arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 685 records · Page 38Linked to original sources

arXiv2020

Tight-Binding Kondo Model and Spin-Exchange Collision Rate of Alkaline-Earth Atoms in a Mixed-Dimensional Optical Lattice

We study the two-body problem of the ultracold fermionic alkaline-earth (like) atoms in the electronic $^1$S$_0$ state ($g$-state) and $^3$P$_0$ state ($e$-state), which are confined in a quasi-one-dimensional (quasi-1D) tube. In addition, in the axial direction, the $g$-atom experience a 1D optical lattice and the $e$-atom is localized by a harmonic potential. In this work, we propose two appropriate tight-binding models, which are applicable for the cases that the odd-wave scattering between the $g$- and $e$-atom is negligible or not, respectively. We further give a microscopic derivation for the inter-atomic interaction parameters of these tight-binding models, by exactly calculating the low-energy inter-atomic scattering amplitude. Our results show that, as one can predict, these interaction parameters can be efficiently controlled by the confinement potentials. We further exam the simple "projection approximation" with which one derives the interaction parameters by directly projecting the 3D Huang-Yang pseudopotential on the ground state of the confinement and the lowest band of the optical lattice. We find that one should be very careful about determining the interaction parameters in the tight-binding models. Furthermore, we calculate the spin-exchanging rate, which dependents on the incident quasi-momentum $k$ of the $g$-atom, for the recent experiment (L. Riegger, {\it et. al.,} Phys. Rev. Lett. {\bf 120}, 143601 (2018)) of $^{173}$Yb atoms in this quasi-(1+0)D system, and study finite-momentum effect in this experiment. Our results show that in this system the finite-momentum effect of the $g$-atom is very significant, and the momentum of the $g$-atoms in this experiment may be pretty high (already in the second Brillouin zone of the optical lattice)

cond-mat.quant-gas↗

arXiv2020

Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In this paper, we propose an adversarial machine learning (AML) approach, which can respond to a wide range of attack behaviors without detecting outliers. It strikes a balance between enhancing a system's robustness against cyberattacks and maintaining a reasonable degree of forecasting accuracy when there is no attack. Attack models and configurations for the adversarial training were selected and evaluated to achieve the desired level of performance in a simulation study. The results validate the effectiveness and excellent performance of the proposed method.

eess.SY↗

arXiv2020

Quantum-Secure Microgrid

Existing microgrid communication relies on classical public key systems, which are vulnerable to attacks from quantum computers. This paper uses quantum key distribution (QKD) to solve these quantum-era microgrid challenges. Specifically, this paper makes the following novel contributions: 1) it offers a QKD-based microgrid communication architecture for microgrids; 2) it shows how to build a quantum-secure microgrid testbed in an RTDS environment; 3) it develops a key pool sharing (KPS) strategy to improve the cyberattack resilience of the QKD-based microgrid; and 4) it analyzes the impacts of critical QKD parameters with the testbed. Test results provide insightful resources for building a quantum-secure microgrid.

cs.CR↗

arXiv2019

Universal Dynamics of a Degenerate Bose Gas Quenched to Unitarity

Motivated by an unexpected experimental observation from the Cambridge group, [Eigen {\it et al.,} Nature {\bf563}, 221 (2018)], we study the evolution of the momentum distribution of a degenerate Bose gas quenched from the weakly interacting to the unitarity regime. For the two-body problem, we establish a relation that connects the momentum distribution at a long time to a sub-leading term in the initial wave function. For the many-body problem, we employ the time-dependent Bogoliubov variational wave function and find that, in certain momentum regimes, the momentum distribution at long times displays the same exponential behavior found by the experiment. Moreover, we find that this behavior is universal and independent of the short-range details of the interaction potential. Consistent with the relation found in the two-body problem, we also numerically show that this exponential form is hidden in the same sub-leading term of the Bogoliubov wave function in the initial stages. Our results establish a consistent picture to understand the universal dynamics observed in the Cambridge experiment.

cond-mat.quant-gas↗

arXiv2019

A Two Dimensional Tunneling Resistance Transmission Line Model for Nanoscale Parallel Electrical Contacts

Contact resistance and current crowding are important to nanoscale electrical contacts. In this paper, we present a self-consistent model to characterize partially overlapped parallel contacts with varying specific contact resistivity along the contact length. For parallel tunneling contacts formed between contacting members separated by a thin insulating gap, we examine the local voltage-dependent variation of potential barrier height and tunneling current along the contact length, by solving the lumped circuit transmission line model (TLM) equations coupled with the tunneling current self consistently. The current and voltage distribution along the parallel tunneling contacts and their overall contact resistance are analyzed in detail, for various input voltage, electrical contact dimension, and material properties (i.e. work function, sheet resistance of the contact members, and permittivity of the insulating layer). It is found the existing one-dimensional (1D) tunneling junction models become less reliable when the tunneling layer thickness becomes smaller or the applied voltage becomes larger. In these regimes, the proposed self-consistent model may provide a more accurate evaluation of the parallel tunneling contacts. This work provides insights on the design, and potential engineering, of nanoscale electrical contacts with controlled current distribution and contact resistance via engineered spatially varying contact layer properties and geometry.

