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Lei Chen

Publications and source records attributed to Lei Chen.

At least 397 records · Page 22Linked to original sources
arXiv2023

Efficient Video Action Detection with Token Dropout and Context Refinement

Streaming video clips with large-scale video tokens impede vision transformers (ViTs) for efficient recognition, especially in video action detection where sufficient spatiotemporal representations are required for precise actor identification. In this work, we propose an end-to-end framework for efficient video action detection (EVAD) based on vanilla ViTs. Our EVAD consists of two specialized designs for video action detection. First, we propose a spatiotemporal token dropout from a keyframe-centric perspective. In a video clip, we maintain all tokens from its keyframe, preserve tokens relevant to actor motions from other frames, and drop out the remaining tokens in this clip. Second, we refine scene context by leveraging remaining tokens for better recognizing actor identities. The region of interest (RoI) in our action detector is expanded into temporal domain. The captured spatiotemporal actor identity representations are refined via scene context in a decoder with the attention mechanism. These two designs make our EVAD efficient while maintaining accuracy, which is validated on three benchmark datasets (i.e., AVA, UCF101-24, JHMDB). Compared to the vanilla ViT backbone, our EVAD reduces the overall GFLOPs by 43% and improves real-time inference speed by 40% with no performance degradation. Moreover, even at similar computational costs, our EVAD can improve the performance by 1.1 mAP with higher resolution inputs. Code is available at https://github.com/MCG-NJU/EVAD.

cs.CV↗
arXiv2023

Distributed Graph Neural Network Training: A Survey

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review on the optimization techniques for the distributed execution of GNN training. In this survey, we analyze three major challenges in distributed GNN training that are massive feature communication, the loss of model accuracy and workload imbalance. Then we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories that are GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol. We carefully discuss the techniques in each category. In the end, we summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and CPU-clusters, respectively, and give a discussion about the future direction on distributed GNN training.

cs.LG↗
arXiv2023

A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design

Logic Synthesis (LS) plays a vital role in chip design -- a cornerstone of the semiconductor industry. A key task in LS is to transform circuits -- modeled by directed acyclic graphs (DAGs) -- into simplified circuits with equivalent functionalities. To tackle this task, many LS operators apply transformations to subgraphs -- rooted at each node on an input DAG -- sequentially. However, we found that a large number of transformations are ineffective, which makes applying these operators highly time-consuming. In particular, we notice that the runtime of the Resub and Mfs2 operators often dominates the overall runtime of LS optimization processes. To address this challenge, we propose a novel data-driven LS operator paradigm, namely PruneX, to reduce ineffective transformations. The major challenge of developing PruneX is to learn models that well generalize to unseen circuits, i.e., the out-of-distribution (OOD) generalization problem. Thus, the major technical contribution of PruneX is the novel circuit domain generalization framework, which learns domain-invariant representations based on the transformation-invariant domain-knowledge. To the best of our knowledge, PruneX is the first approach to tackle the OOD problem in LS operators. We integrate PruneX with the aforementioned Resub and Mfs2 operators. Experiments demonstrate that PruneX significantly improves their efficiency while keeping comparable optimization performance on industrial and very large-scale circuits, achieving up to $3.1\times$ faster runtime.

cs.AR↗
arXiv2023

Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey

Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) non-graph-based knowledge captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) graph-based knowledge captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain.

q-fin.ST↗
arXiv2023

Online Ridesharing with Meeting Points [Technical Report]

