arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 523 records · Page 29Linked to original sources

arXiv2024

Three-dimensional simulation of film boiling on a horizontal surface with magnetic field

This study conducts a numerical investigation into the three-dimensional film boiling of liquid under the influence of external magnetic fields. The numerical method incorporates a sharp phase-change model based on the volume-of-fluid approach to track the liquid-vapor interface. Additionally, a consistent and conservative scheme is employed to calculate the induced current densities and electromagnetic forces. We investigate the magnetohydrodynamic effects on film boiling, particularly examining the pattern transition of the vapor bubble and the evolution of heat transfer characteristics, exposed to either a vertical or horizontal magnetic field. In single-mode scenarios, film boiling under a vertical magnetic field displays an isotropic flow structure, forming a columnar vapor jet at higher magnetic field intensities. In contrast, horizontal magnetic fields result in anisotropic flow, creating a two-dimensional vapor sheet as the magnetic strength increases. In multi-mode scenarios, the patterns observed in single-mode film boiling persist, with the interaction of vapor bubbles introducing additional complexity to the magnetohydrodynamic flow. More importantly, our comprehensive analysis reveals how and why distinct boiling effects are generated by various orientations of magnetic fields, which induce directional electromagnetic forces to suppress flow vortices within the cross-sectional plane.

physics.flu-dyn↗

arXiv2024

Facility Location Problem with Aleatory Agents

In this paper, we introduce and study the Facility Location Problem with Aleatory Agents (FLPAA), where the facility accommodates n agents larger than the number of agents reporting their preferences, namely n_r. The spare capacity is used by n_u=n-n_r aleatory agents sampled from a probability distribution μ. The goal of FLPAA is to find a location that minimizes the ex-ante social cost, which is the expected cost of the n_u agents sampled from μplus the cost incurred by the agents reporting their position. We investigate the mechanism design aspects of the FLPAA under the assumption that the Mechanism Designer (MD) lacks knowledge of the distribution $μ$ but can query k quantiles of μ. We explore the trade-off between acquiring more insights into the probability distribution and designing a better-performing mechanism, which we describe through the strong approximation ratio (SAR). The SAR of a mechanism measures the highest ratio between the cost of the mechanisms and the cost of the optimal solution on the worst-case input x and worst-case distribution μ, offering a metric for efficiency that does not depend on μ. We divide our study into four different information settings: the zero information case, in which the MD has access to no quantiles; the median information case, in which the MD has access to the median of μ; the n_u-quantile information case, in which the MD has access to n_u quantiles of its choice, and the k-quantile information case, in which the MD has access to k<n_u quantiles of its choice. For all frameworks, we propose a mechanism that is optimal or achieves a small constant SAR and pairs it with a lower bound on the SAR. In most cases, the lower bound matches the upper bound, thus no truthful mechanism can achieve a lower SAR. Lastly, we extend the FLPAA to include instances in which we must locate two facilities.

cs.GT↗

arXiv2024

The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions under such cases. In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation. This challenge was developed based on the newly established KITTI-C and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis on robust self-supervised and robust fully-supervised depth estimation, respectively. Out of more than two hundred participants, nine unique and top-performing solutions have appeared, with novel designs ranging from the following aspects: spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive experimental analyses along with insightful observations are drawn to better understand the rationale behind each design. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation and beyond. The datasets, competition toolkit, workshop recordings, and source code from the winning teams are publicly available on the challenge website.

cs.CV↗

arXiv2024

Cambricon-LLM: A Chiplet-Based Hybrid Architecture for On-Device Inference of 70B LLM

Deploying advanced large language models on edge devices, such as smartphones and robotics, is a growing trend that enhances user data privacy and network connectivity resilience while preserving intelligent capabilities. However, such a task exhibits single-batch computing with incredibly low arithmetic intensity, which poses the significant challenges of huge memory footprint and bandwidth demands on limited edge resources. To address these issues, we introduce Cambricon-LLM, a chiplet-based hybrid architecture with NPU and a dedicated NAND flash chip to enable efficient on-device inference of 70B LLMs. Such a hybrid architecture utilizes both the high computing capability of NPU and the data capacity of the NAND flash chip, with the proposed hardware-tiling strategy that minimizes the data movement overhead between NPU and NAND flash chip. Specifically, the NAND flash chip, enhanced by our innovative in-flash computing and on-die ECC techniques, excels at performing precise lightweight on-die processing. Simultaneously, the NPU collaborates with the flash chip for matrix operations and handles special function computations beyond the flash's on-die processing capabilities. Overall, Cambricon-LLM enables the on-device inference of 70B LLMs at a speed of 3.44 token/s, and 7B LLMs at a speed of 36.34 token/s, which is over 22X to 45X faster than existing flash-offloading technologies, showing the potentiality of deploying powerful LLMs in edge devices.

