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Source author record

Yang Wu

Yang Wu 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.mtrl-sciComputer Visioncond-mat.mes-hallquant-phphysics.opticsArtificial Intelligencecond-mat.softcond-mat.supr-coneess.SPApplicationsComputation and Languagecond-mat.othercond-mat.str-elhep-exInformation TheoryMachine Learningmath.COmath.ITMethodologyphysics.acc-phphysics.app-phphysics.chem-phphysics.comp-phphysics.flu-dynSoftware Engineering

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Published work

54 published item(s)

preprint2026arXiv

GEM: Generating LiDAR World Model via Deformable Mamba

World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, progress in LiDAR-based world models has lagged behind those built on camera videos or occupancy data, primarily due to two core challenges: the inherent disorder of LiDAR point clouds and the difficulty of distinguishing dynamic objects from static structures. To address these issues, we propose GEM: a Generative LiDAR world model that leverages deformable mamba architecture, significantly improving fidelity and imaginative capability. Specifically, leveraging the structural similarity between sequential laser scanning and Mamba's processing mechanism, we first tokenize LiDAR sweeps into compact representations via a custom LiDAR scene tokenizer. After unsupervised disentanglement of tokenized features via a dynamic-static separator, a tri-path deformable Mamba is introduced to perform selective scanning and adaptive gating fusion over the disentangled features, leading to enhanced spatial-temporal understanding of the world evolution. Optionally, a planner and a BEV layout controller can be integrated to explore the model's capability for autonomous rollout and its potential to generate ``what-if" scenarios. Extensive experiments show that GEM achieves state-of-the-art performances across diverse benchmarks and evaluation settings, demonstrating its superiority and effectiveness. Project page: https://github.com/wuyang98/GEM.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification

Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, conduction properties, waveform morphology, and overall diagnostic impression. However, most existing approaches predict labels directly from ECG signals without explicit clinical reasoning, resulting in opaque decisions that lack clinical alignment. To bridge this gap, we propose CardioThink, a physician-inspired multimodal large language model (MLLM) framework that explicitly models the diagnostic reasoning process through human-interpretable intermediate stages (rhythm, conduction, morphology, and impression) to derive final classification results. Furthermore, we introduce Structured Set Policy Optimization (SSPO) to jointly optimize adherence to this structured reasoning format and the accuracy of variable-size diagnostic sets, without requiring manually annotated reasoning traces. Extensive experiments on diverse ECG benchmarks demonstrate the significant superiority of our approach in diagnostic accuracy, while simultaneously providing interpretable clinical reasoning. Notably, reasoning quality evaluations confirm that SSPO substantially enhances the clinical validity of the generated rationales. These findings reveal that moving beyond direct label prediction toward structured reasoning offers a more clinically aligned direction for future ECG modeling.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation

In this paper, we propose a novel cascaded diffusion-based generative framework for text-driven human motion synthesis, which exploits a strategy named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy sets up generation objectives by grouping body joints of detailed skeletons in close semantic proximity together and then replacing each of such joint group with a single body-part node. Such an operation recursively abstracts a human pose to coarser and coarser skeletons at multiple granularity levels. With gradually increasing the abstraction level, human motion becomes more and more concise and stable, significantly benefiting the cross-modal motion synthesis task. The whole text-driven human motion synthesis problem is then divided into multiple abstraction levels and solved with a multi-stage generation framework with a cascaded latent diffusion model: an initial generator first generates the coarsest human motion guess from a given text description; then, a series of successive generators gradually enrich the motion details based on the textual description and the previous synthesized results. Notably, we further integrate GUESS with the proposed dynamic multi-condition fusion mechanism to dynamically balance the cooperative effects of the given textual condition and synthesized coarse motion prompt in different generation stages. Extensive experiments on large-scale datasets verify that GUESS outperforms existing state-of-the-art methods by large margins in terms of accuracy, realisticness, and diversity. Code is available at https://github.com/Xuehao-Gao/GUESS.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Constrained Deformable Convolutional Network for Efficient Single Image Dynamic Scene Blind Deblurring with Spatially-Variant Motion Blur Kernels Estimation

Most existing deep-learning-based single image dynamic scene blind deblurring (SIDSBD) methods usually design deep networks to directly remove the spatially-variant motion blurs from one inputted motion blurred image, without blur kernels estimation. In this paper, inspired by the Projective Motion Path Blur (PMPB) model and deformable convolution, we propose a novel constrained deformable convolutional network (CDCN) for efficient single image dynamic scene blind deblurring, which simultaneously achieves accurate spatially-variant motion blur kernels estimation and the high-quality image restoration from only one observed motion blurred image. In our proposed CDCN, we first construct a novel multi-scale multi-level multi-input multi-output (MSML-MIMO) encoder-decoder architecture for more powerful features extraction ability. Second, different from the DLVBD methods that use multiple consecutive frames, a novel constrained deformable convolution reblurring (CDCR) strategy is proposed, in which the deformable convolution is first applied to blurred features of the inputted single motion blurred image for learning the sampling points of motion blur kernel of each pixel, which is similar to the estimation of the motion density function of the camera shake in the PMPB model, and then a novel PMPB-based reblurring loss function is proposed to constrain the learned sampling points convergence, which can make the learned sampling points match with the relative motion trajectory of each pixel better and promote the accuracy of the spatially-variant motion blur kernels estimation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios with the channel fingerprint database established at 0.2 m intervals.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Evolution of the electronic structure of ultrathin MnBi2Te4 Films

