arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2024

The re-entrant ferromagnetism due to symmetric exchange bias in two septuple-layer MnBi2Te4 epitaxial films

MnBi2Te4 (MBT) is a typical magnetic topological insulator with an A-type antiferromagnetic (AFM) ground state. Here we prepared ultra-thin MBT films with controlled anti-site defects and observed rich doping-dependent magnetic behaviors. We find in one-septuple-layer MBT films a ferrimagnetic ground state and in two-septuple-layer ones a kind of re-entrant ferromagnetism (FM) that disappears with increased Bi-on-Mn doping in the FM Mn-layers. This re-entrant behavior is attributed to a kind of symmetric exchange-bias effect that arises in the presence of both AFM and FM sub-systems due to the introduction of high-dense Mn-on-Bi anti-site defects. Furthermore, all MBT films display spin-glass-like behaviors. Our work demonstrates rich magnetic behaviors originating from the competing magnetic interactions between Mn spins at different lattice positions in MBT.

cond-mat.mtrl-sci↗

arXiv2024

Folded multistability and hidden critical point in microwave-driven Rydberg atoms

The interactions between Rydberg atoms and microwave fields provide a valuable framework for studying the complex dynamics out of equilibrium, exotic phases, and critical phenomena in many-body physics. This unique interplay allows us to explore various regimes of nonlinearity and phase transitions. Here, we observe a phase transition from the state in the regime of bistability to that in multistability in strongly interacting Rydberg atoms by varying the microwave field intensity, accompanying with the breaking of Z3-symmetry. During the phase transition, the system experiences a hidden critical point, in which the multistable states are difficult to be identified. Through changing the initial state of system, we can identify a hidden multistable state and reveal a hidden trajectory of phase transition, allowing us to track to a hidden critical point. In addition, we observe multiple phase transitions in spectra, suggesting higher-order symmetry breaking. The reported results shed light on manipulating multistability in dissipative Rydberg atoms systems and hold promise in the applications of non-equilibrium many-body physics.

cond-mat.quant-gas↗

arXiv2024

Tournaments with a Standard

We study tournaments where winning a rank-dependent prize requires passing a minimum performance standard. We show that, for any prize allocation, the optimal standard is always at a mode of performance that is weakly higher than the global mode and identify a necessary and sufficient condition for it to be at the global mode. When the prize scheme can be designed as well, the winner-take-all prize scheme is optimal for noise distributions with an increasing failure rate; and awarding equal prizes to all qualifying agents is optimal for noise distributions with a decreasing failure rate. For distributions with monotone likelihood ratios -- log-concave and log-convex, respectively -- these pay schemes are also optimal in a larger class of anonymous, monotone contracts that may depend on cardinal performance.

econ.TH↗

arXiv2024

Siamese Machine Unlearning with Knowledge Vaporization and Concentration

In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points from trained models. However, existing methods suffer from limitations such as insufficient methodological support, high computational complexity, and significant memory demands. In this work, we propose the concepts of knowledge vaporization and concentration to selectively erase learned knowledge from specific data points while maintaining representations for the remaining data. Utilizing the Siamese networks, we exemplify the proposed concepts and develop an efficient method for machine unlearning. Our proposed Siamese unlearning method does not require additional memory overhead and full access to the remaining dataset. Extensive experiments conducted across multiple unlearning scenarios showcase the superiority of Siamese unlearning over baseline methods, illustrating its ability to effectively remove knowledge from forgetting data, enhance model utility on remaining data, and reduce susceptibility to membership inference attacks.

cs.LG↗

arXiv2024

Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment

Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This situation necessitates cross-model communication between different inference systems, enabling edge devices from one service provider to collaborate effectively with edge servers from another. Independent optimization of diverse edge systems often leads to incoherent feature spaces, which hinders the cross-model inference for existing task-oriented communication. To facilitate and achieve effective cross-model task-oriented communication, this study introduces a novel framework that utilizes shared anchor data across diverse systems. This approach addresses the challenge of feature alignment in both server-based and on-device scenarios. In particular, by leveraging the linear invariance of visual features, we propose efficient server-based feature alignment techniques to estimate linear transformations using encoded anchor data features. For on-device alignment, we exploit the angle-preserving nature of visual features and propose to encode relative representations with anchor data to streamline cross-model communication without additional alignment procedures during the inference. The experimental results on computer vision benchmarks demonstrate the superior performance of the proposed feature alignment approaches in cross-model task-oriented communications. The runtime and computation overhead analysis further confirm the effectiveness of the proposed feature alignment approaches in real-time applications.

