arXiv subjects

Yang Hu

Publications and source records attributed to Yang Hu.

At least 163 records · Page 9Linked to original sources

arXiv2022

Towards Efficient Architecture and Algorithms for Sensor Fusion

The safety of an automated vehicle hinges crucially upon the accuracy of perception and decision-making latency. Under these stringent requirements, future automated cars are usually equipped with multi-modal sensors such as cameras and LiDARs. The sensor fusion is adopted to provide a confident context of driving scenarios for better decision-making. A promising sensor fusion technique is middle fusion that combines the feature representations from intermediate layers that belong to different sensing modalities. However, achieving both the accuracy and latency efficiency is challenging for middle fusion, which is critical for driving automation applications. We present A3Fusion, a software-hardware system specialized for an adaptive, agile, and aligned fusion in driving automation. A3Fusion achieves a high efficiency for the middle fusion of multiple CNN-based modalities by proposing an adaptive multi-modal learning network architecture and a latency-aware, agile network architecture optimization algorithm that enhances semantic segmentation accuracy while taking the inference latency as a key trade-off. In addition, A3Fusion proposes a FPGA-based accelerator that captures unique data flow patterns of our middle fusion algorithm while reducing the overall compute overheads. We enable these contributions by co-designing the neural network, algorithm, and the accelerator architecture.

cs.DC↗

arXiv2022

Towards a High-performance and Secure Memory System and Architecture for Emerging Applications

In this dissertation, we propose a memory and computing coordinated methodology to thoroughly exploit the characteristics and capabilities of the GPU-based heterogeneous system to effectively optimize applications' performance and privacy. Specifically, 1) we propose a task-aware and dynamic memory management mechanism to co-optimize applications' latency and memory footprint, especially in multitasking scenarios. 2) We propose a novel latency-aware memory management framework that analyzes the application characteristics and hardware features to reduce applications' initialization latency and response time. 3) We develop a new model extraction attack that explores the vulnerability of the GPU unified memory system to accurately steal private DNN models. 4) We propose a CPU/GPU Co-Encryption mechanism that can defend against a timing-correlation attack in an integrated CPU/GPU platform to provide a secure execution environment for the edge applications. This dissertation aims at developing a high-performance and secure memory system and architecture in GPU heterogeneous platforms to deploy emerging AI-enabled applications efficiently and safely.

cs.CR↗

arXiv2022

Demystifying Arch-hints for Model Extraction: An Attack in Unified Memory System

The deep neural network (DNN) models are deemed confidential due to their unique value in expensive training efforts, privacy-sensitive training data, and proprietary network characteristics. Consequently, the model value raises incentive for adversary to steal the model for profits, such as the representative model extraction attack. Emerging attack can leverage timing-sensitive architecture-level events (i.e., Arch-hints) disclosed in hardware platforms to extract DNN model layer information accurately. In this paper, we take the first step to uncover the root cause of such Arch-hints and summarize the principles to identify them. We then apply these principles to emerging Unified Memory (UM) management system and identify three new Arch-hints caused by UM's unique data movement patterns. We then develop a new extraction attack, UMProbe. We also create the first DNN benchmark suite in UM and utilize the benchmark suite to evaluate UMProbe. Our evaluation shows that UMProbe can extract the layer sequence with an accuracy of 95% for almost all victim test models, which thus calls for more attention to the DNN security in UM system.

cs.CR↗

arXiv2022

Brief Industry Paper: The Necessity of Adaptive Data Fusion in Infrastructure-Augmented Autonomous Driving System

This paper is the first to provide a thorough system design overview along with the fusion methods selection criteria of a real-world cooperative autonomous driving system, named Infrastructure-Augmented Autonomous Driving or IAAD. We present an in-depth introduction of the IAAD hardware and software on both road-side and vehicle-side computing and communication platforms. We extensively characterize the IAAD system in the context of real-world deployment scenarios and observe that the network condition that fluctuates along the road is currently the main technical roadblock for cooperative autonomous driving. To address this challenge, we propose new fusion methods, dubbed "inter-frame fusion" and "planning fusion" to complement the current state-of-the-art "intra-frame fusion". We demonstrate that each fusion method has its own benefit and constraint.

