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Xiaoyang Wang

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

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cond-mat.str-elcond-mat.supr-conMachine Learningcond-mat.mtrl-sciArtificial IntelligenceComputation and LanguageNetworking and Internet Architecturehep-latquant-phComputer Visioncond-mat.mes-hallDatabaseseess.SPCryptography and SecurityDistributed, Parallel, and Cluster ComputingHardware Architecture

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Published work

35 published item(s)

preprint2026arXiv

A Versatile Multimodal Agent for Multimedia Content Generation

With the advancement of AIGC (AI-generated content) technologies, an increasing number of generative models are revolutionizing fields such as video editing, music generation, and even film production. However, due to the limitations of current AIGC models, most models can only serve as individual components within specific application scenarios and are not capable of completing tasks end-to-end in real-world applications. In real-world applications, editing experts often work with a wide variety of images and video inputs, producing multimodal outputs -- a video typically includes audio, text, and other elements. This level of integration across multiple modalities is something current models are unable to achieve effectively. However, the rise of agent-based systems has made it possible to use AI tools to tackle complex content generation tasks. To deal with the complex scenarios, in this paper, we propose a MultiMedia-Agent designed to automate complex content creation. Our agent system includes a data generation pipeline, a tool library for content creation, and a set of metrics for evaluating preference alignment. Notably, we introduce the skill acquisition theory to model the training data curation and agent training. We designed a two-stage correlation strategy for plan optimization, including self-correlation and model preference correlation. Additionally, we utilized the generated plans to train the MultiMedia-Agent via a three stage approach including base/success plan finetune and preference optimization. The comparison results demonstrate that the our approaches are effective and the MultiMedia-Agent can generate better multimedia content compared to novel models.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Anchor-guided Hypergraph Condensation with Dual-level Discrimination

The increasing prevalence of large-scale hypergraphs poses significant computational challenges for hypergraph neural network (HNN) training. To address this, hypergraph condensation (HGC) distills large real hypergraphs into compact yet informative synthetic ones, beyond graph condensation (GC) methods limited to pairwise relations. However, existing HGC methods rely on decoupled training architectures, where structure generators are pre-trained on the original hypergraph but not jointly optimized with condensed features during refinement, resulting in misaligned structures that degrade downstream utility. Moreover, trajectory-based optimization incurs substantial computational overhead in refinement, limiting condensation efficiency. To tackle these issues, we propose \textbf{A}nchor-guided \textbf{H}yper\textbf{G}raph \textbf{C}ondensation with \textbf{D}ual-level \textbf{D}iscrimination (\textbf{AHGCDD}), which consists of three key components: (1) a node initialization module based on Heat Kernel PageRank (HKPR) to encode structural knowledge into feature semantics; (2) an anchor-guided hyperedge synthesis strategy for joint optimization of condensed features and structure; (3) a theoretically grounded dual-level discrimination objective for utility-preserving condensation without redundant HNN training. Extensive experiments demonstrate the superior effectiveness and efficiency of AHGCDD.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PrivGemo: Privacy-Preserving Dual-Tower Graph Retrieval for Empowering LLM Reasoning with Memory Augmentation

Knowledge graphs (KGs) provide structured evidence that can ground large language model (LLM) reasoning for knowledge-intensive question answering. However, many practical KGs are private, and sending retrieved triples or exploration traces to closed-source LLM APIs introduces leakage risk. Existing privacy treatments focus on masking entity names, but they still face four limitations: structural leakage under semantic masking, uncontrollable remote interaction, fragile multi-hop and multi-entity reasoning, and limited experience reuse for stability and efficiency. To address these issues, we propose PrivGemo, a privacy-preserving retrieval-augmented framework for KG-grounded reasoning with memory-guided exposure control. PrivGemo uses a dual-tower design to keep raw KG knowledge local while enabling remote reasoning over an anonymized view that goes beyond name masking to limit both semantic and structural exposure. PrivGemo supports multi-hop, multi-entity reasoning by retrieving anonymized long-hop paths that connect all topic entities, while keeping grounding and verification on the local KG. A hierarchical controller and a privacy-aware experience memory further reduce unnecessary exploration and remote interactions. Comprehensive experiments on six benchmarks show that PrivGemo achieves overall state-of-the-art results, outperforming the strongest baseline by up to 17.1%. Furthermore, PrivGemo enables smaller models (e.g., Qwen3-4B) to achieve reasoning performance comparable to that of GPT-4-Turbo.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Universal Graph Backdoor Defense: A Feature-based Homophily Perspective

Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) poses a significant barrier to broader adoption in high-stakes applications. Despite recent advances in graph backdoor defense (GBD), existing methods primarily focus on subgraph-based GBAs, relying on the assumption that poisoned target nodes are explicitly connected to subgraph triggers. Our empirical results reveal that such structure-centric approaches fail to defend against emerging feature-based GBAs that preserve graph topology. Therefore, in this paper, we study a novel problem of universal graph backdoor defense. First, we investigate the shared effects of both attack types from a feature-based homophily perspective, which characterizes local feature consistency between nodes and their neighborhoods. Thorough theoretical and empirical analyses demonstrate that, regardless of trigger mechanisms, backdoors induced by GBAs exhibit lower feature-based homophily than clean nodes, indicating a discrepancy in local feature similarity. Motivated by this insight, we propose to leverage node-level local feature consistency, modeled by a neighbor-aware reconstruction loss, to distinguish backdoors from clean nodes. Then, a robust training strategy is developed to eliminate trigger effects while reducing noise induced by detection uncertainty. Extensive experiments demonstrate that our framework significantly degrades the attack success rate and maintains competitive clean accuracy under both subgraph-based and feature-based attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets

The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often rely on heuristic aggregation without a systematic understanding of how individual samples contribute to model performance. In this report, we propose a paradigm shift from ad-hoc curation to a closed-loop dataset engineering framework using OpenDataArena (ODA), which leverages value-anchored rankings and multi-dimensional analysis to transform value benchmarking into feedback signals guiding dataset construction. We instantiate this methodology through two new datasets: \textbf{ODA-Math-460k}, a specialized mathematics reasoning dataset that utilizes a novel two-stage difficulty-aware pipeline to achieve State-of-the-Art (SOTA) results on benchmarks such as AIME and HMMT, and \textbf{ODA-Mixture (100k \& 500k)}, a series of multi-domain instruction datasets built via an ``Anchor-and-Patch'' strategy that outperforms significantly larger open-source baselines. Our empirical results demonstrate that ODA-driven datasets significantly improve both domain-specific reasoning and general utility while achieving superior data efficiency, validating a transition toward data-centric AI where transparent evaluation serves as the primary engine for engineering high-quality training data.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Towards Improving Interpretability of Language Model Generation through a Structured Knowledge Discovery Approach

Knowledge-enhanced text generation aims to enhance the quality of generated text by utilizing internal or external knowledge sources. While language models have demonstrated impressive capabilities in generating coherent and fluent text, the lack of interpretability presents a substantial obstacle. The limited interpretability of generated text significantly impacts its practical usability, particularly in knowledge-enhanced text generation tasks that necessitate reliability and explainability. Existing methods often employ domain-specific knowledge retrievers that are tailored to specific data characteristics, limiting their generalizability to diverse data types and tasks. To overcome this limitation, we directly leverage the two-tier architecture of structured knowledge, consisting of high-level entities and low-level knowledge triples, to design our task-agnostic structured knowledge hunter. Specifically, we employ a local-global interaction scheme for structured knowledge representation learning and a hierarchical transformer-based pointer network as the backbone for selecting relevant knowledge triples and entities. By combining the strong generative ability of language models with the high faithfulness of the knowledge hunter, our model achieves high interpretability, enabling users to comprehend the model output generation process. Furthermore, we empirically demonstrate the effectiveness of our model in both internal knowledge-enhanced table-to-text generation on the RotoWireFG dataset and external knowledge-enhanced dialogue response generation on the KdConv dataset. Our task-agnostic model outperforms state-of-the-art methods and corresponding language models, setting new standards on the benchmark.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

