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IEEE International Conference on Robotics and Automation (ICRA)

作者： David Jin Sushrut Karmalkar Harry Zhang Luca Carlone Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science University of Wisconsin at Madison Madison WI USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set of objects at different poses (and possibly including points belonging to the background) and we want to simultaneously reconstruct how all objects moved between the two point clouds. This setup generalizes standard 3D registration where one wants to reconstruct a single pose, e.g., the motion of the sensor picturing a static scene. Moreover, it provides a mathematically grounded formulation for relevant robotics applications, e.g., where a depth sensor onboard a robot perceives a dynamic scene and has the goal of estimating its own motion (from the static portion of the scene) while simultaneously recovering the motion of all dynamic objects. We assume a correspondence-based setup where we have putative matches between the two point clouds and consider the practical case where these correspondences are plagued with outliers. We then propose a simple approach based on Expectation-Maximization (EM) and establish theoretical conditions under which the EM approach converges to the ground truth. We evaluate the approach in simulated and real datasets ranging from table-top scenes to self-driving scenarios and demonstrate its effectiveness when combined with state-of-the-art scene flow methods to establish dense correspondences.

关键词： Point cloud compression Three-dimensional displays Dynamics Robot sensing systems Distance measurement Standards

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Topology Optimization of Operational Amplifiers using a Performance-Aware Representation

Topology Optimization of Operational Amplifiers using a Perf...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Jinyi Shen Fan Yang Li Shang Changhao Yan Zhaori Bi Dian Zhou Xuan Zeng Microelectronics Department State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

The automatic synthesis of operational amplifiers (opamps) is in high demand to meet the diverse performance requirements of a wide range of analog circuit applications. However, existing opamp topology synthesis methods neglect circuit performance while generating circuit representations, resulting in suboptimal efficiency. To address this issue, this paper proposes a novel opamp topology optimization approach based on a performance-aware topology representation. Specifically, topology information is captured using a customized graph neural network (GNN), while performance information is in-corporated by training the GNN for performance prediction through supervised learning. By combining this performance-aware representation with the genetic algorithm, an efficient opamp topology optimization method is developed. Experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of both optimization efficiency and results.

关键词： Operational amplifiers Training Design automation Circuit optimization Network topology Supervised learning Directed graphs

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On the relationship between the activity at point of interests and road traffic

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Communications in Transportation Research 2023年第1期3卷 150-160页

作者： MátéKolat Tamás Tettamanti TamásBécsi Domokos Esztergár-Kiss Department of Control for Transportation and Vehicle Systems Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Lorand Research NetworkBudapest1053PestHungary Department of Transport Technology and Economics Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green ***,effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management *** contrast to classical traffic detection solutions,this study investigates the correlation between travelers'social activities and road *** s's primary goal is to investigate the presence of the relationship between social activity and road traffic,which might allow an infrastructure-independent traffic monitoring technique as ***'s general activities at Point of Interest(POI)locations(measured as occupancy parameter)are correlated with traffic data so that,finally,proper proxys can be defined for link-level average traffic speed *** method is tested and evaluated using real-world traffic and POI occupancy data from Budapest(District XI.).The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic,which holds promise for future practical applicability.

关键词： Road traffic estimation Google popular times(GPT) Point of interest(POI) POI occupancy Genetic algorithm(GA)

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Cacomp: A Cloud-assisted Collaborative Deep Learning Compiler Framework for DNN Tasks on Edge

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IEEE Transactions on computers 2025年

作者： Lin, Weiwei Lin, Jinhui Zhang, Haotong Wu, Wentai Wu, Weizheng Li, Zhetao Li, Keqin South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Pengcheng Laboratory Shenzhen518066 China South China University of Technology School of Software Engineering Guangzhou510641 China Jinan University Department of Computer Science College of Information Science and Technology Guangzhou510632 China Jinan University College of Information Science and Technology Guangzhou510632 China State University of New York Department of Computer Science New PaltzNY12561 United States

