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Bin Wang

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

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gr-qc hep-th astro-ph.CO Machine Learning Computation and Language hep-ph Computer Vision Artificial Intelligence cond-mat.mes-hall cond-mat.mtrl-sci math.AG Systems and Control astro-ph.IM math.NA Numerical Analysis astro-ph.HE cond-mat.quant-gas eess.SY physics.optics Information Retrieval nucl-th physics.app-ph quant-ph Social and Information Networks Distributed, Parallel, and Cluster Computing Neural and Evolutionary Computing astro-ph astro-ph.GA astro-ph.SR Cryptography and Security eess.AS eess.IV eess.SP math.OC Networking and Internet Architecture Sound Applications Biological Physics Computational Engineering, Finance, and Science Computational Geometry cond-mat.other cond-mat.stat-mech cond-mat.supr-con cs.CY Data Structures and Algorithms Databases Graphics Hardware Architecture math.CO math.CV math.NT math.PR math.RA Mathematical Software Methodology Multimedia physics.atom-ph physics.flu-dyn physics.geo-ph physics.soc-ph physics.space-ph q-bio q-fin.MF Robotics

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preprint2026arXiv

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling

Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning, persistent state, long-horizon consistency, and causal understanding. We argue that the field should move beyond appearance synthesis toward intelligent visual generation: plausible visuals grounded in structure, dynamics, domain knowledge, and causal relations. To frame this shift, we introduce a five-level taxonomy: Atomic Generation, Conditional Generation, In-Context Generation, Agentic Generation, and World-Modeling Generation, progressing from passive renderers to interactive, agentic, world-aware generators. We analyze key technical drivers, including flow matching, unified understanding-and-generation models, improved visual representations, post-training, reward modeling, data curation, synthetic data distillation, and sampling acceleration. We further show that current evaluations often overestimate progress by emphasizing perceptual quality while missing structural, temporal, and causal failures. By combining benchmark review, in-the-wild stress tests, and expert-constrained case studies, this roadmap offers a capability-centered lens for understanding, evaluating, and advancing the next generation of intelligent visual generation systems.

preprint2024arXiv

Black holes with scalar hair: Extending from and beyond the Schwarzschild solution

We construct novel scalarized black hole (BH) solutions beyond the general relativity (GR) framework. These scalarized BH solutions are extended from the Schwarzschild one and the non-Schwarzschild one in the pure Einstein-Weyl gravity. By studying the BH entropy and free energy, we demonstrate that the scalarized BH extending from the Schwarzschild one exhibits thermodynamically preferred. We obtain these novel solutions by directly solving the full fourth-order equations of motion. This narrows the problematic solution space obtained by commonly adopted second-order reduction to physically valid spaces. Our findings also unveil the evasion of the no-hair theorem within the realm of higher-derivative gravity.

preprint2024arXiv

eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning

New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously learned ones in such dynamic environments. It might also be necessary to also eliminate the influence of related categories on the model to adapt to reclassification. We thus introduce class-level machine unlearning (MU) within CIL. Typically, MU methods tend to be time-consuming and can potentially harm the model's performance. A continuous stream of unlearning requests could lead to catastrophic forgetting. To address these issues, we propose a non-destructive eCIL-MU framework based on embedding techniques to map data into vectors and then be stored in vector databases. Our approach exploits the overlap between CIL and MU tasks for acceleration. Experiments demonstrate the capability of achieving unlearning effectiveness and orders of magnitude (upto $\sim 278\times$) of acceleration.

preprint2023arXiv

BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes.

preprint2023arXiv

High-fidelity ptychographic imaging of highly periodic structures enabled by vortex high harmonic beams

Ptychographic Coherent Diffractive Imaging enables diffraction-limited imaging of nanoscale structures at extreme ultraviolet and x-ray wavelengths, where high-quality image-forming optics are not available. However, its reliance on a set of diverse diffraction patterns makes it challenging to use ptychography to image highly periodic samples, limiting its application to defect inspection for electronic and photonic devices. Here, we use a vortex high harmonic light beam driven by a laser carrying orbital angular momentum to implement extreme ultraviolet ptychographic imaging of highly periodic samples with high fidelity and reliability. We also demonstrate, for the first time to our knowledge, ptychographic imaging of an isolated, near-diffraction-limited defect in an otherwise periodic sample using vortex high harmonic beams. This enhanced metrology technique can enable high-fidelity imaging and inspection of highly periodic structures for next-generation nano, energy, photonic and quantum devices.

preprint2022arXiv

$(2+1)$-Dimensional Black Holes in $f(R,ϕ)$ Gravity

We consider a $f(R)$ gravity theory in $(2+1)$-dimensions with a self-interacting scalar field non-minimally coupled to gravity. Without specifying the form of the $f(R)$ function, solving the field equations we find that the Ricci scalar receives a non-linear correction term which breaks the conformal invariance and leads to a massless black hole solution. When the non-linear term decouples, we get a well known hairy black hole solution with the scalar field conformally coupled to gravity. We also find that the entropy of our black hole may be higher than the corresponding conformal black hole which indicates that our solution may be thermodynamically preferred.

preprint2022arXiv

A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA

The numerical reasoning in the financial domain -- performing quantitative analysis and summarizing the information from financial reports -- can greatly increase business efficiency and reduce costs of billions of dollars. Here, we propose a numerical reasoning question answering system to answer numerical reasoning questions among financial text and table data sources, consisting of a retriever module, a generator module, and an ensemble module. Specifically, in the retriever module, in addition to retrieving the whole row data, we innovatively design a cell retriever that retrieves the gold cells to avoid bringing unrelated and similar cells in the same row to the inputs of the generator module. In the generator module, we utilize multiple generators to produce programs, which are operation steps to answer the question. Finally, in the ensemble module, we integrate multiple programs to choose the best program as the output of our system. In the final private test set in FinQA Competition, our system obtains 69.79 execution accuracy.

preprint2022arXiv

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use binocular data to construct a new benchmark for monocular textureless 3D object tracking. The proposed method requires no markers, and the cameras only need to be synchronous, relatively fixed as cross-view and calibrated. Based on our object-centered model, we jointly optimize the object pose by minimizing shape re-projection constraints in all views, which greatly improves the accuracy compared with the single-view approach, and is even more accurate than the depth-based method. Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes. The annotation error is guaranteed to be less than 2mm, according to both theoretical analysis and validation experiments. We re-evaluate the state-of-the-art 3D object tracking methods with our dataset, reporting their performance ranking in real scenes. Our BCOT benchmark and code can be found at https://ar3dv.github.io/BCOT-Benchmark/.

preprint2022arXiv

C3KG: A Chinese Commonsense Conversation Knowledge Graph

Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps. To fill the gap, we curate a large-scale multi-turn human-written conversation corpus, and create the first Chinese commonsense conversation knowledge graph which incorporates both social commonsense knowledge and dialog flow information. To show the potential of our graph, we develop a graph-conversation matching approach, and benchmark two graph-grounded conversational tasks.

preprint2022arXiv

CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations

Translation, rotation, and scaling are three commonly used geometric manipulation operations in image processing. Besides, some of them are successfully used in developing effective knowledge graph embedding (KGE) models such as TransE and RotatE. Inspired by the synergy, we propose a new KGE model by leveraging all three operations in this work. Since translation, rotation, and scaling operations are cascaded to form a compound one, the new model is named CompoundE. By casting CompoundE in the framework of group theory, we show that quite a few scoring-function-based KGE models are special cases of CompoundE. CompoundE extends the simple distance-based relation to relation-dependent compound operations on head and/or tail entities. To demonstrate the effectiveness of CompoundE, we conduct experiments on three popular KG completion datasets. Experimental results show that CompoundE consistently achieves the state of-the-art performance.

preprint2022arXiv

Continuous-stage symplectic adapted exponential methods for charged-particle dynamics with arbitrary electromagnetic fields

This paper is devoted to the numerical symplectic approximation of the charged-particle dynamics (CPD) with arbitrary electromagnetic fields. By utilizing continuous-stage methods and exponential integrators, a general class of symplectic methods is formulated for CPD under a homogeneous magnetic field. Based on the derived symplectic conditions, two practical symplectic methods up to order four are constructed where the error estimates show that the proposed second order scheme has a uniform accuracy in the position w.r.t. the strength of the magnetic field. Moreover, the symplectic methods are extended to CPD under non-homogeneous magnetic fields and three algorithms are formulated. Rigorous error estimates are investigated for the proposed methods and one method is proved to have a uniform accuracy in the position w.r.t. the strength of the magnetic field. Numerical experiments are provided for CPD under homogeneous and non-homogeneous magnetic fields, and the numerical results support the theoretical analysis and demonstrate the remarkable numerical behavior of our methods.

preprint2022arXiv

Critical phenomena in dynamical scalarization of charged black hole

We report a new black hole scalarization mechanism and disclose novel dynamical critical phenomena in the process of the nonlinear accretion of the scalar field into black holes. The accretion process can transform a seed black hole into a final scalarized or bald black hole, depending on the initial parameter of the scalar field $p$. There is a critical parameter $p_{\ast}$ and near it all intermediate solutions are attracted to a critical solution and stay there for a time scaling as $T\propto-γ\ln|p-p_{\ast}|$. At late times, the solutions evolve into scalarized black holes if $p>p_{\ast}$, or bald black holes if $p<p_{\ast}$. The final masses of the resulting scalarized/bald black holes satisfy power-laws $M_{p}-M_{\pm}\propto|p-p_{\ast}|^{γ_{\pm}}$ where $M_{\pm}$ are the masses of the scalarized/bald black holes when $p\to p_\ast$ from above/below, and $γ_{\pm}$ the corresponding exponents.

preprint2022arXiv

Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Previous CDR approaches mostly achieve the goal by following the Embedding and Mapping (EMCDR) idea which attempts to learn a mapping function to transfer the pre-trained user representations (embeddings) from the source domain into the target domain. However, they pre-train the user/item representations independently for each domain, ignoring to consider both domain interactions simultaneously. Therefore, the biased pre-trained representations inevitably involve the domain-specific information which may lead to negative impact to transfer information across domains. In this work, we consider a key point of the CDR task: what information needs to be shared across domains? To achieve the above idea, this paper utilizes the information bottleneck (IB) principle, and proposes a novel approach termed as CDRIB to enforce the representations encoding the domain-shared information. To derive the unbiased representations, we devise two IB regularizers to model the cross-domain/in-domain user-item interactions simultaneously and thereby CDRIB could consider both domain interactions jointly for de-biasing.

preprint2022arXiv

Cuspy and fractured black hole shadows in a toy model with axisymmetry

Cuspy shadow was first reported for hairy rotating black holes, whose metrics deviate significantly from the Kerr one. The non-smooth edge of the shadow is attributed to a transition between different branches of unstable but bounded orbits, known as the fundamental photon orbits, which end up at the light rings. In searching for a minimal theoretical setup to reproduce such a salient feature, in this work, we devise a toy model with axisymmetry, a slowly rotating Kerr black hole enveloped by a thin slowly rotating dark matter shell. Despite its simplicity, we show rich structures regarding fundamental photon orbits explicitly in such a system. We observe two disconnected branches of unstable spherical photon orbits, and the jump between them gives rise to a pair of cusps in the resultant black hole shadow. Besides the cuspy shadow, we explore other intriguing phenomena when the Maxwell construction cannot be established. We find that it is possible to have an incomplete arc of Einstein rings and a "fractured" shadow. The potential astrophysical significance of the corresponding findings is addressed.

preprint2022arXiv

Dynamical spontaneous scalarization in Einstein-Maxwell-scalar theory

We study the linear instability and the nonlinear dynamical evolution of the Reissner-Nordström (RN) black hole in the Einstein-Maxwell-scalar theory in asymptotic flat spacetime. We focus on the coupling function $f(ϕ)=e^{-bϕ^2}$ which allows both the scalar-free RN solution and scalarized black hole solution. We first present the evolution of system parameters during dynamic scalarization. For parameter regions where spontaneous scalarization occurs, we find that the evolution of the scalar field at the horizon is dominated by the fundamental unstable mode from linear analysis at early times. At late times, the nonlinear evolution can be viewed as the perturbation of scalarized black holes.

preprint2022arXiv

Electrified Autonomous Freight Benefit analysis on Fleet, Infrastructure and Grid Leveraging Grid-Electrified Mobility (GEM) Model

This paper analyzes the potential benefit of heavy-duty vehicle (HDV) electrification and automation on fleet cost, infrastructure cost, grid, and environmental impact. In this work, we extended the vehicle electrification benefit analysis tool: Grid-Electrified Mobility (GEM) model, which had primarily been used to study light-duty passenger vehicles (LDVs), to analyze the heavy-duty vehicle electrification. The extended model is derived for freight transportation and key results and findings on the impact of freight electrification and automation are presented and discussed.

preprint2022arXiv

Forecasts on Interacting Dark Energy from 21-cm Angular Power Spectrum with BINGO and SKA observations

Neutral hydrogen (HI) intensity mapping is a promising technique to probe the large-scale structure of the Universe, improving our understanding on the late-time accelerated expansion. In this work, we first scrutinize how an alternative cosmology, interacting dark energy (IDE), can affect the 21-cm angular power spectrum relative to the concordance $Λ$CDM model. We re-derive the 21-cm brightness temperature fluctuation in the context of such interaction and uncover an extra new contribution. Then we estimate the noise level of three upcoming HI intensity mapping surveys, BINGO, SKA1-MID Band$\,$1 and Band$\,$2, respectively, and employ a Fisher matrix approach to forecast their constraints on the IDE model. We find that while $\textit{Planck}\,$ 2018 maintains its dominion over early-Universe parameter constraints, BINGO and SKA1-MID Band$\,$2 put complementary bounding to the latest CMB measurements on dark energy equation of state $w$, the interacting strength $λ_i$ and the reduced Hubble constant $h$, and SKA1-MID Band$\,$1 even outperforms $\textit{Planck}\,$ 2018 in these late-Universe parameter constraints. The expected minimum uncertainties are given by SKA1-MID Band$\,$1+$\textit{Planck}\,$: $\sim 0.34\%$ on $w$, $\sim 0.22\%$ on $h$, $\sim 0.64\%$ on HI bias $b_{\rm HI}$, and an absolute uncertainty of about $3\times10^{-4}$ ($7\times10^{-4}$) on $λ_{1}$ ($λ_{2}$). Moreover, we quantify the effects from systematics of the redshift bin number, redshift-space distortions, foreground residuals and uncertainties on the measured HI fraction, $Ω_{\mathrm{HI}}(z)$. Our results indicate a bright prospect for HI intensity mapping surveys in constraining IDE, whether on their own or further by synergies with other measurements.

preprint2022arXiv

Geometry Contrastive Learning on Heterogeneous Graphs

Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data. Meanwhile, most existing representation learning methods embed the heterogeneous graphs into a single geometric space, either Euclidean or hyperbolic. This kind of single geometric view is usually not enough to observe the complete picture of heterogeneous graphs due to their rich semantics and complex structures. Under these observations, this paper proposes a novel self-supervised learning method, termed as Geometry Contrastive Learning (GCL), to better represent the heterogeneous graphs when supervisory data is unavailable. GCL views a heterogeneous graph from Euclidean and hyperbolic perspective simultaneously, aiming to make a strong merger of the ability of modeling rich semantics and complex structures, which is expected to bring in more benefits for downstream tasks. GCL maximizes the mutual information between two geometric views by contrasting representations at both local-local and local-global semantic levels. Extensive experiments on four benchmarks data sets show that the proposed approach outperforms the strong baselines, including both unsupervised methods and supervised methods, on three tasks, including node classification, node clustering and similarity search.

preprint2022arXiv

Hessian-Free Second-Order Adversarial Examples for Adversarial Learning

Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has been proved as one of the most effective methods to defend against such an attack. At present, most existing adversarial examples generation methods are based on first-order gradients, which can hardly further improve models' robustness, especially when facing second-order adversarial attacks. Compared with first-order gradients, second-order gradients provide a more accurate approximation of the loss landscape with respect to natural examples. Inspired by this, our work crafts second-order adversarial examples and uses them to train DNNs. Nevertheless, second-order optimization involves time-consuming calculation for Hessian-inverse. We propose an approximation method through transforming the problem into an optimization in the Krylov subspace, which remarkably reduce the computational complexity to speed up the training procedure. Extensive experiments conducted on the MINIST and CIFAR-10 datasets show that our adversarial learning with second-order adversarial examples outperforms other fisrt-order methods, which can improve the model robustness against a wide range of attacks.

preprint2022arXiv

Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Recently, mainstream approaches perform this task through adversarial learning, yet still suffer from two limitations. First, they mainly align marginal distribution by unsupervised cross-domain feature matching, and ignore each feature's categorical and positional information that can be exploited for conditional alignment; Second, they treat all classes as equally important for transferring cross-domain knowledge and ignore that different classes usually have different transferability. In this paper, we propose a joint adaptive detection framework (JADF) to address the above challenges. First, an end-to-end joint adversarial adaptation framework for object detection is proposed, which aligns both marginal and conditional distributions between domains without introducing any extra hyperparameter. Next, to consider the transferability of each object class, a metric for class-wise transferability assessment is proposed, which is incorporated into the JADF objective for domain adaptation. Further, an extended study from unsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation (UFDA) is conducted, where only a few unlabeled training images are available in unlabeled target domain. Extensive experiments validate that JADF is effective in both the UDA and UFDA settings, achieving significant performance gains over existing state-of-the-art cross-domain detection methods.

preprint2022arXiv

Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach

In order to improve the penetration of renewable energy resources for distribution networks, a joint planning model of distributed generations (DGs) and energy storage is proposed for an active distribution network by using a bi-level programming approach in this paper. In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices. To solve this model, an improved binary particle swarm optimization (IBPSO) algorithm based on chaos optimization is developed, and the optimal joint planning is achieved through alternating iterations between the two levels. The simulation results on the PG & E 69-bus distribution system demonstrate that the presented approach manages to reduce the planning deviation caused by the uncertainties of DG outputs and remarkably improve the voltage profile and operational economy of distribution systems.

preprint2022arXiv

Just Rank: Rethinking Evaluation with Word and Sentence Similarities

Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method. It leads models to overfit to such evaluations, negatively impacting embedding models' development. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. Further, we propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks. Extensive experiments are conducted based on 60+ models and popular datasets to certify our judgments. Finally, the practical evaluation toolkit is released for future benchmarking purposes.

preprint2022arXiv

Long term analysis of splitting methods for charged-particle dynamics

In this paper, we rigorously analyze the energy, momentum and magnetic moment behaviours of two splitting methods for solving charged-particle dynamics. The near-conservations of these invariants are given for the system under constant magnetic field or quadratic electric potential. By the approach named as backward error analysis, we derive the modified equations and modified invariants of the splitting methods and based on which, the near-conservations over long times are proved. Some numerical experiments are presented to demonstrate these long time behaviours.

preprint2022arXiv

MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress. To address the problems, we propose a novel model \textbf{MISC}, which firstly infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling. Our code and data could be found in \url{https://github.com/morecry/MISC}.

preprint2022arXiv

MOORe: Model-based Offline-to-Online Reinforcement Learning

With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy matters in safe real-world deployment. Besides, fast adaptation of the policy plays a vital role in practical online performance improvement. To tackle these challenges, we propose a simple yet efficient algorithm, Model-based Offline-to-Online Reinforcement learning (MOORe), which employs a prioritized sampling scheme that can dynamically adjust the offline and online data for smooth and efficient online adaptation of the policy. We provide a theoretical foundation for our algorithms design. Experiment results on the D4RL benchmark show that our algorithm smoothly transfers from offline to online stages while enabling sample-efficient online adaption, and also significantly outperforms existing methods.

preprint2022arXiv

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote clusters, huge challenges are faced. In this work, we introduce a general framework, Nebula-I, for collaboratively training deep learning models over remote heterogeneous clusters, the connections between which are low-bandwidth wide area networks (WANs). We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning. To balance the accuracy and communication efficiency, in Nebula-I, parameter-efficient training strategies, hybrid parallel computing methods and adaptive communication acceleration techniques are jointly applied. Meanwhile, security strategies are employed to guarantee the safety, reliability and privacy in intra-cluster computation and inter-cluster communication. Nebula-I is implemented with the PaddlePaddle deep learning framework, which can support collaborative training over heterogeneous hardware, e.g. GPU and NPU. Experiments demonstrate that the proposed framework could substantially maximize the training efficiency while preserving satisfactory NLP performance. By using Nebula-I, users can run large-scale training tasks over cloud clusters with minimum developments, and the utility of existed large pre-trained models could be further promoted. We also introduced new state-of-the-art results on cross-lingual natural language inference tasks, which are generated based upon a novel learning framework and Nebula-I.

preprint2022arXiv

Nonlinear Hall effect induced by internal Coulomb interaction and phase relaxation process in a four-terminal system with time-reversal symmetry

We numerically investigate the second-order nonlinear Hall transport properties of a four-terminal system with time-reversal symmetry and broken inversion symmetry. Within the nonequilibrium Green's function formalism, the second-order nonlinear conductances are derived, where the internal Coulomb potential in response to external voltages is explicitly included to guarantee the gauge invariance. For the system with single mirror symmetry Mx, nonlinear Hall properties are only observable in the y direction and contributed solely from the second-order nonlinear effect. From the symmetry point of view, the observed nonlinear Hall transport phenomena have one-to-one correspondence with the Berry curvature dipole induced nonlinear Hall effect semiclassically obtained for the same Hamiltonian. In addition to the nonlinear Hall effect originated from symmetries of the system, it is found that the internal Coulomb potential has the same symmetry of the four-terminal system, which gives rise to an extra nonlinear Hall response. Moreover, the phase relaxation mechanism modeled by virtual probes leads to the dephasing-induced nonlinear Hall effect.

preprint2022arXiv

Quasinormal modes in two-photon autocorrelation and the geometric-optics approximation

In this work, we study the black hole light echoes in terms of the two-photon autocorrelation and explore their connection with the quasinormal modes. It is shown that the above time-domain phenomenon can be analyzed by utilizing the well-known frequency-domain relations between the quasinormal modes and characteristic parameters of null geodesics. We found that the time-domain correlator, obtained by the inverse Fourier transform, naturally acquires the echo feature, which can be attributed to a collective effect of the asymptotic poles through a weighted summation of the squared modulus of the relevant Green's functions. Specifically, the contour integral leads to a summation taking over both the overtone index and angular momentum. Moreover, the dominant contributions to the light echoes are from those in the eikonal limit, consistent with the existing findings using the geometric-optics arguments. For the Schwarzschild black holes, we demonstrate the results numerically by considering a transient spherical light source. Also, for the Kerr spacetimes, we point out a potential difference between the resulting light echoes using the geometric-optics approach and those obtained by the black hole perturbation theory. Possible astrophysical implications of the present study are addressed.

preprint2022arXiv

Rethinking Reinforcement Learning based Logic Synthesis

Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through extensive experiments, we find out that the learned policy makes decisions independent from the circuit features (i.e., states) and yields an operator sequence that is permutation invariant to some extent in terms of operators. Based on these findings, we develop a new RL-based method that can automatically recognize critical operators and generate common operator sequences generalizable to unseen circuits. Our algorithm is verified on both the EPFL benchmark, a private dataset and a circuit at industrial scale. Experimental results demonstrate that it achieves a good balance among delay, area and runtime, and is practical for industrial usage.

