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Yang Hu

Publications and source records attributed to Yang Hu.

At least 145 records · Page 8Linked to original sources
arXiv2024

On the conjugate interface conditions and Galilean invariance

In the referred paper("H. Karani, C. Huber, Physical Review E, 91(2)(2015) 023304"), a total heat flux continuity condition for conjugate heat transfer problems with moving interfaces was proposed. The authors asserted both conductive and advective heat fluxes are conserved simultaneously in their formulation. This condition had been cited by many subsequent studies. However, it is found that the total heat flux continuity condition violates Galilean invariance. The original diffusion heat flux continuity condition is reasonable for both stationary and moving interfaces.

physics.comp-ph↗
arXiv2024

The sufficient conditions for $k$-leaf-connected graphs in terms of several topological indices

Let $G=(V(G), E(G))$ be a graph with vertex set $V(G)$ and edge set $E(G)$. For $k\geq2$ and given any subset $S\subseteq|V(G)|$ with $|S|=k$, if a graph $G$ of order $|V(G)|\geq k+1$ always has a spanning tree $T$ such that $S$ is precisely the set of leaves of $T$, then the graph $G$ is a $k$-leaf-connected graph. A graph $G$ is called Hamilton-connected if any two vertices of $G$ are connected by a Hamilton path. Based on the definitions of $k$-leaf-connected and Hamilton-connected, we known that a graph is $2$-leaf-connected if and only if it is Hamilton-connected. During the past decades, there have been many results of sufficient conditions for Hamilton-connected with respect to topological indices. In this paper, we present sufficient conditions for a graph $G$ to be $k$-leaf-connected in terms of the Zagreb index, the reciprocal degree distance or the hyper-Zagreb index. Furthermore, we use the first Zagreb index and hyper-Zagreb index of the complement graph $\overline{G}$ to give sufficient conditions for a graph $G$ to be $k$-leaf-connected.

math.CO↗
arXiv2024

High-rectification near-field radiative thermal diode using Weyl semimetals

Thermal diodes, which allow heat transfer in a preferential direction while being blocked in a reverse direction, have numerous applications in thermal management, information processing, energy harvesting, etc. Typical materials of thermal diodes in previous works include phase-change and magneto-optical materials. However, such thermal diodes highly depend on specific working temperatures or external magnetic fields. In this work, we propose a near-field radiative thermal diode (NFRTD) based on two Weyl semimetals (WSMs) nanoparticles (NPs) mediated by WSMs planar substrate, which works without external magnetic field and with flexible temperatures. Numerical results show that the maximum rectification ratio of NFRTD can be up to 2673 when the emitter is 200 K and receiver is 180 K, which exceeds the maximum value reported in previous works by more than 10 times. The underlying physical mechanism is the strong coupling of the localized plasmon modes in the NPs and nonreciprocal surface plasmon polaritons in the substrate. In addition, we calculate the distribution of the Green function and reflection coefficient to investigate nonreciprocal energy transfer in NFRTD. Finally, we discuss the effects of momentum-separation on the rectification performance of the NFRTD. This work demonstrates the great potential of WSMs in thermal rectification and paves a novel path in designing high-performance NFRTD.

physics.optics↗
arXiv2024

Emulating Complex Synapses Using Interlinked Proton Conductors

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.