With the development of edge computing, DNN services have been widely deployed on edge devices. The deployment efficiency of deep learning models relies on the optimization of inference and scheduling policy. However, traditional optimization methods on edge devices still suffer from prohibitively long tuning time due to devices’ low computational power. Meanwhile, the widely used scheduling algorithm, the dominant resource fairness algorithm(DRF algorithm), struggles to maximize the efficiency of model execution on edge devices and inevitably increases average waiting time as it is not applicable in the real-time distributed computing environment. In this paper, we propose Cacomp, a distributed cloud-assisted deep learning compiler framework that features accelerating the optimization on edge devices with assistance from the cloud and a novel inference task scheduling algorithm. Our framework utilizes the tuning records from the cloud devices and proposes a two-step distillation strategy to obtain the best tuning record set for the edge device. For the scheduling process, we propose an RD-DRF algorithm to allocate inference tasks to edge devices based on dominant resource matching in real time. Extensive results show that our framework can achieve up to 2.19× improvement in the optimization time compared with other methods on edge devices. Our proposed scheduling algorithm significantly shortens the average waiting time of inference tasks by 30% and improves resource utilization by 20% on edge devices. © 1968-2012 IEEE.

关键词： Distributed cloud

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Constrained Policy Optimization for Stochastic Optimal Control under Nonstationary Uncertainties*

Constrained Policy Optimization for Stochastic Optimal Contr...

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American Control Conference (ACC)

作者： Sungho Shin François Pacaud Emil Contantinescu Mihai Anitescu Mathematics and Computer Science Division Argonne National Laboratory Lemont IL Department of Statistics University of Chicago Chicago IL

This article presents a constrained policy optimization approach for the optimal control of systems under nonstationary uncertainties. We introduce an assumption that we call Markov embeddability that allows us to cast the stochastic optimal control problem as a policy optimization problem over the augmented state space. Then, the infinite-dimensional policy optimization problem is approximated as a finite-dimensional nonlinear program by applying function approximation, deterministic sampling, and temporal truncation. The approximated problem is solved by using automatic differentiation and condensed-space interior-point methods. We formulate several conceptual and practical open questions regarding the asymptotic exactness of the approximation and the solution strategies for the approximated problem. As proof of concept, we present numerical examples demonstrating the performance of the proposed method.

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Small Models are LLM Knowledge Triggers on Medical Tabular Prediction

arXiv

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arXiv 2024年

作者： Yan, Jiahuan Chen, Jintai Hu, Chaowen Zheng, Bo Hu, Yaojun Sun, Jimeng Wu, Jian College of Computer Science and Technology Zhejiang University China China Computer Science Department University of Illinois Urbana-Champaign United States The Second Affiliated Hospital Zhejiang University School of Medicine China Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence China

Recent development in large language models (LLMs) has demonstrated impressive domain proficiency on unstructured textual or multi-modal tasks. However, despite with intrinsic world knowledge, their application on structured tabular data prediction still lags behind, primarily due to the numerical insensitivity and modality discrepancy that brings a gap between LLM reasoning and statistical tabular learning. Unlike textual or vision data (e.g., electronic clinical notes or medical imaging data), tabular data is often presented in heterogeneous numerical values (e.g., CBC reports). This ubiquitous data format requires intensive expert annotation, and its numerical nature limits LLMs’ capability to effectively transfer untapped domain expertise. In this paper, we propose SERSAL, a general self-prompting method by synergy learning with small models to enhance LLM tabular prediction in an unsupervised manner. Specifically, SERSAL utilizes the LLM’s prior outcomes as original soft noisy annotations, which are dynamically leveraged to teach a better small student model. Reversely, the outcomes from the trained small model are used to teach the LLM to further refine its real capability. This process can be repeatedly applied to gradually distill refined knowledge for continuous progress. Comprehensive experiments on widely used medical domain tabular datasets show that, without access to gold labels, applying SERSAL to OpenAI GPT reasoning process attains substantial improvement compared to linguistic prompting methods, which serves as an orthogonal direction for tabular LLM, and increasing prompting bonus is observed as more powerful LLMs appear. Codes are available at https://***/jyansir/sersal. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Modeling and Control of Intrinsically Elasticity Coupled Soft-Rigid Robots