Nowadays, ridesharing becomes a popular commuting mode. Dynamically arriving riders post their origins and destinations, then the platform assigns drivers to serve them. In ridesharing, different groups of riders can be served by one driver if their trips can share common routes. Recently, many ridesharing companies (e.g., Didi and Uber) further propose a new mode, namely "ridesharing with meeting points". Specifically, with a short walking distance but less payment, riders can be picked up and dropped off around their origins and destinations, respectively. In addition, meeting points enables more flexible routing for drivers, which can potentially improve the global profit of the system. In this paper, we first formally define the Meeting-Point-based Online Ridesharing Problem (MORP). We prove that MORP is NP-hard and there is no polynomial-time deterministic algorithm with a constant competitive ratio for it. We notice that a structure of vertex set, $k$-skip cover, fits well to the MORP. $k$-skip cover tends to find the vertices (meeting points) that are convenient for riders and drivers to come and go. With meeting points, MORP tends to serve more riders with these convenient vertices. Based on the idea, we introduce a convenience-based meeting point candidates selection algorithm. We further propose a hierarchical meeting-point oriented graph (HMPO graph), which ranks vertices for assignment effectiveness and constructs $k$-skip cover to accelerate the whole assignment process. Finally, we utilize the merits of $k$-skip cover points for ridesharing and propose a novel algorithm, namely SMDB, to solve MORP. Extensive experiments on real and synthetic datasets validate the effectiveness and efficiency of our algorithms.

cs.DB↗
arXiv2023

Efficient Non-Learning Similar Subtrajectory Search

Similar subtrajectory search is a finer-grained operator that can better capture the similarities between one query trajectory and a portion of a data trajectory than the traditional similar trajectory search, which requires the two checked trajectories are similar to each other in whole. Many real applications (e.g., trajectory clustering and trajectory join) utilize similar subtrajectory search as a basic operator. It is considered that the time complexity is O(mn^2) for exact algorithms to solve the similar subtrajectory search problem under most trajectory distance functions in the existing studies, where m is the length of the query trajectory and n is the length of the data trajectory. In this paper, to the best of our knowledge, we are the first to propose an exact algorithm to solve the similar subtrajectory search problem in O(mn) time for most of widely used trajectory distance functions (e.g., WED, DTW, ERP, EDR and Frechet distance). Through extensive experiments on three real datasets, we demonstrate the efficiency and effectiveness of our proposed algorithms.

cs.DB↗
arXiv2023

Gate-Tunable Critical Current of the Three-Dimensional Niobium Nano-Bridge Josephson Junction

Recent studies have shown that the critical currents of several metallic superconducting nanowires and Dayem bridges can be locally tuned using a gate voltage {V_g}. Here, we report a gate-tunable Josephson junction structure constructed from a three-dimensional (3D) niobium nano-bridge junction (NBJ) with a voltage gate on top. Measurements up to 6 K showed that the critical current of this structure can be tuned to zero by increasing {V_g}. The critical gate voltage Vgc was reduced to 16 V and may possibly be reduced further by reducing the thickness of the insulation layer between the gate and the NBJ. Furthermore, the flux modulation generated by Josephson interference of two parallel 3D NBJs can also be tuned using {V_g} in a similar manner. Therefore, we believe that this gate-tunable Josephson junction structure is promising for superconducting circuit fabrication at high integration levels.

cond-mat.supr-con↗
arXiv2023

Geometric Scaling of the Current-Phase Relation of Niobium Nano-Bridge Junctions

The nano-bridge junction (NBJ) is a type of Josephson junction that is advantageous for the miniaturization of superconducting circuits. However, the current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal function which has been explained by a simplified model with correlation only to its effective length. Here, we investigated both measured and calculated CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank structure. From a sine-wave to a saw-tooth-like form, we showed that deviated CPRs of NBJs can be described quantitatively by its skewness Δθ. Furthermore, the measured dependency of Δθ on the critical current {I_0} from 108 NBJs turned out to be consistent with the calculated ones derived from the change in geometric dimensions. It suggested that the CPRs of NBJs can be tuned by their geometric dimensions. In addition, the calculated scaling behavior of Δθ versus {I_0} in three-dimensional space was provided for the future design of superconducting circuits of a high integration level by using niobium NBJs.

