadruple temporal-spatial distributions are presented. Finally, the state transition between rogue wave and W-shaped soliton is found where the modulation instability growth rate tends to zero under the low perturbation frequency. Particularly, the dark and double-peak W-shaped solitons are examined.

preprint2020arXiv

Topological Thouless Pumping of Ultracold Fermions

A gas of electrons in a one-dimensional periodic potential can be transported even in the absence of a voltage bias if the potential is modulated slowly and periodically in time. Remarkably, the transferred charge per cycle is only sensitive to the topology of the path in parameter space. Although this so-called Thouless charge pump has first been proposed more than thirty years ago, it has not yet been realized. Here we report the first demonstration of topological Thouless pumping using ultracold atoms in a dynamically controlled optical superlattice. We observe a shift of the atomic cloud as a result of pumping and extract the topological invariance of the pumping process from this shift. We demonstrate the topological nature of the Thouless pump by varying the topology of the pumping path and verify that the topological pump indeed works in the quantum region by varying speed and temperature.

preprint2019arXiv

Gate-Tunable Graphene Hall Sensors with High Magnetic Field Sensitivity

Solid-state magnetic field sensors are important to both modern electronics and fundamental materials science. Many types of these sensors maintain high sensitivity only in a limited range of temperature and background magnetic field, but Hall-effect sensors are in principle able to operate over a broad range of these conditions. Here, we fabricate and characterize micrometer-scale graphene Hall sensors demonstrating high magnetic field sensitivity from liquid-helium to room temperature and in background magnetic field up to several Tesla. By tuning the charge carrier density with an electrostatic gate, we optimize the magnetic field sensitivity for different working conditions. From measurements of the Hall coefficient and the Hall voltage noise at 1 kHz, we estimate an optimum magnetic field sensitivity of 80 nT Hz$^{-1/2}$ at 4.2 K, 700 nT Hz$^{-1/2}$ at room temperature, and 3 $μ$T Hz$^{-1/2}$ in 3 T background magnetic field at 4.2 K. Our devices perform competitively with the best existing Hall sensor technologies at room temperature, outperform any Hall sensors reported in the literature at 4.2 K, and demonstrate high sensitivity for the first time in a few Tesla applied magnetic field.

preprint2019arXiv

Machine Learning Holographic Mapping by Neural Network Renormalization Group

The exact holographic mapping (EHM) provides an explicit duality map between a conformal field theory (CFT) configuration and a massive field propagating on an emergent classical geometry. However, designing the optimal holographic mapping is challenging. Here we introduce the neural network renormalization group as a universal approach to design generic EHM for interacting field theories. Given a field theory action, we train a flow-based hierarchical deep generative neural network to reproduce the boundary field ensemble from uncorrelated bulk field fluctuations. In this way, the neural network develops the optimal renormalization group transformations. Using the machine-designed EHM to map the CFT back to a bulk effective action, we determine the bulk geodesic distance from the residual mutual information. We apply this approach to the complex $ϕ^4$ theory in two-dimensional Euclidian spacetime in its critical phase, and show that the emergent bulk geometry matches the three-dimensional hyperbolic geometry.

preprint2019arXiv

Magic continuum in twisted bilayer WSe2

Emergent quantum phases driven by electronic interactions can manifest in materials with narrowly dispersing, i.e. "flat", energy bands. Recently, flat bands have been realized in a variety of graphene-based heterostructures using the tuning parameters of twist angle, layer stacking and pressure, and resulting in correlated insulator and superconducting states. Here we report the experimental observation of similar correlated phenomena in twisted bilayer tungsten diselenide (tWSe2), a semiconducting transition metal dichalcogenide (TMD). Unlike twisted bilayer graphene where the flat band appears only within a narrow range around a "magic angle", we observe correlated states over a continuum of angles, spanning 4 degree to 5.1 degree. A Mott-like insulator appears at half band filling that can be sensitively tuned with displacement field. Hall measurements supported by ab initio calculations suggest that the strength of the insulator is driven by the density of states at half filling, consistent with a 2D Hubbard model in a regime of moderate interactions. At 5.1 degree twist, we observe evidence of superconductivity upon doping away from half filling, reaching zero resistivity around 3 K. Our results establish twisted bilayer TMDs as a model system to study interaction-driven phenomena in flat bands with dynamically tunable interactions.

preprint2019arXiv

NPSA: Nonorthogonal Principal Skewness Analysis

Principal skewness analysis (PSA) has been introduced for feature extraction in hyperspectral imagery. As a third-order generalization of principal component analysis (PCA), its solution of searching for the locally maximum skewness direction is transformed into the problem of calculating the eigenpairs (the eigenvalues and the corresponding eigenvectors) of a coskewness tensor. By combining a fixed-point method with an orthogonal constraint, it can prevent the new eigenpairs from converging to the same maxima that has been determined before. However, the eigenvectors of the supersymmetric tensor are not inherently orthogonal in general, which implies that the results obtained by the search strategy used in PSA may unavoidably deviate from the actual eigenpairs. In this paper, we propose a new nonorthogonal search strategy to solve this problem and the new algorithm is named nonorthogonal principal skewness analysis (NPSA). The contribution of NPSA lies in the finding that the search space of the eigenvector to be determined can be enlarged by using the orthogonal complement of the Kronecker product of the previous one, instead of its orthogonal complement space. We give a detailed theoretical proof to illustrate why the new strategy can result in the more accurate eigenpairs. In addition, after some algebraic derivations, the complexity of the presented algorithm is also greatly reduced. Experiments with both simulated data and real multi/hyperspectral imagery demonstrate its validity in feature extraction.

preprint2019arXiv

Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits

We extend the ability of unitary quantum circuits by interfacing it with classical autoregressive neural networks. The combined model parametrizes a variational density matrix as a classical mixture of quantum pure states, where the autoregressive network generates bitstring samples as input states to the quantum circuit. We devise an efficient variational algorithm to jointly optimize the classical neural network and the quantum circuit for quantum statistical mechanics problems. One can obtain thermal observables such as the variational free energy, entropy, and specific heat. As a by product, the algorithm also gives access to low energy excitation states. We demonstrate applications to thermal properties and excitation spectra of the quantum Ising model with resources that are feasible on near-term quantum computers.

preprint2018arXiv

Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings

arXiv admin note: This version has been removed as the user did not have the right to agree to the license at the time of submission

preprint2018arXiv

Phononic Weyl Nodal Straight Lines in High-Temperature Superconductor MgB$_2$

Based on first-principles calculations, we predict that the superconducting MgB$_2$ with a AlB$_2$-type centrosymmetric lattice host the so-called phononic topological Weyl nodal lines (PTWNLs) on its bulk phonon spectrum. These PTWNLs can be viewed as countless Weyl points (WPs) closely aligned along the straight lines in the $-$H-K-H direction within the three-dimensional Brillouin zone (BZ). Their topological non-trivial natures are confirmed by the calculated Berry curvature distributions on the planes perpendicular to these lines. These lines are highly unique, because they exactly locate at the high-symmetry boundary of the BZ protected by the mirror symmetry and, simultaneously, straightly transverse the whole BZ, in different from known classifications including nodal rings, nodal chains or nets, and nodal loops. On the (10$\bar{1}$0) crystal surface, the PTWNLs-induced drumhead-like non-trivial surface states appear within the rectangular area confined by the projected lines of the PTWNLs with opposite chirality. Moreover, when the mirror symmetry is broken, the double-degenerate PTWNLs are further lifted to form a pair WPs with opposite chirality. Our results pave the ways for future experimental study on topological phonons on MgB$_2$ and highlights similar results in a series of isostructural AlB$_2$-type metallic diborides.

preprint2018arXiv

Scalable Quantum Tomography with Fidelity Estimation

We propose a quantum tomography scheme for pure qudit systems which adopts random base measurements and generative learning methods, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states. We prove the validity of the scheme theoretically, and we perform numerically simulated experiments on several target states including three typical quantum information states and randomly initiated states, demonstrating its efficiency and robustness. The number of replicas required by a certain convergence criterion grows in the manner of low-degree polynomial when the system scales, thus the scheme achieves high scalability that is crucial for practical quantum state tomography.

preprint2016arXiv

A Cylindrical Basis Function for Solving Partial Differential Equations on Manifolds

Numerical solutions of partial differential equations (PDEs) on manifolds continues to generate a lot of interest among scientists in the natural and applied sciences. On the other hand, recent developments of 3D scanning and computer vision technologies have produced a large number of 3D surface models represented as point clouds. Herein, we develop a simple and efficient method for solving PDEs on closed surfaces represented as point clouds. By projecting the radial vector of standard radial basis function(RBF) kernels onto the local tangent plane, we are able to produce a representation of functions that permits the replacement of surface differential operators with their Cartesian equivalent. We demonstrate, numerically, the efficiency of the method in discretizing the Laplace Beltrami operator.

preprint2016arXiv

A robust optimal control problem with moment constraints on distribution: theoretical analysis and an algorithm

We study an optimal control problem in which both the objective function and the dynamic constraint contain an uncertain parameter. Since the distribution of this uncertain parameter is not exactly known, the objective function is taken as the worst-case expectation over a set of possible distributions of the uncertain parameter. This ambiguity set of distributions is, in turn, defined by the first two moments of the random variables involved. The optimal control is found by minimizing the worst-case expectation over all possible distributions in this set. If the distributions are discrete, the stochastic min-max optimal control problem can be converted into a convensional optimal control problem via duality, which is then approximated as a finite-dimensional optimization problem via the control parametrization. We derive necessary conditions of optimality and propose an algorithm to solve the approximation optimization problem. The results of discrete probability distribution are then extended to the case with one dimensional continuous stochastic variable by applying the control parametrization methodology on the continuous stochastic variable, and the convergence results are derived. A numerical example is present to illustrate the potential application of the proposed model and the effectiveness of the algorithm.

preprint2016arXiv

A simple linear space algorithm for computing a longest common increasing subsequence

This paper reformulates the problem of finding a longest common increasing subsequence of the two given input sequences in a very succinct way. An extremely simple linear space algorithm based on the new formula can find a longest common increasing subsequence of sizes $n$ and $m$ respectively, in time $O(nm)$ using additional $\min\{n,m\}+1$ space.

preprint2016arXiv

An extension of two-Higgs-doublet model and the excesses of 750 GeV diphoton, muon g-2 and $h\toμτ$

In this paper we simultaneously explain the excesses of the 750 GeV diphoton, muon g-2 and $h\to μτ$ in an extension of the two-Higgs-doublet model (2HDM) with additional vector-like fermions and a CP-odd scalar singlet ($P$) which is identified as the 750 GeV resonance. This 750 GeV resonance has a mixing with the CP-odd scalar ($A$) in 2HDM, which leads to a coupling between $P$ and the SM particles as well as a coupling between $A$ and the vector-like fermions. Such a mixing and couplings are strongly constrained by $τ\toμγ$, muon g-2 and the 750 GeV diphoton data. We scan over the parameter space and find that such an extension can simultaneously account for the observed excesses of 750 GeV diphoton, muon g-2 and $h\to μτ$. The 750 GeV resonance decays in exotic modes, such as $P\to hA$, $P\to HZ$, $P\to HA$ and $P\to W^\pm H^\mp$, and its width can be dozens of GeV and is sensitive to the mixing angle.

preprint2016arXiv

Breather transition dynamics, Peregrine combs/walls and modulation instability in a variable-coefficient nonlinear Schrödinger equation with higher-order effects

We study a variable-coefficient nonlinear Schrödinger (vc-NLS) equation with higher-order effects. We show that the breather solution can be converted into four types of nonlinear waves on constant backgrounds including the multi-peak solitons, antidark soliton, periodic wave and W-shaped soliton. The transition condition requiring the group velocity dispersion (GVD) and third-order dispersion (TOD) to scale linearly is obtained analytically. We display several kinds of elastic interactions between the transformed nonlinear waves. We discuss the dispersion management of multi-peak soliton, which indicates that the GVD coefficient controls the number of peaks of the wave while the TOD coefficient has compression effect. The gain or loss has influence on the amplitudes of the multi-peak soliton. We further derive the breather multiple births by using multiple compression points of Akhmediev breathers in optical fiber systems with periodic dispersion. The number of ABs depends on the amplitude of the modulation but not on its wavelength, which affects their separation distance. In the limiting case, the breather multiple births reduce to the Peregrine combs. We discuss the effects of TOD coefficient on the spatiotemporal characteristics of Peregrine combs. When the amplitude of the modulation is equal to 1, the Peregrine comb is converted into a Peregrine wall that can be seen as intermediate state between rogue wave and W-shaped soliton. We finally find that the modulational stability regions with zero growth rate coincide with the transition condition using rogue wave eigenvalues. Our results could be useful for the experimental control and manipulation of the formation of generalized Peregrine rogue waves in diverse physical systems modeled by vc-NLS equation with higher-order effects.

preprint2016arXiv

Breather-to-soliton and rogue wave-to-soliton transitions in a resonant erbium-doped fiber system with higher-order effects

Under investigation in this paper is the higherorder nonlinear Schrodinger and Maxwell-Bloch (HNLSMB) system which describes the wave propagation in an erbium-doped nonlinear fiber with higher-order effects including the fourth-order dispersion and quintic nonKerr nonlinearity. The breather and rogue wave (RW) solutions are shown that they can be converted into various soliton solutions including the multipeak soliton, periodic wave, antidark soliton, M-shaped soliton, and W-shaped soliton. In addition, under different values of higher-order effect, the locus of the eigenvalues on the complex plane which converts breathers or RWs into solitons is calculated.

preprint2016arXiv

Directional Generation of Graphene Plasmons by Near Field Interference

The highly unidirectional excitation of graphene plasmons (GPs) through near-field interference of orthogonally polarized dipoles is investigated. The preferred excitation direction of GPs by a single circularly polarized dipole can be simply understood with the angular momentum conservation law. Moreover, the propagation direction of GPs can be switched not only by changing the phase difference between dipoles, but also by placing the z-polarized dipole to its image position, whereas the handedness of the background field remains the same. The unidirectional excitation of GPs can be extended directly into arc graphene surface as well. Furthermore, our proposal on directional generation of GPs can be realized in a semiconductor nanowire/graphene system, where a semiconductor nanowire can mimic the circularly polarized dipole when illuminated by two orthogonally polarized plane waves.

preprint2016arXiv

Discovering Phase Transitions with Unsupervised Learning

Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems. Starting with raw spin configurations of a prototypical Ising model, we use principal component analysis to extract relevant low dimensional representations the original data and use clustering analysis to identify distinct phases in the feature space. This approach successfully finds out physical concepts such as order parameter and structure factor to be indicators of the phase transition. We discuss future prospects of discovering more complex phases and phase transitions using unsupervised learning techniques.

preprint2016arXiv

Efficient polarization insensitive complex wavefront control using Huygens' metasurfaces based on dielectric resonant meta-atoms

Subwavelength-thin metasurfaces have shown great promises for the control of optical wavefronts, thus opening new pathways for the development of efficient flat optics. In particular, Huygens' metasurfaces based on all-dielectric resonant meta-atoms have already shown a huge potential for practical applications with their polarization insensitivity and high transmittance efficiency. Here, we experimentally demonstrate a polarization insensitive holographic Huygens' metasurface based on dielectric resonant meta-atoms capable of complex wavefront control at telecom wavelengths. Our metasurface produces a hologram image in the far-field with 82% transmittance efficiency and 40% imaging efficiency. Such efficient complex wavefront control shows that Huygens' metasurfaces based on resonant dielectric meta-atoms are a big step towards practical applications of metasurfaces in wavefront design related technologies, including computer-generated holograms, ultra-thin optics, security and data storage devices.

preprint2016arXiv

Electron optics with ballistic graphene junctions

Electrons transmitted across a ballistic semiconductor junction undergo refraction, analogous to light rays across an optical boundary. A pn junction theoretically provides the equivalent of a negative index medium, enabling novel electron optics such as negative refraction and perfect (Veselago) lensing. In graphene, the linear dispersion and zero-gap bandstructure admit highly transparent pn junctions by simple electrostatic gating, which cannot be achieved in conventional semiconductors. Moreover ballistic transport over micron length scales at ambient temperature has been realized, providing an ideal platform to realize a new generation of device based on electron lensing. Robust demonstration of these effects, however, has not been forthcoming. Here we employ transverse magnetic focusing to probe propagation across an electrostatically defined graphene junction. We find perfect agreement with the predicted Snells law for electrons, including observation of both positive and negative refraction. Resonant transmission across the pn junction provides a direct measurement of the angle dependent transmission coefficient, and we demonstrate good agreement with theory. Comparing experimental data with simulation reveals the crucial role played by the effective junction width, providing guidance for future device design. Our results pave the way for realizing novel electron optics based on graphene pn junctions.

preprint2016arXiv

Explaining 750 GeV diphoton excess from top/bottom partner cascade decay in two-Higgs-doublet model extension

In this paper, we interpret the 750 GeV diphoton excess in the Zee-Babu extension of the two-Higgs-doublet model by introducing a top partner ($T$)/bottom partner ($B$). In the alignment limit, the 750 GeV resonance is identified as the heavy CP-even Higgs boson ($H$), which can be sizably produced via the QCD process $pp \to T\bar{T}$ or $pp \to B\bar{B}$ followed by the decay $T\to Ht$ or $B \to Hb$. The diphoton decay rate of $H$ is greatly enhanced by the charged singlet scalars predicted in the Zee-Babu extension and the total width of $H$ can be as large as 7 GeV. Under the current LHC constraints, we scan the parameter space and find that such an extension can account for the observed diphoton excess.

preprint2016arXiv

Exploiting Structure Sparsity for Covariance-based Visual Representation

The past few years have witnessed increasing research interest on covariance-based feature representation. A variety of methods have been proposed to boost its efficacy, with some recent ones resorting to nonlinear kernel technique. Noting that the essence of this feature representation is to characterise the underlying structure of visual features, this paper argues that an equally, if not more, important approach to boosting its efficacy shall be to improve the quality of this characterisation. Following this idea, we propose to exploit the structure sparsity of visual features in skeletal human action recognition, and compute sparse inverse covariance estimate (SICE) as feature representation. We discuss the advantage of this new representation on dealing with small sample, high dimensionality, and modelling capability. Furthermore, utilising the monotonicity property of SICE, we efficiently generate a hierarchy of SICE matrices to characterise the structure of visual features at different sparsity levels, and two discriminative learning algorithms are then developed to adaptively integrate them to perform recognition. As demonstrated by extensive experiments, the proposed representation leads to significantly improved recognition performance over the state-of-the-art comparable methods. In particular, as a method fully based on linear technique, it is comparable or even better than those employing nonlinear kernel technique. This result well demonstrates the value of exploiting structure sparsity for covariance-based feature representation.

preprint2016arXiv

First order topological phase transition of the Haldane--Hubbard model

We study the interplay of topological band structure and conventional magnetic long-range order in spinful Haldane model with onsite repulsive interaction. Using the dynamical cluster approximation with clusters of up to 24 sites we find evidence of a first order phase transition from a Chern insulator at weak coupling to a topologically trivial antiferromagnetic insulator at strong coupling. These results call into question a previously found intermediate state with coexisting topological character and antiferromagnetic long-range order. Experimentally measurable signatures of the first order transition include hysteretic behavior of the double occupancy, single-particle excitation gap and nearest neighbor spin-spin correlations. This first order transition is contrasted with a continuous phase transition from the conventional band insulator to the antiferromagnetic insulator in the ionic Hubbard model on the honeycomb lattice.

preprint2016arXiv

Giant room temperature interface spin Hall and inverse spin Hall effects

The spin Hall angle (SHA) is a measure of the efficiency with which a transverse spin current is generated from a charge current by the spin-orbit coupling and disorder in the spin Hall effect (SHE). In a study of the SHE for a Pt$|$Py (Py=Ni$_{80}$Fe$_{20}$) bilayer using a first-principles scattering approach, we find a SHA that increases monotonically with temperature and is proportional to the resistivity for bulk Pt. By decomposing the room temperature SHE and inverse SHE currents into bulk and interface terms, we discover a giant interface SHA that dominates the total inverse SHE current with potentially major consequences for applications.

preprint2016arXiv

Highly controlled coating of a biomimetic polymer in TiO2 nanotubes

Highly controlled coating of biomimetic polydopamine (PDA) was achieved on titanium dioxide nanotubes (TiO2 NTs) by exposing TiO2 NT arrays to a slightly alkaline dopamine solution. The thin films act as photonic sensitizers (enhancing photocurrents and photodegradation) in the visible light range. The PDA coatings can furthermore be used as a platform for decorating the TiO2 NTs with different co-catalysts and metal nanoparticles (NPs).

preprint2016arXiv

Implication of the 750 GeV diphoton resonance on two-Higgs-doublet model and its extensions with Higgs field

We examine the implication of the 750 GeV diphoton resonance on the two-Higgs-doublet model imposing various theoretical and experimental constraints. The production rate of two-Higgs-doublet model is smaller than the cross section observed at the LHC by two order magnitude. In order to accommodate the 750 GeV diphoton resonance, we extend the two-Higgs-doublet model by introducing additional Higgs fields, and focus on two different extensions, an inert complex Higgs triplet and a real scalar septuplet. With the 125 GeV Higgs being agreement with the observed data, the production rate for the 750 GeV diphoton resonance can be enhanced to 0.6 fb for the former and 4.5 fb for the latter. The results of the latter are well consistent with the 750 GeV diphoton excess at the LHC.

preprint2016arXiv

LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation

This paper addresses the task of separating ground points from airborne LiDAR point cloud data in urban areas. A novel ground filtering method using scan line segmentation is proposed here, which we call SLSGF. It utilizes the scan line information in LiDAR data to segment the LiDAR data. The similarity measurements are designed to make it possible to segment complex roof structures into a single segment as much as possible so the topological relationships between the roof and the ground are simpler, which will benefit the labeling process. In the labeling process, the initial ground segments are detected and a coarse to fine labeling scheme is applied. Data from ISPRS 2011 are used to test the accuracy of SLSGF; and our analytical and experimental results show that this method is computationally-efficient and noise-insensitive, thereby making a denoising process unnecessary before filtering.