cs.NE↗
arXiv2023

Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation

Presently, self-training stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-labels. However, we find two critical limitations in this paradigm. (1) The majority of reliable pixels exhibit a speckle-shaped pattern and are primarily located in the central semantic region. This presents challenges for the model in accurately learning semantics. (2) Category noise in speckle pixels is difficult to locate and correct, leading to error accumulation in self-training. To address these limitations, we propose a novel approach called Semantic Connectivity-driven pseudo-labeling (SeCo). This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics. Specifically, SeCo comprises two key components: Pixel Semantic Aggregation (PSA) and Semantic Connectivity Correction (SCC). Initially, PSA divides semantics into 'stuff' and 'things' categories and aggregates speckled pseudo-labels into semantic connectivity through efficient interaction with the Segment Anything Model (SAM). This enables us not only to obtain accurate boundaries but also simplifies noise localization. Subsequently, SCC introduces a simple connectivity classification task, which enables locating and correcting connectivity noise with the guidance of loss distribution. Extensive experiments demonstrate that SeCo can be flexibly applied to various cross-domain semantic segmentation tasks, including traditional unsupervised, source-free, and black-box domain adaptation, significantly improving the performance of existing state-of-the-art methods. The code is available at https://github.com/DZhaoXd/SeCo.

cs.CV↗
arXiv2023

Wafer-scale Computing: Advancements, Challenges, and Future Perspectives

Nowadays, artificial intelligence (AI) technology with large models plays an increasingly important role in both academia and industry. It also brings a rapidly increasing demand for the computing power of the hardware. As the computing demand for AI continues to grow, the growth of hardware computing power has failed to keep up. This has become a significant factor restricting the development of AI. The augmentation of hardware computing power is mainly propelled by the escalation of transistor density and chip area. However, the former is impeded by the termination of the Moore's Law and Dennard scaling, and the latter is significantly restricted by the challenge of disrupting the legacy fabrication equipment and process. In recent years, advanced packaging technologies that have gradually matured are increasingly used to implement bigger chips that integrate multiple chiplets, while still providing interconnections with chip-level density and bandwidth. Compared to conventional high-performance computing paradigms such as multi-accelerator and datacenter-scale computing, Wafer-scale Computing shows remarkable advantages in communication bandwidth, integration density, and programmability potential. Not surprisingly, disruptive Wafer-scale Computing also brings unprecedented design challenges for hardware architecture, design-system-technology co-optimization, power and cooling systems, and compiler tool chain. At present, there are no comprehensive surveys summarizing the current state and design insights of Wafer-scale Computing. This paper aims to take the first step to help academia and industry review existing wafer-scale chips and essential technologies in a one-stop manner. So that people can conveniently grasp the basic knowledge and key points, understand the achievements and shortcomings of existing research, and contribute to this promising research direction.

cs.AR↗
arXiv2023

DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud

Millimeter-wave (mmWave) radar pointcloud offers attractive potential for 3D sensing, thanks to its robustness in challenging conditions such as smoke and low illumination. However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and strong interference and noise. In this paper, we propose DREAM-PCD, a novel framework that combines signal processing and deep learning methods into three well-designed components to tackle all three challenges: Non-Coherent Accumulation for dense points, Synthetic Aperture Accumulation for improved angular resolution, and Real-Denoise Multiframe network for noise and interference removal. Moreover, the causal multiframe and "real-denoise" mechanisms in DREAM-PCD significantly enhance the generalization performance. We also introduce RadarEyes, the largest mmWave indoor dataset with over 1,000,000 frames, featuring a unique design incorporating two orthogonal single-chip radars, lidar, and camera, enriching dataset diversity and applications. Experimental results demonstrate that DREAM-PCD surpasses existing methods in reconstruction quality, and exhibits superior generalization and real-time capabilities, enabling high-quality real-time reconstruction of radar pointcloud under various parameters and scenarios. We believe that DREAM-PCD, along with the RadarEyes dataset, will significantly advance mmWave radar perception in future real-world applications.

eess.IV↗
arXiv2023

Towards Efficient Control Flow Handling in Spatial Architecture via Architecting the Control Flow Plane