preprint2022arXiv

Risk Concentration and the Mean-Expected Shortfall Criterion

Expected Shortfall (ES, also known as CVaR) is the most important coherent risk measure in finance, insurance, risk management, and engineering. Recently, Wang and Zitikis (2021) put forward four economic axioms for portfolio risk assessment and provide the first economic axiomatic foundation for the family of ES. In particular, the axiom of no reward for concentration (NRC) is arguably quite strong, which imposes an additive form of the risk measure on portfolios with a certain dependence structure. We move away from the axiom of NRC by introducing the notion of concentration aversion, which does not impose any specific form of the risk measure. It turns out that risk measures with concentration aversion are functions of ES and the expectation. Together with the other three standard axioms of monotonicity, translation invariance and lower semicontinuity, concentration aversion uniquely characterizes the family of ES. In addition, we establish an axiomatic foundation for the problem of mean-ES portfolio selection and new explicit formulas for convex and consistent risk measures. Finally, we provide an economic justification for concentration aversion via a few axioms on the attitude of a regulator towards dependence structures.

preprint2022arXiv

Semi-discretization and full-discretization with optimal accuracy for charged-particle dynamics in a strong nonuniform magnetic field

The aim of this paper is to formulate and analyze numerical discretizations of charged-particle dynamics (CPD) in a strong nonuniform magnetic field. A strategy is firstly performed for the two dimensional CPD to construct the semi-discretization and full-discretization which have optimal accuracy. This accuracy is improved in the position and in the velocity when the strength of the magnetic field becomes stronger. This is a better feature than the usual so called "uniformly accurate methods". To obtain this refined accuracy, some reformulations of the problem and two-scale exponential integrators are incorporated, and the optimal accuracy is derived from this new procedure. Then based on the strategy given for the two dimensional case, a new class of uniformly accurate methods with simple scheme is formulated for the three dimensional CPD in maximal ordering case. All the theoretical results of the accuracy are numerically illustrated by some numerical tests.

preprint2022arXiv

Single-frame characterization of ultrafast pulses with spatiotemporal orbital angular momentum

Light carrying spatiotemporal orbital angular momentum (ST-OAM) makes possible new types of optical vortices arising from transverse OAM. ST-OAM pulses exhibit novel properties during propagation, transmission, refraction, diffraction, and nonlinear conversion, attracting growing experimental and theoretical interest and studies. However, one major challenge is the lack of a simple and straightforward method for characterizing ultrafast ST-OAM pulses. Using spatially resolved spectral interferometry, we demonstrate a simple, stationary, single-frame method to quantitatively characterize ultrashort light pulses carrying ST-OAM. Using our method, the presence of an ST-OAM pulse, including its main characteristics such as topological charge numbers and OAM helicity, can be identified easily from the unique and unambiguous features directly seen on the raw data--without any need for a full analysis of the data. After processing and reconstructions, other exquisite features, including pulse dispersion and beam divergence, can also be fully characterized. Our fast characterization method allows high-throughput and quick feedback during the generation and optical alignment processes of ST-OAM pulses. It is straightforward to extend our method to single-shot measurement by using a high-speed camera that matches the pulse repetition rate. This new method can help advance the field of spatially and temporally structured light and its applications in advanced metrologies.

preprint2022arXiv

Strong Effects of Interlayer Interaction on Valence-Band Splitting in Transition Metal Dichalcogenides

Understanding the origin of valence band maxima (VBM) splitting in transition metal dichalcogenides (TMDs) is important because it governs the unique spin and valley physics in monolayer and multilayer TMDs. In this work, we present our systematic study of VBM splitting ($Δ$) in atomically thin MoS$_2$ and WS$_2$ by employing photocurrent spectroscopy as we change the temperature and the layer numbers. We found that VBM splitting in monolayer MoS$_2$ and WS$_2$ depends strongly on temperature, which contradicts the theory that spin-orbit coupling solely determines the VBM splitting in monolayer TMDs. We also found that the rate of change of VBM splitting with respect to temperature ($m=\frac{\partialΔ}{\partial T}$) is the highest for monolayer (-0.14 meV/K for MoS$_2$) and the rate decreases as the layer number increases ($m ~ 0$ meV/K for 5 layers MoS$_2$). We performed density functional theory (DFT) and the GW with Bethe-Salpeter Equation (GW-BSE) calculations to determine the electronic band structure and optical absorption for a bilayer MoS$_2$ with different interlayer separations. Our simulations agree with the experimental observations and demonstrate that the temperature dependence of VBM splitting in atomically thin monolayer and multilayer TMDs originates from the changes in the interlayer coupling strength between the neighboring layers. By studying two different types of TMDs and many different layer thicknesses, we also demonstrate that VBM splitting also depends on the layer numbers and type of transition metals. Our study will help understand the role spin-orbit coupling and interlayer interaction play in determining the VBM splitting in quantum materials and develop next-generation devices based on spin-orbit interactions.

preprint2022arXiv

SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation

Word Mover's Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good performance in sentence similarity evaluation since it does not incorporate word importance and fails to take inherent contextual and structural information in a sentence into account. An improved WMD method using the syntactic parse tree, called Syntax-aware Word Mover's Distance (SynWMD), is proposed to address these two shortcomings in this work. First, a weighted graph is built upon the word co-occurrence statistics extracted from the syntactic parse trees of sentences. The importance of each word is inferred from graph connectivities. Second, the local syntactic parsing structure of words is considered in computing the distance between words. To demonstrate the effectiveness of the proposed SynWMD, we conduct experiments on 6 textual semantic similarity (STS) datasets and 4 sentence classification datasets. Experimental results show that SynWMD achieves state-of-the-art performance on STS tasks. It also outperforms other WMD-based methods on sentence classification tasks.

preprint2022arXiv

The BINGO Project III: Optical design and optimisation of the focal plane

The BINGO telescope was designed to measure the fluctuations of the 21-cm radiation arising from the hyperfine transition of neutral hydrogen and aims to measure the Baryon Acoustic Oscillations (BAO) from such fluctuations, therefore serving as a pathfinder to future deeper intensity mapping surveys. The requirements for the Phase 1 of the projects consider a large reflector system (two 40 m-class dishes in a crossed-Dragone configuration), illuminating a focal plane with 28 horns to measure the sky with two circular polarisations in a drift scan mode to produce measurements of the radiation in intensity as well as the circular polarisation. In this paper we present the optical design for the instrument. We describe the intensity and polarisation properties of the beams and the optical arrangement of the horns in the focal plane to produce a homogeneous and well-sampled map after the end of Phase 1. Our analysis provides an optimal model for the location of the horns in the focal plane, producing a homogeneous and Nyquist sampled map after the nominal survey time. We arrive at an optimal configuration for the optical system, including the focal plane positioning and the beam behavior of the instrument. We present an estimate of the expected side lobes both for intensity and polarisation, as well as the effect of band averaging on the final side lobes. The cross polarisation leakage values for the final configuration allow us to conclude that the optical arrangement meets the requirements of the project. We conclude that the chosen optical design meets the requirements for the project in terms of polarisation purity, area coverage as well as homogeneity of coverage so that BINGO can perform a successful BAO experiment. We further conclude that the requirements on the placement and r.m.s. error on the mirrors are also achievable so that a successful experiment can be conducted.(Abridged)

preprint2022arXiv

The BINGO Project V: Further steps in Component Separation and Bispectrum Analysis

Observing the neutral hydrogen distribution across the Universe via redshifted 21cm line intensity mapping constitutes a powerful probe for cosmology. However, the redshifted 21cm signal is obscured by the foreground emission from our Galaxy and other extragalactic foregrounds. This paper addresses the capabilities of the BINGO survey to separate such signals. Specifically, this paper looks in detail at the different residuals left over by foreground components, shows that a noise-corrected spectrum is unbiased, and shows that we understand the remaining systematic residuals by analyzing nonzero contributions to the three-point function. We use the generalized needlet internal linear combination, which we apply to sky simulations of the BINGO experiment for each redshift bin of the survey. We present our recovery of the redshifted 21cm signal from sky simulations of the BINGO experiment, including foreground components. We test the recovery of the 21cm signal through the angular power spectrum at different redshifts, as well as the recovery of its non-Gaussian distribution through a bispectrum analysis. We find that non-Gaussianities from the original foreground maps can be removed down to, at least, the noise limit of the BINGO survey with such techniques. Our component separation methodology allows us to subtract the foreground contamination in the BINGO channels down to levels below the cosmological signal and the noise, and to reconstruct the 21cm power spectrum for different redshift bins without significant loss at multipoles $20 \lesssim \ell \lesssim 500$. Our bispectrum analysis yields strong tests of the level of the residual foreground contamination in the recovered 21cm signal, thereby allowing us to both optimize and validate our component separation analysis. (Abridged)

preprint2022arXiv

The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

This paper describes our submission to ICASSP 2022 Multi-channel Multi-party Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech. Front-end dereverberation and the direction-of-arrival (DOA) estimation are used to improve the accuracy of speaker diarization. Multi-channel combination and overlap detection are applied to reduce the missed speaker error. A modified DOVER-Lap is also proposed to fuse the results of different systems. We achieve the final DER of 5.79% on the Eval set and 7.23% on the Test set. For Track 2, we develop our system using the Conformer model in a joint CTC-attention architecture. Serialized output training is adopted to multi-speaker overlapped speech recognition. We propose a neural front-end module to model multi-channel audio and train the model end-to-end. Various data augmentation methods are utilized to mitigate over-fitting in the multi-channel multi-speaker E2E system. Transformer language model fusion is developed to achieve better performance. The final CER is 19.2% on the Eval set and 20.8% on the Test set.

preprint2022arXiv

Topological Mirror Symmetry of Parabolic Hitchin Systems

In this paper, we first prove the parabolic Beauvile-Narasimhan-Ramanan correspondence over an arbitrary field which generalizes the corresponding results over algebraically closed fields in [SWW22]. We use the correspondence and the p-adic integration methods developed by Groechenig- Wyss-Ziegler [GWZ20b] to prove the topological mirror symmetry for parabolic Hitchin systems on curves with arbitrary parabolic structures.

preprint2022arXiv

Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training

Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models. However, due to the large and unnecessary increase in the margin along adversarial directions, adversarial training causes heavy cross-over between natural examples and adversarial examples, which is not conducive to balancing the trade-off between robustness and natural accuracy. In this paper, we propose a novel adversarial training scheme to achieve a better trade-off between robustness and natural accuracy. It aims to learn a moderate-inclusive decision boundary, which means that the margins of natural examples under the decision boundary are moderate. We call this scheme Moderate-Margin Adversarial Training (MMAT), which generates finer-grained adversarial examples to mitigate the cross-over problem. We also take advantage of logits from a teacher model that has been well-trained to guide the learning of our model. Finally, MMAT achieves high natural accuracy and robustness under both black-box and white-box attacks. On SVHN, for example, state-of-the-art robustness and natural accuracy are achieved.

preprint2022arXiv

Transformer based Generative Adversarial Network for Liver Segmentation

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking advantage of capturing long range dependence modeling capability in signals, so called attention mechanism. In this study, we propose a new segmentation approach using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach. The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling. This mechanism provides better segmentation performance compared with traditional methods. Furthermore, we encode this generator into the GAN based architecture so that the discriminator network in the GAN can classify the credibility of the generated segmentation masks compared with the real masks coming from human (expert) annotations. This allows us to extract the high dimensional topology information in the mask for biomedical image segmentation and provide more reliable segmentation results. Our model achieved a high dice coefficient of 0.9433, recall of 0.9515, and precision of 0.9376 and outperformed other Transformer based approaches.

preprint2022arXiv

Yes, DLGM! A novel hierarchical model for hazard classification

Hazards can be exposed by HAZOP as text information, and studying their classification is of great significance to the development of industrial informatics, which is conducive to safety early warning, decision support, policy evaluation, etc. However, there is no research on this important field at present. In this paper, we propose a novel model termed DLGM via deep learning for hazard classification. Specifically, first, we leverage BERT to vectorize the hazard and treat it as a type of time series (HTS). Secondly, we build a grey model FSGM(1, 1) to model it, and get the grey guidance in the sense of the structural parameters. Finally, we design a hierarchical-feature fusion neural network (HFFNN) to investigate the HTS with grey guidance (HTSGG) from three themes, where, HFFNN is a hierarchical structure with four types of modules: two feature encoders, a gating mechanism, and a deepening mechanism. We take 18 industrial processes as application cases and launch a series of experiments. The experimental results prove that DLGM has promising aptitudes for hazard classification and that FSGM(1, 1) and HFFNN are effective. We hope our research can contribute added value and support to the daily practice in industrial safety.

preprint2021arXiv

BLOCKEYE: Hunting For DeFi Attacks on Blockchain

Decentralized finance, i.e., DeFi, has become the most popular type of application on many public blockchains (e.g., Ethereum) in recent years. Compared to the traditional finance, DeFi allows customers to flexibly participate in diverse blockchain financial services (e.g., lending, borrowing, collateralizing, exchanging etc.) via smart contracts at a relatively low cost of trust. However, the open nature of DeFi inevitably introduces a large attack surface, which is a severe threat to the security of participants funds. In this paper, we proposed BLOCKEYE, a real-time attack detection system for DeFi projects on the Ethereum blockchain. Key capabilities provided by BLOCKEYE are twofold: (1) Potentially vulnerable DeFi projects are identified based on an automatic security analysis process, which performs symbolic reasoning on the data flow of important service states, e.g., asset price, and checks whether they can be externally manipulated. (2) Then, a transaction monitor is installed offchain for a vulnerable DeFi project. Transactions sent not only to that project but other associated projects as well are collected for further security analysis. A potential attack is flagged if a violation is detected on a critical invariant configured in BLOCKEYE, e.g., Benefit is achieved within a very short time and way much bigger than the cost. We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before. A video of BLOCKEYE is available at https://youtu.be/7DjsWBLdlQU.

preprint2021arXiv

Boosting performance for software defined networks from traffic engineering perspective

Paths selection algorithms and rate adaptation objective functions are usually studied separately. In contrast, this paper evaluates some traffic engineering (TE) systems for software defined networking obtained by combining path selection techniques with average delay and load balancing, the two most popular TE objective functions. Based on TE simulation results, the best TE system suitable for software defined networks is a system where the paths are calculated using an oblivious routing model and its adaptation rate calculated using an average delay objective function. Thus, we propose the RACKE+AD system combining path sets computed using Racke's oblivious routing and traffic splitting objective function using average delay. This model outperforms current state-of-the-art models, maximizes throughput, achieves better network resource utilization, and minimizes delay. The proposed system outperformed SMORE and SWAN by 4.2% and 9.6% respectively, achieving 27% better utilization and delivering 34% more traffic with 50% less latency compared with both systems on a GEANT network.

preprint2021arXiv

CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding

Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed.

preprint2021arXiv

Dynamical scalarization in Einstein-Maxwell-dilaton theory

We study the process of fully nonlinear dynamical scalarization starting from a charged black hole or a naked singularity in asymptotically flat spacetime in the Einstein-Maxwell-dilaton theory. Initially the dilaton field is negligible compared to the gravitational and the Maxwell field. Then the dilaton field experiences an immediate growth, later it oscillates with damping amplitude and finally settles down to a finite value. For a hairy black hole develops from an original Reissner-Nordström black hole, since the dilaton oscillation and decay are almost independent of the coupling parameter, unlike the Anti-de Sitter spacetime it is not easy to distinguish the resulting hairy black hole from the original asymptotically flat charged hole. For a hairy black hole evolves from an original naked singularity, the resulting hairy black hole has rich structures. In the scalarization process, the naked singularity is soon enveloped by one outer horizon, then another horizon is developed and in the end a stable hairy black hole forms and two horizons degenerate into one to protect the singularity. The hairy black hole mass saturates exponentially in the scalarization.

preprint2021arXiv

Evolution of Anti-de Sitter black holes in Einstein-Maxwell-dilaton theory

We study the nonlinear evolution of the spherical symmetric black holes under a small neutral scalar field perturbation in Einstein-Maxwell-dilaton theory with coupling function $f(ϕ)=e^{-bϕ}$ in asymptotic anti-de Sitter spacetime. The non-minimal coupling between scalar and Maxwell fields allows the transmission of the energy from the Maxwell field to the scalar field, but also behaves as a repulsive force for the scalar. The scalar field oscillates with damping amplitude and converges to a final value by a power law. The irreducible mass of the black hole increases abruptly at initial times and then saturates to the final value exponentially. The saturating rate is twice the decaying rate of the dominant mode of the scalar. The effects of the black hole charge, the cosmological constant and the coupling parameter on the evolution are studied in detail. When the initial configuration is a naked singularity spacetime with a large charge to mass ratio, a horizon will form soon and hide the singularity.

preprint2021arXiv

GraphHop: An Enhanced Label Propagation Method for Node Classification

A scalable semi-supervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains attributes of all nodes but labels of a few nodes. The classical label propagation (LP) method and the emerging graph convolutional network (GCN) are two popular semi-supervised solutions to this problem. The LP method is not effective in modeling node attributes and labels jointly or facing a slow convergence rate on large-scale graphs. GraphHop is proposed to its shortcoming. With proper initial label vector embeddings, each iteration of GraphHop contains two steps: 1) label aggregation and 2) label update. In Step 1, each node aggregates its neighbors' label vectors obtained in the previous iteration. In Step 2, a new label vector is predicted for each node based on the label of the node itself and the aggregated label information obtained in Step 1. This iterative procedure exploits the neighborhood information and enables GraphHop to perform well in an extremely small label rate setting and scale well for very large graphs. Experimental results show that GraphHop outperforms state-of-the-art graph learning methods on a wide range of tasks (e.g., multi-label and multi-class classification on citation networks, social graphs, and commodity consumption graphs) in graphs of various sizes. Our codes are publicly available on GitHub (https://github.com/TianXieUSC/GraphHop).

preprint2021arXiv

Inductive Learning on Commonsense Knowledge Graph Completion

Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on the setting where all the entities are presented at training time. Although this setting is standard for conventional KG completion, it has limitations for CKG completion. At test time, entities in CKGs can be unseen because they may have unseen text/names and entities may be disconnected from the training graph, since CKGs are generally very sparse. Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time. We develop a novel learning framework named InductivE. Different from previous approaches, InductiveE ensures the inductive learning capability by directly computing entity embeddings from raw entity attributes/text. InductiveE consists of a free-text encoder, a graph encoder, and a KG completion decoder. Specifically, the free-text encoder first extracts the textual representation of each entity based on the pre-trained language model and word embedding. The graph encoder is a gated relational graph convolutional neural network that learns from a densified graph for more informative entity representation learning. We develop a method that densifies CKGs by adding edges among semantic-related entities and provide more supportive information for unseen entities, leading to better generalization ability of entity embedding for unseen entities. Finally, inductiveE employs Conv-TransE as the CKG completion decoder. Experimental results show that InductiveE significantly outperforms state-of-the-art baselines in both standard and inductive settings on ATOMIC and ConceptNet benchmarks. InductivE performs especially well on inductive scenarios where it achieves above 48% improvement over present methods.

preprint2021arXiv

KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling

Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, named KGBoost, adopts a modularized design and attempts to find hard negative samples so as to train a powerful classifier for missing link prediction. We conduct experiments on multiple benchmark datasets, and demonstrate that KGBoost outperforms state-of-the-art methods across most datasets. Furthermore, as compared with models trained by end-to-end optimization, KGBoost works well under the low-dimensional setting so as to allow a smaller model size.

preprint2021arXiv

Optimal convergence and long-time conservation of exponential integration for Schrödinger equations in a normal or highly oscillatory regime

In this paper, we formulate and analyse exponential integrations when applied to nonlinear Schrödinger equations in a normal or highly oscillatory regime. A kind of exponential integrators with energy preservation, optimal convergence and long time near conservations of actions, momentum and density will be formulated and analysed. To this end, we derive continuous-stage exponential integrators and show that the integrators can exactly preserve the energy of Hamiltonian systems. Three practical energy-preserving integrators are presented. It is shown that these integrators exhibit optimal convergence and have near conservations of actions, momentum and density over long times. A numerical experiment is carried out to support all the theoretical results presented in this paper. Some applications of the integrators to other kinds of ordinary/partial differential equations are also presented.

preprint2021arXiv

Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments

The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR)-enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and micro-gas turbine (MT), as links of multi-energy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory (SOT) and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. Simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a trade-off between economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the Hybrid Intelligent Algorithm in terms of both optimization results and calculation efficiency.

preprint2021arXiv

Passive underwater acoustic barcodes using Rayleigh wave resonance

A passive underwater acoustic marker is presented and its feasibility for underwater recognition, positioning, and navigation is proved by a numerical method and experimental results. These markers are composed of acrylic elastic objects designed by backscattering strong peaks associated with the subsonic Rayleigh wave resonance of a polymer target excited by a broadband pulse, and having a unique acoustic signature for a selected frequency band, akin to acoustic barcodes. Therefore, the backscattering response of markers can be regulated by changing the geometry of elastic objects. These acoustic barcodes naturally operate in a wider frequency band, and have a longer lifetime and lower cost, compared with active acoustic markers.

preprint2021arXiv

Progressive Depth Learning for Single Image Dehazing

The formulation of the hazy image is mainly dominated by the reflected lights and ambient airlight. Existing dehazing methods often ignore the depth cues and fail in distant areas where heavier haze disturbs the visibility. However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase. In turn, the good transmission estimation could facilitate the depth estimation for hazy images. In this paper, a deep end-to-end model that iteratively estimates image depths and transmission maps is proposed to perform an effective depth prediction for hazy images and improve the dehazing performance with the guidance of depth information. The image depth and transmission map are progressively refined to better restore the dehazed image. Our approach benefits from explicitly modeling the inner relationship of image depth and transmission map, which is especially effective for distant hazy areas. Extensive results on the benchmarks demonstrate that our proposed network performs favorably against the state-of-the-art dehazing methods in terms of depth estimation and haze removal.

preprint2021arXiv

Structure-preserving algorithms with uniform error bound and long-time energy conservation for highly oscillatory Hamiltonian systems

Structure-preserving algorithms and algorithms with uniform error bound have constituted two interesting classes of numerical methods. In this paper, we blend these two kinds of methods for solving nonlinear Hamiltonian systems with highly oscillatory solution, and the blended algorithms inherit and respect the advantage of each method. Two kinds of algorithms are presented to preserve the symplecticity and energy of the Hamiltonian systems, respectively. Moreover, the proposed algorithms are shown to have uniform error bound for the highly oscillatory structure. A numerical experiment is carried out to support the theoretical results established in this paper by showing the performance of the blended algorithms.

preprint2021arXiv

Study of form factors and branching ratios for $D\rightarrow S,Al\bar{ν_{l}}$ with light-cone sum rules

We systematically study the semileptonic decay process of $ D\rightarrow S,A l\bar{ν_{l}}(l=e,μ)$ by light-cone sum rules (LCSR) with chiral currents, calculate the form factors containing only the contribution of the leading twist light-cone distribution amplitudes (LCDAs). For scalar mesons $a_{0}(980)$ and $a_{0}(1450)$, we take them as $q\bar{q}$ states. For axial-vector meson, we study $a_{1}(1260)( 1^{3}p^{1})$ and $b_{1}(1235)( 1^{1}p^{1})$. Based on the results of these form factors, we further present the branching ratios of these semileptonic decay processes. The numerical results for $ D\rightarrow a_{0}(980), b_{1}(1235)l\bar{ν_{l}} $ are in good agreement with experiments and that for $ D\rightarrow a_{0}(1450)l\bar{ν_{l}}$ process are expected to be tested experimentally in the future.

preprint2021arXiv

The BINGO Project I: Baryon Acoustic Oscillations from Integrated Neutral Gas Observations