cond-mat.mes-hall↗
arXiv2023

Beyond the Edge of Stability via Two-step Gradient Updates

Gradient Descent (GD) is a powerful workhorse of modern machine learning thanks to its scalability and efficiency in high-dimensional spaces. Its ability to find local minimisers is only guaranteed for losses with Lipschitz gradients, where it can be seen as a `bona-fide' discretisation of an underlying gradient flow. Yet, many ML setups involving overparametrised models do not fall into this problem class, which has motivated research beyond the so-called ``Edge of Stability'' (EoS), where the step-size crosses the admissibility threshold inversely proportional to the Lipschitz constant above. Perhaps surprisingly, GD has been empirically observed to still converge regardless of local instability and oscillatory behavior. The incipient theoretical analysis of this phenomena has mainly focused in the overparametrised regime, where the effect of choosing a large learning rate may be associated to a `Sharpness-Minimisation' implicit regularisation within the manifold of minimisers, under appropriate asymptotic limits. In contrast, in this work we directly examine the conditions for such unstable convergence, focusing on simple, yet representative, learning problems, via analysis of two-step gradient updates. Specifically, we characterize a local condition involving third-order derivatives that guarantees existence and convergence to fixed points of the two-step updates, and leverage such property in a teacher-student setting, under population loss. Finally, starting from Matrix Factorization, we provide observations of period-2 orbit of GD in high-dimensional settings with intuition of its dynamics, along with exploration into more general settings.

cs.LG↗
arXiv2023

The Emerging Weak Antilocalization Effect in Semimetal Ta$_{0.7}$Nb$_{0.3}$Sb$_2$ Single Crystal

Weak antilocalization (WAL) effect is commonly observed in 2D systems, or 3D topological insulators, topological semimetal systems. Here we report the clear sign of WAL effect in high quality Ta$_{0.7}$Nb$_{0.3}$Sb$_2$ single crystals, in below 50$^\circ$ K region. The chemical vapor transport method was employed to grow the single crystal samples, the high crystallization quality and uniform element distribution are verified by X-ray diffractions and electron microscopy techniques. Employing the Hall effect and two-band model fitting, the high carrier mobility (> 1000 cm$^2$V$^{-1}$s$^{-1}$ in 2 to 300$^\circ$ K region) and off-compensation electron/hole ratio are obtained. Due to the different angular dependence of WAL effect and the fermiology of Ta$_{0.7}$Nb$_{0.3}$Sb$_2$ single crystal, interesting magnetic-field-induced symmetry change is observed in angular magnetoresistance. These interesting transport properties will lead to more theoretical and applicational exploration in Ta$_{0.7}$Nb$_{0.3}$Sb$_2$ and related semimetal materials.

cond-mat.mtrl-sci↗
arXiv2023

Topological Insulator VxBi1.08-xSn0.02Sb0.9Te2S as a Promising n-type Thermoelectric Material

As one of the most important n-type thermoelectric (TE) materials, Bi2Te3 has been studied for decades, with efforts to enhance the thermoelectric performance based on element doping, band engineering, etc. In this study, we report a novel bulk-insulating topological material system as a replacement for n-type Bi2Te3 materials: V doped Bi1.08Sn0.02Sb0.9Te2S (V:BSSTS) . The V:BSSTS is a bulk insulator with robust metallic topological surface states. Furthermore, the bulk band gap can be tuned by the doping level of V, which is verified by magnetotransport measurements. Large linear magnetoresistance is observed in all samples. Excellent thermoelectric performance is obtained in the V:BSSTS samples, e.g., the highest figure of merit ZT of ~ 0.8 is achieved in the 2% V doped sample (denoted as V0.02) at 550 K. The high thermoelectric performance of V:BSSTS can be attributed to two synergistic effects: (1) the low conductive secondary phases Sb2S3, and V2S3 are believed to be important scattering centers for phonons, leading to lower lattice thermal conductivity; and (2) the electrical conductivity is increased due to the high-mobility topological surface states at the boundaries. In addition, by replacing one third of costly tellurium with abundant, low-cost, and less-toxic sulfur element, the newly produced BSSTS material is inexpensive but still has comparable TE performance to the traditional Bi2Te3-based materials, which offers a cheaper plan for the electronics and thermoelectric industries. Our results demonstrate that topological materials with unique band structures can provide a new platform in the search for new high performance TE materials.