Ultrathin films of intrinsic magnetic topological insulator MnBi2Te4 exhibit fascinating quantum properties such as quantum anomalous Hall effect and axion insulator state. In this work, we systematically investigate the evolution of the electronic structure of MnBi2Te4 thin films. With increasing film thickness, the electronic structure changes from an insulator-type with a large energy gap to one with in-gap topological surface states, which is, however, still drastically different from the bulk material. By surface doping of alkali-metal atoms, a Rashba split band gradually emerges and hybridizes with topological surface states, which not only reconciles the puzzling difference between the electronic structures of the bulk and thin film MnBi2Te4 but also provides an interesting platform to establish Rashba ferromagnet that is attractive for (quantum) anomalous Hall effect. Our results provide important insights into the understanding and engineering of the intriguing quantum properties of MnBi2Te4 thin films.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Experimental violation of the Leggett-Garg inequality with a single-spin system

Investigation the boundary between quantum mechanical description and classical realistic view is of fundamental importance. The Leggett-Garg inequality provides a criterion to distinguish between quantum systems and classical systems, and can be used to prove the macroscopic superposition state. A larger upper bound of the LG function can be obtained in a multi-level system. Here, we present an experimental violation of the Leggett-Garg inequality in a three-level system using nitrogen-vacancy center in diamond by ideal negative result measurement. The experimental maximum value of Leggett-Garg function is $K_{3}^{exp}=1.625\pm0.022$ which exceeds the Lüders bound with a $5σ$ level of confidence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Influences of the dissipative topological edge state on quantized transport in MnBi2Te4

The beauty of quantum Hall (QH) effect is the metrological precision of Hall resistance quantization that originates from the topological edge states. Understanding the factors that lead to quantization breakdown not only provides important insights on the nature of the topological protection of these edge states, but is beneficial for device applications involving such quantized transport. In this work, we combine conventional transport and real space conductivity mapping to investigate whether the quantization breakdown is tied to the disappearance of edge state in the hotly studied MnBi2Te4 system. Our experimental results unambiguously show that topological edge state does exist when quantization breakdown occurs. Such edge state is dissipative in nature and could lead to a quantization breakdown due to its diffusive character causing overlapping with bulk and other edge states in real devices. Our findings bring attentions to issues that are generally inaccessible in the transport study of QH, but can play important roles in practical measurements and device applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-grained Aligned Annotations

Multimodal fine-grained sentiment analysis has recently attracted increasing attention due to its broad applications. However, the existing multimodal fine-grained sentiment datasets most focus on annotating the fine-grained elements in text but ignore those in images, which leads to the fine-grained elements in visual content not receiving the full attention they deserve. In this paper, we propose a new dataset, the Multimodal Aspect-Category Sentiment Analysis (MACSA) dataset, which contains more than 21K text-image pairs. The dataset provides fine-grained annotations for both textual and visual content and firstly uses the aspect category as the pivot to align the fine-grained elements between the two modalities. Based on our dataset, we propose the Multimodal ACSA task and a multimodal graph-based aligned model (MGAM), which adopts a fine-grained cross-modal fusion method. Experimental results show that our method can facilitate the baseline comparison for future research on this corpus. We will make the dataset and code publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model Averaging for Generalized Linear Models in Fragmentary Data Prediction

Fragmentary data is becoming more and more popular in many areas which brings big challenges to researchers and data analysts. Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response variable is discrete. In this paper we propose a model averaging method for generalized linear models in fragmentary data prediction. The candidate models are fitted based on different combinations of covariate availability and sample size. The optimal weight is selected by minimizing the Kullback-Leibler loss in the com?pleted cases and its asymptotic optimality is established. Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quantifying Wetting Dynamics with Triboelectrification

Wetting is often perceived as an intrinsic surface property of materials, but determining its evolution is complicated by its complex dependence on roughness across the scales. The Wenzel state, where liquids have intimate contact with the rough substrate, and the Cassie-Baxter state, where liquids sit onto air pockets formed between asperities, are only two states among the plethora of wetting behaviors. Furthermore, transitions from the Cassie-Baxter to the Wenzel state dictate completely different surface performance, such as anti-contamination, anti-icing, drag reduction etc.; however, little is known about how transition occurs during time between the several wetting modes. In this paper, we show that wetting dynamics can be accurately quantified and tracked using solid-liquid triboelectrification. Theoretical underpinning reveals how surface micro-/nano-geometries regulate stability/infiltration, also demonstrating the generality of our theoretical approach in understanding wetting transitions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Regulating effect of biaxial strain on electronic, optical and photocatalytic properties in promising X2PAs (X = Si, Ge and Sn) monolayers

Photocatalytic water splitting is an effective way to obtain renewable clean energy. The challenge is to design tunable photocatalyst to meet the needs in different environments. At the same time, the oxygen and hydrogen evolution reactions (OER and HER) on the photocatalyst should be separated, which will be conducive to the separation of products. The electronic, optical and photocatalytic properties of Janus X2PAs (X = Si, Ge and Sn) monolayers are explored by first-principles calculation. All the strain-free X2PAs monolayers exhibit excellent photocatalytic properties with suitable band edge positions straddling the standard redox potential of water and large visible light absorption coefficients (up to 105 cm-1). Interestingly, the intrinsic internal electric field is favorable for separating photogenerated carriers to different surfaces of the monolayer. It contributes to realize the OER and HER separated on different sides of the monolayer. In particular, the energy band edge positions of X2PAs monolayers can be well adjusted by biaxial strain. Then it can effectively modulate photocatalytic reactions, suggesting X2PAs monolayers can be a piezo-photocatalytic switch between the OER, HER and full-reaction of redox for water splitting. This investigation not only highlights that the photocatalyst X2PAs monolayers with the separated OER and HER can be effectively tuned by the mechanical strain, but also provides a new strategy for designing highly adaptable and tunable piezo-photocatalysts.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ultrafast coherent interlayer phonon dynamics in atomically thin layers of MnBi2Te4

