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Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 595 records · Page 33Linked to original sources
arXiv2023

Sluggish and Chemically-Biased Interstitial Diffusion in Concentrated Solid Solution Alloys: Mechanisms and Methods

Interstitial diffusion is a pivotal process that governs the phase stability and irradiation response of materials in non-equilibrium conditions. In this work, we study sluggish and chemically-biased interstitial diffusion in Fe-Ni concentrated solid solution alloys (CSAs) by combining machine learning (ML) and kinetic Monte Carlo (kMC), where ML is used to accurately and efficiently predict the migration energy barriers on-the-fly. The ML-kMC reproduces the diffusivity that was reported by molecular dynamics results at high temperatures. With this powerful tool, we find that the observed sluggish diffusion and the "Ni-Ni-Ni"-biased diffusion in Fe-Ni alloys are ascribed to a unique "Barrier Lock" mechanism, whereas the "Fe-Fe-Fe"-biased diffusion is influenced by a "Component Dominance" mechanism. Inspired by the mentioned mechanisms, a practical AvgS-kMC method is proposed for conveniently and swiftly determining interstitial-mediated diffusivity by only relying on the mean energy barriers of migration patterns. Combining the AvgS-kMC with the differential evolutionary algorithm, an inverse design strategy for optimizing sluggish diffusion properties is applied to emphasize the crucial role of favorable migration patterns.

cond-mat.mtrl-sci↗
arXiv2023

Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through the communication distortion and global update variance. Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods.

cs.LG↗
arXiv2023

Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition

To assist surgeons in the operating theatre, surgical phase recognition is critical for developing computer-assisted surgical systems, which requires comprehensive understanding of surgical videos. Although existing studies made great progress, there are still two significant limitations worthy of improvement. First, due to the compromise of resource consumption, frame-wise visual features are extracted by 2D networks and disregard spatial and temporal knowledge of surgical actions, which hinders subsequent inter-frame modeling for phase prediction. Second, these works simply utilize ordinary classification loss with one-hot phase labels to optimize the phase predictions, and cannot fully explore surgical videos under inadequate supervision. To overcome these two limitations, we propose a Surgical Temporal Action-aware Network with sequence Regularization, named STAR-Net, to recognize surgical phases more accurately from input videos. Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks. Moreover, we devise the dual-classifier sequence regularization (DSR) to facilitate the training of STAR-Net by the sequence guidance of an auxiliary classifier with a smaller capacity. Our STAR-Net with MS-STA and DSR can exploit visual features of surgical actions with effective regularization, thereby leading to the superior performance of surgical phase recognition. Extensive experiments on a large-scale gastrectomy surgery dataset and the public Cholec80 benchmark prove that our STAR-Net significantly outperforms state-of-the-arts of surgical phase recognition.

cs.CV↗
arXiv2023

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model

Most biomedical pretrained language models are monolingual and cannot handle the growing cross-lingual requirements. The scarcity of non-English domain corpora, not to mention parallel data, poses a significant hurdle in training multilingual biomedical models. Since knowledge forms the core of domain-specific corpora and can be translated into various languages accurately, we propose a model called KBioXLM, which transforms the multilingual pretrained model XLM-R into the biomedical domain using a knowledge-anchored approach. We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora. Then we design three corresponding training tasks (entity masking, relation masking, and passage relation prediction) and continue training on top of the XLM-R model to enhance its domain cross-lingual ability. To validate the effectiveness of our model, we translate the English benchmarks of multiple tasks into Chinese. Experimental results demonstrate that our model significantly outperforms monolingual and multilingual pretrained models in cross-lingual zero-shot and few-shot scenarios, achieving improvements of up to 10+ points. Our code is publicly available at https://github.com/ngwlh-gl/KBioXLM.

cs.CL↗
arXiv2023

DESI Legacy Imaging Surveys Data Release 9: Cosmological Constraints from Galaxy Clustering and Weak Lensing using the Minimal Bias Model