cs.AR↗

arXiv2024

Mean Age of Information in Partial Offloading Mobile Edge Computing Networks

The age of information (AoI) performance analysis is essential for evaluating the information freshness in the large-scale mobile edge computing (MEC) networks. This work proposes the earliest analysis of the mean AoI (MAoI) performance of large-scale partial offloading MEC networks. Firstly, we derive and validate the closed-form expressions of MAoI by using queueing theory and stochastic geometry. Based on these expressions, we analyse the effects of computing offloading ratio (COR) and task generation rate (TGR) on the MAoI performance and compare the MAoI performance under the local computing, remote computing, and partial offloading schemes. The results show that by jointly optimising the COR and TGR, the partial offloading scheme outperforms the local and remote computing schemes in terms of the MAoI, which can be improved by up to 51% and 61%, respectively. This encourages the MEC networks to adopt the partial offloading scheme to improve the MAoI performance.

eess.SY↗

arXiv2024

CamLoPA: A Hidden Wireless Camera Localization Framework via Signal Propagation Path Analysis

Hidden wireless cameras pose significant privacy threats, necessitating effective detection and localization methods. However, existing solutions often require spacious activity areas, expensive specialized devices, or pre-collected training data, limiting their practical deployment. To address these limitations, we introduce CamLoPA, a training-free wireless camera detection and localization framework that operates with minimal activity space constraints using low-cost commercial-off-the-shelf (COTS) devices. CamLoPA can achieve detection and localization in just 45 seconds of user activities with a Raspberry Pi board. During this short period, it analyzes the causal relationship between the wireless traffic and user movement to detect the presence of a snooping camera. Upon detection, CamLoPA employs a novel azimuth location model based on wireless signal propagation path analysis. Specifically, this model leverages the time ratio of user paths crossing the First Fresnel Zone (FFZ) to determine the azimuth angle of the camera. Then CamLoPA refines the localization by identifying the camera's quadrant. We evaluate CamLoPA across various devices and environments, demonstrating that it achieves 95.37% snooping camera detection accuracy and an average localization error of 17.23, under the significantly reduced activity space requirements. Our demo are available at https://www.youtube.com/watch?v=GKam04FzeM4.

cs.CR↗

arXiv2024

Interpretable Nonroutine Network Traffic Prediction with a Case Study

This paper pioneers a nonroutine network traffic prediction (NNTP) method to prospectively provide a theoretical basis for avoiding large-scale network disruption by accurately predicting bursty traffic. Certain events that impact user behavior subsequently trigger nonroutine traffic, which significantly constrains the performance of network traffic prediction (NTP) models. By analyzing nonroutine traffic and the corresponding events, the NNTP method is pioneered to construct interpretable NTP model. Based on the real-world traffic data, the network traffic generated during soccer games serves as a case study to validate the performance of the NNTP method. The numerical results indicate that our prediction closely fits the traffic pattern. In comparison to existing researches, the NNTP method is at the forefront of finding a balance among interpretability, accuracy, and computational complexity.

cs.NI↗

arXiv2024

LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement

Speech enhancement (SE) aims to extract the clean waveform from noise-contaminated measurements to improve the speech quality and intelligibility. Although learning-based methods can perform much better than traditional counterparts, the large computational complexity and model size heavily limit the deployment on latency-sensitive and low-resource edge devices. In this work, we propose a lightweight SE network (LiSenNet) for real-time applications. We design sub-band downsampling and upsampling blocks and a dual-path recurrent module to capture band-aware features and time-frequency patterns, respectively. A noise detector is developed to detect noisy regions in order to perform SE adaptively and save computational costs. Compared to recent higher-resource-dependent baseline models, the proposed LiSenNet can achieve a competitive performance with only 37k parameters (half of the state-of-the-art model) and 56M multiply-accumulate (MAC) operations per second.

