Source author record

Qiang Li

Qiang Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record

Catalog footprint

What is connected

138works

53topics

4close collaborators

Actions

Connect this record

Open graph Browse works

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

preprint2026arXiv

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction. However, robotic manipulation is composed of many frequent closed-loop spatial adjustments, for which excessive abstraction may waste computation and weaken low-level geometric cues essential for precise control. Existing early-exit strategies attempt to reduce computation by stopping at predefined layers or applying heuristic rules such as action consistency, but they do not directly answer when a representation is actually sufficient for action. In this paper, we present LoopVLA, a recurrent VLA architecture that jointly learns representation refinement, action prediction, and sufficiency estimation. LoopVLA iteratively applies a shared Transformer block to refine multimodal tokens, and at each iteration produces both a candidate action and a sufficiency score that estimates whether further refinement is necessary. By sharing parameters across iterations, LoopVLA decouples refinement from absolute layer indices and grounds sufficiency estimation in the evolving representation itself. Since sufficiency has no direct supervision, we introduce a self-supervised distribution alignment objective, where intermediate confidence scores are trained to match the relative action quality across refinement steps, thereby linking sufficiency learning to policy optimization signals. Experiments on LIBERO, LIBERO-Plus, and VLA-Arena show that LoopVLA pushes the efficiency-performance frontier of VLA policies, reducing parameters by 45% and improving inference throughput by up to 1.7 times while matching or outperforming strong baselines in task success.

preprint2024arXiv

Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues

In contemporary society, the escalating pressures of life and work have propelled psychological disorders to the forefront of modern health concerns, an issue that has been further accentuated by the COVID-19 pandemic. The prevalence of depression among adolescents is steadily increasing, and traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. Addressing these challenges, numerous AI-based methods for assisting in the diagnosis of mental health issues have emerged. However, most of these methods center around fundamental issues with scales or use multimodal approaches like facial expression recognition. Diagnosis of depression risk based on everyday habits and behaviors has been limited to small-scale qualitative studies. Our research leverages adolescent census data to predict depression risk, focusing on children's experiences with depression and their daily life situations. We introduced a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics. Furthermore, we proposed a cloud-based architecture for automatic online learning and data updates. This study utilized publicly available NSCH youth census data from 2020 to 2022, encompassing nearly 150,000 data entries. We conducted basic data analyses and predictive experiments, demonstrating significant performance improvements over standard machine learning and deep learning algorithms. This affirmed our data processing method's broad applicability in handling imbalanced medical data. Diverging from typical predictive method research, our study presents a comprehensive architectural solution, considering a wider array of user needs.

preprint2024arXiv

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, i.e., general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

preprint2023arXiv

Electromagnetic-Compliant Channel Modeling and Performance Evaluation for Holographic MIMO

Recently, the concept of holographic multiple-input multiple-output (MIMO) is emerging as one of the promising technologies beyond massive MIMO. Many challenges need to be addressed to bring this novel idea into practice, including electromagnetic (EM)-compliant channel modeling and accurate performance evaluation. In this paper, an EM-compliant channel model is proposed for the holographic MIMO systems, which is able to model both the characteristics of the propagation channel and the non-ideal factors caused by mutual coupling at the transceivers, including the antenna pattern distortion and the decrease of antenna efficiency. Based on the proposed channel model, a more realistic performance evaluation is conducted to show the performance of the holographic MIMO system in both the single-user and the multi-user scenarios. Key challenges and future research directions are further provided based on the theoretical analyses and numerical results.

preprint2022arXiv

A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling

Recently, deep learning methods have shown great success in 3D point cloud upsampling. Among these methods, many feature expansion units were proposed to complete point expansion at the end. In this paper, we compare various feature expansion units by both theoretical analysis and quantitative experiments. We show that most of the existing feature expansion units process each point feature independently, while ignoring the feature interaction among different points. Further, inspired by upsampling module of image super-resolution and recent success of dynamic graph CNN on point clouds, we propose a novel feature expansion units named ProEdgeShuffle. Experiments show that our proposed method can achieve considerable improvement over previous feature expansion units.

preprint2022arXiv

Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor landmarks often disturb each other for the homogeneous appearance of vertebrae, which makes vertebral landmark localization extremely difficult. In this paper, we propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps, i.e., center point localization to roughly locate 17 center points of vertebrae, and corner point localization to find 4 corner points for each vertebra without distracted by others. Landmarks in each step are located gradually from a set of initialized points by regressing offsets via cascaded CNNs. Principal Component Analysis (PCA) is employed to preserve a shape constraint in offset regression to resist the mutual attraction of vertebrae. We evaluate our method on the AASCE dataset that consists of 609 tight spinal anterior-posterior X-ray images and each image contains 17 vertebrae composed of the thoracic and lumbar spine for spinal shape characterization. Experimental results demonstrate our superior performance of vertebral landmark localization over other state-of-the-arts with the relative error decreasing from 3.2e-3 to 7.2e-4.

preprint2022arXiv

An adverse-pressure-gradient turbulent boundary layer with nearly-constant $\boldsymbol{β\simeq 1.4}$ up to $\boldsymbol{Re_θ \simeq 8,700}$

In this study, a new well-resolved large-eddy-simulation (LES) of an incompressible near-equilibrium adverse-pressure-gradient (APG) turbulent boundary layer (TBL) over a flat plate is presented. In this simulation, we have established a near-equilibrium APG over a wide Reynolds-number range. In this so-called region of interest (ROI), the Clauser--Rotta pressure-gradient parameter $β$ exhibits an approximately constant value of around 1.4, and the Reynolds number based on momentum thickness reaches $Re_θ=8700$. To the authors' knowledge, this is to date the highest $Re_θ$ achieved for a near-equilibrium APG TBL under an approximately constant moderate APG. We evaluated the self-similarity of the outer region using two scalings, namely the Zagarola--Smits and an alternative one based on edge velocity and displacement thickness. Our results reveal that outer-layer similarity is achieved, and the viscous scaling collapses the near-wall region of the mean flow in agreement with classical theory. Spectral analysis reveals that the APG displaces some small-scale energy from the near-wall to the outer region, an effect observed for all the components of the Reynolds-stress tensor, which becomes more evident at higher Reynolds numbers. Generally, the effects of the APG are more noticeable at lower Reynolds numbers. For instance, the outer peak of turbulent-kinetic-energy (TKE) production is less prominent at higher $Re$. While the scale separation increases with $Re$ in zero-pressure-gradient (ZPG) TBLs, this effect becomes accentuated by the APG. Despite the reduction of the outer TKE production at higher Reynolds numbers, the mechanisms of energization of large scales are still present.

preprint2022arXiv

Animating collider processes with Event-time-frame Format

High Energy Physics processes, such as hard scattering, parton shower, and hadronization, occur at colliders around the world, e.g., the Large Hadron Collider in Europe. The various steps are also components within corresponding Monte-Carlo simulations. They are usually considered to occur in an instant and displayed in MC simulations as intricate paths hard-coded with the HepMC format. We recently developed a framework to convert HEP event records into online 3D animations, aiming for visual Monte-Carlo studies and science popularization, where the most difficult parts are about designing an event timeline and particles' movement. As a by-product, we propose here an event-time-frame format for animation data exchanging and persistence, which is potentially helpful in other visualization works. The code is maintained at https://github.com/lyazj/hepani, and the web service is available at https://ppnp.pku.edu.cn/hepani/index.html.

preprint2022arXiv

Boosted tau lepton as a microscope and macroscope

Anomalies from the LHCb lepton flavour universality and Fermilab muon anomalous magnetic momentum, show tantalizing hints of possible new physics from the lepton sectors. Due to its large mass and shorter lifetime than muon, the tau lepton is believed to couple more to possible new physics beyond the standard model. Traditionally, tau leptons are probed through the decay products due to tau's short life time. On the other hand, at a high energy scale, a large fraction of tau leptons could be boosted to a much longer life time and fly a visible distance from several centimetres up to kilometer length scale, yet very informative to new physics beyond the standard model or high energy cosmic rays. In this article, we show unique yet promising tau physics by exploiting long-lived taus as a microscope or macroscope, to measure tau's anomalous magnetic momentum to an unprecedented level of accuracy and detect high energy cosmic neutrinos at the 1 TeV to 1 PeV scale, respectively.

preprint2022arXiv

Channel Estimation and Multipath Diversity Reception for RIS-Empowered Broadband Wireless Systems Based on Cyclic-Prefixed Single-Carrier Transmission

In this paper, a cyclic-prefixed single-carrier (CPSC) transmission scheme with phase shift keying (PSK) signaling is presented for broadband wireless communications systems empowered by a reconfigurable intelligent surface (RIS). In the proposed CPSC-RIS, the RIS is configured according to the transmitted PSK symbols such that different cyclically delayed versions of the incident signal are created by the RIS to achieve multipath diversity. A practical and efficient channel estimator is developed for CPSC-RIS and the mean square error of the channel estimation is expressed in closed-form. We analyze the bit error rate (BER) performance of CPSC-RIS over frequency-selective Nakagami-$m$ fading channels. An upper bound on the BER is derived by assuming the maximum-likelihood detection. Furthermore, by resorting to the concept of index modulation (IM), we propose an extension of CPSC-RIS, termed CPSC-RIS-IM, which enhances the spectral efficiency. In addition to conventional constellation information of PSK symbols, CPSC-RIS-IM uses the full permutations of cyclic delays caused by the RIS to carry information. A sub-optimal receiver is designed for CPSC-RIS-IM to aim at low computational complexity. Our simulation results in terms of BER corroborate the performance analysis and the superiority of CPSC-RIS(-IM) over the conventional CPSC without an RIS and orthogonal frequency division multiplexing with an RIS.

preprint2022arXiv

Contrast Sensitivity Functions in Autoencoders

Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the current trend of using artificial neural networks for studying vision, and (2) the current knowledge of retinal image representations. As a first contribution, we show that a very popular type of convolutional neural networks (CNNs), called autoencoders, may develop human-like CSFs in the spatio-temporal and chromatic dimensions when trained to perform some basic low-level vision tasks (like retinal noise and optical blur removal), but not others (like chromatic adaptation or pure reconstruction after simple bottlenecks). As an illustrative example, the best CNN (in the considered set of simple architectures for enhancement of the retinal signal) reproduces the CSFs with an RMSE error of 11\% of the maximum sensitivity. As a second contribution, we provide experimental evidence of the fact that, for some functional goals (at low abstraction level), deeper CNNs that are better in reaching the quantitative goal are actually worse in replicating human-like phenomena (such as the CSFs). This low-level result (for the explored networks) is not necessarily in contradiction with other works that report advantages of deeper nets in modeling higher-level vision goals. However, in line with a growing body of literature, our results suggests another word of caution about CNNs in vision science since the use of simplified units or unrealistic architectures in goal optimization may be a limitation for the modeling and understanding of human vision.

preprint2022arXiv

CRIS: CLIP-Driven Referring Image Segmentation

Referring image segmentation aims to segment a referent via a natural linguistic expression.Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmentation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing. The code will be released.

preprint2022arXiv

Enabling Fast and Flexible Distributed Deep Learning with Programmable Switches

Deep learning has been used in a wide range of areas and made a huge breakthrough. With the ever-increasing model size and train-ing data volume, distributed deep learning emerges which utilizes a cluster to train a model in parallel. Unfortunately, the performance is often far from linear speedup due to the communication overhead between cluster nodes. To address this challenge, this paper designs and implements Libra, a network aggregator, that utilizes in-network computation to optimize the communication for distributed DL training in two aspects: 1) reduce active connections and 2) aggregate exchanged network packets. We implemented our Libra on Intel Tofino switches, customized a lightweight host stack and integrated it into an open-source training framework PS-lite. The experimental result shows that our Libra can achieve 1.5~4 times speedup.

preprint2022arXiv

Exploring Set Similarity for Dense Self-supervised Representation Learning

By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks. However, the pixel-level correspondence tends to be noisy because of many similar misleading pixels, e.g., backgrounds. To address this issue, in this paper, we propose to explore \textbf{set} \textbf{sim}ilarity (SetSim) for dense self-supervised representation learning. We generalize pixel-wise similarity learning to set-wise one to improve the robustness because sets contain more semantic and structure information. Specifically, by resorting to attentional features of views, we establish corresponding sets, thus filtering out noisy backgrounds that may cause incorrect correspondences. Meanwhile, these attentional features can keep the coherence of the same image across different views to alleviate semantic inconsistency. We further search the cross-view nearest neighbours of sets and employ the structured neighbourhood information to enhance the robustness. Empirical evaluations demonstrate that SetSim is superior to state-of-the-art methods on object detection, keypoint detection, instance segmentation, and semantic segmentation.

preprint2022arXiv

Forced ignition and oscillating flame propagation in fine ethanol sprays

The present work investigates forced ignition and oscillating propagation of spray flame in a mixture of fine ethanol droplets and air. Eulerian-Eulerian method with two-way coupling is used and detailed chemical mechanism is considered. Different droplet diameters and liquid fuel equivalence ratios (ER) are studied. The evaporation completion front (ECF) is defined to study the interactions between the evaporation zone and flame front. The gas composition at the flame front is quantified through an effective ER. The results show that the kernel trajectory is considerably affected by droplet size and liquid ER. Generally, the flame ER reaches the maximum when the ECF start to move from the spherical center. It gradually decreases and reaches a constant value when the flame freely propagates. Quasi-stationary spherical flame is observed when the liquid ER is low, whilst kernel extinction/re-ignition appears when liquid ER is high. These flame behaviors are essentially affected by the heat conduction and species diffusion timescales between the droplet evaporation zone and flame front. The dependence of the minimum ignition energy (MIE) on liquid ER is U-shaped and there is an optimal liquid equivalence ratio range (ERo) with the smallest MIE. Long and short ignition failure modes are observed, respectively for small and large liquid ERs. When liquid ER is less than ERo (long failure mode), larger energy is required to initiate the kernel due to very lean composition near the spark. For liquid ER larger than ERo (short mode), ignition failure is caused by strong evaporative heat loss and rich gas composition due to heavy droplet loading.

preprint2022arXiv

Higher order monotonicity and submodularity of influence in social networks: from local to global

Kempe, Kleinberg and Tardos (KKT) proposed the following conjecture about the general threshold model in social networks: local monotonicity and submodularity imply global monotonicity and submodularity. That is, if the threshold function of every node is monotone and submodular, then the spread function $σ(S)$ is monotone and submodular, where $S$ is a seed set and the spread function $σ(S)$ denotes the expected number of active nodes at termination of a diffusion process starting from $S$. The correctness of this conjecture has been proved by Mossel and Roch. In this paper, we first provide the concept AD-k (Alternating Difference-$k$) as a generalization of monotonicity and submodularity. Specifically, a set function $f$ is called \adk if all the $\ell$-th order differences of $f$ on all inputs have sign $(-1)^{\ell+1}$ for every $\ell\leq k$. Note that AD-1 corresponds to monotonicity and AD-2 corresponds to monotonicity and submodularity. We propose a refined version of KKT's conjecture: in the general threshold model, local AD-k implies global AD-k. The original KKT conjecture corresponds to the case for AD-2, and the case for AD-1 is the trivial one of local monotonicity implying global monotonicity. By utilizing continuous extensions of set functions as well as social graph constructions, we prove the correctness of our conjecture when the social graph is a directed acyclic graph (DAG). Furthermore, we affirm our conjecture on general social graphs when $k=\infty$.

preprint2022arXiv

Instability of the Electroweak Vacuum in Starobinsky Inflation

We study the stability of the electroweak vacuum during and after the Starobinsky inflation, assuming the existence of the non-minimal Higgs coupling to the Ricci scalar. In the Starobinsky inflation, there exists $R^2$ term (with $R$ being the Ricci scalar), which modifies the evolution equation of the Higgs field. We consider the case that the non-minimal coupling is sizable so that the quantum fluctuation of the Higgs field is suppressed and that the Higgs amplitude is settled near the origin during the inflation. In such a case, the Higgs amplitude may be amplified in the preheating epoch after inflation because of the parametric resonance due to the non-minimal coupling. We perform a detailed analysis of the evolution of the Higgs field in the preheating epoch by a numerical lattice simulation and derive an upper bound on the non-minimal coupling constant $ξ$ in order to realize the electroweak vacuum in the present universe. We find that the upper bound on $ξ$ in the Starobinsky inflation model is more stringent than that in conventional inflation models without the $R^2$ term.