Modeling and Control of Intrinsically Elasticity Coupled Sof...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zach J. Patterson Cosimo Della Santina Daniela Rus Computer Science and Artificial Intelligence Laboratory MIT Department of Cognitive Robotics Delft University of Technology Institute of Robotics and Mechatronics German Aerospace Center (DLR)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure. While these systems have been prevalent in the literature, there is an increasing trend toward designing soft-rigid hybrids with intrinsically coupled elasticity between various degrees of freedom. In this work, we seek to address the issues of modeling and controlling such structures, particularly when underactuated. We introduce several simple models for elastic coupling, typical of those seen in these systems. We then propose a controller that compensates for the elasticity, and we prove its stability with Lyapunov methods without relying on the elastic dominance assumption. This controller is applicable to the general class of underactuated soft robots. After evaluating the controller in simulated cases, we then develop a simple hardware platform to evaluate both the models and the controller. Finally, using the hardware, we demonstrate a novel use case for underactuated, elastically coupled systems in "sensorless" force control.

关键词： Couplings Elasticity Soft robotics Robot sensing systems Market research Hardware Stability analysis

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Multi-rater Prompting for Ambiguous Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wang, Jinhong Cheng, Yi Chen, Jintai Xu, Hongxia Chen, Danny Wu, Jian College of Computer Science and Technology Zhejiang University China Institute of Wenzhou Zhejiang University China Computer Science Department University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering University of Notre Dame United States State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China School of Public Health Zhejiang University China

Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for retraining the entire model on a different dataset domain. We propose a multi-rater prompt-based approach to address these two challenges altogether. Specifically, we introduce a series of rater-aware prompts that can be plugged into the U-Net model for uncertainty estimation to handle multi-annotation cases. During the prompt-based fine-tuning process, only 0.3% of learnable parameters are required to be updated comparing to training the entire model. Further, in order to integrate expert consensus and disagreement, we explore different multi-rater incorporation strategies and design a mix-training strategy for comprehensive insight learning. Extensive experiments verify the effectiveness of our new approach for ambiguous medical image segmentation on two public datasets while alleviating the heavy burden of model re-training. Code will be made available. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Boosting Adversarial Robustness Distillation Via Hybrid Decomposed Knowledge

Boosting Adversarial Robustness Distillation Via Hybrid Deco...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yulun Wu Mingrui Lao Yanming Guo Dongmei Chen Tianyuan Yua Laboratory of Big Data and Decision National University of Defense Technology Changsha China Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

Adversarial Robust Distillation (ARD) has emerged as a potent defense mechanism tailored to small models against adversarial threats. However, mainstream ARD methods typically exploit teachers’ response as the transferred knowledge, while neglecting the analysis of involved target-related knowledge to mitigate adversarial attacks. Furthermore, these methods primarily focus on logits-level distillation, which overlook the features-level knowledge in teacher models. In this paper, we introduce a novel Hybrid Decomposed Distillation (HDD) approach, which attempts to identify the vital knowledge against adversarial threats through dual-level distillation. Specifically, we first seek to separate the predictions of teacher model into target-related and target-unrelated knowledge for flexible yet efficient logits-level distillation. Besides, to further boost the distillation efficacy, HDD leverages the channel correlations to decompose intermediate features into highly and less relevant components. Extensive experiments on two benchmarks demonstrate that our HDD achieves superior performance in both clean accuracy and robustness, in contrast to current state-of-the-art methods.

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Developing an efficient predictive model based on ML and DL approaches to detect diabetes

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Informatica (Slovenia) 2021年第3期45卷 433-440页

作者： Gadri, Said Laboratory of Informatics and its Applications of M’sila LIAM Department of Computer Science Faculty of Mathematics and Informatics University Mohamed Boudiaf of M’sila M’sila28000 Algeria

During the last decade, some important progress in machine learning ML area has been made, especially with the apparition of a new subfield called deep learning DL and CNN networks (Convolutional Neural Networks). This new tendency is used to perform much more sophisticated algorithms allowing high performance in many disciplines such as;pattern recognition, image classification, computer vision, as well as other supervised and unsupervised classification tasks. In this work, we have developed an automatic classifier that permits the classification of a large number of diabetic patients based on some blood characteristics by using ML and DL approaches. Initially, we have proceeded to the classification task using many ML algorithms. Then we proposed a simple DNN model composed of many layers. Finally, we established a comparison between ML and DL algorithms, as well as our model with other existing models. For the programming task, we have used Python, Tensorflow, and Keras which are the most used in the field. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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