preprint2016arXiv

Multiple Hot-Carrier Collection in Photo-Excited Graphene Moire Superlattices

In conventional light harvesting devices, the absorption of a single photon only excites one electron, which sets the standard limit of power-conversion efficiency, such as the Shockley-Queisser limit. In principle, generating and harnessing multiple carriers per absorbed photon can improve the efficiency and possibly overcome this limit. Here, we report the observation of multiple hot carrier collection in graphene-boron-nitride Moire superlattice structures. A record-high zero-bias photoresponsivity of 0.3 ampere per watt, equivalently, an external quantum efficiency exceeding 50 percent, is achieved utilizing graphene photo-Nernst effect, which demonstrates a collection of at least 5 carriers per absorbed photon. We reveal that this effect arises from the enhanced Nernst coefficient through Lifshtiz transition at low energy Van Hove singularities, which is an emergent phenomenon due to the formation of Moire minibands. Our observation points to a new means for extremely efficient and flexible optoelectronics based on van der Waals heterostructures.

preprint2016arXiv

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.

preprint2016arXiv

Phase-locked array of quantum cascade lasers with an intracavity spatial filter

Phase-locking an array of quantum cascade lasers is an effective way to achieve higher output power and beam shaping. In this article, based on Talbot effect, we show a new-type phase-locked array of mid-infrared quantum cascade lasers with an integrated spatial- filtering Talbot cavity. All the arrays show stable in-phase operation from the threshold current to full power current. The beam divergence of the array device is smaller than that of a single-ridge laser. We use the multi-slit Fraunhofer diffraction mode to interpret the far-field radiation profile and give a solution to get better beam quality. The maximum power is just about 5 times that of a single-ridge laser for eleven-laser array device and 3 times for seven-laser array device. Considering the great modal selection ability, simple fabricating process and the potential for achieving better beam quality and smaller cavity loss, this new-type phase-locked array may be a hopeful and elegant solution to get high power or beam shaping.

preprint2016arXiv

Probing a pseudoscalar at the LHC in light of $R(D^{(*)})$ and muon g-2 excesses

We study the excesses of $R(D^{(*)})$ and muon $g-2$ in the framework of a two-Higgs-doublet model with top quark flavor-changing neutral-current (FCNC) couplings. Considering the relevant theoretical and experimental constraints, we find that the $R(D^{(*)})$ and muon $g-2$ excesses can be simultaneously explained in a parameter space allowed by the constraints. In such a parameter space the pseudoscalar ($A$) has a mass between 20 GeV and 150 GeV so that it can be produced from the top quark FCNC decay $t\to A c$. Focusing on its dominant decay $A\to τ\barτ$, we perform a detailed simulation on $pp\to t\bar{t}\to Wb Ac\to jjbcτ\barτ$ and find that the $2σ$ upper limits from a data set of 30 (100) fb$^{-1}$ at the 13 TeV LHC can mostly (entirely) exclude such a parameter space.

preprint2016arXiv

Simulation of heat transport in low-dimensional oscillator lattices

The study of heat transport in low-dimensional oscillator lattices presents a formidable challenge. Theoretical efforts have been made trying to reveal the underlying mechanism of diversified heat transport behaviors. In lack of a unified rigorous treatment, approximate theories often may embody controversial predictions. It is therefore of ultimate importance that one can rely on numerical simulations in the investigation of heat transfer processes in low-dimensional lattices. The simulation of heat transport using the non-equilibrium heat bath method and the Green-Kubo method will be introduced. It is found that one-dimensional (1D), two-dimensional (2D) and three-dimensional (3D) momentum-conserving nonlinear lattices display power-law divergent, logarithmic divergent and constant thermal conductivities, respectively. Next, a novel diffusion method is also introduced. The heat diffusion theory connects the energy diffusion and heat conduction in a straightforward manner. This enables one to use the diffusion method to investigate the objective of heat transport. In addition, it contains fundamental information about the heat transport process which cannot readily be gathered otherwise.

preprint2016arXiv

Stationary nonlinear waves, superposition modes and modulational instability characteristics in the AB system

We study the AB system describing marginally unstable baroclinic wave packets in geophysical fluids and also ultra-short pulses in nonlinear optics. We show that the breather can be converted into different types of stationary nonlinear waves on constant backgrounds, including the multi-peak soliton, M-shaped soliton, W-shaped soliton and periodic wave. We also investigate the nonlinear interactions between these waves, which display some novel patterns due to the non-propagating characteristics of the solitons: (1) Two antidark solitons can produce a W-shaped soliton instead of a higher-order antidark one, (2) The interaction between an antidark soliton and a W-shaped soliton can not only generate a higher-order antidark soliton, but also form a W-shaped solion pair, (3) The interactions between an oscillation W-shaped soliton and an oscillation M-shaped soliton show the multipeak structures. We find that the transition occurs at a modulational stability region in a low perturbation frequency region.

preprint2016arXiv

Stochastic series expansion simulation of the $t$-$V$ model

We present an algorithm for the efficient simulation of the half-filled spinless $t$-$V$ model on bipartite lattices, which combines the stochastic series expansion method with determinantal quantum Monte Carlo techniques widely used in fermionic simulations. The algorithm scales linearly in the inverse temperature, cubically with the system size and is free from the time-discretization error. We use it to map out the finite temperature phase diagram of the spinless $t$-$V$ model on the honeycomb lattice and observe a suppression of the critical temperature of the charge density wave phase in the vicinity of a fermionic quantum critical point.

preprint2016arXiv

Tantalum nitride nanotube photoanodes: establishing a beneficial back-contact by lift-off and transfer to titanium nitride layer

In this work we introduce the use of TiN/Ti2 N layers as a back contact for lifted-off membranes of anodic Ta3N5 nanotube layers. In photoelectrochemical H2 generation experiments under simulated AM 1.5G light, shift of the onset potential for anodic photocurrents to lower potentials is observed, as well as a higher magnitude of the photocurrents compared to conventional Ta3N5 nanotubes (~ 0.5 V RHE ). We ascribe this beneficial effect to the improved conductive properties of the TiNx -based back contact layer that enables a facilitated electron-transport for tantalum-nitride based materials to the conductive substrate.

preprint2016arXiv

Tetravalent edge-transitive Cayley graphs of Frobenius groups

In this paper, we give a characterization for a class of edge-transitive Cayley graphs, and provide methods for constructing Cayley graphs with certain symmetry properties. Also this study leads to construct and characterise a new family of half-transitive graphs.

preprint2016arXiv

The hot pick-up technique for batch assembly of van der Waals heterostructures

The assembly of individual two-dimensional materials into van der Waals heterostructures enables the construction of layered three-dimensional materials with desirable electronic and optical properties. A core problem in the fabrication of these structures is the formation of clean interfaces between the individual two-dimensional materials which would affect device performance. We present here a technique for the rapid batch fabrication of van der Waals heterostructures, demonstrated by the controlled production of 22 mono-, bi- and trilayer graphene stacks encapsulated in hexagonal boron nitride with close to 100% yield. For the monolayer devices we found semiclassical mean free paths up to 0.9 micrometer, with the narrowest samples showing clear indications of the transport being affected by boundary scattering. The presented method readily lends itself to fabrication of van der Waals heterostructures in both ambient and controlled atmospheres, while the ability to assemble pre-patterned layers paves the way for complex three-dimensional architectures.

preprint2016arXiv

Toward a fractal spectrum approach for neutron and gamma pulse shape discrimination

There is a key research issue to accurately select out neutron signals and discriminate gamma signals from a mixed radiation field in the neutron detection. This paper proposes a fractal spectrum discrimination approach by means of different spectrum characteristics of neutron and gamma. Figure of merit and average discriminant error ratio are adopted together to evaluate the discriminant effects. Different neutron and gamma signals with various noises and pulse pile-ups are simulated according to real data in the literature. The proposed approach is compared with the digital charge integration and pulse gradient methods. It is found that the fractal approach exhibits the best discriminant performance among three methods. The fractal spectrum approach is not sensitive to the high frequency noises and pulse pile-ups. It means that the proposed approach takes the advantages of anti-noises and high discriminant ability, and can be used to better discriminate neutron and gamma in neutron detection.

preprint2016arXiv

Tungsten doping of Ta3N5-Nanotubes for Band Gap Narrowing and Enhanced Photoelectrochemical Water Splitting Efficiency

Ordered W-doped Ta2O5 nanotube arrays were grown by self-organizing electrochemical anodization of TaW alloys with different tungsten concentrations and by a suitable high temperature ammonia treatment, fully converted to W:Ta3N5 tubular structures. A main effect found is that W doping can decrease the band gap from 2 eV (bare Ta3N5) down to 1.75 eV. Ta3N5 nanotubes grown on 0.5 at% W alloy and modified with (CoOH)x as co-catalyst show ~33% higher photocurrents in photoelectrochemical (PEC) water splitting than pure Ta3N5.

preprint2015arXiv

A light pseudoscalar of 2HDM confronted with muon g-2 and experimental constraints

A light pseudoscalar of the lepton-specific 2HDM can enhance the muon g-2, but suffer from various constraints easily, such as the 125.5 GeV Higgs signals, non-observation of additional Higgs at the collider and even $B_s\to μ^+μ^-$. In this paper, we take the light CP-even Higgs as the 125.5 GeV Higgs, and examine the implications of those observables on a pseudoscalar with the mass below the half of 125.5 GeV. Also the other relevant theoretical and experimental constraints are considered. We find that the pseudoscalar can be allowed to be as low as 10 GeV, but the corresponding $\tanβ$, $\sin(β-α)$ and the mass of charged Higgs are strongly constrained. In addition, the surviving samples favor the wrong-sign Yukawa coupling region, namely that the 125.5 GeV Higgs couplings to leptons have opposite sign to the couplings to gauge bosons and quarks.

preprint2015arXiv

A Practical O(R\log\log n+n) time Algorithm for Computing the Longest Common Subsequence

In this paper, we revisit the much studied LCS problem for two given sequences. Based on the algorithm of Iliopoulos and Rahman for solving the LCS problem, we have suggested 3 new improved algorithms. We first reformulate the problem in a very succinct form. The problem LCS is abstracted to an abstract data type DS on an ordered positive integer set with a special operation Update(S,x). For the two input sequences X and Y of equal length n, the first improved algorithm uses a van Emde Boas tree for DS and its time and space complexities are O(R\log\log n+n) and O(R), where R is the number of matched pairs of the two input sequences. The second algorithm uses a balanced binary search tree for DS and its time and space complexities are O(R\log L+n) and O(R), where L is the length of the longest common subsequence of X and Y. The third algorithm uses an ordered vector for DS and its time and space complexities are O(nL) and O(R).

preprint2015arXiv

A simplified 2HDM with a scalar dark matter and the galactic center gamma-ray excess

Due to the strong constrain from the LUX experiment, the scalar portal dark matter can not generally explain a gamma-ray excess in the galactic center by the annihilation of dark matter into $b\bar{b}$. With the motivation of eliminating the tension, we add a scalar dark matter to the aligned two-Higgs-doublet model, and focus on a simplified scenario, which has two main characteristics: (i) The heavy CP-even Higgs is the discovered 125 GeV Higgs boson, which has the same couplings to the gauge bosons and fermions as the SM Higgs. (ii) Only the light CP-even Higgs mediates the dark matter interactions with SM particles, which has no couplings to $WW$ and $ZZ$, but the independent couplings to the up-type quarks, down-type quarks and charged leptons. We find that the tension between $<σv>_{SS\to b\bar{b}}$ and the constraint from LUX induced by the scalar portal dark matter can go away for the isospin-violating dark matter-nucleon coupling with $-1.0< f^n/f^p<0.7$, and the constraints from the Higgs search experiments and the relic density of Planck are also satisfied.

preprint2015arXiv

An Efficient Dynamic Programming Algorithm for STR-IC-SEQ-EC-LCS Problem

In this paper, we consider a generalized longest common subsequence problem, in which a constraining sequence of length $s$ must be included as a substring and the other constraining sequence of length $t$ must be excluded as a subsequence of two main sequences and the length of the result must be maximal. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and the given two constraining sequences of length $s$ and $t$, we present an $O(nmst)$ time dynamic programming algorithm for solving the new generalized longest common subsequence problem. The time complexity can be reduced further to cubic time in a more detailed analysis. The correctness of the new algorithm is proved.

preprint2015arXiv

An efficient dynamic programming algorithm for the generalized LCS problem with multiple substring inclusive constraints

In this paper, we consider a generalized longest common subsequence problem with multiple substring inclusive constraints. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a set of $d$ constraints $P=\{P_1,\cdots,P_d\}$ of total length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ including each of constraint string in $P$ as a substring and the length of $Z$ is maximized. A new dynamic programming solution to this problem is presented in this paper. The correctness of the new algorithm is proved. The time complexity of our algorithm is $O(d2^dnmr)$. In the case of the number of constraint strings is fixed, our new algorithm for the generalized longest common subsequence problem with multiple substring inclusive constraints requires $O(nmr)$ time and space.

preprint2015arXiv

Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering

In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the question representation, a Convolutional Neural Network (CNN) to extract the visual representation, an LSTM for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer. We construct a Freestyle Multilingual Image Question Answering (FM-IQA) dataset to train and evaluate our mQA model. It contains over 150,000 images and 310,000 freestyle Chinese question-answer pairs and their English translations. The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test. Specifically, we mix the answers provided by humans and our model. The human judges need to distinguish our model from the human. They will also provide a score (i.e. 0, 1, 2, the larger the better) indicating the quality of the answer. We propose strategies to monitor the quality of this evaluation process. The experiments show that in 64.7% of cases, the human judges cannot distinguish our model from humans. The average score is 1.454 (1.918 for human). The details of this work, including the FM-IQA dataset, can be found on the project page: http://idl.baidu.com/FM-IQA.html

preprint2015arXiv

Benchmarking Big Data Systems: State-of-the-Art and Future Directions

The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about how we design generators to produce data with the 4V properties (i.e. volume, velocity, variety and veracity), as well as implement application-specific but still comprehensive workloads. However, most of the existing big data benchmarks can be described as attempts to solve specific problems in benchmarking systems. This article investigates the state-of-the-art in benchmarking big data systems along with the future challenges to be addressed to realize a successful and efficient benchmark.

preprint2015arXiv

BigDataBench-MT: A Benchmark Tool for Generating Realistic Mixed Data Center Workloads

Long-running service workloads (e.g. web search engine) and short-term data analysis workloads (e.g. Hadoop MapReduce jobs) co-locate in today's data centers. Developing realistic benchmarks to reflect such practical scenario of mixed workload is a key problem to produce trustworthy results when evaluating and comparing data center systems. This requires using actual workloads as well as guaranteeing their submissions to follow patterns hidden in real-world traces. However, existing benchmarks either generate actual workloads based on probability models, or replay real-world workload traces using basic I/O operations. To fill this gap, we propose a benchmark tool that is a first step towards generating a mix of actual service and data analysis workloads on the basis of real workload traces. Our tool includes a combiner that enables the replaying of actual workloads according to the workload traces, and a multi-tenant generator that flexibly scales the workloads up and down according to users' requirements. Based on this, our demo illustrates the workload customization and generation process using a visual interface. The proposed tool, called BigDataBench-MT, is a multi-tenant version of our comprehensive benchmark suite BigDataBench and it is publicly available from http://prof.ict.ac.cn/BigDataBench/multi-tenancyversion/.

preprint2015arXiv

Boosting-like Deep Learning For Pedestrian Detection

This paper proposes boosting-like deep learning (BDL) framework for pedestrian detection. Due to overtraining on the limited training samples, overfitting is a major problem of deep learning. We incorporate a boosting-like technique into deep learning to weigh the training samples, and thus prevent overtraining in the iterative process. We theoretically give the details of derivation of our algorithm, and report the experimental results on open data sets showing that BDL achieves a better stable performance than the state-of-the-arts. Our approach achieves 15.85% and 3.81% reduction in the average miss rate compared with ACF and JointDeep on the largest Caltech benchmark dataset, respectively.

preprint2015arXiv

Characterization and Architectural Implications of Big Data Workloads

Big data areas are expanding in a fast way in terms of increasing workloads and runtime systems, and this situation imposes a serious challenge to workload characterization, which is the foundation of innovative system and architecture design. The previous major efforts on big data benchmarking either propose a comprehensive but a large amount of workloads, or only select a few workloads according to so-called popularity, which may lead to partial or even biased observations. In this paper, on the basis of a comprehensive big data benchmark suite---BigDataBench, we reduced 77 workloads to 17 representative workloads from a micro-architectural perspective. On a typical state-of-practice platform---Intel Xeon E5645, we compare the representative big data workloads with SPECINT, SPECCFP, PARSEC, CloudSuite and HPCC. After a comprehensive workload characterization, we have the following observations. First, the big data workloads are data movement dominated computing with more branch operations, taking up to 92% percentage in terms of instruction mix, which places them in a different class from Desktop (SPEC CPU2006), CMP (PARSEC), HPC (HPCC) workloads. Second, corroborating the previous work, Hadoop and Spark based big data workloads have higher front-end stalls. Comparing with the traditional workloads i. e. PARSEC, the big data workloads have larger instructions footprint. But we also note that, in addition to varied instruction-level parallelism, there are significant disparities of front-end efficiencies among different big data workloads. Third, we found complex software stacks that fail to use state-of-practise processors efficiently are one of the main factors leading to high front-end stalls. For the same workloads, the L1I cache miss rates have one order of magnitude differences among diverse implementations with different software stacks.

preprint2015arXiv

Cosmic reionization of hydrogen and helium: contribution from both mini-quasars and stars

Observations on the high-redshift galaxies at $z>6$ imply that their ionizing emissivity is unable to fully reionize the Universe at $z\sim 6$. Either a high escape fraction of ionizing photons from these galaxies or a large population of faint galaxies below the detection limit are required. However, these requirements are somewhat in tension with present observations. In this work, we explored the combined contribution of mini-quasars and stars to the reionization of cosmic hydrogen and helium. Our model is roughly consistent with: (1) the low escape fractions of ionizing photons from the observed galaxies, (2) the optical depth of Cosmic Microwave Background (CMB) measured by the WMAP-7, and (3) the redshift of the end of hydrogen and helium reionization at $z\approx 6$ and $z\approx 3$, respectively. Neither an extremely high escape fraction nor a large population of fainter galaxies is required in this scenario. In our most optimistic model, more than $\sim20\%$ of the cosmic helium is reionized by $z\sim6$, and the ionized fraction of cosmic helium rapidly climbs to more than $50\%$ by $z\sim5$. These results may imply that better measurements of helium reionization, especially at high redshifts, could be helpful in constraining the growth of intermediate-mass black holes (IMBHs) in the early Universe, which would shed some light on the puzzles concerning the formation of supermassive black holes (SMBHs).

preprint2015arXiv

Efficient Continuous-time Quantum Monte Carlo Method for the Ground State of Correlated Fermions

We present the ground state extension of the efficient quantum Monte Carlo algorithm for lattice fermions of arXiv:1411.0683. Based on continuous-time expansion of imaginary-time projection operator, the algorithm is free of systematic error and scales \emph{linearly} with projection time and interaction strength. Compared to the conventional quantum Monte Carlo methods for lattice fermions, this approach has greater flexibility and is easier to combine with powerful machinery such as histogram reweighting and extended ensemble simulation techniques. We discuss the implementation of the continuous-time projection in detail using the spinless $t-V$ model as an example and compare the numerical results with exact diagonalization, density-matrix-renormalization-group and infinite projected entangled-pair states calculations. Finally we use the method to study the fermionic quantum critical point of spinless fermions on a honeycomb lattice and confirm previous results concerning its critical exponents.

preprint2015arXiv

Entanglement as a resource in adiabatic quantum optimization

We explore the role of entanglement in adiabatic quantum optimization by performing approximate simulations of the real-time evolution of a quantum system while limiting the amount of entanglement. To classically simulate the time evolution of the system with a limited amount of entanglement, we represent the quantum state using matrix-product states and projected entangled-pair states. We show that the probability of finding the ground state of an Ising spin glass on either a planar or non-planar two-dimensional graph increases rapidly as the amount of entanglement in the state is increased. Furthermore, we propose evolution in complex time as a way to improve simulated adiabatic evolution and mimic the effects of thermal cooling of the quantum annealer.

preprint2015arXiv

Fate of the Kondo Effect and Impurity Quantum Phase Transitions Through the Lens of Fidelity Susceptibility

The Kondo effect is an ubiquitous phenomenon appearing at low temperature in quantum confined systems coupled to a continuous bath. Efforts in understanding and controlling it have triggered important developments across several disciplines of condensed matter physics. A recurring pattern in these studies is that the suppression of the Kondo effect often results in intriguing physical phenomena such as impurity quantum phase transitions or non-Fermi-liquid behavior. We show that the fidelity susceptibility is a sensitive indicator for such phenomena because it quantifies the sensitivity of the system's state with respect to its coupling to the bath. We demonstrate the power of fidelity susceptibility approach by using it to identify the crossover and quantum phase transitions in the one and two impurity Anderson models.

preprint2015arXiv

Fidelity susceptibility made simple: A unified quantum Monte Carlo approach

The fidelity susceptibility is a general purpose probe of phase transitions. With its origin in quantum information and in the differential geometry perspective of quantum states, the fidelity susceptibility can indicate the presence of a phase transition without prior knowledge of the local order parameter, as well as reveal the universal properties of a critical point. The wide applicability of the fidelity susceptibility to quantum many-body systems is, however, hindered by the limited computational tools to evaluate it. We present a generic, efficient, and elegant approach to compute the fidelity susceptibility of correlated fermions, bosons, and quantum spin systems in a broad range of quantum Monte Carlo methods. It can be applied both to the ground-state and non-zero temperature cases. The Monte Carlo estimator has a simple yet universal form, which can be efficiently evaluated in simulations. We demonstrate the power of this approach with applications to the Bose-Hubbard model, the spin-$1/2$ XXZ model, and use it to examine the hypothetical intermediate spin-liquid phase in the Hubbard model on the honeycomb lattice.