preprint2022arXiv

ITTR: Unpaired Image-to-Image Translation with Transformers

Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, various techniques have been developed to improve the translation performance. However, CNN-based generators lack the ability to capture long-range dependency to well exploit global semantics. Recently, Vision Transformers have been widely investigated for recognition tasks. Though appealing, it is inappropriate to simply transfer a recognition-based vision transformer to image-to-image translation due to the generation difficulty and the computation limitation. In this paper, we propose an effective and efficient architecture for unpaired Image-to-Image Translation with Transformers (ITTR). It has two main designs: 1) hybrid perception block (HPB) for token mixing from different receptive fields to utilize global semantics; 2) dual pruned self-attention (DPSA) to sharply reduce the computational complexity. Our ITTR outperforms the state-of-the-arts for unpaired image-to-image translation on six benchmark datasets.

preprint2022arXiv

Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion

Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-fine manners simultaneously. Specifically, building on a dual-encoder U-Net, the fixed-moving MRI pair is encoded and decoded into multi-scale deformation sub-fields from coarse to fine. Each decoding block contains two proposed novel modules: i) in Deformation Field Integration (DFI), a single integrated sub-field is calculated, warping by which is equivalent to warping progressively by sub-fields from all previous decoding blocks, and ii) in Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned by DFI-integrated sub-field, and then fused to predict a finer sub-field. Leveraging both DFI and NFF, the target deformation field is factorized into multi-scale sub-fields, where the coarser fields alleviate the estimate of a finer one and the finer field learns to make up those misalignments insolvable by previous coarser ones. The extensive and comprehensive experimental results on both private and public datasets demonstrate a superior registration performance of brain MRI images over progressive registration only and coarse-to-fine estimation only, with an increase by at most 8% in the average Dice.

preprint2022arXiv

Magnetic freeze-out and anomalous Hall effect in ZrTe$_5$

The ultra-quantum limit is achieved when a magnetic field confines an electron gas in its lowest spin-polarised Landau level. Here we show that in this limit, electron doped ZrTe$_5$ shows a metal-insulator transition followed by a sign change of the Hall and Seebeck effects at low temperature. We attribute this transition to a magnetic freeze-out of charge carriers on the ionised impurities. The reduction of the charge carrier density gives way to an anomalous Hall response of the spin-polarised electrons. This behaviour, at odds with the usual magnetic freeze-out scenario, occurs in this Dirac metal because of its tiny Fermi energy, extremely narrow band gap and a large $g$-factor. We discuss the different possible sources (intrinsic or extrinsic) for this anomalous Hall contribution.

preprint2022arXiv

Microwave excitations and hysteretic magnetization dynamics of stripe domain films

FeNi films with the stripe domain (SD) pattern are prepared by electrodeposition and sputtering methods. The magnetic domain, static magnetic parameters, and quality factor, as well as dynamic properties of the two films, are respectively performed. The results show the magnetizations of the film were dependent on the direction of SD, and the rotation of the SD is lagging behind the magnetization reversal. The microwave properties of the SD emerge dynamic hysteresis before the saturation magnetic field. These microwave properties are selectively excited with acoustic mode, optical mode, and spin-wave mode. The frequency and intensity of different resonance modes of stripe domain are determined by the local magnetization. The magnetization variations and the rotation of SD of different modes are further illuminated by the micromagnetic simulation. The magnetic anisotropy and the resonance intensity of permeability of different modes were finally described by the modified resonance equations.

preprint2022arXiv

Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents

We consider a scenario where multiple agents are learning a common decision vector from data which can be influenced by the agents' decisions. This leads to the problem of multi-agent performative prediction (Multi-PfD). In this paper, we formulate Multi-PfD as a decentralized optimization problem that minimizes a sum of loss functions, where each loss function is based on a distribution influenced by the local decision vector. We first prove the necessary and sufficient condition for the Multi-PfD problem to admit a unique multi-agent performative stable (Multi-PS) solution. We show that enforcing consensus leads to a laxer condition for the existence of Multi-PS solution with respect to the distributions' sensitivities, compared to the single agent case. Then, we study a decentralized extension to the greedy deployment scheme [Mendler-Dünner et al., 2020], called the DSGD-GD scheme. We show that DSGD-GD converges to the Multi-PS solution and analyze its non-asymptotic convergence rate. Numerical results validate our analysis.

preprint2022arXiv

Muon Collider Physics Summary

The perspective of designing muon colliders with high energy and luminosity, which is being investigated by the International Muon Collider Collaboration, has triggered a growing interest in their physics reach. We present a concise summary of the muon colliders potential to explore new physics, leveraging on the unique possibility of combining high available energy with very precise measurements.

preprint2022arXiv

New insight into the spectra of turbulent boundary layers with pressure gradients

With the availability of new high-Reynolds-number ($Re$) databases of turbulent boundary layers (TBLs) it has been possible to identify in detail certain regions of the boundary layer with more complex behavior. In this study we consider a unique database at moderately-high $Re$, with a near-constant adverse pressure gradient (APG) (Pozuelo {\it et al.}, {\it J. Fluid Mech.}, {\bf 939}, A34, 2022), and perform spectral analysis of the Reynolds stresses, focusing on the streamwise component. We assess different regions of the APG TBL, comparing this case with the zero-pressure-gradient (ZPG) TBL, and identify the relevant scaling parameters as well as the contribution of the scales of different sizes. The small scales in the near-wall region up to the near-wall spectral peak have been found to scale using viscous units. In APG TBLs, the largest scales close to the wall have a better scaling with the boundary-layer thickness ($δ_{99}$), and they are significantly affected by the APG. In the overlap and wake regions of the boundary layer, the small energetic scales exhibit a good scaling with the displacement thickness ($δ^*$) while the larger scales and the outer spectral peak are better scaled with the boundary-layer thickness. Also note that the wall-normal location of the spectral outer peak scales with the displacement thickness rather than the boundary layer thickness. The various scalings exhibited by the spectra in APG TBLs are reported here for the first time, and shed light on the complex phenomena present in these flows of great scientific and technological importance.

preprint2022arXiv

On the Role of Data Homogeneity in Multi-Agent Non-convex Stochastic Optimization

This paper studies the role of data homogeneity on multi-agent optimization. Concentrating on the decentralized stochastic gradient (DSGD) algorithm, we characterize the transient time, defined as the minimum number of iterations required such that DSGD can achieve comparable performance as its centralized counterpart. When the Hessians for the objective functions are identical at different agents, we show that the transient time of DSGD is $O( n^{4/3} / ρ^{8/3})$ for smooth (possibly non-convex) objective functions, where $n$ is the number of agents and $ρ$ is the spectral gap of connectivity graph. This is improved over the bound of $O( n^2 / ρ^4 )$ without the Hessian homogeneity assumption. Our analysis leverages a property that the objective function is twice continuously differentiable. Numerical experiments are presented to illustrate the essence of data homogeneity to fast convergence of DSGD.

preprint2022arXiv

Reflective Dielectric Cavity Enhanced Emission from Hexagonal Boron Nitride Spin Defect Arrays

Among the various kinds of spin defects in hBN, the negatively charged boron vacancy ($\rm V_B^-$) spin defect that can be deterministically generated is undoubtedly a potential candidate for quantum sensing, but its low quantum efficiency restricts its %use in practical applications. Here, we demonstrate a robust enhancement structure with advantages including easy on-chip integration, convenient processing, low cost and suitable broad-spectrum enhancement for $\rm V_B^-$ defects. %Improved photoluminescence (PL) intensity and optically detected magnetic resonance (ODMR) contrast of $\rm V_B^-$ defect arrays. In the experiment, we used a metal reflective layer under the hBN flakes, filled with a transition dielectric layer in the middle, and adjusted the thickness of the dielectric layer to achieve the best coupling between the reflective dielectric cavity and the hBN spin defect. Using a reflective dielectric cavity, we achieved a PL enhancement of approximately 7-fold, and the corresponding ODMR contrast achieved 18\%. Additionally, the oxide layer of the reflective dielectric cavity can be used as an integrated material for micro-nano photonic devices for secondary processing, which means that it can be combined with other enhancement structures to achieve stronger enhancement. This work has guiding significance for realizing the on-chip integration of spin defects in two-dimensional materials.

preprint2022arXiv

Searching for pulsars with phase characteristics

We present a method by using the phase characteristics of radio observation data for pulsar search and candidate identification. The phase characteristics are relations between the pulsar signal and the phase correction in the frequency-domain, and we regard it as a new search diagnostic characteristic. Based on the phase characteristics, a search method is presented: calculating DM (dispersion measure) -- frequency data to select candidate frequencies, and then confirming of candidates by using the broadband characteristics of pulsar signals. Based on this method, we performed a search test on short observation data of M15 and M71, which were observed by Five-hundred-meter Aperture spherical radio Telescope (FAST), and some of the Galactic Plane Pulsar Snapshot survey (GPPS) data. Results show that it can get similar search results to PRESTO (PulsaR Exploration and Search TOolkit) while having a faster processing speed.

preprint2022arXiv

Semi-supervised Ranking for Object Image Blur Assessment

Assessing the blurriness of an object image is fundamentally important to improve the performance for object recognition and retrieval. The main challenge lies in the lack of abundant images with reliable labels and effective learning strategies. Current datasets are labeled with limited and confused quality levels. To overcome this limitation, we propose to label the rank relationships between pairwise images rather their quality levels, since it is much easier for humans to label, and establish a large-scale realistic face image blur assessment dataset with reliable labels. Based on this dataset, we propose a method to obtain the blur scores only with the pairwise rank labels as supervision. Moreover, to further improve the performance, we propose a self-supervised method based on quadruplet ranking consistency to leverage the unlabeled data more effectively. The supervised and self-supervised methods constitute a final semi-supervised learning framework, which can be trained end-to-end. Experimental results demonstrate the effectiveness of our method.

preprint2022arXiv

Simulated Detector Performance at the Muon Collider

In this paper we report on the current status of studies on the expected performance for a detector designed to operate in a muon collider environment. Beam-induced backgrounds (BIB) represent the main challenge in the design of the detector and the event reconstruction algorithms. The current detector design aims to show that satisfactory performance can be achieved, while further optimizations are expected to significantly improve the overall performance. We present the characterization of the expected beam-induced background, describe the detector design and software used for detailed event simulations taking into account BIB effects. The expected performance of charged-particle reconstruction, jets, electrons, photons and muons is discussed, including an initial study on heavy-flavor jet tagging. A simple method to measure the delivered luminosity is also described. Overall, the proposed design and reconstruction algorithms can successfully reconstruct the high transverse-momentum objects needed to carry out a broad physics program.

preprint2022arXiv

Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation

Symbol-level precoding (SLP) has recently emerged as a new paradigm for physical-layer transmit precoding in multiuser multi-input-multi-output (MIMO) channels. It exploits the underlying symbol constellation structure, which the conventional paradigm of linear precoding does not, to enhance symbol-level performance such as symbol error probability (SEP). It also allows the precoder to take a more general form than linear precoding. This paper aims to better understand the relationships between SLP and linear precoding, subsequent design implications, and further connections beyond the existing SLP scope. Focused on the quadrature amplitude modulation (QAM) constellations, our study is built on a basic signal observation, namely, that SLP can be equivalently represented by a zero-forcing (ZF) linear precoding scheme augmented with some appropriately chosen symbol-dependent perturbation terms, and that some extended form of SLP is equivalent to a vector perturbation (VP) nonlinear precoding scheme augmented with the above-noted perturbation terms. We examine how insights arising from this perturbed ZF and VP interpretations can be leveraged to i) substantially simplify the optimization of certain SLP design criteria, namely, total or peak power minimization subject to SEP quality guarantees; and ii) draw connections with some existing SLP designs. We also touch on the analysis side by showing that, under the total power minimization criterion, the basic ZF scheme is a near-optimal SLP scheme when the QAM order is very high -- which gives a vital implication that SLP is more useful for lower-order QAM cases. Numerical results further indicate the merits and limitations of the different SLP designs derived from the perturbed ZF and VP interpretations.

preprint2022arXiv

The mass spectrum and wave functions of the $B_c$ system

The spectrum and relativistic wave functions of $B_c$ system are investigated via solving the complete Salpeter equation. Emphases are put on the study of the partial waves of each $J^P$ state. Our study shows that there are three categories of $J^P$ states. The first category contains $0^-$ and $0^+$ states, which are ${}^1S_0$ dominant state with a small amount of $P$ wave and ${}^3P_0$ dominant state with a small amount of $S$ wave, respectively. The second category includes the natural parity states, such as $1^-$, $2^+$, $3^-$, etc. Taking the $1^-$ state as an example, we study it in two cases. One is the ${}^3S_1$ dominant state with a small amount of $P$ and $D$ waves, and the other is the ${}^3D_1$ dominant state but contains a large amount of $S$ and $P$ wave components. The third category includes the unnatural parity states, such as $1^+$, $2^-$, $3^+$, etc. For the $1^+$ spectrum, the states are grouped into pairs with different radial quantum numbers. Each pair contains two ${}^1P_1-{}^3P_1$ mixing states, and the corresponding mixing angles are calculated by using the relativistic wave functions.

preprint2022arXiv

The physics case of a 3 TeV muon collider stage

In the path towards a muon collider with center of mass energy of 10 TeV or more, a stage at 3 TeV emerges as an appealing option. Reviewing the physics potential of such muon collider is the main purpose of this document. In order to outline the progression of the physics performances across the stages, a few sensitivity projections for higher energy are also presented. There are many opportunities for probing new physics at a 3 TeV muon collider. Some of them are in common with the extensively documented physics case of the CLIC 3 TeV energy stage, and include measuring the Higgs trilinear coupling and testing the possible composite nature of the Higgs boson and of the top quark at the 20 TeV scale. Other opportunities are unique of a 3 TeV muon collider, and stem from the fact that muons are collided rather than electrons. This is exemplified by studying the potential to explore the microscopic origin of the current $g$-2 and $B$-physics anomalies, which are both related with muons.

preprint2022arXiv

Thermal transport properties and some hydrodynamic-like behavior in 3D topological semimetal ZrTe5

Hydrodynamic fluidity in condensed matter physics has been experimentally demonstrated only in a limited number of compounds due to the stringent conditions that must be met. Herein, we performed thermal and electrical transport experiments in three-dimensional topological semimetal ZrTe5. By measuring the thermal properties in a wide temperature range, two representative experimental evidences of the hydrodynamics are observed in temperature window between the ballistic and diffusive regimes: a faster evolution of the thermal conductivity than in the ballistic regime and the non-monotonic temperature-dependent effective quasiparticle mean-free-path. In addition, magneto-thermal conductivity results indicate that charged quasiparticles, as well as phonons, may also play an important role in this hydrodynamic-like flow in ZrTe5.

preprint2022arXiv

Three kinds of novel multi-symplectic methods for stochastic Hamiltonian partial differential equations

Stochastic Hamiltonian partial differential equations, which possess the multi-symplectic conservation law, are an important and fairly large class of systems. The multi-symplectic methods inheriting the geometric features of stochastic Hamiltonian partial differential equations provide numerical approximations with better numerical stability, and are of vital significance for obtaining correct numerical results. In this paper, we propose three novel multi-symplectic methods for stochastic Hamiltonian partial differential equations based on the local radial basis function collocation method, the splitting technique, and the partitioned Runge-Kutta method. Concrete numerical methods are presented for nonlinear stochastic wave equations, stochastic nonlinear Schrödinger equations, stochastic Korteweg-de Vries equations and stochastic Maxwell equations. We take stochastic wave equations as examples to perform numerical experiments, which indicate the validity of the proposed methods.

preprint2022arXiv

Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression

Learning from a label distribution has achieved promising results on ordinal regression tasks such as facial age and head pose estimation wherein, the concept of adaptive label distribution learning (ALDL) has drawn lots of attention recently for its superiority in theory. However, compared with the methods assuming fixed form label distribution, ALDL methods have not achieved better performance. We argue that existing ALDL algorithms do not fully exploit the intrinsic properties of ordinal regression. In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles. First, the probability corresponding to the ground-truth should be the highest in label distribution. Second, the probabilities of neighboring labels should decrease with the increase of distance away from the ground-truth, i.e., the distribution is unimodal. Third, the label distribution should vary with samples changing, and even be distinct for different instances with the same label, due to the different levels of difficulty and ambiguity. Under the premise of these principles, we propose a novel loss function for fully adaptive label distribution learning, namely unimodal-concentrated loss. Specifically, the unimodal loss derived from the learning to rank strategy constrains the distribution to be unimodal. Furthermore, the estimation error and the variance of the predicted distribution for a specific sample are integrated into the proposed concentrated loss to make the predicted distribution maximize at the ground-truth and vary according to the predicting uncertainty. Extensive experimental results on typical ordinal regression tasks including age and head pose estimation, show the superiority of our proposed unimodal-concentrated loss compared with existing loss functions.

