arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 757 records · Page 42Linked to original sources

arXiv2023

Robust Eigenvectors of Regular Simplex Tensors: Conjecture Proof

The concept of tensor eigenpairs has received more researches in past decades. Recent works have paid attentions to a special class of symmetric tensors termed regular simplex tensors, which is constructed by equiangular tight frame of n + 1 vectors in n-dimensional space, and the robustness of eigenpairs was investigated. In the end of the literature, a conjecture was claimed that the robust eigenvectors of a regular simplex tensor are precisely the vectors in the frame. One later work theoretically proved that the case of n = 2 was true. In this paper, we proceed further and complete the proof for the above conjecture. Some promising directions are discussed in the end for future works.

math.SP↗

arXiv2023

Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework

Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. It aims to make a model trained on multiple source domains generalize to an unseen target domain. Although achieving promising progress, existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. In this paper, we turn to investigate unsupervised domain generalization for ReID, by assuming that no label is available for any source domains. To address this challenging setting, we propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module designed upon the batch and instance normalization techniques. In doing so, we successfully yield reliable pseudo-labels to implement training and also enhance the domain generalization capability of the model as required. In addition, we show that our framework can even be applied to improve person ReID under the settings of supervised domain generalization and unsupervised domain adaptation, demonstrating competitive performance with respect to relevant methods. Extensive experimental study on benchmark datasets is conducted to validate the proposed framework. A significance of our work lies in that it shows the potential of unsupervised domain generalization for person ReID and sets a strong baseline for the further research on this topic.

cs.CV↗

arXiv2023

Particle production at large $p_T$ in Xe+Xe collisions with jet quenching using the higher twist approach

The production of $π^0$, $η$, and $ϕ$ in the most central (0%-5%) Xe+Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV is investigated in the framework of the perturbative QCD (pQCD) improved parton model at an accuracy of next-to-leading order (NLO). The jet quenching effect is effectively incorporated by medium-modified fragmentation functions via the higher-twist approach. Predictions of the nuclear modification factors of $π^0$, $η$, and $ϕ$ as functions of the transverse momentum $p_{\rm T}$ are made with the jet transport parameter $\hat{q}_0$, which is extracted from the available experimental data of charged hadrons provided by ALICE and CMS. The particle ratios $η/π^0,\ ϕ/π^0$ as functions of $p_{\rm T}$ in Xe+Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV as well as in 0%-5% Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are also presented. The numerical simulations of the scaled ratios of charged hadron production in the Xe+Xe 5.44 TeV system over those in the Pb+Pb 5.02 TeV system give a good description of the CMS data, and the scaled ratios of $π^0$, $η$, and $ϕ$ production coincide with the curve of charged hadron production.

hep-ph↗

arXiv2023

Thermodynamics of Non-equilibrium Diffuse-Interfaces in Mesoscale Phase Transformations

We present a new phase-field formulation for the non-equilibrium interface kinetics. The diffuse interface is considered an integral of numerous representative volume elements (RVEs), in which there is a two-phase mixture with two conserved and two non-conserved concentration fields. This particular way of separating concentration fields leads to two distinct dissipative processes besides the phase boundary migration, i.e., trans-sharp-interface diffusion between different phases within a single RVE and trans-diffuse-interface diffusion between different RVEs within a single phase. A two-part mechanism is proposed to balance the driving forces and energy dissipation for diffusionless and diffusional processes in RVEs. Moreover, the classical interface friction and solute drag can be recovered by integrating the diffuse interface. Compared with the sharp interface theory, the present diffuse interface can reproduce the atomic simulated partial-drag self-consistently in thermodynamics.

cond-mat.mtrl-sci↗

arXiv2023

High-level semantic feature matters few-shot unsupervised domain adaptation

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain. We found that the local features are insufficient to FS-UDA, which could introduce noise or bias against classification, and not be used to effectively align the domains. To address the above issues, we aim to refine the local features to be more discriminative and relevant to classification. Thus, we propose a novel task-specific semantic feature learning method (TSECS) for FS-UDA. TSECS learns high-level semantic features for image-to-class similarity measurement. Based on the high-level features, we design a cross-domain self-training strategy to leverage the few labeled samples in source domain to build the classifier in target domain. In addition, we minimize the KL divergence of the high-level feature distributions between source and target domains to shorten the distance of the samples between the two domains. Extensive experiments on DomainNet show that the proposed method significantly outperforms SOTA methods in FS-UDA by a large margin (i.e., 10%).