preprint2015arXiv

Fractional fractal quantum Hall effect in graphene superlattices

The Hofstadter energy spectrum provides a uniquely tunable system to study emergent topological order in the regime of strong interactions. Previous experiments, however, have been limited to the trivial case of low Bloch band filling where only the Landau level index plays a significant role. Here we report measurement of high mobility graphene superlattices where the complete unit cell of the Hofstadter spectrum is accessible. We observe coexistence of conventional fractional quantum Hall effect (QHE) states together with the integer QHE states associated with the fractal Hofstadter spectrum. At large magnetic field, a new series of states appear at fractional Bloch filling index. These fractional Bloch band QHE states are not anticipated by existing theoretical pictures and point towards a new type of many-body state.

preprint2015arXiv

HEp-2 Cell Image Classification with Deep Convolutional Neural Networks

Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper presents an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. This paper elaborates the important components of this framework, discusses multiple key factors that impact the efficiency of training a deep CNN, and systematically compares this framework with the well-established image classification models in the literature. Experiments on benchmark datasets show that i) the proposed framework can effectively outperform existing models by properly applying data augmentation; ii) our CNN-based framework demonstrates excellent adaptability across different datasets, which is highly desirable for classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.

preprint2015arXiv

Higgs pair signal enhanced in the 2HDM with two degenerate 125 GeV Higgs bosons

We discuss a scenario of the type-II 2HDM in which the $b\bar{b}γγ$ rate of the Higgs pair production is enhanced due to the two nearly degenerate 125 GeV Higgs bosons ($h$, $H$). Considering various theoretical and experimental constraints, we figure out the allowed ranges of the trilinear couplings of these two Higgs bosons and calculate the signal rate of $b\bar{b}γγ$ from the productions of Higgs pairs ($hh$, $hH$, $HH$) at the LHC. We find that in the allowed parameter space some trilinear Higgs couplings can be larger than the SM value by an order and the production rate of $b\bar{b}γγ$ can be greatly enhanced. We also consider a "decoupling" benchmark point where the light CP-even Higgs has a SM-like cubic self-coupling while other trilinear couplings are very small. With a detailed simulation on the $b\bar{b}γγ$ signal and backgrounds, we find that in such a "decoupling" scenario the $hh$ and $hH$ channels can jointly enhance the statistical significance to 5$σ$ at 14 TeV LHC with an integrated luminosity of 3000 fb$^{-1}$.

preprint2015arXiv

Identifying Dwarfs Workloads in Big Data Analytics

Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units of computation to represent diversity of big data analytics workloads? Big data dwarfs are abstractions of extracting frequently appearing operations in big data computing. One dwarf represents one unit of computation, and big data workloads are decomposed into one or more dwarfs. Furthermore, dwarfs workloads rather than vast real workloads are more cost-efficient and representative to evaluate big data systems. In this paper, we extensively investigate six most important or emerging application domains i.e. search engine, social network, e-commerce, multimedia, bioinformatics and astronomy. After analyzing forty representative algorithms, we single out eight dwarfs workloads in big data analytics other than OLAP, which are linear algebra, sampling, logic operations, transform operations, set operations, graph operations, statistic operations and sort.

preprint2015arXiv

Immersion and Invariance Stabilization of Nonlinear Systems: A Horizontal Contraction Approach

The main objective of this paper is to propose an alternative procedure to carry out one of the key steps of immersion and invariance stabilising controller design. Namely, the one that ensures attractivity of the manifold whose internal dynamics contains a copy of the desired system behaviour. Towards this end we invoke contraction theory principles and ensure the attractivity of the manifold rendering it horizontally contractive. The main advantage of adopting this alternative approach is to make more systematic the last step of the design with more explicit degrees of freedom to accomplish the task. The classical case of systems in feedback form is used to illustrate the proposed controller design.

preprint2015arXiv

Joint Relay and Jammer Selection Improves the Physical Layer Security in the Face of CSI Feedback Delays

We enhance the physical-layer security (PLS) of amplify-and-forward relaying networks with the aid of joint relay and jammer selection (JRJS), despite the deliterious effect of channel state information (CSI) feedback delays. Furthermore, we conceive a new outage-based characterization approach for the JRJS scheme. The traditional best relay selection (TBRS) is also considered as a benchmark. We first derive closed-form expressions of both the connection outage probability (COP) and of the secrecy outage probability (SOP) for both the TBRS and JRJS schemes. Then, a reliable-and-secure connection probability (RSCP) is defined and analyzed for characterizing the effect of the correlation between the COP and SOP introduced by the corporate source-relay link. The reliability-security ratio (RSR) is introduced for characterizing the relationship between the reliability and security through the asymptotic analysis. Moreover, the concept of effective secrecy throughput is defined as the product of the secrecy rate and of the RSCP for the sake of characterizing the overall efficiency of the system, as determined by the transmit SNR, secrecy codeword rate and the power sharing ratio between the relay and jammer. The impact of the direct source-eavesdropper link and additional performance comparisons with respect to other related selection schemes are further included. Our numerical results show that the JRJS scheme outperforms the TBRS method both in terms of the RSCP as well as in terms of its effective secrecy throughput, but it is more sensitive to the feedback delays. Increasing the transmit SNR will not always improve the overall throughput. Moreover, the RSR results demonstrate that upon reducing the CSI feedback delays, the reliability improves more substantially than the security degrades, implying an overall improvement in terms of the security-reliability tradeoff.

preprint2015arXiv

Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

preprint2015arXiv

Learning Discriminative Stein Kernel for SPD Matrices and Its Applications

Stein kernel has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: i) Eigenvalue estimation becomes biased when the number of samples is inadequate, which may lead to unreliable kernel evaluation; ii) More importantly, eigenvalues only reflect the property of an individual SPD matrix. They are not necessarily optimal for computing Stein kernel when the goal is to discriminate different sets of SPD matrices. To address the two issues in one shot, we propose a discriminative Stein kernel, in which an extra parameter vector is defined to adjust the eigenvalues of the input SPD matrices. The optimal parameter values are sought by optimizing a proxy of classification performance. To show the generality of the proposed method, three different kernel learning criteria that are commonly used in the literature are employed respectively as a proxy. A comprehensive experimental study is conducted on a variety of image classification tasks to compare our proposed discriminative Stein kernel with the original Stein kernel and other commonly used methods for evaluating the similarity between SPD matrices. The experimental results demonstrate that, the discriminative Stein kernel can attain greater discrimination and better align with classification tasks by altering the eigenvalues. This makes it produce higher classification performance than the original Stein kernel and other commonly used methods.

preprint2015arXiv

Parallel Spectral Clustering Algorithm Based on Hadoop

Spectral clustering and cloud computing is emerging branch of computer science or related discipline. It overcome the shortcomings of some traditional clustering algorithm and guarantee the convergence to the optimal solution, thus have to the widespread attention. This article first introduced the parallel spectral clustering algorithm research background and significance, and then to Hadoop the cloud computing Framework has carried on the detailed introduction, then has carried on the related to spectral clustering is introduced, then introduces the spectral clustering arithmetic Method of parallel and relevant steps, finally made the related experiments, and the experiment are summarized.

preprint2015arXiv

Quantum Monte Carlo study of mass-imbalanced Hubbard models

Building on recent solutions of the fermion sign problem for specific models we present two continuous-time quantum Monte Carlo methods for efficient simulation of mass-imbalanced Hubbard models on bipartite lattices at half-filling. For both methods we present the solutions to the fermion sign problem and the algorithms to achieve efficient simulations. As applications, we calculate the dependence of the spin correlation on the mass imbalance in a one-dimensional lattice and study the thermal and quantum phase transitions to an antiferromagnetic Ising long-range ordered state in two dimensions. These results offer unbiased predictions for experiments on ultracold atoms and bridge known exact solutions of Falicov-Kimball model and previous studies of the SU(2)-symmetric Hubbard model.

preprint2015arXiv

Split orthogonal group: A guiding principle for sign-problem-free fermionic simulations

We present a guiding principle for designing fermionic Hamiltonians and quantum Monte Carlo (QMC) methods that are free from the infamous sign problem by exploiting the Lie groups and Lie algebras that appear naturally in the Monte Carlo weight of fermionic QMC simulations. Specifically, rigorous mathematical constraints on the determinants involving matrices that lie in the split orthogonal group provide a guideline for sign-free simulations of fermionic models on bipartite lattices. This guiding principle not only unifies the recent solutions of the sign problem based on the continuous-time quantum Monte Carlo methods and the Majorana representation, but also suggests new efficient algorithms to simulate physical systems that were previously prohibitive because of the sign problem.

preprint2015arXiv

The invariant tori of knot type and the interlinked invariant tori in the Nosé-Hoover system

We revisit the famous Nosé-Hoover system in this paper and show the existence of some averagely conservative regions which are filled with an infinite sequence of nested tori. Depending on initial conditions, some invariant tori are of trefoil knot type, while the others are of trivial knot type. Moreover, we present a variety of interlinked invariant tori whose initial conditions are chosen from different averagely conservative regions and give all the interlinking numbers of those interlinked tori, showing that this quadratic system possesses so rich dynamic properties.

preprint2015arXiv

Understanding Big Data Analytic Workloads on Modern Processors

Big data analytics applications play a significant role in data centers, and hence it has become increasingly important to understand their behaviors in order to further improve the performance of data center computer systems, in which characterizing representative workloads is a key practical problem. In this paper, after investigating three most impor- tant application domains in terms of page views and daily visitors, we chose 11 repre- sentative data analytics workloads and characterized their micro-architectural behaviors by using hardware performance counters, so as to understand the impacts and implications of data analytics workloads on the systems equipped with modern superscalar out-of-order processors. Our study reveals that big data analytics applications themselves share many inherent characteristics, which place them in a different class from traditional workloads and scale-out services. To further understand the characteristics of big data analytics work- loads we performed a correlation analysis of CPI (cycles per instruction) with other micro- architecture level characteristics and an investigation of the big data software stack impacts on application behaviors. Our correlation analysis showed that even though big data ana- lytics workloads own notable pipeline front end stalls, the main factors affecting the CPI performance are long latency data accesses rather than the front end stalls. Our software stack investigation found that the typical big data software stack significantly contributes to the front end stalls and incurs bigger working set. Finally we gave several recommen- dations for architects, programmers and big data system designers with the knowledge acquired from this paper.

preprint2014arXiv

A Generalized Probabilistic Framework for Compact Codebook Creation

Compact and discriminative visual codebooks are preferred in many visual recognition tasks. In the literature, a number of works have taken the approach of hierarchically merging visual words of an initial large-sized codebook, but implemented this approach with different merging criteria. In this work, we propose a single probabilistic framework to unify these merging criteria, by identifying two key factors: the function used to model class-conditional distribution and the method used to estimate the distribution parameters. More importantly, by adopting new distribution functions and/or parameter estimation methods, our framework can readily produce a spectrum of novel merging criteria. Three of them are specifically focused in this work. In the first criterion, we adopt the multinomial distribution with Bayesian method; In the second criterion, we integrate Gaussian distribution with maximum likelihood parameter estimation. In the third criterion, which shows the best merging performance, we propose a max-margin-based parameter estimation method and apply it with multinomial distribution. Extensive experimental study is conducted to systematically analyse the performance of the above three criteria and compare them with existing ones. As demonstrated, the best criterion obtained in our framework achieves the overall best merging performance among the comparable merging criteria developed in the literature.

preprint2014arXiv

A note on the largest number of red nodes in red-black trees

In this paper, we are interested in the number of red nodes in red-black trees. We first present an $O(n^2\log n)$ time dynamic programming solution for computing $r(n)$, the largest number of red internal nodes in a red-black tree on $n$ keys. Then the algorithm is improved to some $O(\log n)$ time recursive and nonrecursive algorithms. Based on these improved algorithms we finally find a closed-form solution of $r(n)$.

preprint2014arXiv

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data).

preprint2014arXiv

BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.

preprint2014arXiv

Characterizing and Subsetting Big Data Workloads

Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates hese challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.

preprint2014arXiv

Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors

Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, % FVC implementations employ the Gaussian mixture model (GMM) to characterize the generation process of local features. This choice has shown to be sufficient for traditional low dimensional local features, e.g., SIFT; and typically, good performance can be achieved with only a few hundred Gaussian distributions. However, the same number of Gaussians is insufficient to model the feature space spanned by higher dimensional local features, which have become popular recently. In order to improve the modeling capacity for high dimensional features, it turns out to be inefficient and computationally impractical to simply increase the number of Gaussians. In this paper, we propose a model in which each local feature is drawn from a Gaussian distribution whose mean vector is sampled from a subspace. With certain approximation, this model can be converted to a sparse coding procedure and the learning/inference problems can be readily solved by standard sparse coding methods. By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC). Moreover, we adopt the recently developed Deep Convolutional Neural Network (CNN) descriptor as a high dimensional local feature and implement image classification with the proposed SCFVC. Our experimental evaluations demonstrate that our method not only significantly outperforms the traditional GMM based Fisher vector encoding but also achieves the state-of-the-art performance in generic object recognition, indoor scene, and fine-grained image classification problems.

preprint2014arXiv

Fermionic Quantum Critical Point of Spinless Fermions on a Honeycomb Lattice

Spinless fermions on a honeycomb lattice provide a minimal realization of lattice Dirac fermions. Repulsive interactions between nearest neighbors drive a quantum phase transition from a Dirac semimetal to a charge-density-wave state through a fermionic quantum critical point, where the coupling of Ising order parameter to the Dirac fermions at low energy drastically affects the quantum critical behavior. Encouraged by a recently discovery of absence of the fermion sign problem in this model, we study the fermionic quantum critical point using the continuous time quantum Monte Carlo method with worm sampling technique. We estimate the transition point $V/t= 1.356(1)$ with the critical exponents $ν=0.80(3)$ and $η=0.302(7)$. Compatible results for the transition point are also obtained with infinite projected entangled-pair states.

preprint2014arXiv

High-Speed Electro-Optic Modulator Integrated with Graphene-Boron Nitride Heterostructure and Photonic Crystal Nanocavity

Nanoscale and power-efficient electro-optic (EO) modulators are essential components for optical interconnects that are beginning to replace electrical wiring for intra- and inter-chip communications. Silicon-based EO modulators show sufficient figures of merits regarding device footprint, speed, power consumption and modulation depth. However, the weak electro-optic effect of silicon still sets a technical bottleneck for these devices, motivating the development of modulators based on new materials. Graphene, a two-dimensional carbon allotrope, has emerged as an alternative active material for optoelectronic applications owing to its exceptional optical and electronic properties. Here, we demonstrate a high-speed graphene electro-optic modulator based on a graphene-boron nitride (BN) heterostructure integrated with a silicon photonic crystal nanocavity. Strongly enhanced light-matter interaction of graphene in a submicron cavity enables efficient electrical tuning of the cavity reflection. We observe a modulation depth of 3.2 dB and a cut-off frequency of 1.2 GHz.

preprint2014arXiv

Interaction effects on topological phase transitions via numerically exact quantum Monte Carlo calculations

We theoretically study topological phase transitions in four generalized versions of the Kane-Mele-Hubbard model with up to $2\times 18^2$ sites. All models are free of the fermion-sign problem allowing numerically exact quantum Monte Carlo (QMC) calculations to be performed to extremely low temperatures. We numerically compute the $\mathbb{Z}_2$ invariant and spin Chern number $C_σ$ directly from the zero-frequency single-particle Green's functions, and study the topological phase transitions driven by the tight-binding parameters at different on-site interaction strengths. The $\mathbb{Z}_2$ invariant and spin Chern number, which are complementary to each another, characterize the topological phases and identify the critical points of topological phase transitions. Although the numerically determined phase boundaries are nearly identical for different system sizes, we find strong system-size dependence of the spin Chern number, where quantized values are only expected upon approaching the thermodynamic limit. For the Hubbard models we considered, the QMC results show that correlation effects lead to shifts in the phase boundaries relative to those in the non-interacting limit, without any spontaneously symmetry breaking. The interaction-induced shift is non-perturbative in the interactions and cannot be captured within a "simple" self-consistent calculation either, such as Hartree-Fock. Furthermore, our QMC calculations suggest that quantum fluctuations from interactions stabilize topological phases in systems where the one-body terms preserve the $D_3$ symmetry of the lattice, and destabilize topological phases when the one-body terms break the $D_3$ symmetry.

preprint2014arXiv

LHC diphoton and Z+photon Higgs signals in the Higgs triplet model with Y=0

We study the implications of the LHC diphoton and Z+photon Higgs signals on the Higgs triplet model with Y=0. We discuss three different scenarios: (i) the observed boson is the light Higgs boson $h$; (ii) it is the heavy Higgs boson $H$; (iii) the observed signal is from the almost degenerate $h$ and $H$. We find that the inclusive Higgs diphoton rates in the first two scenarios can be enhanced or suppressed compared to the SM value, which can respectively fit the ATLAS and CMS diphoton data within $1σ$ range. The inclusive $ZZ^*$ rates are suppressed, which are outside $1σ$ range of ATLAS data and within $1σ$ range of CMS data. Meanwhile, another CP-even Higgs boson production rate can be suppressed enough not to be observed at the collider. For the third scenario, the Higgs diphoton rate is suppressed, which is outside $1σ$ range of ATLAS data, and the $ZZ^*$ rate equals to SM value approximately. In addition, we find that the two rates of $h\to γγ$ and $h\to Zγ$ have the positive correlations for the three scenarios.

preprint2014arXiv

Liquid Phase 3D Printing for Quickly Manufacturing Metal Objects with Low Melting Point Alloy Ink

Conventional 3D printings are generally time-consuming and printable metal inks are rather limited. From an alternative way, we proposed a liquid phase 3D printing for quickly making metal objects. Through introducing metal alloys whose melting point is slightly above room temperature as printing inks, several representative structures spanning from one, two and three dimension to more complex patterns were demonstrated to be quickly fabricated. Compared with the air cooling in a conventional 3D printing, the liquid-phase-manufacturing offers a much higher cooling rate and thus significantly improves the speed in fabricating metal objects. This unique strategy also efficiently prevents the liquid metal inks from air oxidation which is hard to avoid otherwise in an ordinary 3D printing. Several key physical factors (like properties of the cooling fluid, injection speed and needle diameter, types and properties of the printing ink, etc.) were disclosed which would evidently affect the printing quality. In addition, a basic route to make future liquid phase 3D printer incorporated with both syringe pump and needle arrays was also suggested. The liquid phase 3D printing method, which owns potential values not available in a conventional modality, opens an efficient way for quickly making metal objects in the coming time.

preprint2014arXiv

Locally inequivalent four qubit hypergraph states

Hypergraph states as real equally weighted pure states are important resources for quantum codes of non-local stabilizer. Using local Pauli equivalence and permutational symmetry, we reduce the 32768 four qubit real equally weighted pure states to 28 locally inequivalent hypergraph states and several graph states. The calculation of geometric entanglement supplemented with entanglement entropy confirms that further reduction is impossible for true hypergraph states.

preprint2014arXiv

Measurement of Collective Dynamical Mass of Dirac Fermions in Graphene

Individual electrons in graphene behave as massless quasiparticles. In surprising twist, it is inferred from plasmonic investigations that collectively excited graphene electrons must exhibit non-zero mass and its inertial acceleration is essential for graphene plasmonics. Despite such importance, this collective mass has defied direct unequivocal measurement. It may be directly measured by accelerating it with a time-varying voltage and quantifying the phase delay of the resulting current; this voltage-current phase relation would manifest as kinetic inductance, representing the collective inertia's reluctance to accelerate. However, at optical (infrared) frequencies phase measurement of current is generally difficult and at microwave frequencies the inertial phase delay has been buried under electron scattering. Here we directly, precisely measure the kinetic inductance, thus, collective mass, by combining innovative device engineering that reduces electron scattering and delicate microwave phase measurements. Particularly, encapsulation of graphene between hexagonal-boron-nitride layers, one-dimensional edge contacts, and a proximate top gate configured as microwave ground together enable resolving the inertial phase delay from the electron scattering. Beside the fundamental importance, the kinetic inductance demonstrated here to be orders-of-magnitude larger than magnetic inductance can dramatically miniaturize radio-frequency integrated circuits. Moreover, its bias-dependency heralds a solid-state voltage-controlled inductor to complement the prevalent voltage-controlled capacitor.

preprint2014arXiv

Multi-terminal electrical transport measurements of molybdenum disulphide using van der Waals heterostructure device platform

Atomically thin two-dimensional (2D) semiconductors such as molybdenum disulphide (MoS2) hold great promise in electrical, optical, and mechanical devices and display novel physical phenomena such as coupled spin-valley physics and the valley Hall effect. However, the electron mobility of mono- and few-layer MoS2 has so far been substantially below theoretically predicted limits, particularly at low temperature (T), which has hampered efforts to observe its intrinsic quantum transport behaviors. Potential sources of disorder and scattering include both defects such as sulfur vacancies in the MoS2 itself, and extrinsic sources such as charged impurities and remote optical phonons from oxide dielectrics. To reduce extrinsic scattering and approach the intrinsic limit, we developed a van der Waals (vdW) heterostructure device platform where MoS2 layers are fully encapsulated within hexagonal boron nitride (hBN), and electrically contacted in a multi-terminal geometry using gate-tunable graphene electrodes. Multi-terminal magneto-transport measurements show dramatic improvements in performance, including a record-high Hall mobility reaching 34,000 cm2/Vs for 6-layer MoS2 at low T. Comparison to theory shows a decrease of 1-2 orders of magnitude in the density of charged impurities, indicating that performance at low T in previous studies was limited by extrinsic factors rather than defects in the MoS2. We also observed Shubnikov-de Haas (SdH) oscillations for the first time in high-mobility monolayer and few-layer MoS2. This novel device platform therefore opens up a new way toward measurements of intrinsic properties and the study of quantum transport phenomena in 2D semiconducting materials.