preprint2022arXiv

Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various weakly supervised methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network. Furthermore, by incorporating semi-supervised learning, we propose an end-to-end trainable framework named weakly supervised regional and temporal learning (WSRTL) for AU recognition. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.

preprint2021arXiv

Anomalous Hall effect at the Lifshitz transition in ZrTe5

Zirconium pentatelluride ZrTe5 is a topological semimetal. The presence of a temperature induced Lifshitz transition, in which the Fermi level goes from the conduction band to the valence band with increasing temperature, provides unique opportunities to study the interplay between Fermi-surface topology, dynamics of Dirac fermions, and Berry curvature in one system. Here we present a combined experimental and theoretical study and show that a low energy model can be used to understand the complicated Hall response and large anomalous Hall effect observed in ZrTe5 over a wide range of temperature and magnetic field. We found that the anomalous Hall contribution dominates the Hall response in a narrow temperature window around the Lifshitz transition, away from which the orbital contribution dominates. Moreover, our results indicate that a topological phase transition coexists with the Lifshitz transition. Our model provides a unifying framework to understand the Hall effect in semimetals with large Zeeman splitting and non-trivial topology.

preprint2021arXiv

Back-n White Neutron Source at CSNS and its Applications

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source (CSNS) that emit through the incoming proton channel were exploited to build a white neutron beam facility (the so-called Back-n white neutron source), which was completed in March 2018. The Back-n neutron beam is very intense, at approximately 2*10^7 n/cm^2/s at 55 m from the target, and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the slices. In addition, the excellent energy spectrum spanning from 0.5 eV to 200 MeV, and a good time resolution related to the time-of-flight measurements make it a typical white neutron source for nuclear data measurements; its overall performance is among that of the best white neutron sources in the world. Equipped with advanced spectrometers, detectors, and application utilities, the Back-n facility can serve wide applications, with a focus on neutron-induced cross-section measurements. This article presents an overview of the neutron beam characteristics, the experimental setups, and the ongoing applications at Back-n.

preprint2021arXiv

Gravitational effect of a plasma on the shadow of Schwarzschild black holes

Considering a Schwarzschild black hole surrounded by a fully ionized hydrogen plasma, we study the refractive effect and the pure gravitational effect of the plasma on the shadow. The effects are treated in a unified formalism but characterized by two independent parameters. For semi-realistic values of parameters, we find their corrections to the shadow radius are both negligible, and the gravitational correction can overtake the refractive correction for active galactic nuclei of masses larger than $10^9M_{\odot}$. Since the refractive effect is induced by the electromagnetic interaction, this result is in sharp contrast to the textbook knowledge that the ratio of the gravitational force to the electromagnetic force is $Gm_em_p/e^2=4.4\times10^{-40}$ in a hydrogen atom. With unrealistically large values of parameters, we illustrate the two effects on the light trajectories and the intensity map.

preprint2021arXiv

Hidden Dark Matter from Starobinsky Inflation

The Starobinsky inflation model is one of the simplest inflation models that is consistent with the cosmic microwave background observations. In order to explain dark matter of the universe, we consider a minimal extension of the Starobinsky inflation model with introducing the dark sector which communicates with the visible sector only via the gravitational interaction. In Starobinsky inflation model, a sizable amount of dark-sector particle may be produced by the inflaton decay. Thus, a scalar, a fermion or a vector boson in the dark sector may become dark matter. We pay particular attention to the case with dark non-Abelian gauge interaction to make a dark glueball a dark matter candidate. In the minimal setup, we show that it is difficult to explain the observed dark matter abundance without conflicting observational constraints on the coldness and the self-interaction of dark matter. We propose scenarios in which the dark glueball, as well as other dark-sector particles, from the inflaton decay become viable dark matter candidates. We also discuss possibilities to test such scenarios.

preprint2021arXiv

Mass spectra, wave functions and mixing effects of the $(bcq)$ baryon

Mass spectra and wave functions of the $J^P=\frac{1}{2}^+$ $(bcq)$ baryons are calculated by the relativistic Bethe-Salpeter equation\,(BSE) with considering the mixing effects between the $1^+$ and $0^+$ $(bc)$-diquarks inside. Based on the diquark picture, the three-body problem of baryons is transformed into two two-body problems. The BSE and wave functions of the $0^+$ diquark are given, and then solved numerically to obtain the effective mass spectra and form factors. Also we present the wave functions at zero point for the $(bc)$-diquark. Considering the obtained diquark form factors, the $(bcq)$ baryons are then described by the BSE as the bound state of a diquark and a light quark, where the interaction kernel includes the inner transitions between the $0^+$ and $1^+$ diquarks. The general wave function of the $\frac{1}{2}^+$ $(bcq)$ baryons is constructed and solved to obtain the corresponding mass spectra. Especially, by using the obtained wave functions, the mixing effects between $Ξ_{bc}(Ω_{bc})$ and $Ξ_{bc}'(Ω'_{bc})$ in ground states are computed and determined to be small ($\sim \!1\%$). The numerical results indicate that it is a good choice to take $Ξ_{bc}$ and $Ξ'_{bc}$ as the baryon states with the inside $(bc)$-diquarks occupying the definite spin.

preprint2021arXiv

Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition

In facial action unit (AU) recognition tasks, regional feature learning and AU relation modeling are two effective aspects which are worth exploring. However, the limited representation capacity of regional features makes it difficult for relation models to embed AU relationship knowledge. In this paper, we propose a novel multi-level adaptive ROI and graph learning (MARGL) framework to tackle this problem. Specifically, an adaptive ROI learning module is designed to automatically adjust the location and size of the predefined AU regions. Meanwhile, besides relationship between AUs, there exists strong relevance between regional features across multiple levels of the backbone network as level-wise features focus on different aspects of representation. In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level. Experiments on BP4D and DISFA demonstrate the proposed MARGL significantly outperforms the previous state-of-the-art methods.

preprint2021arXiv

Robust and tunable Weyl phases by coherent infrared phonons in ZrTe$_5$

Ultrafast optical control of the structural and electronic properties of various quantum materials has recently sparked great interest. In particular, photoinduced quantum phase transition between distinct topological phases has been considered as a promising route to realize ultrafast topological quantum computers. Here we use first-principles and effective Hamiltonian methods to show that in ZrTe$_5$, a layered topological material, lattice distortions corresponding to all three types of zone-center infrared optical phonon modes can drive the system from the strong or weak topological insulating phase to a Weyl semimetal by breaking the global inversion symmetry. Thus achieved Weyl phases are robust, highly tunable and one of the cleanest ones due to the proximity of the Weyl points to the Fermi level and a lack of other carriers. We further show that the amount of infrared-mode pumping necessary to induce such Weyl phases can be reduced if used in conjunction with an A$_g$ Raman-mode pumping that first drives the system to the Dirac semimetal state. We also find that Berry curvature dipole moment (BCDM), induced by the dynamical inversion symmetry breaking, gives rise to various nonlinear effects that oscillate with the amplitude of the phonon modes. These nonlinear effects present a novel switch for controlling the Weyltronics enabled quantum system.

preprint2021arXiv

Searching for heavy leptoquarks at a muon collider

The LHCb Collaboration recently gave an update on testing lepton flavour universality with $B^+ \rightarrow K^+ \ell^+ \ell^-$, in which a 3.1 standard deviations from the standard model prediction was observed. The g-2 experiment also reports a 3.3 standard deviations from the standard model on muon anomalous magnetic moment measurement. These deviations could be explained by introducing new particles including leptoquarks. In this paper, we show the possibility to search for heavy spin-1 leptoquarks at a future TeV scale muon collider by performing studies from three channels: 1) same flavour final states with either two bottom or two light quarks, 2) different flavour quark final states, and 3) a so-called "VXS" process representing the scattering between a vector boson and a leptoquark to probe the coupling between leptoquark and tau lepton. We conclude that a 3 TeV muon collider with $3~{ab^{-1}}$ of integrated luminosity is already sufficient to cover the leptoquark parameter space in order to explain the LHCb lepton flavour universality anomaly.

preprint2021arXiv

Space Shift Keying with Reconfigurable Intelligent Surfaces: Phase Configuration Designs and Performance Analysis

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this paper, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement.

preprint2021arXiv

Statistical CSI Based Hybrid mmWave MIMO-NOMA with Max-Min Fairness

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider mmWave NOMA systems with max-min fairness constraints. On the one hand, existing beamforming designs aiming at maximizing the spectrum efficiency (SE) are unsuitable for the NOMA systems with fairness in this paper. On the other hand, previous work on about mmWave NOMA mostly depends on full knowledge of channel state information (CSI) which is extremely difficult to obtain accurately in mmWave communication systems. To address this problem, we propose a heuristic hybrid beamforming design based on the statistical CSI (SCSI) user grouping strategy. An analog beamforming scheme is first proposed to integrate the whole cluster users to mitigate the inter-cluster interference in the first stage. Then two digital beamforming designs are proposed to further suppress the interference based on SCSI. One is the widely used zero forcing (ZF) approach and the other is derived from the signal-to leakage-plus-noise ratio (SLNR) metric extended from orthogonal multiple access (OMA) systems. The effective gains fed back from the users are used for the power allocation. We introduce the quadratic transform (QT) method and bisection approach to reformulate this complex problem so as to rend it solvable. Simulation results show that our proposed algorithms outperform the previous algorithms in term of user fairness.

preprint2020arXiv

AliCoCo: Alibaba E-commerce Cognitive Concept Net

One of the ultimate goals of e-commerce platforms is to satisfy various shopping needs for their customers. Much efforts are devoted to creating taxonomies or ontologies in e-commerce towards this goal. However, user needs in e-commerce are still not well defined, and none of the existing ontologies has the enough depth and breadth for universal user needs understanding. The semantic gap in-between prevents shopping experience from being more intelligent. In this paper, we propose to construct a large-scale e-commerce cognitive concept net named "AliCoCo", which is practiced in Alibaba, the largest Chinese e-commerce platform in the world. We formally define user needs in e-commerce, then conceptualize them as nodes in the net. We present details on how AliCoCo is constructed semi-automatically and its successful, ongoing and potential applications in e-commerce.

preprint2020arXiv

Capacity-Aware Edge Caching in Fog Computing Networks

This paper studies edge caching in fog computing networks, where a capacity-aware edge caching framework is proposed by considering both the limited fog cache capacity and the connectivity capacity of base stations (BSs). By allowing cooperation between fog nodes and cloud data center, the average-download-time (ADT) minimization problem is formulated as a multi-class processor queuing process. We prove the convexity of the formulated problem and propose an Alternating Direction Method of Multipliers (ADMM)-based algorithm that can achieve the minimum ADT and converge much faster than existing algorithms. Simulation results demonstrate that the allocation of fog cache capacity and connectivity capacity of BSs needs to be balanced according to the network status. While the maximization of the edge-cache-hit-ratio (ECHR) by utilizing all available fog cache capacity is helpful when the BS connectivity capacity is sufficient, it is preferable to keep a lower ECHR and allocate more traffic to the cloud when the BS connectivity capacity is deficient.

preprint2020arXiv

Coherent control of nitrogen-vacancy center spins in silicon carbide at room temperature

Solid-state color centers with manipulatable spin qubits and telecom-ranged fluorescence are ideal platforms for quantum communications and distributed quantum computations. In this work, we coherently control the nitrogen-vacancy (NV) center spins in silicon carbide at room temperature, in which telecom-wavelength emission is detected. We increase the NV concentration six-fold through optimization of implantation conditions. Hence, coherent control of NV center spins is achieved at room temperature and the coherence time T2 can be reached to around 17.1 μs. Furthermore, investigation of fluorescence properties of single NV centers shows that they are room temperature photostable single photon sources at telecom range. Taking advantages of technologically mature materials, the experiment demonstrates that the NV centers in silicon carbide are promising platforms for large-scale integrated quantum photonics and long-distance quantum networks.

preprint2020arXiv

Deep Reinforcement Learning for Fog Computing-based Vehicular System with Multi-operator Support

This paper studies the potential performance improvement that can be achieved by enabling multi-operator wireless connectivity for cloud/fog computing-connected vehicular systems. Mobile network operator (MNO) selection and switching problem is formulated by jointly considering switching cost, quality-of-service (QoS) variations between MNOs, and the different prices that can be charged by different MNOs as well as cloud and fog servers. A double deep Q network (DQN) based switching policy is proposed and proved to be able to minimize the long-term average cost of each vehicle with guaranteed latency and reliability performance. The performance of the proposed approach is evaluated using the dataset collected in a commercially available city-wide LTE network. Simulation results show that our proposed policy can significantly reduce the cost paid by each fog/cloud-connected vehicle with guaranteed latency services.

preprint2020arXiv

Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

As a real scenes sensing approach, depth information obtains the widespread applications. However, resulting from the restriction of depth sensing technology, the depth map captured in practice usually suffers terrible noise and missing values at plenty of pixels. In this paper, we propose a fast low-rank matrix completion via dual-graph regularization for depth restoration. Specifically, the depth restoration can be transformed into a low-rank matrix completion problem. In order to complete the low-rank matrix and restore it to the depth map, the proposed dual-graph method containing the local and non-local graph regularizations exploits the local similarity of depth maps and the gradient consistency of depth-color counterparts respectively. In addition, the proposed approach achieves the high speed depth restoration due to closed-form solution. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with respect to both objective and subjective quality evaluations, especially for serious depth degeneration.

preprint2020arXiv

HL-LHC Computing Review: Common Tools and Community Software

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

preprint2020arXiv

Joint Long-Term Cache Updating and Short-Term Content Delivery in Cloud-Based Small Cell Networks

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users' quality of service (QoS) and requires substantial power consumption. This paper proposes an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users' QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users' content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective tradeoff between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework.

preprint2020arXiv

Latency-Minimized Design of Secure Transmissions in UAV-Aided Communications

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which presents a major challenge for UAV-aided communications. In this paper, we propose a latency-minimized transmission scheme for satisfying legitimate users' (LUs') content requests securely against Eve. By leveraging physical-layer security (PLS) techniques, we formulate a transmission latency minimization problem by jointly optimizing the UAV trajectory and user association. The resulting problem is a mixed-integer nonlinear program (MINLP), which is known to be NP hard. Furthermore, the dimension of optimization variables is indeterminate, which again makes our problem very challenging. To efficiently address this, we utilize bisection to search for the minimum transmission delay and introduce a variational penalty method to address the associated subproblem via an inexact block coordinate descent approach. Moreover, we present a characterization for the optimal solution. Simulation results are provided to demonstrate the superior performance of the proposed design.

preprint2020arXiv

Measurement of the neutron beam profile of the Back-n white neutron facility at CSNS with a Micromegas detector

The Back-n white neutron beam line, which uses back-streaming white neutrons from the spallation target of the China Spallation Neutron Source, is used for nuclear data measurements. A Micromegas-based neutron detector with two variants was specially developed to measure the beam spot distribution for this beam line. In this article, the design, fabrication, and characterization of the detector are described. The results of the detector performance tests are presented, which include the relative electron transparency, the gain and the gain uniformity, and the neutron beam profile reconstruction capability. The result of the first measurement of the Back-n neutron beam spot distribution is also presented.

preprint2020arXiv

Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning

Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Netwrok (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.

preprint2020arXiv

Minimum Symbol-Error Probability Symbol-Level Precoding with Intelligent Reflecting Surface

Recently, the use of intelligent reflecting surface (IRS) has gained considerable attention in wireless communications. By intelligently adjusting the passive reflection angle, IRS is able to assist the base station (BS) to extend the coverage and improve spectral efficiency. This paper considers a joint symbol-level precoding (SLP) and IRS reflecting design to minimize the symbol-error probability (SEP) of the intended users in an IRS-aided multiuser MISO downlink. We formulate the SEP minimization problems to pursue uniformly good performance for all users for both QAM and PSK constellations. The resulting problem is non-convex and we resort to alternating minimization to obtain a stationary solution. Simulation results demonstrate that under the aid of IRS our proposed design indeed enhances the bit-error rate performance. In particular, the performance improvement is significant when the number of IRS elements is large.

