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Lei Wang

Publications and source records attributed to Lei Wang.

At least 775 records · Page 43Linked to original sources
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↗
arXiv2023

TPU-MLIR: A Compiler For TPU Using MLIR

Multi-level intermediate representations (MLIR) show great promise for reducing the cost of building domain-specific compilers by providing a reusable and extensible compiler infrastructure. This work presents TPU-MLIR, an end-to-end compiler based on MLIR that deploys pre-trained neural network (NN) models to a custom ASIC called a Tensor Processing Unit (TPU). TPU-MLIR defines two new dialects to implement its functionality: 1. a Tensor operation (TOP) dialect that encodes the deep learning graph semantics and independent of the deep learning framework and 2. a TPU kernel dialect to provide a standard kernel computation on TPU. A NN model is translated to the TOP dialect and then lowered to the TPU dialect for different TPUs according to the chip's configuration. We demonstrate how to use the MLIR pass pipeline to organize and perform optimization on TPU to generate machine code. The paper also presents a verification procedure to ensure the correctness of each transform stage.

cs.PL↗
arXiv2023

Spontaneous CP violation electroweak baryogenesis and gravitational wave through multi-step phase transitions

In a singlet pseudoscalar extension of the two-Higgs-doublet model we discuss spontaneous CP violation electroweak baryogenesis via two different patterns of phase transitions (PTs): (i) two-step PTs whose first-step and second-step are strongly first-order; (ii) three-step PTs whose first-step is second-order and the second-step and third-step are strongly first-order. For the case of the two-step pattern, the first-step PT takes place at a high temperature, converting the origin phase into an electroweak symmetry broken phase and breaking the CP symmetry spontaneously. Thus, the baryon number is produced during the first-step PT. At the second-step PT, the phase is converted into the observed vacuum at zero temperature, and the CP-symmetry is restored. In both phases the sphaleron processes are sufficiently suppressed, which keep the baryon number unchanged. For the case of the three-step PTs, the pseudoscalar field firstly acquires a nonzero VEV, and VEVs of other fields still remain zero during the first-step PT. The following PTs and electroweak baryogenesis are similar to the case of the two-step PTs. In addition, the gravitational wave spectra can have one or two peaks through the two-step and the three-step PTs, and we discuss the detectability at the future gravitational wave detectors.

hep-ph↗
arXiv2023

Large Eddy Simulations of Fully-Developed Turbulent Flows Over Additively Manufactured Rough Surfaces

In the last decade, progresses in additive manufacturing (AM) have paved the way for optimized heat exchangers, whose disruptive design will depend on predictive numerical simulations. Typical AM rough surfaces show limited resemblance to the artificially constructed rough surfaces that have been the basis of most prior fundamental research on turbulent flow over rough walls. Therefore, a high-fidelity LES database is built to develop and assess novel wall models for AM. This article investigates the flow in rough pipes built from the surfaces created using AM techniques at Siemens based on Nickel Alloy IN939 material. We developed a code to generate the desired rough pipes from scanned planar surfaces and performed high-fidelity LES of turbulent rough pipe flows at Re = 11,700 to reveal the influence of roughness on turbulence, mainly the average roughness height and the Effective Slope. The equivalent sand-grain roughnesses, ks, of the present AM rough surfaces are predicted using the Colebrook correlation. In the present study, the existence of a logarithmic layer is marked even for high values of ks. The mean flow, the velocity fluctuations, and the Reynolds stresses show turbulence's strong dependence on the roughness topography. Profiles of turbulence statistics are compared by introducing an effective wall-normal distance. The effective distance collapses the shear stresses and the velocity fluctuations outside the roughness sublayer; thus, Townsend's similarity of the streamwise mean velocity is marked for the present roughnesses. Furthermore, a mixed scaling is introduced to improve the collapse of turbulence statistics in the roughness sublayer.

physics.flu-dyn↗
arXiv2023

Effect of temperature-dependent thermophysical properties on turbulent forced convection under constant heat flux boundary condition

In this study, we performed highly resolved large-eddy simulations (LES) to investigate the influence of variable properties on the forced turbulent convection in a channel. The constant heat flux boundary condition permits wall temperature fluctuations and thus induces variations of fluid properties. Since the effect of viscosity on the flow exhibits $Re_τ^{-1}$ scaling, we only considered $Re_τ= 180$ in the present study. Compared to the flow with constant properties, results indicate that the variable properties have trivial effects on the mean velocity and temperature profiles, Reynolds shear stress, wall-normal heat flux, as well as the small-scale turbulence characteristics. However, we also observed that the turbulence intensities, low-speed streaks, burst motions, and budgets for temperature variance and wall-normal heat flux are modified by the variable properties in a perceptible way. In addition, we showed that the classic wall scaling is a good choice for flow with small and moderate variations of fluid properties.

physics.flu-dyn↗
arXiv2023

A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provides fast convergence speed as well as satisfactory accuracy of deducting optimized reallocation of the thruster forces to compensate for the power loss in different fault cases. Simulations with or without environmental perturbations under different fault cases and comparisons to other traditional FTCs are presented, thus verifying the effectiveness and robustness of the proposed GOA-based fault-tolerant trajectory tracking design.

cs.RO↗