The atomically thin MnBi2Te4 crystal is a novel magnetic topological insulator, exhibiting exotic quantum physics. Here we report a systematic investigation of ultrafast carrier dynamics and coherent interlayer phonons in few-layer MnBi2Te4 as a function of layer number using time-resolved pump-probe reflectivity spectroscopy. Pronounced coherent phonon oscillations from the interlayer breathing mode are directly observed in the time domain. We find that the coherent oscillation frequency, the photocarrier and coherent phonon decay rates all depend sensitively on the sample thickness. The time-resolved measurements are complemented by ultralow-frequency Raman spectroscopy measurements, which both confirm the interlayer breathing mode and additionally enable observation of the interlayer shear mode. The layer dependence of these modes allows us to extract both the out-of-plane and in-plane interlayer force constants. Our studies not only reveal the interlayer van der Waals coupling strengths, but also shed light on the ultrafast optical properties of this novel two-dimensional material.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection

Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era. Nevertheless, jointly conducting moment retrieval and highlight detection is an emerging research topic, even though its component problems and some related tasks have already been studied for a while. In this paper, we present the first unified framework, named Unified Multi-modal Transformers (UMT), capable of realizing such joint optimization while can also be easily degenerated for solving individual problems. As far as we are aware, this is the first scheme to integrate multi-modal (visual-audio) learning for either joint optimization or the individual moment retrieval task, and tackles moment retrieval as a keypoint detection problem using a novel query generator and query decoder. Extensive comparisons with existing methods and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and TVSum datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method under various settings. Source code and pre-trained models are available at https://github.com/TencentARC/UMT.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Magnetization-tuned topological quantum phase transition in MnBi2Te4 devices

Recently, the intrinsic magnetic topological insulator MnBi2Te4 has attracted enormous research interest due to the great success in realizing exotic topological quantum states, such as the quantum anomalous Hall effect (QAHE), axion insulator state, high-Chern-number and high-temperature Chern insulator states. One key issue in this field is to effectively manipulate these states and control topological phase transitions. Here, by systematic angle-dependent transport measurements, we reveal a magnetization-tuned topological quantum phase transition from Chern insulator to magnetic insulator with gapped Dirac surface states in MnBi2Te4 devices. Specifically, as the magnetic field is tilted away from the out-of-plane direction by around 40-60 degrees, the Hall resistance deviates from the quantization value and a colossal, anisotropic magnetoresistance is detected. The theoretical analyses based on modified Landauer-Buttiker formalism show that the field-tilt-driven switching from ferromagnetic state to canted antiferromagnetic state induces a topological quantum phase transition from Chern insulator to magnetic insulator with gapped Dirac surface states in MnBi2Te4 devices. Our work provides an efficient means for modulating topological quantum states and topological quantum phase transitions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation

We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement. However, it remains challenging for refining MOTS results, which could be attributed to that appearance features are not adapted to target videos and it is also difficult to find proper thresholds to discriminate them. To tackle this issue, we propose a self-supervised refining MOTS (i.e., ReMOTS) framework. ReMOTS mainly takes four steps to refine MOTS results from the data association perspective. (1) Training the appearance encoder using predicted masks. (2) Associating observations across adjacent frames to form short-term tracklets. (3) Training the appearance encoder using short-term tracklets as reliable pseudo labels. (4) Merging short-term tracklets to long-term tracklets utilizing adopted appearance features and thresholds that are automatically obtained from statistical information. Using ReMOTS, we reached the $1^{st}$ place on CVPR 2020 MOTS Challenge 1, with an sMOTSA score of $69.9$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories -- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are avaible at: https://github.com/lightChaserX/Awesome-Hetero-reID

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution (the first as far as we know). It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data. The source code of CTracker can be found at: github.com/pjl1995/CTracker.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Compressing 3DCNNs Based on Tensor Train Decomposition

Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs). To miniaturize 3DCNNs for the deployment in confining environments such as embedded devices, neural network compression is a promising approach. In this work, we adopt the tensor train (TT) decomposition, a straightforward and simple in situ training compression method, to shrink the 3DCNN models. Through proposing tensorizing 3D convolutional kernels in TT format, we investigate how to select appropriate TT ranks for achieving higher compression ratio. We have also discussed the redundancy of 3D convolutional kernels for compression, core significance and future directions of this work, as well as the theoretical computation complexity versus practical executing time of convolution in TT. In the light of multiple contrast experiments based on VIVA challenge, UCF11, and UCF101 datasets, we conclude that TT decomposition can compress 3DCNNs by around one hundred times without significant accuracy loss, which will enable its applications in extensive real world scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electronic states and magnetic response of MnBi2Te4 by scanning tunneling microscopy and spectroscopy

Exotic quantum phenomena have been demonstrated in recently discovered intrinsic magnetic topological insulator MnBi2Te4. At its two-dimensional limit, quantum anomalous Hall (QAH) effect and axion insulator state are observed in odd and even layers of MnBi2Te4, respectively. The measured band structures exhibit intriguing and complex properties. Here we employ low-temperature scanning tunneling microscopy to study its surface states and magnetic response. The quasiparticle interference patterns indicate that the electronic structures on the topmost layer of MnBi2Te4 is different from that of the expected out-of-plane A-type antiferromagnetic phase. The topological surface states may be embedded in deeper layers beneath the topmost surface. Such novel electronic structure presumably related to the modification of crystalline structure during sample cleaving and re-orientation of magnetic moment of Mn atoms near the surface. Mn dopants substituted at the Bi site on the second atomic layer are observed. The ratio of Mn/Bi substitutions is 5%. The electronic structures are fluctuating at atomic scale on the surface, which can affect the magnetism of MnBi2Te4. Our findings shed new lights on the magnetic property of MnBi2Te4 and thus the design of magnetic topological insulators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Energy-Efficient Trajectory Design for UAV-Enabled Communication Under Malicious Jamming