preprint2014arXiv

Plasmon-phonon coupled modes in graphene tuned by the carrier concentration of the semiconductor substrates

The interaction between graphene plasmons and surface phonons of a semiconductor substrate is investigated, which can be efficiently controlled by the carrier injection of the substrate. The energy and lifetime of surface phonons in a substrate depend a lot on the carrier concentration, which provides a new machanism to tune plasmon-phonon coupled modes (PPCMs). More specifically, the dispersion and lifetime of PPCMs can be controlled by the carrier concentration change of the substrate. The energy of PPCMs for a given momentum increases as the carrier concentration of a substrate increases. On the other hand, the momentum of PPCMs for a given energy decreases when the carrier concentration of the substrate increases. Lifetime of PPCMs is always larger than the intrinsic lifetime of graphene plasmons without plasmon-phonon coupling.

preprint2014arXiv

Protecting sing-photon multi-mode W state from photon loss

Single-photon entanglement is of major importance in current quantum communications. However, it is sensitive to photon loss. In this paper, we discuss the protection of single-photon multi-mode W state with noiseless linear amplification. It is shown that the amplification factor is only decided with the transmission coefficient of the variable fiber beam splitters, and it does not change with the number of the spatial mode. This protocol may be useful in current quantum information processing.

preprint2014arXiv

Renyi Entanglement Entropy of Interacting Fermions Calculated Using Continuous-Time Quantum Monte Carlo Method

We present a new algorithm for calculating the Renyi entanglement entropy of interacting fermions using the continuous-time quantum Monte Carlo method. The algorithm only samples interaction correction of the entanglement entropy, which by design ensures efficient calculation of weakly interacting systems. Combined with Monte Carlo reweighting, the algorithm also performs well for systems with strong interactions. We demonstrate the potential of this method by studying the quantum entanglement signatures of the charge-density-wave transition of interacting fermions on a square lattice.

preprint2014arXiv

Rigidity of proper holomorphic mappings between certain unbounded non-hyperbolic domains

The Fock-Bargmann-Hartogs domain $D_{n,m}(μ)$ ($μ>0$) in $\mathbf{C}^{n+m}$ is defined by the inequality $\|w\|^2<e^{-μ\|z\|^2},$ where $(z,w)\in \mathbf{C}^n\times \mathbf{C}^m$, which is an unbounded non-hyperbolic domain in $\mathbf{C}^{n+m}$. Recently, Yamamori gave an explicit formula for the Bergman kernel of the Fock-Bargmann-Hartogs domains in terms of the polylogarithm functions and Kim-Ninh-Yamamori determined the automorphism group of the domain $D_{n,m}(μ)$. In this article, we obtain rigidity results on proper holomorphic mappings between two equidimensional Fock-Bargmann-Hartogs domains. Our rigidity result implies that any proper holomorphic self-mapping on the Fock-Bargmann-Hartogs domain $D_{n,m}(μ)$ with $m\geq 2$ must be an automorphism.

preprint2014arXiv

Rigidity of proper holomorphic mappings between equidimensional Hua domains

Hua domain, named after Chinese mathematician Loo-Keng Hua, is defined as a domain in $\mathbb{C}^{n}$ fibered over an irreducible bounded symmetric domain $Ω\subset \mathbb{C}^{d}\;(d<n)$ with the fiber over $z\in Ω$ being a $(n-d)$-dimensional generalized complex ellipsoid $Σ(z)$. In general, a Hua domain is a nonhomogeneous domain without smooth boundary. The purpose of this paper is twofold. Firstly, we obtain what seems to be the first rigidity results on proper holomorphic mappings between two equidimensional Hua domains. Secondly, we determine the explicit form of the biholomorphisms between two equidimensional Hua domains. As a special conclusion of this paper, we completely describe the group of holomorphic automorphisms of the Hua domain.

preprint2014arXiv

Rigidity of Proper Holomorphic Self-mappings of the Pentablock

The pentablock is a Hartogs domain over the symmetrized bidisc. The domain is a bounded inhomogeneous pseudoconvex domain, and does not have a $\mathcal{C}^{1}$ boundary. Recently, Agler-Lykova-Young constructed a special subgroup of the group of holomorphic automorphisms of the pentablock, and Kosiński completely described the group of holomorphic automorphisms of the pentablock. The purpose of this paper is to prove that any proper holomorphic self-mapping of the pentablock must be an automorphism.

preprint2014arXiv

Spin alignments of spiral galaxies within the large-scale structure from SDSS DR7

Using a sample of spiral galaxies selected from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) and Galaxy Zoo 2 (GZ2), we investigate the alignment of spin axes of spiral galaxies with their surrounding large scale structure, which is characterized by the large-scale tidal field reconstructed from the data using galaxy groups above a certain mass threshold. We find that the spin axes of only have weak tendency to be aligned with (or perpendicular to) the intermediate (or minor) axis of the local tidal tensor. The signal is the strongest in a \cluster environment where all the three eigenvalues of the local tidal tensor are positive. Compared to the alignments between halo spins and local tidal field obtained in N-body simulations, the above observational results are in best agreement with those for the spins of inner regions of halos, suggesting that the disk material traces the angular momentum of dark matter halos in the inner regions.

preprint2014arXiv

Status of the aligned two-Higgs-doublet model confronted with the Higgs data

Imposing the theoretical constraints from vacuum stability, unitarity and perturbativity as well as the experimental constraints from the electroweak precision data, flavor observables and the non-observation of additional Higgs at collider, we study the implications of available Higgs signals on a two-Higgs-doublet model with the alignment of the down-type quarks and charged lepton Yukawa coupling matrices. Compared to the four traditional types of two-Higgs-doublet models, the model has two additional mixing angles $θ_d$ and $θ_l$ in the down-type quark and charged lepton Yukawa interactions. We find that the mixing angle $θ_d$ can loose the constraints on $sin(β-α)$, $tanβ$ and $m_{H^{\pm}}$ sizably. The model can provide the marginally better fit to available Higgs signals data than SM, which requires the Higgs couplings with gauge bosons, $u\bar{u}$ and $d\bar{d}$ to be properly suppressed, and favors (1 <θ_d< 2, 0.5 <θ_l< 2.2) for $m_h=$ 125.5 GeV and (0.5 <θ_d< 2, 0.5 <θ_l< 2.2) for $m_H=$ 125.5 GeV. However, these Higgs couplings are allowed to have sizable deviations from SM for ($m_h=$ 125.5 GeV, 125.5 $\leq m_H\leq$ 128 GeV) and (125 GeV $\leq m_h\leq$ 125.5 GeV, $m_H=$ 125.5 GeV).

preprint2014arXiv

Study of the heavy CP-even Higgs with mass 125 GeV in two-Higgs-doublet models at the LHC and ILC

We assume that the 125 GeV Higgs discovered at the LHC is the heavy CP-even Higgs of the two-Higgs-doublet models, and examine the parameter space in the Type-I, Type-II, Lepton-specific and Flipped models allowed by the latest Higgs signal data, the relevant experimental and theoretical constraints. Further, we show the projected limits on $\tanβ$, $\sin(β-α)$, $Hf\bar{f}$ and $HVV$ couplings from the future measurements of the 125 GeV Higgs at the LHC and ILC, including the LHC with integrated luminosity of 300 fb$^{-1}$ (LHC-300 fb$^{-1}$) and 3000 fb$^{-1}$ (LHC-3000 fb$^{-1}$) as well as the ILC at $\sqrt{s}=250$ GeV (ILC-250 GeV), $\sqrt{s}=500$ GeV (ILC-500 GeV) and $\sqrt{s}=1000$ GeV (ILC-1000 GeV). Assuming that the future Higgs signal data have no deviation from the SM expectation, the LHC-300 fb$^{-1}$, LHC-3000 fb$^{-1}$ and ILC-1000 GeV can exclude the wrong-sign Yukawa coupling regions of the Type-II, Flipped and Lepton-specific models at the $2σ$ level, respectively. The future experiments at the LHC and ILC will constrain the Higgs couplings to be very close to SM values, especially for the $HVV$ coupling.

preprint2014arXiv

Topological Phase Transition in the Hofstadter-Hubbard Model

We study the interplay between topological and conventional long range order of attractive fermions in a time reversal symmetric Hofstadter lattice using quantum Monte Carlo simulations, focussing on the case of one-third flux quantum per plaquette. At half-filling, the system is unstable towards s-wave pairing and charge-density-wave order at infinitesimally small interactions. At one-third-filling, the noninteracting system is a topological insulator, and a nonzero critical interaction strength is needed to drive a transition from the quantum spin Hall insulator to a superfluid. We probe the topological signature of the phase transition by threading a magnetic flux through a cylinder and observe quantized topological charge pumping.

preprint2014arXiv

Tunable Fractional Quantum Hall Phases in Bilayer Graphene

Symmetry breaking in a quantum system often leads to complex emergent behavior. In bilayer graphene (BLG), an electric field applied perpendicular to the basal plane breaks the inversion symmetry of the lattice, opening a band gap at the charge neutrality point. In a quantizing magnetic field electron interactions can cause spontaneous symmetry breaking within the spin and valley degrees of freedom, resulting in quantum Hall states (QHS) with complex order. Here we report fractional quantum Hall states (FQHS) in bilayer graphene which show phase transitions that can be tuned by a transverse electric field. This result provides a model platform to study the role of symmetry breaking in emergent states with distinct topological order.

preprint2013arXiv

130 GeV gamma-ray line and enhancement of $h\toγγ$ in the Higgs triplet model plus a scalar dark matter

With a discrete $Z_2$ symmetry being imposed, we introduce a real singlet scalar $S$ to the Higgs triplet model with the motivation of explaining the tentative evidence for a spectral feature at $E_γ$ = 130 GeV in the Fermi LAT data. The model can naturally satisfy the experimental constraints of the dark matter relic density and direct detection data from Xenon100. The doubly charged and one charged scalars can enhance the annihilation cross section of $SS\toγγ$ via the one-loop contributions, and give the negligible contributions to the relic density. $<\sigmav>_{SS\toγγ}$ for $m_{S}=130$ GeV can reach $\ord(1)\times10^{-27} cm^3 s^{-1}$ for the small charged scalar masses and the coupling constant of larger than 1. Besides, this model also predict a second photon peak at 114 GeV from the annihilation $SS\toγZ$, and the cross section is approximately 0.76 times that of $SS\toγγ$, which is below the upper limit reported by Fermi LAT. Finally, the light charged scalars can enhance LHC diphoton Higgs rate, and make it to be consistent with the experimental data reported by ATLAS and CMS.

preprint2013arXiv

A Dynamic Programming Solution to a Generalized LCS Problem

In this paper, we consider a generalized longest common subsequence problem, the string-excluding constrained LCS problem. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a constraint string $P$ of length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ excluding $P$ as a substring and the length of $Z$ is maximized. The problem and its solution were first proposed by Chen and Chao\cite{1}, but we found that their algorithm can not solve the problem correctly. A new dynamic programming solution for the STR-EC-LCS problem is then presented in this paper. The correctness of the new algorithm is proved. The time complexity of the new algorithm is $O(nmr)$.

preprint2013arXiv

Adaptive Low-rank Constrained Constant Modulus Beamforming Algorithms using Joint Iterative Optimization of Parameters

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The scheme provides an efficient way to deal with filters with large number of elements. It consists of a bank of full-rank adaptive filters that forms a transformation matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. The transformation matrix projects the received vector onto a low-dimension vector, which is processed by the reduced-rank filter to estimate the desired signal. The expressions of the transformation matrix and the reduced-rank weight vector are derived according to the constrained constant modulus (CCM) criterion. Two novel low-complexity adaptive algorithms are devised for the implementation of the proposed scheme with respect to different constrained conditions. Simulations are performed to show superior performance of the proposed algorithms in comparison with the existing methods.

preprint2013arXiv

Adaptive Reduced-Rank Constrained Constant Modulus Beamforming Algorithms Based on Joint Iterative Optimization of Filters

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The novel scheme is designed according to the constant modulus (CM) criterion subject to different constraints, and consists of a bank of full-rank adaptive filters that forms the transformation matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters to estimate the desired signal. We describe the proposed scheme for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. For each structure, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithms for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to the adaptive algorithms for reformulating the transformation matrix and improving performance. An automatic rank selection technique is developed and employed to determine the most adequate rank for the derived algorithms. The complexity and convexity analyses are carried out. Simulation results show that the proposed algorithms outperform the existing full-rank and reduced-rank methods in convergence and tracking performance.

preprint2013arXiv

Adaptive Set-Membership Reduced-Rank Least Squares Beamforming Algorithms

This paper presents a new adaptive algorithm for the linearly constrained minimum variance (LCMV) beamformer design. We incorporate the set-membership filtering (SMF) mechanism into the reduced-rank joint iterative optimization (JIO) scheme to develop a constrained recursive least squares (RLS) based algorithm called JIO-SM-RLS. The proposed algorithm inherits the positive features of reduced-rank signal processing techniques to enhance the output performance, and utilizes the data-selective updates (around 10-15%) of the SMF methodology to save the computational cost significantly. An effective time-varying bound is imposed on the array output as a constraint to circumvent the risk of overbounding or underbounding, and to update the parameters for beamforming. The updated parameters construct a set of solutions (a membership set) that satisfy the constraints of the LCMV beamformer. Simulations are performed to show the superior performance of the proposed algorithm in terms of the convergence rate and the reduced computational complexity in comparison with the existing methods.

preprint2013arXiv

Alignments of galaxies within cosmic filaments from SDSS DR7

Using a sample of galaxy groups selected from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we examine the alignment between the orientation of galaxies and their surrounding large scale structure in the context of the cosmic web. The latter is quantified using the large-scale tidal field, reconstructed from the data using galaxy groups above a certain mass threshold. We find that the major axes of galaxies in filaments tend to be preferentially aligned with the directions of the filaments, while galaxies in sheets have their major axes preferentially aligned parallel to the plane of the sheets. The strength of this alignment signal is strongest for red, central galaxies, and in good agreement with that of dark matter halos in N-body simulations. This suggests that red, central galaxies are well aligned with their host halos, in quantitative agreement with previous studies based on the spatial distribution of satellite galaxies. There is a luminosity and mass dependence that brighter and more massive galaxies in filaments and sheets have stronger alignment signals. We also find that the orientation of galaxies is aligned with the eigenvector associated with the smallest eigenvalue of the tidal tensor. These observational results indicate that galaxy formation is affected by large-scale environments, and strongly suggests that galaxies are aligned with each other over scales comparable to those of sheets and filaments in the cosmic web.

preprint2013arXiv

An Efficient Dual Approach to Distance Metric Learning

Distance metric learning is of fundamental interest in machine learning because the distance metric employed can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive. Standard interior-point SDP solvers typically have a complexity of $O(D^{6.5})$ (with $D$ the dimension of input data), and can thus only practically solve problems exhibiting less than a few thousand variables. Since the number of variables is $D (D+1) / 2 $, this implies a limit upon the size of problem that can practically be solved of around a few hundred dimensions. The complexity of the popular quadratic Mahalanobis metric learning approach thus limits the size of problem to which metric learning can be applied. Here we propose a significantly more efficient approach to the metric learning problem based on the Lagrange dual formulation of the problem. The proposed formulation is much simpler to implement, and therefore allows much larger Mahalanobis metric learning problems to be solved. The time complexity of the proposed method is $O (D ^ 3) $, which is significantly lower than that of the SDP approach. Experiments on a variety of datasets demonstrate that the proposed method achieves an accuracy comparable to the state-of-the-art, but is applicable to significantly larger problems. We also show that the proposed method can be applied to solve more general Frobenius-norm regularized SDP problems approximately.

preprint2013arXiv

An Efficient Dynamic Programming Algorithm for the Generalized LCS Problem with Multiple Substring Exclusion Constrains

In this paper, we consider a generalized longest common subsequence problem with multiple substring exclusion constrains. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a set of $d$ constrains $P=\{P_1,...,P_d\}$ of total length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ excluding each of constrain string in $P$ as a substring and the length of $Z$ is maximized. The problem was declared to be NP-hard\cite{1}, but we finally found that this is not true. A new dynamic programming solution for this problem is presented in this paper. The correctness of the new algorithm is proved. The time complexity of our algorithm is $O(nmr)$.

preprint2013arXiv

BigDataBench: a Big Data Benchmark Suite from Web Search Engines

This paper presents our joint research efforts on big data benchmarking with several industrial partners. Considering the complexity, diversity, workload churns, and rapid evolution of big data systems, we take an incremental approach in big data benchmarking. For the first step, we pay attention to search engines, which are the most important domain in Internet services in terms of the number of page views and daily visitors. However, search engine service providers treat data, applications, and web access logs as business confidentiality, which prevents us from building benchmarks. To overcome those difficulties, with several industry partners, we widely investigated the open source solutions in search engines, and obtained the permission of using anonymous Web access logs. Moreover, with two years' great efforts, we created a sematic search engine named ProfSearch (available from http://prof.ict.ac.cn). These efforts pave the path for our big data benchmark suite from search engines---BigDataBench, which is released on the web page (http://prof.ict.ac.cn/BigDataBench). We report our detailed analysis of search engine workloads, and present our benchmarking methodology. An innovative data generation methodology and tool are proposed to generate scalable volumes of big data from a small seed of real data, preserving semantics and locality of data. Also, we preliminarily report two case studies using BigDataBench for both system and architecture researches.

preprint2013arXiv

Blind Adaptive Beamforming Based on Constrained Constant Modulus RLS Algorithm for Smart Antennas

In this paper, we study the performance of blind adaptive beamforming algorithms for smart antennas in realistic environments. A constrained constant modulus (CCM) design criterion is described and used for deriving a recursive least squares (RLS) type optimization algorithm. Furthermore, two kinds of scenarios are considered in the paper for analyzing its performance. Simulations are performed to compare the performance of the proposed method to other well-known methods for blind adaptive beamforming. Results indicate that the proposed method has a significant faster convergence rate, better robustness to changeable environments and better tracking capability.

preprint2013arXiv

Characterizing Data Analysis Workloads in Data Centers

As the amount of data explodes rapidly, more and more corporations are using data centers to make effective decisions and gain a competitive edge. Data analysis applications play a significant role in data centers, and hence it has became increasingly important to understand their behaviors in order to further improve the performance of data center computer systems. In this paper, after investigating three most important application domains in terms of page views and daily visitors, we choose eleven representative data analysis workloads and characterize their micro-architectural characteristics by using hardware performance counters, in order to understand the impacts and implications of data analysis workloads on the systems equipped with modern superscalar out-of-order processors. Our study on the workloads reveals that data analysis applications share many inherent characteristics, which place them in a different class from desktop (SPEC CPU2006), HPC (HPCC), and service workloads, including traditional server workloads (SPECweb2005) and scale-out service workloads (four among six benchmarks in CloudSuite), and accordingly we give several recommendations for architecture and system optimizations. On the basis of our workload characterization work, we released a benchmark suite named DCBench for typical datacenter workloads, including data analysis and service workloads, with an open-source license on our project home page on http://prof.ict.ac.cn/DCBench. We hope that DCBench is helpful for performing architecture and small-to-medium scale system researches for datacenter computing.

preprint2013arXiv

Comment on: "Classical signature of quantum annealing"

In a recent preprint (arXiv:1305.4904) entitled "Classical signature of quantum annealing" Smolin and Smith point out that a bimodal distribution presented in (arXiv:1304.4595) for the success probability in the D-Wave device does not in itself provide sufficient evidence for quantum annealing, by presenting a classical model that also exhibits bimodality. Here we analyze their model and in addition present a similar model derived from the semi-classical limit of quantum spin dynamics, which also exhibits a bimodal distribution. We find that in both cases the correlations between the success probabilities of these classical models and the D-Wave device are weak compared to the correlations between a simulated quantum annealer and the D-Wave device. Indeed, the evidence for quantum annealing presented in arXiv:1304.4595 is not limited to the bimodality, but relies in addition on the success probability correlations between the D-Wave device and the simulated quantum annealer. The Smolin-Smith model and our semi-classical spin model both fail this correlation test.

preprint2013arXiv

Complete Solutions for a Combinatorial Puzzle in Linear Time

In this paper we study a single player game consisting of $n$ black checkers and $m$ white checkers, called shifting the checkers. We have proved that the minimum number of steps needed to play the game for general $n$ and $m$ is $nm + n + m$. We have also presented an optimal algorithm to generate an optimal move sequence of the game consisting of $n$ black checkers and $m$ white checkers, and finally, we present an explicit solution for the general game.

preprint2013arXiv

Dark matter in little Higgs model under current experimental constraints from LHC, Planck and Xenon

We examine the status of dark matter (heavy photon) in the littlest Higgs model with T-parity (LHT) in light of the new results from the LHC Higgs search, the Planck dark matter relic density and the XENON100 limit on the dark matter scattering off the nucleon. We obtain the following observations: (i) For the LHC Higgs data, the LHT can well be consistent with the CMS results but disfavored by the ATLAS observation of diphoton enhancement; (ii) For the dark matter relic density, the heavy photon in the LHT can account for the Planck data for the small mass splitting of mirror lepton and heavy photon; (iii) For the dark matter scattering off the nucleon, the heavy photon can give a spin-independent cross section below the XENON100 upper limit for $m_{A_H}>95$ GeV ($f> 665$ GeV); (iv) A fit using the CMS Higgs data gives the lowest chi-square of 2.63 (the SM value is 4.75) at $f\simeq$ 1120 GeV and $m_{A_H}\simeq$ 170 GeV (at this point the dark matter constraints from Planck and XENON100 can also be satisfied). Such a best point and its nearby favored region (even for a $f$ value up to 3.8 TeV) can be covered by the future XENON1T (2017) experiment.

preprint2013arXiv

Direct measurement of topological invariants in optical lattices

We propose an experimental technique for classifying the topology of band structures realized in optical lattices, based on a generalization of topological charge pumping in quantum Hall systems to cold atom in optical lattices. Time-of-flight measurement along one spatial direction combined with in situ detection along the transverse direction provide a direct measure of the system's Chern number, as we illustrate by calculations for the Hofstadter lattice. Based on an analogy with Wannier functions techniques of topological band theory, the method is very general and also allows the measurement of other topological invariants, such as the $Z_2$ topological invariant of time-reversal symmetric insulators.

preprint2013arXiv

Ferromagnetism of the Repulsive Atomic Fermi Gas: three-body recombination and domain formation

The simplest model for itinerant ferromagnetism, the Stoner model, has so far eluded experimental observation in repulsive ultracold fermions due to rapid three-body recombination at large scattering lengths. Here we show that a ferromagnetic phase can be stabilised by imposing a moderate optical lattice. The reduced kinetic energy drop upon formation of a polarized phase in an optical lattice extends the ferromagnetic phase to smaller scattering lengths where three-body recombination is small enough to permit experimental detection of the phase. We also show, using time dependent density functional theory, that in such a setup ferromagnetic domains emerge rapidly from a paramagnetic initial state.

preprint2013arXiv

High Volume Computing: Identifying and Characterizing Throughput Oriented Workloads in Data Centers

For the first time, this paper systematically identifies three categories of throughput oriented workloads in data centers: services, data processing applications, and interactive real-time applications, whose targets are to increase the volume of throughput in terms of processed requests or data, or supported maximum number of simultaneous subscribers, respectively, and we coin a new term high volume computing (in short HVC) to describe those workloads and data center computer systems designed for them. We characterize and compare HVC with other computing paradigms, e.g., high throughput computing, warehouse-scale computing, and cloud computing, in terms of levels, workloads, metrics, coupling degree, data scales, and number of jobs or service instances. We also preliminarily report our ongoing work on the metrics and benchmarks for HVC systems, which is the foundation of designing innovative data center computer systems for HVC workloads.

preprint2013arXiv

How are mortality rates affected by population density?