InFoBench: Evaluating Instruction Following Ability in Large Language Models

This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs' compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR's higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Federated Meta-Learning for Traffic Steering in O-RAN

The vision of 5G lies in providing high data rates, low latency (for the aim of near-real-time applications), significantly increased base station capacity, and near-perfect quality of service (QoS) for users, compared to LTE networks. In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi. Each radio access technology (RAT) provides different types of access, and these should be allocated and managed optimally among the users. Besides resource management, 5G systems will also support a dual connectivity service. The orchestration of the network therefore becomes a more difficult problem for system managers with respect to legacy access technologies. In this paper, we propose an algorithm for RAT allocation based on federated meta-learning (FML), which enables RAN intelligent controllers (RICs) to adapt more quickly to dynamically changing environments. We have designed a simulation environment which contains LTE and 5G NR service technologies. In the simulation, our objective is to fulfil UE demands within the deadline of transmission to provide higher QoS values. We compared our proposed algorithm with a single RL agent, the Reptile algorithm and a rule-based heuristic method. Simulation results show that the proposed FML method achieves higher caching rates at first deployment round 21% and 12% respectively. Moreover, proposed approach adapts to new tasks and environments most quickly amongst the compared methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. The reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) The irregularity of graphs can result in severe resource under-utilization and load-imbalance problems. This paper presents a GNNear accelerator to tackle these challenges. GNNear adopts a DIMM-based memory system to provide sufficient memory capacity. To match the heterogeneous nature of GNN training, we offload the memory-intensive Reduce operations to in-DIMM Near-Memory-Engines (NMEs), making full use of the high aggregated local bandwidth. We adopt a Centralized-Acceleration-Engine (CAE) to process the computation-intensive Update operations. We further propose several optimization strategies to deal with the irregularity of input graphs and improve GNNear's performance. Comprehensive evaluations on 16 GNN training tasks demonstrate that GNNear achieves 30.8$\times$/2.5$\times$ geomean speedup and 79.6$\times$/7.3$\times$(geomean) higher energy efficiency compared to Xeon E5-2698-v4 CPU and NVIDIA V100 GPU.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Measurement Error Mitigation in Quantum Computers Through Classical Bit-Flip Correction

We develop a classical bit-flip correction method to mitigate measurement errors on quantum computers. This method can be applied to any operator, any number of qubits, and any realistic bit-flip probability. We first demonstrate the successful performance of this method by correcting the noisy measurements of the ground-state energy of the longitudinal Ising model. We then generalize our results to arbitrary operators and test our method both numerically and experimentally on IBM quantum hardware. As a result, our correction method reduces the measurement error on the quantum hardware by up to one order of magnitude. We finally discuss how to pre-process the method and extend it to other errors sources beyond measurement errors. For local Hamiltonians, the overhead costs are polynomial in the number of qubits, even if multi-qubit correlations are included.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sim2real for Reinforcement Learning Driven Next Generation Networks

The next generation of networks will actively embrace artificial intelligence (AI) and machine learning (ML) technologies for automation networks and optimal network operation strategies. The emerging network structure represented by Open RAN (O-RAN) conforms to this trend, and the radio intelligent controller (RIC) at the centre of its specification serves as an ML applications host. Various ML models, especially Reinforcement Learning (RL) models, are regarded as the key to solving RAN-related multi-objective optimization problems. However, it should be recognized that most of the current RL successes are confined to abstract and simplified simulation environments, which may not directly translate to high performance in complex real environments. One of the main reasons is the modelling gap between the simulation and the real environment, which could make the RL agent trained by simulation ill-equipped for the real environment. This issue is termed as the sim2real gap. This article brings to the fore the sim2real challenge within the context of O-RAN. Specifically, it emphasizes the characteristics, and benefits that the digital twins (DT) could have as a place for model development and verification. Several use cases are presented to exemplify and demonstrate failure modes of the simulations trained RL model in real environments. The effectiveness of DT in assisting the development of RL algorithms is discussed. Then the current state of the art learning-based methods commonly used to overcome the sim2real challenge are presented. Finally, the development and deployment concerns for the RL applications realisation in O-RAN are discussed from the view of the potential issues like data interaction, environment bottlenecks, and algorithm design.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Using classical bit-flip correction for error mitigation including 2-qubit correlations