In this letter, we investigate a UAV-enabled communication system, where a UAV is deployed to communicate with the ground node (GN) in the presence of multiple jammers. We aim to maximize the energy efficiency (EE) of the UAV by optimizing its trajectory, subject to the UAV's mobility constraints. However, the formulated problem is difficult to solve due to the non-convex and fractional form of the objective function. Thus, we propose an iterative algorithm based on successive convex approximation (SCA) technique and Dinkelbach's algorithm to solve it. Numerical results show that the proposed algorithm can strike a better balance between the throughput and energy consumption by the optimized trajectory and thus improve the EE significantly as compared to the benchmark algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhancement of superconductivity in organic-inorganic hybrid topological materials

Inducing or enhancing superconductivity in topological materials is an important route toward topological superconductivity. Reducing the thickness of transition metal dichalcogenides (e.g. WTe2 and MoTe2) has provided an important pathway to engineer superconductivity in topological matters; for instance, emergent superconductivity with Tc=0.82 K was observed in monolayer WTe2 which also hosts intriguing quantum spin Hall effect, although the bulk crystal is nonsuperconducting. However, such monolayer sample is difficult to obtain, unstable in air, and with extremely low Tc, which could pose a grand challenge for practical applications. Here we report an experimentally convenient approach to control the interlayer coupling to achieve tailored topological properties, enhanced superconductivity and good sample stability through organic cation intercalation of the Weyl semimetals MoTe2 and WTe2. The as-formed organic-inorganic hybrid crystals are weak topological insulators with enhanced Tc of 7.0 K for intercalated MoTe2 (0.25 K for pristine crystal) and 2.3 K for intercalated WTe2 (2.8 times compared to monolayer WTe2). Such organic-cationintercalation method can be readily applied to many other layered crystals, providing a new pathway for manipulating their electronic, topological and superconducting properties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

High-Chern-Number and High-Temperature Quantum Hall Effect without Landau Levels

The quantum Hall effect (QHE) with quantized Hall resistance of h/νe2 starts the research on topological quantum states and lays the foundation of topology in physics. Afterwards, Haldane proposed the QHE without Landau levels, showing nonzero Chern number |C|=1, which has been experimentally observed at relatively low temperatures. For emerging physics and low-power-consumption electronics, the key issues are how to increase the working temperature and realize high Chern numbers (C>1). Here, we report the experimental discovery of high-Chern-number QHE (C=2) without Landau levels and C=1 Chern insulator state displaying nearly quantized Hall resistance plateau above the Néel temperature in MnBi2Te4 devices. Our observations provide a new perspective on topological matter and open new avenues for exploration of exotic topological quantum states and topological phase transitions at higher temperatures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Make Skeleton-based Action Recognition Model Smaller, Faster and Better

Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition. By using a lightweight network structure (i.e., 0.15 million parameters), DD-Net can reach a super fast speed, as 3,500 FPS on one GPU, or, 2,000 FPS on one CPU. By employing robust features, DD-Net achieves the state-of-the-art performance on our experimental datasets: SHREC (i.e., hand actions) and JHMDB (i.e., body actions). Our code will be released with this paper later.

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preprint2020arXiv

Metallic Microswimmers Driven up the Wall by Gravity

As a natural and functional behavior, various microorganisms exhibit gravitaxis by orienting and swimming upwards against gravity. Swimming autophoretic nanomotors described herein, comprising bimetallic nanorods, preferentially orient upwards and swim up along a wall, when tail-heavy (i.e. when the density of one of the metals is larger than the other). Through experiment and theory, two mechanisms were identified that contribute to this gravitactic behavior. First, a buoyancy or gravitational torque acts on these rods to align them upwards. Second, hydrodynamic interactions of the rod with the inclined wall induce a fore-aft drag asymmetry on the rods that reinforces their orientation bias and promotes their upward motion.

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preprint2020arXiv

Multiple Object Tracking by Flowing and Fusing

Most of Multiple Object Tracking (MOT) approaches compute individual target features for two subtasks: estimating target-wise motions and conducting pair-wise Re-Identification (Re-ID). Because of the indefinite number of targets among video frames, both subtasks are very difficult to scale up efficiently in end-to-end Deep Neural Networks (DNNs). In this paper, we design an end-to-end DNN tracking approach, Flow-Fuse-Tracker (FFT), that addresses the above issues with two efficient techniques: target flowing and target fusing. Specifically, in target flowing, a FlowTracker DNN module learns the indefinite number of target-wise motions jointly from pixel-level optical flows. In target fusing, a FuseTracker DNN module refines and fuses targets proposed by FlowTracker and frame-wise object detection, instead of trusting either of the two inaccurate sources of target proposal. Because FlowTracker can explore complex target-wise motion patterns and FuseTracker can refine and fuse targets from FlowTracker and detectors, our approach can achieve the state-of-the-art results on several MOT benchmarks. As an online MOT approach, FFT produced the top MOTA of 46.3 on the 2DMOT15, 56.5 on the MOT16, and 56.5 on the MOT17 tracking benchmarks, surpassing all the online and offline methods in existing publications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Robust axion insulator and Chern insulator phases in a two-dimensional antiferromagnetic topological insulator