physics.app-ph↗

arXiv2019

Confinement-Induced Resonance with Weak Background Interaction

We studied the scattering problem of two distinguishable atoms with unequal mass, where one atom (atom $α$) is trapped in a quasi-one-dimensional (quasi-1D) tube and the other one (atom $β$) is localized by a 3D harmonic trap. We show that in such a system if atom $α$ is much heavier than $β$, confinement-induced resonance (CIR) can appear when the 3D $s$-wave scattering length $a_s$ of these two atoms is much smaller than the characteristic lengths (CLs) of the confinements, for either $a_s>0$ or $a_s<0$. This is quite different from the usual CIRs which occurs only when $a_s$ is comparable with the CL of confinement. Moreover, the CIRs we find are broad enough that can serve as a tool for the control of effective inter-atomic interaction. We further show the mechanism of these CIRs via the Born-Oppenheimer approximation. Our results can be used for the realization of strongly-interacting systems with ultracold atoms with weak 3D background interaction (i.e., small $a_s$), e.g., the realization of ultracold gases with strong spin-dependent interaction at zero magnetic fields.

cond-mat.quant-gas↗

arXiv2019

A Tensorized Transformer for Language Modeling

Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, has led to breakthroughs in Natural Language Processing (NLP) tasks. However, the multi-head attention mechanism, as a key component of Transformer, limits the effective deployment of the model to a resource-limited setting. In this paper, based on the ideas of tensor decomposition and parameters sharing, we propose a novel self-attention model (namely Multi-linear attention) with Block-Term Tensor Decomposition (BTD). We test and verify the proposed attention method on three language modeling tasks (i.e., PTB, WikiText-103 and One-billion) and a neural machine translation task (i.e., WMT-2016 English-German). Multi-linear attention can not only largely compress the model parameters but also obtain performance improvements, compared with a number of language modeling approaches, such as Transformer, Transformer-XL, and Transformer with tensor train decomposition.

cs.CL↗

arXiv2019

Dirac surface states in intrinsic magnetic topological insulators EuSn2As2 and MnBi2nTe3n+1

In magnetic topological insulators (TIs), the interplay between magnetic order and nontrivial topology can induce fascinating topological quantum phenomena, such as the quantum anomalous Hall effect, chiral Majorana fermions and axion electrodynamics. Recently, a great deal of attention has been focused on the intrinsic magnetic TIs, where disorder effects can be eliminated to a large extent, which is expected to facilitate the emergence of topological quantum phenomena. In despite of intensive efforts, experimental evidence of the topological surface states (SSs) remains elusive. Here, by combining first-principles calculations and angle-resolved photoemission spectroscopy (ARPES) experiments, we have revealed that EuSn2As2 is an antiferromagnetic TI with observation of Dirac SSs consistent with our prediction. We also observe nearly gapless Dirac SSs in antiferromagnetic TIs MnBi2nTe3n+1 (n = 1 and 2), which were absent in previous ARPES results. These results provide clear evidence for nontrivial topology of these intrinsic magnetic TIs. Furthermore, we find that the topological SSs show no observable changes across the magnetic transition within the experimental resolution, indicating that the magnetic order has quite small effect on the topological SSs, which can be attributed to weak hybridization between the localized magnetic moments, from either 4f or 3d orbitals, and the topological electronic states. This provides insights for further research that the correlations between magnetism and topological states need to be strengthened to induce larger gaps in the topological SSs, which will facilitate the realization of topological quantum phenomena at higher temperatures.

cond-mat.mtrl-sci↗

arXiv2019

Realization of nonlinear optical nonreciprocity on a few-photon level based on atoms strongly coupled to an asymmetric cavity

Optical nonreciprocity is important in photonic information processing to route the optical signal or prevent the reverse flow of noise. By adopting the strong nonlinearity associated with a few atoms in a strongly coupled cavity QED system and an asymmetric cavity configuration, we experimentally demonstrate the nonreciprocal transmission between two counterpropagating light fields with extremely low power. This nonreciprocity can even occur on a few-photon level due to the high optical nonlinearity of the system. The working power can be flexibly tuned by changing the effective number of atoms strongly coupled to the cavity. The idea and result can be applied to optical chips as optical diodes by using fiber-based cavity QED systems. Our work opens up new perspectives for realizing optical nonreciprocity on a few-photon level based on the nonlinearities of atoms strongly coupled to an optical cavity.