Observations of the redshifted 21-cm line of neutral hydrogen (HI) are a new and powerful window of observation that offers us the possibility to map the spatial distribution of cosmic HI and learn about cosmology. BINGO (Baryon Acoustic Oscillations [BAO] from Integrated Neutral Gas Observations) is a new unique radio telescope designed to be one of the first to probe BAO at radio frequencies. BINGO has two science goals: cosmology and astrophysics. Cosmology is the main science goal and the driver for BINGO's design and strategy. The key of BINGO is to detect the low redshift BAO to put strong constraints in the dark sector models. Given the versatility of the BINGO telescope, a secondary goal is astrophysics, where BINGO can help discover and study Fast Radio Bursts (FRB) and other transients, Galactic and extragalactic science. In this paper, we introduce the latest progress of the BINGO project, its science goals, describing the scientific potential of the project in each science and the new developments obtained by the collaboration. We introduce the BINGO project and its science goals and give a general summary of recent developments in construction, science potential and pipeline development obtained by the BINGO collaboration in the past few years. We show that BINGO will be able to obtain competitive constraints for the dark sector, and also that will allow for the discovery of several FRBs in the southern hemisphere. The capacity of BINGO in obtaining information from 21-cm is also tested in the pipeline introduced here. There is still no measurement of the BAO in radio, and studying cosmology in this new window of observations is one of the most promising advances in the field. The BINGO project is a radio telescope that has the goal to be one of the first to perform this measurement and it is currently being built in the northeast of Brazil. (Abridged)

preprint2021arXiv

The BINGO Project II: Instrument Description

The measurement of diffuse 21-cm radiation from the hyperfine transition of neutral hydrogen (HI signal) in different redshifts is an important tool for modern cosmology. However, detecting this faint signal with non-cryogenic receivers in single-dish telescopes is a challenging task. The BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas Observations) radio telescope is an instrument designed to detect baryonic acoustic oscillations (BAOs) in the cosmological HI signal, in the redshift interval $0.127 \le z \le 0.449$. This paper describes the BINGO radio telescope, including the current status of the optics, receiver, observational strategy, calibration, and the site. BINGO has been carefully designed to minimize systematics, being a transit instrument with no moving dishes and 28 horns operating in the frequency range $980 \le ν\le 1260$ MHz. Comprehensive laboratory tests were conducted for many of the BINGO subsystems and the prototypes of the receiver chain, horn, polarizer, magic tees, and transitions have been successfully tested between 2018 - 2020. The survey was designed to cover $\sim 13\%$ of the sky, with the primary mirror pointing at declination $δ=-15^{\circ}$. The telescope will see an instantaneous declination strip of $14.75^{\circ}$. The results of the prototype tests closely meet those obtained during the modeling process, suggesting BINGO will perform according to our expectations. After one year of observations with a $60\%$ duty cycle and 28 horns, BINGO should achieve an expected sensitivity of 102 $μK$ per 9.33 MHz frequency channel, one polarization, and be able to measure the HI power spectrum in a competitive time frame.

preprint2021arXiv

The BINGO Project IV: Simulations for mission performance assessment and preliminary component separation steps

The large-scale distribution of neutral hydrogen (HI) in the Universe is luminous through its 21 cm emission. The goal of the Baryon Acoustic Oscillations from Integrated Neutral Gas Observations -- BINGO -- radio telescope is to detect baryon acoustic oscillations (BAOs) at radio frequencies through 21 cm intensity mapping (IM). The telescope will span the redshift range 0.127 $< z <$ 0.449 with an instantaneous field-of-view of $14.75^{\circ} \times 6.0^{\circ}$. In this work we investigate different constructive and operational scenarios of the instrument by generating sky maps as they would be produced by the instrument. In doing this we use a set of end-to-end IM mission simulations. The maps will additionally be used to evaluate the efficiency of a component separation method (GNILC). We have simulated the kind of data that would be produced in a single-dish IM experiment such as BINGO. According to the results obtained, we have optimized the focal plane design of the telescope. In addition, the application of the GNILC method on simulated data shows that it is feasible to extract the cosmological signal across a wide range of multipoles and redshifts. The results are comparable with the standard principal component analysis method.

preprint2021arXiv

The BINGO Project VI: HI Halo Occupation Distribution and Mock Building

BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas Observations.) is a radio telescope designed to survey from 980 MHz to 1260 MHz, observe the neutral Hydrogen (HI) 21-cm line and detect BAO (Baryon Acoustic Oscillation) signal with Intensity Mapping technique. Here we present our method to generate mock maps of the 21-cm Intensity Mapping signal covering the BINGO frequency range and related test results. (Abridged)

preprint2021arXiv

The BINGO Project VII: Cosmological Forecasts from 21cm Intensity Mapping

The 21cm line of neutral hydrogen (HI) opens a new avenue in our exploration of the structure and evolution of the Universe. It provides complementary data to the current large-scale structure observations with different systematics, and thus it will be used to improve our understanding of the $Λ$CDM model. Among several radio cosmological surveys designed to measure this line, BINGO is a single-dish telescope mainly designed to detect baryon acoustic oscillations (BAOs) at low redshifts ($0.127< z<0.449$). Our goal is to assess the fiducial BINGO setup and its capabilities of constraining the cosmological parameters, and to analyze the effect of different instrument configurations. We used the Phase 1 fiducial configuration of the BINGO telescope to perform our cosmological forecasts. In addition, we investigated the impact of several instrumental setups, taking into account some instrumental systematics, and different cosmological models. Combining BINGO with Planck temperature and polarization data, the projected constraint improves from a $13\%$ and $25\%$ precision measurement at the $68\%$ confidence level with Planck only to $1\%$ and $3\%$ for the Hubble constant and the dark energy equation of state (EoS), respectively, within the wCDM model. Assuming a Chevallier-Polarski-Linder parameterization, the EoS parameters have standard deviations given by $σ_{w_0} = 0.30$ and $σ_{w_a} = 1.2$, which are improvements on the order of $30\%$ with respect to Planck alone. Also, we can access information about the HI density and bias, obtaining $\sim 8.5\%$ and $\sim 6\%$ precision, respectively, assuming they vary with redshift at three independent bins. The fiducial BINGO configuration will be able to extract significant cosmological information from the HI distribution and provide constraints competitive with current and future cosmological surveys. (Abridged)

preprint2021arXiv

The collider tests of a leptophilic scalar for the anomalous magnetic moments

We study the anomalous muon and electron magnetic moments by introducing a scalar with CP-violating Yukawa couplings to the lepton sector. By fitting these two magnetic moments with the recent experimental measurements, we find that such a leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be a possible source for the current experimental deviations from the Standard Model (SM) predictions, with $\mathcal{O}(0.1) - \mathcal{O}(1)$ Yukawa couplings. The current electron and muon EDM constraints to the general CP-violating Yukawa couplings are discussed. We propose to search such a leptophilic scalar mediated at the future high-luminosity LHC (HL-LHC) runs, as well as the high-energy lepton colliders, including the CEPC and the muon collider. Our results show that the leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be fully probed by the future experimental searches at the HL-LHC and the lepton colliders at their early stages.

preprint2021arXiv

Thermodynamics of 2+1 dimensional Coulomb-Like Black Holes from Non Linear Electrodynamics with a traceless energy momentum tensor

In this work we study thermodynamics of 2+1-dimensional static black holes with a nonlinear electric field. Besides employing the standard thermodynamic approach, we investigate the black hole thermodynamics by studying its thermodynamic geometry. We compute the Weinhold and Ruppeiner metrics and compare the thermodynamic geometry with the standard description on the black hole thermodynamics. We further consider the cosmological constant as an additional extensive thermodynamic variable. In the thermodynamic equilibrium three dimensional space, we compute the efficiency of the heat engine and show that it is possible to be built with this black hole.

preprint2021arXiv

Towards Speeding up Adversarial Training in Latent Spaces

Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable time, due to the fact that they need to generate adversarial examples in the large input space. To speed up adversarial training, we propose a novel adversarial training method that does not need to generate real adversarial examples. By adding perturbations to logits to generate Endogenous Adversarial Examples (EAEs) -- the adversarial examples in the latent space, the time consuming gradient calculation can be avoided. Extensive experiments are conducted on CIFAR-10 and ImageNet, and the results show that comparing to state-of-the-art methods, our EAE adversarial training not only shortens the training time, but also enhances the robustness of the model and has less impact on the accuracy of clean examples than the existing methods.

preprint2021arXiv

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

This paper presents a simple unsupervised visual representation learning method with a pretext task of discriminating all images in a dataset using a parametric, instance-level classifier. The overall framework is a replica of a supervised classification model, where semantic classes (e.g., dog, bird, and ship) are replaced by instance IDs. However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy. This work presents several novel techniques to handle these difficulties. First, we introduce a hybrid parallel training framework to make large-scale training feasible. Second, we present a raw-feature initialization mechanism for classification weights, which we assume offers a contrastive prior for instance discrimination and can clearly speed up converge in our experiments. Finally, we propose to smooth the labels of a few hardest classes to avoid optimizing over very similar negative pairs. While being conceptually simple, our framework achieves competitive or superior performance compared to state-of-the-art unsupervised approaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation protocol and on several downstream visual tasks, verifying that full instance classification is a strong pretraining technique for many semantic visual tasks.

preprint2020arXiv

A Modal-Space Method for Online Power System Steady-State Stability Monitoring

This paper proposes a novel approach to estimate the steady-state angle stability limit (SSASL) by using the nonlinear power system dynamic model in the modal space. Through two linear changes of coordinates and a simplification introduced by the steady-state condition, the nonlinear power system dynamic model is transformed into a number of single-machine-like power systems whose power-angle curves can be derived and used for estimating the SSASL. The proposed approach estimates the SSASL of angles at all machines and all buses without the need for manually specifying the scenario, i.e. setting sink and source areas, and also without the need for solving multiple nonlinear power flows. Case studies on 9-bus and 39-bus power systems demonstrate that the proposed approach is always able to capture the aperiodic instability in an online environment, showing promising performance in the online monitoring of the steady-state angle stability over the traditional power flow-based analysis.

preprint2020arXiv

A New Software Framework for Traffic Engineering: Path Cardinality and the Effect of Multipath on Residual Capacity

In this paper, we present a new traffic engineering (TE) software framework to analyze, configure, and optimize (with the aid of a linear programming solver) a network for service provisioning. The developed software tool is based on our new data-driven traffic engineering approach that analyzes a large volume of network configuration data generated given the user input. By analyzing the data, one can then make efficient decisions later when designing a traffic engineering solution. We focus on three well-known traffic engineering objective functions: minimum cost routing (MCR), load balancing (LB), and average delay (AD). With this new tool, one can answer numerous traffic engineering questions. For example, what are the differences among the three objective functions? What is the impact of an objective function on link utilization? How many candidate paths are enough to achieve optimality or near-optimality with respect to a specific objective. This new software tool allows us to conveniently perform various experiments and visualize the results for performance analysis. As case studies, this paper presents examples that answer the questions for two traffic engineering problems: (1) how many paths are required to obtain a solution that is within a few percent from the optimal solution and whether that number is fixed for any network size? (2) how the choice of single-path/multi-path routing affects the load in the network? For the first problem, it turns out that the number of paths needed to achieve optimality increases as the number of links in the network increases.

preprint2020arXiv

A novel combination of theoretical analysis and data-driven method for reconstruction of structural defects

Ultrasonic guided wave technology has played a significant role in the field of non-destructive testing as it employs acoustic waves that have advantages of high propagation efficiency and low energy consumption during the inspect process. However, theoretical solutions to guided wave scattering problems using assumptions such as Born approximation, have led to the poor quality of the reconstructed results. To address this issue, a novel approach to quantitative reconstruction of defects using the integration of data-driven method with the guided wave scattering analysis has been proposed in this paper. Based on the geometrical information of defects and initial results by the theoretical analysis of defect reconstructions, a deep learning neural network model is built to reveal the physical relationship between defects and the received signals. This data-driven model is then applied to quantitatively assess and characterize defect profiles in structures, reduce the inaccuracy of the theoretical modelling and eliminate the impact of noise pollution in the process of inspection. To demonstrate advantages of the developed approach to reconstructions of defects with complex profiles, numerical examples including basic defect profiles and a defect with the noisy fringe have been examined. Results show that this approach has greater accuracy for reconstruction of defects in structures as compared with the analytical method and provides a valuable insight into the development of artificial intelligence-assisted inspection systems with high accuracy and efficiency in the field of non-destructive testing.

preprint2020arXiv

Accelerating linear solvers for Stokes problems with C++ metaprogramming

The efficient solution of large sparse saddle point systems is very important in computational fluid mechanics. The discontinuous Galerkin finite element methods have become increasingly popular for incompressible flow problems but their application is limited due to high computational cost. We describe the C++ programming techniques that may help to accelerate linear solvers for such problems. The approach is based on the policy-based design pattern and partial template specialization, and is implemented in the open source AMGCL library. The efficiency is demonstrated with the example of accelerating an iterative solver of a discontinuous Galerkin finite element method for the Stokes problem. The implementation allows selecting algorithmic components of the solver by adjusting template parameters without any changes to the codebase. It is possible to switch the system matrix to use small statically sized blocks to store the nonzero values, or use a mixed precision solution, which results in up to 4 times speedup, and reduces the memory footprint of the algorithm by about 40\%. We evaluate both monolithic and composite preconditioning strategies for the 3 benchmark problems. The performance of the proposed solution is compared with a multithreaded direct Pardiso solver and a parallel iterative PETSc solver.

preprint2020arXiv

Acoustic black hole in Schwarzschild spacetime: quasi-normal modes, analogous Hawking radiation and shadows

Various properties of acoustic black holes constructed in Minkowski spacetime have been widely studied in the past decades. Recently the acoustic black holes in general spacetime were proposed . In this paper, we first investigate the basic characteristics of `curved' acoustic black hole in Schwarzschild spacetime, including the quasi-normal modes, grey-body factor and analogous Hawking radiation. We find that the signal of quasi-normal mode is weaker than that of Schwarzschild black hole. Moreover, as the tuning parameter increases, both the positive real part and negative imaginal part of the quasi-normal frequency approach to the horizonal axis, but they will not change sign. This means that all the perturbations could die off and the system is stable under those perturbations. Since the larger tuning parameter suppresses the effective potential barrier, so it enhances the grey-body factor. The energy emission rate of Hawking radiation does not monotonically increase of the tuning parameter because of the non-monotonicity of the Hawking temperature. Finally, as a first attempt, we study the acoustic black hole shadow. The radius of acoustic shadow becomes larger as the tuning parameter increases, because both the related acoustic horizon and the acoustic sphere become larger. Our studies could help us to further understand the near horizon geometrical features of the black hole. We also expect that our observations could be detected experimentally in the near future.

preprint2020arXiv

Anapole mediated giant photothermal nonlinearity in nanostructured silicon

Featured with a plethora of electric and magnetic Mie resonances, high index dielectric nanostructures offer a versatile platform to concentrate light-matter interactions at the nanoscale. By integrating unique features of far-field scattering control and near-field concentration from radiationless anapole states, here, we demonstrate a giant photothermal nonlinearity in single subwavelength-sized silicon nanodisks. The nanoscale energy concentration and consequent near-field enhancements mediated by the anapole mode yield a reversible nonlinear scattering with a large modulation depth and a broad dynamic range, unveiling a record-high nonlinear index change up to 0.5 at mild incident light intensities on the order of MW/cm2. The observed photothermal nonlinearity showcases three orders of magnitude enhancement compared with that of unstructured bulk silicon, as well as nearly one order of magnitude higher than that through the radiative electric dipolar mode. Such nonlinear scattering can empower distinctive point spread functions in confocal reflectance imaging, offering the potential for far-field localization of nanostructured Si with an accuracy approaching 40 nm. Our findings shed new light on active silicon photonics based on optical anapoles.

preprint2020arXiv

Attention-based Transducer for Online Speech Recognition

Recent studies reveal the potential of recurrent neural network transducer (RNN-T) for end-to-end (E2E) speech recognition. Among some most popular E2E systems including RNN-T, Attention Encoder-Decoder (AED), and Connectionist Temporal Classification (CTC), RNN-T has some clear advantages given that it supports streaming recognition and does not have frame-independency assumption. Although significant progresses have been made for RNN-T research, it is still facing performance challenges in terms of training speed and accuracy. We propose attention-based transducer with modification over RNN-T in two aspects. First, we introduce chunk-wise attention in the joint network. Second, self-attention is introduced in the encoder. Our proposed model outperforms RNN-T for both training speed and accuracy. For training, we achieves over 1.7x speedup. With 500 hours LAIX non-native English training data, attention-based transducer yields ~10.6% WER reduction over baseline RNN-T. Trained with full set of over 10K hours data, our final system achieves ~5.5% WER reduction over that trained with the best Kaldi TDNN-f recipe. After 8-bit weight quantization without WER degradation, RTF and latency drop to 0.34~0.36 and 268~409 milliseconds respectively on a single CPU core of a production server.

preprint2020arXiv

Centrality dependence of multiplicity fluctuations from a hydrodynamical approach

As one of the possible signals for the whereabouts of the critical point on the QCD phase diagram, recently, the multiplicity fluctuations in heavy-ion collisions have aroused much attention. It is a crucial observable of the Beam Energy Scan program of the Relativistic Heavy Ion Collider. In this work, we investigate the centrality dependence of the multiplicity fluctuations regarding the recent measurements from STAR Collaboration. By employing a hydrodynamical approach, the present study is dedicated to the noncritical aspects of the phenomenon. To be specific, in addition to the thermal fluctuations, finite volume corrections, and resonance decay at the freeze-out surface, the model is focused on the properties of the hydrodynamic expansion of the system and the event-by-event initial fluctuations. It is understood that the real signal of the critical point can only be obtained after appropriately subtracting the background, the latter is investigated in the present work. Besides the experimental data, our results are also compared to those of the hadronic resonance gas, as well as transport models.

preprint2020arXiv

Creation of Synthetic Networked PMU Data: A Generative Adversarial Network Approach

This paper introduces a machine learning-based approach to synthetically creating realistic phasor measurement unit (PMU) data streams of multiple transient types. In contrast to the existing literature of transient simulation-based data generation methods, we propose a generative adversarial network (GAN) based approach to learning directly from the historical data and simultaneously reproduce multiple PMU data streams. The synthetic PMU data streams reflect meaningful dynamic characteristics which observe first principles such as Kirchhoff's laws. The efficacy of this approach is demonstrated by numerical studies on the IEEE 39-bus system. We validate the fidelity and flexibility of the synthetic data via statistical resemblance and modal analysis approaches. Finally we illustrate a practical application scenario for the usage of the synthetic PMU data, i.e. leverage the synthetic data to improve the performance of the event classification algorithms.

preprint2020arXiv

Design-controlled Synthesis of IrO$_2$ sub-monolayers on Au Nanodendrites: Marrying Plasmonic and Electrocatalytic Properties

We develop herein plasmonic-catalytic Au-IrO$_2$ nanostructures with a morphology optimized for efficient light harvesting and catalytic surface area; the nanoparticles have a dendritic morphology, with closely spaced Au branches all partially covered by an ultrathin (1 nm) IrO$_2$ shell. This nanoparticle architecture optimizes optical features due to the interactions of closely spaced plasmonic branches forming electromagnetic hot spots, and the ultra-thin IrO$_2$ layer maximizes efficient use of this expensive catalyst. This concept was evaluated towards the enhancement of the electrocatalytic performances towards the oxygen evolution reaction (OER) as a model transformation. The OER can play a central role in meeting future energy demands but the performance of conventional electrocatalysts in this reaction is limited by the sluggish OER kinetics. We demonstrate an improvement of the OER performance for one of the most active OER catalysts, IrO$_2$, by harvesting plasmonic effects from visible light illumination in multimetallic nanoparticles. We find that the OER activity for the Au-IrO$_2$ nanodendrites can be improved under LSPR excitation, matching best properties reported in the literature. Our simulations and electrocatalytic data demonstrate that the enhancement in OER activities can be attributed to an electronic interaction between Au and IrO$_2$ and to the activation of Ir-O bonds by LSPR excited hot holes, leading to a change in the reaction mechanism (rate-determinant step) under visible light illumination.

preprint2020arXiv

Echoes from phantom wormholes

We study the time evolution of the test scalar and electromagnetic fields perturbations in configurations of phantom wormholes surrounded by dark energy with state parameter $ω< -1$. We observe obvious signals of echoes reflecting wormholes properties and disclose the physical reasons behind such phenomena. In particular, we find that the dark energy equation of state has a clear imprint in echoes in wave perturbations. When $ω$ approaches the phantom divide $ω=-1$ from below, the delay time of echoes becomes longer. The echo of gravitational wave is likely to be detected in the near future, the signature of the dark energy equation of state in the echo spectrum can serve as a local measurement of the dark energy.

preprint2020arXiv

Efficient Sentence Embedding via Semantic Subspace Analysis

A novel sentence embedding method built upon semantic subspace analysis, called semantic subspace sentence embedding (S3E), is proposed in this work. Given the fact that word embeddings can capture semantic relationship while semantically similar words tend to form semantic groups in a high-dimensional embedding space, we develop a sentence representation scheme by analyzing semantic subspaces of its constituent words. Specifically, we construct a sentence model from two aspects. First, we represent words that lie in the same semantic group using the intra-group descriptor. Second, we characterize the interaction between multiple semantic groups with the inter-group descriptor. The proposed S3E method is evaluated on both textual similarity tasks and supervised tasks. Experimental results show that it offers comparable or better performance than the state-of-the-art. The complexity of our S3E method is also much lower than other parameterized models.

preprint2020arXiv

Error estimates of some splitting schemes for charged-particle dynamics under strong magnetic field

In this work, we consider the error estimates of some splitting schemes for the charged-particle dynamics under a strong magnetic field. We first propose a novel energy-preserving splitting scheme with computational cost per step independent from the strength of the magnetic field. Then under the maximal ordering scaling case, we establish for the scheme and in fact for a class of Lie-Trotter type splitting schemes, a uniform (in the strength of the magnetic field) and optimal error bound in the position and in the velocity parallel to the magnetic field. For the general strong magnetic field case, the modulated Fourier expansions of the exact and the numerical solutions are constructed to obtain a convergence result. Numerical experiments are presented to illustrate the error and energy behaviour of the splitting schemes.

preprint2020arXiv

Explicit symplectic adapted exponential integrators for charged-particle dynamics in a strong and constant magnetic field

This paper studies explicit symplectic adapted exponential integrators for solving charged-particle dynamics in a strong and constant magnetic field. We first formulate the scheme of adapted exponential integrators and then derive its symplecticity conditions. Based on the symplecticity conditions, we propose five practical explicit symplectic adapted exponential integrators. Two numerical experiments are carried out and the numerical results demonstrate the remarkable numerical behavior of the new methods.

preprint2020arXiv

Feature-Driven Super-Resolution for Object Detection

Although some convolutional neural networks (CNNs) based super-resolution (SR) algorithms yield good visual performances on single images recently. Most of them focus on perfect perceptual quality but ignore specific needs of subsequent detection task. This paper proposes a simple but powerful feature-driven super-resolution (FDSR) to improve the detection performance of low-resolution (LR) images. First, the proposed method uses feature-domain prior which extracts from an existing detector backbone to guide the HR image reconstruction. Then, with the aligned features, FDSR update SR parameters for better detection performance. Comparing with some state-of-the-art SR algorithms with 4$\times$ scale factor, FDSR outperforms the detection performance mAP on MS COCO validation, VOC2007 databases with good generalization to other detection networks.

preprint2020arXiv

Forecasting Interacting Vacuum-Energy Models using Gravitational Waves

The physics of the dark sector has remained one of the controversial areas of modern cosmology at present and hence it naturally attracts massive attention to the scientific community. With the developments of the astronomical data, the physics of the dark sector is becoming much more transparent than it was some twenty years back. The detection of gravitational waves (GWs) has now opened a cluster of possibilities in the cosmological regime. Being motivated by the detection of GWs and its possible impact on the physics of dark matter and dark energy, in this work we focus on the interacting dark energy models. Assuming the simplest possibility in which the vacuum energy with equation-of-state $w_x =-1$ is allowed to interact with the pressureless dark matter, we have extracted the constraints of the cosmological parameters. We find that the addition of the GWs data to the CMB measurements significantly improves up to a factor 4 of the parameters space, and up to a factor 2 for the full combination of current cosmological datasets, namely CMB+BAO+Pantheon+RSD+R16+CC+WL. The most affected parameters by the inclusion of the GWs are $Ω_ch^2$, $θ_{MC}$, $ξ$, and the derived parameters $Ω_{m0}$, $σ_8$ and $H_0$.

