arXiv subjects

Chao Li

Publications and source records attributed to Chao Li.

At least 253 records · Page 14Linked to original sources

arXiv2024

Achieving AoI Fairness in Spatially Distributed Wireless Networks: From Theory to Implementation

We design and implement two variants of a practical random access protocol called WiFair, based on IEEE 802.11 and designed to mitigate spatial unfairness in Age of Information (AoI). Drawing on previous theoretical work, we modify the mechanics of 802.11 to fairly minimize AoI in a wireless network consisting of several update nodes and a single base station. We implement this protocol on a testbed of software defined radios (SDRs) and measure its performance under a variety of settings compared to standard 802.11. We observe a 32% reduction in network average AoI and an 89% reduction in peak AoI in a last come first served (LCFS) single-packet queue setting, as well as a 76% reduction in network average AoI and an 82% reduction in peak AoI in a first come first served (FCFS) queue setting when the network is congested. We further show that when the network is uncongested, WiFair achieves the same performance as 802.11, and we demonstrate its robustness to more bursty traffic by streaming live video.

cs.NI↗

arXiv2024

Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization

The deep neural networks are known to be vulnerable to well-designed adversarial attacks. The most successful defense technique based on adversarial training (AT) can achieve optimal robustness against particular attacks but cannot generalize well to unseen attacks. Another effective defense technique based on adversarial purification (AP) can enhance generalization but cannot achieve optimal robustness. Meanwhile, both methods share one common limitation on the degraded standard accuracy. To mitigate these issues, we propose a novel pipeline to acquire the robust purifier model, named Adversarial Training on Purification (AToP), which comprises two components: perturbation destruction by random transforms (RT) and purifier model fine-tuned (FT) by adversarial loss. RT is essential to avoid overlearning to known attacks, resulting in the robustness generalization to unseen attacks, and FT is essential for the improvement of robustness. To evaluate our method in an efficient and scalable way, we conduct extensive experiments on CIFAR-10, CIFAR-100, and ImageNette to demonstrate that our method achieves optimal robustness and exhibits generalization ability against unseen attacks.

cs.CV↗

arXiv2024

High quality epitaxial piezoelectric and ferroelectric wurtzite Al$_{1-x}$Sc$_x$N thin films

Piezoelectric and ferroelectric wurtzite are promising to reshape modern microelectronics because they can be easily integrated with mainstream semiconductor technology. Sc doped AlN (Al$_{1-x}$Sc$_x$N) has attracted much attention for its enhanced piezoelectric and emerging ferroelectric properties, yet the commonly used sputtering results in polycrystalline Al$_{1-x}$Sc$_x$N films with high leakage current. Here we report the pulsed laser deposition of single crystalline epitaxial Al$_{1-x}$Sc$_x$N thin films on sapphire and 4H-SiC substrates. Pure wurtzite phase is maintained up to $x = 0.3$ with minimal oxygen contamination. Polarization is estimated to be 140 $μ$C/cm$^2$ via atomic scale microscopy imaging and found to be switchable via a scanning probe. The piezoelectric coefficient is found to be 5 times of undoped one when $x = 0.3$, making it desirable for high frequency radiofrequency (RF) filters and three-dimensional nonvolatile memories.

physics.app-ph↗

arXiv2024

Spherical World-Locking for Audio-Visual Localization in Egocentric Videos

Egocentric videos provide comprehensive contexts for user and scene understanding, spanning multisensory perception to behavioral interaction. We propose Spherical World-Locking (SWL) as a general framework for egocentric scene representation, which implicitly transforms multisensory streams with respect to measurements of head orientation. Compared to conventional head-locked egocentric representations with a 2D planar field-of-view, SWL effectively offsets challenges posed by self-motion, allowing for improved spatial synchronization between input modalities. Using a set of multisensory embeddings on a worldlocked sphere, we design a unified encoder-decoder transformer architecture that preserves the spherical structure of the scene representation, without requiring expensive projections between image and world coordinate systems. We evaluate the effectiveness of the proposed framework on multiple benchmark tasks for egocentric video understanding, including audio-visual active speaker localization, auditory spherical source localization, and behavior anticipation in everyday activities.

cs.CV↗

arXiv2024

Nanoscale Control of Quantum States in Radical Molecules on Superconducting Pb(111)