preprint2020arXiv

Observation and control of the weak topological insulator state in ZrTe5

A quantum spin Hall insulator hosts topological states at the one-dimensional edge, along which backscattering by nonmagnetic impurities is strictly prohibited and dissipationless current flows. Its 3D analogue, a weak topological insulator (WTI), possesses similar quasi-1D topological states confined at side surfaces of crystals. The enhanced confinement could provide a route for dissipationless current and better advantages for applications relative to the widely studied strong topological insulators. However, the topological side surface is usually not cleavable and is thus hard to observe by angle-resolved photoemission spectroscopy (ARPES), which has hindered the revealing of the electronic properties of WTIs. Here, we visualize the topological surface states of the WTI candidate ZrTe5 for the first time by spin and angle-resolved photoemission spectroscopy: a quasi-1D band with spin-momentum locking was revealed on the side surface. We further demonstrate that the bulk band gap in ZrTe5 is controlled by strain to the crystal, realizing a more stabilized WTI state or an ideal Dirac semimetal state depending on the direction of the external strain. The highly directional spin-current and the tunable band gap we found in ZrTe5 will provide an excellent platform for applications.

preprint2020arXiv

Population aging caused by rise in sex ratio at birth

Despite its historical and biological stability, the sex ratio at birth (SRB) has risen in parts of the world in the last several decades. The resultant demographic consequences, mostly on sex imbalance, are well documented, typically including "missing girls/women" and "marriage squeeze." However, the SRB-induced impact on demographic dynamics, particularly its underlying mechanism, has not been explored in depth. We aim to investigate the impact of the SRB rise on the size, structure, and growth of a population, particularly emphasizing on population aging. We provide a simple framework, derived from classical stable population models, to analyze how the SRB rise can reduce the population size and make the population old. We demonstrate that the cohorts born with a higher SRB are smaller in size than those with a lower SRB. As the affected cohorts are born into the population, their smaller size will reduce the total population size, thereby lifting the fraction of old people that were born with the original SRB and have the same size as before. The resultant population aging speed increases as the cohorts with the new SRB take an increasing share of the population. This study adds that, in addition to fertility and mortality, the SRB can be a driving factor of population dynamics, especially when it moves far above normal biological levels.

preprint2020arXiv

Propagation of weakly stretched premixed spherical spray flames in localized homogeneous and heterogeneous reactants

Propagation of weakly stretched spherical flames in partially pre-vaporized fuel sprays is theoretically investigated in this work. A general theory is developed to describe flame propagation speed, flame temperature, droplet evaporation onset and completion locations. The influences of liquid fuel and gas mixture properties on spherical spray flame propagation are studied. The results indicate that the spray flame propagation speed is enhanced with increased droplet mass loading and/or evaporation heat exchange coefficient (or evaporation rate). Opposite trends are found when the latent heat is high, due to strong evaporation heat absorption. Fuel vapor and temperature gradients are observed in the post-flame evaporation zone of heterogeneous flames. Evaporation completion front location considerably changes with flame radius, but the evaporation onset location varies little relative to the flame front when the flame propagates. For larger droplet loading and smaller evaporation rate, the fuel droplet tends to complete evaporation behind the flame front. Flame bifurcation occurs with high droplet mass loading under large latent heat, leading to multiplicity of flame propagation speed, droplet evaporation onset and completion fronts. The flame enhancement or weakening effects by the fuel droplet sprays are revealed by enhanced or suppressed heat and mass diffusion process in the pre-flame zone. Besides, for heterogeneous flames, heat and mass diffusion in the post-flame zone also exists. The mass diffusion for both homogeneous and heterogeneous flames is enhanced with decreased Lewis number. The magnitude of Markstein length is considerably reduced with increased droplet loading. Moreover, post-flame droplet burning behind heterogeneous flame influences the flame propagation speed and Markstein length when the liquid fuel loading is relatively low.

preprint2020arXiv

Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing

Whole slide imaging (WSI) is an emerging technology for digital pathology. The process of autofocusing is the main influence of the performance of WSI. Traditional autofocusing methods either are time-consuming due to repetitive mechanical motions, or require additional hardware and thus are not compatible to current WSI systems. In this paper, we propose the concept of \textit{virtual autofocusing}, which does not rely on mechanical adjustment to conduct refocusing but instead recovers in-focus images in an offline learning-based manner. With the initial focal position, we only perform two-shot imaging, in contrast traditional methods commonly need to conduct as many as 21 times image shooting in each tile scanning. Considering that the two captured out-of-focus images retain pieces of partial information about the underlying in-focus image, we propose a U-Net-inspired deep neural network based approach for fusing them into a recovered in-focus image. The proposed scheme is fast in tissue slides scanning, enabling a high-throughput generation of digital pathology images. Experimental results demonstrate that our scheme achieves satisfactory refocusing performance.

preprint2020arXiv

Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures

Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Firstly, we propose an FTN signal detection based on DL and connect it with the successive interference cancellation (SIC) to replace traditional detection algorithms. Simulation results show that this architecture can achieve near-optimal performance in both uncoded and coded scenarios. Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers. The performance of this new architecture has also been illustrated by simulation results. Finally, both the proposed DL-based receiver architecture has the robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for the FTN receiver design.

preprint2020arXiv

Recently observed $P_c$ as molecular states and possible mixture of $P_c(4457)$

Recently observed spectrum of $P_c$ states exhibits a strong link to $Σ_c \bar{D}^{(*)}$ thresholds. In spite of successful molecular interpretations, we still push forward to wonder whether there exist finer structures. Utilizing the effecitve lagrangians respecting heavy quark symmetry and chiral symmetry, as well as instantaneous Bethe-Salpeter equations, we investigate the $Σ_c \bar{D}^{(*)}$ interactions and three $P_c$ states. We confirm that $P_c(4312)$ and $P_c(4440)$ are good candidates of $Σ_c \bar{D}$ and $Σ_c \bar{D}^{*}$ molecules with spin-$\frac12$, respectively. Unlike other molecular calculations, our results indicate $P_c(4457)$ signal might be a mixture of spin-$\frac32$ and spin-$\frac12$ $Σ_c \bar{D}^{*}$ molecules, where the latter one appears to be an excitation of $P_c(4440)$. Therefore we conclude that, confronting three LHCb $P_c$ signals, there may exist not three, but four molecular states.

preprint2020arXiv

Room-temperature coherent control of implanted defect spins in silicon carbide

Recently, vacancy-related spin defects in silicon carbide (SiC) have been demonstrated to be potentially suitable for versatile quantum interface building and scalable quantum network construction. Significant efforts have been undertaken to identify spin systems in SiC and to extend their quantum capabilities using large-scale growth and advanced nanofabrication methods. Here we demonstrated a type of spin defect in the 4H polytype of SiC generated via hydrogen ion implantation with high-temperature post-annealing, which is different from any known defects. These spin defects can be optically addressed and coherently controlled even at room temperature, and their fluorescence spectrum and optically detected magnetic resonance spectra are different from those of any previously discovered defects. Moreover, the generation of these defects can be well controlled by optimizing the annealing temperature after implantation. These defects demonstrate high thermal stability with coherently controlled electron spins, facilitating their application in quantum sensing and masers under harsh conditions.

preprint2020arXiv

Siamese Keypoint Prediction Network for Visual Object Tracking

Visual object tracking aims to estimate the location of an arbitrary target in a video sequence given its initial bounding box. By utilizing offline feature learning, the siamese paradigm has recently been the leading framework for high performance tracking. However, current existing siamese trackers either heavily rely on complicated anchor-based detection networks or lack the ability to resist to distractors. In this paper, we propose the Siamese keypoint prediction network (SiamKPN) to address these challenges. Upon a Siamese backbone for feature embedding, SiamKPN benefits from a cascade heatmap strategy for coarse-to-fine prediction modeling. In particular, the strategy is implemented by sequentially shrinking the coverage of the label heatmap along the cascade to apply loose-to-strict intermediate supervisions. During inference, we find the predicted heatmaps of successive stages to be gradually concentrated to the target and reduced to the distractors. SiamKPN performs well against state-of-the-art trackers for visual object tracking on four benchmark datasets including OTB-100, VOT2018, LaSOT and GOT-10k, while running at real-time speed.

preprint2020arXiv

Single-RF MIMO: From Spatial Modulation to Metasurface-Based Modulation

In multiple-input multiple-output (MIMO), multiple radio frequency (RF) chains are usually required to simultaneously transmit multiple data streams. As a special MIMO technology, spatial modulation (SM) activates one transmit antenna with one RF chain and exploits the index of the active antenna for information transfer at each time slot. Recently, reconfigurable metasurfaces have emerged as a promising technology that is able to reconfigure the wireless propagation environment by altering the amplitude and/or phase of the incident signal. In this article, we aim for the implementation of single-RF MIMO by shifting the focus from SM to metasurface-based modulation. Specifically, the principles of SM and metasurfaces are first presented. After reviewing the evolution of SM, we elaborate on the idea of metasurface-aided single-RF MIMO and discuss some implementations for it in line with notable variants of SM. A comparison between antenna-based and metasurface-based modulation is made to highlight the advantages of using metasurfaces. We finally investigate the research challenges and opportunities in the context of metasurface-based modulation.

preprint2020arXiv

Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution

Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, most existing models can not effectively explore spatial information and spectral information between bands simultaneously, obtaining relatively low performance. To address this issue, in this paper, we propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet). Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. Furthermore, we design a spectral-spatial residual module (SSRM) to adaptively learn more effective features from all the hierarchical features in units through local feature fusion, significantly improving the performance of the algorithm. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.

preprint2020arXiv

Trend and forecasting of the COVID-19 outbreak in China

By using the public data from Jan. 20 to Feb. 11, 2020, we perform data-driven analysis and forecasting on the COVID-19 epidemic in mainland China, especially Hubei province. Our results show that the turning points of the daily infections are predicted to be Feb. 6 and Feb. 1, 2020, for Hubei and China other than Hubei, respectively. The epidemic in China is predicted to end up after Mar. 10, 2020, and the number of the total infections are predicted to be 51600. The data trends reveal that quick and active strategies taken by China to reduce human exposure have already had a good impact on the control of the epidemic.

preprint2019arXiv

Measurements of differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

Differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li, $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions have been measured at CSNS Back-n white neutron source. Two enriched (90%) $^{10}$B samples 5.0 cm in diameter and ~85.0 $μ$g/cm$^{2}$ in thickness each with an aluminum backing were prepared, and back-to-back mounted at the sample holder. The charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array (LPDA) system. The neutron energy E$_{n}$ was determined by TOF (time-of-flight) method, and the valid $α$ events were extracted from the E$_{n}$-Amplitude two-dimensional spectrum. With 15 silicon detectors, the differential cross sections of $α$-particles were measured from 19.2° to 160.8°. Fitted with the Legendre polynomial series, the ($n, α$) cross sections were obtained through integration. The absolute cross sections were normalized using the standard cross sections of the $^{10}$B($n, α$)$^{7}$Li reaction in the 0.3 - 0.5 MeV neutron energy region. The measurement neutron energy range for the $^{10}$B($n, α$)$^{7}$Li reaction is 1.0 eV $\le$ En < 2.5 MeV (67 energy points), and for the $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions is 1.0 eV $\le$ En < 1.0 MeV (59 energy points). The present results have been analyzed by the resonance reaction mechanism and the level structure of the $^{11}$B compound system, and compared with existing measurements and evaluations.

preprint2019arXiv

One-Bit Sigma-Delta MIMO Precoding

Coarsely quantized MIMO signalling methods have gained popularity in the recent developments of massive MIMO as they open up opportunities for massive MIMO implementation using cheap and power-efficient radio-frequency front-ends. This paper presents a new one-bit MIMO precoding approach using spatial Sigma-Delta ($ΣΔ$) modulation. In previous one-bit MIMO precoding research, one mainly focuses on using optimization to tackle the difficult binary signal optimization problem that arises from the precoding design. Our approach attempts a different route. Assuming angular MIMO channels, we apply $ΣΔ$ modulation---a classical concept in analog-to-digital conversion of temporal signals---in space. The resulting $ΣΔ$ precoding approach has two main advantages: First, we no longer need to deal with binary optimization in $ΣΔ$ precoding design. Particularly, the binary signal restriction is replaced by peak signal amplitude constraints. Second, the impact of the quantization error can be well controlled via modulator design and under appropriate operating conditions. Through symbol error probability analysis, we reveal that the very large number of antennas in massive MIMO provides favorable operating conditions for $ΣΔ$ precoding. In addition, we develop a new $ΣΔ$ modulation architecture that is capable of adapting the channel to achieve nearly zero quantization error for a targeted user. Furthermore, we consider multi-user $ΣΔ$ precoding using the zero-forcing and symbol-level precoding schemes. These two $ΣΔ$ precoding schemes perform considerably better than their direct one-bit quantized counterparts, as simulation results show.

preprint2019arXiv

Optimization of the power broadening in optically detected magnetic resonance of defect spins in silicon carbide

Defect spins in silicon carbide have become promising platforms with respect to quantum information processing and quantum sensing. Indeed, the optically detected magnetic resonance (ODMR) of defect spins is the cornerstone of the applications. In this work, we systematically investigate the contrast and linewidth of laser-and microwave power-dependent ODMR with respect to ensemble-divacancy spins in silicon carbide at room temperature. The results suggest that magnetic field sensing sensitivity can be improved by a factor of 10 for the optimized laser and microwave power range. The experiment will be useful for the applications of silicon carbide defects in quantum information processing and ODMR-dependent quantum sensing.

preprint2017arXiv

Interference Minimization in 5G Heterogeneous Networks

In this paper, we focus on one of the representative 5G network scenarios, namely multi-tier heterogeneous cellular networks. User association is investigated in order to reduce the down-link co-channel interference. Firstly, in order to analyze the multi-tier heterogeneous cellular networks where the base stations in different tiers usually adopt different transmission powers, we propose a Transmission Power Normalization Model (TPNM), which is able to convert a multi-tier cellular network into a single-tier network, such that all base stations have the same normalized transmission power. Then using TPNM, the signal and interference received at any point in the complex multi-tier environment can be analyzed by considering the same point in the equivalent single-tier cellular network model, thus significantly simplifying the analysis. On this basis, we propose a new user association scheme in heterogeneous cellular networks, where the base station that leads to the smallest interference to other co-channel mobile stations is chosen from a set of candidate base stations that satisfy the quality-of-service (QoS) constraint for an intended mobile station. Numerical results show that the proposed user association scheme is able to significantly reduce the down-link interference compared with existing schemes while maintaining a reasonably good QoS.

preprint2016arXiv

$D$-Wave Charmonia $η_{c2}$($1{^1D_2}$), $ψ_2$($1{^3D_2}$), and $ψ_3$($1{^3D_3}$) in $B_c$ Decays

We study the semi-leptonic and non-leptonic decays of $B_c$ meson to $D$-wave charmonia, namely, $η_{c2}(1^1\!D_2)$, $ψ_2(1^3\!D_2)$, and $ψ_3(1^3\!D_3)$. In our calculations, the instantaneous Bethe-Salpeter method is applied to achieve the hadronic matrix elements. This method includes relativistic corrections which are important especially for the higher orbital excited states. For the semi-leptonic decay channels with electron as the final lepton, we get the branching ratios $\mathcal{B}[B_c \rightarrow η_{c2}e\barν_e] = 5.9^{-0.8}_{+1.0}\times 10^{-4}$, $\mathcal{B}[B_c \rightarrow ψ_2e\barν_e]=1.5^{-0.2}_{+0.3}\times 10^{-4}$, and $\mathcal{B}[B_c \rightarrow ψ_3e\barν_e]=3.5^{-0.6}_{+0.8}\times 10^{-4}$. The transition form factors, forward-backward asymmetries, and lepton spectra in these processes are also presented. For the non-leptonic decay channels, those with $ρ$ as the lighter meson have the largest branching ratios, $\mathcal{B}[B_c \rightarrow η_{c2}ρ] = 8.1^{-1.0}_{+1.0}\times 10^{-4}$, $\mathcal{B}[B_c \rightarrow ψ_2ρ]=9.6^{-1.0}_{+1.0}\times 10^{-5}$, and $\mathcal{B}[B_c \rightarrow ψ_3ρ]=4.1^{-0.7}_{+0.8}\times 10^{-4}$.

preprint2016arXiv

5G Multimedia Massive MIMO Communications Systems

In the Fifth generation (5G) wireless communication systems, a majority of the traffic demands is contributed by various multimedia applications. To support the future 5G multimedia communication systems, the massive multiple-input multiple-output (MIMO) technique is recognized as a key enabler due to its high spectral efficiency. The massive antennas and radio frequency (RF) chains not only improve the implementation cost of 5G wireless communication systems but also result in an intense mutual coupling effect among antennas because of the limited space for deploying antennas. To reduce the cost, an optimal equivalent precoding matrix with the minimum number of RF chains is proposed for 5G multimedia massive MIMO communication systems considering the mutual coupling effect. Moreover, an upper bound of the effective capacity is derived for 5G multimedia massive MIMO communication systems. Two antenna receive diversity gain models are built and analyzed. The impacts of the antenna spacing, the number of antennas, the quality of service (QoS) statistical exponent, and the number of independent incident directions on the effective capacity of 5G multimedia massive MIMO communication systems are analyzed. Comparing with the conventional zero-forcing precoding matrix, simulation results demonstrate that the proposed optimal equivalent precoding matrix can achieve a higher achievable rate for 5G multimedia massive MIMO communication systems.