cs.CV↗

arXiv2023

Periodic traveling waves in the ϕ^4 model: Instability, stability and localized structures

We consider the instability and stability of periodic stationary solutions to the classical ϕ^4 equation numerically. In the superluminal regime, the model possesses dnoidal and cnoidal waves. The former are modulationally unstable and the spectrum forms a figure eight intersecting at the origin of the spectral plane. The latter can be modulationally stable and the spectrum near the origin in that case is represented by vertical bands along the purely imaginary axis. The instability of the cnoidal states in that case stems from elliptical bands of complex eigenvalues far from the spectral plane origin. In the subluminal regime, there exist only snoidal waves which are modulationally unstable. Considering the subharmonic perturbations, we show that the snoidal waves in the subluminal regime are spectrally unstable with respect to all subharmonic perturbations, while for the dnoidal and cnoidal waves in the superluminal regime, the transition between the spectrally stable state and the spectrally unstable state occurs through a Hamiltonian Hopf bifurcation. The dynamical evolution of the unstable states is also considered, leading to some interesting spatio-temporal localization events.

nlin.PS↗

arXiv2023

Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation

Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The elimination of the autocorrelation time leads to a stochastic algorithm with provable cubic scaling (with a potentially large prefactor), i.e. the number of operations for producing an uncorrelated sample and for calculating the local energy scales like $\mathcal{O}(N_s^3)$ with the number of orbitals $N_s$. The implementation is benchmarked on the two-dimensional $t-V$ model of spinless fermions on the square lattice.

cond-mat.str-el↗

arXiv2023

Lifts of Brauer characters in characteristic two

A conjecture raised by Cossey in 2007 asserts that if $G$ is a finite $p$-solvable group and $φ$ is an irreducible $p$-Brauer character of $G$ with vertex $Q$, then the number of lifts of $φ$ is at most $|Q:Q'|$. This conjecture is now known to be true in several situations for $p$ odd, but there has been little progress for $p$ even. The main obstacle appeared in characteristic two is that all the vertex pairs of a lift are neither linear nor conjugate. In this paper we show that if $χ$ is a lift of an irreducible $2$-Brauer character in a solvable group, then $χ$ has a linear Navarro vertex if and only if all the vertex pairs of $χ$ are linear, and in that case all of the twisted vertices of $χ$ are conjugate. Our result can also be used to study other lifting problems of Brauer characters in characteristic two. As an application, we prove a weaker form of Cossey's conjecture for $p=2$ "one vertex at a time".

math.GR↗

arXiv2023

Pilot-Free Unsourced Random Access Via Dictionary Learning and Error-Correcting Codes

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests itself by focusing only on unwrapping the received signals via a common codebook. In this paper, we propose a URA protocol for a massive access cellular system equipped with multiple antennas at the base station. The proposed scheme encompasses a codebook enabling construction of sparse transmission frame, a receiver equipped with dictionary learning and error-correcting codes and a collision resolution strategy for the collided codeword. Discrepant to the existing schemes with necessary overhead for preamble signals, no overhead or pre-defined pilot sequences are needed in the proposed scheme, which is favorable for energy-efficient transmission and latency reduction. Numerical results verify the viability of the proposed scheme in practical massive access scenario.

cs.IT↗

arXiv2023

REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation

Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential for improving the recommendation persuasiveness, informativeness and user satisfaction. Despite a lot of promising explainable recommender models have been proposed in the past few years, the evaluation strategies of these models suffer from several limitations. For example, the explanation ground truths are not labeled by real users, the explanations are mostly evaluated based on only one aspect and the evaluation strategies can be hard to unify. To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths. In specific, we firstly develop a video recommendation platform, where a series of questions around the recommendation explainability are carefully designed. Then, we recruit about 3000 users with different backgrounds to use the system, and collect their behaviors and feedback to our questions. In this paper, we detail the construction process of our dataset and also provide extensive analysis on its characteristics. In addition, we develop a library, where ten well-known explainable recommender models are implemented in a unified framework. Based on this library, we build several benchmarks for different explainable recommendation tasks. At last, we present many new opportunities brought by our dataset, which are expected to shed some new lights to the explainable recommendation field. Our dataset, library and the related documents have been released at https://reasoner2023.github.io/.