quant-ph↗

arXiv2019

Confinement Induced Resonance with Weak Bare Interaction in a Quasi 3+0 Dimensional Ultracold Gas

Confinement induced resonance (CIR) is a useful tool for the control of the interaction between ultracold atoms. In most cases the CIR occurs when the characteristic length atrap of the confinement is similar as the scattering length as of the two atoms in the free three-dimensional (3D) space. If there is a CIR which can occur with weak bare interaction, i.e., under the condition $a_{\rm{trap}}\gg a_s$, then it can be realized for much more systems, even without the help of a magnetic Feshbach resonance, and would be very useful. In a previous research by Massignan and Castin (Phys Rev A 74:013616, 2006), it was shown that it is possible to realize such a CIR in a quasi-(3+0)D system, where one ultracold atom is moving in the 3D space and another one is localized by a 3D harmonic trap. In this work we carefully investigate the properties of the CIRs in this system. We show that the CIR with $a_{\rm{trap}}\gg a_s$ can really occur, and the number of the CIRs of this type increases with the mass ratio between the moving and localized atoms. However, when $a_{\rm{trap}}\gg a_s$ the CIR becomes extremely narrow, and thus are difficult to be controlled in realistic experiments.

physics.atom-ph↗

arXiv2019

Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network

Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the complex domain has not yet thoroughly examined. This paper presents a novel complex-valued deep fully convolutional neural network (CV-FCN) designed for PolSAR image classification. Specifically, CV-FCN uses PolSAR CV data that includes the phase information and utilizes the deep FCN architecture that performs pixel-level labeling. It integrates the feature extraction module and the classification module in a united framework. Technically, for the particularity of PolSAR data, a dedicated complex-valued weight initialization scheme is defined to initialize CV-FCN. It considers the distribution of polarization data to conduct CV-FCN training from scratch in an efficient and fast manner. CV-FCN employs a complex downsampling-then-upsampling scheme to extract dense features. To enrich discriminative information, multi-level CV features that retain more polarization information are extracted via the complex downsampling scheme. Then, a complex upsampling scheme is proposed to predict dense CV labeling. It employs complex max-unpooling layers to greatly capture more spatial information for better robustness to speckle noise. In addition, to achieve faster convergence and obtain more precise classification results, a novel average cross-entropy loss function is derived for CV-FCN optimization. Experiments on real PolSAR datasets demonstrate that CV-FCN achieves better classification performance than other state-of-art methods.

eess.IV↗

arXiv2019

Protracted Kondo coherence with dilute carrier density in Cerium based nickel pnictides

Nozi$\grave{e}$res' exhaustion theory argues the temperature for coherently screening of all local moments in Kondo lattice could be much lower than the temperature of single moment screening with insufficient number of conduction electrons. Recent experiment [Luo et al, PNAS, 112,13520 (2015)] indicates the cerium based nickel pnictides $CeNi_{2-δ}As_2 (δ\approx0.28)$ with low carrier density is an ideal material to exam such protracted Kondo screening. Using the density functional theory and dynamical mean-field theory, we calculated the respective electronic structures of paramagnetic $CeNi_2As_2$/$CeNi_2P_2$. In contrast to structurally analogous layered iron pnictides, the electronic structures of the present systems show strong three-dimensionality with substantially small contributions of Ni-3d electrons to the carrier density. Moreover, we find significant Kondo resonance peaks in the compressed $CeNi_2As_2$ and $CeNi_2P_2$ at low temperatures, accompanied by topological changes of the Fermi surfaces. We also find similar quantum phase transition in $CeNi_2As_2$ driven by chemical pressure via the isovalence As$\rightarrow$P substitution.

cond-mat.str-el↗

arXiv2019

CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots

Natural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks - intent classification (IC) and slot labeling (SL), which are then followed by a dialogue management (DM) component. Such NLU systems cater to utterances in isolation, thus pushing the problem of context management to DM. However, contextual information is critical to the correct prediction of intents and slots in a conversation. Prior work on contextual NLU has been limited in terms of the types of contextual signals used and the understanding of their impact on the model. In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals, such as previous intents, slots, dialog acts and utterances over a variable context window, in addition to the current user utterance. CASA-NLU outperforms a recurrent contextual NLU baseline on two conversational datasets, yielding a gain of up to 7% on the IC task for one of the datasets. Moreover, a non-contextual variant of CASA-NLU achieves state-of-the-art performance for IC task on standard public datasets - Snips and ATIS.