preprint2020arXiv

From Trend Analysis to Virtual World System Design Requirement Satisfaction Study

Virtual worlds have become global platforms connecting millions of people and containing various technologies. The development of technology, shift of market value, and change of user preference shape the features of virtual worlds. In this paper, we first study the new features of virtual worlds and emergent requirements of system development through trend analysis. Based on the trend analysis, we constructed the new design requirement space. We then discuss the requirement satisfaction of existing virtual world system architectures and highlight their limitations through a literature survey. The comparison of existing system architectures sheds some light on future virtual world system development to match the changing trends of the user market. At the end of this study, we briefly introduce our ongoing study, a new architecture, called Virtual Net, and discuss its possibility in requirement satisfaction and new research challenges.

preprint2020arXiv

Full analytical formulas for frequency response of space-based gravitational wave detectors

The discovery of gravitational waves, which are ripples of space-time itself, opened a new window to test general relativity, because it predicts that there are only plus and cross polarizations for gravitational waves. For alternative theories of gravity, there may be up to six polarizations. The measurement of the polarization is one of the major scientific goals for future gravitational wave detectors. To evaluate the capability of the detector, we need to use the frequency dependent response functions averaged over the source direction and polarization angle. We derive the full analytical formulas of the averaged response functions for all six possible polarizations and present their asymptotic behaviors based on these analytical formulas. Compared with the numerical simulation, the full analytical formulas are more efficient and valid for any equal-arm interferometric gravitational wave detector without optical cavities in the arms and for a time-delay-interferometry Michelson combination.

preprint2020arXiv

Generic Fibers of Parahoric Hitchin Systems

In this paper, we talk about parahoric Hitchin systems over smooth projective curves with structure group a semisimple simply connected group. We describe the geometry of generic fibers of parahoric Hitchin fibrations using root stacks. We work over an algebraically closed field with a mild assumption of the characteristic. All of these can be treated as a generalization of GLn case in [SWW19]

preprint2020arXiv

Graph Structured Network for Image-Text Matching

Image-text matching has received growing interest since it bridges vision and language. The key challenge lies in how to learn correspondence between image and text. Existing works learn coarse correspondence based on object co-occurrence statistics, while failing to learn fine-grained phrase correspondence. In this paper, we present a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence. The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase. This is achieved by node-level matching and structure-level matching. The node-level matching associates each node with its relevant nodes from another modality, where the node can be object, relation or attribute. The associated nodes then jointly infer fine-grained correspondence by fusing neighborhood associations at structure-level matching. Comprehensive experiments show that GSMN outperforms state-of-the-art methods on benchmarks, with relative Recall@1 improvements of nearly 7% and 2% on Flickr30K and MSCOCO, respectively. Code will be released at: https://github.com/CrossmodalGroup/GSMN.

preprint2020arXiv

Great Chiral Fluorescence from Optical Duality Silver Nanostructures Enabled by 3D Laser Printing

Featured by prominent flexibility and fidelity in producing sophisticated stereoscopic structures transdimensionally, three-dimensional (3D) laser printing technique has vastly extended the toolkit for delivering diverse functional devices. Yet chiral nanoemitters heavily resorting to artificial structures that manifest efficient emission and tightly confined light-mater interactions simultaneously remains alluring but dauntingly challenging for this technique at this moment. In this work, we assert the chiral photoluminescence is implemented from silver nanostructures of optical duality in one go via a twofold three-dimensional laser printing scheme. Such laser printing protocol allows the highly desired duality by simultaneously producing uniformly distributed fluorescent silver nanoclusters and aggregated plasmonic silver nanoparticles to tightly confine chiral interactions at the nanoscale. A helical emitter of 550 nm-helix-diameter as fabricated has seen a record-high luminescence anisotropic factor with the absolute value up to 0.58, which is two orders of magnitude greater than fluorescent chiral silver clusters. This method holds great promise for future versatile applications in chiroptical nanodevices.

preprint2020arXiv

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter

With the rapid development of mobile Internet technology and the widespread use of mobile devices, it becomes much easier for people to express their opinions on social media. The openness and convenience of social media platforms provide a free expression for people but also cause new social problems. The widespread of false rumors on social media can bring about the panic of the public and damage personal reputation, which makes rumor automatic detection technology become particularly necessary. The majority of existing methods for rumor detection focus on mining effective features from text contents, user profiles, and patterns of propagation. Nevertheless, these methods do not take full advantage of global semantic relations of the text contents, which characterize the semantic commonality of rumors as a key factor for detecting rumors. In this paper, we construct a tweet-word-user heterogeneous graph based on the text contents and the source tweet propagations of rumors. A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection. Experiments on real-world Twitter data demonstrate the superiority of the proposed approach, which also has a comparable ability to detect rumors at a very early stage.

preprint2020arXiv

Holographic Principle and the Second Law in Stephani Cosmology Revisited

We show in a model-independent way that the inhomogeneous cosmological class II Stephani model fulfills both the the cosmological holographic principle, and that the entropy is increasing with time. By this we mean the result does not depend on any assumption on the time evolution of the scale factor, or on the matter content of the Universe, we also do not need to use the numerical values of the cosmological parameters, which are inferred in the framework of the usual homogeneous Friedmann model. Therefore our analysis is not affected by the tension of the current estimates of the Hubble parameter, and does not rely on any model-dependent assumption of the entropy amount at the present epoch. Our analysis allows us to set an upper limit for the inhomogeneity parameter of the model, an upper bound for the size that this type of universe can reach during the time evolution, a lower bound for the entropy abundance, and an estimate of the present day value of the deceleration parameter.

preprint2020arXiv

Hydrodynamic results on multiplicity fluctuations in heavy-ion collisions

Multiplicity fluctuations are one of the most crucial observables in the Beam Energy Scan program of the Relativistic Heavy Ion Collider. It is understood that they can be utilized to probe the whereabouts of the critical point on the phase diagram of the QCD matter. However, a significant portion of these fluctuations is, apart from that related to the QCD phase transition, attributed to the other origins, which we refer to as "noncritical" ones. The present study is dedicated to the noncritical aspects of the multiplicity fluctuations in heavy-ion collisions. In particular, we focus on those of dynamical origin, such as the hydrodynamic expansion of the system and the event-by-event initial fluctuations, in addition to the usual thermal fluctuations, finite volume corrections, and resonance decay at the freeze-out surface. The obtained results are compared to those of the hadronic resonance gas model as well as to the experimental data.

preprint2020arXiv

Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain adaptation is limited. Previous work tackles the problem in a transductive manner, by assuming access to the full set of test data, which is too restrictive for many real-world applications. In this paper, we set out to tackle this issue by introducing a inductive framework, DaFeC, to improve Domain adaptation performance for Few-shot classification via Clustering. We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner. The generated pseudo-labeled data and the labeled source-domain data are used as supervision to update the parameters of the few-shot classifier. In order to derive high-quality pseudo labels, we propose a Clustering Promotion Mechanism, to learn better features for the target domain via Similarity Entropy Minimization and Adversarial Distribution Alignment, which are combined with a Cosine Annealing Strategy. Experiments are performed on the FewRel 2.0 dataset. Our approach outperforms previous work with absolute gains (in classification accuracy) of 4.95%, 9.55%, 3.99% and 11.62%, respectively, under four few-shot settings.

preprint2020arXiv

InsteaDMatic: Towards cross-platform automated continuous rotation electron diffraction

A DigitalMicrograph script InsteaDMatic has been developed to facilitate rapid automated continuous rotation electron diffraction (cRED) data acquisition. The script coordinates microscope functions, such as stage rotation, camera functions relevant for data collection, and stores the experiment metadata. The script is compatible with any microscope that can be controlled by DigitalMicrograph and has been tested on both JEOL and Thermo Fisher Scientific microscopes. A proof-of-concept has been performed through employing InsteaDMatic for data collection and structure determination of a ZSM-5 zeolite. The influence of illumination settings and electron dose rate on the quality of diffraction data, unit cell determination and structure solution has been investigated in order to optimize the data acquisition procedure.

preprint2020arXiv

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior work typically solves this task in the extract-then-classify or unified labeling manner. However, these methods either suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first decompose the joint extraction task into two interrelated subtasks, namely HE extraction and TER extraction. The former subtask is to distinguish all head-entities that may be involved with target relations, and the latter is to identify corresponding tail-entities and relations for each extracted head-entity. Next, these two subtasks are further deconstructed into several sequence labeling problems based on our proposed span-based tagging scheme, which are conveniently solved by a hierarchical boundary tagger and a multi-span decoding algorithm. Owing to the reasonable decomposition strategy, our model can fully capture the semantic interdependency between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that our method outperforms previous work by 5.2%, 5.9% and 21.5% (F1 score), achieving a new state-of-the-art on three public datasets

preprint2020arXiv

MeshODE: A Robust and Scalable Framework for Mesh Deformation

We present MeshODE, a scalable and robust framework for pairwise CAD model deformation without prespecified correspondences. Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm. We address two challenges in this problem, namely the design of a powerful deformation function and obtaining a feature-preserving CAD deformation. While traditional deformation directly optimizes for the coordinates of the mesh vertices or the vertices of a control cage, we introduce a deep bijective mapping that utilizes a flow model parameterized as a neural network. Our function has the capacity to handle complex deformations, produces deformations that are guaranteed free of self-intersections, and requires low rigidity constraining for geometry preservation, which leads to a better fitting quality compared with existing methods. It additionally enables continuous deformation between two arbitrary shapes without supervision for intermediate shapes. Furthermore, we propose a robust preprocessing pipeline for raw CAD meshes using feature-aware subdivision and a uniform graph template representation to address artifacts in raw CAD models including self-intersections, irregular triangles, topologically disconnected components, non-manifold edges, and nonuniformly distributed vertices. This facilitates a fast deformation optimization process that preserves global and local details. Our code is publicly available.

preprint2020arXiv

Modeling Discourse Structure for Document-level Neural Machine Translation

Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN.

preprint2020arXiv

New probe of gravity: strongly lensed gravitational wave multi-messenger approach

Strong gravitational lensing by galaxies provides us with a unique opportunity to understand the nature of gravity on galactic and extra-galactic scales. In this paper, we propose a new multimessenger approach using data from both gravitational wave (GW) and the corresponding electromagnetic (EM) counterpart to infer the constraint of the modified gravity (MG) theory denoted by the scale dependent phenomenological parameter. To demonstrate the robustness of this approach, we calculate the time-delay predictions by choosing various values of the phenomenological parameters and then compare them with that from the general relativity (GR). For the third generation ground-based GW observatory, with one typical strongly lensed GW+EM event, and assuming that the dominated error from the stellar velocity dispersions is 5\%, the GW time-delay data can distinguish an 18\% MG effect on a scale of tens of kiloparsecs with a $68\%$ confidence level. Assuming GR and a Singular Isothermal Sphere mass model, there exists a simplified consistency relationship between time-delay and imaging data. This relationship does not require for the velocity dispersion measurement, and hence can avoid major uncertainties. By using this relationship, the multimessenger approach is able to distinguish an $8\%$ MG effect. Our results show that the GW multimessenger approach can play an important role in revealing the nature of gravity on the galactic and extra-galactic scales.

preprint2020arXiv

No static regular black holes in Einstein-complex-scalar-Gauss-Bonnet gravity

In this brief report, we investigate the existence of 4-dimensional static spherically symmetric black holes (BHs) in the Einstein-complex-scalar-Gauss-Bonnet (EcsGB) gravity with an arbitrary potential $V(ϕ)$ and a coupling $f(ϕ)$ between the scalar field $ϕ$ and the Gauss-Bonnet (GB) term. We find that static regular BH solutions with complex scalar hairs do not exist. This conclusion does not depend on the coupling between the GB term and the scalar field, nor on the scalar potential $V(ϕ)$ and the presence of a cosmological constant $Λ$ (which can be either positive or negative), as longer as the scalar field remains complex and is regular across the horizon.

preprint2020arXiv

Object picture of scalar field perturbation on Kerr black hole in scalar-Einstein-Gauss-Bonnet theory

Scalar perturbations around the Kerr black hole in scalar-Einstein-Gauss-Bonnet (sEGB) theory are studied in the time domain. To overcome the "outer boundary problem" that usually encountered in traditional numerical calculations, we apply the hyperboloidal compactification technique to perform a $(2+1)$-dimensional simulation aiming to obtain a precise object picture of the wave propagation under the scalar field perturbation. We find that the big enough coupling constant between the scalar field and the Gauss-Bonnet curvature is responsible to destroy the original Kerr black hole. The breakdown of the Kerr spacetime happens earlier and the instability becomes more violent when the coupling becomes stronger. We further present object confirmations on the special case for the negative coupling where there exists a minimum rotation and below which the instability can never happen no matter how strong the coupling is. We also illustrate the fine structure property in the quasinormal ringing frequency once there is the coupling, and present the characteristic imprint of the sEGB theory. We expect that such a fine structure can be detected in the future gravitational wave observation to test the sEGB theory.

preprint2020arXiv

On nonlinearity in hydrodynamic response to the initial geometry in relativistic heavy-ion collisions

In the context of event-by-event hydrodynamic description, we analyze the implications of two models characterized by distinct initial conditions. The initial energy density of the first model adopts a Gaussian-type distribution, while those of the second one are features by high energy peripheral tubes. We calibrate the initial conditions of both models so that their initial probability distribution of eccentricity are mostly identical. Subsequently, the resultant scaled probability distributions of collective flow and the correlations between flow harmonic and eccentricity coefficients are investigated. Besides, the calculations are carried out for particle correlations regarding the symmetric cumulant, mixed harmonics, and nonlinear response coefficients. Although the resultant two-particle correlations possess similar shapes, numerical calculations indicate a subtle difference between the two models. To be specific, the difference resides in more detailed observables such as the probability distributions of elliptic flow as well as Pearson correlation coefficient regarding higher-order harmonics. We discuss several essential aspects concerning the linearity and nonlinearity between initial eccentricities and final state anisotropies. Further implications are addressed.

preprint2020arXiv

Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks

Deep Convolutional Neural Networks (CNNs) have been widely used in image classification tasks, but the process of designing CNN architectures is very complex, so Neural Architecture Search (NAS), automatically searching for optimal CNN architectures, has attracted more and more research interests. However, the computational cost of NAS is often too high to apply NAS on real-life applications. In this paper, an efficient particle swarm optimisation method named EPSOCNN is proposed to evolve CNN architectures inspired by the idea of transfer learning. EPSOCNN successfully reduces the computation cost by minimising the search space to a single block and utilising a small subset of the training set to evaluate CNNs during evolutionary process. Meanwhile, EPSOCNN also keeps very competitive classification accuracy by stacking the evolved block multiple times to fit the whole dataset. The proposed EPSOCNN algorithm is evaluated on CIFAR-10 dataset and compared with 13 peer competitors comprised of deep CNNs crafted by hand, learned by reinforcement learning methods and evolved by evolutionary computation approaches, which shows very promising results by outperforming all of the peer competitors with regard to the classification accuracy, number of parameters and the computational cost.

preprint2020arXiv

SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models

Sentence embedding is an important research topic in natural language processing (NLP) since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word representation, called BERT, achieves the state-of-the-art performance in quite a few NLP tasks. Yet, it is an open problem to generate a high quality sentence representation from BERT-based word models. It was shown in previous study that different layers of BERT capture different linguistic properties. This allows us to fusion information across layers to find better sentence representation. In this work, we study the layer-wise pattern of the word representation of deep contextualized models. Then, we propose a new sentence embedding method by dissecting BERT-based word models through geometric analysis of the space spanned by the word representation. It is called the SBERT-WK method. No further training is required in SBERT-WK. We evaluate SBERT-WK on semantic textual similarity and downstream supervised tasks. Furthermore, ten sentence-level probing tasks are presented for detailed linguistic analysis. Experiments show that SBERT-WK achieves the state-of-the-art performance. Our codes are publicly available.

preprint2020arXiv

Searching for the Shortest Path to the Point of Voltage Collapse on the Algebraic Manifold

Voltage instability is one of the main causes of power system blackouts. Emerging technologies such as renewable energy integration, distributed energy resources and demand responses may introduce significant uncertainties in analyzing of system-wide voltage stability. This paper starts with summarizing different known voltage instability mechanisms, and then focuses on a class of voltage instability which is induced by the singular surface of the algebraic manifold. We argue and demonstrate that this class can include both dynamic and static voltage instabilities. To determine the minimum distance to the point of voltage collapse, a new formulation is proposed on the algebraic manifold. This formulation is further converted into an optimal control framework for identifying the path with minimum distance on the manifold. Comprehensive numerical studies are conducted on some manifolds of different power system test cases and demonstrate that the proposed method yields candidates for the local shortest paths to the singular surface on the manifold for both the dynamic model and the static model. Simulations show that the proposed method can identify shorter paths on the manifold than the paths associated with the minimum Euclidean distances. Furthermore, the proposed method always locates the right path ending at the correct singular surface which is responsible for the voltage instability; while the Euclidean distance formulation can mistakenly find solutions on the wrong singular surface. A broad range of potential applications using the proposed method are also discussed.

preprint2020arXiv

Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification

Deep convolutional neural networks have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fast depth growth and the increasingly complex topologies of convolutional neural networks. As a result, neural architecture search has emerged to automatically design convolutional neural networks that outperform handcrafted counterparts. However, the computational cost is immense, e.g. 22,400 GPU-days and 2,000 GPU-days for two outstanding neural architecture search works named NAS and NASNet, respectively, which motivates this work. A new effective and efficient surrogate-assisted particle swarm optimisation algorithm is proposed to automatically evolve convolutional neural networks. This is achieved by proposing a novel surrogate model, a new method of creating a surrogate dataset and a new encoding strategy to encode variable-length blocks of convolutional neural networks, all of which are integrated into a particle swarm optimisation algorithm to form the proposed method. The proposed method shows its effectiveness by achieving competitive error rates of 3.49% on the CIFAR-10 dataset, 18.49% on the CIFAR-100 dataset, and 1.82% on the SVHN dataset. The convolutional neural network blocks are efficiently learned by the proposed method from CIFAR-10 within 3 GPU-days due to the acceleration achieved by the surrogate model and the surrogate dataset to avoid the training of 80.1% of convolutional neural network blocks represented by the particles. Without any further search, the evolved blocks from CIFAR-10 can be successfully transferred to CIFAR-100 and SVHN, which exhibits the transferability of the block learned by the proposed method.

preprint2020arXiv

TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples

Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation.Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem.The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples. If it is used for adversarial training, the DNNs can be effectively regularized and the defects of the model can be improved.

preprint2020arXiv

TEAM: We Need More Powerful Adversarial Examples for DNNs

Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassification of DNNs easily. To overcome this challenge, many defensive methods are proposed. Indeed, a powerful adversarial example is a key benchmark to measure these defensive mechanisms. In this paper, we propose a novel method (TEAM, Taylor Expansion-Based Adversarial Methods) to generate more powerful adversarial examples than previous methods. The main idea is to craft adversarial examples by minimizing the confidence of the ground-truth class under untargeted attacks or maximizing the confidence of the target class under targeted attacks. Specifically, we define the new objective functions that approximate DNNs by using the second-order Taylor expansion within a tiny neighborhood of the input. Then the Lagrangian multiplier method is used to obtain the optimize perturbations for these objective functions. To decrease the amount of computation, we further introduce the Gauss-Newton (GN) method to speed it up. Finally, the experimental result shows that our method can reliably produce adversarial examples with 100% attack success rate (ASR) while only by smaller perturbations. In addition, the adversarial example generated with our method can defeat defensive distillation based on gradient masking.

preprint2020arXiv

The Horizon of the McVittie Black Hole: On the Role of the Cosmic Fluid Modeling

In this paper, we investigate the existence and time evolution of the cosmological and event horizons in a McVittie universe whose expansion is driven by the Redlich-Kwong, (Modified) Berthelot, Dieterici, and Peng-Robinson fluids, respectively. The equations of state of these fluids are rich enough to account for both exotic and regular, as well as ideal and non-ideal matter contents of the universe. We show that the cosmological horizon is expanding, while the event horizon is shrinking along the cosmic time evolution. The former achieves larger size for regular types of matter, contrary to the latter. The strength of interactions within the cosmic fluid are shown to play a more important role in affecting the evolution of the event horizon, rather than of the cosmological horizon in the case of a singularity-free universe. While the cosmological horizon always exists during the time evolution, the event horizon can exist only when a certain relationship between the Hawking-Hayward quasi-local mass and the Hubble function is fulfilled. In this manner, we can study the role played by the large-scale physics (cosmic evolution) on the local scale physics (evolution of a black hole).

preprint2020arXiv

The parameter-free Finger-Of-God model and its application to 21cm intensity mapping

Using the galaxy catalog built from ELUCID N-body simulation and the semi-analytical galaxy formation model, we have built a mock HI intensity mapping map. We have implemented the Finger-of-God (FoG) effect in the map by considering the galaxy HI gas velocity dispersion. By comparing the HI power spectrum in the redshift space with the measurement from IllustrisTNG simulation, we have found that such FoG effect can explain the discrepancy between current mock map built from N-body simulation and Illustris TNG simulation. Then we built a parameter-free FoG model and a shot-noise model to calculate the HI power spectrum. We found that our model can accurately fit both the monopole and quadrupole moments of the HI matter power spectrum. Our method of building the mock HI intensity map and the parameter-free FoG model will be widely useful for the up-coming 21cm intensity mapping experiments, such as CHIME, Tianlai, BINGO, FAST and SKA. It is also crucial for us to study the non-linear effects in 21cm intensity mapping.

preprint2020arXiv

The TianQin project: current progress on science and technology

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about $10^5~{\rm km}$. The satellites will form a equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4}~{\rm Hz}$ and $1~{\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe or exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new generation $17~{\rm cm}$ single-body hollow corner-cube retro-reflector which has been launched with the QueQiao satellite on 21 May 2018; a new laser ranging station equipped with a $1.2~{\rm m}$ telescope has been constructed and the station has successfully ranged to all the five retro-reflectors on the Moon; and the TianQin-1 experimental satellite has been launched on 20 December 2019 and the first round result shows that the satellite has exceeded all of its mission requirements.

preprint2020arXiv

Thermodynamics of Shearing Massless Scalar Field Spacetimes is Inconsistent With the Weyl Curvature Hypothesis

Our Universe has an arrow of time. In accordance with the second law of thermodynamics, entropy has been increasing ever since the Big Bang. The fact that matter is in thermal equilibrium in the very early Universe, as indicated by the cosmic microwave background, has led to the idea that gravitational entropy must be very low in the beginning. Penrose proposed that gravitational entropy can be quantified by the Weyl curvature, which increases as structures formed. A concrete realization of such a measure is the Clifton-Ellis-Tavakol gravitational entropy, which has been shown to be increasing in quite a number of cosmological models. In this work, we show a counter-example involving a class of inhomogeneous universes that are supported by a chameleon massless scalar field and exhibit anisotropic spacetime shearing effects. In fact, in our model the Clifton-Ellis-Tavakol gravitational entropy is increasing although the magnitude of the Weyl curvature is decreasing; this is due to the growth of the spacetime shear. The topology and the values of the three free parameters of the model are constrained by imposing a positive energy density for the cosmic fluid, and the thermodynamical requirements which follow from the cosmological holographic principle and the second law. It is shown that a negative deceleration parameter and a time decreasing Weyl curvature automatically follow from those conditions. Thus, we argue that our model can account for the formation of some primordial structures, like the Large Quasar Groups, which has required a non-standard evolution of the spatial anisotropies.