Magnetic impurities on superconductors present a viable platform for building advanced applications in quantum technologies. However, a controlled manipulation of their quantum states continues to pose a significant challenge, hindering the progress in the field. Here we show the manipulation of magnetic states in the radical molecule 4,5,9,10-tetrabromo-1,3,6,8-tetraazapyrene (TBTAP) on a Pb(111) superconducting surface using low-temperature scanning tunneling microscopy. Tunneling spectra reveal Yu-Shiba-Rusinov (YSR) states near the Fermi energy in isolated molecules. A quantum phase transition from singlet to doublet ground state is induced by changing the tip-molecule distance. Additionally, the presence of a second TBTAP molecule allows tuning of the YSR state position by altering the relative distance and can induce splitting of the YSR states for certain orientations. The construction of molecular chains up to pentamers shows periodic arrangements of charged and neutral molecules, with even-numbered chains forming a charged dimer structure at one end. Information can be encoded in these chains by switching the dimer position. These findings elucidate interactions between molecular assemblies and superconducting substrates, paving the way for advanced quantum-state engineering.

cond-mat.mes-hall↗

arXiv2024

Long-distance distribution of telecom time-energy entanglement generated on a silicon chip

Entanglement distribution is a critical technique that enables numerous quantum applications. Most fiber-based long-distance experiments reported to date have utilized photon pair sources generated in bulk optical crystals, with the entanglement encoded in the polarization degree of freedom. Here, we create time-energy entanglement for photon pairs generated from an on-chip silicon ring resonator via SFWM process and report the distribution of the entanglement over standard optical fiber with distance >81 km. Our work paves the way for future large-scale quantum networks with connect of distant quantum nodes.

quant-ph↗

arXiv2024

Blockchain Takeovers in Web 3.0: An Empirical Study on the TRON-Steem Incident

A fundamental goal of Web 3.0 is to establish a decentralized network and application ecosystem, thereby enabling users to retain control over their data while promoting value exchange. However, the recent Tron-Steem takeover incident poses a significant threat to this vision. In this paper, we present a thorough empirical analysis of the Tron-Steem takeover incident. By conducting a fine-grained reconstruction of the stake and election snapshots within the Steem blockchain, one of the most prominent social-oriented blockchains, we quantify the marked shifts in decentralization pre and post the takeover incident, highlighting the severe threat that blockchain network takeovers pose to the decentralization principle of Web 3.0. Moreover, by employing heuristic methods to identify anomalous voters and conducting clustering analyses on voter behaviors, we unveil the underlying mechanics of takeover strategies employed in the Tron-Steem incident and suggest potential mitigation strategies, which contribute to the enhanced resistance of Web 3.0 networks against similar threats in the future. We believe the insights gleaned from this research help illuminate the challenges imposed by blockchain network takeovers in the Web 3.0 era, suggest ways to foster the development of decentralized technologies and governance, as well as to enhance the protection of Web 3.0 user rights.

cs.SI↗

arXiv2024

AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs

Recently, the use of large language models (LLMs) for software code generation, e.g., C/C++ and Python, has proven a great success. However, LLMs still suffer from low syntactic and functional correctness when it comes to the generation of register-transfer level (RTL) code, such as Verilog. To address this issue, in this paper, we develop AutoVCoder, a systematic open-source framework that significantly improves the LLMs' correctness of generating Verilog code and enhances the quality of its output at the same time. Our framework integrates three novel techniques, including a high-quality hardware dataset generation approach, a two-round LLM fine-tuning method and a domain-specific retrieval-augmented generation (RAG) mechanism. Experimental results demonstrate that AutoVCoder outperforms both industrial and academic LLMs in Verilog code generation. Specifically, AutoVCoder shows a 0.5% and 2.2% improvement in functional correctness on the EvalMachine and EvalHuman benchmarks compared with BetterV, and also achieves a 3.4% increase in syntax correctness and a 3.4% increase in functional correctness on the RTLLM benchmark compared with RTLCoder.

cs.AR↗

arXiv2024

What Radio Waves Tell Us about Sleep

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

cs.LG↗

arXiv2024

SOFA: A Compute-Memory Optimized Sparsity Accelerator via Cross-Stage Coordinated Tiling

Benefiting from the self-attention mechanism, Transformer models have attained impressive contextual comprehension capabilities for lengthy texts. The requirements of high-throughput inference arise as the large language models (LLMs) become increasingly prevalent, which calls for large-scale token parallel processing (LTPP). However, existing dynamic sparse accelerators struggle to effectively handle LTPP, as they solely focus on separate stage optimization, and with most efforts confined to computational enhancements. By re-examining the end-to-end flow of dynamic sparse acceleration, we pinpoint an ever-overlooked opportunity that the LTPP can exploit the intrinsic coordination among stages to avoid excessive memory access and redundant computation. Motivated by our observation, we present SOFA, a cross-stage compute-memory efficient algorithm-hardware co-design, which is tailored to tackle the challenges posed by LTPP of Transformer inference effectively. We first propose a novel leading zero computing paradigm, which predicts attention sparsity by using log-based add-only operations to avoid the significant overhead of prediction. Then, a distributed sorting and a sorted updating FlashAttention mechanism are proposed with a cross-stage coordinated tiling principle, which enables fine-grained and lightweight coordination among stages, helping optimize memory access and latency. Further, we propose a SOFA accelerator to support these optimizations efficiently. Extensive experiments on 20 benchmarks show that SOFA achieves $9.5\times$ speed up and $71.5\times$ higher energy efficiency than Nvidia A100 GPU. Compared to 8 SOTA accelerators, SOFA achieves an average $15.8\times$ energy efficiency, $10.3\times$ area efficiency and $9.3\times$ speed up, respectively.