Spatial architecture is a high-performance architecture that uses control flow graphs and data flow graphs as the computational model and producer/consumer models as the execution models. However, existing spatial architectures suffer from control flow handling challenges. Upon categorizing their PE execution models, we find that they lack autonomous, peer-to-peer, and temporally loosely-coupled control flow handling capability. This leads to limited performance in intensive control programs. A spatial architecture, Marionette, is proposed, with an explicit-designed control flow plane. The Control Flow Plane enables autonomous, peer-to-peer and temporally loosely-coupled control flow handling. The Proactive PE Configuration ensures timely and computation-overlapped configuration to improve handling Branch Divergence. The Agile PE Assignment enhance the pipeline performance of Imperfect Loops. We develop full stack of Marionette (ISA, compiler, simulator, RTL) and demonstrate that in a variety of challenging intensive control programs, compared to state-of-the-art spatial architectures, Marionette outperforms Softbrain, TIA, REVEL, and RipTide by geomean 2.88x, 3.38x, 1.55x, and 2.66x.

cs.AR↗
arXiv2023

Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models

Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology. However, the interpretability of these approaches is still limited. In particular, they do not report whether high-attention regions are positively or negatively associated with the class labels or how well these regions correspond to previously established clinical and biological knowledge. We address this by introducing a post-training methodology to analyse MIL models. Firstly, we introduce prediction-attention-weighted (PAW) maps by combining tile-level attention and prediction scores produced by a refined encoder, allowing us to quantify the predictive contribution of high-attention regions. Secondly, we introduce a biological feature instantiation technique by integrating PAW maps with nuclei segmentation masks. This further improves interpretability by providing biologically meaningful features related to the cellular organisation of the tissue and facilitates comparisons with known clinical features. We illustrate the utility of our approach by comparing PAW maps obtained for prostate cancer diagnosis (i.e. samples containing malignant tissue, 381/516 tissue samples) and prognosis (i.e. samples from patients with biochemical recurrence following surgery, 98/663 tissue samples) in a cohort of patients from the international cancer genome consortium (ICGC UK Prostate Group). Our approach reveals that regions that are predictive of adverse prognosis do not tend to co-locate with the tumour regions, indicating that non-cancer cells should also be studied when evaluating prognosis.

q-bio.QM↗
arXiv2023

WindMill: A Parameterized and Pluggable CGRA Implemented by DIAG Design Flow

With the cross-fertilization of applications and the ever-increasing scale of models, the efficiency and productivity of hardware computing architectures have become inadequate. This inadequacy further exacerbates issues in design flexibility, design complexity, development cycle, and development costs (4-d problems) in divergent scenarios. To address these challenges, this paper proposed a flexible design flow called DIAG based on plugin techniques. The proposed flow guides hardware development through four layers: definition(D), implementation(I), application(A), and generation(G). Furthermore, a versatile CGRA generator called WindMill is implemented, allowing for agile generation of customized hardware accelerators based on specific application demands. Applications and algorithm tasks from three aspects is experimented. In the case of reinforcement learning algorithm, a significant performance improvement of $2.3\times$ compared to GPU is achieved.

cs.AR↗
arXiv2023

Perspectives in closed-loop supply chains network design considering risk and uncertainty factors

Risk and uncertainty in each stage of CLSC have greatly increased the complexity and reduced process efficiency of the closed-loop networks, impeding the sustainable and resilient development of industries and the circular economy. Recently, increasing interest in academia have been raised on the risk and uncertainty analysis of closed-loop supply chain, yet there is no comprehensive review paper focusing on closed-loop network design considering risk and uncertainty. This paper examines previous research on the domain of closed-loop network design under risk and uncertainties to provide constructive prospects for future study. We selected 106 papers published in the Scopus database from the year 2004 to 2022. We analyse the source of risk and uncertainties of the CLSC network and identified appropriate methods for handling uncertainties in addition to algorithms for solving uncertain CLSCND problems. We also illustrate the evolution of objectives for designing a closed-loop supply chain that is expos to risk or uncertainty, and investigate the application of uncertain network design models in practical industry sectors. Finally, we draw proper research gaps for each category and clarify some novel insights for future study. By considering the impacts of risk or uncertainties of different sources on closed-loop supply chain network design, we can approach the economical, sustainable, social, and resilient objectives effectively and efficiently.

econ.GN↗
arXiv2023

Machine learning of twin/matrix interfaces from local stress field

Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg.