Biologists have found that the death rate of cells in culture depends upon their spatial density. Permanent "Stay alive" signals from their neighbours seem to prevent them from dying. In a previous paper (Wang et al. 2013) we gave evidence for a density effect for ants. In this paper we examine whether there is a similar effect in human demography. We find that although there is no observable relationship between population density and overall death rates, there is a clear relationship between density and the death rates of young age-groups. Basically their death rates decrease with increasing density. However, this relationship breaks down around 300 inhabitants per square kilometre. Above this threshold the death rates remains fairly constant. The same density effect is observed in Canada, France, Japan and the United States. We also observe a striking parallel between the density effect and the so-called marital status effect in the sense that they both lead to higher suicide rates and are both enhanced for younger age-groups. However, it should be noted that the strength of the density effect is only a fraction of the strength of the marital status effect. In spite of the fact that this parallel does not give us an explanation by itself, it invites us to focus on explanations that apply to both effects. In this light the "Stay alive" paradigm set forth by Prof. Martin Raff appears as a natural interpretation. It can be seen as an extension of the "social ties" framework proposed at the end of the 19th century by the sociologist Emile Durkheim in his study about suicide.

preprint2013arXiv

How does group interaction and its severance affect life expectancy?

The phenomenon of apoptosis observed in cell cultures consists in the fact that unless cells permanently receive a "Stay alive" signal from their neighbors, they are bound to die. A natural question is whether manifestations of this apoptosis paradigm can also be observed in other organizations of living organisms. In this paper we report results from a two-year long campaign of experiments on three species of ants and one species of (tephritid) fruit flies. In these experiments individuals were separated from their colony and kept in isolation either alone or in groups of 10 individuals. The overall conclusion is that "singles" have a shorter life expectancy than individuals in the groups of 10. This observation holds for ants as well as for fruit flies. The paper also provides compelling evidence of a similar effect in married versus unmarried (i.e. single, widowed or divorced) people. A natural question concerns the dynamic of the transition between the two regimes. Observation suggests an abrupt (rather than smooth) transition and this conclusion seems to hold for ants, fruit flies and humans as well. We call it a shock transition. In addition, for red fire ants Solenopsis invicta, it was observed that individuals in groups of 10 that also comprise one queen, die much faster than those in similar groups without queens. The paper also examines the corresponding survivorship curves from the perspective of the standard classification into 3 types. The survivorship curves of ants (whether single or in groups of 10) are found to be of type II whereas those of the fruit fly Bactrocera dorsalis are rather of type III. In this connection it is recalled that the survivorship curve of the fruit fly Drosophila melanogaster is of type I, i.e. of same type as for humans.

preprint2013arXiv

Little Higgs theory confronted with the LHC Higgs data

We confront the little Higgs theory with the LHC Higgs search data (up to 17 fb$^{-1}$ of the combined 7 and 8 TeV run). Considering some typical models, namely the littlest Higgs model (LH), the littlest Higgs model with T-parity (LHT-A and LHT-B) and the simplest little Higgs model (SLH), we scan over the parameter space in the region allowed by current experiments. We find that in these models the inclusive and exclusive (via gluon-gluon fusion) diphoton and $ZZ^*$ signal rates of the Higgs boson are always suppressed and approach to the SM predictions for a large scale $f$. Thus, the $ZZ^*$ signal rate is within the $1σ$ range of the experimental data while the inclusive diphoton signal rate is always outside the $2σ$ range. Especially, in the LHT-A the diphoton signal rate is outside the $3σ$ range of the experimental data for $f < 800$ GeV. We also perform a global $χ^2$ fit to the available LHC and Tevatron Higgs data, and find that these models provide no better global fit to the whole data set (only for some special channels a better fit can be obtained, specially in the LHT-B).

preprint2013arXiv

Low-Complexity Adaptive Set-Membership Reduced-rank LCMV Beamforming

This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the set-membership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based algorithm for the beamformer design. An effective time-varying bound is employed in the proposed method to adjust the step sizes, avoid the misadjustment and the risk of overbounding or underbounding. Simulations are performed to show the improved performance of the proposed algorithm in comparison with existing full-rank and reduced-rank methods.

preprint2013arXiv

Low-Complexity Constrained Constant Modulus SG-based Beamforming Algorithms with Variable Step Size

In this paper, two low-complexity adaptive step size algorithms are investigated for blind adaptive beamforming. Both of them are used in a stochastic gradient (SG) algorithm, which employs the constrained constant modulus (CCM) criterion as the design approach. A brief analysis is given for illustrating their properties. Simulations are performed to compare the performances of the novel algorithms with other well-known methods. Results indicate that the proposed algorithms achieve superior performance, better convergence behavior and lower computational complexity in both stationary and non-stationary environments.

preprint2013arXiv

Measuring the X-ray luminosities of SDSS DR7 clusters from RASS

We use ROSAT All Sky Survey (RASS) broadband X-ray images and the optical clusters identified from SDSS DR7 to estimate the X-ray luminosities around $\sim 65,000$ candidate clusters with masses $\ga 10^{13}\msunh$ based on an Optical to X-ray (OTX) code we develop. We obtain a catalogue with X-ray luminosity for each cluster. This catalog contains 817 clusters (473 at redshift $z\le 0.12$) with $S/N> 3$ in X-ray detection. We find about $65\%$ of these X-ray clusters have their most massive member located near the X-ray flux peak; for the rest $35\%$, the most massive galaxy is separated from the X-ray peak, with the separation following a distribution expected from a NFW profile. We investigate a number of correlations between the optical and X-ray properties of these X-ray clusters, and find that: the cluster X-ray luminosity is correlated with the stellar mass (luminosity) of the clusters, as well as with the stellar mass (luminosity) of the central galaxy and the mass of the halo, but the scatter in these correlations is large. Comparing the properties of X-ray clusters of similar halo masses but having different X-ray luminosities, we find that massive halos with masses $\ga 10^{14}\msunh$ contain a larger fraction of red satellite galaxies when they are brighter in X-ray. ... A cluster catalog containing the optical properties of member galaxies and the X-ray luminosity is available at {\it http://gax.shao.ac.cn/data/Group.html}.

preprint2013arXiv

Pomeranchuk cooling of the SU($2N$) ultra-cold fermions in optical lattices

We investigate the thermodynamic properties of a half-filled SU(2N) Fermi-Hubbard model in the two-dimensional square lattice using the determinantal quantum Monte Carlo simulation, which is free of the fermion "sign problem". The large number of hyperfine-spin components enhances spin fluctuations, which facilitates the Pomeranchuk cooling to temperatures comparable to the superexchange energy scale at the case of SU$(6)$. Various quantities including entropy, charge fluctuation, and spin correlations have been calculated.

preprint2013arXiv

Quantum magnetic properties of the SU(2N) Hubbard model in the square lattice: a quantum Monte Carlo study

We employ the determinant projector quantum Monte-Carlo method to investigate the ground state magnetic properties in the Mott insulating states of the half-filled SU(4) and SU(6) Fermi-Hubbard model in the 2D square lattice, which is free of the sign problem. The long-range antiferromagnetic Neel order is found for the SU(4) case with a small residual Neel moment. Quantum fluctuations are even stronger in the SU(6) case. Numeric results are consistent with either a vanishing or even weaker Neel ordering than that of SU(4).

preprint2013arXiv

Reduced-rank Adaptive Constrained Constant Modulus Beamforming Algorithms based on Joint Iterative Optimization of Filters

This paper proposes a reduced-rank scheme for adaptive beamforming based on the constrained joint iterative optimization of filters. We employ this scheme to devise two novel reduced-rank adaptive algorithms according to the constant modulus (CM) criterion with different constraints. The first devised algorithm is formulated as a constrained joint iterative optimization of a projection matrix and a reduced-rank filter with respect to the CM criterion subject to a constraint on the array response. The constrained constant modulus (CCM) expressions for the projection matrix and the reduced-rank weight vector are derived, and a low-complexity adaptive algorithm is presented to jointly estimate them for implementation. The second proposed algorithm is extended from the first one and implemented according to the CM criterion subject to a constraint on the array response and an orthogonal constraint on the projection matrix. The Gram-Schmidt (GS) technique is employed to achieve this orthogonal constraint and improve the performance. Simulation results are given to show superior performance of the proposed algorithms in comparison with existing methods.

preprint2013arXiv

Reduced-Rank DOA Estimation based on Joint Iterative Subspace Optimization and Grid Search

In this paper, we propose a novel reduced-rank algorithm for direction of arrival (DOA) estimation based on the minimum variance (MV) power spectral evaluation. It is suitable to DOA estimation with large arrays and can be applied to arbitrary array geometries. The proposed DOA estimation algorithm is formulated as a joint optimization of a subspace projection matrix and an auxiliary reduced-rank parameter vector with respect to the MV and grid search. A constrained least squares method is employed to solve this joint optimization problem for the output power over the grid. The proposed algorithm is described for problems of large number of users' direction finding with or without exact information of the number of sources, and does not require the singular value decomposition (SVD). The spatial smoothing (SS) technique is also employed in the proposed algorithm for dealing with correlated sources problem. Simulations are conducted with comparisons against existent algorithms to show the improved performance of the proposed algorithm in different scenarios.

preprint2013arXiv

Robust Auxiliary Vector Filtering with Constrained Constant Modulus Design for Beamforming

This paper proposes an auxiliary vector filtering (AVF) algorithm based on a constrained constant modulus (CCM) design for robust adaptive beamforming. This scheme provides an efficient way to deal with filters with a large number of elements. The proposed beamformer decomposes the adaptive filter into a constrained (reference vector filters) and an unconstrained (auxiliary vector filters) components. The weight vector is iterated by subtracting the scaling auxiliary vector from the reference vector. The scalar factor and the auxiliary vector depend on each other and are jointly calculated according to the CCM criterion. The proposed robust AVF algorithm provides an iterative exchange of information between the scalar factor and the auxiliary vector and thus leads to a fast convergence and an improved steady-state performance over the existing techniques. Simulations are performed to show the performance and the robustness of the proposed scheme and algorithm in several scenarios.

preprint2013arXiv

Seeing Hofstadter's Butterfly in Atomic Fermi Gases

We propose a novel way to detect the fractal energy spectrum of the Hofstadter model from the density distributions of ultracold fermions in an external trap. At low temperature, the local compressibility is proportional to the density of states of the system which reveals the fractal energy spectrum. However, thermal broadening and noises in the real experimental situation inevitably smear out fine features in the density distribution. To overcome this difficulty, we use the maximum entropy method to extract the density of states directly from the noisy thermal density distributions. Simulations show that one is able to restore the core feature of the Hofstadter's butterfly spectrum with current experimental techniques. By further reducing the noise or the temperature, one can refine the resolution and observe fine structures of the butterfly spectrum.

preprint2013arXiv

Set-Membership Conjugate Gradient Constrained Adaptive Filtering Algorithm for Beamforming

We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the data-selective updates that are controlled by a time-varying bound related to the parameters. For the update, the CG-based vector is calculated iteratively (one iteration per update) to obtain the filter parameters and to avoid the matrix inversion. The resulting iterations construct a space of feasible solutions that satisfy the constraints of the LCMV optimization problem. The proposed algorithm reduces the computational complexity significantly and shows an enhanced convergence and tracking performance over existing algorithms.

preprint2013arXiv

Splashing phenomena of room temperature liquid metal droplet striking on the pool of the same liquid under ambient air environment

In this article, the fluid dynamics of room temperature liquid metal (RTLM) droplet impacting onto a pool of the same liquid in ambient air was investigated. A series of experiments were conducted in order to disclose the influence of the oxidation effect on the impact dynamics. The droplet shape and impact phenomenology were recorded with the aid of a high-speed digital camera. The impact energy stored in the splash structures was estimated via a theoretical model and several morphological parameters obtained from instantaneous images of the splash. It was observed that the droplet shape and the splashing morphology of RTLM were drastically different from those of water, so was the impact dynamics between room temperature LM pool and high temperature LM pool. The energy analysis disclosed that the height of the jet is highly sensitive to the viscosity of the fluid, which is subjected to the oxidation effect and temperature effect simultaneously, and thus perfectly explained the phenomena. These basic findings are important for the application of RTLM in a series of newly emerging technologies such as liquid metal based spray cooling, ink-jet printed electronics, interface material painting and coating, metallurgy, and 3D packages, etc.

preprint2013arXiv

The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems

Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we focus on measuring the performance impacts of diverse applications and scalable volumes of data sets on big data systems. For four typical data analysis applications---an important class of big data applications, we find two major results through experiments: first, the data scale has a significant impact on the performance of big data systems, so we must provide scalable volumes of data sets in big data benchmarks. Second, for the four applications, even all of them use the simple algorithms, the performance trends are different with increasing data scales, and hence we must consider not only variety of data sets but also variety of applications in benchmarking big data systems.

preprint2013arXiv

The LAMOST Survey of Background Quasars in the Vicinity of the Andromeda and Triangulum Galaxies -- II. Results from the Commissioning Observations and the Pilot Surveys

We present new quasars discovered in the vicinity of the Andromeda and Triangulum galaxies with the LAMOST during the 2010 and 2011 observational seasons. Quasar candidates are selected based on the available SDSS, KPNO 4 m telescope, XSTPS optical, and WISE near infrared photometric data. We present 509 new quasars discovered in a stripe of ~135 sq. deg from M31 to M33 along the Giant Stellar Stream in the 2011 pilot survey datasets, and also 17 new quasars discovered in an area of ~100 sq. deg that covers the central region and the southeastern halo of M31 in the 2010 commissioning datasets. These 526 new quasars have i magnitudes ranging from 15.5 to 20.0, redshifts from 0.1 to 3.2. They represent a significant increase of the number of identified quasars in the vicinity of M31 and M33. There are now 26, 62 and 139 known quasars in this region of the sky with i magnitudes brighter than 17.0, 17.5 and 18.0 respectively, of which 5, 20 and 75 are newly-discovered. These bright quasars provide an invaluable collection with which to probe the kinematics and chemistry of the ISM/IGM in the Local Group of galaxies. A total of 93 quasars are now known with locations within 2.5 deg of M31, of which 73 are newly discovered. Tens of quasars are now known to be located behind the Giant Stellar Stream, and hundreds behind the extended halo and its associated substructures of M31. The much enlarged sample of known quasars in the vicinity of M31 and M33 can potentially be utilized to construct a perfect astrometric reference frame to measure the minute PMs of M31 and M33, along with the PMs of substructures associated with the Local Group of galaxies. Those PMs are some of the most fundamental properties of the Local Group.

preprint2013arXiv

Thermodynamics and magnetic properties of the anisotropic 3D Hubbard model

We study the 3D Hubbard model with anisotropic nearest neighbor tunneling amplitudes using the dynamical cluster approximation and compare the results with a quantum simulation experiment using ultracold fermions in an optical lattice, focussing on magnetic correlations. We find that the short-range spin correlations are significantly enhanced in the direction with stronger tunneling amplitudes. Our results agree with the experimental observations and show that the experimental temperature is lower than the strong tunneling amplitude. We characterize the system by examining the spin correlations beyond neighboring sites and determine the distribution of density, entropy and spin correlation in the trapped system. We furthermore investigate the dependence of the critical entropy at the Néel transition on anisotropy.

preprint2013arXiv

Topological charge pumping in a one-dimensional optical lattice

A topological charge pump [1] transfers charge in a quantized fashion. The quantization is stable against the detailed form of the pumping protocols and external noises and shares the same topological origin as the quantum Hall effect. We propose an experiment setup to realize topological charge pumping of cold atoms in a one-dimensional optical lattice. The quantization of the pumped charge is confirmed by first-principle simulations of the dynamics of uniform and trapped systems. Quantum effects are shown to be crucial for the topological protection of the charge quantization. Finite-temperature and non-adiabatic effect on the experimental observables are discussed. Realization of such a topological charge pump servers as a firm step towards exploring topological states and non-equilibrium dynamics using cold atoms.

preprint2013arXiv

Topological phase transition in a generalized Kane-Mele-Hubbard model: A combined Quantum Monte Carlo and Green's function study

We study a generalized Kane-Mele-Hubbard model with third-neighbor hopping, an interacting two-dimensional model with a topological phase transition as a function of third-neighbor hopping, by means of the determinant projector Quantum Monte Carlo (QMC) method. This technique is essentially numerically exact on models without a fermion sign problem, such as the one we consider. We determine the interaction-dependence of the Z2 topological insulator/trivial insulator phase boundary by calculating the Z2 invariants directly from the single-particle Green's function. The interactions push the phase boundary to larger values of third-neighbor hopping, thus stabilizing the topological phase. The observation of boundary shifting entirely stems from quantum °uctuations. We also identify qualitative features of the single-particle Green's function which are computationally useful in numerical searches for topological phase transitions without the need to compute the full topological invariant.

preprint2013arXiv

Validity of Fourier's law in one-dimensional momentum-conserving lattices with asymmetric interparticle interactions

We have numerically studied heat conduction in a few one-dimensional momentum-conserving lattices with asymmetric interparticle interactions by the nonequilibrium heat bath method, the equilibrium Green-Kubo method, and the heat current power spectra analysis. Very strong finite-size effects are clearly observed. Such effects make the heat conduction obey a Fourier-like law in a wide range of lattice lengths. However, in yet longer lattice lengths, the heat conductivity regains its power-law divergence. Therefore the power-law divergence of the heat conductivity in the thermodynamic limit is verified, as is expected by many existing theories.

preprint2012arXiv

A load balancing strategy for parallel computation of sparse permanents

The research in parallel machine scheduling in combinatorial optimization suggests that the desirable parallel efficiency could be achieved when the jobs are sorted in the non-increasing order of processing times. In this paper, we find that the time spending for computing the permanent of a sparse matrix by hybrid algorithm is strongly correlated to its permanent value. A strategy is introduced to improve a parallel algorithm for sparse permanent. Methods for approximating permanents, which have been studied extensively, are used to approximate the permanent values of sub-matrices to decide the processing order of jobs. This gives an improved load balancing method. Numerical results show that the parallel efficiency is improved remarkably for the permanents of fullerene graphs, which are of great interests in nanoscience.

preprint2012arXiv

Calculation of the transport critical current density of c-axis textured 122 iron-based superconductors

The c-axis textured Sr1-xKxFe2As2 tapes produced by cold rolling and post-annealing, could carry a high super-current over 2*104 A/cm2. However, the magnitude is far from its maximum, because of the current obstacles associated with various defects in the material. To predict the maximal transport critical current density, we modeled the current paths in a c-axis textured polycrystal as a three-dimensional flow network, and calculated the maximum flow with the Ford-Fulkerson algorithm. It indicates that a much higher super-current of about 2*105 A/cm2 could be achieved in an ideal c-axis textured K-doped 122 polycrystal. The dependences of transport Jc on density, content of invalid boundary and grain size and shape were also studied. The results imply that, over 30% of the grain boundaries in the reported c-axis textured Sr1-xKxFe2As2 tapes may act as current obstacles, and the large ratio of width to thickness was expected to be the most favorable grain shape for high transport Jc in c-axis textured 122 superconducting tapes.

preprint2012arXiv

Cross identification between X-ray and Optical Clusters of Galaxies in the SDSS DR7 Field

