arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 739 records · Page 41Linked to original sources

arXiv2023

An ultra-stable cryogenic sapphire cavity laser with an instability of $1.9\times10^{-16}$ based on a low vibration level cryostat

Cryogenic ultra-stable lasers have extremely low thermal noise limits and frequency drifts, but they are more seriously affected by vibration noise from cryostats. Main material candidates for cryogenic ultra-stable cavities include silicon and sapphire. Although sapphire has many excellent properties at low temperature, the development of sapphire-based cavities is less advanced than that of silicon-based. Using a homemade cryogenic sapphire cavity, we develop an ultra-stable laser source with a frequency instability of $1.9\times10^{-16}$. This is the best frequency instability level among similar systems using cryogenic sapphire cavities reported so far. Low vibration performance of the cryostat is demonstrated with a two-stage vibration isolation, and the vibration suppression is further improved by different mixing ratio of the gas-liquid helium. With this technique, vibrations at frequencies higher than tens of hertz are greatly suppressed.

physics.ins-det↗

arXiv2023

Neural Airport Ground Handling

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://github.com/RoyalSkye/AGH.

cs.AI↗

arXiv2023

A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings

Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-SOT) and joint training of single-channel target-speaker separation and ASR (SC-TS-ASR) can be exploited to partially solve this problem. In this paper, we propose three corresponding multichannel (MC) SA-ASR approaches, namely MC-FD-SOT, MC-WD-SOT and MC-TS-ASR. For different tasks/models, different multichannel data fusion strategies are considered, including channel-level cross-channel attention for MC-FD-SOT, frame-level cross-channel attention for MC-WD-SOT and neural beamforming for MC-TS-ASR. Results on the AliMeeting corpus reveal that our proposed models can consistently outperform the corresponding single-channel counterparts in terms of the speaker-dependent character error rate.

eess.AS↗

arXiv2023

From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization

In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training by efficiently utilizing their computational resources. It is well acknowledged that step asynchronism leads to objective inconsistency under non-i.i.d. data, which degrades the model's accuracy. To address this issue, we propose a new algorithm FedaGrac, which calibrates the local direction to a predictive global orientation. Taking advantage of the estimated orientation, we guarantee that the aggregated model does not excessively deviate from the global optimum while fully utilizing the local updates of faster nodes. We theoretically prove that FedaGrac holds an improved order of convergence rate than the state-of-the-art approaches and eliminates the negative effect of step asynchronism. Empirical results show that our algorithm accelerates the training and enhances the final accuracy.

cs.DC↗

arXiv2023

The yielding of granular matter is marginally stable and critical

The mechanical yield of dense granular materials is a fascinating rheological phenomenon, beyond which stress no longer increases with strain at a sufficiently large deformation. Understanding the behavior of mechanical responses associated with yielding is a fundamental goal in granular physics, and other related fields including glassy physics, material sciences, geophysics, and active matter biophysics. However, despite nearly half a century of theoretical efforts, the nature of yielding in amorphous solids remains largely elusive compared to its crystalline counterpart. Here, we experimentally investigate the mechanical responses of two-dimensional bidisperse jammed disks subjected to volume-invariant pure shear, focusing on the behavior of yielding. We show that the microscopic mechanical and geometrical features of configurations under shear can be characterized by two critical exponents of weak-force and small-gap distributions originally proposed for the isotropic jamming transition. We find that the yielding transition satisfies the condition of marginal mechanical stability through a scaling relationship between the two exponents, and after yielding global instability emerges. The criticality of yielding is revealed by a significant peak of susceptibility that quantifies the fluctuation of a glass overlap order parameter. Moreover, we find a distinct transition before yielding, which is associated with the onset of structural anisotropy.