cs.DC↗

arXiv2022

Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction

Link prediction is one important application of graph neural networks (GNNs). Most existing GNNs for link prediction are based on one-dimensional Weisfeiler-Lehman (1-WL) test. 1-WL-GNNs first compute node representations by iteratively passing neighboring node features to the center, and then obtain link representations by aggregating the pairwise node representations. As pointed out by previous works, this two-step procedure results in low discriminating power, as 1-WL-GNNs by nature learn node-level representations instead of link-level. In this paper, we study a completely different approach which can directly obtain node pair (link) representations based on \textit{two-dimensional Weisfeiler-Lehman (2-WL) tests}. 2-WL tests directly use links (2-tuples) as message passing units instead of nodes, and thus can directly obtain link representations. We theoretically analyze the expressive power of 2-WL tests to discriminate non-isomorphic links, and prove their superior link discriminating power than 1-WL. Based on different 2-WL variants, we propose a series of novel 2-WL-GNN models for link prediction. Experiments on a wide range of real-world datasets demonstrate their competitive performance to state-of-the-art baselines and superiority over plain 1-WL-GNNs.

cs.LG↗

arXiv2022

Actively tuning anisotropic light-matter interaction in biaxial hyperbolic material $α$-MoO$_3$ using phase change material VO$_2$ and graphene

Anisotropic hyperbolic phonon polaritons (PhPs) in natural biaxial hyperbolic material MoO$_3$ has opened up new avenues for mid-infrared nanophotonics, while active tunability of $α$-MoO$_3$ PhPs is still an urgent problem needing to be solved.In this study, we present a theoretical demonstration of actively tuning $α$-MoO$_3$ PhPs using phase change material VO$_2$ and graphene. It is observed that $α$-MoO$_3$ PhPs are greatly depending on the propagation plane angle of PhPs. The metal-to-insulator phase transition of VO$_2$ has a significant effect on the hybridization PhPs of the $α$-MoO$_3$/VO$_2$ structure and allows to obtain an actively tunable $α$-MoO$_3$ PhPs, which is especially obvious when the propagation plane angle of PhPs is 90.

physics.app-ph↗

arXiv2022

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

cs.CV↗

arXiv2022

Negative differential thermal conductance between Weyl semimetals nanoparticles through vacuum

In this work, the near-field radiative heat transfer (NFRHT) between two Weyl semimetal (WSM) nanoparticles (NPs) is investigated. The numerical results show that negative differential thermal conductance (NDTC) effect can be obtained in this system, i.e., when the temperature of the emitter is fixed, the heat flux does not decrease monotonically with the increase of the temperature of the receiver. Specifically, when the temperature of the emitter is 300 K, the heat flux is identical when the temperature of the receiver is 50 K or 280 K. The NDTC effect is attributed to the fact that the permittivity of the WSMs changes with the temperature. The coupling effects of polarizability of two WSM NPs have been further identified at different temperature to reveal the physical mechanism of the NDTC effect. In addition, the NFRHT between two Weyl WSM NPs can be greatly enhanced by exciting the localized plasmon and circular modes. This work indicates that the WSMs maybe promising candidate materials for manipulating NFRHT.

cond-mat.mes-hall↗

arXiv2022

Spontaneous emission modulation in biaxial hyperbolic van der Waals material

As a natural van der Waals crystal, α-MoO3 has excellent in-plane hyperbolic properties and essential nanophotonics applications. However, its actively tunable properties are generally neglected. In this work, we achieved active modulation of spontaneous emission from a single-layer flat plate using the rotation method for the first time. Numerical results and theoretical analysis show that α-MoO3 exhibits good tunability when rotated in the y-z or x-y plane. A modulation factor of more than three orders of magnitude can be obtained at 634 cm-1. However, when the rotation is in the x-z plane, the spontaneous emission of the material exhibits strong angle independence. The theoretical formulation and the physical mechanism analysis explain the above phenomenon well. In addition, for the semi-infinite α-MoO3 flat structure, we give the distribution of the modulation factor of spontaneous emission with wavenumber and rotation angle. Finally, we extended the calculation results from semi-infinite media to finite thickness films. We obtained the general evolution law of the peak angle of the modulation factor with thickness, increasing the modulation factor to about 2000. We believe that the results of this paper can guide the active modulation of spontaneous emission based on anisotropic materials.

