arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 685 records · Page 38Linked to original sources

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↗

arXiv2019

GraphNAS: Graph Neural Architecture Search with Reinforcement Learning

Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.

cs.LG↗

arXiv2019

Interaction Control of Ultracold Alkaline-Earth Atoms

Ultracold alkaline-earth atoms have now been widely explored for precision measurements and quantum simulation. Because of its unique atomic structure, alkaline earth atoms possess great advantages for quantum simulation and studying quantum many-body matters, such as simulating synthetic gauge field, Kondo physics and $SU(N)$ physics. To fully explore the potential of ultracold alkaline-earth atoms, these systems also need to be equipped with the capability of tuning the inter-atomic interaction to the strongly interacting regime. Recently several theoretical proposals and experimental demonstrations have shown that both spin-independent and spin-exchanging interaction can be tuned to resonance. In this perspective, we will review these progress and discuss the new opportunities brought by these interaction control tools for future quantum simulation studies with ultracold alkaline-earth atoms.

cond-mat.quant-gas↗

arXiv2019

Unsupervised Online Video Object Segmentation with Motion Property Understanding

Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in literature and their performance is still far from satisfactory, because the complementary information from future frames cannot be processed under online setting. To solve this challenging problem, in this paper, we propose a novel Unsupervised Online Video Object Segmentation (UOVOS) framework by construing the motion property to mean moving in concurrence with a generic object for segmented regions. By incorporating salient motion detection and object proposal, a pixel-wise fusion strategy is developed to effectively remove detection noise such as dynamic background and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is employed to deal with unreliable motion detection and object proposals. Experimental results on several benchmark datasets demonstrate the efficacy of the proposed method. Compared to the state-of-the-art unsupervised online segmentation algorithms, the proposed method achieves an absolute gain of 6.2%. Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset. Our code is available on the project website: https://github.com/visiontao/uovos.

cs.CV↗

arXiv2019

REAPS: Towards Better Recognition of Fine-grained Images by Region Attending and Part Sequencing

Fine-grained image recognition has been a hot research topic in computer vision due to its various applications. The-state-of-the-art is the part/region-based approaches that first localize discriminative parts/regions, and then learn their fine-grained features. However, these approaches have some inherent drawbacks: 1) the discriminative feature representation of an object is prone to be disturbed by complicated background; 2) it is unreasonable and inflexible to fix the number of salient parts, because the intended parts may be unavailable under certain circumstances due to occlusion or incompleteness, and 3) the spatial correlation among different salient parts has not been thoroughly exploited (if not completely neglected). To overcome these drawbacks, in this paper we propose a new, simple yet robust method by building part sequence model on the attended object region. Concretely, we first try to alleviate the background effect by using a region attention mechanism to generate the attended region from the original image. Then, instead of localizing different salient parts and extracting their features separately, we learn the part representation implicitly by applying a mapping function on the serialized features of the object. Finally, we combine the region attending network and the part sequence learning network into a unified framework that can be trained end-to-end with only image-level labels. Our extensive experiments on three fine-grained benchmarks show that the proposed method achieves the state of the art performance.

cs.CV↗

arXiv2019

Plate-like precipitate effects on plasticity of Al-Cu micro-pillar: {100}-interfacial slip

In this paper, we study the effects of $θ^\prime$-Al$_2$Cu plate-like precipitates on the plasticity of Al-Cu micro-pillars, with a sample size allowing the precipitates to cross the entire micro-pillar. {100}-slip traces are identified for the first time in Al and Al alloys at room temperature. We investigate the underlying mechanisms of this unusual {100}-slip, and show that it operates along the coherent $θ^\prime$-Al$_2$Cu precipitate/$α$-Al matrix interface. A combination of molecular dynamics simulations and stress analysis indicates that screw dislocations can cross-slip from the {111} plane onto the {100} $θ^\prime$-Al$_2$Cu/$α$-Al interface, then move on it through a kink-pair mechanism, providing a reasonable explanation to the observed {100}-slips. The roles of the $θ^\prime$-Al$_2$Cu/$α$-Al matrix interface on the properties of interfacial dislocations are studied within the Peierls-Nabarro framework, showing that the interface can stabilize the {100} screw dislocations from the spreading of the core, and increases the Peierls stress. These results improve our understanding of the mechanical behavior of Al-Cu micro-pillars at room temperature, and imply an enhanced role of interfacial slip in Al-Cu based alloys at elevated temperature in consideration of the underlying kink-pair mechanism.

cond-mat.mtrl-sci↗

arXiv2019

Plate-like precipitate effects on plasticity of Al-Cu alloys at micrometer to submicrometer scales

The continuous miniaturization of modern electromechanical systems calls for a comprehensive understanding of the mechanical properties of metallic materials specific to micrometer and sub-micrometer scales. At these scales, the nature of dislocation-mediated plasticity changes radically: sub-micrometer metallic samples exhibit high yield strengths, however accompanied by detrimental intermittent strain fluctuations compromising forming processes and endangering structural stability. In this paper, we studied the effects of plate-like $θ^\prime$-Al$_2$Cu precipitates on the strength, plastic fluctuations and deformation mechanisms of Al-Cu alloys from micro-pillar compression testing. The plate-like precipitates have diameters commensurate with the external size of the Al-Cu micro-pillars. Our results show that these plate-like precipitates can strengthen the materials and suppress plastic fluctuations efficiently at large sample sizes ($\geq 3 μm$). However, the breakdown of the mean-field pinning landscape at smaller scales weakens its taming effect on intermittency. Over an intermediate range of sample sizes allowing the precipitates to cross the entire pillar, an enhanced apparent strain hardening and a sharp decrease of jerkiness are observed, in association with the presence of {100}-slip traces along the coherent $θ^\prime$-Al$_2$Cu precipitate/$α$-Al matrix interface and precipitate shearing. These complex effects of plate-like precipitates on plasticity are analyzed, experimentally and theoretically, in view of the interferences between external and internal sizes, and the related modifications of the underlying plastic mechanisms.