We use the ROSAT all sky survey X-ray cluster catalogs and the optical SDSS DR7 galaxy and group catalogs to cross-identify X-ray clusters with their optical counterparts, resulting in a sample of 201 X-ray clusters in the sky coverage of SDSS DR7. We investigate various correlations between the optical and X-ray properties of these X-ray clusters, and find that the following optical properties are correlated with the X-ray luminosity: the central galaxy luminosity, the central galaxy mass, the characteristic group luminosity ($\propto \Lx^{0.43}$), the group stellar mass ($\propto \Lx^{0.46}$), with typical 1-$σ$ scatter of $\sim 0.67$ in $\log \Lx$. Using the observed number distribution of X-ray clusters, we obtain an unbiased scaling relation between the X-ray luminosity, the central galaxy stellar mass and the characteristic satellite stellar mass as ${\log L_X} = -0.26 + 2.90 [\log (M_{\ast, c} + 0.26 M_{\rm sat}) -12.0]$ (and in terms of luminosities, as ${\log L_X} = -0.15 + 2.38 [\log (L_{c} + 0.72 L_{\rm sat}) -12.0]$). We find that the systematic difference between different halo mass estimations, e.g., using the ranking of characteristic group stellar mass or using the X-ray luminosity scaling relation can be used to constrain cosmology. Comparing the properties of groups of similar stellar mass (or optical luminosities) and redshift that are X-ray luminous or under-luminous, we find that X-ray luminous groups have more faint satellite galaxies and higher red fraction in their satellites. The cross-identified X-ray clusters together with their optical properties are provided in Appendix B.

preprint2012arXiv

Double transfer through Dirac points in a tunable honeycomb optical lattice

We report on Bloch-Zener oscillations of an ultracold Fermi gas in a tunable honeycomb lattice. The quasi-momentum distribution of the atoms is measured after sequentially passing through two Dirac points. We observe a double-peak feature in the transferred fraction to the second band, both as a function of the band gap at the Dirac points and the quasi-momentum of the trajectory. Our results are in good agreement with a simple analytical model based on two successive Landau-Zener transitions. Owing to the variation of the potential gradient over the cloud size, coherent Stückelberg oscillations are not visible in our measurements. This effect of the harmonic confinement is confirmed by a numerical simulation of the dynamics of a trapped 2D system.

preprint2012arXiv

Energy Conditions and Stability in generalized $f(R)$ gravity with arbitrary coupling between matter and geometry

The energy conditions and the Dolgov-Kawasaki criterion in generalized $f(R)$ gravity with arbitrary coupling between matter and geometry are derived in this paper, which are quite general and can degenerate to the well-known energy conditions in GR and $f(R)$ gravity with non-minimal coupling and non-coupling as special cases. In order to get some insight on the meaning of these energy conditions and the Dolgov- Kawasaki criterion, we apply them to a class of models in the FRW cosmology and give some corresponding results.

preprint2012arXiv

Graphene growth on h-BN by Molecular Beam Epitaxy

The growth of single layer graphene nanometer size domains by solid carbon source molecular beam epitaxy on hexagonal boron nitride (h-BN) flakes is demonstrated. Formation of single-layer graphene is clearly apparent in Raman spectra which display sharp optical phonon bands. Atomic-force microscope images and Raman maps reveal that the graphene grown depends on the surface morphology of the h-BN substrates. The growth is governed by the high mobility of the carbon atoms on the h-BN surface, in a manner that is consistent with van der Waals epitaxy. The successful growth of graphene layers depends on the substrate temperature, but is independent of the incident flux of carbon atoms.

preprint2012arXiv

Improved transport critical currents in Ag and Pb co-doped BaxK1-xFe2As2 superconducting tapes

Fe-clad BaxK1-xFe2As2 superconducting tapes were fabricated by the ex situ powder-in-tube method combined with a short high-temperature annealing technique. The effect of annealing time and different dopants on the transport properties of the BaxK1-xFe2As2 tapes were systematically studied. By co-doping with Ag and Pb, the transport critical current density Jc of BaxK1-xFe2As2 tapes was significantly improved in whole field region and the highest transport Jc was up to 1.4x10^4 A/cm^2 (Ic=100 A) at 4.2K in self field. It is proposed that the superior Jc in the co-doped samples are due to the combine effect of Pb doping at low fields and Ag doping at high fields.

preprint2012arXiv

Interaction induced topological phase transition in Bernevig-Hughes-Zhang model

We study interaction induced topological phase transition in Bernevig-Hughes-Zhang model. Topological nature of the phase transition is revealed by directly calculating the Z2 index of the interacting system from the single-particle Green's function. The interacting Z2 index is also consistently checked through the edge spectra. Combined with ab initio methods, present approach is a useful tool searching for correlated topological insulating materials from the first-principle point of view.

preprint2012arXiv

LHC diphoton Higgs signal and top quark forward-backward asymmetry in quasi-inert Higgs doublet model

In the quasi-inert Higgs doublet model, we study the LHC diphoton rate for a standard model-like Higgs boson and the top quark forward-backward asymmetry at Tevatron. Taking into account the constraints from the vacuum stability, unitarity, electroweak precision tests, flavor physics and the related experimental data of top quark, we find that compared with the standard model prediction, the diphoton rate of Higgs boson at LHC can be enhanced due to the light charged Higgs contributions, while the measurement of the top quark forward-backward asymmetry at Tevatron can be explained to within $1σ$ due to the non-standard model neutral Higgs bosons contributions. Finally, the correlations between the two observables are discussed.

preprint2012arXiv

Optimal entanglement concentration for quantum dot and optical microcavities systems

A recent paper [Chuan Wang, Phys. Rev. A \textbf{86}, 012323 (2012)] discussed an entanglement concentration protocol (ECP) for partially entangled electrons using a quantum dot and microcavity coupled system. In his paper, each two-electron spin system in a partially entangled state can be concentrated with the assistance of an ancillary quantum dot and a single photon. In this paper, we will present an optimal ECP for such entangled electrons with the help of only one single photon. Compared with the protocol of Wang, the most significant advantage is that during the whole ECP, the single photon only needs to pass through one microcavity which will increase the total success probability if the cavity is imperfect. The whole protocol can be repeated to get a higher success probability. With the feasible technology, this protocol may be useful in current long-distance quantum communications.

preprint2012arXiv

Phononics: Manipulating heat flow with electronic analogs and beyond

The form of energy termed heat that typically derives from lattice vibrations, i.e. the phonons, is usually considered as waste energy and, moreover, deleterious to information processing. However, with this colloquium, we attempt to rebut this common view: By use of tailored models we demonstrate that phonons can be manipulated like electrons and photons can, thus enabling controlled heat transport. Moreover, we explain that phonons can be put to beneficial use to carry and process information. In a first part we present ways to control heat transport and how to process information for physical systems which are driven by a temperature bias. Particularly, we put forward the toolkit of familiar electronic analogs for exercising phononics; i.e. phononic devices which act as thermal diodes, thermal transistors, thermal logic gates and thermal memories, etc.. These concepts are then put to work to transport, control and rectify heat in physical realistic nanosystems by devising practical designs of hybrid nanostructures that permit the operation of functional phononic devices and, as well, report first experimental realizations. Next, we discuss yet richer possibilities to manipulate heat flow by use of time varying thermal bath temperatures or various other external fields. These give rise to a plenty of intriguing phononic nonequilibrium phenomena as for example the directed shuttling of heat, a geometrical phase induced heat pumping, or the phonon Hall effect, that all may find its way into operation with electronic analogs.

preprint2012arXiv

Pole expansion of self-energy and interaction effect on topological insulators

We study effect of interactions on time-reversal-invariant topological insulators. Their topological indices are expressed by interacting Green's functions. Under the local self-energy approximation, we connect topological index and surface states of an interacting system to an auxiliary noninteracting system, whose Hamiltonian is related to the pole-expansions of the local self-energy. This finding greatly simplifies the calculation of interacting topological indices and gives an noninteracting pictorial description of interaction driven topological phase transitions. Our results also bridge studies of the correlated topological insulating materials with the practical dynamical-mean-field-theory calculations.

preprint2012arXiv

Positive Semidefinite Metric Learning Using Boosting-like Algorithms

The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time.

preprint2012arXiv

Quantum Information transmission

We present a scheme of quantum information transmission, which transmits the quantum information contained in a single qubit via the quantum correlation shared by two parties (a two-qubit channel), whose quantum discord is non-zero. We demonstrate that the quantum correlation, which may have no entanglement, is sufficient to transmit the information of a quantum state. When the correlation matrix of the two-qubit channel is of full rank (rank three), the information of the qubit in either a mixed state or a pure state can be transmitted. The quantum discord of a channel with rank larger than or equal to three is always non-zero. Therefore, non-zero quantum discord is also necessary for our quantum information transmission protocol.

preprint2012arXiv

Separability criterion for bipartite states and its generalization to multipartite systems

A group of symmetric operators are introduced to carry out the separability criterion for bipartite and multipartite quantum states. Every symmetric operator, represented by a symmetric matrix with only two nonzero elements, and their arbitrary linear combinations are found to be entanglement witnesses. By using these symmetric operators, Wootters' separability criterion for two-qubit states can be generalized to bipartite and multipartite systems in arbitrary dimensions.

preprint2012arXiv

Spin and valley quantum Hall ferromagnetism in graphene

In a graphene Landau level (LL), strong Coulomb interactions and the fourfold spin/valley degeneracy lead to an approximate SU(4) isospin symmetry. At partial filling, exchange interactions can spontaneously break this symmetry, manifesting as additional integer quantum Hall plateaus outside the normal sequence. Here we report the observation of a large number of these quantum Hall isospin ferromagnetic (QHIFM) states, which we classify according to their real spin structure using temperature-dependent tilted field magnetotransport. The large measured activation gaps confirm the Coulomb origin of the broken symmetry states, but the order is strongly dependent on LL index. In the high energy LLs, the Zeeman effect is the dominant aligning field, leading to real spin ferromagnets with Skyrmionic excitations at half filling, whereas in the `relativistic' zero energy LL, lattice scale anisotropies drive the system to a spin unpolarized state, likely a charge- or spin-density wave.

preprint2012arXiv

The recent Higgs boson data and Higgs triplet model with vectorlike quarks

Some vectorlike quarks are added to the Higgs triplet model with the motivation of fitting the recent Higgs boson data released by LHC and Tevatron collaborations. These vectorlike quarks can suppress the cross section of $gg\to h$ sizably, while the charged scalars, especially for the doubly charged scalar, can enhance $Br(h\to γγ)$ more sizably. Besides, the Higgs couplings to $WW$, $ZZ$ and light fermions can be the same as their SM values. Thus, the model will enhance the Higgs production rates into $γγ$ and $jjγγ$, while those for $WW^*$, $ZZ^*$ and $τ\barτ$ at the LHC are reduced relative to their SM predictions. The Higgs production rates into $Vb\bar{b}$ at the Tevatron are the same as the SM values.

preprint2012arXiv

Top quark forward-backward asymmetry and charge asymmetry in left-right twin Higgs model

In order to explain the Tevatron anomaly of the top quark forward-backward asymmetry $A_{FB}^t$ in the left-right twin Higgs model, we choose to give up the lightest neutral particle of $\hat{h}$ field as a stable dark matter candidate. Then a new Yukawa interaction for $\hat{h}$ is allowed, which can be free from the constraint of same-sign top pair production and contribute sizably to $A_{FB}^t$. Considering the constraints from the production rates of the top pair ($t\bar t$), the top decay rates and $t\bar{t}$ invariant mass distribution, we find that this model with such new Yukawa interaction can explain $A_{FB}^t$ measured at the Tevatron while satisfying the charge asymmetry $A_{C}^t$ measured at the LHC.Moreover, this model predicts a strongly correlation between $A_{C}^t$ at the LHC and $A_{FB}^t$ at the Tevatron, i.e., $A_{C}^t$ increases as $A_{FB}^t$ increases.

preprint2011arXiv

A new criteria for zero quantum discord

We propose a new criterion to judge zero quantum discord for arbitrary bipartite states. A bipartite quantum state has zero quantum discord if and only if all blocks of its density matrix are normal matrices and commute with each other. Given a bipartite state with zero quantum discord, how to find out the set of local projectors, which do not disturb the whole state after being imposed on one subsystem, is also presented. A class of two-qubit X-state is used to test the criterion, and an experimental scheme is proposed to realize it. Consequently, we prove that the positive operator-valued measurement can not extinguish the quantum correlation of a bipartite state with nonzero quantum discord.

preprint2011arXiv

Automatic Performance Debugging of SPMD-style Parallel Programs

The simple program and multiple data (SPMD) programming model is widely used for both high performance computing and Cloud computing. In this paper, we design and implement an innovative system, AutoAnalyzer, that automates the process of debugging performance problems of SPMD-style parallel programs, including data collection, performance behavior analysis, locating bottlenecks, and uncovering their root causes. AutoAnalyzer is unique in terms of two features: first, without any apriori knowledge, it automatically locates bottlenecks and uncovers their root causes for performance optimization; second, it is lightweight in terms of the size of performance data to be collected and analyzed. Our contributions are three-fold: first, we propose two effective clustering algorithms to investigate the existence of performance bottlenecks that cause process behavior dissimilarity or code region behavior disparity, respectively; meanwhile, we present two searching algorithms to locate bottlenecks; second, on a basis of the rough set theory, we propose an innovative approach to automatically uncovering root causes of bottlenecks; third, on the cluster systems with two different configurations, we use two production applications, written in Fortran 77, and one open source code-MPIBZIP2 (http://compression.ca/mpibzip2/), written in C++, to verify the effectiveness and correctness of our methods. For three applications, we also propose an experimental approach to investigating the effects of different metrics on locating bottlenecks.

preprint2011arXiv

Clustering experiments

It is well known that bees cluster together in cold weather, in the process of swarming (when the ``old'' queen leaves with part of the colony) or absconding (when the queen leaves with all the colony) and in defense against intruders such as wasps or hornets. In this paper we describe a fairly different clustering process which occurs at any temperature and independently of any special stimulus or circumstance. As a matter of fact, this process is about four times faster at 28 degree Celsius than at 15 degrees. Because of its simplicity and low level of ``noise'' we think that this phenomenon can provide a means for exploring the strength of inter-individual attraction between bees or other living organisms. For instance, and at first sight fairly surprisingly, our observations showed that this attraction does also exist between bees belonging to different colonies. As this study is aimed at providing a comparative perspective, we also describe a similar clustering experiment for red fire ants.

preprint2011arXiv

D-wave bosonic pair in an optical lattice

We present a bosonic model, in which two bosons may form a bound pair with d-wave symmetry via the four-site ring exchange interaction. A d-wave pairing superfluid as well as a d-wave density wave (DDW) state, are proposed to be achievable in this system. This exotic bosonic system can be realized in the BEC zone of a two-dimensional attractive p-band spinless fermionic system. By the mean field approach, we find that at low densities, the d-wave pairs may condensate, leading to a d-wave bosonic paired superfluid. At some particular filling factors, a novel phase, d-wave density wave state, emerges. We study this DDW state and its corresponding quantum phase transition in a two-leg ladder by the time-evolving block decimation (TEBD) method.

preprint2011arXiv

Development of Powder-in-Tube Processed Iron Pnictide Wires and Tapes

The development of the PIT fabrication process of iron pnictide superconducting wires and tapes has been carried out in order to enhance their transport properties. Silver was found to be the best sheath material, since no reaction layer was observed between the silver sheath and the superconducting core. The grain connectivity of iron pnictide wires and tapes has been markedly improved by employing Ag or Pb as dopants. At present, critical current densities in excess of 3750 A/cm^2 (Ic = 37.5 A) at 4.2 K have been achieved on Ag-sheathed SrKFeAs wires prepared with the above techniques, which is the highest in iron-based wires and tapes so far. Moreover, Ag-sheathed Sm-1111 superconducting tapes were successfully prepared by PIT method at temperatures as low as 900C, instead of commonly used temperatures of 1200C. These results demonstrate the feasibility of producing superconducting pnictide composite wires, even grain boundary properties require much more attention.

preprint2011arXiv

Direct observation of nanometer-scale amorphous layers and oxide crystallites at grain boundaries in polycrystalline Sr1-xKxFe2As2 superconductors

We report here an atomic resolution study of the structure and composition of the grain boundaries in polycrystalline Sr0.6K0.4Fe2As2 superconductor. A large fraction of grain boundaries contain amorphous layers larger than the coherence length, while some others contain nanometer-scale particles sandwiched in between amorphous layers. We also find that there is significant oxygen enrichment at the grain boundaries. Such results explain the relatively low transport critical current density (Jc) of polycrystalline samples with respect to that of bicrystal films.

preprint2011arXiv

Enhanced critical current properties in Ba0.6K0.4+xFe2As2 superconductor by over-doping of potassium

Phase-pure polycrystalline Ba0.6K0.4+xFe2As2 with were prepared using a one-step solid-state reaction method. We found that over-doping of potassium can improve critical current density (Jc). High-field Jc for samples with x = 0.1 is three times higher than that for samples with x = 0. Over-doping of K has minimal effect on the critical transition temperature (Tc). Less than 0.5 K degradations in Tc was measured for samples with x = 0.1. TEM revealed high concentration of dislocations in samples with x = 0.1, resulting in enhanced flux pining. Further analyses on magnetization loops for powder samples confirm that K over-doping can promote intra-grain Jc. Our results indicate that slight excess of K in Ba0.6K0.4Fe2As2 samples is beneficial to high-field applications.

preprint2011arXiv

Feature selection via simultaneous sparse approximation for person specific face verification

There is an increasing use of some imperceivable and redundant local features for face recognition. While only a relatively small fraction of them is relevant to the final recognition task, the feature selection is a crucial and necessary step to select the most discriminant ones to obtain a compact face representation. In this paper, we investigate the sparsity-enforced regularization-based feature selection methods and propose a multi-task feature selection method for building person specific models for face verification. We assume that the person specific models share a common subset of features and novelly reformulated the common subset selection problem as a simultaneous sparse approximation problem. To the best of our knowledge, it is the first time to apply the sparsity-enforced regularization methods for person specific face verification. The effectiveness of the proposed methods is verified with the challenging LFW face databases.

preprint2011arXiv

Frequency domain winding number and interaction effect on topological insulators

We study the effect of interactions on the time reversal invariant topological insulators in four and three spatial dimensions. Their topological indices are expressed by the interacting Green's functions. Under the local self-energy approximation, we find that interaction could induce nontrivial frequency-domain winding numbers and change the topological classes of the system. Our results suggest that the topological phases could be destroyed without developing long range orders. Practical issues on the accurate frequency-momentum integration combined with DMFT and diagrammatic calculations of the interacting Green's functions are also addressed.

preprint2011arXiv

High transport critical current densities in textured Fe-sheathed Sr1-xKxFe2As2+Sn superconducting tapes

We report the realization of grain alignment in Sn-added Sr1-xKxFe2As2 superconducting tapes prepared by ex-situ powder-in-tube method. At 4.2 K, high transport critical current densities Jc of 2.5x10^4 A/cm^2 (Ic = 180 A) in self-field and 3.5x10^3 A/cm^2 (Ic = 25.5 A) in 10 T have been measured. These values are the highest ever reported so far for Fe-based superconducting wires and tapes. We believe the superior Jc in our tape samples are due to well textured grains and strengthened intergrain coupling achieved by Sn addition. Our results demonstrated an encouraging prospect for application of iron based superconductors.

preprint2011arXiv

Learning Valuation Functions

In this paper we study the approximate learnability of valuations commonly used throughout economics and game theory for the quantitative encoding of agent preferences. We provide upper and lower bounds regarding the learnability of important subclasses of valuation functions that express no-complementarities. Our main results concern their approximate learnability in the distributional learning (PAC-style) setting. We provide nearly tight lower and upper bounds of $\tildeΘ(n^{1/2})$ on the approximation factor for learning XOS and subadditive valuations, both widely studied superclasses of submodular valuations. Interestingly, we show that the $\tildeΩ(n^{1/2})$ lower bound can be circumvented for XOS functions of polynomial complexity; we provide an algorithm for learning the class of XOS valuations with a representation of polynomial size achieving an $O(n^{\eps})$ approximation factor in time $O(n^{1/\eps})$ for any $\eps > 0$. This highlights the importance of considering the complexity of the target function for polynomial time learning. We also provide new learning results for interesting subclasses of submodular functions. Our upper bounds for distributional learning leverage novel structural results for all these valuation classes. We show that many of these results provide new learnability results in the Goemans et al. model (SODA 2009) of approximate learning everywhere via value queries. We also introduce a new model that is more realistic in economic settings, in which the learner can set prices and observe purchase decisions at these prices rather than observing the valuation function directly. In this model, most of our upper bounds continue to hold despite the fact that the learner receives less information (both for learning in the distributional setting and with value queries), while our lower bounds naturally extend.

preprint2011arXiv

On the 3-$γ_t$-Critical Graphs of Order $Δ(G)+3$

Let $γ_t(G)$ be the total domination number of graph $G$, a graph $G$ is $k$-total domination vertex critical (or\ just\ $k$-$γ_t$-critical) if $γ_t(G)=k$, and for any vertex $v$ of $G$ that is not adjacent to a vertex of degree one, $γ_t(G-v)=k-1$. Mojdeh and Rad \cite{MR06} proposed an open problem: Does there exist a 3-$γ_t$-critical graph $G$ of order $Δ(G)+3$ with $Δ(G)$ odd? In this paper, we prove that there exists a 3-$γ_t$-critical graph $G$ of order $Δ(G)+3$ with odd $Δ(G)\geq 9$.

preprint2011arXiv

Quantum Spinon Oscillations

The full quantum dynamics of a spinon under external magnetic fields is investigated by using the time-evolving block decimation (TEBD) method within the microcanonical picture of transport. We show that the center of the spinon oscillates back and forth in the absence of dissipation. The quantum many-body behavior can be understood in a single-particle picture of transport and Bloch oscillations, where quantum fluctuations induce finite life times. Transport, oscillations and lifetimes can be tuned to some degree separately by external fields. Other nontrivial dynamics such as resonance as well as chaos have also been discussed.

preprint2011arXiv

Rare Z-decay into light pseudoscalar bosons in the simplest little Higgs model

The simplest little Higgs model predicts a light pseudoscalar boson $η$ and opens up some new decay modes for $Z$-boson, such as $Z \to \bar{f} f η$, $Z\to ηηη$, $Z\to ηγ$ and $Z\to ηgg$. We examine these decay modes in the parameter space allowed by current experiments, and find that the branching ratios can reach $10^{-7}$ for $Z\to \bar{b}bη$, $10^{-8}$ for $Z\to \barττη$, and $10^{-8}$ for $Z\to ηγ$, which should be accessible at the GigaZ option of the ILC. However, the branching ratios can reach $10^{-12}$ for $Z\to ηηη$, and $10^{-11}$ for $Z\to ηgg$, which are hardly accessible at the GigaZ option.