We present a tentative constraint on cosmological parameters $Ω_m$ and $σ_8$ from a joint analysis of galaxy clustering and galaxy-galaxy lensing from DESI Legacy Imaging Surveys Data Release 9 (DR9), covering approximately 10000 square degrees and spanning the redshift range of 0.1 to 0.9. To study the dependence of cosmological parameters on lens redshift, we divide lens galaxies into seven approximately volume-limited samples, each with an equal width in photometric redshift. To retrieve the intrinsic projected correlation function $w_{\rm p}(r_{\rm p})$ from the lens samples, we employ a novel method to account for redshift uncertainties. Additionally, we measured the galaxy-galaxy lensing signal $ΔΣ(r_{\rm p})$ for each lens sample, using source galaxies selected from the shear catalog by applying our \texttt{Fourier\_Quad} pipeline to DR9 images. We model these observables within the flat $Λ$CDM framework, employing the minimal bias model. To ensure the reliability of the minimal bias model, we apply conservative scale cuts: $r_{\rm p} > 8$ and $12 ~h^{-1}{\rm Mpc}$, for $w_{\rm p}(r_{\rm p})$ and $ΔΣ(r_{\rm p})$, respectively. Our findings suggest a mild tendency that $S_8 \equiv σ_8 \sqrt{Ω_m/0.3} $ increases with lens redshift, although this trend is only marginally significant. When we combine low redshift samples, the value of $S_8$ is determined to be $0.84 \pm 0.02$, consistent with the Planck results but significantly higher than the 3$\times$ 2pt analysis by 2-5$σ$. Despite the fact that further refinements in measurements and modeling could improve the accuracy of our results, the consistency with standard values demonstrates the potential of our method for more precise and accurate cosmology in the future.

astro-ph.CO↗
arXiv2023

Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design

Federated learning (FL) has achieved great success as a privacy-preserving distributed training paradigm, where many edge devices collaboratively train a machine learning model by sharing the model updates instead of the raw data with a server. However, the heterogeneous computational and communication resources of edge devices give rise to stragglers that significantly decelerate the training process. To mitigate this issue, we propose a novel FL framework named stochastic coded federated learning (SCFL) that leverages coded computing techniques. In SCFL, before the training process starts, each edge device uploads a privacy-preserving coded dataset to the server, which is generated by adding Gaussian noise to the projected local dataset. During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices. We design a gradient aggregation scheme to ensure that the aggregated model update is an unbiased estimate of the desired global update. Moreover, this aggregation scheme enables periodical model averaging to improve the training efficiency. We characterize the tradeoff between the convergence performance and privacy guarantee of SCFL. In particular, a more noisy coded dataset provides stronger privacy protection for edge devices but results in learning performance degradation. We further develop a contract-based incentive mechanism to coordinate such a conflict. The simulation results show that SCFL learns a better model within the given time and achieves a better privacy-performance tradeoff than the baseline methods. In addition, the proposed incentive mechanism grants better training performance than the conventional Stackelberg game approach.

cs.DC↗
arXiv2023

Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer

Deep biasing for the Transducer can improve the recognition performance of rare words or contextual entities, which is essential in practical applications, especially for streaming Automatic Speech Recognition (ASR). However, deep biasing with large-scale rare words remains challenging, as the performance drops significantly when more distractors exist and there are words with similar grapheme sequences in the bias list. In this paper, we combine the phoneme and textual information of rare words in Transducers to distinguish words with similar pronunciation or spelling. Moreover, the introduction of training with text-only data containing more rare words benefits large-scale deep biasing. The experiments on the LibriSpeech corpus demonstrate that the proposed method achieves state-of-the-art performance on rare word error rate for different scales and levels of bias lists.

cs.CL↗
arXiv2023

Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning

This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining controller adaptivity and scalability stands out as a primary challenge. Capturing the spatial-temporal correlation among traffic lights under the framework of Multi-Agent Reinforcement Learning (MARL) is a promising solution. Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents. In this paper, we design a new decentralized control architecture with improved environmental observability to capture the spatial-temporal correlation. Specifically, we first develop a topology-aware information aggregation strategy to extract correlation-related information from unstructured data gathered in the road network. Particularly, we transfer the road network topology into a graph shift operator by forming a diffusion process on the topology, which subsequently facilitates the construction of graph signals. A diffusion convolution module is developed, forming a new MARL algorithm, which endows agents with the capabilities of graph learning. Extensive experiments based on both synthetic and real-world datasets verify that our proposal outperforms existing decentralized algorithms.

cs.LG↗
arXiv2023

Halo mass-observable proxy scaling relations and their dependencies on galaxy and group properties