cond-mat.soft↗

arXiv2023

Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated based on real scenarios, where we integrate imitation learning and graph convolutional network (GCN) to learn a destroy operator to automatically select variables, and employ an off-the-shelf solver as the repair operator to reoptimize the selected variables. Experimental results based on a real airport show that the proposed method allows for handling up to 200 flights with 10 types of operations simultaneously, and outperforms state-of-the-art methods. Moreover, the learned method performs consistently accompanying different solvers, and generalizes well on larger instances, verifying the versatility and scalability of our method.

cs.AI↗

arXiv2023

MUX64, an analogue 64-to-1 multiplexer ASIC for the ATLAS High Granularity Timing Detector

We present the design and the performance of MUX64, a 64-to-1 analogue multiplexer ASIC for the ATLAS High Granularity Timing Detector (HGTD). The MUX64 transmits one of its 64 inputs selected by six address lines for the voltages or temperatures being monitored to an lpGBT ADC channel. The prototype ASICs fabricated in TSMC 130 nm CMOS technology were prepared in wire-bonding and QFN88 packaging format. A total of 280 chips was examined for functionality and quality assurance. The accelerated aging test conducted at 85 degrees celsius shows negligible degradation over 16 days.

physics.ins-det↗

arXiv2023

Snake net and balloon force with a neural network for detecting multiple phases

Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys.Rev.Lett. 120, 176401(2018)]. It guesses the phase boundary as an initial snake and then drives the snake to convergence with forces estimated by the artificial neural network. In this paper, we extend this unsupervised learning method with one contour to a snake net with multiple contours for the purpose of obtaining several phase boundaries in a phase diagram. For the classical Blume-Capel model, the phase diagram containing three and four phases is obtained. Moreover, to overcome the limitations of the initial position and speed up the movement of the snake, the balloon force decaying with the iteration steps is introduced and applied to the snake net structure. Our method is helpful in determining the phase diagram with multiple phases, using just snapshots of configurations from cold atoms or other experiments without knowledge of the phases.

cond-mat.stat-mech↗

arXiv2023

Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network

Model inversion (MI) attacks have raised increasing concerns about privacy, which can reconstruct training data from public models. Indeed, MI attacks can be formalized as an optimization problem that seeks private data in a certain space. Recent MI attacks leverage a generative adversarial network (GAN) as an image prior to narrow the search space, and can successfully reconstruct even the high-dimensional data (e.g., face images). However, these generative MI attacks do not fully exploit the potential capabilities of the target model, still leading to a vague and coupled search space, i.e., different classes of images are coupled in the search space. Besides, the widely used cross-entropy loss in these attacks suffers from gradient vanishing. To address these problems, we propose Pseudo Label-Guided MI (PLG-MI) attack via conditional GAN (cGAN). At first, a top-n selection strategy is proposed to provide pseudo-labels for public data, and use pseudo-labels to guide the training of the cGAN. In this way, the search space is decoupled for different classes of images. Then a max-margin loss is introduced to improve the search process on the subspace of a target class. Extensive experiments demonstrate that our PLG-MI attack significantly improves the attack success rate and visual quality for various datasets and models, notably, 2~3 $\times$ better than state-of-the-art attacks under large distributional shifts. Our code is available at: https://github.com/LetheSec/PLG-MI-Attack.

cs.CV↗

arXiv2023

Delving into the Adversarial Robustness of Federated Learning

In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largely unexplored. This paper casts light on the challenge of adversarial robustness of federated learning. To facilitate a better understanding of the adversarial vulnerability of the existing FL methods, we conduct comprehensive robustness evaluations on various attacks and adversarial training methods. Moreover, we reveal the negative impacts induced by directly adopting adversarial training in FL, which seriously hurts the test accuracy, especially in non-IID settings. In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems. Extensive experiments on multiple datasets demonstrate that DBFAT consistently outperforms other baselines under both IID and non-IID settings.