preprint2016arXiv

A Dual-Directional Path-loss Model in 5G Wireless Fractal Small Cell Networks

With the anticipated increase in the number of low power base stations (BSs) deployed in small cell networks, blockage effects becoming more sensitive on wireless transmissions over high spectrums, variable propagation fading scenarios make it hard to describe coverage of small cell networks. In this paper, we propose a dual-directional path loss model cooperating with Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) transmissions for the fifth generation (5G) fractal small cell networks. Based on the proposed path loss model, a LoS transmission probability is derived as a function of the coordinate azimuth of the BS and the distance between the mobile user (MU) and the BS. Moreover, the coverage probability and the average achievable rate are analyzed for 5G fractal small cell networks. Numerical results imply that the minimum intensity of blockages and the maximum intensity of BSs can not guarantee the maximum average achievable rate in 5G fractal small cell networks. Our results explore the relationship between the anisotropic path loss fading and the small cell coverage in 5G fractal small cell networks.

preprint2016arXiv

A Stochastic Beamformed Amplify-and-Forward Scheme in a Multigroup Multicast MIMO Relay Network with Per-Antenna Power Constraints

In this paper, we consider a two-hop one-way relay network for multigroup multicast transmission between long-distance users, in which the relay is equipped with multiple antennas, while the transmitters and receivers are all with a single antenna. Assuming that perfect channel state information is available, we study amplify-and-forward (AF) schemes that aim at optimizing the max-min-fair (MMF) rate. We begin by considering the classic beamformed AF (BF-AF) scheme, whose corresponding MMF design problem can be formulated as a rank-constrained fractional semidefinite program (SDP). We show that the gap between the BF-AF rate and the SDR rate associated with an optimal SDP solution is sensitive to the number of users as well as the number of power constraints in the relay system. This reveals that the BF-AF scheme may not be well suited for large-scale systems. We therefore propose the stochastic beamformed AF (SBF-AF) schemes, which differ from the BF-AF scheme in that time-varying AF weights are used. We prove that the MMF rates of the proposed SBF-AF schemes are at most $0.8317$ bits/s/Hz less than the SDR rate, irrespective of the number of users or power constraints. Thus, SBF-AF can outperform BF-AF especially in large-scale systems. Finally, we present numerical results to demonstrate the viability of our proposed schemes.

preprint2016arXiv

Boundary element method for normal non-adhesive and adhesive contacts of power-law graded elastic materials

Recently proposed formulation of the Boundary Element Method for adhesive contacts has been generalized for contacts of functionally graded materials with and without adhesion. First, proceeding from the fundamental solution for single force acting on the surface of a half space with a power-law varying elastic modulus, the deformation produced by constant pressure acting on a rectangular element was calculated and the influence matrix was obtained for a rectangular grid. The inverse problem for the calculation of required stress in contact area from a known surface deformation was solved by use of conjugate-gradient technique. For the transformation between the stresses and displacements, the Fast Fourier Transformation is used which drastically reduces the computation time. For the adhesive contact of graded material, the detachment criterion based on the method of Pohrt and Popov was proposed. A number of numerical test for the problem having exact analytical solution have been carried out confirming the correctness of underlying ideas and numerical implementation.

preprint2016arXiv

Control over emissivity of zero-static-power thermal emitters based on phase changing material GST

Controlling the emissivity of a thermal emitter has attracted growing interest with a view towards a new generation of thermal emission devices. So far, all demonstrations have involved sustained external electric or thermal consumption to maintain a desired emissivity. Here control over the emissivity of a thermal emitter consisting of a phase changing material Ge2Sb2Te5 (GST) film on top of a metal film is demonstrated. This thermal emitter shows broad wavelength-selective spectral emissivity in the mid-infrared. The peak emissivity approaches the ideal blackbody maximum and a maximum extinction ratio of above 10dB is attainable by switching GST between the crystalline and amorphous phases. By controlling the intermediate phases, the emissivity can be continuously tuned. This switchable, tunable, wavelength-selective and thermally stable thermal emitter will pave the way towards the ultimate control of thermal emissivity in the field of fundamental science as well as for energy-harvesting and thermal control applications, including thermophotovoltaics, light sources, infrared imaging and radiative coolers.

preprint2016arXiv

Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising

In this work, we study the guaranteed delivery model which is widely used in online display advertising. In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers. A crucial problem for the advertising platform is how to fully utilize the valuable user traffic to generate as much as possible revenue. Different from previous works which usually minimize the penalty of unsatisfied contracts and some other cost (e.g. representativeness), we propose the novel consumption minimization model, in which the primary objective is to minimize the user traffic consumed to satisfy all contracts. Under this model, we develop a near optimal method to deliver ads for users. The main advantage of our method lies in that it consumes nearly as least as possible user traffic to satisfy all contracts, therefore more contracts can be accepted to produce more revenue. It also enables the publishers to estimate how much user traffic is redundant or short so that they can sell or buy this part of traffic in bulk in the exchange market. Furthermore, it is robust with regard to priori knowledge of user type distribution. Finally, the simulation shows that our method outperforms the traditional state-of-the-art methods.

preprint2016arXiv

Energy Saving of Base Stations Sleep Scheduling for Multi-Hop Vehicular Networks

This paper investigates the energy saving of base station (BS) deployed in a 1-D multi-hop vehicular network with sleep scheduling strategy. We consider cooperative BS scheduling strategy where BSs can switch between sleep and active modes to reduce the average energy consumption utilizing the information of vehicular speeds and locations. Assuming a Poisson distribution of vehicles, we derive an appropriate probability distribution function of distance between two adjacent cluster heads, where a cluster is a maximal set of vehicles in which every two adjacent vehicles can communicate directly when their Euclidean distance is less than or equal to a threshold, known as the communication range of vehicles. Furthermore, the expected value of the sojourn time in the sleep mode and energy saving are obtained. The numerical results show that the sleep scheduling strategy significantly reduces the energy consumption of the base stations.

preprint2016arXiv

Generalized master curve procedure for elastomer friction taking into account dependencies on velocity, temperature and normal force

In the sliding contact of elastomer on a rigid substrate, the coefficient of friction may depend on a large number of system and loading parameters, including normal force, sliding velocity, shape of contacting bodies, surface roughness and so on. It was argued earlier that the contact configuration is determined more immediately through the indentation depth than the normal force, and thus the indentation depth can be considered as one of "robust governing parameters" of friction. Both models of friction of simple shapes and fractal surfaces demonstrate that the coefficient of friction of elastomers should be generally a function of dimensionless combinations of sliding velocity, surface gradient, relaxation time and size of micro-contacts. The relaxation time does depend only on temperature and the surface slope and the size of micro contacts mostly on the indentation depth. Based on this general structure of the law of friction, we propose a generalized master curve procedure for elastomer friction where the significant governing parameter - indentation depth (or normal force) was taken into account. Unlike the generation of the classical master curve by horizontal shifting of dependence "friction - logarithm of velocity" for different temperatures, in the case of various indentation depth the shifting in both horizontal and vertical direction is required. We experimentally investigated coefficient of friction of elastomer on sliding velocity for different indentation depths and temperatures, and generated a master curve according to this hypothesis.

preprint2016arXiv

High spatial frequency periodic structures induced on ferric ion-doped Polyvinyl Pyrrolidone film by femtosecond laser pulses

Utilizing continues-wave or pulsed laser to induce nano-structures on various material surfaces is one significant method in nano-fabrication technology. In this report, we investigate the formation of high spatial frequency periodic structures on Polyvinyl Pyrrolidone (PVP) film by a linearly polarized femtosecond laser. Ferric (Fe) ions are introduced into the film to improve the photosensitivity. Regular nano-gratings with spatial periods at the range of 60-100nm, which are about one tenth of the irradiating wavelength, can be induced. The period direction of the nano-gratings is perpendicular to the polarization of the femtosecond laser. By tuning the laser energy and scanning speed, we find that the nano-gratings can be formed in a wide range of experimental parameters. As high laser energy can excite not only metals, but also semiconductors and polymers, we believe the formation of the nano-gratings is due to the interaction between the incident femtosecond laser and surface plasmons. The laser processable PVP-based materials and the induced nano-gratings will have potential applications in biophotonics and nanophotonics.

preprint2016arXiv

Millimeter Wave Communications with OAM-SM Scheme for Future Mobile Networks

The orbital angular momentum (OAM) technique provides a new degree of freedom for information transmissions in millimeter wave communications. Considering the spatial distribution characteristics of OAM beams, a new OAM spatial modulation (OAM-SM) millimeter wave communication system is first proposed for future mobile networks. Furthermore, the capacity, average bit error probability and energy efficiency of OAM-SM millimeter wave communication systems are analytically derived for performance analysis. Compared with the conventional multi-input multi-output (MIMO) millimeter wave communication systems, the maximum capacity and energy efficiency of OAM-SM millimeter wave communication systems are improved by 36% and 472.3%, respectively. Moreover, numerical results indicate that the proposed OAM-SM millimeter wave communication systems are more robust to path-loss attenuations than the conventional MIMO millimeter wave communication systems, which makes it suitable for long-range transmissions. Therefore, OAM-SM millimeter wave communication systems provide a great growth space for future mobile networks.

preprint2016arXiv

On Energy Efficiency of the Nearest-Neighbor Cooperative Communication in Heterogeneous Networks

In this paper, we consider a two-dimensional heterogeneous cellular network scenario consisting of one base station (BS) and some mobile stations (MSs) whose locations follow a Poisson point process (PPP). The MSs are equipped with multiple radio access interfaces including a cellular access interface and at least one short-range communication interface. We propose a nearest-neighbor cooperation communication (NNCC) scheme by exploiting the short-range communication between a MS and its nearest neighbor to collaborate on their uplink transmissions. In the proposed cooperation scheme, a MS and its nearest neighbor first exchange data by the short-range communication. Upon successful decoding of the data from each other, they proceed to send their own data, as well as the data received from the other to the BS respectively in orthogonal time slots. The energy efficiency analysis for the proposed scheme is presented based on the characteristics of the PPP and the Rayleigh fading channel. Numerical results show that the NNCC scheme significantly improves the energy efficiency compared to the conventional non-cooperative uplink transmissions.

preprint2016arXiv

On the relationship between G-band bright point dynamics and their magnetic field strengths

G-band bright points (GBPs) are regarded as good manifestations of magnetic flux concentrations. We aim to investigate the relationship between the dynamic properties of GBPs and their longitudinal magnetic field strengths. High spatial and temporal resolution observations were recorded simultaneously with G-band filtergrams and Narrow-band Filter Imager (NFI) Stokes I and V images with Hinode /Solar Optical Telescope. The GBPs are identified and tracked in the G-band images automatically, and the corresponding longitudinal magnetic field strength of each GBP is extracted from the calibrated NFI magnetograms by a point-to-point method. After categorizing the GBPs into five groups by their longitudinal magnetic field strengths, we analyze the dynamics of GBPs of each group. The results suggest that with increasing longitudinal magnetic field strengths of GBPs correspond to a decrease in their horizontal velocities and motion ranges as well as by showing more complicated motion paths. This suggests that magnetic elements showing weaker magnetic field strengths prefer to move faster and farther along straighter paths, while stronger ones move more slowly in more erratic paths within a smaller region. The dynamic behaviors of GBPs with different longitudinal magnetic field strengths can be explained by that the stronger flux concentrations withstand the convective flows much better than weaker ones.

preprint2016arXiv

Quantum entanglement swapping between two multipartite entangled states

Quantum entanglement swapping is one of the most promising ways to realize the quantum connection among local quantum nodes. In this Letter, we present an experimental demonstration of the entanglement swapping between two independent multipartite entangled states, each of which involves a tripartite Greenberger-Horne-Zeilinger (GHZ) entangled state of an optical field. The entanglement swapping is implemented deterministically by means of a joint measurement on two optical modes coming from the two multipartite entangled states respectively and the classical feedforward of the measurement results. After entanglement swapping the two independent multipartite entangled states are merged into a large entangled state in which all unmeasured quantum modes are entangled. The entanglement swapping between a tripartite GHZ state and an Einstein-Podolsky-Rosen entangled state is also demonstrated and the dependence of the resultant entanglement on transmission loss is investigated. The presented experiment provides a feasible technical reference for constructing more complicated quantum networks.

preprint2016arXiv

Secure MIMO Relaying Network: An Artificial Noise Aided Robust Design Approach

Owing to the vulnerability of relay-assisted and device-to-device (D2D) communications, improving wireless security from a physical layer signal processing perspective is attracting increasing interest. Hence we address the problem of secure transmission in a relay-assisted network, where a pair of legitimate user equipments (UEs) communicate with the aid of a multiple-input multiple output (MIMO) relay in the presence of multiple eavesdroppers (eves). Assuming imperfect knowledge of the eves' channels, we jointly optimize the power of the source UE, the amplify-and-forward (AF) relaying matrix and the covariance of the artificial noise (AN) transmitted by the relay, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination, while imposing a set of robust secrecy constraints. To tackle the resultant nonconvex optimization problem, a globally optimal solution based on a bi-level optimization framework is proposed, but with high complexity. Then a low-complexity sub-optimal method relying on a new penalized difference-of-convex (DC) algorithmic framework is proposed, which is specifically designed for non-convex semidefinite programs (SDPs). We show how this penalized DC framework can be invoked for solving our robust secure relaying problem with proven convergence. Our extensive simulation results show that both proposed solutions are capable of ensuring the secrecy of the relay-aided transmission and significantly improve the robustness towards the eves' channel uncertainties as compared to the non-robust counterparts. It is also demonstrated the penalized DC-based method advocated yields a performance close to the globally optimal solution.

preprint2016arXiv

Sum Secrecy Rate Maximization for Full-Duplex Two-Way Relay Networks Using Alamouti-based Rank-Two Beamforming

Consider a two-way communication scenario where two single-antenna nodes, operating under full-duplex mode, exchange information to one another through the aid of a (full-duplex) multi-antenna relay, and there is another single-antenna node who intends to eavesdrop. The relay employs artificial noise (AN) to interfere the eavesdropper's channel, and amplify-forward (AF) Alamouti-based rank-two beamforming to establish the two-way communication links of the legitimate nodes. Our problem is to optimize the rank-two beamformer and AN covariance for sum secrecy rate maximization (SSRM). This SSRM problem is nonconvex, and we develop an efficient solution approach using semidefinite relaxation (SDR) and minorization-maximization (MM). We prove that SDR is tight for the SSRM problem and thus introduces no loss. Also, we consider an inexact MM method where an approximately but computationally cheap MM solution update is used in place of the exact update in conventional MM. We show that this inexact MM method guarantees convergence to a stationary solution to the SSRM problem. The effectiveness of our proposed approach is further demonstrated by an energy-harvesting scenario extension, and by extensive simulation results.

preprint2016arXiv

The Routing of Complex Contagion in Kleinberg's Small-World Networks

In Kleinberg's small-world network model, strong ties are modeled as deterministic edges in the underlying base grid and weak ties are modeled as random edges connecting remote nodes. The probability of connecting a node $u$ with node $v$ through a weak tie is proportional to $1/|uv|^α$, where $|uv|$ is the grid distance between $u$ and $v$ and $α\ge 0$ is the parameter of the model. Complex contagion refers to the propagation mechanism in a network where each node is activated only after $k \ge 2$ neighbors of the node are activated. In this paper, we propose the concept of routing of complex contagion (or complex routing), where we can activate one node at one time step with the goal of activating the targeted node in the end. We consider decentralized routing scheme where only the weak ties from the activated nodes are revealed. We study the routing time of complex contagion and compare the result with simple routing and complex diffusion (the diffusion of complex contagion, where all nodes that could be activated are activated immediately in the same step with the goal of activating all nodes in the end). We show that for decentralized complex routing, the routing time is lower bounded by a polynomial in $n$ (the number of nodes in the network) for all range of $α$ both in expectation and with high probability (in particular, $Ω(n^{\frac{1}{α+2}})$ for $α\le 2$ and $Ω(n^{\fracα{2(α+2)}})$ for $α> 2$ in expectation), while the routing time of simple contagion has polylogarithmic upper bound when $α= 2$. Our results indicate that complex routing is harder than complex diffusion and the routing time of complex contagion differs exponentially compared to simple contagion at sweetspot.