eess.AS↗

arXiv2024

Geometry-Constrained EEG Channel Selection for Brain-Assisted Speech Enhancement

Brain-assisted speech enhancement (BASE) aims to extract the target speaker in complex multi-talker scenarios using electroencephalogram (EEG) signals as an assistive modality, as the auditory attention of the listener can be decoded from electroneurographic signals of the brain. This facilitates a potential integration of EEG electrodes with listening devices to improve the speech intelligibility of hearing-impaired listeners, which was shown by the recently-proposed BASEN model. As in general the multichannel EEG signals are highly correlated and some are even irrelevant to listening, blindly incorporating all EEG channels would lead to a high economic and computational cost. In this work, we therefore propose a geometry-constrained EEG channel selection approach for BASE. We design a new weighted multi-dilation temporal convolutional network (WDTCN) as the backbone to replace the Conv-TasNet in BASEN. Given a raw channel set that is defined by the electrode geometry for feasible integration, we then propose a geometry-constrained convolutional regularization selection (GC-ConvRS) module for WD-TCN to find an informative EEG subset. Experimental results on a public dataset show the superiority of the proposed WD-TCN over BASEN. The GC-ConvRS can further refine the useful EEG subset subject to the geometry constraint, resulting in a better trade-off between performance and integration cost.

eess.AS↗

arXiv2024

Dispersionless Flat Mode and Vibrational Anomaly in Active Brownian Vibrators Induced by String-like Dynamical Defects

In recent years, active Brownian particles have emerged as a prominent model system for comprehending the behaviors of active matter, wherein particles demonstrate self-propelled motion by harnessing energy from the surrounding environment. A fundamental objective of studying active matter is to elucidate the physical mechanisms underlying its collective behaviors. Drawing inspiration from advancements in molecular glasses, our study unveils a low-energy ``flat mode" within the transverse spectrum of active Brownian vibrators -- a nearly two-dimensional, bi-disperse granular assembly. We demonstrate that this collective excitation induces an anomalous excess in the vibrational density of states (VDOS) beyond the phononic Debye contribution. We characterize the properties of this flat mode by exploring the parameter space of our experimental system and tuning the packing fraction, the vibrational frequency, the particle size ratio and the mixture ratio. Additionally, we establish through empirical evidence that string-like dynamical defects, discerned via the spatial distribution of each particle's contribution to the reduced transverse VDOS, serve as the microscopic origin of the flat mode and its associated anomalies.

cond-mat.soft↗

arXiv2024

NeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering

The Compute Express Link (CXL) interconnect makes it feasible to integrate diverse types of memory into servers via its byte-addressable SerDes links. Considering the various access latency, harnessing the full potential of CXL-based heterogeneous memory systems requires efficient memory tiering. However, prior work can hardly make a fundamental progress owing to low-resolution and high-overhead memory access profiling techniques. To address this critical challenge, we propose a novel memory tiering solution called NeoMem, which features a hardware/software co-design. NeoMem offloads memory profiling functions to CXL device-side controllers, integrating a dedicated hardware unit called NeoProf. NeoProf readily monitors memory accesses and provides the OS with crucial page hotness statistics and other useful system state information. On the OS kernel side, we design a revamped memory-tiering strategy, enabling accurate and timely hot page promotion based on NeoProf statistics. We implement NeoMem on a real FPGA-based CXL memory platform and Linux kernel v6.3. Comprehensive evaluations demonstrate that NeoMem achieves 32% to 67% geomean speedup over several existing memory tiering solutions.

cs.AR↗

arXiv2024

Demystifying Datapath Accelerator Enhanced Off-path SmartNIC

Network speeds grow quickly in the modern cloud, so SmartNICs are introduced to offload network processing tasks, even application logic. However, typical multicore SmartNICs such as BlueFiled-2 are only capable of processing control-plane tasks with their embedded processors that have limited memory bandwidth and computing power. On the other hand, cloud applications evolve rapidly, such that a limited number of fixed hardware engines in a SmartNIC cannot satisfy the requirements of cloud applications. Therefore, SmartNIC programmers call for a programmable datapath accelerator (DPA) to process network traffic at line rate. However, no existing work has unveiled the performance characteristics of the existing DPA. To this end, we present the first architectural characterization of the latest DPA-enhanced BlueFiled-3 (BF3) SmartNIC. Our evaluation results indicate that BF3's DPA is significantly wimpier than the off-path Arm processor and the host CPU. However, we still identify that DPA has three unique architectural characteristics that unleash the performance potential of DPA. Specifically, we demonstrate how to take advantage of DPA's three architectural characteristics regarding computing, networking, and memory subsystems. Then we propose three important guidelines for programmers to fully unleash the potential of DPA. To demonstrate the effectiveness of our approach, we conduct detailed case studies regarding each guideline. Our case study on key-value aggregation achieves up to 4.3$\times$ higher throughput by using our guidelines to optimize memory combinations.