cond-mat.mtrl-sci↗

arXiv2019

A new Majorana platform in an Fe-As bilayer superconductor

Recently, iron-chalcogenide superconductors have emerged as a new and promising platform for studying and manipulating Majorana zero mode (MZM). By combining topological band structure and superconductivity in a multiband material, they provide significant advantages such as higher superconducting transition temperature (Tc) and isolated Majorana mode. However, iron-chalcogenide superconductors, especially Fe(Te,Se), suffer from strong inhomogeneity which may hamper their practical application. On the other hand, some iron-pnictide (Fe-As) superconductors, such as LiFeAs, have been demonstrated to have a similar topological band structure, yet no MZM has been observed in its vortex cores, raising a question of universality of MZM presence in iron-based superconductors. In this work, by using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/spectroscopy, we identify the first Fe-As superconductor CaKFe4As4 (Tc = 35 K) which has both the superconducting Dirac surface states and the MZMs inside its vortex cores. The topological band inversion is largely due to the down-shift of the pz band caused by the bilayer band folding in this material. More strikingly, the energies and spatial line profiles of MZM and multiple quantized Caroli-de Gennes-Matricon bound states observed inside the topological vortex can be accurately reproduced by a simple theoretical model derived from a surface Dirac cone, firmly establishing Majorana nature of the zero mode.

cond-mat.supr-con↗

arXiv2019

Unified Viscous-to-inertial Scaling in Liquid Droplet Coalescence

This letter presents a theory on the coalescence of two spherical liquid droplets that are initially stationary. The evolution of the radius of a liquid neck formed upon coalescence was formulated as an initial value problem and then solved to yield an exact solution without free parameters, with its two asymptotic approximations reproducing the well-known scaling relations in the viscous and inertial regimes. The viscous-to-inertial crossover observed by Paulsen et al. [Phys. Rev. Lett. 106, 114501 (2011)] is also recovered by the theory, rendering the collapse of data of different viscosities onto a single curve.

physics.flu-dyn↗

arXiv2019

Using Restart Heuristics to Improve Agent Performance in Angry Birds

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However none of these agents attempt one of the key strategies which humans employ to solve Angry Birds levels, which is restarting levels. Restarting is important in Angry Birds because sometimes the level is no longer solvable or some given shot made has little to no benefit towards the ultimate goal of the game. This paper proposes a framework and experimental evaluation for when to restart levels in Angry Birds. We demonstrate that restarting is a viable strategy to improve agent performance in many cases.

cs.AI↗

arXiv2019

Two-body bound state of ultracold Fermi atoms with two-dimensional spin-orbit coupling

In a recent experiment, a two-dimensional spin-orbit coupling (SOC) was realized for fermions in the continuum [Nat. Phys. 12, 540 (2016)], which represents an important step forward in the study of synthetic gauge field using cold atoms. In the experiment, it was shown that a Raman-induced two-dimensional SOC exists in the dressed-state basis close to a Dirac point of the single-particle spectrum. By contrast, the short-range inter-atomic interactions of the system are typically expressed in the hyperfine-spin basis. The interplay between synthetic SOC and interactions can potentially lead to interesting few- and many-body phenomena but has so far eluded theoretical attention. Here we study in detail properties of two-body bound states of such a system. We find that, due to the competition between SOC and interaction, the stability region of the two-body bound state is in general reduced. Particularly, the threshold of the lowest two-body bound state is shifted to a positive, SOC-dependent scattering length. Furthermore, the center-of-mass momentum of the lowest two-body bound state becomes nonzero, suggesting the emergence of Fulde-Ferrell pairing states in a many-body setting. Our results reveal the critical difference between the experimentally realized two-dimensional SOC and the more symmetric Rashba or Dresselhaus SOCs in an interacting system, and paves the way for future characterizations of topological superfluid states in the experimentally relevant systems.

cond-mat.quant-gas↗

arXiv2019

Multicore fiber-based quantum access network

We propose a quantum access network based on multicore fiber (MCF) to scale up the number of users in quantum key distribution (QKD) networks. The MCF is used as feeder fiber and single core single mode fibers (SSMFs) are used as drop fibers. Quantum signals (QSs) are integrated with classical signals (CSs) in both MCF and SSMFs to save deployment cost since access networks are cost sensitive. Due to the integration, spontaneous Raman scattering (SRS) and intercore crosstalk (ICXT) are the main impairment sources to QKD. To alleviate the noise, we propose a core and wavelength assignment scheme (CWAS) that a dedicated core of MCF is used to transmit QSs and the wavelengths of QSs are set lower than those of upstream signals. Also, we demonstrate that wavelength time division multiplexing (WTDM) is suitable for QSs which are required to support large number of quantum users, since WTDM can realize higher secure key rate (SKR) than time division multiplexing (TDM) and require lower cost than wavelength division multiplexing (WDM). Finally, the proposed quantum access network is verified experimentally. The experiment results are consistent with our analysis. The properties of SRS in the proposed architecture are shown in the experiments through the quantum bit error rates (QBERs) in different experimental case, which verifies the superiority of the proposed CWAS. Also, the characteristics of the SKRs prove that the number of receivers has a great impact on the performance of QSs using WTDM.

quant-ph↗