preprint2020arXiv

Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary skill selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

preprint2020arXiv

Unified Multi-Criteria Chinese Word Segmentation with BERT

Multi-Criteria Chinese Word Segmentation (MCCWS) aims at finding word boundaries in a Chinese sentence composed of continuous characters while multiple segmentation criteria exist. The unified framework has been widely used in MCCWS and shows its effectiveness. Besides, the pre-trained BERT language model has been also introduced into the MCCWS task in a multi-task learning framework. In this paper, we combine the superiority of the unified framework and pretrained language model, and propose a unified MCCWS model based on BERT. Moreover, we augment the unified BERT-based MCCWS model with the bigram features and an auxiliary criterion classification task. Experiments on eight datasets with diverse criteria demonstrate that our methods could achieve new state-of-the-art results for MCCWS.

preprint2019arXiv

Fractional Dark Matter decay: cosmological imprints and observational constraints

If a fraction $f_{\rm dcdm}$ of the Dark Matter decays into invisible and massless particles (so-called "dark radiation") with the decay rate (or inverse lifetime) $Γ_{\rm dcdm}$, such decay will leave distinctive imprints on cosmological observables. With a full consideration of the Boltzmann hierarchy, we calculate the decay-induced impacts not only on the CMB but also on the redshift distortion and the kinetic Sunyaev-Zel'dovich effect, while providing detailed physical interpretations based on evaluating the evolution of gravitational potential. By using the current cosmological data with a combination of Planck 2015, Baryon Acoustic Oscillation and redshift distortion measurements which can improve the constraints, we update the $1σ$ bound on the fraction of decaying DM from $f_{\rm dcdm}\lesssim5.26\%$ to $f_{\rm dcdm}\lesssim2.73\%$ for the short-lived DM (assuming $Γ_{\rm dcdm}/H_0\gtrsim10^4$). However, no constraints are improved from RSD data ($f_{\rm dcdm}\lesssim0.94\%$) for the long-lived DM (i.e., $Γ_{\rm dcdm}/H_0\lesssim10^4$). We also find the fractional DM decay can only slightly reduce the $H_0$ and $σ_8$ tensions, which is consistent with other previous works. Furthermore, our calculations show that the kSZ effect in future would provide a further constraining power on the decaying DM.

preprint2019arXiv

Graph Representation Learning: A Survey

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several state-of-the-art methods against small and large datasets and compare their performance. Finally, potential applications and future directions are presented.

preprint2019arXiv

Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding

Although embedded vector representations of words offer impressive performance on many natural language processing (NLP) applications, the information of ordered input sequences is lost to some extent if only context-based samples are used in the training. For further performance improvement, two new post-processing techniques, called post-processing via variance normalization (PVN) and post-processing via dynamic embedding (PDE), are proposed in this work. The PVN method normalizes the variance of principal components of word vectors while the PDE method learns orthogonal latent variables from ordered input sequences. The PVN and the PDE methods can be integrated to achieve better performance. We apply these post-processing techniques to two popular word embedding methods (i.e., word2vec and GloVe) to yield their post-processed representations. Extensive experiments are conducted to demonstrate the effectiveness of the proposed post-processing techniques.

preprint2019arXiv

Single-photon computational 3D imaging at 45 km

Long-range active imaging has a variety of applications in remote sensing and target recognition. Single-photon LiDAR (light detection and ranging) offers single-photon sensitivity and picosecond timing resolution, which is desirable for high-precision three-dimensional (3D) imaging over long distances. Despite important progress, further extending the imaging range presents enormous challenges because only weak echo photons return and are mixed with strong noise. Herein, we tackled these challenges by constructing a high-efficiency, low-noise confocal single-photon LiDAR system, and developing a long-range-tailored computational algorithm that provides high photon efficiency and super-resolution in the transverse domain. Using this technique, we experimentally demonstrated active single-photon 3D-imaging at a distance of up to 45 km in an urban environment, with a low return-signal level of $\sim$1 photon per pixel. Our system is feasible for imaging at a few hundreds of kilometers by refining the setup, and thus represents a significant milestone towards rapid, low-power, and high-resolution LiDAR over extra-long ranges.

preprint2019arXiv

Strong Cosmic Censorship in Charged de Sitter spacetime with Scalar Field Non-minimally Coupled to Curvature

We examine the stability and the strong cosmic censorship in the Reissner-Nordstrom-de Sitter (RN-dS) black hole by investigating the evolution of a scalar field non-minimally coupled to the curvature. We find that when the coupling parameter is negative, the RN-dS black hole experiences instability. The instability disappears when the coupling parameter becomes non-negative. With the increase of the coupling parameter, the violation of the strong cosmic censorship occurs at a larger critical charge ratio. But such an increase of the critical charge is suppressed by the increase of the cosmological constant. Different from the minimal coupling situation, it is possible to accommodate $β\ge1$ in the near extremal black hole when the scalar field is non-minimally coupled to curvature. The increase of the cosmological constant can allow $β\ge1$ to be satisfied for even smaller value of the coupling parameter. The existence of $β\ge1$ implies that the resulting curvature can continuously cross the Cauchy horizon.

preprint2017arXiv

Transmission spectra and valley processing of graphene and carbon nanotube superlattices with inter-valley coupling

We numerically investigate the electronic transport properties of graphene nanoribbons and carbon nanotubes with inter-valley coupling, e.g., in \sqrt{3}N \times \sqrt{3}N and 3N \times 3N superlattices. By taking the \sqrt{3} \times \sqrt{3} graphene superlattice as an example, we show that tailoring the bulk graphene superlattice results in rich structural configurations of nanoribbons and nanotubes. After studying the electronic characteristics of the corresponding armchair and zigzag nanoribbon geometries, we find that the linear bands of carbon nanotubes can lead to the Klein tunnelling-like phenomenon, i.e., electrons propagate along tubes without backscattering even in the presence of a barrier. Due to the coupling between K and K' valleys of pristine graphene by \sqrt{3} \times \sqrt{3} supercells,we propose a valley-field-effect transistor based on the armchair carbon nanotube, where the valley polarization of the current can be tuned by applying a gate voltage or varying the length of the armchair carbon nanotubes.

preprint2016arXiv

A mesoscopic approach on stability and phase transition between different traffic flow states

It is understood that congestion in traffic can be interpreted in terms of the instability of the equation of dynamic motion. The evolution of a traffic system from an unstable or metastable state to a globally stable state bears a strong resemblance to the phase transition in thermodynamics. In this work, we explore the underlying physics of the traffic system, by examining closely the physical properties and mathematical constraints of the phase transitions therein. By using a mesoscopic approach, one entitles the catastrophe model the same physical content as in the Landau's theory, and uncovers its close connections to the instability of the equation of motion and to the transition between different traffic states. In addition to the one-dimensional configuration space, we generalize our discussions to the higher-dimensional case, where the observed temporal oscillation in traffic flow data is attributed to the curl of a vector field. We exhibit that our model can reproduce the main features of the observed fundamental diagram including the inverse-$λ$ shape and the wide scattering of congested traffic data. When properly parameterized, the main feature of the data can be reproduced reasonably well either in terms of the oscillating congested traffic or in terms of the synchronized flow.

preprint2016arXiv

Analytic study of the effect of dark energy-dark matter interaction on the growth of structures

Large-scale structure has been shown as a promising cosmic probe for distinguishing and constraining dark energy models. Using the growth index parametrization, we obtain an analytic formula for the growth rate of structures in a coupled dark energy model in which the exchange of energy-momentum is proportional to the dark energy density. We find that the evolution of $f σ_8$ can be determined analytically once we know the coupling, the dark energy equation of state, the present value of the dark energy density parameter and the current mean amplitude of dark matter fluctuations. After correcting the growth function for the correspondence with the velocity field through the continuity equation in the interacting model, we use our analytic result to compare the model's predictions with large-scale structure observations.

preprint2016arXiv

Charged scalar gravitational collapse in de Sitter spacetime

We study the charged scalar collapse in de Sitter spacetimes. With the electric charge, there is one more competitor to join the competition of dynamics in the gravitational collapse. We find that two factors can influence the electric charge. If we just adjust the charge conjugation, the electric charge effect is always perturbative at the black hole threshold. The electric charge can also be influenced by the initial conditions of perturbations. These initial parameters can be tuned to control the competition in dynamics and present us new and rich physics in the process of gravitational collapse. We give physical explanations on these phenomena found in dynamics. Furthermore we show that the properties of the gravitational collapse are universal and do not depend on spacetime dimensions.

preprint2016arXiv

Dirac loops in trigonally connected 3D lattices

We consider different generalizations of the honeycomb lattice to three dimensional structures. We address the family of the hyper-honeycomb lattice, which is made up of alternating layers of 2D honeycomb nano-ribbons, with each layer rotated by $π/2$ with the layer below. When the orbitals of the lattice sites are symmetric with respect to the planes of the trigonal links, these structures can produce a Dirac loop, a closed line of Dirac nodes in momentum space. For orbitals that break that symmetry, such as the carbon $p$-wave orbitals, hyper-honeycomb lattices do not possess the loop structure. We also consider a new structure, the screw hyper-honeycomb, in which the successive layers of parallel units are rotated by $2π/3$. This structure has a Dirac loop if reflection symmetry in the unit cell is imposed, regardless the symmetry of the onsite orbitals. We discuss the implementation of those systems in optical lattices.

preprint2016arXiv

Dynamical analysis of modified gravity with nonminimal gravitational coupling to matter

We perform a phase space analysis of a generalized modified gravity theory with nonminimally coupling between geometry and matter. We apply the dynamical system approach to this generalized model and find that in the cosmological context, different choices of Lagrangian density will apparently result in different phases of the Universe. By carefully choosing the variables, we prove that there is an attractor solution to describe the late time accelerating universe when the modified gravity is chosen in a simple power-law form of the curvature scalar. We further examine the temperature evolution based on the thermodynamic understanding of the model. Confronting the model with supernova type Ia data sets, we find that the nonminimally coupled theory of gravity is a viable model to describe the late time Universe acceleration.

preprint2016arXiv

Effective p-wave interaction and topological superfluids in s-wave quantum gases

P-wave interaction in cold atoms may give rise to exotic topological superfluids. However, the realization of p-wave interaction in cold atom system is experimentally challenging. Here we propose a simple scheme to synthesize effective $p$-wave interaction in conventional $s$-wave interacting quantum gases. The key idea is to load atoms into spin-dependent optical lattice potential. Using two concrete examples involving spin-1/2 fermions, we show how the original system can be mapped into a model describing spinless fermions with nearest neighbor p-wave interaction, whose ground state can be a topological superfluid that supports Majorana fermions under proper conditions. Our proposal has the advantage that it does not require spin-orbit coupling or loading atoms onto higher orbitals, which is the key in earlier proposals to synthesize effective $p$-wave interaction in $s$-wave quantum gases, and may provide a completely new route for realizing $p$-wave topological superfluids.

preprint2016arXiv

Emulated Inertia and Damping of Converter-Interfaced Power Source

Converter-interfaced power sources (CIPSs), like wind turbine and energy storage, can be switched to the inertia emulation mode when the detected frequency deviation exceeds a pre-designed threshold, i.e. dead band, to support the frequency response of a power grid. This letter proposes an approach to derive the emulated inertia and damping from a CIPS based on the linearized model of the CIPS and the power grid, where the grid is represented by an equivalent single machine. The emulated inertia and damping can be explicitly expressed in time and turn out to be time-dependent.

preprint2016arXiv

Gravitational collapse of massless scalar field in $f(R)$ gravity

We study the spherically symmetric gravitational collapse of massless scalar matter field in asymptotic flat spacetime in $f(R)$ gravity. In the Einstein frame of $f(R)$ gravity, an additional scalar field arises due to the conformal transformation. We find that besides the usual competition between gravitational energy and kinetic energy in the process of gravitational collapse, the new scalar field brought by the conformal transformation adds one more competing force in the dynamical system. The dynamical competition can be controlled by tuning the amplitudes of the initial perturbations of the new scalar field and the matter field. To understand the physical reasons behind these phenomena, we analyze the gravitational potential behavior and calculate the Ricci scalar at center with the change of initial amplitudes of perturbations. We find rich physics on the formation of black holes through gravitational collapse in $f(R)$ gravity.

preprint2016arXiv

Incorporating Semantic Knowledge into Latent Matching Model in Search

The relevance between a query and a document in search can be represented as matching degree between the two objects. Latent space models have been proven to be effective for the task, which are often trained with click-through data. One technical challenge with the approach is that it is hard to train a model for tail queries and tail documents for which there are not enough clicks. In this paper, we propose to address the challenge by learning a latent matching model, using not only click-through data but also semantic knowledge. The semantic knowledge can be categories of queries and documents as well as synonyms of words, manually or automatically created. Specifically, we incorporate semantic knowledge into the objective function by including regularization terms. We develop two methods to solve the learning task on the basis of coordinate descent and gradient descent respectively, which can be employed in different settings. Experimental results on two datasets from an app search engine demonstrate that our model can make effective use of semantic knowledge, and thus can significantly enhance the accuracies of latent matching models, particularly for tail queries.

preprint2016arXiv

Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition

The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals. In contrast, a new trans-dimensional random field (TRF) LM has been recently introduced to show superior performances, where the whole sentence is modeled as a random field. In this paper, we examine how the TRF models can be interpolated with the NN models, and obtain 12.1\% and 17.9\% relative error rate reductions over 6-gram LMs for English and Chinese speech recognition respectively through log-linear combination.

preprint2016arXiv

Non-equilibrium condensation process in holographic superconductor with nonlinear electrodynamics

We study the non-equilibrium condensation process in a holographic superconductor with nonlinear corrections to the U(1) gauge field. We start with an asymptotic Anti-de-Sitter(AdS) black hole against a complex scalar perturbation at the initial time, and solve the dynamics of the gravitational systems in the bulk. When the black hole temperature T is smaller than a critical value Tc, the scalar perturbation grows exponentially till saturation, the final state of spacetime approaches to a hairy black hole. In the bulk theory, we find the clue of the influence of nonlinear corrections in the gauge field on the process of the scalar field condensation. We show that the bulk dynamics in the non-equilibrium process is completely consistent with the observations on the boundary order parameter. Furthermore we examine the time evolution of horizons in the bulk non-equilibrium transformation process from the bald AdS black hole to the AdS hairy hole. Both the evolution of apparent and event horizons show that the original AdS black hole configuration requires more time to finish the transformation to become a hairy black hole if there is nonlinear correction to the electromagnetic field. We generalize our non-equilibrium discussions to the holographic entanglement entropy and find that the holographic entanglement entropy can give us further understanding of the influence of the nonlinearity in the gauge field on the scalar condensation. In our analysis, we also compare the effect of different models on the corrections to the gauge field on the formation of holographic superconductor.

preprint2016arXiv

Nonlinear Modal Decoupling of Multi-Oscillator Systems with Applications to Power Systems

Many natural and manmade dynamical systems that are modeled as large nonlinear multi-oscillator systems like power systems are hard to analyze. For such a system, we propose a nonlinear modal decoupling (NMD) approach inversely constructing as many decoupled nonlinear oscillators as the system oscillation modes so that individual decoupled oscillators can easily be analyzed to infer dynamics and stability of the original system. The NMD follows a similar idea to the normal form except that we eliminate inter-modal terms but allow intra-modal terms of desired nonlinearities in decoupled systems, so decoupled systems can flexibly be shaped into desired forms of nonlinear oscillators. The NMD is then applied to power systems towards two types of nonlinear oscillators, i.e. the single-machine-infinite-bus (SMIB) systems and a proposed non-SMIB oscillator. Numerical studies on a 3-machine 9-bus system and New England 10-machine 39-bus system show that (i) decoupled oscillators keep a majority of the original system modal nonlinearities and the NMD provides a bigger validity region than the normal form, and (ii) decoupled non-SMIB oscillators may keep more authentic dynamics of the original system than decoupled SMIB systems.

preprint2016arXiv

Optimal Operation of Stationary and Mobile Batteries in Distribution Grids

The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem size, a distributed method with a guaranteed convergence is applied to shift the centralized computational burden to distributed controllers. To verify the proposed DSM, real-life EV data collected on UCLA campus is used to test the proposed DSM in an IEEE benchmark test system. Numerical results demonstrate the correctness and merits of the proposed approach.

preprint2016arXiv

Visualization of defect-induced excitonic properties of the edges and grain boundaries in synthesized monolayer molybdenum disulfide

Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDCs) are attractive materials for next generation nanoscale optoelectronic applications. Understanding nanoscale optical behavior of the edges and grain boundaries of synthetically grown TMDCs is vital for optimizing their optoelectronic properties. Elucidating the nanoscale optical properties of 2D materials through far-field optical microscopy requires a diffraction-limited optical beam diameter sub-micron in size. Here we present our experimental work on spatial photoluminescence (PL) scanning of large size ( $\geq 50$ microns) monolayer MoS$_2$ grown by chemical vapor deposition (CVD) using a diffraction limited blue laser beam spot (wavelength 405 nm) with a beam diameter as small as 200 nm allowing us to probe nanoscale excitonic phenomena which was not observed before. We have found several important features: (i) there exists a sub-micron width strip ($\sim 500$ nm) along the edges that fluoresces $\sim 1000 \%$ brighter than the region far inside; (ii) there is another brighter wide region consisting of parallel fluorescing lines ending at the corners of the zig-zag peripheral edges; (iii) there is a giant blue shifted A-excitonic peak, as large as $\sim 120$ meV, in the PL spectra from the edges. Using density functional theory calculations, we attribute this giant blue shift to the adsorption of oxygen dimers at the edges, which reduces the excitonic binding energy. Our results not only shed light on defect-induced excitonic properties, but also offer an attractive route to tailor optical properties at the TMDC edges through defect engineering.

preprint2015arXiv

An Analytical Formulation of Power System Oscillation Frequency

This letter proposes an analytical approach to formulate the power system oscillation frequency under a large disturbance. A fact is revealed that the oscillation frequency is only the function of the oscillation amplitude when the system's model and operating condition are fixed. Case studies also show that this function is damping-insensitive and could be applied to an inter-area model of a multi-machine power system.

preprint2015arXiv

Can FSRQs produce the IceCube detected diffuse neutrino emission?

IceCube has reported the detection of a diffuse TeV-PeV neutrino emission, for which the flat spectrum radio quasars (FSRQs) have been proposed to be the candidate sources. Here we assume that the neutrino flux from FSRQs is proportional to their gamma-ray ones, and obtain the gamma-ray/neutrino flux ratio by the diffuse gamma-ray flux from Fermi-LAT measurement of FSRQs and the diffuse neutrino flux detected by IceCube. We apply this ratio to individual FSRQs and hence predict their neutrino flux. We find that a large fraction of candidate FSRQs from the northern sky in the IceCube point source search has predicted neutrino flux above the IceCube upper limit; and for the sample of stacking search for neutrinos by IceCube, the predicted stacked flux is even larger than the upper limit of stacked flux by orders of magnitude. Therefore the IceCube limit from stacking searches, combined with the Fermi-LAT observations, already rejects FSRQs as the main sources of IceCube-detected diffuse neutrinos: FSRQs can only account for <10% (4%) of the IceCube-detected diffuse neutrino flux, according to the stacking searches from the whole (northern) sky. The derived neutrino/gamma-ray flux ratio is <<1, also implying that the gamma-ray emission from FSRQs cannot be produced by the secondary leptons and photons from the pion production processes. The caveat in the assumptions is discussed.

preprint2015arXiv

Charged Scalar Perturbations around Garfinkle-Horowitz-Strominger Black Holes

We examine the stability of the Garfinkle-Horowitz-Strominger (GHS) black hole under charged scalar perturbations. We find that different from the neutral scalar field perturbations, only two numerical methods, such as the continued fraction method and the asymptotic iteration method, can keep high efficiency and accuracy requirements in the frequency domain computations. The comparisons of the efficiency between these two methods have also been done. Employing the appropriate numerical method, we show that the GHS black hole is always stable against charged scalar perturbations. This is different from the result obtained in the de Sitter and Anti-de Sitter black holes. Furthermore we argue that in the GHS black hole background there is no amplification of the incident charged scalar wave to cause the superradiance, so that the superradiant instability cannot exist in this spacetime.

preprint2015arXiv

Dynamically generated gap from holography in the charged black brane with hyperscaling violation

We holographically investigate the effects of a dipole coupling between a fermion field and a $U(1)$ gauge field on the dual fermionic sector in the charged gravity bulk with hyperscaling violation. We analytically study the features of the ultraviolet and infrared Green's functions of the dual fermionic system and we show that as the dipole coupling and the hyperscaling violation exponent are varied, the fluid possess Fermi, marginal Fermi, non-Fermi liquid phases and also an additional Mott insulating phase. We find that the increase of the hyperscaling violation exponent which effectively reduces the dimensionality of the system makes it harder for the Mott gap to be formed. We also show that the observed duality between zeros and poles in the presence of a dipole moment coupling still persists in theories with hyperscaling violation.

preprint2015arXiv

Early dark energy and its interaction with dark matter

We study a class of early dark energy models which has substantial amount of dark energy in the early epoch of the universe. We examine the impact of the early dark energy fluctuations on the growth of structure and the CMB power spectrum in the linear approximation. Furthermore we investigate the influence of the interaction between the early dark energy and the dark matter and its effect on the structure growth and CMB. We finally constrain the early dark energy model parameters and the coupling between dark sectors by confronting to different observations.

preprint2015arXiv

Fermionic phase transition induced by the effective impurity in holography

We investigate the holographic fermionic phase transition induced by the effective impurity in holography, which is introduced by massless scalar fields in Einstein-Maxwell-massless scalar gravity. We obtain a phase diagram in $(α, T)$ plane separating the Fermi liquid phase and the non-Fermi liquid phase.

preprint2015arXiv

Hilbert schemes of rational curves on Fano hypersurfaces

In this paper we try to further explore the linear model of the moduli of rational maps. Our attempt yields following results. Let $X\subset \mathbf P^n$ be a generic hypersurface of degree $h$. Let $R_d(X, h)$ denote the open set of the Hilbert scheme parameterizing irreducible rational curves of degree $d$ on $X$. We obtain that (1) If $4\leq h\leq n-1$, $R_d(X, h)$ is an integral, local complete intersection of dimension \begin{equation} (n+1-h)d+n-4. \end{equation} (2) If furthermore $(h^2-n)d+h\leq 0$ and $h\geq 4$, in addition to part (1), $R_d(X, h)$ is also rationally connected.

preprint2015arXiv

Holographic thermalization in Gauss-Bonnet gravity with de Sitter boundary

We introduce higher-derivative Gauss-Bonnet correction terms in the gravity sector and we relate the modified gravity theory in the bulk to the strongly coupled quantum field theory on a de Sitter boundary. We study the process of holographic thermalization by examining three nonlocal observables, the two-point function, the Wilson loop and the holographic entanglement entropy. We study the time evolution of these three observables and we find that as the strength of the Gauss-Bonnet coupling is increased, the saturation time of the thermalization process to reach thermal equilibrium becomes shorter with the dominant effect given by the holographic entanglement entropy.

preprint2015arXiv

Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform

This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014. With real-world user action data provided by Tmall, one of the largest B2C online retail platforms in China, this competition requires to predict future user purchases on Tmall website. Predictions are judged on F1Score, which considers both precision and recall for fair evaluation. The data set provided by Tmall contains more than half billion action records from over ten million distinct users. Such massive data volume poses a big challenge, and drives competitors to write every single program in MapReduce fashion and run it on distributed cluster. We model the purchase prediction problem as standard machine learning problem, and mainly employ regression and classification methods as single models. Individual models are then aggregated in a two-stage approach, using linear regression for blending, and finally a linear ensemble of blended models. The competition is approaching the end but still in running during writing this paper. In the end, our team achieves F1Score 6.11 and ranks 7th (out of 7,276 teams in total).