cs.CV↗

arXiv2024

How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the model owners after training and are only concerned about the model's generalization performance on their local data. Due to the data heterogeneity issue, asking all the clients to join a single FL training process may result in model performance degradation. To investigate the effectiveness of collaboration, we first derive a generalization bound for each client when collaborating with others or when training independently. We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution. Our analysis allows us to formulate a client utility maximization problem by partitioning clients into multiple collaborating groups. A hierarchical clustering-based collaborative training (HCCT) scheme is then proposed, which does not need to fix in advance the number of groups. We further analyze the convergence of HCCT for general non-convex loss functions which unveils the effect of data similarity among clients. Extensive simulations show that HCCT achieves better generalization performance than baseline schemes, whereas it degenerates to independent training and conventional FL in specific scenarios.

cs.LG↗

arXiv2024

A facile route to synthesize cubic gauche polymeric nitrogen

In this work, the long-sought cg-N with N-N single bond has been synthesized for the first time by a thermal-driven-only chemical route at ambient conditions. The successful synthesis of cg-N was achieved by first creating a solution of azides, which was then pretreated under vacuum conditions. Following the pretreatment, the resultant concentrated azide was heated at temperatures ranging from 260°C to 330°C for a reaction time of 3 hours, ultimately leading to the formation of cg-N. The emergent intense Raman peak characterized of cg-N provides solid evidence that the double bonded nitrogen-nitrogen transforms into a single bond form, which agrees well with cg-N structure. To date, this is the only work achieving the quantity of cg-N synthesized at ambient conditions by a facile route that can be further developed for the scalable synthesis and applications of polymerized nitrogen-based materials as high energy density materials.

cond-mat.mtrl-sci↗

arXiv2024

Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks

Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data. To lessen the computational burden on resource-limited devices, combining low-rank adaptation (LoRA) with federated learning enables parameter-efficient fine-tuning. Additionally, the split FedFT architecture partitions an FM between edge devices and a central server, reducing the necessity for complete model deployment on individual devices. However, the risk of privacy eavesdropping attacks in FedFT remains a concern, particularly in sensitive areas such as healthcare and finance. In this paper, we propose a split FedFT framework with differential privacy (DP) over wireless networks, where the inherent wireless channel noise in the uplink transmission is utilized to achieve DP guarantees without adding an extra artificial noise. We shall investigate the impact of the wireless noise on convergence performance of the proposed framework. We will also show that by updating only one of the low-rank matrices in the split FedFT with DP, the proposed method can mitigate the noise amplification effect. Simulation results will demonstrate that the proposed framework achieves higher accuracy under strict privacy budgets compared to baseline methods.

cs.LG↗

arXiv2024

SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation

Full-duplex multimodal large language models (LLMs) provide a unified framework for addressing diverse speech understanding and generation tasks, enabling more natural and seamless human-machine conversations. Unlike traditional modularised conversational AI systems, which separate speech recognition, understanding, and text-to-speech generation into distinct components, multimodal LLMs operate as single end-to-end models. This streamlined design eliminates error propagation across components and fully leverages the rich non-verbal information embedded in input speech signals. We introduce SALMONN-omni, a codec-free, full-duplex speech understanding and generation model capable of simultaneously listening to its own generated speech and background sounds while speaking. To support this capability, we propose a novel duplex spoken dialogue framework incorporating a ``thinking'' mechanism that facilitates asynchronous text and speech generation relying on embeddings instead of codecs (quantized speech and audio tokens). Experimental results demonstrate SALMONN-omni's versatility across a broad range of streaming speech tasks, including speech recognition, speech enhancement, and spoken question answering. Additionally, SALMONN-omni excels at managing turn-taking, barge-in, and echo cancellation scenarios, establishing its potential as a robust prototype for full-duplex conversational AI systems. To the best of our knowledge, SALMONN-omni is the first codec-free model of its kind. A full technical report along with model checkpoints will be released soon.

eess.AS↗

arXiv2024

Exploring Selective Layer Fine-Tuning in Federated Learning

Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner. Under limited computational resources, clients often find it more practical to fine-tune a selected subset of layers, rather than the entire model, based on their task-specific data. In this study, we provide a thorough theoretical exploration of selective layer fine-tuning in FL, emphasizing a flexible approach that allows the clients to adjust their selected layers according to their local data and resources. We theoretically demonstrate that the layer selection strategy has a significant impact on model convergence in two critical aspects: the importance of selected layers and the heterogeneous choices across clients. Drawing from these insights, we further propose a strategic layer selection method that utilizes local gradients and regulates layer selections across clients. The extensive experiments on both image and text datasets demonstrate the effectiveness of the proposed strategy compared with several baselines, highlighting its advances in identifying critical layers that adapt to the client heterogeneity and training dynamics in FL.