preprint2011arXiv

Superconducting properties of FeSe wires and tapes prepared by gas diffusion technique

Superconducting FeSe in the form of wires and tapes were successfully fabricated using a novel gas diffusion procedure. Structural analysis by mean of x-ray diffraction shows that themain phase of tetragonal PbO-type FeSe was obtained by this synthesis method. The zero resistivity transition temperature of the FeSe was confirmed to be 9.3 K. The critical current density as high as 137 A/cm^2 (4 K, self field) has been observed. The results suggest that the diffusion procedure is promising in preparing high-quality FeSe wires and tapes.

preprint2011arXiv

Superconductivity induced by doping Rh in CaFe2-xRhxAs2

In this paper we report the synthesis of iron-based superconductors CaFe2-xRhxAs2 using one-step solid state reaction method, which crystallizes in the ThCr2Si2-type structure with a space group I4/mmm. The systematic evolution of the lattice constants demonstrates that the Fe ions are successfully replaced by the Rh. By increasing the doping content of Rh, the spin-density-wave (SDW) transition in the parent compound is suppressed and superconductivity emerges. The maximum superconducting transition temperature is found at 18.5 K with the doping level of x = 0.15. The temperature dependence of DC magnetization confirms superconducting transitions at around 15 K. The general phase diagram was obtained and found to be similar to the case of Rh-doping Sr122 system. Our results explicitly demonstrate the feasibility of inducing superconductivity in Ca122 compounds by higher d-orbital electrons doping, however, different Rh-doping effect between FeAs122 compounds and FeAs1111 systems still remains an open question.

preprint2011arXiv

Surface plasmons at the interface between graphene and kerr-type nonlinear medium

The properties of surface plasmons localized at the interface between graphene and kerr-type nonlinear medium in three dimensions are investigated. Compared with surface plasmons at the surface of metal, with the inevitable nonlinear refractive effect, the confinement of plasmon can be improved to three times than graphene plasmons without nonlinear contribution, but also with almost the same relative propagation length. Moreover, the dispersion relation and propation distance of graphene plasmons can be easily controlled by changing the fermi energy, temperature and relaxation time of graphene. Our results suggest a simple but useful potential application for precise nonlinear material sensor using graphene plasmons.

preprint2011arXiv

Synthesis and properties of La-doped CaFe2As2 single crystals with Tc = 42.7 K

Large single crystals of La-doped CaFe2As2 were successfully synthesized by the FeAs self-flux method. The x-ray diffraction patterns suggest high crystalline quality and c-axis orientation. By substitution of trivalent La3+ ions for divalent Ca2+, the resistivity anomaly in the parent compound CaFe2As2 is completely suppressed and a superconducting transition reaches the value of 42.7 K, which is higher than that of about 30 K reported in Saha S. R. et al., arXiv:1105.4798v1. The upper critical field has been determined with the magnetic field along ab-plane and c-axis, yielding an anisotropy of about 3.3.

preprint2011arXiv

The first returning speed and the last exit speed of a type of Markov chain

Let $\{X_n\}$ be a Markov chain with transition probability $p_{ij}=a_{j-(i-1)^+},\forall i,j\ge 0$, where $a_j=0$ provided $j<0$, $a_0>0$, $a_0+a_1<1$ and $\sum_{n=0}^\infty a_n=1$. Let $μ=\sum_{n=1}^\infty na_n$. It's known that $\{X_n\}$ is positive recurrent when $μ<1$; is null recurrent when $μ=1$; and is transient when $μ>1$. In this paper, we shall discuss the first returning speed and the last exit speed more precisely by means of $\{a_n\}$

preprint2011arXiv

The LHC di-photon Higgs signal predicted by little Higgs models

Little Higgs theory naturally predicts a light Higgs boson whose most important discovery channel at the LHC is the di-photon signal $pp\to h\to γγ$. In this work we perform a comparative study for this signal in some typical little Higgs models, namely the littlest Higgs model (LH), two littlest Higgs models with T-parity (named LHT-I and LHT-II) and the simplest little Higgs modes (SLH). We find that compared with the Standard Model prediction, the di-photon signal rate is always suppressed and the suppression extent can be quite different for different models. The suppression is mild ($\lsim 10%$) in the LH model but can be quite severe ($\simeq 90%$) in other three models. This means that discovering the light Higgs boson predicted by the little Higgs theory through the di-photon channel at the LHC will be more difficult than discovering the SM Higgs boson.

preprint2011arXiv

Transport properties and anisotropy in rare earth doped CaFe2As2 single crystals with Tc above 40 K

In this paper we report the superconductivity above 40 K in the electron doping single crystal Ca1-xRexFe2As2 (Re = La, Ce, Pr). The x-ray diffraction patterns indicate high crystalline quality and c-axis orientation. the resistivity anomaly in the parent compound CaFe2As2 is completely suppressed by partial replacement of Ca by rare earth and a superconducting transition reaches as high as 43 K, which is higher than the value in electron doping FeAs-122 compounds by substituting Fe ions with transition metal, even surpasses the highest values observed in hole doping systems with a transition temperature up to 38 K. The upper critical field has been determined with the magnetic field along ab-plane and c-axis, yielding the anisotropy of 2~3. Hall-effect measurements indicate that the conduction in this material is dominated by electron like charge carriers. Our results explicitly demonstrate the feasibility of inducing superconductivity in Ca122 compounds via electron doping using aliovalent rare earth substitution into the alkaline earth site, which should add more ingredients to the underlying physics of the iron-based superconductors.

preprint2011arXiv

Upper fields and critical current density of K0.58Fe1.56Se2 single crystals grown by one step technique

Single crystals of K0.58Fe1.56Se2 were successfully synthesized by a new single step process with the onset superconducting transition temperatures 31.9 K. The x-ray diffraction patterns suggest that they have high crystalline quality and c-axis orientation. A possible modulation structure of Fe-vacancy along c axis was observed. The upper critical field has been determined with the magnetic field along ab-plane and c-axis, yielding an anisotropy of about 3.3. It has also been shown that the critical current density of the K0.58Fe1.56Se2 is about 1.7x10^4 A/cm^2 at 5 K.

preprint2010arXiv

An Impurity Solver Using the Time-Dependent Variational Matrix Product State Approach

We use the time dependent variational matrix product state (tVMPS) approach to investigate the dynamical properties of the single impurity Anderson model (SIAM). Under the Jordan-Wigner transformation, the SIAM is reformulated into two spin-1/2 XY chains with local magnetic fields along the z-axis. The chains are connected by the longitudinal Ising coupling at the end points. The ground state of the model is searched variationally within the space spanned by the matrix product state (MPS). The temporal Green's functions are calculated both by the imaginary and real time evolutions, from which the spectral information can be extracted. The possibility of using the tVMPS approach as an impurity solver for the dynamical mean field theory is also addressed. Finite temperature density operator is obtained by the ancilla approach. The results are compared to those from the Lanczos and the Hirsch-Fye quantum Monte-Carlo methods.

preprint2010arXiv

Charge-density-wave and topological transitions in interacting Haldane model

Haldane model is a noninteracting model for spinless fermions showing nontrivial topological properties. Effect of the electron-electron interaction on the topological phase poses an intriguing question. By means of the Hartree-Fock mean field, the exact diagonalization and the constrained-path Monte Carlo methods we mapped out the phase diagram of the interacting Haldane model. It is found that interaction breaks down the topological phase and drives the system into the charge-density-wave state. Sequence of the two transitions depends on the strength of next-nearest-neighbor hopping. Many-body Chern number and the charge excitation gap are used to characterize the topological transition.

preprint2010arXiv

Dark matter and Higgs phenomenology predicted by left-right twin Higgs model in light of CDMS II results

The left-right twin Higgs model predicts a light stable scalar \hat{S}, which is a candidate for WIMP dark matter. We study its scattering on nucleon and find that the cross section is below the CDMS II upper bound but can reach the SuperCDMS sensitivity. Then we study the Higgs phenomenology by paying special attention to the decay h -> \hat{S} \hat{S} which is strongly correlated with the dark matter scattering on nucleon. We find that such an invisible decay can be sizable, which can severely suppress the conventional decay modes like h->VV (V=W,Z) and h->b\bar{b}. On the other hand, compared to the SM prediction, the rates of Higgs boson productions at the LHC via gluon-gluon fusion, weak boson fusion or in association with top quark pairs are all reduced significantly, e.g., the gluon-gluon fusion channel can be suppressed by about 30%.

preprint2010arXiv

Fabrication and some properties of biaxially aligned Sr0.6K0.4Fe2As2 superconductors by processing in high magnetic field

We fabricated the c axis and ab-plane biaxially aligned Sr0.6K0.4Fe2As2 superconductor using a two-step magnetic field procedure. The effect of magnetic fields on the structure and superconducting properties of Sr0.6K0.4Fe2As2 has been investigated by using X-ray diffraction and magnetic measurements. The degree of orientation of the samples was about 0.39 for the c axis and 0.51 for ab-plane orientation, as evaluated from the Lotgering factor of X-ray diffraction. This technology might be useful in a variety of potential applications, including preparing iron based superconducting bulks and wires with high critical currents.

preprint2010arXiv

Heat treatment effects on the superconducting properties of Ag-doped SrKFeAs compound

The superconducting properties of polycrystalline Sr0.6K0.4Fe2As2 were strongly influenced by Ag doping (Supercond. Sci. Technol. 23 (2010) 025027). Ag addition is mainly dominated by silver diffusing, so the annealing process is one of the essential factors to achieve high quality Ag doped Sr0.6K0.4Fe2As2. In this paper, the optimal annealing conditions were studied for Ag doped Sr0.6K0.4Fe2As2 bulks prepared by a one-step solid reaction method. It is found that the annealing temperature has a strong influence on the superconducting properties, especially on the critical current density Jc. As a result, higher heat treatment temperature (~900C) is helpful in diffusing Ag and reducing the impurity phase gathered together to improve the grain connectivity. In contrast, low-temperature sintering is counterproductive for Ag doped samples. These results clearly suggest that annealing at ~900C is necessary for obtaining high Jc Ag-doped samples.

preprint2010arXiv

Higgs boson production in photon-photon collision at ILC: a comparative study in different little Higgs models

We study the process γγ->h->bb_bar at ILC as a probe of different little Higgs models, including the simplest little Higgs model (SLH), the littlest Higgs model (LH), and two types of littlest Higgs models with T-parity (LHT-I, LHT-II). Compared with the Standard Model (SM) prediction, the production rate is found to be sizably altered in these little Higgs models and, more interestingly, different models give different predictions. We find that the production rate can be possibly enhanced only in the LHT-II for some part of the parameter space, while in all other cases the rate is suppressed. The suppression can be 10% in the LH and as much as 60% in both the SLH and the LHT-I/LHT-II. The severe suppression in the SLH happens for a large \tanβand a small m_h, in which the new decay mode h->ηη(ηis a light pseudo-scalar) is dominant; while for the LHT-I/LHT-II the large suppression occurs when f and m_h are both small so that the new decay mode h->A_H A_H is dominant. Therefore, the precision measurement of such a production process at the ILC will allow for a test of these models and even distinguish between different scenarios.

preprint2010arXiv

In Cloud, Can Scientific Communities Benefit from the Economies of Scale?

The basic idea behind Cloud computing is that resource providers offer elastic resources to end users. In this paper, we intend to answer one key question to the success of Cloud computing: in Cloud, can small or medium-scale scientific computing communities benefit from the economies of scale? Our research contributions are three-fold: first, we propose an enhanced scientific public cloud model (ESP) that encourages small- or medium-scale organizations to rent elastic resources from a public cloud provider; second, on a basis of the ESP model, we design and implement the DawningCloud system that can consolidate heterogeneous scientific workloads on a Cloud site; third, we propose an innovative emulation methodology and perform a comprehensive evaluation. We found that for two typical workloads: high throughput computing (HTC) and many task computing (MTC), DawningCloud saves the resource consumption maximally by 44.5% (HTC) and 72.6% (MTC) for service providers, and saves the total resource consumption maximally by 47.3% for a resource provider with respect to the previous two public Cloud solutions. To this end, we conclude that for typical workloads: HTC and MTC, DawningCloud can enable scientific communities to benefit from the economies of scale of public Clouds.

preprint2010arXiv

In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale?

In this paper, we intend to answer one key question to the success of cloud computing: in cloud, do many task computing (MTC) or high throughput computing (HTC) service providers, which offer the corresponding computing service to end users, benefit from the economies of scale? Our research contributions are three-fold: first, we propose an innovative usage model, called dynamic service provision (DSP) model, for MTC or HTC service providers. In the DSP model, the resource provider provides the service of creating and managing runtime environments for MTC or HTC service providers, and consolidates heterogeneous MTC or HTC workloads on the cloud platform; second, according to the DSP model, we design and implement DawningCloud, which provides automatic management for heterogeneous workloads; third, a comprehensive evaluation of DawningCloud has been performed in an emulatation experiment. We found that for typical workloads, in comparison with the previous two cloud solutions, DawningCloud saves the resource consumption maximally by 46.4% (HTC) and 74.9% (MTC) for the service providers, and saves the total resource consumption maximally by 29.7% for the resource provider. At the same time, comparing with the traditional solution that provides MTC or HTC services with dedicated systems, DawningCloud is more cost-effective. To this end, we conclude that for typical MTC and HTC workloads, on the cloud platform, MTC and HTC service providers and the resource provider can benefit from the economies of scale.

preprint2010arXiv

Influence of Pb addition on the superconducting properties of polycrystalline Sr0.6K0.4Fe2As2

Polycrystalline Sr0.6K0.4Fe2As2 samples with various Pb additions (0-20 wt%) were prepared using a one-step solid state reaction. X-ray diffraction analysis shows no evidence for chemical reaction between the Pb and the FeAs-based superconductor. However, the presence of the Pb can affect the microstructure and superconducting properties of the final products. The critical transition temperature Tc indicates no degradation up to 20 wt% Pb addition, and dramatic improvements of magnetic Jc and irreversibility field Hirr were observed for appropriate Pb concentration. Transport critical current property of pure and Pb-added Sr0.6K0.4Fe2As2 tapes was also measured by a four-probe technique, and a remarkable enhancement of Jc at low fields was detected for the Pb added tapes.

preprint2010arXiv

Low-temperature synthesis of SmFeAsO0.7F0.3 wires with high transport critical current density

Ag-sheathed SmFeAsO0.7F0.3 (Sm-1111) superconducting wires were prepared by a one-step solid state reaction at temperatures as low as 850~900C, instead of commonly used temperatures of 1150~1250C. The X-ray diffraction pattern of the as-sintered samples is well indexed on the basis of tetragonal ZrCuSiAs-type structure. We characterized transport critical current density Jc of the SmFeAsO0.7F0.3 wires in increasing and subsequently decreasing fields, by a resistive four-probe method. A transport Jc as high as ~1300 A/cm^2 at 4.2 K and self field has been observed for the first time in Sm-1111 type polycrystalline superconductors. The Jc also shows a rapid depression in small applied fields as well as a magnetic-history dependence, indicating weak-linked grain boundaries. The low-temperature synthesis method can be very beneficial to fabricating the RE-1111 iron oxynictides in a convenient and safe way.

preprint2010arXiv

Low-temperature synthesis of SmO0.8F0.2FeAs superconductor with Tc = 56.1K

We report a systematic study on the effect of sintering temperature on the structural and superconducting properties of nominal SmO0.8F0.2FeAs fabricated by simple one-step solid state reaction method. A detailed correlation between the sintering temperature, structure, onset transition temperature (Tc) and critical current density (Jc) has been found in all samples of each batch. Most importantly, samples sintered at a low temperature clearly shows high Tc, for example the Tc of the samples sintered at 850C is even above 53 K, and the samples prepared at 1000C display the highest Tc of 56.1 K reported so far. Furthermore, the samples sintered at 1000C also show the highest RRR and the lowest resistivity(57K), indicating the low impurity scattering and enhanced carrier density. However, the maximum Jc of 10510 A/cm^2 at 5 K in self field was achieved in the samples sintered at 1100oC. The dependence of Tc on a-axis lattice constant indicates that the sintering temperature has strong influence on the effective substitution level of F for O. This result suggests that annealing at a temperature of ~1000C seems much better for obtaining high quality 1111 phase oxypnictides, compared to commonly used temperatures of around 1200C.

preprint2010arXiv

One-step method to grow Ba0.6K0.4Fe2As2 single crystals without fluxing agent

Single crystals of Ba0.6K0.4Fe2As2 with excellent quality have been successfully grown without fluxing agent through a simple one-step method for the first time. X-ray diffraction patterns demonstrate that the samples have high crystalline quality and c-axis orientation. The onset transition temperature is up to 38 K with the zero resistivity temperature about 36.7 K. Both the R-T and M-T data show a very sharp superconducting transition with transition width 0.4 K. We also found that the samples possess very large current carrying ability and high upper critical fields, indicating potential applications requiring very high field. The above simple and safe one-step technique of single crystal growth can be effective in other systems of Fe-based superconductors.

preprint2010arXiv

Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization

Different departments of a large organization often run dedicated cluster systems for different computing loads, like HPC (high performance computing) jobs or Web service applications. In this paper, we have designed and implemented a cloud management system software Phoenix Cloud to consolidate heterogeneous workloads from different departments affiliated to the same organization on the shared cluster system. We have also proposed cooperative resource provisioning and management policies for a large organization and its affiliated departments, running HPC jobs and Web service applications, to share the consolidated cluster system. The experiments show that in comparison with the case that each department operates its dedicated cluster system, Phoenix Cloud significantly decreases the scale of the required cluster system for a large organization, improves the benefit of the scientific computing department, and at the same time provisions enough resources to the other department running Web services with varying loads.

preprint2010arXiv

PhoenixCloud: Provisioning Resources for Heterogeneous Cloud Workloads

As more and more service providers choose Cloud platforms, a resource provider needs to provision resources and supporting runtime environments (REs) for heterogeneous workloads in different scenarios. Previous work fails to resolve this issue in several ways: (1) it fails to pay attention to diverse RE requirements, and does not enable creating coordinated REs on demand; (2) few work investigates coordinated resource provisioning for heterogeneous workloads. In this paper, our contributions are three-fold: (1) we present an RE agreement that expresses diverse RE requirements, and build an innovative system PhoenixCloud that enables a resource provider to create REs on demand according to RE agreements; (2) we propose two coordinated resource provisioning solutions for heterogeneous workloads in two typical Cloud scenarios: first, a large organization operates a private Cloud for two heterogeneous workloads; second, a large organization or two service providers running heterogeneous workloads revert to a public Cloud; and (3) A comprehensive evaluation has been performed in experiments. For typical workload traces of parallel batch jobs and Web services, our experiments show that: a) In the first Cloud scenario, when the throughput is almost same like that of a dedicated cluster system, our solution decreases the configuration size of cluster by about 40%; b) in the second scenario, our solution decreases not only the total resource consumption, but also the peak resource consumption maximally to 31% with respect to that of EC2 + RightScale solution.

preprint2010arXiv

PhoenixCloud: Provisioning Resources for Heterogeneous Workloads in Cloud Computing

As more and more service providers choose Cloud platforms, which is provided by third party resource providers, resource providers needs to provision resources for heterogeneous workloads in different Cloud scenarios. Taking into account the dramatic differences of heterogeneous workloads, can we coordinately provision resources for heterogeneous workloads in Cloud computing? In this paper we focus on this important issue, which is investigated by few previous work. Our contributions are threefold: (1) we respectively propose a coordinated resource provisioning solution for heterogeneous workloads in two typical Cloud scenarios: first, a large organization operates a private Cloud for two heterogeneous workloads; second, a large organization or two service providers running heterogeneous workloads revert to a public Cloud; (2) we build an agile system PhoenixCloud that enables a resource provider to create coordinated runtime environments on demand for heterogeneous workloads when they are consolidated on a Cloud site; and (3) A comprehensive evaluation has been performed in experiments. For two typical heterogeneous workload traces: parallel batch jobs and Web services, our experiments show that: a) in a private Cloud scenario, when the throughput is almost same like that of a dedicated cluster system, our solution decreases the configuration size of a cluster by about 40%; b) in a public Cloud scenario, our solution decreases not only the total resource consumption, but also the peak resource consumption maximally to 31% with respect to that of EC2 +RightScale solution.

preprint2010arXiv

PowerTracer: Tracing requests in multi-tier services to save cluster power consumption

As energy proportional computing gradually extends the success of DVFS (Dynamic voltage and frequency scaling) to the entire system, DVFS control algorithms will play a key role in reducing server clusters' power consumption. The focus of this paper is to provide accurate cluster-level DVFS control for power saving in a server cluster. To achieve this goal, we propose a request tracing approach that online classifies the major causal path patterns of a multi-tier service and monitors their performance data as a guide for accurate DVFS control. The request tracing approach significantly decreases the time cost of performance profiling experiments that aim to establish the empirical performance model. Moreover, it decreases the controller complexity so that we can introduce a much simpler feedback controller, which only relies on the single-node DVFS modulation at a time as opposed to varying multiple CPU frequencies simultaneously. Based on the request tracing approach, we present a hybrid DVFS control system that combines an empirical performance model for fast modulation at different load levels and a simpler feedback controller for adaption. We implement a prototype of the proposed system, called PowerTracer, and conduct extensive experiments on a 3-tier platform. Our experimental results show that PowerTracer outperforms its peer in terms of power saving and system performance.

preprint2010arXiv

Precise Request Tracing and Performance Debugging for Multi-tier Services of Black Boxes