preprint2015arXiv

Monte Carlo Predictions of Proton SEE Cross-Sections from Heavy Ion Test Data

The limits of previous methods promote us to design a new approach (named PRESTAGE) to predict proton single event effect (SEE) cross-sections using heavy-ion test data. To more realistically simulate the SEE mechanisms, we adopt Geant4 and the location-dependent strategy to describe the physics processes and the sensitivity of the device. Cross-sections predicted by PRESTAGE for over twenty devices are compared with the measured data. Evidences show that PRESTAGE can calculate not only single event upsets induced by proton indirect ionization, but also direct ionization effects and single event latch-ups. Most of the PRESTAGE calculated results agree with the experimental data within a factor of 2-3.

preprint2015arXiv

Rational curves on generic quintic threefolds

Let $X_0$ be a generic quintic threefold in projective space $\mathbf P^4$ over complex numbers and $C_0$ be an irreducible rational curve on $X_0$. Let $$c_0: \mathbf P^1\to C_0\subset X_0$$ be its normalization. In this paper, we show (1) $c_0$ must be an immersion, i.e. the differential $(c_0)_\ast: T_{t}\mathbf P^1 \to T_{c_0(t)} X_0$ is injective at each $t\in \mathbf P^1$, (2) the normal bundle of $c_0$ satisfies $$H^1(N_{c_0/X_0})=0.$$

preprint2015arXiv

Testing the tensor-vector-scalar Theory with the latest cosmological observations

The tensor-vector-scalar (TeVeS) model is considered a viable theory of gravity. It produces the Milgrom's modified Newtonian dynamics in the nonrelativistic weak field limit and is free from ghosts. This model has been tested against various cosmological observations. Here we investigate whether new observations such as the galaxy velocity power spectrum measured by 6dF and the kinetic Sunyaev Zel'dovich effect power spectrum measured by ACT/SPT can put further constraints on the TeVeS model. Furthermore, we perform the test of TeVeS cosmology with a sterile neutrino by confronting to Planck data, and find that it is ruled out by cosmic microwave background measurements from the Planck mission.

preprint2015arXiv

The chiral phase transition with a chiral chemical potential in the framework of Dyson-Schwinger equations

Within the framework of Dyson-Schwinger equations (DSEs), we discuss the chiral phase transition of QCD with a chiral chemical potential $μ_5$ as an additional scale. We focus especially on the issues related to the widely accepted as well as interested critical end point (CEP). With the help of a scalar susceptibility, we find that there might be no CEP$_5$ in the $T-μ_5$ plane, and the phase transition in the $T-μ_5$ plane might be totally crossover when $μ<50$ MeV, which has apparent consistency with the Lattice QCD calculation. Our study may also provide some useful hints to some other studies related to $μ_5$.

preprint2015arXiv

Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

preprint2014arXiv

A model study of the chiral phase diagram of QCD

In this paper we study the chiral phase transition of QCD at finite temperature and density by using the rank-2 confining separable gluon propagator model in the framework of Dyson-Schwinger Equations. The critical end point is located at (T_CEP, μ_CEP) = (69 MeV, 270.3 MeV). It is also found that the first order phase transition might not end at one point, but experiences a two-phase coexisting meta-stable state. A comparison with the results in the previous literature is given.

preprint2014arXiv

An example on deformation of a pair

In this paper we give an example to show Clemens' conjecture is not a first order deformation problem.

preprint2014arXiv

Compound Effect of Alfvén Waves and Ion-cyclotron Waves on Heating/Acceleration of Minor Ions via the Pickup Process

A scenario is proposed to explain the preferential heating of minor ions and differential streaming velocity between minor ions and protons observed in the solar corona and in the solar wind. It is demonstrated by test particle simulations that minor ions can be nearly fully picked up by intrinsic Alfvén-cyclotron waves observed in the solar wind based on the observed wave energy density. Both high frequency ion-cyclotron waves and low frequency Alfvén waves play crucial roles in the pickup process. A minor ion can first gain a high magnetic moment through the resonant wave-particle interaction with ion-cyclotron waves, and then this ion with a large magnetic moment can be trapped by magnetic mirror-like field structures in the presence of the lower-frequency Alfvén waves. As a result, the ion is picked up by these Alfvén-cyclotron waves. However, minor ions can only be partially picked up in the corona due to low wave energy density and low plasma beta. During the pickup process, minor ions are stochastically heated and accelerated by Alfvén-cyclotron waves so that they are hotter and flow faster than protons. The compound effect of Alfvén waves and ion-cyclotron waves is important on the heating and acceleration of minor ions. The kinetic properties of minor ions from simulation results are generally consistent with in situ and remote features observed in the solar wind and solar corona.

preprint2014arXiv

Critical behavior of Born-Infeld AdS black holes in the extended phase space thermodynamics

We study the thermodynamics of $D$-dimensional Born-Infeld AdS black holes in the extended phase space. We find that the usual small-large black hole phase transition, which exhibits analogy with the Van de Waals liquid-gas system holds in all dimensions greater than three. However, different from the four-dimensional case, in the system of higher dimensional Born-Infeld AdS black holes there is no reentrant phase transition. For the three-dimensional Born-Infeld AS black hole, there does not exist critical phenomena.

preprint2014arXiv

Critical behavior of charged Gauss-Bonnet AdS black holes in the grand canonical ensemble

We study the thermodynamics in the grand canonical ensemble of D-dimensional charged Gauss-Bonnet-AdS black holes in the extended phase space. We find that the usual small-large black hole phase transition, which exhibits analogy with the Van de Waals liquid-gas system holds in five-dimensional spherical charged Gauss-Bonnet-AdS black holes when its potential is fixed within the range $0<Φ<\frac{\sqrt{3}π}{4}$. For the other higher dimensional and topological charged Gauss-Bonnet-AdS black holes, there is no such phase transition. In the limiting case, Reissner-Nordstrom-AdS black holes, with vanishing Gauss-Bonnet parameter, there is no critical behavior in the grand canonical ensemble. This result holds independent of the spacetime dimensions and topologies. We also examine the behavior of physical quantities in the vicinity of the critical point in the five-dimensional spherical charged Gauss-Bonnet-AdS black holes.

preprint2014arXiv

Entanglement Entropy as a Probe of the Proximity Effect in Holographic Superconductors

We study the entanglement entropy as a probe of the proximity effect of a superconducting system by using the gauge/gravity duality in a fully back-reacted gravity system. While the entanglement entropy in the superconducting phase is less than the entanglement entropy in the normal phase, we find that near the contact interface of the superconducting to normal phase the entanglement entropy has a different behavior due to the leakage of Cooper pairs to the normal phase. We verify this behavior by calculating the conductivity near the boundary interface.

preprint2014arXiv

Extreme Negative Dependence and Risk Aggregation

We introduce the concept of an extremely negatively dependent (END) sequence of random variables with a given common marginal distribution. The END structure, as a new benchmark for negative dependence, is comparable to comonotonicity and independence. We show that an END sequence always exists for any given marginal distributions with a finite mean and we provide a probabilistic construction. Through such a construction, the partial sum of identically distributed but dependent random variables is controlled by a random variable that depends only on the marginal distribution of the sequence. The new concept and derived results are used to obtain asymptotic bounds for risk aggregation with dependence uncertainty.

preprint2014arXiv

Formation of Fermi surfaces and the appearance of liquid phases in holographic theories with hyperscaling violation

We consider a holographic fermionic system in which the fermions are interacting with a U(1) gauge field in the presence of a dilaton field in a gravity bulk of a charged black hole with hyperscaling violation. Using both analytical and numerical methods, we investigate the properties of the infrared and ultaviolet Green's functions of the holographic fermionic system. Studying the spectral functions of the system, we find that as the hyperscaling violation exponent is varied, the fermionic system possesses Fermi, non-Fermi, marginal-Fermi and log-oscillating liquid phases. Various liquid phases of the fermionic system with hyperscaling violation are also generated with the variation of the fermionic mass. We also explore the properties of the flat band and the Fermi surface of the non-relativistic fermionic fixed point dual to the hyperscaling violation gravity.

preprint2014arXiv

Holographic parity violating charged fluid dual to Chern-Simons modified gravity

We discuss the $(2+1)$-dimensional parity violating charged fluid on a finite cutoff surface $Σ_c$, dual to the nondynamical and dynamical Chern-Simons (CS) modified gravities. Using nonrelativistic long-wavelength expansion method, the field equations are solved up to $\mathcal{O}(ε^2)$ in the nondynamical model. It is shown that there exists nonvortical dual fluid with shear viscosity $η$ and Hall viscosity $η_A$ on the cutoff surface $Σ_c$. The ratio of shear viscosity over entropy density $η/s$ of the fluid takes the universal value $1/{4π}$, while the ratio of Hall viscosity over entropy density $η_A/s$ depends on the $Σ_c$ and black brane charge $q$. Moreover the nonvortical dual fluid obeys the magnetohydrodynamic (MHD) equation. However, these kinematic viscosities $ν$ and $ν_A$ related to $η$ and $η_A$ do not appear in this MHD equation, due to the constraint condition $\tilde{\partial}^2β_j=0$ for the $(2+1)$-dimensional dual fluid. Then, we extend our discussion to the dynamical CS modified gravity and show that the dual vortical fluid possesses another so-called Curl viscosity $ζ_A$, whose ratio to entropy density $ζ_A/s$ also depends on the $Σ_c$ and $q$. Moreover, the value of $η/s$ still equals to $1/4π$ and the result of $η_A/s$ agrees to the previous result under the probe limit of the pseudo scalar field at the infinite boundary in the charged black brane background for the dynamical CS modified gravity. This vortical dual fluid corresponds to the magnetohydrodynamic (MHD) turbulence equation in plasma physics.

preprint2014arXiv

Implications of Fermi-LAT observations on the origin of IceCube neutrinos

The IceCube (IC) collaboration recently reported the detection of TeV-PeV extraterrestrial neutrinos whose origin is yet unknown. By the photon-neutrino connection in $pp$ and $pγ$ interactions, we use the \fermi-LAT observations to constrain the origin of the IC detected neutrinos. We find that Galactic origins, i.e., the diffuse Galactic neutrinos due to cosmic ray (CR) propagation in the Milky Way, and the neutrinos from the Galactic point sources, may not produce the IC neutrino flux, thus these neutrinos should be of extragalactic origin. Moreover, the extragalactic gamma-ray bursts (GRBs) may not account for the IC neutrino flux, the jets of active galactic nuclei may not produce the IC neutrino spectrum, but the starburst galaxies (SBGs) may be promising sources. As suggested by the consistency between the IC detected neutrino flux and the Waxman-Bahcall bound, GRBs in SBGs may be the sources of both the ultrahigh energy, $>10^{19}$eV, CRs and the $1-100$~PeV CRs that produce the IC detected TeV-PeV neutrinos.

preprint2014arXiv

Multibrane DGP model: Our universe as a stack of $(2+1)$-dim branes

We consider scenario in which our $(3+1)$-dim universe is actually a dense stack of multiple parallel $(2+1)$-dim branes. For this purpose, we generalize the DGP model to a multi-brane case. We solve for the propagation of the scalar field and gravity in this setup. At short distances (high momenta) along the branes interactions follow the $(2+1)$-dim laws, while at large distances (low momenta) interactions follow the usual $(3+1)$-dim laws. This feature is inherited from the original DGP model. In the direction perpendicular to the brane, we show that interactions become $(3+1)$-dim at low momenta which are unable to resolve the interbrane separation. Thus, this is one of the explicit constructs of the "vanishing dimensions" scenario where high energy physics appears to be lower dimensional rather than higher dimensional.

preprint2014arXiv

On Zeilberger Conjecture

We prove a conjecture by D. Zeilberger on the determinant of a certain matrix and relate it to a problem of non-existence of 1-cycles in this note.

preprint2014arXiv

Probing excitonic states in ultraclean suspended two-dimensional semiconductors by photocurrent spectroscopy

The optical response of semiconducting monolayer transition-metal dichalcogenides (TMDCs) is dominated by strongly bound excitons that are stable even at room temperature. However, substrate-related effects such as screening and disorder in currently available specimens mask many anticipated physical phenomena and limit device applications of TMDCs. Here, we demonstrate that that these undesirable effects are strongly suppressed in suspended devices. Extremely robust (photogain >1,000) and fast (response time <1ms) photoresponse combined with the high quality of our devices allow us to study, for the first time, the formation, binding energies, and dissociation mechanisms of excitons in TMDCs through photocurrent spectroscopy. By analyzing the spectral positions of peaks in the photocurrent and by comparing them with first-principles calculations, we obtain binding energies, band gaps and spin-orbit splitting in monolayer TMDCs. For monolayer MoS2, in particular, we estimate an extremely large binding energy for band-edge excitons, Ebind > 570meV. Along with band-edge excitons, we observe excitons associated with a van Hove singularity of rather unique nature. The analysis of the source-drain voltage dependence of photocurrent spectra reveals exciton dissociation and photoconversion mechanisms in TMDCs.

preprint2014arXiv

Rational curves on hypersurfaces

In this paper, we prove three related results; (1) Extension of our result in [10] to all generic hypersurfaces. More precisely, the normal sheaf of a generic rational map $c_0$ to a generic hypersurface $X_0$ of $\mathbf P^n, n\geq 4$ has a vanishing higher cohomology, \begin{equation} H^1(N_{c_0/X_0})=0. \end{equation} As applications we give (2) A solution to a Voisin's conjecture [9] on a covering of a generic hypersurface by rational curves (3) A classification of rational curves on hypersurfaces of general type--a solution to another Voisin's conjecture [9].

preprint2014arXiv

Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.

preprint2014arXiv

Signature of the Van der Waals like small-large charged AdS black hole phase transition in quasinormal modes

We calculate the quasinormal modes of massless scalar perturbations around small and large four-dimensional Reissner-Nordstrom-Anti de Sitter (RN-AdS) black holes. We find a dramatic change in the slopes of quasinormal frequencies in small and large black holes near the critical point where the Van der Waals like thermodynamic phase transition happens. This further supports that the quasinormal mode can be a dynamic probe of the thermodynamic phase transition.

preprint2014arXiv

Stability of Reissner-Nordström black hole in de Sitter background under charged scalar perturbation

We find a new instability in the four-dimensional Reissner-Nordstrom-de Sitter black holes against charged scalar perturbations with vanishing angular momentum, $l=0$. We show that such an instability is caused by superradiance. The instability does not occur for an larger angular index, as explicitely proven for $l=1$. Our results are obtained from a numerical investigation of the time domain profiles of the perturbations.

preprint2014arXiv

Superradiant instability of Kerr-de Sitter black holes in scalar-tensor theory

We investigate in detail the mechanism of superradiance to render the instability of Kerr-de Sitter black holes in scalar-tensor gravity. Our results provide more clues to examine the scalar-tensor gravity in the astrophysical black holes in the universe with cosmological constant. We also discuss the spontaneous scalarization in the de Sitter background and find that this instability can also happen in the spherical de Sitter configuration in a special style.

preprint2014arXiv

Testing the Interaction between Dark Energy and Dark Matter with Planck Data

Interacting Dark Energy and Dark Matter is used to go beyond the standard cosmology. We base our arguments on Planck data and conclude that an interaction is compatible with the observations and can provide a strong argument towards consistency of different values of cosmological parameters.

preprint2014arXiv

Thermodynamics of rotating black holes with scalar hair in three dimensions

Introducing a new form of scalar potential $V(ϕ)$, we derive a proper form of the rotating black hole solution in three-dimensional Einstein gravity with nonminimally coupled scalar field and find that the first law of thermodynamics of this new rotating hairy black hole can be protected, where the scalar field parameter $B$ is constrained to relate to the black hole size. We also disclose the Hawking-Page phase transition between this rotating hairy black holes and the pure thermal radiation. Moreover, we study phase transitions between this rotating hairy black hole and rotating BTZ black hole. Considering the matchings for the temperature and angular momentum, we find that the rotating BTZ black hole always has smaller free energy which is a thermodynamically more preferred phase. Additionally, we evaluate the thermodynamics of the rotating black hole with minimally coupled scalar hair in three dimensions, which exhibits that the thermodynamical behaviors of this rotating hairy black hole are very similar to those of the rotating black hole with nonminimally coupled scalar hair.

preprint2013arXiv

A Model for Dark Energy decay

We discuss a model of non perturbative decay of dark energy into hot and cold dark matter. This model provides a mechanism from the field theory to realize the energy transfer from dark energy into dark matter, which is the requirement to alleviate the coincidence problem. The advantage of the model is the fact that we accommodate a mean life compatible with the age of the universe. We also argue that supersymmetry is a natural set up, though not essential.

preprint2013arXiv

Bandgap Engineering of Strained Monolayer and Bilayer MoS2

We report the influence of uniaxial tensile mechanical strain in the range 0-2.2% on the phonon spectra and bandstructures of monolayer and bilayer molybdenum disulfide (MoS2) two-dimensional crystals. First, we employ Raman spectroscopy to observe phonon softening with increased strain, breaking the degeneracy in the E' Raman mode of MoS2, and extract a Grüneisen parameter of ~1.06. Second, using photoluminescence spectroscopy we measure a decrease in the optical band gap of MoS2 that is roughly linear with strain, ~45 meV% strain for monolayer MoS2 and ~120 meV% strain for bilayer MoS2. Third, we observe a pronounced strain-induced decrease in the photoluminescence intensity of monolayer MoS2 that is indicative of the direct-to-indirect transition of the character of the optical band gap of this material at applied strain of ~1.5%, a value supported by first-principles calculations that include excitonic effects. These observations constitute the first demonstration of strain engineering the band structure in the emergent class of two-dimensional crystals, transition-metal dichalcogenides.

preprint2013arXiv

Building a Holographic Superconductor with Higher-derivative Couplings

We discuss the gravitational dual of a holographic superconductor consisting of a U(1) gauge field, a complex scalar field coupled to a charged AdS black hole and a higher-derivative coupling between the U(1) gauge field and the scalar with coupling constant η. In the presence of a magnetic field, the system possesses localized spatially dependent droplet solutions which, in the low temperature limit, have smaller critical temperature for η>0 than the droplet solutions without the interaction term (η=0). In the weak magnetic field limit, the opposite behavior is observed: the critical temperature increases as we increase η. We also calculate the energy gap in the probe limit and find that it is larger for η<0 than the energy gap in the conventional case (η=0).

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

Dynamical gap from holography in the charged dilaton black hole

We study the holographic non-relativistic fermions in the presence of bulk dipole coupling in charged dilatonic black hole background. We explore the nontrivial effects of the bulk dipole coupling, the fermion charge as well as the dilaton field on the flat band, the Fermi surface and the emergence of the gap by investigating the spectral function of the non-relativistic fermion system. In particular, we find that the presence of the flat band in the non-relativistic case will suppress the Fermi momentum. Besides, we observe that the effect of the dipole coupling in the dilaton gravity is more explicit. Finally, we consider the non-relativistic fermions at nonzero temperature. A phase transition from insulator to a conducting state is observed as the fermion system becomes hotter.

preprint2013arXiv

Holographic charged fluid dual to third order Lovelock gravity

We study the dual fluid on a finite cutoff surface outside the black brane horizon in the third order Lovelock gravity. Using nonrelativistic long-wavelength expansion, we obtain the incompressible Navier-Stokes equations of dual fluid with external force density on the finite cutoff surface. The viscosity to entropy density ratio $η/s$ is independent of cutoff surface and does not get modification from the third order Lovelock gravity influence. The obtained ratio agrees with the results obtained by using other methods, such as the Kubo formula at the AdS boundary and the membrane paradigm at the horizon in the third order Lovelock gravity. These results can be related by Wilson renormalization group flow. However the kinematic viscosity receives correction from the third order Lovelock term. We show that the equivalence between the isentropic flow of the fluid and the radial component of the gravitational equation observed in the Einstein and Gauss-Bonnet gravities also holds in the third order Lovelock gravity. This generalization brings more understandings of relating the gravity theory to the dual fluid.

preprint2013arXiv

Horizon instability of massless scalar perturbations of an extreme Reissner-Nordström-AdS black hole

We study the stability of extreme Reissner-Nordström-AdS black hole under massless scalar perturbations. We show that the perturbation on the horizon of the extreme Reissner-Nordström-AdS black hole experiences a power-law decay, instead of an exponential decay as observed in the nonextreme AdS black hole. On the horizon of the extreme Reissner-Nordström-AdS black hole, the blow up happens at lower order derivative of the scalar field compared with that of the extreme Reissner-Nordström black hole, which shows that extreme AdS black holes tend to instability in comparison to black holes in asymptotic flat space-times.

preprint2013arXiv

Observing the inhomogeneity in the holographic models of superconductors

We study the gravity duals of striped holographic superconductors in the AdS black hole and AdS soliton backgrounds. We show the dependences of the condensation and the critical temperature/critical chemical potential on the inhomogeneity in these two different spacetimes. By exploring the dynamics of the normal phase through the scalar field perturbation, we argue that the pair susceptibility and the conductivity can be possible phenomenological indications to disclose the property of inhomogeneity.

preprint2013arXiv

Study on perturbation schemes for achieving the real PMNS matrix from various symmetric textures

The PMNS matrix displays an obvious symmetry, but not exact. There are several textures proposed in literature, which possess various symmetry patterns and seem to originate from different physics scenarios at high energy scales. To be consistent with the experimental measurement, all of the regularities slightly decline, i.e. the symmetry must be broken. Following the schemes given in literature, we modify the matrices (9 in total) to gain the real PMNS matrix by perturbative rotations. The transformations may provide hints about the underlying physics at high energies and the breaking mechanisms which apply during the evolution to the low energy scale, especially the results may be useful for the future model builders.

preprint2013arXiv

Superradiant instability of extremal brane-world Reissner-Nordström black holes to charged scalar perturbations

We examine the stability of extremal braneworld Reissner-Nordström black holes under massive charged scalar perturbations. We show that similar to the four-dimensional case, the superradiant amplification can occur in braneworld charged holes. More interestingly we find that when the spacetime dimension is higher than four, a trapping potential well can emerge outside the braneworld black hole. This potential well is the extra dimensional effect and does not exist in four dimensions. It triggers the superradiant instability of the extremal braneworld charged holes.

preprint2013arXiv

Surface term, Virasoro algebra, and Wald entropy of black holes in higher-curvature gravity

Recently, in the Einstein gravity, Majhi and Padmanabhan proposed a straightforward and transparent way in obtaining the Bekenstein-Hawking entropy by using the approach based on the Virasoro algebra and central charge. In this work, we generalize their approach to the modified gravity with higher curvature corrections and show that their approach can successfully lead to the corresponding Wald entropy in the higher curvature gravity. Our result shows that the approach is physically general.

preprint2013arXiv

Testing lepton flavor universality in terms of data of BES III and charm-tau factory

The recent measurements on $R_K$ and $R_π$ imply that there exists a possible violation of the leptonic flavor universality which is one of the cornerstones of the standard model. It is suggested that a mixing between sterile and active neutrinos might induce such a violation. In this work we consider the scenarios with one or two sterile neutrinos to explicitly realize the data while the constraints from the available experiments have been taken into account. Moreover, as indicated in literature, the deviation of the real PMNS matrix from the symmetric patterns may be due to a $μ-τ$ asymmetry, therefore the measurements on $R_{D(D_s)eμ} = Γ(D(D_s)\rightarrow e^+ν_e)/Γ(D(D_s)\rightarrow μ^+ν_μ)$ and $R_{D(D_s)μτ} = Γ(D(D_s)\rightarrow μ^+ν_μ)/Γ(D(D_s)\rightarrow μ^+τ_τ)$ (and for some other heavy mesons $B^{\pm}$ and $B_c$ etc.) may shed more light on physics responsible for the violation of the leptonic flavor universality. The data of BES III are available to test the universality and that of the future charm-tau factory will provide more accurate information towards the aspect, in this work, we will discuss $R_{D(D_s)eμ}$ and $R_{D(D_s)μτ}$ in all details and also briefly consider the cases for $B^{\pm}$ and $B_c$.