We present an error mitigation scheme which corrects readout errors on Noisy Intermediate-Scale Quantum (NISQ) computers [1,2]. After a short review of applying the method to one qubit, we proceed to discuss the case when correlations between different qubits occur. We demonstrate how the readout error can be mitigated in this case. By performing experiments on IBMQ hardware, we show that such correlations do not have a strong effect on the results, justifying to neglect them.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model

Measuring customer experience on mobile data is of utmost importance for global mobile operators. The reference signal received power (RSRP) is one of the important indicators for current mobile network management, evaluation and monitoring. Radio data gathered through the minimization of drive test (MDT), a 3GPP standard technique, is commonly used for radio network analysis. Collecting MDT data in different geographical areas is inefficient and constrained by the terrain conditions and user presence, hence is not an adequate technique for dynamic radio environments. In this paper, we study a generative model for RSRP prediction, exploiting MDT data and a digital twin (DT), and propose a data-driven, two-tier neural network (NN) model. In the first tier, environmental information related to user equipment (UE), base stations (BS) and network key performance indicators (KPI) are extracted through a variational autoencoder (VAE). The second tier is designed as a likelihood model. Here, the environmental features and real MDT data features are adopted, formulating an integrated training process. On validation, our proposed model that uses real-world data demonstrates an accuracy improvement of about 20% or more compared with the empirical model and about 10% when compared with a fully connected prediction network.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Optimization of graded filleted lattice structures subject to yield and buckling constraints

To reduce the stress concentration and ensure the structural safety for lattice structure designs, in this paper, a new optimization framework is developed for the optimal design of graded lattice structures, innovatively integrating fillet designs as well as yield and elastic buckling constraints. Both strut and fillet radii are defined as design variables. Homogenization method is employed to characterize the effective elastic constants and yield stresses of the lattice metamaterials. Metamaterial models are developed to represent the relationships between the metamaterial effective properties and lattice geometric variables. A yield constraint, based on the modified Hills yield criterion, is developed as a function of relative strut radii and fillet parameters. An elastic buckling constraint, based on the Euler buckling formula and the Johnson formula, is developed as a function of relative strut radii. Both yield and buckling constraints are integrated into an optimization problem formulation; a new optimization framework is proposed and a case study of minimizing the compliance of a Messerschmitt-Bolkow-Blohm beam is conducted. The yield and buckling constraints guarantee the safety of the optimized beams composed of BCC and PC lattices. Reductions in compliance and stress concentration are achieved by the optimized MBB beams.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Robusta: Robust AutoML for Feature Selection via Reinforcement Learning

Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks. As ML systems are increasingly being used in a variety of mission-critical applications, improving the robustness of ML systems has become of utmost importance. In this paper, we propose the first robust AutoML framework, Robusta--based on reinforcement learning (RL)--to perform feature selection, aiming to select features that lead to both accurate and robust ML systems. We show that a variation of the 0-1 robust loss can be directly optimized via an RL-based combinatorial search in the feature selection scenario. In addition, we employ heuristics to accelerate the search procedure based on feature scoring metrics, which are mutual information scores, tree-based classifiers feature importance scores, F scores, and Integrated Gradient (IG) scores, as well as their combinations. We conduct extensive experiments and show that the proposed framework is able to improve the model robustness by up to 22% while maintaining competitive accuracy on benign samples compared with other feature selection methods.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Self-play Learning Strategies for Resource Assignment in Open-RAN Networks

Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs), which allows flexible software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the mapping of variable RU requirements to local mobile edge computing centres for future centralized processing would significantly reduce the power consumption in cellular networks. In this paper, we study the RU-DU resource assignment problem in an ORAN system, modelled as a 2D bin packing problem. A deep reinforcement learning-based self-play approach is proposed to achieve efficient RU-DU resource management, with AlphaGo Zero inspired neural Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing environment and real sites data show that the self-play learning strategy achieves intelligent RU-DU resource assignment for different network conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Towards Quantum Simulations in Particle Physics and Beyond on Noisy Intermediate-Scale Quantum Devices

We review two algorithmic advances that bring us closer to reliable quantum simulations of model systems in high energy physics and beyond on noisy intermediate-scale quantum (NISQ) devices. The first method is the dimensional expressivity analysis of quantum circuits, which allows for constructing minimal but maximally expressive quantum circuits. The second method is an efficient mitigation of readout errors on quantum devices. Both methods can lead to significant improvements in quantum simulations, e.g., when variational quantum eigensolvers are used.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Matrix Factorization (MF) has been widely applied in machine learning and data mining. A large number of algorithms have been studied to factorize matrices. Among them, stochastic gradient descent (SGD) is a commonly used method. Heterogeneous systems with multi-core CPUs and GPUs have become more and more promising recently due to the prevalence of GPUs in general-purpose data-parallel applications. Due to the large computational cost of MF, we aim to improve the efficiency of SGD-based MF computation by utilizing the massive parallel processing power of heterogeneous multiprocessors. The main challenge in parallel SGD algorithms on heterogeneous CPU-GPU systems lies in the granularity of the matrix division and the strategy to assign tasks. We design a novel strategy to divide the matrix into a set of blocks by considering two aspects. First, we observe that the matrix should be divided nonuniformly, and relatively large blocks should be assigned to GPUs to saturate the computing power of GPUs. In addition to exploiting the characteristics of hardware, the workloads assigned to two types of hardware should be balanced. Aiming at the final division strategy, we design a cost model tailored for our problem to accurately estimate the performance of hardware on different data sizes. A dynamic scheduling policy is also used to further balance workloads in practice. Extensive experiments show that our proposed algorithm achieves high efficiency with a high quality of training quality.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Location Anomalies Detection for Connected and Autonomous Vehicles

Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres will have access to suitably anonymized CAV mobility information. Safe and secure operations will then be contingent on early detection of anomalies. In this paper, a novel unsupervised learning model based on deep autoencoder is proposed to detect the self-reported location anomaly in CAVs, using vehicle locations and the Received Signal Strength Indicator (RSSI) as features. Quantitative experiments on simulation datasets show that the proposed approach is effective and robust in detecting self-reported location anomalies.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Disappearance of Nodal Gap across the Insulator-Superconductor Transition in a Copper-Oxide Superconductor

The parent compound of the copper-oxide high temperature superconductors is a Mott insulator. Superconductivity is realized by doping an appropriate amount of charge carriers. How a Mott insulator transforms into a superconductor is crucial in understanding the unusual physical properties of high temperature superconductors and the superconductivity mechanism. Here we report high resolution angle-resolved photoemission measurement on heavily underdoped Bi2Sr2-xLaxCuO6+d system. The electronic structure of the lightly-doped samples exhibit a number of characteristics: existence of an energy gap along the nodal direction, d-wave-like anisotropic energy gap along the underlying Fermi surface, and coexistence of a coherence peak and a broad hump in the photoemission spectra. Our results reveal a clear insulator-superconductor transition at a critical doping level of ~0.10 where the nodal energy gap approaches zero, the three-dimensional antiferromagnetic order disappears, and superconductivity starts to emerge. These observations clearly signal a close connection between the nodal gap, antiferromagnetism and superconductivity.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Orbital-Selective Spin Texture and its Manipulation in a Topological Insulator

Topological insulators represent a new quantum state of matter that are insulating in the bulk but metallic on the edge or surface. In the Dirac surface state, it is well-established that the electron spin is locked with the crystal momentum. Here we report a new phenomenon of the spin texture locking with the orbital texture in a topological insulator Bi2Se3. We observe light-polarization-dependent spin texture of both the upper and lower Dirac cones that constitutes strong evidence of the orbital-dependent spin texture in Bi2Se3. The different spin texture detected in variable polarization geometry is the manifestation of the spin-orbital texture in the initial state combined with the photoemission matrix element effects. Our observations provide a new orbital degree of freedom and a new way of light manipulation in controlling the spin structure of the topological insulators that are important for their future applications in spin-related technologies.