cs.LG↗

arXiv2023

Speech Enhancement with Multi-granularity Vector Quantization

With advances in deep learning, neural network based speech enhancement (SE) has developed rapidly in the last decade. Meanwhile, the self-supervised pre-trained model and vector quantization (VQ) have achieved excellent performance on many speech-related tasks, while they are less explored on SE. As it was shown in our previous work that utilizing a VQ module to discretize noisy speech representations is beneficial for speech denoising, in this work we therefore study the impact of using VQ at different layers with different number of codebooks. Different VQ modules indeed enable to extract multiple-granularity speech features. Following an attention mechanism, the contextual features extracted by a pre-trained model are fused with the local features extracted by the encoder, such that both global and local information are preserved to reconstruct the enhanced speech. Experimental results on the Valentini dataset show that the proposed model can improve the SE performance, where the impact of choosing pre-trained models is also revealed.

eess.AS↗

arXiv2023

An optimized online filter stack spectrometer

The spectrum of laser-plasma-generated X-rays is very important as it can characterize electron dynamics and also be useful for applications, and nowadays with the forthcoming high-repetition-rate laser-plasma experiments, there is a raising demand for online diagnosis for the X-ray spectrum. In this paper, scintillators and silicon PIN diodes are used to build a wideband online filter stack spectrometer. The genetic algorithm is used to optimize the arrangements of the X-ray sensors and filters by minimizing the condition number of the response matrix, thus the unfolding error can be significantly decreased according to the numerical experiments. The detector responses are quantitatively calibrated by irradiating the scintillator and PIN diode using different nuclides and comparing the measured gamma-ray peaks. Finally, a 15-channel spectrometer prototype has been implemented. The X-ray detector, front-end electronics, and back-end electronics are integrated into the prototype, and the prototype can determine the spectrum with 1 kHz repetition rates.

physics.ins-det↗

arXiv2023

Clusters and collective motions in Brownian vibrators

Using Brownian vibrators, where single particles can undergo Brownian motion under vibration, we experimentally investigated self-organized structures and dynamics of quasi-two-dimensional (quasi-2d) granular materials with volume fractions $0.111\leϕ\le0.832$. We show rich structures and dynamics in hard-disk systems of inelastic particle collisions, with four phases corresponding to cluster fluid, collective fluid, poly-crystal, and crystal. While poly-crystal and crystal are strikingly similar to the equilibrium hard disks, the first two phases differ substantially from the equilibrium ones and the previous quasi-2d experiments of uniformly driven spheres. Our investigation provides single-particle-scale evidence that granular materials subject to uniform random forcing are weakly cohesive with complex internal structures and dynamics. Moreover, our experiment shows that large-scale collective motion can arise in a purely repulsive hard-disk system. The collective motion emerges near $ϕ=0.317$, where the most significant clusters span half of the system, and disappears near $ϕ=0.713$, around which the system crystallizes and the melting transition occurs in the equilibrium hard disks.

cond-mat.soft↗

arXiv2023

Rectified Rotational Dynamics of Mobile Inclusions in Two-Dimensional Active Nematics

We investigate the dynamics of mobile inclusions embedded in 2D active nematics. The interplay between the inclusion shape, the boundary-induced nematic order, and the autonomous flow powers the inclusion motion. Disks and achiral gears exhibit unbiased rotational motion, but with distinct dynamics. In comparison, chiral gear-shaped inclusions exhibit long-term rectified rotation, which is correlated with dynamics and polarization of nearby +1/2 topological defects. The chirality of defect polarities and the active nematic texture around the inclusion correlate with the inclusion's instantaneous rotationr ate. Inclusions provide a promising tool for probing the rheological properties of active nematics and extracting ordered motion from the inherently chaotic motion of active nematics.