preprint2015arXiv

Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks

We study the downlink linear precoder design problem in a multi-cell dense heterogeneous network (HetNet). The problem is formulated as a general sum-utility maximization (SUM) problem, which includes as special cases many practical precoder design problems such as multi-cell coordinated linear precoding, full and partial per-cell coordinated multi-point transmission, zero-forcing precoding and joint BS clustering and beamforming/precoding. The SUM problem is difficult due to its non-convexity and the tight coupling of the users' precoders. In this paper we propose a novel convex approximation technique to approximate the original problem by a series of convex subproblems, each of which decomposes across all the cells. The convexity of the subproblems allows for efficient computation, while their decomposability leads to distributed implementation. {Our approach hinges upon the identification of certain key convexity properties of the sum-utility objective, which allows us to transform the problem into a form that can be solved using a popular algorithmic framework called BSUM (Block Successive Upper-Bound Minimization).} Simulation experiments show that the proposed framework is effective for solving interference management problems in large HetNet.

preprint2015arXiv

Probe Higgs boson pair production via the $3 \ell 2 j$ + missing $E_T$ mode

We perform a detailed hadron-level study on the sensitivity of Higgs boson pair production via the $WW^{*}WW^{*}$ channel with the final state $3 \ell 2 j$ + missing $E_T$ at the LHC with the collision energy $\sqrt{S} = 14$ TeV and a future 100 TeV collider. To avoid the huge background from $pp \to Z W + \textrm{jets}$ processes, we confine to consider the four lepton patterns: $e^\pm e^\pm μ^\mp $ and $μ^\pm μ^\pm e^\mp$. We propose a partial reconstruction method to determine the most reliable combination. After that, we examine a few crucial observables which can discriminate efficiently signal and background events, especially we notice that the observable $m_{\rm T2}$ is very efficient. For the LHC 14 TeV collisions, with an accumulated 3000 fb$^{-1}$ dataset, we find that the sensitivity of this mode can reach up to 1.5 $σ$ for the Standard Model and the triple coupling of Higgs boson $λ_3$ in the simplest effective theory can be constrained into the range [-1, 8] at $95\%$ confidence level; at a 100 TeV collider with the integrated luminosity 3000 fb$^{-1}$, the sensitivity can reach up to 13 $σ$ for the Standard Model and we find that all values of $λ_3$ in the effective theory can be covered up to 3$σ$ even without optimising signals. To precisely measure the triple coupling of Higgs boson $λ_3=1$ of the Standard Model at a 100 TeV collider, by using the invariant mass of three leptons which is robust to against the contamination of underlying events and pileup effects and by performing a $χ^2$ analysis, we find that it can be determined into a range [0.8, 1.5] at $95\%$ confidence level.

preprint2015arXiv

Probing Triple-W Production and Anomalous WWWW Coupling at the CERN LHC and future 100TeV proton-proton collider

Triple gauge boson production at the LHC can be used to test the robustness of the Standard Model and provide useful information for VBF di-boson scattering measurement. Especially, any derivations from SM prediction will indicate possible new physics. In this paper we present a detailed Monte Carlo study on measuring WWW production in pure leptonic and semileptonic decays, and probing anomalous quartic gauge WWWW couplings at the CERN LHC and future hadron collider, with parton shower and detector simulation effects taken into account. Apart from cut-based method, multivariate boosted decision tree method has been exploited for possible improvement. For the leptonic decay channel, our results show that at the sqrt{s}=8(14)[100] TeV pp collider with integrated luminosity of 20(100)[3000] fb-1, one can reach a significance of 0.4(1.2)[10]sigma to observe the SM WWW production. For the semileptonic decay channel, one can have 0.5(2)[14]sigma to observe the SM WWW production. We also give constraints on relevant Dim-8 anomalous WWWW coupling parameters.

preprint2015arXiv

Substitution of Ni for Fe in superconducting Fe$_{0.98}$Te$_{0.5}$Se$_{0.5}$ depresses the normal-state conductivity but not the magnetic spectral weight

We have performed systematic resistivity and inelastic neutron scattering measurements on Fe$_{0.98-z}$Ni$_z$Te$_{0.5}$Se$_{0.5}$ samples to study the impact of Ni substitution on the transport properties and the low-energy ($\le$ 12 meV) magnetic excitations. It is found that, with increasing Ni doping, both the conductivity and superconductivity are gradually suppressed; in contrast, the low-energy magnetic spectral weight changes little. Comparing with the impact of Co and Cu substitution, we find that the effects on conductivity and superconductivity for the same degree of substitution grow systematically as the atomic number of the substituent deviates from that of Fe. The impact of the substituents as scattering centers appears to be greater than any contribution to carrier concentration. The fact that low-energy magnetic spectral weight is not reduced by increased electron scattering indicates that the existence of antiferromagnetic correlations does not depend on electronic states close to the Fermi energy.

preprint2015arXiv

Surface-state-dominated transport in crystals of the topological crystalline insulator In-doped Pb$_{1-x}$Sn$_x$Te

Three-dimensional topological insulators and topological crystalline insulators represent new quantum states of matter, which are predicted to have insulating bulk states and spin-momentum-locked gapless surface states. Experimentally, it has proven difficult to achieve the high bulk resistivity that would allow surface states to dominate the transport properties over a substantial temperature range. Here we report a series of indium-doped Pb$_{1-x}$Sn$_x$Te compounds that manifest huge bulk resistivities together with strong evidence of topological surface states, based on thickness-dependent transport studies and magnetoresistance measurements. For these bulk-insulating materials, the surface states determine the resistivity for temperatures approaching 30 K.

preprint2015arXiv

User Mobility Evaluation for 5G Small Cell Networks Based on Individual Mobility Model

With small cell networks becoming core parts of the fifth generation (5G) cellular networks, it is an important problem to evaluate the impact of user mobility on 5G small cell networks. However, the tendency and clustering habits in human activities have not been considered in traditional user mobility models. In this paper, human tendency and clustering behaviors are first considered to evaluate the user mobility performance for 5G small cell networks based on individual mobility model (IMM). As key contributions, user pause probability, user arrival and departure probabilities are derived in this paper for evaluat-ing the user mobility performance in a hotspot-type 5G small cell network. Furthermore, coverage probabilities of small cell and macro cell BSs are derived for all users in 5G small cell networks, respectively. Compared with the traditional random waypoint (RWP) model, IMM provides a different viewpoint to investigate the impact of human tendency and clustering behaviors on the performance of 5G small cell networks.

preprint2014arXiv

A probabilistic approach to quantum Bayesian games of incomplete information

A Bayesian game is a game of incomplete information in which the rules of the game are not fully known to all players. We consider the Bayesian game of Battle of Sexes that has several Bayesian Nash equilibria and investigate its outcome when the underlying probability set is obtained from generalized Einstein-Podolsky-Rosen experiments. We find that this probability set, which may become non-factorizable, results in a unique Bayesian Nash equilibrium of the game.

preprint2014arXiv

Energy Efficiency Optimization for MIMO-OFDM Mobile Multimedia Communication Systems with QoS Constraints

It is widely recognized that besides the quality of service (QoS), the energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy efficiency model is first proposed for multiple-input multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) mobile multimedia communication systems with statistical QoS constraints. Employing the channel matrix singular-value-decomposition (SVD) method, all subchannels are classified by their channel characteristics. Furthermore, the multi-channel joint optimization problem in conventional MIMO-OFDM communication systems is transformed into a multi-target single channel optimization problem by grouping all subchannels. Therefore, a closed-form solution of the energy efficiency optimization is derived for MIMO-OFDM mobile mlutimedia communication systems. As a consequence, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems. Simulation comparisons validate that the proposed EEOPA algorithm can guarantee the required QoS with high energy efficiency in MIMO-OFDM mobile multimedia communication systems.

preprint2014arXiv

Higgs Pair Production: Improved Description by Matrix Element Matching

Higgs pair production is crucial for measuring the Higgs boson self-coupling. The dominant channel at hadron colliders is gluon fusion via heavy-quark loops. We present the results of a fully exclusive simulation of gluon fusion Higgs pair production based on the matrix elements for hh + 0, 1 partons including full heavy-quark loop dependence, matched to a parton shower. We examine and validate this new description by comparing it with (a) Higgs Effective Theory predictions, (b) exact hh + 0-parton sample showered by pythia, and (c) exact hh+1-parton distributions, by looking at the most relevant kinematic distributions, such as PTh, PThh, Mhh spectra, and jet rate as well. We find that matched samples provide an state-of-the-art accurate exclusive description of the final state. The relevant LHE files for Higgs pair productions at the LHC can be accessed via http://hepfarm02.phy.pku.edu.cn/foswiki/CMS/HH, which can be used for relevant experimental analysis.

preprint2014arXiv

Magneto-optical characterizations of FeTe$_{0.5}$Se$_{0.5}$ thin films with critical current density over 1 MA/cm$^2$

We performed magneto-optical (MO) measurements on FeTe$_{0.5}$Se$_{0.5}$ thin films grown on LaAlO$_3$ (LAO) and Yttria-stabilized zirconia (YSZ) single-crystalline substrates. These thin films show superconducting transition temperature, ${T_c}$ $\sim$ 19 K, 4 K higher than the bulk sample. Typical roof-top patterns can be observed in the MO images of thin films grown on LAO and YSZ, from which a large and homogeneous critical current density, ${J_c}$, over 1 $\times$ 10$^6$ A/cm$^2$ at 5 K was obtained. Magnetic flux penetration measurement reveals that the current is almost isotropically distributed in the two thin films. Compared with bulk crystals, FeTe$_{0.5}$Se$_{0.5}$ thin film demonstrates not only higher ${T_c}$, but also much larger ${J_c}$, which is attractive for applications.

preprint2014arXiv

Observation of the chiral magnetic effect in ZrTe5

The chiral magnetic effect is the generation of electric current induced by chirality imbalance in the presence of magnetic field. It is a macroscopic manifestation of the quantum anomaly in relativistic field theory of chiral fermions (massless spin $1/2$ particles with a definite projection of spin on momentum) -- a dramatic phenomenon arising from a collective motion of particles and antiparticles in the Dirac sea. The recent discovery of Dirac semimetals with chiral quasi-particles opens a fascinating possibility to study this phenomenon in condensed matter experiments. Here we report on the first observation of chiral magnetic effect through the measurement of magneto-transport in zirconium pentatelluride, ZrTe_5. Our angle-resolved photoemission spectroscopy experiments show that this material's electronic structure is consistent with a 3D Dirac semimetal. We observe a large negative magnetoresistance when magnetic field is parallel with the current. The measured quadratic field dependence of the magnetoconductance is a clear indication of the chiral magnetic effect. The observed phenomenon stems from the effective transmutation of Dirac semimetal into a Weyl semimetal induced by the parallel electric and magnetic fields that represent a topologically nontrivial gauge field background.

preprint2014arXiv

Optimization of a Superconducting Magnetic Energy Storage Device via a CPU-Efficient Semi-Analytical Simulation

Recent advances in second generation (YBCO) high temperature superconducting wire could potentially enable the design of super high performance energy storage devices that combine the high energy density of chemical storage with the high power of superconducting magnetic storage. However, the high aspect ratio and considerable filament size of these wires requires the concomitant development of dedicated optimization methods that account for both the critical current density and ac losses in type II superconductors. Here, we report on the novel application and results of a CPU-efficient semi-analytical computer code based on the Radia 3D magnetostatics software package. Our algorithm is used to simulate and optimize the energy density of a superconducting magnetic energy storage device model, based on design constraints, such as overall size and number of coils. The rapid performance of the code is pivoted on analytical calculations of the magnetic field based on an efficient implementation of the Biot-Savart law for a large variety of 3D "base" geometries in the Radia package. The significantly-reduced CPU time and simple data input in conjunction with the consideration of realistic input variables, such as material-specific, temperature and magnetic field-dependent critical current densities have enabled the Radia-based algorithm to outperform finite element approaches by a twenty fold reduction in CPU time at the same accuracy levels. Comparative simulations of MgB2 and YBCO-based devices are performed at 4.2 K, and finally, calculations of the ac losses are computed in order to ascertain the realistic efficiency of the design configurations.

preprint2014arXiv

Remote-excitation and remote-detection of single quantum dot using propagating surface plasmons on silver nanowire

Using propagating surface plasmons (SPs) on silver nanowire (NW), we demonstrate that focused laser light at the end of silver nanowire can excite single quantum dot (QD) microns away from the excitation spot. The QD-NW interaction allows the excited QD convert part of its energy into propagating SPs which then can be detected at the remote sites. Simultaneous multi-QDs remote-excitation and detection are also realized. Furthermore, the tight confinement of propagating SPs around the NW surface enables selective excitation of QDs very close in space, which cannot be realized under conventional excitation condition. This remote excitation and detection approach may find applications in optical imaging and sensing of chemical and biological systems.

preprint2013arXiv

Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice. This paper describes the solution of team "binghsu & MLRush & BrickMover". We conduct simple feature engineering work and train different models by each individual team member. Afterwards, we use listwise ensemble method to combine each model's output. Besides describing effective model and features, we will discuss about the lessons we learned while using deep learning in this competition.

preprint2013arXiv

Enhanced low-energy magnetic excitations via suppression of the itinerancy in Fe0.98-zCuzTe0.5Se0.5

We have performed resistivity and inelastic neutron scattering measurements on three samples of Fe0.98-zCuzTe0.5Se0.5 with z = 0, 0.02, and 0.1. It is found that with increasing Cu doping the sample's resistivity deviates progressively from that of a metal. However, in contrast to expectations that replacing Fe with Cu would suppress the magnetic correlations, the low-energy (no larger than 12 meV) magnetic scattering is enhanced in strength, with greater spectral weight and longer dynamical spin-spin correlation lengths. Such enhancements can be a consequence of either enlarged local moments or a slowing down of the spin fluctuations. In either case, the localization of the conduction states induced by the Cu doping should play a critical role. Our results are not applicable to models that treat 3d transition metal dopants simply as effective electron donors.

preprint2013arXiv

Evolution of superconducting correlations within magnetic-field-decoupled CuO(2) layers of La(1.905)Ba(0.095)CuO(4)

We explore the evolution of superconductivity in La(2-x)Ba(x)CuO(4) with x=0.095 in magnetic fields of up to 35 T applied perpendicular to the CuO(2) planes. Previous work on this material has shown that perpendicular fields enhance both charge and spin stripe order within the planes. We present measurements of the resistivity parallel and perpendicular to the planes, as well as the Hall effect. Measurements of magnetic susceptibility for fields of up to 15 T applied both parallel and perpendicular to the planes provide complementary measures of the superconductivity. We show that fields sufficient to destroy pair tunneling between the planes do not disrupt the superconducting correlations within the planes. In fact, we observe an onset of large amplitude but phase disordered superconductivity within the planes at approximately 30 K that is remarkably insensitive to field. With further cooling, we observe a phase-transition-like drop in the in-plane resistivity to an apparent state of superconductivity, despite the lack of phase coherence between the layers. These observations raise interesting questions concerning the identification of the upper critical field, where pairing is destroyed, in underdoped cuprates.

preprint2013arXiv

Photon-pair jet production via gluon fusion at the LHC

Photon-pair direct or jet-associated productions are important for relevant standard model measurement, Higgs and new physics searches at the LHC. The loop-induced gluon-fusion process gg -> γγg, which although formally contributes only at the next-to-next-to-leading order to γγj productions, may get enhanced by the large gluon flux at the LHC. We have checked and confirmed previous results on gg -> γγ, γγg at one loop, using now the traditional Feynman diagram based approach and taking into account the quark mass effects, and further updated them for the 7 and 14 TeV LHC with new inputs and settings. We provide the details and results of the calculations, which involves manipulation of rank-5 pentagon integrals. Our results show that the gluon-fusion process can contribute about 10% of the Born result, especially at small M_{γγ} and PT_{γγ}, and increase further the overall scale uncertainty. Top quark loop effects are examined in detail, which shows importance near or above the threshold M_{γγ} \gsim 2m_t.

preprint2013arXiv

Probing W+W-gamma Production and Anomalous Quartic Gauge Boson Couplings at the CERN LHC