physics.optics↗

arXiv2022

SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions

Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However, the huge combinatorial search space of candidate pipelines means that current AutoML techniques, generate sub-optimal pipelines, or none at all, especially on large, complex datasets. In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset. To combat the search space explosion of AutoML, SapientML employs a novel divide-and-conquer strategy realized as a three-stage program synthesis approach, that reasons on successively smaller search spaces. The first stage uses a machine-learned model to predict a set of plausible ML components to constitute a pipeline. In the second stage, this is then refined into a small pool of viable concrete pipelines using syntactic constraints derived from the corpus and the machine-learned model. Dynamically evaluating these few pipelines, in the third stage, provides the best solution. We instantiate SapientML as part of a fully automated tool-chain that creates a cleaned, labeled learning corpus by mining Kaggle, learns from it, and uses the learned models to then synthesize pipelines for new predictive tasks. We have created a training corpus of 1094 pipelines spanning 170 datasets, and evaluated SapientML on a set of 41 benchmark datasets, including 10 new, large, real-world datasets from Kaggle, and against 3 state-of-the-art AutoML tools and 2 baselines. Our evaluation shows that SapientML produces the best or comparable accuracy on 27 of the benchmarks while the second best tool fails to even produce a pipeline on 9 of the instances.

cs.LG↗

arXiv2022

Metastable complex vector bundles over complex projective spaces

We apply Weiss calculus to compute the number of topological complex vector bundles of rank $n-2$ with vanishing Chern classes over $\mathbb{C}P^n$ for $n>3$, as given by the list $1, 1, 12, 2, 1, 3, 2, 2, 3, 1, 4, 6, 1, 1, 6, 2, 1, 3, 4, 2, 3, 1, 2, 6$, where the $i$-th entry in this list is the number of such bundles whenever $n$ is congruent to $i$ modulo $24$, starting with $i = 0$. Similarly, the number of rank $n-1$ bundles with vanishing Chern classes over $\mathbb{C}P^n$ for $n>2$ is $2$ when $n$ is odd and $1$ when $n$ is even.

math.AT↗

arXiv2022

Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving

Autonomous driving demands accurate perception and safe decision-making. To achieve this, automated vehicles are now equipped with multiple sensors (e.g., camera, Lidar, etc.), enabling them to exploit complementary environmental context by fusing data from different sensing modalities. With the success of Deep Convolutional Neural Network(DCNN), the fusion between DCNNs has been proved as a promising strategy to achieve satisfactory perception accuracy. However, mainstream existing DCNN fusion schemes conduct fusion by directly element-wisely adding feature maps extracted from different modalities together at various stages, failing to consider whether the features being fused are matched or not. Therefore, we first propose a feature disparity metric to quantitatively measure the degree of feature disparity between the feature maps being fused. We then propose Fusion-filter as a feature-matching techniques to tackle the feature-mismatching issue. We also propose a Layer-sharing technique in the deep layer that can achieve better accuracy with less computational overhead. Together with the help of the feature disparity to be an additional loss, our proposed technologies enable DCNN to learn corresponding feature maps with similar characteristics and complementary visual context from different modalities to achieve better accuracy. Experimental results demonstrate that our proposed fusion technique can achieve better accuracy on KITTI dataset with less computational resources demand.

cs.CV↗

arXiv2022

On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory

Stabilizing an unknown dynamical system is one of the central problems in control theory. In this paper, we study the sample complexity of the learn-to-stabilize problem in Linear Time-Invariant (LTI) systems on a single trajectory. Current state-of-the-art approaches require a sample complexity linear in $n$, the state dimension, which incurs a state norm that blows up exponentially in $n$. We propose a novel algorithm based on spectral decomposition that only needs to learn "a small part" of the dynamical matrix acting on its unstable subspace. We show that, under proper assumptions, our algorithm stabilizes an LTI system on a single trajectory with $\tilde{O}(k)$ samples, where $k$ is the instability index of the system. This represents the first sub-linear sample complexity result for the stabilization of LTI systems under the regime when $k = o(n)$.