Based on the DECaLS shear catalog, we study the scaling relations between halo mass($M_{\rm h}$) and various proxies for SDSS central galaxies, including stellar mass($M_*$), stellar velocity dispersion($σ_*$), abundance matching halo mass($M_{\rm AM}$) and satellite velocity dispersion($σ_{\rm s}$), and their dependencies on galaxy and group properties. In general, these proxies all have strong positive correlations with $M_{\rm h}$, consistent with previous studies. We find that the $M_{\rm h}$-$M_*$ and $M_{\rm h}$-$σ_*$ relations depend strongly on group richness($N_{\rm sat}$), while the $M_{\rm h}$-$M_{\rm AM}$ and $M_{\rm h}$-$σ_{\rm s}$ relations are independent of it. Moreover, the dependence on star formation rate(SFR) is rather weak in the $M_{\rm h}$-$σ_*$ and $M_{\rm h}$-$σ_{\rm s}$ relations, but very prominent in the other two. $σ_{\rm s}$ is thus the best proxy among them, and its scaling relation is in good agreement with hydro-dynamical simulations. However, estimating $σ_{\rm s}$ accurately for individual groups/clusters is challenging because of interlopers and the requirement for sufficient satellites. We construct new proxies by combining $M_*$, $σ_*$, and $M_{\rm AM}$, and find the proxy with 30\% contribution from $M_{\rm AM}$ and 70\% from $σ_*$ can minimize the dependence on $N_{\rm sat}$ and SFR. We obtain the $M_{\rm h}$-supermassive black hole(SMBH) mass relation via the SMBH scaling relation and find indications for rapid and linear growth phases for SMBH. We also find that correlations among $M_{\rm h}$, $M_*$ and $σ_*$ change with $M_*$, indicating that different processes drive the growth of galaxies and SMBH at different stages.

astro-ph.GA↗
arXiv2023

Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to the superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.

cs.IT↗
arXiv2023

Two-stage solution for ancilla-assisted quantum process tomography: error analysis and optimal design

Quantum process tomography (QPT) is a fundamental task to characterize the dynamics of quantum systems. In contrast to standard QPT, ancilla-assisted process tomography (AAPT) framework introduces an extra ancilla system such that a single input state is needed. In this paper, we extend the two-stage solution, a method originally designed for standard QPT, to perform AAPT. Our algorithm has $O(Md_A^2d_B^2)$ computational complexity where $ M $ is the type number of the measurement operators, $ d_A $ is the dimension of the quantum system of interest, and $d_B$ is the dimension of the ancilla system. Then we establish an error upper bound and further discuss the optimal design on the input state in AAPT. A numerical example on a phase damping process demonstrates the effectiveness of the optimal design and illustrates the theoretical error analysis.

quant-ph↗
arXiv2023

Compact free-running InGaAs/InP single-photon detector with 40% detection efficiency and 2.3 kcps dark count rate

Free-running InGaAs/InP single-photon detectors (SPDs) based on negative-feedback avalanche diodes (NFADs) are the key components for applications requiring asynchronous single-photon detection in the near-infrared region. From the perspective of practical applications, the features of SPDs in terms of high photon detection efficiency (PDE), low noise, large sensitive area, and compactness are highly desired for system integration and performance enhancement. Here, we present the implementation of a compact four-channel multimode fiber coupling free-running InGaAs/InP SPD, with the best overall performance to date. On the one hand, we design and fabricate structure-optimized InGaAs/InP NFAD devices with 25 $μ$m diameter active area and integrated thin film resistors to enhance the maximum achievable PDE. On the other hand, we apply a compact thermoacoustic cryocooler to regulate the operating temperature of NFADs within a large range, and design a dedicated readout circuit with minimized parasitic parameters and tunable settings of hold-off time to suppress the afterpulsing effect. The SPD is then characterized to achieve remarkable overall performance simultaneously at 1550 nm, i.e., 40% PDE, 2.3 kcps dark count rate, 8% afterpulse probability and 49 ps timing jitter (full width at half maximum) under the conditions of 5.9 V excess bias voltage, 10 $μ$s hold-off time and 213 K operation temperature. Such performance and the results of the long-term stability tests indicate that the SPD could be a favorable solution for practical applications.

physics.ins-det↗
arXiv2023

Detecting anomalous images in astronomical datasets

Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical datasets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the Entropy Method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the Entropy Method on the CFHTLenS and DECaLS DR3 data. In CFHTLenS, with 1171 exposures, the Entropy Method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the Entropy method detects a significant number of anomalous exposures while keeping a low false positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the Entropy Method.

astro-ph.IM↗
arXiv2023

O(1) benchmarking of precise rotation in a spin-squeezed Bose-Einstein condensate

Benchmarking a high-precision quantum operation is a big challenge for many quantum systems in the presence of various noises as well as control errors. Here we propose an $O(1)$ benchmarking of a dynamically corrected rotation by taking the quantum advantage of a squeezed spin state in a spin-1 Bose-Einstein condensate. Our analytical and numerical results show that tiny rotation infidelity, defined by $1-F$ with $F$ the rotation fidelity, can be calibrated in the order of $1/N^2$ by only several measurements of the rotation error for $N$ atoms in an optimally squeezed spin state. Such an $O(1)$ benchmarking is possible not only in a spin-1 BEC but also in other many-spin or many-qubit systems if a squeezed or entangled state is available.