cond-mat.mtrl-sci↗
arXiv2023

Colossal magnetoresistance in Ti lightly-doped Cr2Se3 single crystals with layered structure

Stoichiometric Cr2Se3 single crystals are particular layer-structured antiferromagnets which possess noncolinear spin configuration, weak ferromagnetic moments, moderate magnetoresistance (MR ~ 14.3%), and bad metallic conductivity below the antiferromagnetic phase transition temperature. Here, we report an interesting >16000% colossal magnetoresistance (CMR) effect in Ti (1.5 atomic percent) lightly-doped Cr2Se3 single crystals. Such a CMR is approximately 1143 times larger than that of the stoichiometric Cr2Se3 crystals and is rarely observed in layered antiferromagnets and is attributed to the frustrated spin configuration. Moreover, the Ti doping not only dramatically changes the electronic conductivity of the Cr2Se3 crystal from a bad metal to a semiconductor with a gap of ~ 15 meV, but also induces a change of the magnetic anisotropy of the Cr2Se3 crystal from strong out-of-plane to weak in plane. Further, magnetotransport measurements reveal that the low-field MR scales with the square of the reduced magnetization, which is a signature of CMR materials. The layered Ti:Cr2Se3 with CMR effect could be used as 2D heterostructure building blocks to provide colossal negative MR in spintronic devices.

cond-mat.mtrl-sci↗
arXiv2023

Magnetotransport and Berry phase Tuning in Gd-doped Bi2Se3 Topological Insulator Single Crystals

The Berry phase is an important concept in solids, correlated to the band topology, axion electrodynamics and potential applications of topological materials. Here, we investigate the magnetotransport and Berry phase of rare earth element Gd doped Bi2Se3 (Gd_Bi2Se3) topological insulator at low temperatures and high magnetic fields. Gd_Bi2Se3 single crystals show Shubnikov-de Haas (SdH) oscillations with nontrivial Berry phase while Bi2Se3 single crystals show zero Berry phase in SdH oscillations. The temperature dependent magnetization curves can be well fitted with the Curie-Weiss law in 3-300 K region, indicating no magnetic ordering in Gd_Bi2Se3 crystals. Moreover, Gd doping has limited influence on the quantum oscillation parameters (e.g., frequency of oscillations, the area of the Fermi surface, effective electron mass, Fermi wave vectors etc.), but has an impact on the Hall mobility, carrier density, and band topology. Our results demonstrate that Gd doping can tune the Berry phase of topological insulators effectively, which may pave a way for the future realization of many predicted exotic transport phenomena of topological origin.

cond-mat.mtrl-sci↗
arXiv2023

Coexistence of Logarithmic and SdH Quantum Oscillations in Ferromagnetic Cr-doped Tellurium Single Crystals

We report the synthesis of transition-metal-doped ferromagnetic elemental single-crystal semiconductors with quantum oscillations using the physical vapor transport method. The 7.7 atom% Cr-doped Te crystals (Cr_Te) show ferromagnetism, butterfly-like negative magnetoresistance in the low temperature (< 3.8 K) and low field (< 0.15 T) region, and high Hall mobility, e.g., 1320 cm2 V-1 s-1 at 30 K and 350 cm2 V-1 s-1 at 300 K, implying that Cr_Te crystals are ferromagnetic elemental semiconductors. When B // c // I, the maximum negative MR is -27% at T = 20 K and B = 8 T. In the low temperature semiconducting region, Cr_Te crystals show strong discrete scale invariance dominated logarithmic quantum oscillations when the direction of the magnetic field B is parallel to the [100] crystallographic direction and show Landau quantization dominated Shubnikov-de Haas (SdH) oscillations for B // [210] direction, which suggests the broken rotation symmetry of the Fermi pockets in the Cr_Te crystals. The findings of coexistence of multiple quantum oscillations and ferromagnetism in such an elemental quantum material may inspire more study of narrow bandgap semiconductors with ferromagnetism and quantum phenomena.