math.OC↗

arXiv2022

RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals

Human silhouette segmentation, which is originally defined in computer vision, has achieved promising results for understanding human activities. However, the physical limitation makes existing systems based on optical cameras suffer from severe performance degradation under low illumination, smoke, and/or opaque obstruction conditions. To overcome such limitations, in this paper, we propose to utilize the radio signals, which can traverse obstacles and are unaffected by the lighting conditions to achieve silhouette segmentation. The proposed RFMask framework is composed of three modules. It first transforms RF signals captured by millimeter wave radar on two planes into spatial domain and suppress interference with the signal processing module. Then, it locates human reflections on RF frames and extract features from surrounding signals with human detection module. Finally, the extracted features from RF frames are aggregated with an attention based mask generation module. To verify our proposed framework, we collect a dataset containing 804,760 radio frames and 402,380 camera frames with human activities under various scenes. Experimental results show that the proposed framework can achieve impressive human silhouette segmentation even under the challenging scenarios(such as low light and occlusion scenarios) where traditional optical-camera-based methods fail. To the best of our knowledge, this is the first investigation towards segmenting human silhouette based on millimeter wave signals. We hope that our work can serve as a baseline and inspire further research that perform vision tasks with radio signals. The dataset and codes will be made in public.

cs.CV↗

arXiv2021

Radio-Assisted Human Detection

In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based onestage detectors and two-stage detectors. We extract the radio localization and identifer information from the radio signals to assist the human detection, due to which the problem of false positives and false negatives can be greatly alleviated. For both detectors, we use the confidence score revision based on the radio localization to improve the detection performance. For two-stage detection methods, we propose to utilize the region proposals generated from radio localization rather than relying on region proposal network (RPN). Moreover, with the radio identifier information, a non-max suppression method with the radio localization constraint has also been proposed to further suppress the false detections and reduce miss detections. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate of the state-of-the-art detection methods can be improved with the aid of radio information. Finally, we conduct experiments in real-world scenarios to demonstrate the feasibility of our proposed method in practice.

cs.CV↗

arXiv2021

Learning Token-based Representation for Image Retrieval

In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract deep local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.

eess.IV↗

arXiv2021

RFGAN: RF-Based Human Synthesis

This paper demonstrates human synthesis based on the Radio Frequency (RF) signals, which leverages the fact that RF signals can record human movements with the signal reflections off the human body. Different from existing RF sensing works that can only perceive humans roughly, this paper aims to generate fine-grained optical human images by introducing a novel cross-modal RFGAN model. Specifically, we first build a radio system equipped with horizontal and vertical antenna arrays to transceive RF signals. Since the reflected RF signals are processed as obscure signal projection heatmaps on the horizontal and vertical planes, we design a RF-Extractor with RNN in RFGAN for RF heatmap encoding and combining to obtain the human activity information. Then we inject the information extracted by the RF-Extractor and RNN as the condition into GAN using the proposed RF-based adaptive normalizations. Finally, we train the whole model in an end-to-end manner. To evaluate our proposed model, we create two cross-modal datasets (RF-Walk & RF-Activity) that contain thousands of optical human activity frames and corresponding RF signals. Experimental results show that the RFGAN can generate target human activity frames using RF signals. To the best of our knowledge, this is the first work to generate optical images based on RF signals.

cs.MM↗

arXiv2021

Containerized Distributed Value-Based Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning tasks put a high demand on the volume of training samples. Different from its single-agent counterpart, distributed value-based multi-agent reinforcement learning faces the unique challenges of demanding data transfer, inter-process communication management, and high requirement of exploration. We propose a containerized learning framework to solve these problems. We pack several environment instances, a local learner and buffer, and a carefully designed multi-queue manager which avoids blocking into a container. Local policies of each container are encouraged to be as diverse as possible, and only trajectories with highest priority are sent to a global learner. In this way, we achieve a scalable, time-efficient, and diverse distributed MARL learning framework with high system throughput. To own knowledge, our method is the first to solve the challenging Google Research Football full game $5\_v\_5$. On the StarCraft II micromanagement benchmark, our method gets $4$-$18\times$ better results compared to state-of-the-art non-distributed MARL algorithms.

cs.LG↗