cs.NI↗

arXiv2024

InstInfer: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference

The widespread of Large Language Models (LLMs) marks a significant milestone in generative AI. Nevertheless, the increasing context length and batch size in offline LLM inference escalate the memory requirement of the key-value (KV) cache, which imposes a huge burden on the GPU VRAM, especially for resource-constraint scenarios (e.g., edge computing and personal devices). Several cost-effective solutions leverage host memory or SSDs to reduce storage costs for offline inference scenarios and improve the throughput. Nevertheless, they suffer from significant performance penalties imposed by intensive KV cache accesses due to limited PCIe bandwidth. To address these issues, we propose InstInfer, a novel LLM inference system that offloads the most performance-critical computation (i.e., attention in decoding phase) and data (i.e., KV cache) parts to Computational Storage Drives (CSDs), which minimize the enormous KV transfer overheads. InstInfer designs a dedicated flash-aware in-storage attention engine with KV cache management mechanisms to exploit the high internal bandwidths of CSDs instead of being limited by the PCIe bandwidth. The optimized P2P transmission between GPU and CSDs further reduces data migration overheads. Experimental results demonstrate that for a 13B model using an NVIDIA A6000 GPU, InstInfer improves throughput for long-sequence inference by up to 11.1$\times$, compared to existing SSD-based solutions such as FlexGen.

cs.AR↗

arXiv2024

Multiple types of spin textures and robust valley physics in MP$_2$X$_6$

Both spin textures and multiple valleys in the momentum space have attracted great attentions due to their versatile applications in spintronics and valleytronics. It is highly desirable to realize multiple types of spin textures in a single material and further couple the spin textures to valley degree of freedom. Here, we study electronic properties of SnP$_{2}$Se$_{6}$ monolayer by first-principles calculations. The monolayer exhibits rare Weyl-type and Ising-type spin textures at different valleys, which can be conveniently expressed by electron and hole dopings, respectively. Besides valley-contrasting spin textures, Berry-curvature-driven anomalous Hall currents and optical selectivity are found to be valley dependent as well. These valley-related properties also have generalizations to SnP$_{2}$Se$_{6}$ few-layers and other MP$_{2}$X$_{6}$. Our findings open new avenue for exploring appealing interplay between spin textures and multiple valleys, and designing advanced device paradigms based on spin and valley degrees of freedom.

cond-mat.mtrl-sci↗

arXiv2024

Evaluations of Machine Learning Privacy Defenses are Misleading

Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries. We identify severe pitfalls in existing empirical privacy evaluations (based on membership inference attacks) that result in misleading conclusions. In particular, we show that prior evaluations fail to characterize the privacy leakage of the most vulnerable samples, use weak attacks, and avoid comparisons with practical differential privacy baselines. In 5 case studies of empirical privacy defenses, we find that prior evaluations underestimate privacy leakage by an order of magnitude. Under our stronger evaluation, none of the empirical defenses we study are competitive with a properly tuned, high-utility DP-SGD baseline (with vacuous provable guarantees).

cs.CR↗

arXiv2024

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models

Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs' trustworthiness. In this paper, to reveal the untapped potential of pre-training, we pioneer the exploration of LLMs' trustworthiness during this period, focusing on five key dimensions: reliability, privacy, toxicity, fairness, and robustness. To begin with, we apply linear probing to LLMs. The high probing accuracy suggests that \textit{LLMs in early pre-training can already distinguish concepts in each trustworthiness dimension}. Therefore, to further uncover the hidden possibilities of pre-training, we extract steering vectors from a LLM's pre-training checkpoints to enhance the LLM's trustworthiness. Finally, inspired by~\citet{choi2023understanding} that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with mutual information to investigate the dynamics of trustworthiness during pre-training. We are the first to observe a similar two-phase phenomenon: fitting and compression~\citep{shwartz2017opening}. This research provides an initial exploration of trustworthiness modeling during LLM pre-training, seeking to unveil new insights and spur further developments in the field. We will make our code publicly accessible at \url{https://github.com/ChnQ/TracingLLM}.

cs.CL↗

arXiv2024

Optimal Dispatch Strategy for a Multi-microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism

To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism. Initially, the strategy assesses electric energy interactions between microgrids and distribution networks to establish a foundation for collaborative scheduling. The two-stage pricing mechanism initiates with a leader-follower game, wherein the microgrid operator acts as the leader and users as followers. Subsequently, it adjusts EV tariffs based on the game's equilibrium, taking into account factors such as battery degradation and travel needs to optimize EVs' electricity consumption. Furthermore, a bi-level optimization model refines power interactions and pricing strategies across the network, significantly enhancing demand response capabilities and economic outcomes. Simulation results demonstrate that this strategy not only increases renewable energy consumption but also reduces energy costs, thereby improving the overall efficiency and sustainability of the system.

eess.SY↗

arXiv2024

Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models

The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications.

eess.SP↗