The intricate interplay between nontrivial topology and magnetism in two-dimensional (2D) materials has led to the emergence of many novel phenomena and functionalities. An outstanding example is the quantum anomalous Hall (QAH) effect, which was realized in magnetically doped topological insulators (TIs) in the absence of magnetic field. Recently, the layered van der Waals compound MnBi2Te4 has been theoretically predicted and experimentally verified to be a TI with interlayer antiferromagnetic (AFM) order. It is a rare stoichiometric material with coexisting topology and magnetism, thus represents a perfect building block for complex topological-magnetic structures. Here we investigate the quantum transport behaviors of both bulk crystal and exfoliated MnBi2Te4 flakes in a field effect transistor geometry. In the 6 septuple layers (SLs) device tuned into the insulating regime, we observe a large longitudinal resistance and zero Hall plateau, which are characteristic of the axion insulator state. The robust axion insulator state occurs in zero magnetic field, over a wide magnetic field range, and at relatively high temperatures. Moreover, a moderate magnetic field drives a quantum phase transition from the axion insulator phase to a Chern insulator phase with zero longitudinal resistance and quantized Hall resistance h/e2 (h is the Plank constant and e is the elemental charge). These results pave the road for using even-number-SL MnBi2Te4 to realize the quantized topological magnetoelectric effect and axion electrodynamics in condensed matter systems.

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preprint2020arXiv

Spin-orbit torque magnetization switching in MoTe2/permalloy heterostructures

The ability to switch magnetic elements by spin-orbit-induced torques has recently attracted much attention for a path towards high-performance, non-volatile memories with low power consumption. Realizing efficient spin-orbit-based switching requires harnessing both new materials and novel physics to obtain high charge-to-spin conversion efficiencies, thus making the choice of spin source crucial. Here we report the observation of spin-orbit torque switching in bilayers consisting of a semimetallic film of 1T'-MoTe2 adjacent to permalloy. Deterministic switching is achieved without external magnetic fields at room temperature, and the switching occurs with currents one order of magnitude smaller than those typical in devices using the best-performing heavy metals. The thickness dependence can be understood if the interfacial spin-orbit contribution is considered in addition to the bulk spin Hall effect. Further threefold reduction in the switching current is demonstrated with resort to dumbbell-shaped magnetic elements. These findings foretell exciting prospects of using MoTe2 for low-power semimetal material based spin devices.

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preprint2020arXiv

Unified spectral hamiltonian results of balanced bipartite graphs and complementary graphs

There have been researches on sufficient spectral conditions for Hamiltonian properties and path-coverable properties of graphs. Utilizing the Bondy-Chvátal closure, we provide a unified approach to study sufficient graph eigenvalue conditions for these properties and sharpen former spectral results in [{\em Linear Algebra Appl.}, 432 (2010), 566-570], [{\em Linear Algebra Appl.}, 432 (2010), 2170-2173], [{\em Appl. Mech. Mater.}, 336-338 (2013), 2329-2334], [{\em Linear Algebra Appl.}, 467 (2015), 254-266], [{\em Linear Multilinear Algebra}, 64 (2016), 2252-2269], and [{\em J. Comb. Optim.}, 35 (2018), 1104-1127], among others.

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preprint2020arXiv

Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate

3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data. In this work, we propose an effective and efficient approach at a low cost. First, we obtain panoramic videos with four normal cameras. Then, we transform human locations from a 2D panoramic image coordinate to a 3D panoramic camera coordinate using camera geometry and human bio-metric property (i.e., height). Finally, we generate 3D tracklets by associating human appearance and 3D trajectory. We verify the effectiveness of our method on three datasets including a new one built by us, in terms of 3D single-view multi-person localization, 3D single-view multi-person tracking, and 3D panoramic multi-person localization and tracking. Our code and dataset are available at \url{https://github.com/fandulu/MPLT}.

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preprint2020arXiv

Video Region Annotation with Sparse Bounding Boxes

Video analysis has been moving towards more detailed interpretation (e.g. segmentation) with encouraging progresses. These tasks, however, increasingly rely on densely annotated training data both in space and time. Since such annotation is labour-intensive, few densely annotated video data with detailed region boundaries exist. This work aims to resolve this dilemma by learning to automatically generate region boundaries for all frames of a video from sparsely annotated bounding boxes of target regions. We achieve this with a Volumetric Graph Convolutional Network (VGCN), which learns to iteratively find keypoints on the region boundaries using the spatio-temporal volume of surrounding appearance and motion. The global optimization of VGCN makes it significantly stronger and generalize better than existing solutions. Experimental results using two latest datasets (one real and one synthetic), including ablation studies, demonstrate the effectiveness and superiority of our method.

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preprint2020arXiv

When Person Re-identification Meets Changing Clothes

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://wanfb.github.io/dataset.html.

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preprint2016arXiv

Enhanced spin-orbit torque via modulation of spin current absorption

The magnitude of spin-orbit torque (SOT), exerted to a ferromagnet (FM) from an adjacent heavy metal (HM), strongly depends on the amount of spin currents absorbed in the FM. We exploit the large spin absorption at the Ru interface to manipulate the SOTs in HM/FM/Ru multilayers. While the FM thickness is smaller than its spin dephasing length of 1.2 nm, the top Ru layer largely boosts the absorption of spin currents into the FM layer and substantially enhances the strength of SOT acting on the FM. Spin-pumping experiments induced by ferromagnetic resonance support our conclusions that the observed increase in the SOT efficiency can be attributed to an enhancement of the spin-current absorption. A theoretical model that considers both reflected and transmitted mixing conductances at the two interfaces of FM is developed to explain the results.