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preprint2014arXiv

Weak Electron-Phonon Coupling and Unusual Electron Scattering of Topological Surface States in Sb(111) by Laser-Based Angle-Resolved Photoemission Spectroscopy

High resolution laser-based angle-resolved photoemission measurements have been carried out on Sb(111) single crystal. Two kinds of Fermi surface sheets are observed that are derived from the topological surface states: one small hexagonal electron-like Fermi pocket around $Γ$ point and the other six elongated lobes of hole-like Fermi pockets around the electron pocket. Clear Rashba-type band splitting due to the strong spin-orbit coupling is observed that is anisotropic in the momentum space. Our super-high-resolution ARPES measurements reveal no obvious kink in the surface band dispersions indicating a weak electron-phonon interaction in the surface states. In particular, the electron scattering rate for these topological surface states is nearly a constant over a large energy window near the Fermi level that is unusual in terms of the conventional picture.

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preprint2013arXiv

Tunable Dirac Fermion Dynamics in Topological Insulators

Three-dimensional topological insulators are characterized by insulating bulk state and metallic surface state involving Dirac fermions that behave as massless relativistic particles. These Dirac fermions are responsible for achieving a number of novel and exotic quantum phenomena in the topological insulators and for their potential applications in spintronics and quantum computations. It is thus essential to understand the electron dynamics of the Dirac fermions, i.e., how they interact with other electrons, phonons and disorders. Here we report super-high resolution angle-resolved photoemission studies on the Dirac fermion dynamics in the prototypical Bi2(Te,Se)3 topological insulators. We have directly revealed signatures of the electron-phonon coupling in these topological insulators and found that the electron-disorder interaction is the dominant factor in the scattering process. The Dirac fermion dynamics in Bi2(Te3-xSex) topological insulators can be tuned by varying the composition, x, or by controlling the charge carriers. Our findings provide crucial information in understanding the electron dynamics of the Dirac fermions in topological insulators and in engineering their surface state for fundamental studies and potential applications.

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preprint2012arXiv

Coexistence of Two Sharp-Mode Couplings and Their Unusual Momentum Dependence in the Superconducting State of Bi2Sr2CaCu2O8+d Superconductor Revealed by Laser-Based Angle-Resolved Photoemission

Super-high resolution laser-based angle-resolved photoemission measurements have been carried out on Bi2Sr2CaCu2O8+d (Bi2212) superconductors to investigate momentum dependence of electron coupling with collective excitations (modes). Two coexisting energy scales are clearly revealed over a large momentum space for the first time in the superconducting state of an overdoped Bi2212 superconductor. These two energy scales exhibit distinct momentum dependence: one keeps its energy near 78 meV over a large momentum space while the other changes its energy from $\sim$40 meV near the antinodal region to $\sim$70 meV near the nodal region. These observations provide a new picture on momentum evolution of electron-boson coupling in Bi2212 that electrons are coupled with two sharp modes simultaneously over a large momentum space in the superconducting states. Their unusual momentum dependence poses a challenge to our current understanding of electron-mode-coupling and its role for high temperature superconductivity in cuprate superconductors.