cs.AR↗

arXiv2024

Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis

Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers through undersampled dMRI. To generate more comprehensive raw dMRI, generative adversarial network based methods are proposed to include b-values and b-vectors as conditions, but they are limited by unstable training and less desirable diversity. The emerging diffusion model (DM) promises to improve generative performance. However, it remains challenging to include essential information in conditioning DM for more relevant generation, i.e., the physical principles of dMRI and white matter tract structures. In this study, we propose a physics-guided diffusion model to generate high-quality dMRI. Our model introduces the physical principles of dMRI in the noise evolution in the diffusion process and introduce a query-based conditional mapping within the difussion model. In addition, to enhance the anatomical fine detials of the generation, we introduce the XTRACT atlas as prior of white matter tracts by adopting an adapter technique. Our experiment results show that our method outperforms other state-of-the-art methods and has the potential to advance dMRI enhancement.

eess.IV↗

arXiv2024

Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization

Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly focus on white-box attacks and are merely valid at a specific viewpoint, while the universal multi-view black-box attack is less explored, limiting their generalization in practice. In this paper, we propose a novel universal multi-view black-box attack against object detectors, which optimizes a universal adversarial UV texture constructed by multiple image stickers for a 3D object via the designed layout optimization algorithm. Specifically, we treat the placement of image stickers on the UV texture as a circle-based layout optimization problem, whose objective is to find the optimal circle layout filled with image stickers so that it can deceive the object detector under the multi-view scenario. To ensure reasonable placement of image stickers, two constraints are elaborately devised. To optimize the layout, we adopt the random search algorithm enhanced by the devised important-aware selection strategy to find the most appropriate image sticker for each circle from the image sticker pools. Extensive experiments conducted on four common object detectors suggested that the detection performance decreases by a large magnitude of 74.29% on average in multi-view scenarios. Additionally, a novel evaluation tool based on the photo-realistic simulator is designed to assess the texture-based attack fairly.

cs.CV↗

arXiv2024

Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction

The characterization of Tumor MicroEnvironment (TME) is challenging due to its complexity and heterogeneity. Relatively consistent TME characteristics embedded within highly specific tissue features, render them difficult to predict. The capability to accurately classify TME subtypes is of critical significance for clinical tumor diagnosis and precision medicine. Based on the observation that tumors with different origins share similar microenvironment patterns, we propose PathoTME, a genomics-guided Siamese representation learning framework employing Whole Slide Image (WSI) for pan-cancer TME subtypes prediction. Specifically, we utilize Siamese network to leverage genomic information as a regularization factor to assist WSI embeddings learning during the training phase. Additionally, we employ Domain Adversarial Neural Network (DANN) to mitigate the impact of tissue type variations. To eliminate domain bias, a dynamic WSI prompt is designed to further unleash the model's capabilities. Our model achieves better performance than other state-of-the-art methods across 23 cancer types on TCGA dataset. Our code is available at https://github.com/Mengflz/PathoTME.

cs.CV↗

arXiv2024

MSTF: Multiscale Transformer for Incomplete Trajectory Prediction

Motion forecasting plays a pivotal role in autonomous driving systems, enabling vehicles to execute collision warnings and rational local-path planning based on predictions of the surrounding vehicles. However, prevalent methods often assume complete observed trajectories, neglecting the potential impact of missing values induced by object occlusion, scope limitation, and sensor failures. Such oversights inevitably compromise the accuracy of trajectory predictions. To tackle this challenge, we propose an end-to-end framework, termed Multiscale Transformer (MSTF), meticulously crafted for incomplete trajectory prediction. MSTF integrates a Multiscale Attention Head (MAH) and an Information Increment-based Pattern Adaptive (IIPA) module. Specifically, the MAH component concurrently captures multiscale motion representation of trajectory sequence from various temporal granularities, utilizing a multi-head attention mechanism. This approach facilitates the modeling of global dependencies in motion across different scales, thereby mitigating the adverse effects of missing values. Additionally, the IIPA module adaptively extracts continuity representation of motion across time steps by analyzing missing patterns in the data. The continuity representation delineates motion trend at a higher level, guiding MSTF to generate predictions consistent with motion continuity. We evaluate our proposed MSTF model using two large-scale real-world datasets. Experimental results demonstrate that MSTF surpasses state-of-the-art (SOTA) models in the task of incomplete trajectory prediction, showcasing its efficacy in addressing the challenges posed by missing values in motion forecasting for autonomous driving systems.