Triple gauge boson associated production at the LHC serves as an interesting channel to test the robustness of the Standard Model. Any deviation from its SM prediction may indicate possible existence of relevant new physics, e.g., anomalous quartic gauge boson couplings. In this paper, a Monte-Carlo feasibility study of measuring WWA production with pure leptonic decays and probing anomalous quartic gauge-boson (e.g., WWAA) couplings, is presented in detail for the first time, with parton shower and detector simulation effects taken into account. Our results show that at the sqrt{s} = 14 TeV LHC with an integrated luminosity of 100 (30) fb-1, one can reach a significance of 9 (5) sigma to observe the SM WWA production, and can constrain at the 95% CL the anomalous WWAA coupling parameters, e.g., a_{0,c}^W/Λ^2 (see Ref.[15] for their definitions), at 10^{-5} GeV^{-2}, respectively.

preprint2013arXiv

Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization

Consider an MISO channel overheard by multiple eavesdroppers. Our goal is to design an artificial noise (AN)-aided transmit strategy, such that the achievable secrecy rate is maximized subject to the sum power constraint. AN-aided secure transmission has recently been found to be a promising approach for blocking eavesdropping attempts. In many existing studies, the confidential information transmit covariance and the AN covariance are not simultaneously optimized. In particular, for design convenience, it is common to prefix the AN covariance as a specific kind of spatially isotropic covariance. This paper considers joint optimization of the transmit and AN covariances for secrecy rate maximization (SRM), with a design flexibility that the AN can take any spatial pattern. Hence, the proposed design has potential in jamming the eavesdroppers more effectively, based upon the channel state information (CSI). We derive an optimization approach to the SRM problem through both analysis and convex conic optimization machinery. We show that the SRM problem can be recast as a single-variable optimization problem, and that resultant problem can be efficiently handled by solving a sequence of semidefinite programs. Our framework deals with a general setup of multiple multi-antenna eavesdroppers, and can cater for additional constraints arising from specific application scenarios, such as interference temperature constraints in interference networks. We also generalize the framework to an imperfect CSI case where a worst-case robust SRM formulation is considered. A suboptimal but safe solution to the outage-constrained robust SRM design is also investigated. Simulation results show that the proposed AN-aided SRM design yields significant secrecy rate gains over an optimal no-AN design and the isotropic AN design, especially when there are more eavesdroppers.

preprint2013arXiv

The CERN LHC Sensitivity on measuring WZGamma Production and Anomalous WWZGamma Coupling

In this paper we present for the first time a detailed Monte Carlo study of measuring WZGamma production with pure leptonic decays and probing anomalous quartic gauge-boson WWZGamma couplings at the sqrt(s) = 14 TeV LHC, with parton shower and detector simulation effects taken into account. We find that with an integrated luminosity of 100 fb-1 and proper selection cuts, the Standard Model WZGamma signal significance can be improved to as much as 3 sigma. After reviewing previous parametrization on anomalous WWZGamma couplings (see e.g.an/Lambda^2 or k2m/Lambda^2 as shown in Ref. [17]), we propose a more general parametrization scheme with 4 free inputs leading only to genuine WWZGamma aQGC couplings. Finally, our numerical results show that one can reach constraints at 95% confidence level of -5.7 * 10^{-5} GeV^{-2} < k2m/Lambda^2 < 5.5 * 10^{-5} GeV^{-2} and -2.2 x 10^{-5} GeV^{-2} < an/Lambda^2 < 2.4 * 10^{-5} GeV^{-2}, which are more stringent than LEP's results by three orders of magnitude.

preprint2012arXiv

Conductance of single-atom magnetic junctions: A first-principles study

We present a first-principles investigation to show that the contact conductance of a half conductance quantum ($G_0/2$) found previously does not generally hold for single-atom magnetic junctions composed of a tip and an adatom dsorbed on a surface. The contact conductance of the Ni-Co/Co(111) junction is approximately $G_0/2$, while for the Co-Co/Co(111), Ni-Ni/Ni(111), and Ni-Ni/Ni(001) junctions the contact conductances are 0.80$G_0$, 1.55$G_0$, and 1.77$G_0$, respectively. The deviation from $G_0/2$ is mainly caused by the variation of the spin-down conductance largely determined by the minority $d$ orbitals, as the spin-up one changes little for different junctions.

preprint2012arXiv

Probing New Physics via pp-> W+W- -> lvjj at the CERN LHC

TeV scale new Physics, e.g., Large Extra Dimensions or Models with anomalous triple vector boson couplings, can lead to excesses in various kinematic regions on the semi-leptonic productions of pp -> WW -> lvjj at the CERN LHC, which, although suffers from large QCD background compared with the pure leptonic channel, can benefit from larger production rates and the reconstructable 4-body mass Mlvjj. We study the search sensitivity through the lvjj channel at the 7TeV LHC on relevant new physics, via probing the hard tails on the reconstructed Mlvjj and the transverse momentum of W-boson (PTW), taking into account main backgrounds and including the parton shower and detector simulation effects. Our results show that with integrated luminosity of 5fb-1, the LHC can already discovery or exclude a large parameter region of the new physics, e.g., 95% CL. limit can be set on the Large Extra Dimensions with a cut-off scale up to 1.5 TeV, and the WWZ anomalous coupling down to, e.g. |λ_Z|~0.1. Brief results are also given for the 8TeV LHC.

preprint2012arXiv

Probing the connections between superconductivity, stripe order, and structure in La1.905Ba0.095Cu1-yZnyO4

The superconducting system La2-xBaxCuO4 is known to show a minimum in the transition temperature, Tc, at x = 1/8 where maximal stripe order is pinned by the anisotropy within the CuO2 planes that occurs in the low-temperature-tetragonal (LTT) crystal structure. For x = 0.095, where Tc reaches its maximum value of 32 K, there is a roughly coincident structural transition to a phase that is very close to LTT. Here we present a neutron scattering study of the structural transition, and demonstrate how features of it correlate with anomalies in the magnetic susceptibility, electrical resistivity, thermal conductivity, and thermoelectric power. We also present measurements on a crystal with 1% Zn substituted for Cu, which reduces Tc to 17 K, enhances the spin stripe order, but has much less effect on the structural transition. We make the case that the structural transition correlates with a reduction of the Josephson coupling between the CuO2 layers, which interrupts the growth of the superconducting order. We also discuss evidence for two-dimensional superconducting fluctuations in the normal state, analyze the effective magnetic moment per Zn impurity, and consider the significance of the anomalous thermopower often reported in the stripe-ordered phase.

preprint2012arXiv

Transport Properties of Stripe-Ordered High Tc Cuprates

Transport measurements provide important characterizations of the nature of stripe order in the cuprates. Initial studies of systems such as La(1.6-x)Nd(0.4)Sr(x)CuO(4) demonstrated the strong anisotropy between in-plane and c-axis resistivities, but also suggested that stripe order results in a tendency towards insulating behavior within the planes at low temperature. More recent work on La(2-x)Ba(x)CuO(4) with x=1/8 has revealed the occurrence of quasi-two-dimensional superconductivity that onsets with spin-stripe order. The suppression of three-dimensional superconductivity indicates a frustration of the interlayer Josephson coupling, motivating a proposal that superconductivity and stripe order are intertwined in a pair-density-wave state. Complementary characterizations of the low-energy states near the Fermi level are provided by measurements of the Hall and Nernst effects, each revealing intriguing signatures of stripe correlations and ordering. We review and discuss this work.

preprint2012arXiv

Uniaxial linear resistivity of superconducting La(1.905)Ba(0.095)CuO(4) induced by an external magnetic field

We present an experimental study of the anisotropic resistivity of superconducting La(2-x)Ba(x)CuO(4) with x=0.095 and transition temperature Tc=32 K. In a magnetic field perpendicular to the CuO(2) layers, H(perp), we observe that the resistivity perpendicular to the layers, ρ(perp), becomes finite at a temperature consistent with previous studies on very similar materials; however, the onset of finite parallel resistivity, ρ(par), occurs at a much higher temperature. This behavior contradicts conventional theory, which predicts that ρ(perp) and ρ(par) should become finite at the same temperature. Voltage vs. current measurements near the threshold of voltage detectability indicate linear behavior perpendicular to the layers, becoming nonlinear at higher currents, while the behavior is nonlinear from the onset parallel to the layers. These results, in the presence of moderate H(perp), appear consistent with superconducting order parallel to the layers with voltage fluctuations between the layers due to thermal noise. In search of uncommon effects that might help to explain this behavior, we have performed diffraction measurements that provide evidence for H(perp)-induced charge and spin stripe order. The field-induced decoupling of superconducting layers is similar to the decoupled phase observed previously in La(2-x)Ba(x)CuO(4) with x=1/8 in zero field.

preprint2012arXiv

ZZ+jet production via gluon fusion at the LHC

Pair production of Z bosons in association with a hard jet is an important background for Higgs particle or new physics searches at the LHC. The loop-induced gluon-fusion process gg > ZZg contributes formally only at the next-to-next-to-leading order. Nevertheless, it can get enhanced by the large gluon flux at the LHC, and thus should be taken into account in relevant experimental searches. We provide the details and results of this calculation, which involves the manipulation of rank-5 pentagon integrals. Our results show that the gluon-fusion process can contribute more than 10% to the next-to-leading order QCD result and increases the overall scale uncertainty. Moreover, interference effects between Higgs and non-Higgs contributions can become large in phase-space regions where the Higgs is far off-shell.

preprint2011arXiv

A Robust Artificial Noise Aided Transmit Design for Miso Secrecy

This paper considers an artificial noise (AN) aided secrecy rate maximization (SRM) problem for a multi-input single-output (MISO) channel overheard by multiple single-antenna eavesdroppers. We assume that the transmitter has perfect knowledge about the channel to the desired user but imperfect knowledge about the channels to the eavesdroppers. Therefore, the resultant SRM problem is formulated in the way that we maximize the worst-case secrecy rate by jointly designing the signal covariance ${\bf W}$ and the AN covariance ${\bf Σ}$. However, such a worst-case SRM problem turns out to be hard to optimize, since it is nonconvex in ${\bf W}$ and ${\bf Σ}$ jointly. Moreover, it falls into the class of semi-infinite optimization problems. Through a careful reformulation, we show that the worst-case SRM problem can be handled by performing a one-dimensional line search in which a sequence of semidefinite programs (SDPs) are involved. Moreover, we also show that the optimal ${\bf W}$ admits a rank-one structure, implying that transmit beamforming is secrecy rate optimal under the considered scenario. Simulation results are provided to demonstrate the robustness and effectiveness of the proposed design compared to a non-robust AN design.

preprint2011arXiv

Gluon Fusion induced Zg and Zgg Productions in the Standard Model at the LHC

We report calculations of the gluon induced Zg and Zgg productions in the Standard Model at the LHC operating at both 7 TeV and 14 TeV collision energy. We present total cross sections and differential distributions of the processes and compare them with the leading and next-to-leading order QCD pp -> Z+1 jet, Z+2 jets results. Our results show that the gluon induced Zg and Zgg productions contribute to pp -> Z+1 jet, Z+2 jets at 1% level.

preprint2011arXiv

Higgs Boson Production via Gluon Fusion in the Standard Model with four Generations

Higgs bosons can be produced copiously at the LHC via gluon fusion induced by top and bottom quark loops, and can be enhanced strongly if extra heavy quarks exist. We present results for Higgs +zero-, one- and two-jet production at the LHC, in both the Standard Model and the 4th generation model, by evaluating the corresponding heavy quark triangle, box and pentagon Feynman diagrams. We compare the results by using the effective Higgs-gluon interactions in the limit of heavy quarks with the cross sections including the full mass dependences. NLO effects with full mass dependence are included in the inclusive Higgs boson production rate. Our results improve the theoretical basis for fourth generation effects on the Higgs boson search at the LHC.

preprint2011arXiv

Iron-chalcogenide FeSe$_{0.5}$Te$_{0.5}$ coated superconducting tapes for high field applications

The high upper critical field characteristic of the recently discovered iron-based superconducting chalcogenides opens the possibility of developing a new type of non-oxide high-field superconducting wires. In this work, we utilize a buffered metal template on which we grow a textured FeSe$_{0.5}$Te$_{0.5}$ layer, an approach developed originally for high temperature superconducting coated conductors. These tapes carry high critical current densities (>1$\times10^{4}$A/cm$^{2}$) at about 4.2K under magnetic field as high as 25 T, which are nearly isotropic to the field direction. This demonstrates a very promising future for iron chalcogenides for high field applications at liquid helium temperatures. Flux pinning force analysis indicates a point defect pinning mechanism, creating prospects for a straightforward approach to conductor optimization.

preprint2011arXiv

Matched predictions for Higgs production via heavy-quark loops in the SM and beyond

The main Higgs production channel at hadron colliders is gluon fusion via heavy-quark loops. We present the results of a fully exclusive simulation of gluon fusion Higgs production based on the matrix elements for h + 0, 1, 2 partons including full heavy-quark loop dependence, matched to a parton shower. We consider a Higgs with standard model couplings as well as models where the Higgs has enhanced couplings to bottom quarks (b-philic). We study the most relevant kinematic distributions, such as jet and Higgs pT spectra and find that matched samples provide an accurate description of the final state. For the SM Higgs, we confirm the excellent accuracy of the large heavy-quark-mass approximation also in differential distributions over all phase space, with significant effects arising only at large pT . For a b-philic Higgs however, the loops have a dramatic impact on the kinematics of the Higgs as well as of the jets and need to be accounted for exactly to achieve reliable event simulations.

preprint2011arXiv

Optimal and Robust Transmit Designs for MISO Channel Secrecy by Semidefinite Programming

In recent years there has been growing interest in study of multi-antenna transmit designs for providing secure communication over the physical layer. This paper considers the scenario of an intended multi-input single-output channel overheard by multiple multi-antenna eavesdroppers. Specifically, we address the transmit covariance optimization for secrecy-rate maximization (SRM) of that scenario. The challenge of this problem is that it is a nonconvex optimization problem. This paper shows that the SRM problem can actually be solved in a convex and tractable fashion, by recasting the SRM problem as a semidefinite program (SDP). The SRM problem we solve is under the premise of perfect channel state information (CSI). This paper also deals with the imperfect CSI case. We consider a worst-case robust SRM formulation under spherical CSI uncertainties, and we develop an optimal solution to it, again via SDP. Moreover, our analysis reveals that transmit beamforming is generally the optimal transmit strategy for SRM of the considered scenario, for both the perfect and imperfect CSI cases. Simulation results are provided to illustrate the secrecy-rate performance gains of the proposed SDP solutions compared to some suboptimal transmit designs.

preprint2011arXiv

Quantum Strategies Win in a Defector-Dominated Population

Quantum strategies are introduced into evolutionary games. The agents using quantum strategies are regarded as invaders whose fraction generally is 1% of a population in contrast to the 50% defectors. In this paper, the evolution of strategies on networks is investigated in a defector-dominated population, when three networks (Regular Lattice, Newman-Watts small world network, scale-free network) are constructed and three games (Prisoners' Dilemma, Snowdrift, Stag-Hunt) are employed. As far as these three games are concerned, the results show that quantum strategies can always invade the population successfully. Comparing the three networks, we find that the regular lattice is most easily invaded by agents that adopt quantum strategies. However, for a scale-free network it can be invaded by agents adopting quantum strategies only if a hub is occupied by an agent with a quantum strategy or if the fraction of agents with quantum strategies in the population is significant.

preprint2011arXiv

Simulating graviton production at hadron colliders

Spin-2 particles and in particular gravitons are predicted in many new physics scenarios at the TeV scale. Depending on the details of models such new states might show up as a continuum, massless particles, or TeV scale resonances. Correspondingly, very different discovery signatures should be exploited, from the search of excesses in events with multi jets and large missing transverse energy, to resonances in weak boson or jet pair productions. We present a very general and flexible implementation in MadGraph/MadEvent of spin-2 particles interacting with the standard model particles via the energy momentum tensor, which encompasses all of the most popular TeV scale models featuring gravitons. By merging matrix elements with parton shower, we can generate inclusive samples of graviton+jets at the hadron colliders in several scenarios (ADD, zero-mass graviton and RS). We compare and validate our results against the corresponding next-to-leading order QCD calculations.