preprint2013arXiv

The effect of Dark Matter and Dark Energy interactions on the peculiar velocity field and the kinetic Sunyaev-Zel'dovich effect

The interaction between Dark Matter and Dark Energy has been proposed as a mechanism to alleviate the coincidence problem. We analyze the effect of the interaction on the evolution of the gravitational field and propose two new observables based on its effect on the matter peculiar velocity field. We find that for different model parameters the matter peculiar velocity could be factor 2 times larger or 5 times smaller than the amplitude of velocity perturbations in the concordance LCDM cosmological model at the same scales. We compare the effect on the peculiar velocities with those on the Integrated Sachs-Wolfe effect and we show that velocities can potentially provide constraints on the strength of the interaction stronger than those currently available. We show that the current upper limits on the amplitude of the kinetic Sunyaev-Zel'dovich power spectrum provide constraints on the coupling within the dark sectors that are consistent with those obtained previously from the CMB and galaxy clusters. In particular, we show that current upper limits from the Atacama Cosmology Telescope and the South Pole Telescope favor Dark Energy decaying into Dark Matter, as required to solve the coincidence problem.

preprint2012arXiv

ALAE: Accelerating Local Alignment with Affine Gap Exactly in Biosequence Databases

We study the problem of local alignment, which is finding pairs of similar subsequences with gaps. The problem exists in biosequence databases. BLAST is a typical software for finding local alignment based on heuristic, but could miss results. Using the Smith-Waterman algorithm, we can find all local alignments in O(mn) time, where m and n are lengths of a query and a text, respectively. A recent exact approach BWT-SW improves the complexity of the Smith-Waterman algorithm under constraints, but still much slower than BLAST. This paper takes on the challenge of designing an accurate and efficient algorithm for evaluating local-alignment searches, especially for long queries. In this paper, we propose an efficient software called ALAE to speed up BWT-SW using a compressed suffix array. ALAE utilizes a family of filtering techniques to prune meaningless calculations and an algorithm for reusing score calculations. We also give a mathematical analysis and show that the upper bound of the total number of calculated entries using ALAE could vary from 4.50mn0.520 to 9.05mn0.896 for random DNA sequences and vary from 8.28mn0.364 to 7.49mn0.723 for random protein sequences. We demonstrate the significant performance improvement of ALAE on BWT-SW using a thorough experimental study on real biosequences. ALAE guarantees correctness and accelerates BLAST for most of parameters.

preprint2012arXiv

Analytical study on holographic superconductors with backreactions

We employ the variational method for the Sturm-Liouville eigenvalue problem to analytically investigate the properties of the holographic superconductors. We find that the analytic method is still powerful when the backreaction is turned on. Reducing step size in the iterative procedure, we observe that the consistency of results between the analytic and numerical computations can be further improved. The obtained analytic result can be used to back up the numerical computations in the holographic superconductor in the fully backreacted spacetime.

preprint2012arXiv

Bose-Einstein Condensates in a Cavity-mediated Triple-well

We investigate the energy structures and the dynamics of a Bose-Einstein condensates (BEC) in a triple-well potential coupled a high finesse optical cavity within a mean field approach. Due to the intrinsic atom-cavity field nonlinearity, several interesting phenomena arise which are the focuses of this work. For the energy structure, the bistability appears in the energy levels due to this atoms-cavity field nonlinearity, and the same phenomena can be found in the intra-cavity photons number. With an increase of the pump-cavity detunings, the higher and lower energy levels show a loop structure due to this cavity-mediated effects. In the dynamical process, an extensive numerical simulation of localization of the BECs for atoms initially trapped in one-, two-, and three-wells are performed for the symmetric and asymmetric cases in detail. It is shown that the the transition from oscillation to the localization can be modified by the cavity-mediated potential, which will enlarge the regions of oscillation. With the increasing of the atomic interaction, the oscillation is blocked and the localization emerges. The condensates atoms can be trapped either in one-, two-, or in three wells eventually where they are initially uploaded for certain parameters. In particular, we find that the transition from the oscillation to the localization is accompanied with some irregular regime where tunneling dynamics is dominated by chaos for this cavity-mediated system.

preprint2012arXiv

d-Wave holographic superconductors with backreaction in external magnetic fields

We study the d-wave holographic superconductors (the d-wave model proposed in [arXiv:1003.2991[hep-th]]) immersed in constant external magnetic fields by using the analytic matching method and numerical computation. In the probe limit, we calculate the spatially dependent condensate solution in the presence of the magnetism and find that the expression for the upper critical magnetic field satisfies the relation given in the Ginzburg-Landau theory. The result shows that the upper critical field gradually increases to its maximum value $B_{c2}$ at absolute zero temperature T=0, while vanishing at the critical temperature $T=T_c$. Moving away from the probe limit, we investigate the effect of spacetime backreaction on the critical temperature and the upper critical magnetic field. The magnetic fields as well as the electric fields acting as gravitational sources reduce the critical temperature of the superconductor and actually result in a dyonic black hole solution to the leading order. We obtain the expression for the upper critical magnetic field up to $\mathcal{O}(κ^2)$ order. The analytic result is consistent with the numerical findings.

preprint2012arXiv

Dipole Coupling Effect of Holographic Fermion in the Background of Charged Gauss-Bonnet AdS Black Hole

We investigate the holographic fermions in the charged Gauss-Bonnet $AdS_{d}$ black hole background with the dipole coupling between fermion and gauge field in the bulk. We show that in addition to the strength of the dipole coupling, the spacetime dimension and the higher curvature correction in the gravity background also influence the onset of the Fermi gap and the gap distance. We find that the higher curvature effect modifies the fermion spectral density and influences the value of the Fermi momentum for the appearance of the Fermi surface. There are richer physics in the boundary fermion system due to the modification in the bulk gravity.

preprint2012arXiv

Gauss-Bonnet holographic superconductors in Born-Infeld electrodynamics with backreactions

We develop a general holographic superconductor away from the probe limit by considering the corrections both in the gravity and in the gauge matter fields. We find the consistent effects of the high curvature correction in the gravity, the nonlinear correction in the gauge matter field and the backreaction on the dynamics of bulk AdS background and boundary CFT. They all have the effect to protect the stability of the bulk spacetime and hinder the formation of the scalar hair condensation on the boundary.

preprint2012arXiv

Gravitational thermodynamics and universal holographic duality in dynamical spacetimes

We construct a generalized Smarr formula which could provide a thermodynamic route to derive the covariant field equation of general theories of gravity in dynamic spacetimes. Combining some thermodynamic variables and a new chemical potential conjugated to the number of degree of freedom on the holographic screen, we find a universal Cardy-Verlinde formula and give its braneworld interpretation. We demonstrate that the associated AdS-Bekenstein bound is tighten than the previous expression for multi-charge black holes in the gauged supergravities. The Cardy-Verlinde formula and the AdS-Bekenstein bound are derived from the thermodynamics of bulk trapping horizons, which strongly suggests the underlying holographic duality between dynamical bulk spacetime and boundary field theory.

preprint2012arXiv

Modeling $f(R)$ gravity in terms of mass dilation rate

We review the conformal equivalence in describing the background expansion of the universe by $f(R)$ gravity both in the Jordan frame and the Einstein frame. In the Jordan frame, we present the general analytic expression for $f(R)$ models that have the same expansion history as the $Λ$CDM model. This analytic form can provide further insights on how cosmology can be used to test the $f(R)$ gravity at the largest scales. Moreover we present a systematic and self-consistent way to construct the viable $f(R)$ model in Jordan frame using the mass dilation rate function from the Einstein frame through the conformal transformation. In addition, we extend our study to the linear perturbation theories and we further exhibit the equivalence of the $f(R)$ gravity presented in the Jordan frame and Einstein frame in the perturbed space-time. We argue that this equivalence has solid physics root.

preprint2012arXiv

Observing various phase transitions in the holographic model of superfluidity

We study the gravity duals of supercurrent solutions in the AdS black hole background with general phase structure to describe both the first and the second order phase transitions at finite temperature in strongly interacting systems. We argue that the conductivity and the pair susceptibility can be possible phenomenological indications to distinguish the order of phase transitions. We extend our discussion to the AdS soliton configuration. Different from the black hole spacetime, in the probe limit the first order phase transition cannot be brought by introducing the spatial component of the vector potential of the gauge field in the AdS soliton background.

preprint2012arXiv

On the traces of elements of modular group

We prove a conjecture by W. Bergweiler and A. Eremenko on the traces of elements of modular group in this paper

preprint2012arXiv

Phase transition in the holographic model of superfluidity with backreactions

In the fully back reacted geometry we develop a supercurrent solution which corresponds to a deformation of superconducting black holes by the spatial component of gauge fields with a non- trivial radial dependence. We investigate the influence of the backreaction on the condensation and the phase structure in the AdS black hole spacetime. Different from that observed in the probe limit, we find that two operators cannot give consistent information on the phase diagram. We argue that only one of these two operators can reflect the real property of the condensation and phase structure in the holographic superconductor. We also generalize our study to the associated phase diagram in the AdS soliton background.

preprint2012arXiv

Probing charge scattering mechanisms in suspended graphene by varying its dielectric environment

Graphene with high carrier mobility μ is required both for graphene-based electronic devices and for the investigation of the fundamental properties of graphene's Dirac fermions. It is largely accepted that the mobility-limiting factor in graphene is the Coulomb scattering off of charged impurities that reside either on graphene or in the underlying substrate. This is true both for traditional graphene devices on SiO2 substrates and possibly for the recently reported high-mobility suspended and supported devices. An attractive approach to reduce such scattering is to place graphene in an environment with high static dielectric constant κ that would effectively screen the electric field due to the impurities. However, experiments so far report only a modest effect of high-κ environment on mobility. Here, we investigate the effect of the dielectric environment of graphene by studying electrical transport in multi-terminal graphene devices that are suspended in liquids with κ ranging from 1.9 to 33. For non-polar liquids (κ<5) we observe a rapid increase of μ with κ and report a record room-temperature mobility as large as ~60,000 cm2/Vs for graphene devices in anisole (κ=4.3), while in polar liquids (κ>18) we observe a drastic drop in mobility. We demonstrate that non-polar liquids enhance mobility by screening charged impurities adsorbed on graphene, while charged ions in polar liquids cause the observed mobility suppression. Furthermore, using molecular dynamics simulation we establish that scattering by out-of-plane flexural phonons, a dominant scattering mechanism in suspended graphene in vacuum at room temperature, is suppressed by the presence of liquids. We expect that our findings may provide avenues to control and reduce carrier scattering in future graphene-based electronic devices.

preprint2012arXiv

Revisiting f(R) gravity models that reproduce $Λ$CDM expansion

We reconstruct an $f(R)$ gravity model that gives rise to the particular $Λ$CDM background evolution of the universe. We find well-defined, real-valued analytical forms for the $f(R)$ model to describe the universe both in the early epoch from the radiation to matter dominated eras and the late time acceleration period. We further examine the viability of the derived $f(R)$ model and find that it is viable to describe the evolution of the universe in the past and there does not exist the future singularity in the Lagrangian.

preprint2012arXiv

Shot noise of spin current and spin transfer torque

We report the theoretical investigation of noise spectrum of spin current and spin transfer torque for non-colinear spin polarized transport in a spin-valve device which consists of normal scattering region connected by two ferromagnetic electrodes. Our theory was developed using non-equilibrium Green's function method and general non-linear $S^σ-V$ and $S^τ-V$ relations were derived as a function of angle $θ$ between magnetization of two leads. We have applied our theory to a quantum dot system with a resonant level coupled with two ferromagnetic electrodes. It was found that for the MNM system, the auto-correlation of spin current is enough to characterize the fluctuation of spin current. For a system with three ferromagnetic layers, however, both auto-correlation and cross-correlation of spin current are needed to characterize the noise spectrum of spin current. Furthermore, the spin transfer torque and the torque noise were studied for the MNM system. For a quantum dot with a resonant level, the derivative of spin torque with respect to bias voltage is proportional to $\sinθ$ when the system is far away from the resonance. When the system is near the resonance, the spin transfer torque becomes non-sinusoidal function of $θ$. The derivative of noise spectrum of spin transfer torque with respect to the bias voltage $N_τ$ behaves differently when the system is near or far away from the resonance. Specifically, the differential shot noise of spin transfer torque $N_τ$ is a concave function of $θ$ near the resonance while it becomes convex function of $θ$ far away from resonance. For certain bias voltages, the period $N_τ(θ)$ becomes $π$ instead of $2π$. For small $θ$, it was found that the differential shot noise of spin transfer torque is very sensitive to the bias voltage and the other system parameters.

preprint2012arXiv

Slow Magneto-acoustic Waves Observed above Quiet-Sun Region in a Dark Cavity

Waves play a crucial role in diagnosing the plasma properties of various structures in the solar corona and coronal heating. Slow magneto-acoustic (MA) waves are one of the important magnetohydrodynamic waves. In past decades, numerous slow MA waves were detected above the active regions and coronal holes, but rarely found elsewhere. Here, we investigate a `tornado'-like structure consisting of quasi-periodic streaks within a dark cavity at about 40--110 Mm above the quiet-Sun region on 2011 September 25. Our analysis reveals that these streaks are actually slow MA wave trains. The properties of these wave trains, including the phase speed, compression ratio, kinetic energy density, etc., are similar to those of the reported slow MA waves, except that the period of these waves is about 50 s, much shorter than the typical reported values (3--5 minutes).

preprint2012arXiv

Temperature-dependent Casimir-Polder forces on polarizable molecules

We demonstrate that the thermal Casimir-Polder forces on molecules near a conducting surface whose transition wavelengths are comparable to the molecule-surface separation are dependent on the ambient temperature and molecular polarization and they can even be changed from attractive to repulsive via varying the temperature across a threshold value for anisotropically polarizable molecules. Remarkably, this attractive-to-repulsive transition may be realized at room temperature. Let us note that the predicted repulsion is essentially a nonequilibrium effect since the force we calculated on a ground-state (or an excited-stated) molecule actually contains the contribution of the absorption (or emission) of thermal photons.

preprint2012arXiv

The signature of the scattering between dark sectors in large scale cosmic microwave background anisotropies

We study the interaction between dark sectors by considering the momentum transfer caused by the dark matter scattering elastically within the dark energy fluid. Describing the dark scattering analogy to the Thomson scattering which couples baryons and photons, we examine the impact of the dark scattering in CMB observations. Performing global fitting with the latest observational data, we find that for a dark energy equation of state $w<-1$, the CMB gives tight constraints on dark matter-dark energy elastic scattering. Assuming a dark matter particle of proton mass, we derive an elastic scattering cross section of $σ_D < 3.295 \times 10^{-10} σ_T$ where $σ_T$ is the cross section of Thomson scattering. For $w>-1$, however, the constraints are poor. For $w=-1$, $σ_D$ can formally take any value.

preprint2011arXiv

Analytical investigation of the phase transition between holographic insulator and superconductor in Gauss-Bonnet gravity

We employ the variational method for the Sturm-Liouville eigenvalue problem to analytically study the phase transition between the holographic insulator and superconductor in the Gauss-Bonnet gravity. By investigating the s-wave and p-wave holographic insulator/superconductor models, we find that this analytic method is more effective to obtain the analytic results on the condensation and the critical phenomena in the AdS soliton background in Gauss-Bonnet gravity. Our analytic result can be used to back up the numerical computations in the AdS soliton with Gauss-Bonnet correction.

preprint2011arXiv

Bandwidth Selection for Weighted Kernel Density Estimation

In the this paper, the authors propose to estimate the density of a targeted population with a weighted kernel density estimator (wKDE) based on a weighted sample. Bandwidth selection for wKDE is discussed. Three mean integrated squared error based bandwidth estimators are introduced and their performance is illustrated via Monte Carlo simulation. The least-squares cross-validation method and the adaptive weight kernel density estimator are also studied. The authors also consider the boundary problem for interval bounded data and apply the new method to a real data set subject to informative censoring.

preprint2011arXiv

Breaking parameter degeneracy in interacting dark energy models from observations

We study the interacting dark energy model with time varying dark energy equation of state. We examine the stability in the perturbation formalism and the degeneracy among the coupling between dark sectors, the time-dependent dark energy equation of state and dark matter abundance in the cosmic microwave background radiation. Further we discuss the possible ways to break such degeneracy by doing global fitting using the latest observational data and we get a tight constraint on the interaction between dark sectors.

preprint2011arXiv

Calculation of bulk viscosity of QCD at zero temperature and finite chemical potential

In this letter, based on Kubo's formula and the QCD low energy theorem, we propose a direct formula for calculating the bulk viscosity of QCD at finite chemical potential $μ$ and zero temperature. According to this formula, the bulk viscosity at finite $μ$ is totally determined by the dressed quark propagator at finite $μ$. We then use a dynamical, confining Dyson-Schwinger equation model of QCD to calculate the bulk viscosity at finite $μ$. It is found that no sharp peak behavior of the bulk viscosity at finite $μ$ is observed, which is quite different from that of the bulk viscosity at finite temperature.

preprint2011arXiv

Clemens' conjecture: part I

This is a series of two papers in which we solve the Clemens conjecture: there are only finitely many smooth rational curves of each degree in a generic quintic threefold. In this first paper, we deal with a family of smooth Calabi-Yau threefolds f_εfor a small complex number ε. We give an geometric obstruction, deviated quasi-regular deformations B_b of c_ε, to a deformation of the rational curve c_εin a Calabi-Yau threefold f_ε.

preprint2011arXiv

Clemens' conjecture: part II

This is the part II of our series of two papers, "Clemens' conjecture: part I", "Clemens' conjecture: part II". Continuing from part I, in this paper we turn our attention to general quintic threefolds. In a universal quintic threefold X, we construct a family of quasi-regular deformation B_b such that the generic member in this family is non-deviated, but some special member is deviated. By the result from part I, this is impossible unless there is no one parameter family of smooth rational curves in a generic quintic threefold.

preprint2011arXiv

Deep connection between f(R) gravity and the interacting dark sector model

We examine the conformal equivalence between the $f(R)$ gravity and the interacting dark sector model. We review the well-known result that the conformal transformation physically corresponds to the mass dilation which marks the strength of interaction between dark sectors. Instead of modeling f(R) gravity in the Jordan frame, we construct the $f(R)$ gravity in terms of mass dilation function in the Einstein frame. We find that the condition to keep $f(R)$ gravity consistent with CMB observations ensures the energy flow from dark energy to dark matter in the corresponding interacting model, which meets the requirement to alleviate the coincidence problem in the Einstein framework.

preprint2011arXiv

Earthquake Forecast via Neutrino Tomography

We discuss the possibility of forecasting earthquakes by means of (anti)neutrino tomography. Antineutrinos emitted from reactors are used as a probe. As the antineutrinos traverse through a region prone to earthquakes, observable variations in the matter effect on the antineutrino oscillation would provide a tomography of the vicinity of the region. In this preliminary work, we adopt a simplified model for the geometrical profile and matter density in a fault zone. We calculate the survival probability of electron antineutrinos for cases without and with an anomalous accumulation of electrons which can be considered as a clear signal of the coming earthquake, at the geological region with a fault zone, and find that the variation may reach as much as 3% for $\bar ν_e$ emitted from a reactor. The case for a $ν_e$ beam from a neutrino factory is also investigated, and it is noted that, because of the typically high energy associated with such neutrinos, the oscillation length is too large and the resultant variation is not practically observable. Our conclusion is that with the present reactor facilities and detection techniques, it is still a difficult task to make an earthquake forecast using such a scheme, though it seems to be possible from a theoretical point of view while ignoring some uncertainties. However, with the development of the geology, especially the knowledge about the fault zone, and with the improvement of the detection techniques, etc., there is hope that a medium-term earthquake forecast would be feasible.

preprint2011arXiv

Genus of curves in generic hypersurfaces

This is the continuation of our paper [10]. In this paper which is self contained, we would like to give a different obstruction formula to the FIRST order deformation of the pair of a smooth curve and a smooth hypersurface. This obstruction formula leads to a genus formula for a smooth curve in a smooth hypersurface. As an application we show that smooth elliptic curves in a smooth hypersurface of degree $$h\geq 2n-1$$ in the projective space $\mathbf P^n, n\geq 3,$ can't deform in the first order to all hypersurfaces of the same degree. In particular, there are no smooth elliptic curves in generic hypersurfaces of degree $$h\geq 2n-1.$$ This application in return leads to a study of the deformation of the pair mentioned above.

preprint2011arXiv

Geometrical Model for Non-Zero theta_13

Based on Friedberg and Lee's geometric picture by which the tribimaximal Pontecorvo-Maki-Nakawaga-Sakata leptonic mixing matrix is constructed, namely, corresponding mixing angles correspond to the geometric angles among the sides of a cube. We suggest that the three realistic mixing angles, which slightly deviate from the values determined for the cube, are due to a viable deformation from the perfectly cubic shape. Taking the best-fitted results of $θ_{12}$ and $θ_{23}$ as inputs, we determine the central value of $\sin^22θ_{13}$ should be 0.0238, with a relatively large error tolerance; this value lies in the range of measurement precision of the Daya Bay experiment and is consistent with recent results from the T2K Collaboration.

preprint2011arXiv

Holographic superconductor developed in BTZ black hole background with backreactions

We develop a holographic superconductor in BTZ black hole background with backreactions. We investigate the influence of the backreaction on the condensation of the scalar hair and the dynamics of perturbation in the background spacetime. When the Breitenlohner-Freedman bound is approached, we argue that only one of two possible operators can reflect the real property of the condensation in the holographic superconductor. This argument is supported by the investigation in dynamics.

preprint2011arXiv

Holographic superconductor models with the Maxwell field strength corrections

We study the effect of the quadratic field strength correction to the usual Maxwell field on the holographic dual models in the backgrounds of AdS black hole and AdS soliton. We find that in the black hole background, the higher correction to the Maxwell field makes the condensation harder to form and changes the expected relation in the gap frequency. This effect is similar to that caused by the curvature correction. However, in the soliton background we find that different from the curvature effect, the correction to the Maxwell field does not influence the holographic superconductor and insulator phase transition.

preprint2011arXiv

Obstructions to the deformations of curves to other hypersurfaces

We are interested in obstructions to the FIRST order deformation of a pair of a smooth hypersurface $f_0$ and a smooth curve $C_0$ contained in $f_0$. In the first half of the paper, we give necessary conditions for the pair to deform in the first order. In particular, for a rational curve $C_0$, this necessary condition is $$H^1(N_{C_0}f_0(1))=0.$$ In the second half, we apply the necessary conditions from the first half of the paper to study the geometry of smooth curves in hypersurfaces. The main application is for the case where $C_0$ is a rational curve.

preprint2011arXiv

Perturbations around the AdS Born-Infeld black holes

We study the quasinormal mode of the perturbation of the scalar field interacting with the electromagnetic field in the backgrounds of Schwarzschild AdS black hole, the Reissner-Nordstrom AdS black hole and the Born-Infeld AdS black hole. We disclose influence by the electric charge $Q$, the coupling between the scalar and electromagnetic field $q$ and the Born-Infeld parameter $b$ on the quasinormal frequencies of the scalar perturbation. We observe the possible growing mode in the perturbation when the scalar field strongly couples to the electromagnetic field. When the electromagnetic field becomes nonlinear described by the Born-Infeld electrodynamics, the nonlinearity described by the Born-Infeld parameter can hinder the growing mode to appear in the scalar perturbation. The quasinormal mode of the scalar field perturbation can help us further understand the physics in the holographic superconductor.

preprint2011arXiv

Quasinormal behavior of massless scalar field perturbation in Reissner-Nordstrom anti-de Sitter spacetimes