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preprint2016arXiv

Experimental observation of topological Fermi arcs in type-II Weyl semimetal MoTe2

Weyl semimetal is a new quantum state of matter [1-12] hosting the condensed matter physics counterpart of relativisticWeyl fermion [13] originally introduced in high energy physics. The Weyl semimetal realized in the TaAs class features multiple Fermi arcs arising from topological surface states [10, 11, 14-16] and exhibits novel quantum phenomena, e.g., chiral anomaly induced negative mag-netoresistance [17-19] and possibly emergent supersymmetry [20]. Recently it was proposed theoretically that a new type (type-II) of Weyl fermion [21], which does not have counterpart in high energy physics due to the breaking of Lorentz invariance, can emerge as topologically-protected touching between electron and hole pockets. Here, we report direct spectroscopic evidence of topological Fermi arcs in the predicted type-II Weyl semimetal MoTe2 [22-24]. The topological surface states are confirmed by directly observing the surface states using bulk-and surface-sensitive angle-resolved photoemission spectroscopy (ARPES), and the quasi-particle interference (QPI) pattern between the two putative Fermi arcs in scanning tunneling microscopy (STM). Our work establishes MoTe2 as the first experimental realization of type-II Weyl semimetal, and opens up new opportunities for probing novel phenomena such as exotic magneto-transport [21] in type-II Weyl semimetals.

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preprint2016arXiv

Experimental time-optimal universal control of spin qubits in solids

Quantum control of systems plays important roles in modern science and technology. The ultimate goal of quantum control is to achieve high fidelity universal control in the time-optimal way. Although high fidelity universal control has been reported in various quantum systems, experimental implementation of time-optimal universal control remains elusive. Here we report the experimental realization of time-optimal universal control of spin qubits in diamond. By generalizing a recent method for solving quantum brachistochrone equations [X. Wang, et al., Phys. Rev. Lett. 114, 170501 (2015)], we obtained accurate minimum time protocols for multiple qubits with fixed qubits' interactions and constrained control field. Single- and two-qubit time-optimal gates are experimentally implemented with fidelities of 99% obtained via quantum process tomography. Our work provides a time-optimal route to achieve accurate quantum control, and unlocks new capabilities for emerging field of time-optimal control in general quantum systems.

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preprint2016arXiv

Helicity dependent photovoltaic effect in Bi2Se3 under normal incident light

Topological insulators (TIs) form a new class of materials with insulating bulk and surface conduction ensured by topologically protected surface states (TPSS). We investigate the impact of the helicity of a normally incident laser beam on the photovoltaic effect in the TI Bi2Se3. The observation of a helicity dependent photovoltaic effect for normally incident light indicates the presence of out-of-plane spin components for some TPSSs due to the hexagonal warping. In addition, fluctuations in the electrostatic potential at the surface locally break the rotational symmetry of the film allowing the helicity dependent photovoltaic effect. Our result suggests that engineering local electrostatic potentials in Bi2Se3 would allow the control of optically generated spin currents, which may be useful for applications in spin-optoelectronics.

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preprint2016arXiv

High performance THz emitters based on ferromagnetic/nonmagnetic heterostructures

We report a THz emitter with excellent performances based on nonmagnetic (NM) and ferromagnetic (FM) heterostructures. The spin currents are first excited by the femtosecond laser beam in the NM/FM bilayer, and then transient charge currents are generated by inverse spin Hall effect, leading to THz emission out of the structure. The broadband THz waves emitted from our film stacks have a peak intensity exceeding 500 um thick ZnTe crystals (standard THz emitters). Our device is insensitive to the polarization of an incident laser beam which indicates the noise resistive feature. In contrast, the polarization of THz waves is fully controllable by an external magnetic field. We have also fabricated the devices on flexible substrates with a great performance, and demonstrated that the devices can be driven by low power lasers. Together with the low cost and mass productive sputtering growth method for the film stacks, the proposed THz emitters can be readily applied to a wide range of THz equipment. Our study also points towards an alternative approach to characterize spintronic devices with NM/FM bilayers.

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preprint2016arXiv

Raman signatures of inversion symmetry breaking and structural phase transition in type-II Weyl semimetal MoTe2

Transition metal dichalcogenide MoTe$_2$ is an important candidate for realizing the newly predicted type-IIWeyl fermions, for which the breaking of the inversion symmetry is a prerequisite. Here we present direct spectroscopic evidence for the inversion symmetry breaking in the low temperature phase of MoTe$_2$ by systematic Raman experiments and first principles calculations. We identify five lattice vibrational modes which are Raman active only in noncentrosymmetric structure at low temperature. A hysteresis is also observed in the peak intensity of inversion symmetry activated Raman modes, confirming a temperature induced structural phase transition with a concomitant change in the inversion symmetry. Our results provide definitive evidence for the low temperature noncentrosymmetric T$_d$ phase from vibrational spectroscopy, and suggest MoTe$_2$ as an ideal candidate for investigating the temperature induced topological phase transition.

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preprint2016arXiv

Spin orbit torques and Dzyaloshinskii-Moriya interaction in dual-interfaced Co-Ni multilayers

We study the spin orbit torque (SOT) and Dzyaloshinskii-Moriya interaction (DMI) in the dual-interfaced Co-Ni perpendicular multilayers. Through the combination of top and bottom layer materials (Pt, Ta, MgO and Cu), SOT and DMI are efficiently manipulated due to an enhancement or cancellation of the top and bottom contributions. However, SOT is found to originate mostly from the bulk of a heavy metal (HM), while DMI is more of interfacial origin. In addition, we find that the direction of the domain wall (DW) motion can be either along or against the electron flow depending on the DW tilting angle when there is a large DMI. Such an abnormal DW motion induces a large assist field required for hysteretic magnetization reversal. Our results provide insight into the role of DMI in SOT driven magnetization switching, and demonstrate the feasibility of achieving desirable SOT and DMI for spintronic devices.