cond-mat.soft↗

arXiv2022

Experimental evidence of detailed balance in granular systems

The principle of detailed balance (DB) states that every kinetic transition in a system with many micro-states, $μ$, is balanced, on average, with the opposite transition, $μ_i\leftrightharpoonsμ_j$. Since its introduction by Boltzmann, this principle has been used by luminaries, such as Einstein, Eddington, Kramers, Pauli, Ehrenfest, Dirac, Onsager, and many others to derive significant results that underpin much of our scientific understanding. The current belief is that DB is satisfied only in equilibrium systems, while non-equilibrium steady states can only be balanced by cycles, such as $A\to B\to C\to A$. We show here experimentally that DB can exist and is commonly and robustly satisfied in a family of quasi-statically cyclically sheared granular systems. We further study the approach to DB as a function of system size and time. Given the significant impact that this principle has had on equilibrium systems, we believe that this discovery paves the way for better models of the dynamics of non-equilibrium systems.

cond-mat.soft↗

arXiv2022

Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning

Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model. Existing works usually search the invariant subnetwork using modular risk minimization (MRM) with out-domain data. Such a paradigm may bring about two potential weaknesses: 1) Unfairness, due to the insufficient observation of out-domain data during training; and 2) Sub-optimal OOD generalization, due to the feature-untargeted model pruning on the whole data distribution. In this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above weaknesses. Specifically, SFP identifies in-distribution (ID) features during training using our theoretically verified task loss, upon which, SFP can perform ID targeted-model pruning that removes branches with strong dependencies on ID features. Notably, by attenuating the projections of spurious features into model space, SFP can push the model learning toward invariant features and pull that out of environmental features, devising optimal OOD generalization. Moreover, we also conduct detailed theoretical analysis to provide the rationality guarantee and a proof framework for OOD structures via model sparsity, and for the first time, reveal how a highly biased data distribution affects the model's OOD generalization. Extensive experiments on various OOD datasets show that SFP can significantly outperform both structure-based and non-structure OOD generalization SOTAs, with accuracy improvement up to 4.72% and 23.35%, respectively.

cs.LG↗

arXiv2022

Spectrum of the S-wave fully-heavy tetraquark states

In present work, spectrum of the $S$-wave fully-heavy tetraquark states $QQ\bar{Q}\bar{Q}$ ($Q=c,b$), i.e., $cc\bar{c}\bar{c}$, $bb\bar{b}\bar{b}$, $cc\bar{b}\bar{b}$/$bb\bar{c}\bar{c}$, $bc\bar{c}\bar{c}$/ $cc\bar{b}\bar{c}$, $bb\bar{c}\bar{b}$/$cb\bar{b}\bar{b}$, and $bc\bar{b}\bar{c}$ are systematically investigated within an nonrelativistic constituent quark model, in which the Instanton-induced and one-gluon-exchange interactions are taken into account as the residual spin-dependent hyperfine interaction. Our results show that the states with $cc\bar{c}\bar{c}$ and $bb\bar{b}\bar{b}$ components could be located around $ 6500$ MeV and $ 19200$ MeV, respectively. Based on our calculations, the new $X(6900)$ state observed by LHCb may be not a ground $cc\bar{c}\bar{c}$ tetraquark state, while it could be an orbitally or radially excited state of $cc\bar{c}\bar{c}$ system. On the other hand, the recently reported $X(6600)$ state by CMS and ATLAS can be explained as a ground $cc\bar{c}\bar{c}$ tetraquark state with spin-parity $J^{PC} =0^{++}$.

hep-ph↗

arXiv2022

Wireless Channel Delay Spread Performance Evaluation of a Building Layout

Evaluating a building's wireless performance during the building design process is a new paradigm for wireless communications and building design. This paper proposes the earliest building wireless performance (BWP) evaluation theory focusing on the channel delay spread (DS). The novel contributions of this paper lie in the following aspects. 1) We define a new metric called DS gain, which is the first metric for evaluating the channel DS performance of a building under design. 2) We propose an analytical model to compute the metric quickly and accurately. 3) The proposed scheme is validated via Monte-Carlo simulations under typical indoor scenarios. Numerical results reveal that building design has a clear impact on the root mean square (RMS) DS. In the future, architects need to design a building taking its DS gain into account carefully. Otherwise, indoor networks in it will suffer from severe signal inter-symbol interference (ISI) due to an over large DS gain.

cs.NI↗