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arXiv 2023年

作者： Haddadin, Zaid Khan, Shahrose Poulikakos, Lisa V. Department of Electrical & Computer Engineering UC San Diego United States Department of Mechanical & Aerospace Engineering UC San Diego United States Materials Science & Engineering Program UC San Diego United States

Mie resonators as lattice resonant metasurfaces have the capability to produce structural colour. However, design criteria for these metasurfaces are still being investigated. In this work, we numerically examine how the two-dimensional nanostructure shape in a lattice array affects the colorimetric response of the metasurface under linearly polarised light excitation. First, the transformation from a square-shaped to rectangle-shaped nanostructure array resulted in polarisation-sensitive metasurfaces with colorimetric outputs bound along a line on the CIE 1931 2-degree Standard Observer colour space. The bounds of the colorimetry line were tuneable to any desired chromatic range. Second, the removal of the corners in square- or rectangle-shaped nanostructures to create t-shaped nanostructure arrays displayed a dampening effect on the high-order resonance. Finally, we analytically determined that the colour saturation could increase when moving from rectangle-shaped to t-shaped nanostructure arrays. From these results, we present two design guidelines for lattice resonant metasurfaces: (1) Constructing the nanostructure to support fundamental resonances at different wavelengths enables two-colour-bound movement when excited by successive angles of linearly polarised light;(2) Removing portions of the nanostructure that only support high-order resonances dampens these modes while maintaining support for fundamental resonances. These results present first-principles guidelines for engineering nanoparticles in lattice resonant metasurfaces, offering a new toolbox for polarised-light sensing and colorimetric applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Nanostructures

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Taking an (embodied) cue from community health: Designing dementia caregiver support technology to advance health equity 21

Taking an (embodied) cue from community health: Designing de...

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2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Guan, Connie Bouzida, Anya Oncy-Avila, Ramzy M. Computer Science and Engineering Uc San Diego San DiegoCA United States

ISBN: (纸本)9781450380966

Dementia afects >50 million worldwide, causing progressive cognitive and physical disabilities. Its caregiving burden falls largely onto informal caregivers, who experience their own health problems, and face tremendous stress with little support-all exacerbated during COVID-19. In this paper, we present a new caregiver support perspective, where the lenses of health equity and community health can shape future technology design. Through a 1.5 year long, in-depth research process with dementia community health workers, we learned how caregiving support technology can refect key concepts in dementia community health practice. This paper makes two contributions: 1)We propose employing embodied cueing, such as imitation or action mimicry, as a communication modality that can align technology with community caregiving approaches, promote agency in people with dementia, and relieve caregiver burden, and 2) We suggest new avenues for HCI research to advance health equity in the context of dementia technology design. © 2021 ACM.

关键词： Machine design

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Efficient Host Intrusion Detection using Hyperdimensional Computing

Efficient Host Intrusion Detection using Hyperdimensional Co...

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IEEE International Conference on Big Data

作者： Yujin Nam Rachel King Quinn Burke Minxuan Zhou Patrick McDaniel Tajana Rosing Dept. of Computer Science and Engineering UC San Diego USA Dept. of Computer Sciences University of Wisconsin-Madison Madison USA Computer Science Department Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs—graph representations of activity history on a system—to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS.

关键词： Accuracy Costs Intrusion detection Vectors Encoding Threat assessment Security History Hardware acceleration Engines

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FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs

FastStamp: Accelerating Neural Steganography and Digital Wat...

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IEEE International Conference on computer-Aided Design

作者： Shehzeen Hussain Nojan Sheybani Paarth Neekhara Xinqiao Zhang Javier Duarte Farinaz Koushanfar Department of Electrical and Computer Engineering UC San Diego Department of Computer Science and Engineering UC San Diego Department of Physics UC San Diego

Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are quickly replacing traditional hand-engineered pipelines. DNN based watermarking techniques have drastically improved the message capacity, imperceptibility and robustness of the embedded watermarks. However, this improvement comes at the cost of increased computational overhead of the watermark encoder neural network. In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware. We first propose a parameter efficient DNN model for embedding recoverable bit-strings in image pixels. Our proposed model can match the success metrics of prior state-of-the-art DNN based watermarking methods while being significantly faster and lighter in terms of memory footprint. We then design an FPGA based accelerator framework to further improve the model throughput and power consumption by leveraging data parallelism and customized computation paths. FastStamp allows embedding hardware signatures into images to establish media authenticity and ownership of digital media. Our best design achieves 68× faster inference as compared to GPU implementations of prior DNN based watermark encoder while consuming less power.

关键词： Steganography Computational modeling Pipelines Neural networks Watermarking Media Throughput

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A Grammar for the Representation of Unmanned Aerial Vehicles with 3D Topologies

arXiv

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arXiv 2023年

作者： Mallozzi, Piergiuseppe Sibai, Hussein Incer, Inigo Seshia, Sanjit A. Sangiovanni-Vincentelli, Alberto Electrical Engineering and Computer Sciences UC Berkeley United States Computer Science and Engineering Washington University in St. Louis United States

We propose a context-sensitive grammar for the systematic exploration of the design space of the topology of 3D robots, particularly unmanned aerial vehicles. It defines production rules for adding components to an incomplete design topology modeled over a 3D grid. The rules are local. The grammar is simple, yet capable of modeling most existing UAVs as well as novel ones. It can be easily generalized to other robotic platforms. It can be thought of as a building block for any design exploration and optimization algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Tight Lower Bounds on Non-Adaptive Group Testing Estimation