cs.LG↗

arXiv2024

FairAdapter: Detecting AI-generated Images with Improved Fairness

The high-quality, realistic images generated by generative models pose significant challenges for exposing them.So far, data-driven deep neural networks have been justified as the most efficient forensics tools for the challenges. However, they may be over-fitted to certain semantics, resulting in considerable inconsistency in detection performance across different contents of generated samples. It could be regarded as an issue of detection fairness. In this paper, we propose a novel framework named Fairadapter to tackle the issue. In comparison with existing state-of-the-art methods, our model achieves improved fairness performance. Our project: https://github.com/AppleDogDog/FairnessDetection

cs.CV↗

arXiv2024

Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models

Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance. Then, the second stage establishes a dense correspondence between the source and target images using the global features from the previous stage, and an inpainting conditional diffusion model is proposed to further align and enhance the contextual features, generating a coarse-grained person image. In the third stage, we propose a refining conditional diffusion model to utilize the coarsely generated image from the previous stage as a condition, achieving texture restoration and enhancing fine-detail consistency. The three-stage PCDMs work progressively to generate the final high-quality and high-fidelity synthesized image. Both qualitative and quantitative results demonstrate the consistency and photorealism of our proposed PCDMs under challenging scenarios.The code and model will be available at https://github.com/tencent-ailab/PCDMs.

cs.CV↗

arXiv2024

Towards 250-m gigabits-per-second underwater wireless optical communication using a low-complexity ANN equalizer

The breakthroughs of communication distance and data rate have been eagerly anticipated by scientists in the area of underwater wireless optical communication (UWOC), which is seriously limited by the obvious aquatic attenuation in underwater channel. High-power laser source and ultra-sensitive photodetector are straightforward to extend the UWOC distance. However, nonlinear impairments caused by bandwidth-limited high-power transmitter and sensitive receiver severely degrade the data rate of long-distance UWOC. In this paper, we develop a UWOC system using a high-power transmitter by beam combining of 8-channel cascaded laser diodes (LD) and a sensitive receiver by a silicon photomultiplier (SiPM). The combined linear equalizer and low-complexity Artificial Neural Network (ANN) equalizer are used to achieve 1-Gbps data transmission over a 250-m UWOC system. To the best of our knowledge, this is the first Gbps-level UWOC experimental demonstration in >250-meter underwater transmission that has ever been reported. To lower the complexity of the ANN equalizer, a linear equalizer is applied first in order to prune the input size of the ANN equalizer. The optimal input size of the ANN equalizer is identified as 9. And the ANN architecture consists of two hidden layers, with 10 neurons in the first layer and a single neuron in the second layer. The performance of the proposed ANN-based system is compared with that of systems employing Volterra and linear equalizers. The bit error rate (BER) at data rate of 1 Gbps over a 250-m UWOC is reduced to with the combined linear and ANN equalizer, which is below the hard-decision forward error correction (HD-FEC) limit. In contrast, the linear and Volterra equalizer-based systems achieve data rates of 500 Mbps and 750 Mbps, respectively.

physics.optics↗

arXiv2024

A Dense Subframe-based SLAM Framework with Side-scan Sonar

Side-scan sonar (SSS) is a lightweight acoustic sensor that is commonly deployed on autonomous underwater vehicles (AUVs) to provide high-resolution seafloor images. However, leveraging side-scan images for simultaneous localization and mapping (SLAM) presents a notable challenge, primarily due to the difficulty of establishing sufficient amount of accurate correspondences between these images. To address this, we introduce a novel subframe-based dense SLAM framework utilizing side-scan sonar data, enabling effective dense matching in overlapping regions of paired side-scan images. With each image being evenly divided into subframes, we propose a robust estimation pipeline to estimate the relative pose between each paired subframes, by using a good inlier set identified from dense correspondences. These relative poses are then integrated as edge constraints in a factor graph to optimize the AUV pose trajectory. The proposed framework is evaluated on three real datasets collected by a Hugin AUV. Among one of them includes manually-annotated keypoint correspondences as ground truth and is used for evaluation of pose trajectory. We also present a feasible way of evaluating mapping quality against multi-beam echosounder (MBES) data without the influence of pose. Experimental results demonstrate that our approach effectively mitigates drift from the dead-reckoning (DR) system and enables quasi-dense bathymetry reconstruction. An open-source implementation of this work is available.