cond-mat.mtrl-sci↗
arXiv2023

Antiferromagnetic topological insulating state in Tb$_{0.02}$Bi$_{1.08}$Sb$_{0.9}$Te$_2$S single crystals

Topological insulators are emerging materials with insulating bulk and symmetry protected nontrivial surface states. One of the most fascinating transport behaviors in a topological insulator is the quantized anomalous Hall insulator, which has been observed inmagnetic-topological-insulator-based devices. In this work, we report a successful doping of rare earth element Tb into Bi$_{1.08}$Sb$_{0.9}$Te$_2$S topological insulator single crystals, in which the Tb moments are antiferromagnetically ordered below ~10 K. Benefiting from the in-bulk-gap Fermi level, transport behavior dominant by the topological surface states is observed below ~ 150 K. At low temperatures, strong Shubnikov-de Haas oscillations are observed, which exhibit 2D-like behavior. The topological insulator with long range magnetic ordering in rare earth doped Bi$_{1.08}$Sb$_{0.9}$Te$_2$S single crystal provides an ideal platform for quantum transport studies and potential applications.

cond-mat.mtrl-sci↗
arXiv2023

A survey on deep learning approaches for data integration in autonomous driving system

The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment. Recent advancements in deep learning have led to the rapid development of approaches that integrate multi-sensory measurements to enhance perception capabilities. This paper surveys the latest deep learning integration techniques applied to the perception module in autonomous driving systems, categorizing integration approaches based on "what, how, and when to integrate". A new taxonomy of integration is proposed, based on three dimensions: multi-view, multi-modality, and multi-frame. The integration operations and their pros and cons are summarized, providing new insights into the properties of an "ideal" data integration approach that can alleviate the limitations of existing methods. After reviewing hundreds of relevant papers, this survey concludes with a discussion of the key features of an optimal data integration approach.

cs.RO↗
arXiv2023

From Large Language Models to Databases and Back: A discussion on research and education

This discussion was conducted at a recent panel at the 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), held April 17-20, 2023 in Tianjin, China. The title of the panel was "What does LLM (ChatGPT) Bring to Data Science Research and Education? Pros and Cons". It was moderated by Lei Chen and Xiaochun Yang. The discussion raised several questions on how large language models (LLMs) and database research and education can help each other and the potential risks of LLMs.

cs.DB↗
arXiv2023

Reversible Non-Volatile Electronic Switching in a Near Room Temperature van der Waals Ferromagnet

The ability to reversibly toggle between two distinct states in a non-volatile method is important for information storage applications. Such devices have been realized for phase-change materials, which utilizes local heating methods to toggle between a crystalline and an amorphous state with distinct electrical properties. To expand such kind of switching between two topologically distinct phases requires non-volatile switching between two crystalline phases with distinct symmetries. Here we report the observation of reversible and non-volatile switching between two stable and closely-related crystal structures with remarkably distinct electronic structures in the near room temperature van der Waals ferromagnet Fe$_{5-δ}$GeTe$_2$. From a combination of characterization techniques we show that the switching is enabled by the ordering and disordering of an Fe site vacancy that results in distinct crystalline symmetries of the two phases that can be controlled by a thermal annealing and quenching method. Furthermore, from symmetry analysis as well as first principle calculations, we provide understanding of the key distinction in the observed electronic structures of the two phases: topological nodal lines compatible with the preserved global inversion symmetry in the site-disordered phase, and flat bands resulting from quantum destructive interference on a bipartite crystaline lattice formed by the presence of the site order as well as the lifting of the topological degeneracy due to the broken inversion symmetry in the site-ordered phase. Our work not only reveals a rich variety of quantum phases emergent in the metallic van der Waals ferromagnets due to the presence of site ordering, but also demonstrates the potential of these highly tunable two-dimensional magnets for memory and spintronics applications.

cond-mat.str-el↗