SSRN

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SSRN 2024年

作者： Bshouty, Nader H. Cheung, Tsun-Ming Harcos, Gergely Hatami, Hamed Ostuni, Anthony Department of Computer Science Technion School of Computer Science McGill University Canada Number Theory Division Alfréd Rényi Institute of Mathematics Hungary Department of Computer Science and Engineering UC San Diego United States

Efficiently counting or detecting defective items is a crucial task in various fields ranging from biological testing to quality control to streaming algorithms. The group testing estimation problem concerns estimating the number of defective elements d in a collection of n total within a given factor. We primarily consider the classical query model, in which a query reveals whether the selected group of elements contains a defective one. We show that any non-adaptive randomized algorithm that estimates the value of d within a constant factor requires Ω(log n) queries. This confirms that a known O(log n) upper bound by Bshouty (2019) is tight and resolves a conjecture by Damaschke and Sheikh Muhammad (2010). Additionally, we prove similar matching upper and lower bounds in the threshold query model. © 2024, The Authors. All rights reserved.

关键词： Defects

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Full-distance evasion of pedestrian detectors in the physical world 24

Full-distance evasion of pedestrian detectors in the physica...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhi Cheng Zhanhao Hu Yuqiu Liu Jianmin Li Hang Su Xiaolin Hu Department of Computer Science and Technology Tsinghua University Beijing China Department of Electrical Engineering and Computer Sciences UC Berkeley Department of Technology Beijing Forestry University Beijing China

ISBN: (纸本)9798331314385

Many studies have proposed attack methods to generate adversarial patterns for evading pedestrian detection, alarming the computer vision community about the need for more attention to the robustness of detectors. However, adversarial pat terns optimized by these methods commonly have limited performance at medium to long distances in the physical world. To overcome this limitation, we identify two main challenges. First, in existing methods, there is commonly an appearance gap between simulated distant adversarial patterns and their physical world coun terparts, leading to incorrect optimization. Second, there exists a conflict between adversarial losses at different distances, which causes difficulties in optimization. To overcome these challenges, we introduce a Full Distance Attack (FDA) method. Our physical world experiments demonstrate the effectiveness of our FDA patterns across various detection models like YOLOv5, Deformable-DETR, and Mask RCNN. Codes available at https://***/zhicheng2T0/***

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A Generalized Acquisition Function for Preference-based Reward Learning

A Generalized Acquisition Function for Preference-based Rewa...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Evan Ellis Gaurav R. Ghosal Stuart J. Russell Anca Dragan Erdem Bıyık Department of Electrical Engineering and Computer Sciences UC Berkeley Machine Learning Department Carnegie Mellon University Thomas Lord Department of Computer Science University of Southern California

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that actively synthesizing preference queries to maximize information gain about the reward function parameters improves data efficiency. The information gain criterion focuses on precisely identifying all parameters of the reward function. This can potentially be wasteful as many parameters may result in the same reward, and many rewards may result in the same behavior in the downstream tasks. Instead, we show that it is possible to optimize for learning the reward function up to a behavioral equivalence class, such as inducing the same ranking over behaviors, distribution over choices, or other related definitions of what makes two rewards similar. We introduce a tractable framework that can capture such definitions of similarity. Our experiments in a synthetic environment, an assistive robotics environment with domain transfer, and a natural language processing problem with real datasets demonstrate the superior performance of our querying method over the state-of-the-art information gain method.

关键词： Measurement Learning systems Autonomous systems Education Artificial neural networks Natural language processing Bayes methods

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Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks

arXiv

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arXiv 2022年

作者： Yu, Chenning Gao, Sicun Computer Science and Engineering UC San Diego United States

Sampling-based motion planning is a popular approach in robotics for finding paths in continuous configuration spaces. Checking collision with obstacles is the major computational bottleneck in this process. We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perform path exploration and path smoothing. Given random geometric graphs (RGGs) generated from batch sampling, the path exploration component iteratively predicts collision-free edges to prioritize their exploration. The path smoothing component then optimizes paths obtained from the exploration stage. The methods benefit from the ability of GNNs of capturing geometric patterns from RGGs through batch sampling and generalize better to unseen environments. Experimental results show that the learned components can significantly reduce collision checking and improve overall planning efficiency in challenging high-dimensional motion planning tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Mitigating over-exploration in latent space optimization using LES

arXiv

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arXiv 2024年

作者： Ronen, Omer Humayun, Ahmed Imtiaz Baraniuk, Richard Balestriero, Randall Yu, Bin Department of Statistics UC Berkeley United States Department of Electrical and Computer Engineering Rice University United States Department of Computer Science Brown University United States

We develop Latent Exploration Score (LES) to mitigate over-exploration in Latent Space Optimization (LSO), a popular method for solving black-box discrete optimization problems. LSO utilizes continuous optimization within the latent space of a Variational Autoencoder (VAE) and is known to be susceptible to over-exploration, which manifests in unrealistic solutions that reduce its practicality. LES leverages the trained decoder’s approximation of the data distribution, and can be employed with any VAE decoder–including pretrained ones–without additional training, architectural changes or access to the training data. Our evaluation across five LSO benchmark tasks and twenty-two VAE models demonstrates that LES always enhances the quality of the solutions while maintaining high objective values, leading to improvements over existing solutions in most cases. We believe that new avenues to LSO will be opened by LES’ ability to identify out of distribution areas, differentiability, and computational tractability. Open source code for LES is available at https://***/OmerRonen/les. © 2024, CC BY.

关键词： Decoding

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