cs.CL↗

arXiv2019

MarlRank: Multi-agent Reinforced Learning to Rank

When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are similar in terms of the same query, they are more likely to have similar relevance score. To mitigate this problem, in this paper, we propose a multi-agent reinforced ranking model, named MarlRank. In particular, by considering each document as an agent, we formulate the ranking process as a multi-agent Markov Decision Process (MDP), where the mutual interactions among documents are incorporated in the ranking process. To compute the ranking list, each document predicts its relevance to a query considering not only its own query-document features but also its similar documents features and actions. By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure. Our experimental results on two LETOR benchmark datasets show that our model has significant performance gains over the state-of-art baselines. We also find that the NDCG shows an overall increasing trend along with the step of interactions, which demonstrates that the mutual information among documents helps improve the ranking performance.

cs.LG↗

arXiv2019

Calibration of liquid argon detector with $^{83m}Kr$ and $^{22}Na$ in different drift field

$^{83m}Kr$ and $^{22}Na$ have been used in calibrating a liquid argon (LAr) detector.$^{83m}Kr$ atoms are produced through the decay of $^{83}Rb$ and introduced into the LAr detector through the circulating purification system. The light yield reaches 7.26$\pm$0.02 photonelectrons/keV for 41.5keV from $^{83m}Kr$ and 7.66$\pm$0.01 photonelectrons/keV for the 511keV from $^{22}Na$, as a comparison. The light yield varies with the drift electric field from 50 to 200V/cm have been also reported. After stopping fill, the decay rate of $^{83m}Kr$ with a fitted half-life of 1.83$\pm$0.11 h, which is consistent with the reported value of 1.83$\pm$0.02 h.

physics.ins-det↗

arXiv2019

Minimum Label s-t Cut has Large Integrality Gaps

Given a graph G=(V,E) with a label set L = {l_1, l_2, ..., l_q}, in which each edge has a label from L, a source s in V, and a sink t in V, the Min Label s-t Cut problem asks to pick a set L' subseteq L of labels with minimized cardinality, such that the removal of all edges with labels in L' from G disconnects s and t. This problem comes from many applications in real world, for example, information security and computer networks. In this paper, we study two linear programs for Min Label s-t Cut, proving that both of them have large integrality gaps, namely, Omega(m) and Omega(m^{1/3-epsilon}) for the respective linear programs, where m is the number of edges in the graph and epsilon > 0 is any arbitrarily small constant. As Min Label s-t Cut is NP-hard and the linear programming technique is a main approach to design approximation algorithms, our results give negative answer to the hope that designs better approximation algorithms for Min Label s-t Cut that purely rely on linear programming.

cs.DS↗

arXiv2019

Explainable Video Action Reasoning via Prior Knowledge and State Transitions

Human action analysis and understanding in videos is an important and challenging task. Although substantial progress has been made in past years, the explainability of existing methods is still limited. In this work, we propose a novel action reasoning framework that uses prior knowledge to explain semantic-level observations of video state changes. Our method takes advantage of both classical reasoning and modern deep learning approaches. Specifically, prior knowledge is defined as the information of a target video domain, including a set of objects, attributes and relationships in the target video domain, as well as relevant actions defined by the temporal attribute and relationship changes (i.e. state transitions). Given a video sequence, we first generate a scene graph on each frame to represent concerned objects, attributes and relationships. Then those scene graphs are linked by tracking objects across frames to form a spatio-temporal graph (also called video graph), which represents semantic-level video states. Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans. Compared to previous works, the action reasoning results of our method can be explained by both logical rules and semantic-level observations of video content changes. Besides, the proposed method can be used to detect multiple concurrent actions with detailed information, such as who (particular objects), when (time), where (object locations) and how (what kind of changes). Experiments on a re-annotated dataset CAD-120 show the effectiveness of our method.

cs.CV↗

arXiv2019

Optically active beams: non-reciprocal optical activity in free space induced by spin-orbital interaction of light

Optical activity, the power of a medium to rotate the polarization of a light beam, has contributed significantly to molecular structure assessments in stereochemistry, biomolecular science and crystallography. Thus far, it is commonly believed that optical activity is manifested only in the chiral media which can give rise to circular birefringence of light. Here, we experimentally demonstrate that free space can also support the implementation of non-reciprocal optical activity with Bessel beams by spin-orbital interaction. Specifically, non-diffractive optically active beams are realized, with their optical rotatory power readily controlled by simple optical elements. We show that such free-space optical activity can be exploited to form non-reciprocal optical components such as polarization rotators, isolators, and circulators. Our results may bring about new possibilities of media-independent optical activity to other transverse waves ranging from radio to optical frequencies.

physics.optics↗