As more and more multi-tier services are developed from commercial components or heterogeneous middleware without the source code available, both developers and administrators need a precise request tracing tool to help understand and debug performance problems of large concurrent services of black boxes. Previous work fails to resolve this issue in several ways: they either accept the imprecision of probabilistic correlation methods, or rely on knowledge of protocols to isolate requests in pursuit of tracing accuracy. This paper introduces a tool named PreciseTracer to help debug performance problems of multi-tier services of black boxes. Our contributions are two-fold: first, we propose a precise request tracing algorithm for multi-tier services of black boxes, which only uses application-independent knowledge; secondly, we present a component activity graph abstraction to represent causal paths of requests and facilitate end-to-end performance debugging. The low overhead and tolerance of noise make PreciseTracer a promising tracing tool for using on production systems.

preprint2010arXiv

Precise, Scalable and Online Request Tracing for Multi-tier Services of Black Boxes

As more and more multi-tier services are developed from commercial off-the-shelf components or heterogeneous middleware without source code available, both developers and administrators need a request tracing tool to (1) exactly know how a user request of interest travels through services of black boxes; (2) obtain macro-level user request behavior information of services without the necessity of inundating within massive logs. Previous research efforts either accept imprecision of probabilistic correlation methods or present precise but unscalable tracing approaches that have to collect and analyze large amount of logs; Besides, previous precise request tracing approaches of black boxes fail to propose macro-level abstractions that enables debugging performance-in-the-large, and hence users have to manually interpret massive logs. This paper introduces a precise, scalable and online request tracing tool, named PreciseTracer, for multi-tier services of black boxes. Our contributions are four-fold: first, we propose a precise request tracing algorithm for multi-tier services of black boxes, which only uses application-independent knowledge; second, we respectively present micro-level and macro-level abstractions: component activity graphs and dominated causal path patterns to represent causal paths of each individual request and repeatedly executed causal paths that account for significant fractions; third, we present two mechanisms: tracing on demand and sampling to significantly increase system scalability; fourth, we design and implement an online request tracing tool. PreciseTracer's fast response, low overhead and scalability make it a promising tracing tool for large-scale production systems.

preprint2010arXiv

Pseudoscalar boson and SM-like Higgs boson productions at LHC in simplest little Higgs model

In the framework of the simplest little Higgs model (SLHM), we perform a comprehensive study for the pair productions of the pseudoscalar boson η and SM-like Higgs boson h at LHC, namely gg(b\bar{b})-> ηη, gg(q\bar{q})-> ηh and gg(b\bar{b})-> hh. These production processes provide a way to probe the couplings between Higgs bosons. We find that the cross section of gg-> ηη always dominates over that of b\bar{b}-> ηη. When the Higgs boson h which mediates these two processes is on-shell, their cross sections can reach several thousand $fb$ and several hundred $fb$, respectively. When the intermediate state h is off-shell, those two cross sections are reduced by two orders of magnitude, respectively. The cross sections of gg-> ηh and q\bar{q}-> ηh are about in the same order of magnitude, which can reach $\ord{(10^2fb)}$ for a light η boson. Besides, compared with the SM prediction, the cross section of a pair of SM-like Higgs bosons production at LHC can be enhanced sizably. Finally, we briefly discuss the observable signatures of ηη, ηh and hh at the LHC, respectively.

preprint2010arXiv

Scalable Group Management in Large-Scale Virtualized Clusters

To save cost, recently more and more users choose to provision virtual machine resources in cluster systems, especially in data centres. Maintaining a consistent member view is the foundation of reliable cluster managements, and it also raises several challenge issues for large scale cluster systems deployed with virtual machines (which we call virtualized clusters). In this paper, we introduce our experiences in design and implementation of scalable member view management on large-scale virtual clusters. Our research contributions are three-fold: 1) we propose a scalable and reliable management infrastructure that combines a peer-to-peer structure and a hierarchy structure to maintain a consistent member view in virtual clusters; 2) we present a light-weighted group membership algorithm that can reach the consistent member view within a single round of message exchange; and 3) we design and implement a scalable membership service that can provision virtual machines and maintain a consistent member view in virtual clusters. Our work is verified on Dawning 5000A, which ranked No.10 of Top 500 super computers in November, 2008.

preprint2010arXiv

Scalable Large-Margin Mahalanobis Distance Metric Learning

For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance metric. By employing the principle of margin maximization to achieve better generalization performances, this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite (psd) matrix is the unknown variable. a specialized gradient descent method is proposed. our algorithm is much more efficient and has a better performance in scalability compared with existing methods. Experiments on benchmark data sets suggest that, compared with state-of-the-art metric learning algorithms, our algorithm can achieve a comparable classification accuracy with reduced computational complexity.

preprint2010arXiv

Spin current through an ESR quantum dot: A real-time study

The spin transport in a strongly interacting spin-pump nano-device is studied using the time-dependent variational-matrix-product-state (VMPS) approach. The precession magnetic field generates a dissipationless spin current through the quantum dot. We compute the real time spin current away from the equilibrium condition. Both transient and stationary states are reached in the simulation. The essentially exact results are compared with those from the Hartree-Fock approximation (HFA). It is found that correlation effect on the physical quantities at quasi-steady state are captured well by the HFA for small interaction strength. However the HFA misses many features in the real time dynamics. Results reported here may shed light on the understanding of the ultra-fast processes as well as the interplay of the non-equilibrium and strongly correlated effect in the transport properties.

preprint2009arXiv

A Fast Impurity Solver Based on Gutzwiller variational approach

A fast impurity solver for the dynamical mean field theory(DMFT) named Two Mode Approxi- mation (TMA) is proposed based on the Gutzwiller variational approach, which captures the main features of both the coherent and incoherent motion of the electrons. The new solver works with real frequency at zero temperature and it provides directly the spectral function of the electrons. It can be easily generalized to multi-orbital impurity problems with general on-site interactions, which makes it very useful in LDA+DMFT. Benchmarks on one and two band Hubbard models are presented, and the results agree well with those of Exact Diagonalization (ED).

preprint2009arXiv

Antiferromagnetism of Repulsively Interacting Fermions in a harmonic trap

We propose a Real-Space Gutzwiller variational approach and apply it to a system of repulsively interacting ultracold fermions with spin 1/2 trapped in an optical lattice with a harmonic confinement. Using the Real-Space Gutzwiller variational approach, we find that in system with balanced spin-mixtures on a square lattice, antiferromagnetism either appears in a checkerboard pattern or forms a ring and antiferromagnetic order is stable in the regions where the particle density is close to one, which is consistent with the recent results obtained by the Real-Space Dynamical Mean-field Theory approach. We also investigate the imbalanced case and find that antiferromagnetic order is suppressed there.

preprint2009arXiv

Fabrication and characterization of iron pnictide wires and bulk materials through the powder-in-tube method

The recent discovery of superconductivity in the iron based superconductors with very high upper critical fields presents a new possibility for practical applications, but fabricating fine-wire is a challenge because of mechanically hard and brittle powders and the toxicity and volatility of arsenic. In this paper, we report the synthesis and the physical characterization of iron pnictide wires and bulks prepared by the powder-in-tube method (PIT). A new class of high-Tc iron pnictide composite wires, such as LaFeAsO1-xFx, SmFeAsO1-xFx and Sr1-xKxFeAs, has been fabricated by the in situ PIT technique using Fe, Ta and Nb tubes. Microscopy and x-ray analysis show that the superconducting core is continuous, and retains phase composition after wire drawing and heat treatment. Furthermore, the wires exhibit a very weak Jc-field dependence behavior even at high temperatures. The upper critical field Hc2(0) value can exceed 100 T, surpassing those of MgB2 and all the low temperature superconductors and indicating a strong potential for applications requiring very high field. These results demonstrate the feasibility of producing superconducting pnictide composite wire. We also applied the one step PIT method to synthesize the iron-based bulks, due to its convenience and safety. In fact, by using this technique, we have successfully discovered superconductivity at 35 K and 15 K in Eu0.7Na0.3Fe2As2 and SmCoFeAsO compounds, respectively. These clearly suggest that the one-step PIT technique is unique and versatile and hence can be tailored easily for other rare earth derivatives of novel iron-based superconductors.

preprint2009arXiv

Higgs boson decays and production via gluon fusion at LHC in littlest Higgs models with T-parity

We study the Higgs boson decays and production via gluon fusion at the LHC as a probe of two typical littlest Higgs models which introduce a top quark partner with different (even and odd) T-parity to cancel the Higgs mass quadratic divergence contributed by the top quark. For each model we consider two different choices for the down-type quark Yukawa couplings. We first examine the branching ratios of the Higgs boson decays and then study the production via gluon fusion followed by the decay into two photons or two weak gauge bosons. We find that the predictions can be quite different for different models or different choices of down-type quark Yukawa couplings and all these predictions can sizably deviate from the SM predictions. So the Higgs boson processes at the LHC can be a sensitive probe for these littlest Higgs models.

preprint2009arXiv

Interaction-induced anomalous transport behavior in one dimensional optical lattice

The non-equilibrium dynamics of spin impurity atoms in a strongly interacting one-dimensional (1D) Bose gas under the gravity field is studied. We show that due to the non-equilibrium preparation of the initial state as well as the interaction between the impurity atoms and other bosons, a counterintuitive phenomenon may emerge: the impurity atoms could propagate upwards automatically in the gravity field . The effects of the strength of interaction, the gradient of the gravity field, as well as the different configurations of the initial state are investigated by studying the time-dependent evolution of the 1D strongly interacting bosonic system using time-evolving block decimation (TEBD) method. A profound connection between this counterintuitive phenomenon and the repulsive bound pair is also revealed.

preprint2009arXiv

Large transport critical currents of powder-in-tube Sr0.6K0.4Fe2As2/Ag superconducting wires and tapes

We report significant transport critical currents firstly achieved in Sr0.6K0.4Fe2As2 wires and tapes with a Tc = 34 K, which were fabricated through an in-situ powder-in-tube process. Silver was used as a chemical addition as well as a sheath material. Transport measurements were performed by a standard four-probe resistive method. All the wire and tape samples have shown transport properties. Critical current density Jc was enhanced upon silver addition, and at 4.2 K, a best Jc of ~1200 A/cm^2 (Ic = 9 A) was achieved for 20 % silver added tapes, which is the highest in iron-based wires and tapes so far. The Jc is almost field independent between 1 T and 10 T, exhibiting a strong vortex pinning. Such a high transport critical current density is attributed to the absence of reaction layer between the silver sheath and superconducting core, as well as an improved connectivity between grains. We also identify a weak-link behavior from the creep drop of Jc at low fields and a hysteretic phenomenon. Finally, we found that compared to Fe, Ta and Nb tubes, Ag was the best sheath material for the fabrication of high-performance 122 type pnictide wires and tapes.

preprint2009arXiv

Numerical study of the topological Anderson insulator in HgTe/CdTe quantum wells

We study the disorder effect on the transport properties in the HgTe/CdTe semiconductor quantum wells. We confirm that at a moderate disorder strength, the initially un-quantized two terminal conductance becomes quantized, and the system makes a transition to the novel topological Anderson insulator (TAI). Conductances calculated for the stripe and cylinder samples reveal the topological feature of TAI and supports the idea that the helical edge states may cause the anomalous quantized plateaus. The influence of disorder is studied by calculating the distributions of local currents. Base on the above-mentioned picture, the phenomena induced by disorder in the quantum spin Hall region and TAI region are directly explained. Our study of the local current configurations shed further light on the mechanism of the anomalous plateau.

preprint2009arXiv

Superconductivity in SmFe1-xMxAsO (M = Co, Rh, Ir)

In this paper we report the comparative study of superconductivity by 3d (Co), 4d (Rh), 5d (Ir) element doping in SmFeAsO. X-ray diffraction patterns indicate that the material has formed the ZrCuSiAs-type structure with a space group P4/nmm. It is found that the antiferromagnetic spin-density-wave (SDW) order in the parent compounds is rapidly suppressed by Co, Rh, and Ir doping, and superconductivity emerges. Both electrical resistance and magnetization measurements show superconductivity up to around 10 K in SmFe1-xMxAsO (M = Co, Rh, Ir). Co, Rh and Ir locate in the same column in the periodic table of elements but have different electronic band structure, so comparative study would add more ingredients to the underlying physics of the iron-based superconductors.

preprint2009arXiv

Superconductivity induced by doping Ru in SrFe2-xRuxAs2

Using one-step solid state reaction method, we have successfully synthesized the superconductor SrFe1-xRuxAs. X-ray diffraction indicates that the material has formed the ThCr2Si2-type structure with a space group I4/mmm. The systematic evolution of the lattice constants demonstrates that the Fe ions are successfully replaced by the Ru. By increasing the doping content of Ru, the spin-density-wave (SDW) transition in the parent compound is suppressed and superconductivity emerges. The maximum superconducting transition temperature is found at 13.5 K with the doping level of x = 0.7. The temperature dependence of DC magnetization confirms superconducting transitions at around 12 K. Our results indicate that similar to non-isoelectronic substitution, isoelectronic substitution contributes to changes in both the carrier concentration and internal pressure, and superconductivity could be induced by isoelectronic substitution.

preprint2009arXiv

Superconductivity of powder-in-tube Sr0.6K0.4Fe2As2 wires

Nb-sheathed Sr0.6K0.4Fe2As2 superconducting wires have been fabricated using the powder-in-tube (PIT) method for the first time and the superconducting properties of the wires have been investigated. The transition temperature (Tc) of the Sr0.6K0.4Fe2As2 wires is confirmed to be as high as 35.3 K. Most importantly, Sr0.6K0.4Fe2As2 wires exhibit a very weak Jc-field dependence behavior even the temperature is very close to Tc. The upper critical field Hc2(0) value can exceed 140 T, surpassing those of MgB2 and all the low temperature superconductors. Such high Hc2 and superior Jc-field performance make the 122 phase SrKFeAs wire conductors a powerful competitor potentially useful in very high field applications.

preprint2009arXiv

The role of silver addition on the structural and superconducting properties of polycrystalline Sr0.6K0.4Fe2As2

The effect of Ag addition (0-20 wt%) on polycrystalline Sr0.6K0.4Fe2As2 superconductor has been investigated. It is found that the critical transition temperature Tc was not depressed, and the irreversibility field Hirr and hysteresis magnetization were significantly enhanced upon Ag addition. Characterization study reveals that larger grains are observed in the Ag-added samples. Moreover, the formation of glassy phase as well as amorphous layer, which are present in almost all the grain edges and boundaries in pure samples, are suppressed by Ag addition. The improvement of superconducting properties in Ag-added samples may originate from the enlargement of grains as well as better connections between grains

preprint2008arXiv

LDA+Gutzwiller Method for Correlated Electron Systems: Formalism and Its Applications

We introduce in detail our newly developed \textit{ab initio} LDA+Gutzwiller method, in which the Gutzwiller variational approach is naturally incorporated with the density functional theory (DFT) through the "Gutzwiller density functional theory (GDFT)" (which is a generalization of original Kohn-Sham formalism). This method can be used for ground state determination of electron systems ranging from weakly correlated metal to strongly correlated insulators with long-range ordering. We will show that its quality for ground state is as high as that by dynamic mean field theory (DMFT), and yet it is computationally much cheaper. In additions, the method is fully variational, the charge-density self-consistency can be naturally achieved, and the quantities, such as total energy, linear response, can be accurately obtained similar to LDA-type calculations. Applications on several typical systems are presented, and the characteristic aspects of this new method are clarified. The obtained results using LDA+Gutzwiller are in better agreement with existing experiments, suggesting significant improvements over LDA or LDA+U.

preprint2008arXiv

Magnetism of Cold Fermionic Atoms on p-Band of an Optical Lattice

We carry out \textit{ab initio} study of ground state phase diagram of spin-1/2 cold fermionic atoms within two-fold degenerate $p$-band of an anisotropic optical lattice. Using the Gutzwiller variational approach, we show that a robust ferromagnetic phase exists for a vast range of band fillings and interacting strengths. The ground state crosses over from spin density wave state to spin-1 Neel state at half filling. Additional harmonic trap will induce spatial separation of varies phases. We also discuss several relevant observable consequences and detection methods. Experimental test of the results reported here may shed some light on the long-standing issue of itinerant ferromagnetism.

preprint2006arXiv

Calculation of Bit Error Ratio for Optically Pre-Amplified DPSK Receivers Using Optical Mach-Zehnder Interferometer Demodulation and Balanced Detection

This paper presents an analysis of how to calculate bit error ratio (BER) with physical explanation for optically pre-amplified DPSK receivers using optical Mach-Zehnder interferometer (MZI) demodulation and balanced detection. It is shown that BER calculation method for this kind of receivers is different from the conventional calculation method used widely for IM/DD receivers. An analytical relationship in receiver sensitivity between DPSK receivers using MZI demodulation with balanced detection and IM/DD receivers (or DPSK receivers using MZI demodulation and single-port detection) is given based on the Gaussian noise approximation. Our calculation method correctly predicts the 3-dB improvement of receiver sensitivity by using balanced detection over single-port detection or IM/DD receivers. Furthermore, quantum-limited DPSK receivers with MZI demodulation are also analyzed.

preprint2006arXiv

Negative differential thermal resistance and thermal transistor

We report on the first model of a thermal transistor to control heat flow. Like its electronic counterpart, our thermal transistor is a three-terminal device with the important feature that the current through the two terminals can be controlled by small changes in the temperature or in the current through the third terminal. This control feature allows us to switch the device between "off" (insulating) and "on" (conducting) states or to amplify a small current. The thermal transistor model is possible because of the negative differential thermal resistance.

Lei Wang

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321 published item(s)

Delta Score Matters! Spatial Adaptive Multi Guidance in Diffusion Models

VulTriage: Triple-Path Context Augmentation for LLM-Based Vulnerability Detection

Modern applications of machine learning in quantum sciences

An Inexact Preconditioned Zeroth-order Proximal Method for Composite Optimization

An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification

Broadband miniaturized spectrometers with a van der Waals tunnel diode

A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation

Effect of temperature-dependent thermophysical properties on turbulent forced convection under constant heat flux boundary condition

Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers

A Communication-Efficient and Privacy-Aware Distributed Algorithm for Sparse PCA

A joint explanation of W-mass and muon g-2 in 2HDM

A Medical Semantic-Assisted Transformer for Radiographic Report Generation

A New High Energy Efficiency Scheme Based on Two-Dimension Resource Blocks in Wireless Communication Systems

A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints

Ab-initio study of interacting fermions at finite temperature with neural canonical transformation

Active and Passive Hybrid Detection Method for Power CPS False Data Injection Attacks with Improved AKF and GRU-CNN

An efficient thermal lattice Boltzmann method for simulating three-dimensional liquid-vapor phase change

Attitude estimation from vector measurements: Necessary and sufficient conditions and convergent observer design

CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs

Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification

Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

Dissipation-enabled hydrodynamic conductivity in a tunable bandgap semiconductor

Factorizations of almost simple orthogonal groups of plus type

Fast and Arbitrary Beam Pattern Design for RIS-Assisted Terahertz Wireless Communication

Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition

Graph Neural Network with Curriculum Learning for Imbalanced Node Classification

Hardy-Sobolev inequalities with distance to the boundary weight functions

Indirect Adaptive Control of Nonlinearly Parameterized Nonlinear Dissipative Systems

Instance Image Retrieval by Learning Purely From Within the Dataset

Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation

LibFewShot: A Comprehensive Library for Few-shot Learning

Machine Learning assisted excess noise suppression for continuous-variable quantum key distribution

Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

OLxPBench: Real-time, Semantically Consistent, and Domain-specific are Essential in Benchmarking, Designing, and Implementing HTAP Systems

Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy

Progressive Hard-case Mining across Pyramid Levels for Object Detection

Projective-truncation-approximation study of the one-dimensional $ϕ^4$ lattice model

Pursuing the Precision Study for Color Glass Condensate in Forward Hadron Productions

Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data

Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection

Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations

StyTr$^2$: Image Style Transfer with Transformers

Testing gravitational redshift based on microwave frequency links onboard China Space Station

The Shigesada-Kawasaki-Teramoto cross-diffusion system beyond detailed balance

Three-dimensional study of double droplets impact on a wettability-patterned surface

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Two-dimensional Obstructed Atomic Insulators with Fractional Corner Charge in MA$_2$Z$_4$ Family

Two-stream Hierarchical Similarity Reasoning for Image-text Matching

An efficient HTS electromagnetic model combining thin-strip, homogeneous and multi-scale methods by T-A formulation

Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function

Differentially Private Distributed Computation via Public-Private Communication Networks

Distributed Algorithms that Solve Boolean Equations with Local and Differential Privacies

Fast Evaporation Enabled Ultrathin Polymeric Coatings on Nanoporous Substrates for Highly Permeable Membranes

Giant Crystal Hall Effect in Collinear Antiferromagnetic $γ$-FeMn

HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking

Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events

Multiscale analysis of crystal defect formation in rapid solidification of pure aluminium and aluminium-copper alloys

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Novel Two-Dimensional Layered MSi$_2$N$_4$ (M = Mo, W): New Promising Thermal Management Materials

Robust I&I Adaptive Tracking Control of Systems with Nonlinear Parameterization: An ISS Perspective

Robust Implementable Regulator Design of General Linear Systems

Robust Output Feedback Stabilization of MIMO Invertible Nonlinear Systems with Output-Dependent Multipliers (extended version)

Robust Output Feedback Stabilization of Multivariable Invertible Nonlinear Systems: A Feedback Linearization-Based Method

Simulation of an imaging system for internal contamination of lungs using MPA-MURA coded aperture collimator

Tropical Tensor Network for Ground States of Spin Glasses

Unified First-Principles Study of the Anomalous Hall Effect Based on Exact Muffin-Tin Orbitals

A Neural Architecture Search based Framework for Liquid State Machine Design

A Noise Filter for Dynamic Vision Sensors using Self-adjusting Threshold

AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite

Artificial Neural Network Approach to the Analytic Continuation Problem

ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease