arXiv subjects

Yang Hu

Publications and source records attributed to Yang Hu.

At least 127 records · Page 7Linked to original sources

arXiv2024

Equivariant Weiss Calculus

In this paper, we introduce an equivariant analog of Weiss calculus of functors for all finite group $\mathrm{G}$. In our theory, Taylor approximations and derivatives are index by finite dimensional $\mathrm{G}$-representations, and homogeneous layers are classified by orthogonal $\mathrm{G}$-spectra. Further, our framework permits a notion of restriction as well as a notion of fixed-point at the level of Weiss functors. We establish various results comparing Taylor approximations and derivatives of fixed-point (resp. restrictions) functors to that of the fixed-point (resp. restrictions) of Taylor approximations and derivatives.

math.AT↗

arXiv2024

Primal-Dual Spectral Representation for Off-policy Evaluation

Off-policy evaluation (OPE) is one of the most fundamental problems in reinforcement learning (RL) to estimate the expected long-term payoff of a given target policy with only experiences from another behavior policy that is potentially unknown. The distribution correction estimation (DICE) family of estimators have advanced the state of the art in OPE by breaking the curse of horizon. However, the major bottleneck of applying DICE estimators lies in the difficulty of solving the saddle-point optimization involved, especially with neural network implementations. In this paper, we tackle this challenge by establishing a linear representation of value function and stationary distribution correction ratio, i.e., primal and dual variables in the DICE framework, using the spectral decomposition of the transition operator. Such primal-dual representation not only bypasses the non-convex non-concave optimization in vanilla DICE, therefore enabling an computational efficient algorithm, but also paves the way for more efficient utilization of historical data. We highlight that our algorithm, SpectralDICE, is the first to leverage the linear representation of primal-dual variables that is both computation and sample efficient, the performance of which is supported by a rigorous theoretical sample complexity guarantee and a thorough empirical evaluation on various benchmarks.

cs.LG↗

arXiv2024

Risk-sensitive Affine Control Synthesis for Stationary LTI Systems

To address deviations from expected performance in stochastic systems, we propose a risk-sensitive control synthesis method to minimize certain risk measures over the limiting stationary distribution. Specifically, we extend Worst-case Conditional Value-at-Risk (W-CVaR) optimization for Linear Time-invariant (LTI) systems to handle nonzero-mean noise and affine controllers, using only the first and second moments of noise, which enhances robustness against model uncertainty. Highlighting the strong coupling between the linear and bias terms of the controller, we reformulate the synthesis problem as a Bilinear Matrix Inequality (BMI), and propose an alternating optimization algorithm with guaranteed convergence. Finally, we demonstrate the numerical performance of our approach in two representative settings, which shows that the proposed algorithm successfully synthesizes risk-sensitive controllers that outperform the naïve LQR baseline.

eess.SY↗

arXiv2024

Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive Control

Aerial grasping, particularly soft aerial grasping, holds significant promise for drone delivery and harvesting tasks. However, controlling UAV dynamics during aerial grasping presents considerable challenges. The increased mass during payload grasping adversely affects thrust prediction, while unpredictable environmental disturbances further complicate control efforts. In this study, our objective aims to enhance the control of the Soft Aerial Vehicle (SAV) during aerial grasping by incorporating a disturbance observer into a Nonlinear Model Predictive Control (NMPC) SAV controller. By integrating the disturbance observer into the NMPC SAV controller, we aim to compensate for dynamic model idealization and uncertainties arising from additional payloads and unpredictable disturbances. Our approach combines a disturbance observer-based NMPC with the SAV controller, effectively minimizing tracking errors and enabling precise aerial grasping along all three axes. The proposed SAV equipped with Disturbance Observer-based Nonlinear Model Predictive Control (DOMPC) demonstrates remarkable capabilities in handling both static and non-static payloads, leading to the successful grasping of various objects. Notably, our SAV achieves an impressive payload-to-weight ratio, surpassing previous investigations in the domain of soft grasping. Using the proposed soft aerial vehicle weighing 1.002 kg, we achieve a maximum payload of 337 g by grasping.

cs.RO↗

arXiv2024

Near-field radiative heat transfer between graphene-covered Weyl semimetals

Polariton manipulations introduce novel approaches to modulate the near-field radiative heat transfer (NFRHT). Our theoretical investigation in this study centers on NFRHT in graphene-covered Weyl semimetals (WSMs). Our findings indicate variable heat flux enhancement or attenuation, contingent on chemical potential of graphene. Enhancement or attenuation mechanisms stem from the coupling or decoupling of surface plasmon polaritons (SPPs) in the graphene/WSM heterostructure. The graphene-covered WSM photon tunneling probabilities variation is demonstrated in detail. This research enhances our comprehension of SPPs within the graphene/WSM heterostructure and suggests methods for actively controlling NFRHT.

cond-mat.mes-hall↗

arXiv2024

Poplar: Efficient Scaling of Distributed DNN Training on Heterogeneous GPU Clusters

Scaling Deep Neural Networks (DNNs) requires significant computational resources in terms of GPU quantity and compute capacity. In practice, there usually exists a large number of heterogeneous GPU devices due to the rapid release cycle of GPU products. It is highly needed to efficiently and economically harness the power of heterogeneous GPUs, so that it can meet the requirements of DNN research and development. The paper introduces Poplar, a distributed training system that extends Zero Redundancy Optimizer (ZeRO) with heterogeneous-aware capabilities. We explore a broader spectrum of GPU heterogeneity, including compute capability, memory capacity, quantity and a combination of them. In order to achieve high computational efficiency across all heterogeneous conditions, Poplar conducts fine-grained measurements of GPUs in each ZeRO stage. We propose a novel batch allocation method and a search algorithm to optimize the utilization of heterogeneous GPUs clusters. Furthermore, Poplar implements fully automated parallelism, eliminating the need for deploying heterogeneous hardware and finding suitable batch size. Extensive experiments on three heterogeneous clusters, comprising six different types of GPUs, demonstrate that Poplar achieves a training throughput improvement of 1.02-3.92x over current state-of-the-art heterogeneous training systems.

cs.DC↗

arXiv2024

Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference

Remote photoplethysmography (rPPG) has gained significant attention in recent years for its ability to extract physiological signals from facial videos. While existing rPPG measurement methods have shown satisfactory performance in intra-dataset and cross-dataset scenarios, they often overlook the incremental learning scenario, where training data is presented sequentially, resulting in the issue of catastrophic forgetting. Meanwhile, most existing class incremental learning approaches are unsuitable for rPPG measurement. In this paper, we present a novel method named ADDP to tackle continual learning for rPPG measurement. We first employ adapter to efficiently finetune the model on new tasks. Then we design domain prototypes that are more applicable to rPPG signal regression than commonly used class prototypes. Based on these prototypes, we propose a feature augmentation strategy to consolidate the past knowledge and an inference simplification strategy to convert potentially forgotten tasks into familiar ones for the model. To evaluate ADDP and enable fair comparisons, we create the first continual learning protocol for rPPG measurement. Comprehensive experiments demonstrate the effectiveness of our method for rPPG continual learning. Source code is available at \url{https://github.com/MayYoY/rPPGDIL}

cs.CV↗

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

Uncovering the origin of interface stress enhancement and compressive-to-tensile stress transition in immiscible nanomultilayers

The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a chemically-accurate neural network potential reveal the role of interfacial intermixing and metastable phase formation on the interface stress levels. These results rationalize an experimentally-reported compressive- to-tensile transition as a function of NML deposition conditions and the extremely high interface stresses under some conditions.

cond-mat.mtrl-sci↗

arXiv2024

In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow

Recall initiator identification and assessment are the preliminary steps to prevent medical device recall. Conventional analysis tools are inappropriate for processing massive and multi-formatted data comprehensively and completely to meet the higher expectations of delicacy management with the increasing overall data volume and textual data format. This study presents a bigdata-analytics-based machine learning-natural language processing work tool to address the shortcomings in dealing efficiency and data process versatility of conventional tools in the practical context of big data volume and muti data format. This study identified, assessed and analysed the medical device recall initiators according to the public medical device recall database from 2018 to 2024 with the ML-NLP tool. The results suggest that the unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm can present each single recall initiator in a specific manner, therefore helping practitioners to identify the recall reasons comprehensively and completely within a short time frame. This is then followed by text similarity-based textual classification to assist practitioners in controlling the group size of recall initiators and provide managerial insights from the operational to the tactical and strategical levels. This ML-NLP work tool can not only capture specific details of each recall initiator but also interpret the inner connection of each existing initiator and can be implemented for risk identification and assessment in the forward SC. Finally, this paper suggests some concluding remarks and presents future works. More proactive practices and control solutions for medical device recalls are expected in the future.

cs.CL↗

arXiv2024

PALM: A Efficient Performance Simulator for Tiled Accelerators with Large-scale Model Training

Deep learning (DL) models are piquing high interest and scaling at an unprecedented rate. To this end, a handful of tiled accelerators have been proposed to support such large-scale training tasks. However, these accelerators often incorporate numerous cores or tiles even extending to wafer-scale, substantial on-chip bandwidth, and distributed memory systems. This results in an exceedingly complex design space. Moreover, conducting actual training experiments to find optimal configurations is impractical due to time constraints. Hence, predicting the optimal mapping of various parallelisms to such tiled system architectures becomes crucial. In this study, leveraging an analysis of existing mainstream DL model training strategies, we introduce a performance simulator named PALM. PALM targets both the training and inference processes for tiled accelerators, aiming to inspire the design of current and future accelerators. Specifically, (i) we establish a scheduling mechanism among tiled accelerators based on an event-driven framework; (ii) we support user-configurable pipeline, tensor, and data parallelism on tiled accelerators, determining the absolute performance throughput under these parallelism strategies; (iii) we model the interaction of on-chip SRAM, NoC, and off-chip DRAM during operator execution. This work is available here: https://github.com/fangjh21/PALM.

cs.DC↗

arXiv2024

Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity

Robust Markov Decision Processes (MDPs) and risk-sensitive MDPs are both powerful tools for making decisions in the presence of uncertainties. Previous efforts have aimed to establish their connections, revealing equivalences in specific formulations. This paper introduces a new formulation for risk-sensitive MDPs, which assesses risk in a slightly different manner compared to the classical Markov risk measure (Ruszczyński 2010), and establishes its equivalence with a class of soft robust MDP (RMDP) problems, including the standard RMDP as a special case. Leveraging this equivalence, we further derive the policy gradient theorem for both problems, proving gradient domination and global convergence of the exact policy gradient method under the tabular setting with direct parameterization. This forms a sharp contrast to the Markov risk measure, known to be potentially non-gradient-dominant (Huang et al. 2021). We also propose a sample-based offline learning algorithm, namely the robust fitted-Z iteration (RFZI), for a specific soft RMDP problem with a KL-divergence regularization term (or equivalently the risk-sensitive MDP with an entropy risk measure). We showcase its streamlined design and less stringent assumptions due to the equivalence and analyze its sample complexity

math.OC↗

arXiv2024

Atomistic-informed phase field modeling of magnesium twin growth by disconnections

The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. Alternatively, mesoscale modeling approaches (such as the phase field method) address these constraints of atomistic simulations and can maintain atomic-level accuracy when integrated with atomic-level information. In this work, a phase field model is used to simulate the disconnection-mediated twinning, informed by molecular dynamics (MD) simulations. This work considers the specific case of the growth of $\{10\bar{1}2\}$ twin in magnesium. MD simulations are first conducted to obtain the orientation-dependent interface mobility and motion threshold, and to simulate twin embryo growth and collect facet velocities, which can be used for calibrating the continuum model. The phase field disconnections model, based on the principle of minimum dissipation potential, provides the theoretical framework. This model incorporates a nonconvex grain boundary energy, elasticity and shear coupling, and simulates disconnections as a natural emergence under the elastic driving force. The phase field model is further optimized by including the anisotropic interface mobility and motion threshold suggested by MD simulations. Results agree with MD simulations of twin embryo growth in the aspects of final twin thickness, twin shape, and twin size, as well as the kinetic behavior of twin boundaries and twin tips. The simulated twin microstructure is also consistent with experimental observations, demonstrating the fidelity of the model.

cond-mat.mtrl-sci↗

arXiv2024

Efficient Orchestrated AI Workflows Execution on Scale-out Spatial Architecture

Given the increasing complexity of AI applications, traditional spatial architectures frequently fall short. Our analysis identifies a pattern of interconnected, multi-faceted tasks encompassing both AI and general computational processes. In response, we have conceptualized "Orchestrated AI Workflows," an approach that integrates various tasks with logic-driven decisions into dynamic, sophisticated workflows. Specifically, we find that the intrinsic Dual Dynamicity of Orchestrated AI Workflows, namely dynamic execution times and frequencies of Task Blocks, can be effectively represented using the Orchestrated Workflow Graph. Furthermore, the intrinsic Dual Dynamicity poses challenges to existing spatial architecture, namely Indiscriminate Resource Allocation, Reactive Load Rebalancing, and Contagious PEA Idleness. To overcome these challenges, we present Octopus, a scale-out spatial architecture and a suite of advanced scheduling strategies optimized for executing Orchestrated AI Workflows, such as the Discriminate Dual-Scheduling Mechanism, Adaptive TBU Scheduling Strategy, and Proactive Cluster Scheduling Strategy. Our evaluations demonstrate that Octopus significantly outperforms traditional architectures in handling the dynamic demands of Orchestrated AI Workflows, and possesses robust scalability in large scale hardware such as wafer-scale chip.

cs.AI↗

arXiv2024

NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks

Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Mainstream modern SAT solvers are based on the Conflict-Driven Clause Learning (CDCL) algorithm. Recent work aimed to enhance CDCL SAT solvers using Graph Neural Networks (GNNs). However, so far this approach either has not made solving more effective, or required substantial GPU resources for frequent online model inferences. Aiming to make GNN improvements practical, this paper proposes an approach called NeuroBack, which builds on two insights: (1) predicting phases (i.e., values) of variables appearing in the majority (or even all) of the satisfying assignments are essential for CDCL SAT solving, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts. Once trained, the offline model inference allows NeuroBack to execute exclusively on the CPU, removing its reliance on GPU resources. To train NeuroBack, a new dataset called DataBack containing 120,286 data samples is created. NeuroBack is implemented as an enhancement to a state-of-the-art SAT solver called Kissat. As a result, it allowed Kissat to solve up to 5.2% and 7.4% more problems on two recent SAT competition problem sets, SATCOMP-2022 and SATCOMP-2023, respectively. NeuroBack therefore shows how machine learning can be harnessed to improve SAT solving in an effective and practical manner.

cs.AI↗

arXiv2024

IFNet: Deep Imaging and Focusing for Handheld SAR with Millimeter-wave Signals

Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve robust imaging and focusing for handheld mmWave systems. We first formulate the handheld imaging model by integrating multiple priors about mmWave images and handheld phase errors. Furthermore, we transform the optimization processes into an iterative network structure for improved and efficient imaging performance. Extensive experiments demonstrate that IFNet effectively compensates for handheld phase errors and recovers high-fidelity images from severely distorted signals. In comparison with existing methods, IFNet can achieve at least 11.89 dB improvement in average peak signal-to-noise ratio (PSNR) and 64.91% improvement in average structural similarity index measure (SSIM) on a real-world dataset.

cs.CV↗

arXiv2024

Modeling Hierarchical Structural Distance for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to estimate a transferable model for unlabeled target domains by exploiting labeled source data. Optimal Transport (OT) based methods have recently been proven to be a promising solution for UDA with a solid theoretical foundation and competitive performance. However, most of these methods solely focus on domain-level OT alignment by leveraging the geometry of domains for domain-invariant features based on the global embeddings of images. However, global representations of images may destroy image structure, leading to the loss of local details that offer category-discriminative information. This study proposes an end-to-end Deep Hierarchical Optimal Transport method (DeepHOT), which aims to learn both domain-invariant and category-discriminative representations by mining hierarchical structural relations among domains. The main idea is to incorporate a domain-level OT and image-level OT into a unified OT framework, hierarchical optimal transport, to model the underlying geometry in both domain space and image space. In DeepHOT framework, an image-level OT serves as the ground distance metric for the domain-level OT, leading to the hierarchical structural distance. Compared with the ground distance of the conventional domain-level OT, the image-level OT captures structural associations among local regions of images that are beneficial to classification. In this way, DeepHOT, a unified OT framework, not only aligns domains by domain-level OT, but also enhances the discriminative power through image-level OT. Moreover, to overcome the limitation of high computational complexity, we propose a robust and efficient implementation of DeepHOT by approximating origin OT with sliced Wasserstein distance in image-level OT and accomplishing the mini-batch unbalanced domain-level OT.

cs.CV↗

arXiv2024

Efficient Duple Perturbation Robustness in Low-rank MDPs

The pursuit of robustness has recently been a popular topic in reinforcement learning (RL) research, yet the existing methods generally suffer from efficiency issues that obstruct their real-world implementation. In this paper, we introduce duple perturbation robustness, i.e. perturbation on both the feature and factor vectors for low-rank Markov decision processes (MDPs), via a novel characterization of $(ξ,η)$-ambiguity sets. The novel robust MDP formulation is compatible with the function representation view, and therefore, is naturally applicable to practical RL problems with large or even continuous state-action spaces. Meanwhile, it also gives rise to a provably efficient and practical algorithm with theoretical convergence rate guarantee. Examples are designed to justify the new robustness concept, and algorithmic efficiency is supported by both theoretical bounds and numerical simulations.

cs.LG↗