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preprint2015arXiv

Experimental fault-tolerant universal quantum gates with solid-state spins under ambient conditions

Quantum computation provides great speedup over its classical counterpart for certain problems. One of the key challenges for quantum computation is to realize precise control of the quantum system in the presence of noise. Control of the spin-qubits in solids with the accuracy required by fault-tolerant quantum computation under ambient conditions remains elusive. Here, we quantitatively characterize the source of noise during quantum gate operation and demonstrate strategies to suppress the effect of these. A universal set of logic gates in a nitrogen-vacancy centre in diamond are reported with an average single-qubit gate fidelity of 0.999952 and two-qubit gate fidelity of 0.992. These high control fidelities have been achieved at room temperature in naturally abundant 13C diamond via composite pulses and an optimized control method.

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preprint2015arXiv

Feedback-optimized Extraordinary Optical Transmission of Continuous-variable Entangled States

We report on the feedback-optimized extraordinary optical transmission of continuous-variable entangled states through a hexagonal metal-hole array. The continuous-variable entanglements from a nondegenerate optical parametric amplifier are first demonstrated to survive after a photon-plasmon-photon conversion process. By controlling the reflected light from the metal-hole array, a significant enhancement of quantum correlations has then been experimentally achieved comparing to the case of without such coherent feedback control. This result presents a useful technique to efficiently recover the substantial reflective losses in the plasmonic circuits for quantum information processing.

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preprint2015arXiv

Graphene terahertz modulators by ionic liquid gating

Graphene based THz modulators are promising due to the conical band structure and high carrier mobility of graphene. Here, we tune the Fermi level of graphene via electrical gating with the help of ionic liquid to control the THz transmittance. It is found that, in the THz range, both the absorbance and reflectance of the device increase proportionately to the available density of states due to intraband transitions. Compact, stable, and repeatable THz transmittance modulation up to 93% (or 99%) for a single (or stacked) device has been demonstrated in a broad frequency range from 0.1 to 2.5 THz, with an applied voltage of only 3 V at room temperature.

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preprint2015arXiv

Spin-orbit torque engineering via oxygen manipulation

Spin transfer torques allow the electrical manipulation of the magnetization at room temperature, which is desirable in spintronic devices such as spin transfer torque memories. When combined with spin-orbit coupling, they give rise to spin-orbit torques which are a more powerful tool for magnetization control and can enrich device functionalities. The engineering of spin-orbit torques, based mostly on the spin Hall effect, is being intensely pursued. Here we report that the oxidation of spin-orbit torque devices triggers a new mechanism of spin-orbit torque, which is about two times stronger than that based on the spin Hall effect. We thus introduce a way to engineer spin-orbit torques via oxygen manipulation. Combined with electrical gating of the oxygen level, our findings may also pave the way towards reconfigurable logic devices.

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preprint2015arXiv

Thermal and Vibrational Properties of Thermoelectric ZnSb - Exploring the Origin of Low Thermal Conductivity

The intermetallic compound ZnSb is an interesting thermoelectric material, largely due to its low lattice thermal conductivity. The origin of the low thermal conductivity has so far been speculative. Using multi-temperature single crystal X-ray diffraction (9 - 400 K) and powder X-ray diffraction (300 - 725 K) measurements we characterized the volume expansion and the evolution of structural properties with temperature and identify an increasingly anharmonic behavior of the Zn atoms. From a combination of Raman spectroscopy and first principles calculations of phonons we consolidate the presence of low-energy optic modes with wavenumbers below 60 cm-1. Heat capacity measurements between 2 and 400 K can be well described by a Debye-Einstein model containing one Debye and two Einstein contributions with temperatures ΘD = 195K, ΘE1 = 78 K and ΘE2 = 277 K as well as a significant contribution due to anharmonicity above 150 K. The presence of a multitude of weakly dispersed low-energy optical modes (which couple with the acoustic, heat carrying phonons) combined with anharmonic thermal behavior provides an effective mechanism for low lattice thermal conductivity. The peculiar vibrational properties of ZnSb are attributed to its chemical bonding properties which are characterized by multicenter bonded structural entities. We argue that the proposed mechanism to explain the low lattice thermal conductivity of ZnSb might also control the thermoelectric properties of electron poor semiconductors, such as Zn4Sb3, CdSb, Cd4Sb3, Cd13-xInyZn10, and Zn5Sb4In2-x.

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preprint2014arXiv

Collaborative Representation for Classification, Sparse or Non-sparse?

Sparse representation based classification (SRC) has been proved to be a simple, effective and robust solution to face recognition. As it gets popular, doubts on the necessity of enforcing sparsity starts coming up, and primary experimental results showed that simply changing the $l_1$-norm based regularization to the computationally much more efficient $l_2$-norm based non-sparse version would lead to a similar or even better performance. However, that's not always the case. Given a new classification task, it's still unclear which regularization strategy (i.e., making the coefficients sparse or non-sparse) is a better choice without trying both for comparison. In this paper, we present as far as we know the first study on solving this issue, based on plenty of diverse classification experiments. We propose a scoring function for pre-selecting the regularization strategy using only the dataset size, the feature dimensionality and a discrimination score derived from a given feature representation. Moreover, we show that when dictionary learning is taking into account, non-sparse representation has a more significant superiority to sparse representation. This work is expected to enrich our understanding of sparse/non-sparse collaborative representation for classification and motivate further research activities.