cs.IR↗

arXiv2023

WPC: Whole-picture Workload Characterization

This article raises an important and challenging workload characterization issue: can we uncover each critical component across the stacks contributing what percentages to any specific bottleneck? The typical critical components include languages, programming frameworks, runtime environments, instruction set architectures (ISA), operating systems (OS), and microarchitecture. Tackling this issue could help propose a systematic methodology to guide the software and hardware co-design and critical component optimizations. We propose a whole-picture workload characterization (WPC) methodology to answer the above issue. In essence, WPC is an iterative ORFE loop consisting of four steps: Observation, Reference, Fusion, and Exploration. WPC observes different level data (observation), fuses and normalizes the performance data (fusion) with respect to the well-designed standard reference workloads suite (reference), and explores the software and hardware co-design space (exploration) to investigate the impacts of critical components across the stacks. We build and open-source the WPC tool. Our evaluations confirm WPC can quantitatively reveal the contributions of the language, framework, runtime environment, ISA, OS, and microarchitecture to the primary pipeline efficiency.

cs.PF↗

arXiv2023

DCNetBench: Scaleable Data Center Network Benchmarking

Data center networking is the central infrastructure of the modern information society. However, benchmarking them is very challenging as the real-world network traffic is difficult to model, and Internet service giants treat the network traffic as confidential. Several industries have published a few publicly available network traces. However, these traces are collected from specific data center environments, e.g., applications, network topology, protocols, and hardware devices, and thus cannot be scaled to different users, underlying technologies, and varying benchmarking requirements. This article argues we should scale different data center applications and environments in designing, implementing, and evaluating data center networking benchmarking. We build DCNetBench, the first application-driven data center network benchmarking that can scale to different users, underlying technologies, and varying benchmarking requirements. The methodology is as follows. We built an emulated system that can simulate networking with different configurations. Then we run applications on the emulated systems to capture the realistic network traffic patterns; we analyze and classify these patterns to model and replay those traces. Finally, we provide an automatic benchmarking framework to support this pipeline. The evaluations on DCNetBench show its scaleability, effectiveness, and diversity for data center network benchmarking.

cs.NI↗

arXiv2023

MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion

Multi-view radar-camera fused 3D object detection provides a farther detection range and more helpful features for autonomous driving, especially under adverse weather. The current radar-camera fusion methods deliver kinds of designs to fuse radar information with camera data. However, these fusion approaches usually adopt the straightforward concatenation operation between multi-modal features, which ignores the semantic alignment with radar features and sufficient correlations across modals. In this paper, we present MVFusion, a novel Multi-View radar-camera Fusion method to achieve semantic-aligned radar features and enhance the cross-modal information interaction. To achieve so, we inject the semantic alignment into the radar features via the semantic-aligned radar encoder (SARE) to produce image-guided radar features. Then, we propose the radar-guided fusion transformer (RGFT) to fuse our radar and image features to strengthen the two modals' correlation from the global scope via the cross-attention mechanism. Extensive experiments show that MVFusion achieves state-of-the-art performance (51.7% NDS and 45.3% mAP) on the nuScenes dataset. We shall release our code and trained networks upon publication.

cs.CV↗

arXiv2023

MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts

Monocular 3D object detection reveals an economical but challenging task in autonomous driving. Recently center-based monocular methods have developed rapidly with a great trade-off between speed and accuracy, where they usually depend on the object center's depth estimation via 2D features. However, the visual semantic features without sufficient pixel geometry information, may affect the performance of clues for spatial 3D detection tasks. To alleviate this, we propose MonoPGC, a novel end-to-end Monocular 3D object detection framework with rich Pixel Geometry Contexts. We introduce the pixel depth estimation as our auxiliary task and design depth cross-attention pyramid module (DCPM) to inject local and global depth geometry knowledge into visual features. In addition, we present the depth-space-aware transformer (DSAT) to integrate 3D space position and depth-aware features efficiently. Besides, we design a novel depth-gradient positional encoding (DGPE) to bring more distinct pixel geometry contexts into the transformer for better object detection. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on the KITTI dataset.