preprint2011arXiv

Study of Thick CZT Detectors for X-ray and Gamma-Ray Astronomy

CdZnTe (CZT) is a wide bandgap II-VI semiconductor developed for the spectroscopic detection of X-rays and γ-rays at room temperature. The Swift Burst Alert Telescope is using an 5240 cm2 array of 2 mm thick CZT detectors for the detection of 15-150 keV X-rays from Gamma-Ray Bursts. We report on the systematic tests of thicker (\geq 0.5 cm) CZT detectors with volumes between 2 cm3 and 4 cm3 which are potential detector choices for a number of future X-ray telescopes that operate in the 10 keV to a few MeV energy range. The detectors contacted in our laboratory achieve Full Width Half Maximum energy resolutions of 2.7 keV (4.5%) at 59 keV, 3 keV (2.5%) at 122 keV and 4 keV (0.6%) at 662 keV. The 59 keV and 122 keV energy resolutions are among the world-best results for \geq 0.5 cm thick CZT detectors. We use the data set to study trends of how the energy resolution depends on the detector thickness and on the pixel pitch. Unfortunately, we do not find clear trends, indicating that even for the extremely good energy resolutions reported here, the achievable energy resolutions are largely determined by the properties of individual crystals. Somewhat surprisingly, we achieve the reported results without applying a correction of the anode signals for the depth of the interaction. Measuring the interaction depths thus does not seem to be a pre-requisite for achieving sub-1% energy resolutions at 662 keV.

preprint2011arXiv

Three-dimensional Orientation Sensors by Defocused Imaging of Gold Nanorods through an Ordinary Wide-Field Microscope

Gold (Au) nanoparticles particularly nanorods are actively exploited as imaging probes because of their special nonblinking and nonbleaching absorption, scattering and emitting properties that arise from the excitation of surface plasmons. Herein, we report a novel orientation sensor at nanoscale by defocused imaging of single Au nanorods (AuNRs) through an ordinary wide-field optical microscope. By simultaneously recording defocused images and two-photon luminescence intensities for a large number of single AuNRs, we correlate their defocused images with their three-dimensional spatial orientations. As many AuNRs can be monitored in parallel, defocused imaging of single AuNRs is a high throughput sensing technique that allows us to obtain their spatial orientations within one frame in situ and real-time. The probe size can be down to several nanometers, which is highly desirable in order to minimize any potential interference from the probe itself. Furthermore, the sensing property is insensitive to the excitation polarization and the distribution of the probe aspect ratio, which allows AuNRs of any length in a proper regime to be used as orientation sensors without changing the laser frequency and polarization. These unique features make the orientation probes proposed here outstanding candidates for optical imaging and sensing in material science and biology.

preprint2010arXiv

A Novel Clustering Algorithm Based Upon Games on Evolving Network

This paper introduces a model based upon games on an evolving network, and develops three clustering algorithms according to it. In the clustering algorithms, data points for clustering are regarded as players who can make decisions in games. On the network describing relationships among data points, an edge-removing-and-rewiring (ERR) function is employed to explore in a neighborhood of a data point, which removes edges connecting to neighbors with small payoffs, and creates new edges to neighbors with larger payoffs. As such, the connections among data points vary over time. During the evolution of network, some strategies are spread in the network. As a consequence, clusters are formed automatically, in which data points with the same evolutionarily stable strategy are collected as a cluster, so the number of evolutionarily stable strategies indicates the number of clusters. Moreover, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the comparison with other algorithms also provides an indication of the effectiveness of the proposed algorithms.

preprint2010arXiv

Disappearance of static magnetic order and evolution of spin fluctuations in Fe$_{1+δ}$Se$_{x}$Te$_{1-x}$

We report neutron scattering studies on static magnetic orders and spin excitations in the Fe-based chalcogenide system Fe$_{1+δ}$Se$_{x}$Te$_{1-x}$ with different Fe and Se compositions. Short-range static magnetic order with the "bicollinear" spin configuration is found in all non-superconducting samples, with strong low-energy magnetic excitations near the $(0.5,0)$ in-plane wave-vector (using the two-Fe unit cell) for Se doping up to 45%. When the static order disappears and bulk superconductivity emerges, the spectral weight of the magnetic excitations shifts to the region of reciprocal space near the in-plane wave-vector $(0.5,0.5)$, corresponding to the "collinear" spin configuration. Our results suggest that spin fluctuations associated with the collinear magnetic structure appear to be universal in all Fe-based superconductors, and there is a strong correlation between superconductivity and the character of the magnetic order/fluctuations in

preprint2010arXiv

Effect of magnetic field on the spin resonance in FeTe(0.5)Se(0.5) as seen via inelastic neutron scattering

Inelastic neutron scattering and susceptibility measurements have been performed on the optimally-doped Fe-based superconductor FeTe(0.5)Se(0.5), which has a critical temperature, Tc of 14 K. The magnetic scattering at the stripe antiferromagnetic wave-vector Q = (0.5,0.5) exhibits a "resonance" at ~ 6 meV, where the scattering intensity increases abruptly when cooled below Tc. In a 7-T magnetic field parallel to the a-b plane, Tc is slightly reduced to ~ 12 K, based on susceptibility measurements. The resonance in the neutron scattering measurements is also affected by the field. The resonance intensity under field cooling starts to rise at a lower temperature ~ 12 K, and the low temperature intensity is also reduced from the zero-field value. Our results provide clear evidence for the intimate relationship between superconductivity and the resonance measured in magnetic excitations of Fe-based superconductors.

preprint2010arXiv

Einstein Modes in the Phonon Density of States of the Single-Filled Skutterudite Yb$_{0.2}$Co$_{4}$Sb$_{12}$

Measurements of the phonon density of states by inelastic neutron \emph{time-of-flight} scattering and specific heat measurements along with first principles calculations, provide compelling evidence for the existence of an Einstein oscillator ("\emph{rattler}") at $ω_{E1} \approx$ 5.0 meV in the filled skutterudite Yb$_{0.2}$Co$_{4}$Sb$_{12}$. Multiple dispersionless modes in the measured density of states of Yb$_{0.2}$Co$_{4}$Sb$_{12}$ at intermediate transfer energies (14 meV $\leq$ \emph{$ω$} $\leq$ 20 meV) are exhibited in both the experimental and theoretical \emph{density-of-states} of the Yb-filled specimen. A peak at 12.4 meV is shown to coincide with a second Einstein mode at \emph{$ω_{E2} \approx$} 12.8 meV obtained from heat capacity data. The local modes at intermediate transfer energies are attributed to altered properties of the host CoSb$_{3}$ cage as a result of Yb-filling. It is suggested that these modes are owed to a complementary mechanism for the scattering of heat-carrying phonons in addition to the mode observed at $ω_{E1} \, \approx$ 5.0 meV. Our observations offer a plausible explanation for the significantly-higher \textit{dimensionless figures of merit} of filled skutterudites, compared to their parent compounds.

preprint2010arXiv

Nanostructures and Defects in Non-equilibrium Synthesized Filled Skutterudite CeFe4Sb12

We studied nanoprecipitates and defects in p-type filled skutterudite CeFe4Sb12 prepared by non-equilibrium melt-spinning plus spark plasma sintering method using transmission electron microscopy. Nanoprecipitates with mostly spherical shapes and different sizes (from several nm to several tens of nm) have been observed. The most typically observed nanoprecipitates are shown to be Sb-rich. Superlattices with a periodicity of about 3.576 nm were induced by the ordering of excessive Sb atoms along the c direction. These nanoprecipitates usually share coherent interfaces with the surrounding matrix and induce anisotropic and strong strain fields in the surrounding matrix. Nanoprecipitates with compositions close to CeSb2 are much larger in size (~ 30 nm) and have orthorhombic structures. Various defects were typically observed on the interfaces between these nanoprecipitates and the matrix. The strain fields induced by these nanoprecipitates are less distinct, possibly because part of the strains has been released by the formation of defects.

preprint2010arXiv

NLO QCD corrections to graviton production at hadron colliders

Models with large extra dimensions predict the existence of Kaluza-Klein graviton resonances. We compute the next-to-leading order QCD corrections to graviton plus jet hadro-production, which is an important channel for graviton searches at the Tevatron and the LHC. The QCD corrections are sizable and lead to a significant reduction of the scale dependence. We present numerical results for cross sections and distributions, and discuss the uncertainty from parton distribution functions and the ultraviolet sensitivity of the theoretical prediction.

preprint2010arXiv

Optical properties of the iron-chalcogenide superconductor FeTe0.55Se0.45

The complex optical properties of the iron-chalcogenide superconductor FeTe0.55Se0.45 with Tc=14K have been examined over a wide frequency range for light polarized in the Fe-Te(Se) planes above and below Tc. At room temperature the optical response may be described by a weakly-interacting Fermi liquid; however, just above Tc this picture breaks down and the scattering rate takes on a linear frequency dependence. Below Tc there is evidence for two gap features in the optical conductivity at Delta_1 ~ 2.5 meV and Delta_2 ~ 5.1 meV. Less than 20% of the free carriers collapse into the condensate for T << Tc, and this material is observed to fall on the universal scaling line for a BCS dirty-limit superconductor in the weak-coupling limit.

preprint2010arXiv

Strong impact of grain boundaries on the thermoelectric properties of non-equilibrium synthesized p-type Ce1.05Fe4Sb12.04 filled skutterudites with nanostructure

p-type Ce1.05Fe4Sb12.04 filled skutterudites with much improved thermoelectric properties have been synthesized by rapidly converting nearly amorphous ribbons into crystalline pellets under pressure. It is found that this process greatly suppresses grain growth and second phase formation/segregation, and hence results in the samples consisting of nano-sized grains with strongly-coupled grain boundaries, as observed by transmission electron microscopy. The room temperature carrier mobility in these samples is significantly higher (nearly double) than those in the samples of the same starting composition made by the conventional solid-state reaction. Nanostructure reduces the lattice thermal conductivity, while cleaner grain boundaries permit higher electron conduction.

preprint2010arXiv

ZZ+jet and Graviton+jet at NLO QCD: recent applications using GOLEM methods

In this talk we discuss recent progress concerning precise predictions for hadron colliders. We show results of two applications of tensor reduction using GOLEM methods: the next-to-leading order (NLO) corrections to $pp \to ZZ+$jet as an important background for Higgs particle and new physics searches at hadron colliders, and the NLO corrections to graviton plus jet hadro-production, which is an important channel for graviton searches at the Tevatron and the LHC.

preprint2009arXiv

A Novel Clustering Algorithm Based on Quantum Games

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

preprint2009arXiv

Jet angular correlation in vector-boson fusion processes at hadron colliders

Higgs boson and massive-graviton productions in association with two jets via vector-boson fusion (VBF) processes and their decays into a vector-boson pair at hadron colliders are studied. They include scalar and tensor boson production processes via weak-boson fusion in quark-quark collisions, gluon fusion in quark-quark, quark-gluon and gluon-gluon collisions, as well as their decays into a pair of weak bosons or virtual gluons which subsequently decay into $\ell\bar\ell$, $q\bar q$ or $gg$. We give the helicity amplitudes explicitly for all the VBF subprocesses, and show that the VBF amplitudes dominate the exact matrix elements not only for the weak-boson fusion processes but also for all the gluon fusion processes when appropriate selection cuts are applied, such as a large rapidity separation between two jets and a slicing cut for the transverse momenta of the jets. We also show that our off-shell vector-boson current amplitudes reduce to the standard quark and gluon splitting amplitudes with appropriate gluon-polarization phases in the collinear limit. Nontrivial azimuthal angle correlations of the jets in the production and in the decay of massive spin-0 and -2 bosons are manifestly expressed as the quantum interference among different helicity states of the intermediate vector-bosons. Those correlations reflect the spin and the CP nature of the Higgs bosons and the massive gravitons.

preprint2005arXiv

Effects of R-parity violation in unpolarized top quark decay into polarized W-boson

We calculate one-loop R-parity violating corrections to the top quark decay into a bottom quark and a polarized W-gauge boson. The corrections are presented according to the total corrections, the longitudinal corrections and the transverse corrections, respectively. We compared our results with the ${\cal O}(α_s)$ QCD corrections, the ${\cal O}(α)$ electroweak (EW) and finite width corrections, and also the supersymmetric (SUSY) corrections with R-parity conservation, respectively.

preprint2004arXiv

Effects of R-parity Violation on the Charged Higgs Boson Decays

We calculate one-loop R-parity-violating couplings corrections to the processes $H^-\to τ\bar{ν_τ}$ and $H^-\to b\bar{t}$. We find that the corrections to the $H^-\to τ\bar{ν_τ}$ decay mode are generally about 0.1%, and can be negligible. But the corrections to the $H^-\to b\bar{t}$ decay mode can reach a few percent for the favored parameters.

Qiang Li

What is connected

Connect this record

See the researcher in context

Building this map preview

138 published item(s)

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Electromagnetic-Compliant Channel Modeling and Performance Evaluation for Holographic MIMO

A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling

Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

An adverse-pressure-gradient turbulent boundary layer with nearly-constant $\boldsymbol{β\simeq 1.4}$ up to $\boldsymbol{Re_θ \simeq 8,700}$

Animating collider processes with Event-time-frame Format

Boosted tau lepton as a microscope and macroscope

Channel Estimation and Multipath Diversity Reception for RIS-Empowered Broadband Wireless Systems Based on Cyclic-Prefixed Single-Carrier Transmission

Contrast Sensitivity Functions in Autoencoders

CRIS: CLIP-Driven Referring Image Segmentation

Enabling Fast and Flexible Distributed Deep Learning with Programmable Switches

Exploring Set Similarity for Dense Self-supervised Representation Learning

Forced ignition and oscillating flame propagation in fine ethanol sprays

Higher order monotonicity and submodularity of influence in social networks: from local to global

Instability of the Electroweak Vacuum in Starobinsky Inflation

ITTR: Unpaired Image-to-Image Translation with Transformers

Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion

Magnetic freeze-out and anomalous Hall effect in ZrTe$_5$

Microwave excitations and hysteretic magnetization dynamics of stripe domain films

Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents

Muon Collider Physics Summary

New insight into the spectra of turbulent boundary layers with pressure gradients

On the Role of Data Homogeneity in Multi-Agent Non-convex Stochastic Optimization

Reflective Dielectric Cavity Enhanced Emission from Hexagonal Boron Nitride Spin Defect Arrays

Searching for pulsars with phase characteristics

Semi-supervised Ranking for Object Image Blur Assessment

Simulated Detector Performance at the Muon Collider

Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation

The mass spectrum and wave functions of the $B_c$ system

The physics case of a 3 TeV muon collider stage

Thermal transport properties and some hydrodynamic-like behavior in 3D topological semimetal ZrTe5

Three kinds of novel multi-symplectic methods for stochastic Hamiltonian partial differential equations

Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression

Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

Anomalous Hall effect at the Lifshitz transition in ZrTe5

Back-n White Neutron Source at CSNS and its Applications

Gravitational effect of a plasma on the shadow of Schwarzschild black holes

Hidden Dark Matter from Starobinsky Inflation

Mass spectra, wave functions and mixing effects of the $(bcq)$ baryon

Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition

Robust and tunable Weyl phases by coherent infrared phonons in ZrTe$_5$

Searching for heavy leptoquarks at a muon collider

Space Shift Keying with Reconfigurable Intelligent Surfaces: Phase Configuration Designs and Performance Analysis

Statistical CSI Based Hybrid mmWave MIMO-NOMA with Max-Min Fairness

AliCoCo: Alibaba E-commerce Cognitive Concept Net

Capacity-Aware Edge Caching in Fog Computing Networks

Coherent control of nitrogen-vacancy center spins in silicon carbide at room temperature

Deep Reinforcement Learning for Fog Computing-based Vehicular System with Multi-operator Support

Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

HL-LHC Computing Review: Common Tools and Community Software

Joint Long-Term Cache Updating and Short-Term Content Delivery in Cloud-Based Small Cell Networks

Latency-Minimized Design of Secure Transmissions in UAV-Aided Communications

Measurement of the neutron beam profile of the Back-n white neutron facility at CSNS with a Micromegas detector

Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning

Minimum Symbol-Error Probability Symbol-Level Precoding with Intelligent Reflecting Surface

Observation and control of the weak topological insulator state in ZrTe5

Population aging caused by rise in sex ratio at birth

Propagation of weakly stretched premixed spherical spray flames in localized homogeneous and heterogeneous reactants

Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing

Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures

Recently observed $P_c$ as molecular states and possible mixture of $P_c(4457)$

Room-temperature coherent control of implanted defect spins in silicon carbide

Siamese Keypoint Prediction Network for Visual Object Tracking

Single-RF MIMO: From Spatial Modulation to Metasurface-Based Modulation

Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution

Trend and forecasting of the COVID-19 outbreak in China

Measurements of differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

One-Bit Sigma-Delta MIMO Precoding

Optimization of the power broadening in optically detected magnetic resonance of defect spins in silicon carbide

Interference Minimization in 5G Heterogeneous Networks

$D$-Wave Charmonia $η_{c2}$($1{^1D_2}$), $ψ_2$($1{^3D_2}$), and $ψ_3$($1{^3D_3}$) in $B_c$ Decays

5G Multimedia Massive MIMO Communications Systems