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preprint2014arXiv

Strain-enhanced tunneling magnetoresistance in MgO magnetic tunnel junctions

While the effects of lattice mismatch-induced strain, mechanical strain, as well as the intrinsic strain of thin films are sometimes detrimental, resulting in mechanical deformation and failure, strain can also be usefully harnessed for applications such as data storage, transistors, solar cells, and strain gauges, among other things. Here, we demonstrate that quantum transport across magnetic tunnel junctions (MTJs) can be significantly affected by the introduction of controllable mechanical strain, achieving an enhancement factor of ~2 in the experimental tunneling magnetoresistance (TMR) ratio. We further correlate this strain-enhanced TMR with coherent spin tunneling through the MgO barrier. Moreover, the strain-enhanced TMR is analyzed using non-equilibrium Green's function (NEGF) quantum transport calculations. Our results help elucidate the TMR mechanism at the atomic level and can provide a new way to enhance, as well as tune, the quantum properties in nanoscale materials and devices.

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preprint2013arXiv

A new route to spin-orbit torque engineering via oxygen manipulation

Spin transfer torques allow for electrical manipulation of magnetization at room temperature, which is utilized to build future electronic devices such as spin transfer torque memories. Recent experiments have discovered that the combination of the spin transfer torque with the spin Hall effect enables more efficient manipulation. A versatile control mechanism of such spin-orbit torques is beneficial to envision device applications with competitive advantages over the existing schemes. Here we report that the oxidation manipulation of spin-orbit torque devices triggers a new mechanism, and the resulting torques are estimated to be about two times stronger than that of the spin Hall effect. Our result introduces an entirely new way to engineer the spin-orbit torques for device operation via oxygen manipulation. Combined with electrical gating for the control of the oxygen content, our finding may also pave the way for towards reconfigurable logic devices.

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preprint2013arXiv

Dynamics of Open-Source Software Developer's Commit Behavior: An Empirical Investigation of Subversion

Commit is an important operation of revision control for open-source software (OSS). Recent research has been pursued to explore the statistical laws of such an operation, but few of those papers conduct empirical investigations on commit interval (i.e., the waiting time between two consecutive commits). In this paper, we investigated software developer's collective and individual commit behavior in terms of the distribution of commit intervals, and found that 1) the data sets of project-level commit interval within both the lifecycle and each release of the projects analyzed roughly follow power-law distributions; and 2) lifecycle- and release-level collective commit interval on class files can also be best fitted with power laws. These findings reveal some general (collective) collaborative development patterns of OSS projects, e.g., most of the waiting times between two consecutive commits to a central repository are short, but only a few of them experience a long duration of waiting. Then, the implications of what we found for OSS research were outlined, which could provide an insight into understanding OSS development processes better based on software developers' historical commit behavior.

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preprint2013arXiv

Graphene/liquid crystal based terahertz phase shifters

Due to its high electrical conductivity and excellent transmittance at terahertz frequencies, graphene is a promising candidate as transparent electrodes for terahertz devices. We demonstrate a liquid crystal based terahertz phase shifter with the graphene films as transparent electrodes. The maximum phase shift is 10.8 degree and the saturation voltage is 5 V with a 50 um liquid crystal cell. The transmittance at terahertz frequencies and electrical conductivity depending on the number of graphene layer are also investigated. The proposed phase shifter provides a continuous tunability, fully electrical controllability, and low DC voltage operation.

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preprint2013arXiv

Ground state pairing correlations in the $S_4$ symmetric microscopic model for iron-based superconductors

We present the ground state pairing correlations in the $S_4$ symmetric microscopic model for iron-based superconductors, computed with the constrained-path Monte Carlo method. For various electron fillings and interaction strengths, we find that the $s_{xy}$ pairing dominates over other pairing correlations and is positive when the pair separation exceeds several lattice constants, whatever for iron pnictides and iron chlcogenides. These ground state properties, especially the long range part pairing correlations re-confirm the previous finite temperature results published in Phys. Rev. Lett. 110, 107002(2013). We further our study by including the nearest neighbour interaction $V$ and it is found that the $s_{xy}$ pairing correlation is slightly suppressed by the increasing $V$.

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preprint2012arXiv

Post-Acceleration Study for Neutrino Super-beam at CSNS

A post-acceleration system based on the accelerators at CSNS (China Spallation Neutron Source) is proposed to build a super-beam facility for neutrino physics. Two post-acceleration schemes, one using superconducting dipole magnets in the main ring and the other using room-temperature magnets have been studied, both to achieve the final proton energy of 128 GeV and the beam power of 4 MW by taking 10% of the CSNS beam from the neutron source. The main design features and the comparison for the two schemes are presented. The CSNS super-beam facility will be very competitive in long-baseline neutrino physics studies, compared with other super-beam facilities proposed in the world.

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preprint2007arXiv

Variable Electron-Phonon Coupling in Isolated Metallic Carbon Nanotubes Observed by Raman Scattering

We report the existence of broad and weakly asymmetric features in the high-energy (G) Raman modes of freely suspended metallic carbon nanotubes of defined chiral index. A significant variation in peak width (from 12 cm-1 to 110 cm-1) is observed as a function of the nanotube's chiral structure. When the nanotubes are electrostatically gated, the peak widths decrease. The broadness of the Raman features is understood as the consequence of coupling of the phonon to electron-hole pairs, the strength of which varies with the nanotube chiral index and the position of the Fermi energy.

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