cs.CV↗

arXiv2023

Navarro vertices and lifts in solvable groups

Let $Q$ be a $p$-subgroup of a finite $p$-solvable group $G$, where $p$ is a prime, and suppose that $δ$ is a linear character of $Q$ with the property that $δ(x)=δ(y)$ whenever $x,y\in Q$ are conjugate in $G$. In this situation, we show that restriction to $p$-regular elements defines a canonical bijection from the set of those irreducible ordinary characters of $G$ with Navarro vertex $(Q,δ)$ onto the set of irreducible Brauer characters of $G$ with Green vertex $Q$. Also, we use this correspondence to examine the behavior of lifts of Brauer characters with respect to normal subgroups.

math.GR↗

arXiv2023

NHtapDB: Native HTAP Databases

Native database (1) provides a near-data machine learning framework to facilitate generating real-time business insight, and predefined change thresholds will trigger online training and deployment of new models, and (2) offers a mixed-format store to guarantee the performance of HTAP workloads, especially the hybrid workloads that consist of OLAP queries in-between online transactions. We make rigorous test plans for native database with an enhanced state-of-the-art HTAP benchmark.

cs.DB↗

arXiv2023

Hysteresis and training effect in the electric control of spin current in Pt/Y3Fe5O12 heterostructures

We have reported on the hysteresis and training effect of spin current in Pt/Y3Fe5O12 heterostructures during subsequent cycles of ionic liquid gate voltage Vg. The inverse spin Hall effect voltage in spin pumping and spin Hall magnetoresistance exhibit diode-like behaviors in the first half cycle of Vg andalsoshowhysteresisinthe first cycle of Vg. Both the diode-like behavior and the hysteresis become weak and even vanish in the second cycle of Vg due to the training effect. The above experimental results can be well explained by the screening charge doping model, in which the charge and the local magnetic moment are asymmetrically distributed in the Pt layer. The applicability of this model is further confirmed by measurements of anisotropic magnetoresistance and ferromagnetic resonance. The diode-like behavior is attributed to interplay between the asymmetrically distributed local magnetic moment and the spin current relaxation in the Pt layer. The hysteresis and the training effect arise from the incompletely reversible process between oxidation and reduction of Pt atoms and the evolution of the surface morphology at the ionic liquid/Pt interface under electric gating. This work provides new insights to improve the functional performance of electrically controlled spin current devices.

cond-mat.mtrl-sci↗

arXiv2023

Cloak: Transitioning States on Legacy Blockchains Using Secure and Publicly Verifiable Off-Chain Multi-Party Computation

In recent years, the confidentiality of smart contracts has become a fundamental requirement for practical applications. While many efforts have been made to develop architectural capabilities for enforcing confidential smart contracts, a few works arise to extend confidential smart contracts to Multi-Party Computation (MPC), i.e., multiple parties jointly evaluate a transaction off-chain and commit the outputs on-chain without revealing their secret inputs/outputs to each other. However, existing solutions lack public verifiability and require O(n) transactions to enable negotiation or resist adversaries, thus suffering from inefficiency and compromised security. In this paper, we propose Cloak, a framework for enabling Multi-Party Transaction (MPT) on existing blockchains. An MPT refers to transitioning blockchain states by an publicly verifiable off-chain MPC. We identify and handle the challenges of securing MPT by harmonizing TEE and blockchain. Consequently, Cloak secures the off-chain nondeterministic negotiation process (a party joins an MPT without knowing identities or the total number of parties until the MPT proposal settles), achieves public verifiability (the public can validate that the MPT correctly handles the secret inputs/outputs from multiple parties and reads/writes states on-chain), and resists Byzantine adversaries. According to our proof, Cloak achieves better security with only 2 transactions, superior to previous works that achieve compromised security at O(n) transactions cost. By evaluating examples and real-world MPTs, the gas cost of Cloak reduces by 32.4% on average.

cs.CR↗