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preprint2011arXiv

Common Fermi Surface Topology and Nodeless Superconducting Gap in K0.68Fe1.79Se2 and (Tl0.45K0.34)Fe1.84Se2 Superconductors Revealed from Angle-Resolved Photoemission Spectroscopy

We carried out high resolution angle-resolved photoemission measurements on the electronic structure and superconducting gap of K_0.68Fe_1.79Se_2 (T_c=32 K) and (Tl_0.45K_0.34)Fe_1.84Se_2 (T_c=28 K) superconductors. In addition to the electron-like Fermi surface near M(π,π), two electron-like Fermi pockets are revealed around the zone center Γ(0,0) in K0.68Fe1.79Se_2. This observation makes the Fermi surface topology of K_0.68Fe_1.79Se_2 consistent with that of (Tl,Rb)_xFe_{2-y}Se_2 and (Tl,K)_xFe_{2-y}Se_2 compounds. A nearly isotropic superconducting gap (Δ) is observed along the electron-like Fermi pocket near the M point in K_0.68Fe_1.79Se_2 (Δ\sim 9 meV) and (Tl_0.45K_0.34)Fe_1.84Se_2 (Δ\sim 8 meV). The establishment of a universal picture on the Fermi surface topology and superconducting gap in the A_xFe_2-ySe_2 (A=K, Tl, Cs, Rb and etc.) superconductors will provide important information in understanding the superconductivity mechanism of the iron-based superconductors.

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preprint2011arXiv

Distinct Fermi Surface Topology and Nodeless Superconducting Gap in (Tl0.58Rb0.42)Fe1.72Se2 Superconductor

High resolution angle-resolved photoemission measurements have been carried out to study the electronic structure and superconducting gap of the (Tl$_{0.58}$Rb$_{0.42}$)Fe$_{1.72}$Se$_2$ superconductor with a T$_c$=32 K. The Fermi surface topology consists of two electron-like Fermi surface sheets around $Γ$ point which is distinct from that in all other iron-based compounds reported so far. The Fermi surface around the M point shows a nearly isotropic superconducting gap of $\sim$12 meV. The large Fermi surface near the $Γ$ point also shows a nearly isotropic superconducting gap of $\sim$15 meV while no superconducting gap opening is clearly observed for the inner tiny Fermi surface. Our observed new Fermi surface topology and its associated superconducting gap will provide key insights and constraints in understanding superconductivity mechanism in the iron-based superconductors.

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preprint2011arXiv

Extraction of Electron Self-Energy and Gap Function in the Superconducting State of Bi_2Sr_2CaCu_2O_8 Superconductor via Laser-Based Angle-Resolved Photoemission

Super-high resolution laser-based angle-resolved photoemission measurements have been performed on a high temperature superconductor Bi_2Sr_2CaCu_2O_8. The band back-bending characteristic of the Bogoliubov-like quasiparticle dispersion is clearly revealed at low temperature in the superconducting state. This makes it possible for the first time to experimentally extract the complex electron self-energy and the complex gap function in the superconducting state. The resultant electron self-energy and gap function exhibit features at ~54 meV and ~40 meV, in addition to the superconducting gap-induced structure at lower binding energy and a broad featureless structure at higher binding energy. These information will provide key insight and constraints on the origin of electron pairing in high temperature superconductors.

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preprint2011arXiv

Growth, Characterization and Fermi Surface of Heavy Fermion CeCoIn5 Superconductor

High quality single crystals of heavy Fermion CeCoIn5 superconductor have been grown by flux method with a typical size of (1~2)mm x (1~2)mm x ~0.1 mm. The single crystals are characterized by structural analysis from X-ray diffraction and Laue diffraction, as well as compositional analysis. Magnetic and electrical measurements on the single crystals show a sharp superconducting transition with a transition temperature at Tc(onset) ~ 2.3 K and a transition width of ~0.15 K. The resistivity of the CeCoIn5 crystal exhibits a hump at ~45 K which is typical of a heavy Fermion system. High resolution angle-resolved photoemission spectroscopy (ARPES) measurements of CeCoIn5 reveal clear Fermi surface sheets that are consistent with the band structure calculations when assuming itinerant Ce 4f electrons at low temperature. This work provides important information on the electronic structure of heavy Fermion CeCoIn5 superconductor. It also lays a foundation for further studies on the physical properties and superconducting mechanism of the heavy Fermion superconductors.