cond-mat.mtrl-sci↗
arXiv2023

A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis

High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.

cs.LG↗
arXiv2023

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Stock market plays an important role in the economic development. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. The traditional time series model ARIMA can not describe the nonlinearity, and can not achieve satisfactory results in the stock prediction. As neural networks are with strong nonlinear generalization ability, this paper proposes an attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price. The model constructed in this paper integrates the time series model, the Convolutional Neural Networks with Attention mechanism, the Long Short-Term Memory network, and XGBoost regressor in a non-linear relationship, and improves the prediction accuracy. The model can fully mine the historical information of the stock market in multiple periods. The stock data is first preprocessed through ARIMA. Then, the deep learning architecture formed in pretraining-finetuning framework is adopted. The pre-training model is the Attention-based CNN-LSTM model based on sequence-to-sequence framework. The model first uses convolution to extract the deep features of the original stock data, and then uses the Long Short-Term Memory networks to mine the long-term time series features. Finally, the XGBoost model is adopted for fine-tuning. The results show that the hybrid model is more effective and the prediction accuracy is relatively high, which can help investors or institutions to make decisions and achieve the purpose of expanding return and avoiding risk. Source code is available at https://github.com/zshicode/Attention-CLX-stock-prediction.

q-fin.ST↗
arXiv2022

RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory

This paper introduces a novel framework, i.e., RFPose-OT, to enable the 3D human pose estimation from Radio Frequency (RF) signals. Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in basic indoor environment, occlusion indoor environment, and outdoor environment, all demonstrate that RFPose-OT can predict 3D human poses with higher precision than the state-of-the-art methods.

cs.CV↗
arXiv2022

An improvement of sufficient condition for $k$-leaf-connected graphs

For integer $k\geq2,$ a graph $G$ is called $k$-leaf-connected if $|V(G)|\geq k+1$ and given any subset $S\subseteq V(G)$ with $|S|=k,$ $G$ always has a spanning tree $T$ such that $S$ is precisely the set of leaves of $T.$ Thus a graph is $2$-leaf-connected if and only if it is Hamilton-connected. In this paper, we present a best possible condition based upon the size to guarantee a graph to be $k$-leaf-connected, which not only improves the results of Gurgel and Wakabayashi [On $k$-leaf-connected graphs, J. Combin. Theory Ser. B 41 (1986) 1-16] and Ao, Liu, Yuan and Li [Improved sufficient conditions for $k$-leaf-connected graphs, Discrete Appl. Math. 314 (2022) 17-30], but also extends the result of Xu, Zhai and Wang [An improvement of spectral conditions for Hamilton-connected graphs, Linear Multilinear Algebra, 2021]. Our key approach is showing that an $(n+k-1)$-closed non-$k$-leaf-connected graph must contain a large clique if its size is large enough. As applications, sufficient conditions for a graph to be $k$-leaf-connected in terms of the (signless Laplacian) spectral radius of $G$ or its complement are also presented.

math.CO↗
arXiv2022

Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity

We study Model Predictive Control (MPC) and propose a general analysis pipeline to bound its dynamic regret. The pipeline first requires deriving a perturbation bound for a finite-time optimal control problem. Then, the perturbation bound is used to bound the per-step error of MPC, which leads to a bound on the dynamic regret. Thus, our pipeline reduces the study of MPC to the well-studied problem of perturbation analysis, enabling the derivation of regret bounds of MPC under a variety of settings. To demonstrate the power of our pipeline, we use it to generalize existing regret bounds on MPC in linear time-varying (LTV) systems to incorporate prediction errors on costs, dynamics, and disturbances. Further, our pipeline leads to regret bounds on MPC in systems with nonlinear dynamics and constraints.

math.OC↗
arXiv2022

Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92\%, 99.18\%, and 98.76\% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recognition and sensing at extreme angles, all of which demonstrate the superior robustness and effectiveness of our system.

cs.CV↗