cs.CV↗

arXiv2024

Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks

Traditional image processing methods employing partial differential equations (PDEs) offer a multitude of meaningful regularizers, along with valuable theoretical foundations for a wide range of image-related tasks. This makes their integration into neural networks a promising avenue. In this paper, we introduce a novel regularization approach inspired by the reverse process of PDE-based evolution models. Specifically, we propose inverse evolution layers (IELs), which serve as bad property amplifiers to penalize neural networks of which outputs have undesired characteristics. Using IELs, one can achieve specific regularization objectives and endow neural networks' outputs with corresponding properties of the PDE models. Our experiments, focusing on semantic segmentation tasks using heat-diffusion IELs, demonstrate their effectiveness in mitigating noisy label effects. Additionally, we develop curve-motion IELs to enforce convex shape regularization in neural network-based segmentation models for preventing the generation of concave outputs. Theoretical analysis confirms the efficacy of IELs as an effective regularization mechanism, particularly in handling training with label issues.

cs.LG↗

arXiv2024

Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning

Multi-modal learning plays a crucial role in cancer diagnosis and prognosis. Current deep learning based multi-modal approaches are often limited by their abilities to model the complex correlations between genomics and histology data, addressing the intrinsic complexity of tumour ecosystem where both tumour and microenvironment contribute to malignancy. We propose a biologically interpretative and robust multi-modal learning framework to efficiently integrate histology images and genomics by decomposing the feature subspace of histology images and genomics, reflecting distinct tumour and microenvironment features. To enhance cross-modal interactions, we design a knowledge-driven subspace fusion scheme, consisting of a cross-modal deformable attention module and a gene-guided consistency strategy. Additionally, in pursuit of dynamically optimizing the subspace knowledge, we further propose a novel gradient coordination learning strategy. Extensive experiments demonstrate the effectiveness of the proposed method, outperforming state-of-the-art techniques in three downstream tasks of glioma diagnosis, tumour grading, and survival analysis. Our code is available at https://github.com/helenypzhang/Subspace-Multimodal-Learning.

eess.IV↗

arXiv2024

KernelWarehouse: Rethinking the Design of Dynamic Convolution

Dynamic convolution learns a linear mixture of n static kernels weighted with their input-dependent attentions, demonstrating superior performance than normal convolution. However, it increases the number of convolutional parameters by n times, and thus is not parameter efficient. This leads to no research progress that can allow researchers to explore the setting n>100 (an order of magnitude larger than the typical setting n<10) for pushing forward the performance boundary of dynamic convolution while enjoying parameter efficiency. To fill this gap, in this paper, we propose KernelWarehouse, a more general form of dynamic convolution, which redefines the basic concepts of ``kernels", ``assembling kernels" and ``attention function" through the lens of exploiting convolutional parameter dependencies within the same layer and across neighboring layers of a ConvNet. We testify the effectiveness of KernelWarehouse on ImageNet and MS-COCO datasets using various ConvNet architectures. Intriguingly, KernelWarehouse is also applicable to Vision Transformers, and it can even reduce the model size of a backbone while improving the model accuracy. For instance, KernelWarehouse (n=4) achieves 5.61%|3.90%|4.38% absolute top-1 accuracy gain on the ResNet18|MobileNetV2|DeiT-Tiny backbone, and KernelWarehouse (n=1/4) with 65.10% model size reduction still achieves 2.29% gain on the ResNet18 backbone. The code and models are available at https://github.com/OSVAI/KernelWarehouse.

cs.CV↗

arXiv2024

Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology

Multimodal learning, integrating histology images and genomics, promises to enhance precision oncology with comprehensive views at microscopic and molecular levels. However, existing methods may not sufficiently model the shared or complementary information for more effective integration. In this study, we introduce a Unified Modeling Enhanced Multimodal Learning (UMEML) framework that employs a hierarchical attention structure to effectively leverage shared and complementary features of both modalities of histology and genomics. Specifically, to mitigate unimodal bias from modality imbalance, we utilize a query-based cross-attention mechanism for prototype clustering in the pathology encoder. Our prototype assignment and modularity strategy are designed to align shared features and minimizes modality gaps. An additional registration mechanism with learnable tokens is introduced to enhance cross-modal feature integration and robustness in multimodal unified modeling. Our experiments demonstrate that our method surpasses previous state-of-the-art approaches in glioma diagnosis and prognosis tasks, underscoring its superiority in precision neuro-Oncology.

cs.CV↗