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preprint2011arXiv

Persistence of Topological Order and Formation of Quantum Well States in Topological Insulators Bi2(Se,Te)3 under Ambient Conditions

The topological insulators represent a unique state of matter where the bulk is insulating with an energy gap while the surface is metallic with a Dirac cone protected by the time reversal symmetry. These characteristics provide a venue to explore novel quantum phenomena in fundamental physics and show potential applications in spintronics and quantum computing. One critical issue directly related with the applications as well as the fundamental studies is how the topological surface state will behave under ambient conditions (1 atmosphere air and room temperature). In this paper, we report high resolution angle-resolved photoemission measurements on the surface state of the prototypical topological insulators, Bi2Se3, Bi2Te3 and Bi2(Se0.4Te2.6), upon exposing to ambient conditions. We find that the topological order persists even when the surface is exposed to air at room temperature. However, the surface state is strongly modified after such an exposure. Particularly, we have observed the formation of two-dimensional quantum well states near the surface of the topological insulators after the exposure which depends sensitively on the original composition, x, in Bi2(Se3-xTex). These rich information are crucial in utilizing the surface state and in probing its physical properties under ambient conditions.

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preprint2010arXiv

Quantitative Determination of Eliashberg Function and Evidence of Strong Electron Coupling with Multiple Phonon Modes in Heavily Over doped (Bi,Pb)2Sr2CuO6

Super-high resolution laser-based angle-resolved photoemission spectroscopy measurements have been carried out on a heavily overdoped (Bi,Pb)2Sr2CuO6 (Tc> 5 K) superconductor. Taking advantage of the high-precision data on the subtle change of the quasi-particle dispersion at different temperatures, we develop a general procedure to determine the bare band dispersion and extract the bosonic spectral function quantitatively. Our results show unambiguously that the 70 meV nodal kink is due to the electron coupling with the multiple phonon modes, with a large mass enhancement factor Lamda= 0.42 even in the heavily over-doped regime.

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preprint2009arXiv

Coexistence of Fermi arcs and Fermi pockets in a high Tc copper oxide superconductor

In the pseudogap state of the high-Tc copper-oxide (cuprate) superconductors, angle-resolved photoemission (ARPES) measurements have seen an Fermi arc, i.e., an open-ended gapless section in the large Fermi surface, rather than a closed loop expected of an ordinary metal. This is all the more puzzling because Fermi pockets (small closed Fermi surface features) have been suggested from recent quantum oscillation measurements. The Fermi arcs have worried the high-Tc community for many years because they cannot be understood in terms of existing theories. Theorists came up with a way out in the form of conventional Fermi surface pockets associated with competing order, with a back side that is for detailed reasons invisible by photoemission. Here we report ARPES measurements of La-Bi2201 that give direct evidence of the Fermi pocket. The charge carriers in the pocket are holes and the pockets show an unusual dependence upon doping, namely, they exist in underdoped but not overdoped samples. A big surprise is that these Fermi pockets appear to coexist with the Fermi arcs. This coexistence has not been expected theoretically and the understanding of the mysterious pseudogap state in the high-Tc cuprate superconductors will rely critically on understanding such a new finding.

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preprint2008arXiv

Identification of Nodal Kink in Electron-Doped (Nd$_{1.85}$Ce$_{0.15}$)CuO$_4$ Superconductor from Laser-Based Angle-Resolved Photoemission Spectroscopy

High-resolution laser-based angle-resolved photoemission measurements have been carried out on the electron-doped (Nd$_{1.85}$Ce$_{0.15}$)CuO$_4$ high temperature superconductor. We have revealed a clear kink at $\sim$60 meV in the dispersion along the (0,0)-($π$,$π$) nodal direction, accompanied by a peak-dip-hump feature in the photoemission spectra. This indicates that the nodal electrons are coupled to collective excitations (bosons) in electron-doped superconductors, with the phonons as the most likely candidate of the boson. This finding has established a universality of nodal electron coupling in both hole- and electron-doped high temperature cuprate superconductors.

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