cs.RO↗

arXiv2024

Exciton Enhanced Giant Correlated Stoke AntiStokes Scattering of Multiorder Phonons in Semiconductor

The correlated Stoke antiStokes (SaS) scattering plays a crucial role in quantum information processing, such as heralded light sources, Fock state dynamics, and write read protocol for quantum memory. However, several reported materials exhibit low degree of SaS correlation and require high-power pulse laser excitation, limiting further applications. Herein, we explore the giant correlated multiorder SaS scattering under low power continuous laser excitation through red-sideband resonance of exciton in semiconductor ZnTe nanobelts. At low temperatures, we observe an unexpectedly strong anti-Stokes signal for multiorder longitudinal optical phonons, with SaS correlations two or four orders of magnitude larger than reported results. Furthermore, we observed the mitigation of laser heating effect for longitudinal optical phonon in SaS scattering. This finding paves a new pathway to study multiorder quantum correlated photon pairs produced through exciton-resonant Raman scattering.

cond-mat.mes-hall↗

arXiv2024

Cavity-enhanced circular dichroism in a van der Waals antiferromagnet

Broken symmetry plays a pivotal role in determining the macroscopic electrical, optical, magnetic, and topological properties of materials. Circular dichroism (CD) has been widely employed to probe broken symmetry in various systems, from small molecules to bulk crystals, but designing CD responses on demand remains a challenge, especially for antiferromagnetic materials. Here, we develop a cavity-enhanced CD technique to sensitively probe the magnetic order and broken symmetry in the van der Waals antiferromagnet FePS3. By introducing interfacial inversion asymmetry in cavity-coupled FePS3 crystals, we demonstrate that the induced CD is strongly coupled with the zig-zag antiferromagnetic order of FePS3 and can be tuned both spectrally and in magnitude by varying the cavity length and FePS3 thickness. Our findings open new avenues for using cavity-modulated CD as a sensitive diagnostic probe to detect weak broken symmetries, particularly at hidden interfaces, and in systems exhibiting hidden spin polarization or strong correlations.

cond-mat.mes-hall↗

arXiv2024

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion

In this paper, we introduce \textbf{DimensionX}, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins with the insight that both the spatial structure of a 3D scene and the temporal evolution of a 4D scene can be effectively represented through sequences of video frames. While recent video diffusion models have shown remarkable success in producing vivid visuals, they face limitations in directly recovering 3D/4D scenes due to limited spatial and temporal controllability during generation. To overcome this, we propose ST-Director, which decouples spatial and temporal factors in video diffusion by learning dimension-aware LoRAs from dimension-variant data. This controllable video diffusion approach enables precise manipulation of spatial structure and temporal dynamics, allowing us to reconstruct both 3D and 4D representations from sequential frames with the combination of spatial and temporal dimensions. Additionally, to bridge the gap between generated videos and real-world scenes, we introduce a trajectory-aware mechanism for 3D generation and an identity-preserving denoising strategy for 4D generation. Extensive experiments on various real-world and synthetic datasets demonstrate that DimensionX achieves superior results in controllable video generation, as well as in 3D and 4D scene generation, compared with previous methods.

cs.CV↗

arXiv2024

Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitations in generalization to unseen environments, prediction fidelity of critical details, and action controllability for flexible application. In this paper, we present Vista, a generalizable driving world model with high fidelity and versatile controllability. Based on a systematic diagnosis of existing methods, we introduce several key ingredients to address these limitations. To accurately predict real-world dynamics at high resolution, we propose two novel losses to promote the learning of moving instances and structural information. We also devise an effective latent replacement approach to inject historical frames as priors for coherent long-horizon rollouts. For action controllability, we incorporate a versatile set of controls from high-level intentions (command, goal point) to low-level maneuvers (trajectory, angle, and speed) through an efficient learning strategy. After large-scale training, the capabilities of Vista can seamlessly generalize to different scenarios. Extensive experiments on multiple datasets show that Vista outperforms the most advanced general-purpose video generator in over 70% of comparisons and surpasses the best-performing driving world model by 55% in FID and 27% in FVD. Moreover, for the first time, we utilize the capacity of Vista itself to establish a generalizable reward for real-world action evaluation without accessing the ground truth actions.

cs.CV↗