We present a comprehensive study of the massless scalar field perturbation in the Reissner-Nordstrom anti-de Sitter (RNAdS) spacetime and compute its quasinormal modes (QNM). For the lowest lying mode, we confirm and extend the dependence of the QNM frequencies on the black hole charge got in previous works. In near extreme limit, under the scalar perturbation we find that the imaginary part of the frequency tends to zero, which is consistent with the previous conjecture based on electromagnetic and axial gravitational perturbations. For the extreme value of the charge, the asymptotic field decay is dominated by a power-law tail, which shows that the extreme black hole can still be stable to scalar perturbations. We also study the higher overtones for the RNAdS black hole and find large variations of QNM frequencies with the overtone number and black hole charge. The nontrivial dependence of frequencies on the angular index is also discussed.

preprint2011arXiv

Quench induced Mott insulator to superfluid quantum phase transition

Mott insulator to superfluid quenches have been used by recent experiments to generate exotic superfluid phases. While the final Hamiltonian following the sudden quench is that of a superfluid, it is not a priori clear how close the final state of the system approaches the ground state of the superfluid Hamiltonian. To understand the nature of the final state we calculate the temporal evolution of the momentum distribution following a Mott insulator to superfluid quench. Using the numerical infinite time-evolving block decimation approach and the analytical rotor model approximation we establish that the one and two dimensional Mott insulators following the quench equilibriate to thermal states with spatially short-ranged coherence peaks in the final momentum distribution and therefore are not strict superfluids. However, in three dimensions we find a divergence in the momentum distribution indicating the emergence of true superfluid order.

preprint2011arXiv

Spin Polarized and Valley Helical Edge Modes in Graphene Nanoribbons

Inspired by recent progress in fabricating precisely zigzag-edged graphene nanoribbons and the observation of edge magnetism, we find that spin polarized edge modes with well-defined valley index can exist in a bulk energy gap opened by a staggered sublattice potential such as that provided by a hexagonal Boron-Nitride substrate. Our result is obtained by both tight-binding model and first principles calculations. These edge modes are helical with respect to the valley degree of freedom, and are robust against scattering, as long as the disorder potential is smooth over atomic scale, resulting from the protection of the large momentum separation of the valleys.

preprint2011arXiv

Testing the interaction between dark energy and dark matter via latest observations

Cosmological analysis based on currently available observations are unable to rule out a sizeable coupling between dark energy and dark matter. However, the signature of the coupling is not easy to grasp, since the coupling is degenerate with other cosmological parameters, such as the dark energy equation of state and the dark matter abundance. We discuss the possible ways to break such degeneracy. Based on the perturbation formalism, we carry out the global fitting by using latest observational data and get a tight constraint on the interaction between dark sectors. We find that the appropriate interaction can alleviate the coincidence problem.

preprint2011arXiv

The Prediction of a Gapless Topological "Haldane Liquid" Phase in a One-Dimensional Cold Polar Molecular Lattice

We show that ultracold two-component fermionic dipolar gases in an optical lattice with strong two-body on-site loss can be used to realize a tunable effective spin-one model. Fermion number conservation provides an unusual constraint that $\sum_i (S^z_i)^2$ is conserved, leading to a novel topological liquid phase in one dimension which can be thought of as the gapless analog of the Haldane gapped phase of a spin-one Heisenberg chain. The properties of this phase are calculated numerically via the infinite time-evolving block decimation method and analytically via a mapping to a one-mode Luttinger liquid with hidden spin information.

preprint2011arXiv

Various types of phase transitions in the AdS soliton background

We study the basic holographic insulator and superconductor phase transition in the AdS soliton background by generalizing the spontaneous breaking of a global U(1) symmetry to occur via St$\ddot{u}$ckelberg mechanism. We construct the soliton solutions with backreaction and examine the effects of the backreaction on the condensation of the scalar hair in the generalized St$\ddot{u}$ckelberg Lagrangian. We disclose rich physics in various phase transitions. In addition to the AdS soliton configuration, we also examine the property of the phase transition in the AdS black hole background.

preprint2010arXiv

Analytical study on holographic superconductors in external magnetic field

We investigate the holographic superconductors immersed in an external magnetic field by using the analytical approach. We obtain the spatially dependent condensate solutions in the presence of the magnetism and find analytically that the upper critical magnetic field satisfies the relation given in the Ginzburg-Landau theory. We observe analytically the reminiscent of the Meissner effect where the magnetic field expels the condensate. Extending to the D-dimensional Gauss-Bonnet AdS black holes, we examine the influence given by the Gauss-Bonnet coupling on the condensation. Different from the positive coupling, we find that the negative Gauss-Bonnet coupling enhances the condensation when the external magnetism is not strong enough.

preprint2010arXiv

Bose-Fermi solid and its quantum melting in an one-dimensional optical lattice

We investigate the quantum phase diagram of Bose-Fermi mixtures of ultracold dipolar particles trapped in one-dimensional optical lattices in the thermodynamic limit. With the presence of nearest-neighbor (N.N.) interactions, a long-ranged ordered crystalline phase (Bose-Fermi solid) is found stabilized between a Mott insulator of bosons and a band-insulator of fermions in the limit of weak inter-site tunneling ($J$). When $J$ is increased, such a Bose-Fermi solid can be quantum melted into a Bose-Fermi liquid through either a two-stage or a three-stage transition, depending on whether the crystalline order is dominated by the N.N. interaction between fermions or bosons. These properties can be understood as quantum competition between a pseudo-spin frustration and a pseudo-spin-charge separation, qualitatively different from the classical picture of solid-liquid phase transition.

preprint2010arXiv

Constraining the interacting dark energy models from weak gravity conjecture and recent observations

We examine the effectiveness of the weak gravity conjecture in constraining the dark energy by comparing with observations. For general dark energy models with plausible phenomenological interactions between dark sectors, we find that although the weak gravity conjecture can constrain the dark energy, the constraint is looser than that from the observations.

preprint2010arXiv

Deriving the gravitational field equation and horizon entropy for arbitrary diffeomorphism-invariant gravity from spacetime solid

Motivated by the analogy between the spacetime and the solid with inhomogeneous elasticity modulus, we present an alternative method to obtain the field equation of any diffeomorphism-invariant gravity, by extremizing the constructed entropy function of the displacement vector field of spacetime solid. In general stationary spacetimes, we show that the Wald entropy of horizon arises from the on-shell entropy function of spacetime solid.

preprint2010arXiv

Dynamical perturbations and critical phenomena in Gauss-Bonnet-AdS black holes

We investigate the perturbations of charged scalar field in $5$-dimensional Gauss-Bonnet AdS black hole backgrounds. From the perturbation behaviors we obtain the objective picture on how the high curvature influence the spacetime perturbation and the condensation of the scalar hair. The high curvature effects can also be read from the linear response function such as the susceptibility and the correlation length, when the system approaches the critical point. We find that the Gauss-Bonnet term does not affect the critical exponents of the system and they still take the mean-field values.

preprint2010arXiv

First-principles investigation of dynamical properties of molecular devices under a steplike pulse

We report a computationally tractable approach to first principles investigation of time-dependent current of molecular devices under a step-like pulse. For molecular devices, all the resonant states below Fermi level contribute to the time-dependent current. Hence calculation beyond wideband limit must be carried out for a quantitative analysis of transient dynamics of molecules devices. Based on the exact non-equilibrium Green's function (NEGF) formalism of calculating the transient current in Ref.\onlinecite{Maciejko}, we develop two approximate schemes going beyond the wideband limit, they are all suitable for first principles calculation using the NEGF combined with density functional theory. Benchmark test has been done by comparing with the exact solution of a single level quantum dot system. Good agreement has been reached for two approximate schemes. As an application, we calculate the transient current using the first approximated formula with opposite voltage $V_L(t)=-V_R(t)$ in two molecular structures: Al-${\rm C}_{5}$-Al and Al-${\rm C}_{60}$-Al. As illustrated in these examples, our formalism can be easily implemented for real molecular devices. Importantly, our new formula has captured the essential physics of dynamical properties of molecular devices and gives the correct steady state current at $t=0$ and $t\rightarrow \infty$.

preprint2010arXiv

General holographic superconductor models with backreactions

We study general models of holographic superconductors away from the probe limit. We find that the backreaction of the spacetime can bring richer physics in the phase transition. Moreover we observe that the ratio $ω_g/T_c$ changes with the strength of the backreaction and is not a universal constant.

preprint2010arXiv

General holographic superconductor models with Gauss-Bonnet corrections

We study general models for holographic superconductors in Einstein-Gauss-Bonnet gravity. We find that different values of Gauss-Bonnet correction term and model parameters can determine the order of phase transitions and critical exponents of second-order phase transitions. Moreover we find that the size and strength of the conductivity coherence peak can be controlled. The ratios $ω_g/Tc$ for various model parameters have also been examined.

preprint2010arXiv

Gröbner-Shirshov Bases and Hilbert Series of Free Dendriform Algebras

In this paper, we give a Gröbner-Shirshov basis of the free dendriform algebra as a quotient algebra of an $L$-algebra. As applications, we obtain a normal form of the free dendriform algebra. Moreover, Hilbert series and Gelfand-Kirillov dimension of finitely generated free dendriform algebras are obtained.

preprint2010arXiv

Holographic Superconductors with various condensates in Einstein-Gauss-Bonnet gravity

We study holographic superconductors in Einstein-Gauss-Bonnet gravity. We consider two particular backgrounds: a $d$-dimensional Gauss-Bonnet-AdS black hole and a Gauss-Bonnet-AdS soliton. We discuss in detail the effects that the mass of the scalar field, the Gauss-Bonnet coupling and the dimensionality of the AdS space have on the condensation formation and conductivity. We also study the ratio $ω_g/T_c $ for various masses of the scalar field and Gauss-Bonnet couplings.

preprint2010arXiv

Influence on the entropic force by the virtual degree of freedom on the holographic screen

We generalize the study of entropic force to a general static spherical spacetime and examine the acceleration, temperature, equation of gravity and the energy associated with the holographic screen in this general background. We show that the virtual degree of freedom on the holographic screen plays a crucial role in interpreting field equations of gravity based on thermodynamical perspective.

preprint2010arXiv

Probing the cosmic acceleration from combinations of different data sets

We examine in some detail the influence of the systematics in different data sets including type Ia supernova sample, baryon acoustic oscillation data and the cosmic microwave background information on the fitting results of the Chevallier-Polarski-Linder parametrization. We find that the systematics in the data sets does influence the fitting results and leads to different evolutional behavior of dark energy. To check the versatility of Chevallier-Polarski-Linder parametrization, we also perform the analysis on the Wetterich parametrization of dark energy. The results show that both the parametrization of dark energy and the systematics in data sets influence the evolutional behavior of dark energy.

preprint2010arXiv

Quasinormal Modes of Charged Fermions and Phase Transition of Black Holes

We study the quasinormal modes of massless charged fermions in a Reissner-Nordstrom-anti-de Sitter black hole spacetime. In the probe limit, we find that the imaginary part of quasinormal frequency will become positive when the temperature of the black hole is below a critical value. This indicates an instability of the black hole occurs and a phase transition happens. In the AdS/CFT correspondence, this transition can be viewed as a superconducting phase transition and the bulk fermion is regarded as the order parameter. When the coupling of the fermions and the background electric field becomes stronger, the critical temperature of the phase transition becomes higher. If the interaction between the fermion and the electric field can be ignored, namely in the case of a neutral fermion, the imaginary part of the quasinormal modes is always negative, which indicates that the black hole is stable and no phase transition occurs.

preprint2010arXiv

Signature of the black hole phase transition in quasinormal modes

We study the perturbation of the scalar field interacting with the Maxwell field in the background of d-dimensional charged AdS black hole and AdS soliton. Different from the single classical field perturbation, which always has the decay mode in the black hole background, we observe the possible growing mode when the perturbation of the scalar field strongly couples to the Maxwell field. Our results disclose the signature of how the phase transition happens when the interaction among classical fields is strong. The sudden change of the perturbation to growing mode is also observed in the AdS soliton with electric potential. However in the magnetic charged AdS soliton background, we observe the consistent perturbation behavior when the interaction between scalar field and Maxwell field is considered. This implies that for the magnetic charged AdS soliton configuration, unlike the situation with electric potential, there is no scalar field condensation which causes the phase change.

preprint2010arXiv

Stability of the warped black string with nontrivial topology in five-dimensional Anti-de Sitter spacetime

We investigate the stability of a new warped black string with nontrivial topologies in five-dimensional Anti-de Sitter spacetime. After studying the linear gravitational perturbation, we find that this black string is unstable when the Kaluza-Klein mass falls in a certain range and the instability exists for all topological spacetimes.

preprint2010arXiv

The imprint of the interaction between dark sectors in galaxy clusters

Based on perturbation theory, we study the dynamics of how dark matter and dark energy in the collapsing system approach dynamical equilibrium while interacting. We find that the interaction between dark sectors cannot ensure the dark energy to fully cluster along with dark, leading to the energy non-conservation problem in the collapsing system We examine the cluster number counts dependence on the interaction between dark sectors. Furthermore, we analyze how dark energy inhomogeneities affect cluster abundances. It is shown that cluster number counts can provide specific signature of dark sectors interaction and dark energy inhomogeneities.

preprint2009arXiv

Generalized second law of thermodynamics in Gauss-Bonnet braneworld

We investigate the validity the generalized second law of thermodynamics in a general braneworld model with curvature correction terms on the brane and in the bulk, respectively. Employing the derived entropy expression associated with the apparent horizon, we examine the time evolution of the total entropy, including the derived entropy of the apparent horizon and the entropy of the matter fields inside the apparent horizon. We show that the generalized second law of thermodynamics is fulfilled on the 3-brane embedded in the 5D spacetime with curvature corrections.

preprint2009arXiv

Generalized second law of thermodynamics in warped DGP braneworld

We investigate the validity of the generalized second law of thermodynamics on the $(n-1)$-dimensional brane embedded in the $(n+1)$-dimensional bulk. We examine the evolution of the apparent horizon entropy extracted through relation between gravitational equation and the first law of thermodynamics together with the matter field entropy inside the apparent horizon. We find that the apparent horizon entropy extracted through connection between gravity and the first law of thermodynamics satisfies the generalized second law of thermodynamics. This result holds regardless of whether there is the intrinsic curvature term on the brane or a cosmological constant in the bulk. The observed satisfaction of the generalized second law provides further support on the thermodynamical interpretation of gravity based on the profound connection between gravity and thermodynamics.

preprint2009arXiv

Temperature effect on the power spectrum in inflation

We examine the effect of the thermal vacuum on the power spectrum of inflation by using the thermal field dynamics. We find that the thermal effect influences the CMB anisotropy at large length scale. After removing the divergence by using the holographic cutoff, we observe that the thermal vacuum explains well the observational CMB result at low multipoles. This shows that the temperature dependent factor should be considered in the study of power spectrum in inflation, especially at large length scale.

preprint2009arXiv

Transient dynamics of molecular devices under step-like pulse bias

We report first principles investigation of time-dependent current of molecular devices under a step-like pulse.Our results show that although the switch-on time of the molecular device is comparable to the transit time, much longer time is needed to reach the steady state. In reaching the steady state the current is dominated by resonant states below Fermi level. The contribution of each resonant state to the current shows the damped oscillatory behavior with frequency equal to the bias of the step-like pulse and decay rate determined by the life time of the corresponding resonant state. We found that all the resonant states below Fermi level have to be included for accurate results. This indicates that going beyond wideband limit is essential for a quantitative analysis of transient dynamics of molecular devices.

preprint2007arXiv

The phase transition and the Quasi-Normal Modes of black Holes

We reexamined the argument that the quasinormal modes could be a probe of the phase transition of a topological black hole to a hairy configuration by investigating general scalar perturbations. We found further evidence in the quasinormal modes for this phase transition. For the general black hole configurations, we observed that although the quasinormal modes can present us different phases of different configurations, there is no dramatic change in the slope of quasinormal frequencies at the critical point of the phase transition. More detailed studies of quasinormal modes are needed to reveal the subtle behavior of the phase transition.

preprint2007arXiv

Thermodynamical properties of the Universe with dark energy

We have investigated the thermodynamical properties of the Universe with dark energy. Adopting the usual assumption in deriving the constant co-moving entropy density that the physical volume and the temperature are independent, we observed some strange thermodynamical behaviors. However, these strange behaviors disappeared if we consider the realistic situation that the physical volume and the temperature of the Universe are related. Based on the well known correspondence between the Friedmann equation and the first law of thermodynamics of the apparent horizon, we argued that the apparent horizon is the physical horizon in dealing with thermodynamics problems. We have concentrated on the volume of the Universe within the apparent horizon and considered that the Universe is in thermal equilibrium with the Hawking temperature on the apparent horizon. For dark energy with $w\ge -1$, the holographic principle and the generalized second law are always respected.

preprint2006arXiv

Soliton with a Pion Field in the Global Color Symmetry Model

We calculate the property of the global color symmetry model soliton with the pion field being included explicitly. The calculated results indicate that the pion field provides a strong attraction so that the eigen-energy of a quark and the mass of a soliton reduce drastically, in contrast to those with only the sigma field.

preprint2005arXiv

Thermodynamic Geometry and Critical Behavior of Black Holes

Based on the observations that there exists an analogy between the Reissner-Nordström-anti-de Sitter (RN-AdS) black holes and the van der Waals-Maxwell liquid-gas system, in which a correspondence of variables is $(ϕ, q) \leftrightarrow (V,P)$, we study the Ruppeiner geometry, defined as Hessian matrix of black hole entropy with respect to the internal energy (not the mass) of black hole and electric potential (angular velocity), for the RN, Kerr and RN-AdS black holes. It is found that the geometry is curved and the scalar curvature goes to negative infinity at the Davies' phase transition point for the RN and Kerr black holes. Our result for the RN-AdS black holes is also in good agreement with the one about phase transition and its critical behavior in the literature.

preprint2000arXiv

A time series model of CDS sequences in complete genome

A time series model of CDS sequences in complete genome is proposed. A map of DNA sequence to integer sequence is given. The correlation dimensions and Hurst exponents of CDS sequences in complete genome of bacteria are calculated. Using the average of correlation dimensions, some interesting results are obtained.

preprint2000arXiv

Extreme black hole entropy obtained in an operational approach

The entropy of anti-de Sitter Reissner-Nordstrom black hole is found to be stored in the material which gathers to form it and equals to $A/4$ regardless of material states. Extending the study to two kinds of extreme black holes, we find different entropy results for the first kind of extreme black hole due to different material states. However for the second kind of extreme black hole the results of entropy are uniform independently of the material states. Relations between these results and the stability of two kinds of extreme black holes have been addressed.

preprint1999arXiv

Entropy, topology of two-dimensional extreme black holes

Through direct thermodynamic calculations we have shown that different classical entropies of two-dimensional extreme black holes appear due to two different treatments, namely Hawking's treatment and Zaslavskii's treatment. Geometrical and topological properties corresponding to these different treatments are investigated. Quantum entropies of the scalar fields on the backgrounds of these black holes concerning different treatments are also exhibited. Different results of entropy and geometry lead us to argue that there are two kinds of extreme black holes in the nature. Explanation of black hole phase transition has also been given from the quantum point of view.

Bin Wang

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240 published item(s)

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling

Black holes with scalar hair: Extending from and beyond the Schwarzschild solution

eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning

BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation

High-fidelity ptychographic imaging of highly periodic structures enabled by vortex high harmonic beams

$(2+1)$-Dimensional Black Holes in $f(R,ϕ)$ Gravity

A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

C3KG: A Chinese Commonsense Conversation Knowledge Graph

CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations

Continuous-stage symplectic adapted exponential methods for charged-particle dynamics with arbitrary electromagnetic fields

Critical phenomena in dynamical scalarization of charged black hole

Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

Cuspy and fractured black hole shadows in a toy model with axisymmetry

Dynamical spontaneous scalarization in Einstein-Maxwell-scalar theory

Electrified Autonomous Freight Benefit analysis on Fleet, Infrastructure and Grid Leveraging Grid-Electrified Mobility (GEM) Model

Forecasts on Interacting Dark Energy from 21-cm Angular Power Spectrum with BINGO and SKA observations

Geometry Contrastive Learning on Heterogeneous Graphs

Hessian-Free Second-Order Adversarial Examples for Adversarial Learning

Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach

Just Rank: Rethinking Evaluation with Word and Sentence Similarities

Long term analysis of splitting methods for charged-particle dynamics

MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

MOORe: Model-based Offline-to-Online Reinforcement Learning

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

Nonlinear Hall effect induced by internal Coulomb interaction and phase relaxation process in a four-terminal system with time-reversal symmetry

Quasinormal modes in two-photon autocorrelation and the geometric-optics approximation

Rethinking Reinforcement Learning based Logic Synthesis

Risk Concentration and the Mean-Expected Shortfall Criterion

Semi-discretization and full-discretization with optimal accuracy for charged-particle dynamics in a strong nonuniform magnetic field

Single-frame characterization of ultrafast pulses with spatiotemporal orbital angular momentum

Strong Effects of Interlayer Interaction on Valence-Band Splitting in Transition Metal Dichalcogenides

SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation

The BINGO Project III: Optical design and optimisation of the focal plane

The BINGO Project V: Further steps in Component Separation and Bispectrum Analysis

The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

Topological Mirror Symmetry of Parabolic Hitchin Systems

Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training

Transformer based Generative Adversarial Network for Liver Segmentation

Yes, DLGM! A novel hierarchical model for hazard classification

BLOCKEYE: Hunting For DeFi Attacks on Blockchain

Boosting performance for software defined networks from traffic engineering perspective

CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding

Dynamical scalarization in Einstein-Maxwell-dilaton theory

Evolution of Anti-de Sitter black holes in Einstein-Maxwell-dilaton theory

GraphHop: An Enhanced Label Propagation Method for Node Classification

Inductive Learning on Commonsense Knowledge Graph Completion

KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling

Optimal convergence and long-time conservation of exponential integration for Schrödinger equations in a normal or highly oscillatory regime

Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments

Passive underwater acoustic barcodes using Rayleigh wave resonance

Progressive Depth Learning for Single Image Dehazing

Structure-preserving algorithms with uniform error bound and long-time energy conservation for highly oscillatory Hamiltonian systems

Study of form factors and branching ratios for $D\rightarrow S,Al\bar{ν_{l}}$ with light-cone sum rules

The BINGO Project I: Baryon Acoustic Oscillations from Integrated Neutral Gas Observations

The BINGO Project II: Instrument Description

The BINGO Project IV: Simulations for mission performance assessment and preliminary component separation steps

The BINGO Project VI: HI Halo Occupation Distribution and Mock Building

The BINGO Project VII: Cosmological Forecasts from 21cm Intensity Mapping

The collider tests of a leptophilic scalar for the anomalous magnetic moments

Thermodynamics of 2+1 dimensional Coulomb-Like Black Holes from Non Linear Electrodynamics with a traceless energy momentum tensor

Towards Speeding up Adversarial Training in Latent Spaces

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

A Modal-Space Method for Online Power System Steady-State Stability Monitoring

A New Software Framework for Traffic Engineering: Path Cardinality and the Effect of Multipath on Residual Capacity

A novel combination of theoretical analysis and data-driven method for reconstruction of structural defects

Accelerating linear solvers for Stokes problems with C++ metaprogramming

Acoustic black hole in Schwarzschild spacetime: quasi-normal modes, analogous Hawking radiation and shadows

Anapole mediated giant photothermal nonlinearity in nanostructured silicon

Attention-based Transducer for Online Speech Recognition

Centrality dependence of multiplicity fluctuations from a hydrodynamical approach

Creation of Synthetic Networked PMU Data: A Generative Adversarial Network Approach

Design-controlled Synthesis of IrO$_2$ sub-monolayers on Au Nanodendrites: Marrying Plasmonic and Electrocatalytic Properties