cond-mat.quant-gas↗
arXiv2023

FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data

Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant performance degradation due to heterogeneous data at clients. Common solutions involve designing an auxiliary loss to regularize weight divergence or feature inconsistency during local training. However, we discover that these approaches fall short of the expected performance because they ignore the existence of a vicious cycle between feature inconsistency and classifier divergence across clients. This vicious cycle causes client models to be updated in inconsistent feature spaces with more diverged classifiers. To break the vicious cycle, we propose a novel framework named Federated learning with Feature Anchors (FedFA). FedFA utilizes feature anchors to align features and calibrate classifiers across clients simultaneously. This enables client models to be updated in a shared feature space with consistent classifiers during local training. Theoretically, we analyze the non-convex convergence rate of FedFA. We also demonstrate that the integration of feature alignment and classifier calibration in FedFA brings a virtuous cycle between feature and classifier updates, which breaks the vicious cycle existing in current approaches. Extensive experiments show that FedFA significantly outperforms existing approaches on various classification datasets under label distribution skew and feature distribution skew.

cs.LG↗
arXiv2023

Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition

Integrating a low-spatial-resolution hyperspectral image (LR-HSI) with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance on preserving the spatial-spectral correlation. Furthermore, the low-rank property in some TR factors has been exploited via the matrix nuclear norm regularization along mode-2. On the other hand, the tensor nuclear norm (TNN)-based approaches have recently demonstrated to be more efficient on keeping high-dimensional low-rank structures in tensor recovery. Here, we study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation which is to promote the continuity of HR-HSI in the spatial-spectral domain. Meanwhile, we have devised a highly efficient proximal alternating minimization algorithm to solve the proposed model. The experimental results indicate that our method improves the visual quality and exceeds the existing state-of-the-art fusion approaches with respect to various quantitative metrics.

eess.IV↗
arXiv2023

Effortless Cross-Platform Video Codec: A Codebook-Based Method

Under certain circumstances, advanced neural video codecs can surpass the most complex traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the high performance of existing neural video codecs is the use of the entropy model, which can provide more accurate probability distribution estimations for compressing the latents. This also implies the rigorous requirement that entropy models running on different platforms should use consistent distribution estimations. However, in cross-platform scenarios, entropy models running on different platforms usually yield inconsistent probability distribution estimations due to floating point computation errors that are platform-dependent, which can cause the decoding side to fail in correctly decoding the compressed bitstream sent by the encoding side. In this paper, we propose a cross-platform video compression framework based on codebooks, which avoids autoregressive entropy modeling and achieves video compression by transmitting the index sequence of the codebooks. Moreover, instead of using optical flow for context alignment, we propose to use the conditional cross-attention module to obtain the context between frames. Due to the absence of autoregressive modeling and optical flow alignment, we can design an extremely minimalist framework that can greatly benefit computational efficiency. Importantly, our framework no longer contains any distribution estimation modules for entropy modeling, and thus computations across platforms are not necessarily consistent. Experimental results show that our method can outperform the traditional H.265 (medium) even without any entropy constraints, while achieving the cross-platform property intrinsically.

cs.CV↗
arXiv2023

Attentive Multi-Layer Perceptron for Non-autoregressive Generation

Autoregressive~(AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive~(NAR) generation gains increasing popularity for its efficiency and growing efficacy. However, its efficiency is still bottlenecked by quadratic complexity in sequence lengths, which is prohibitive for scaling to long sequence generation and few works have been done to mitigate this problem. In this paper, we propose a novel MLP variant, \textbf{A}ttentive \textbf{M}ulti-\textbf{L}ayer \textbf{P}erceptron~(AMLP), to produce a generation model with linear time and space complexity. Different from classic MLP with static and learnable projection matrices, AMLP leverages adaptive projections computed from inputs in an attentive mode. The sample-aware adaptive projections enable communications among tokens in a sequence, and model the measurement between the query and key space. Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity. Empirical results show that such marriage architecture surpasses competitive efficient NAR models, by a significant margin on text-to-speech synthesis and machine translation. We also test AMLP's self- and cross-attention ability separately with extensive ablation experiments, and find them comparable or even superior to the other efficient models. The efficiency analysis further shows that AMLP extremely reduces the memory cost against vanilla non-autoregressive models for long sequences.

cs.CL↗