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International Conference on Field Programmable Logic and Applications

作者： Jonas Krautter Paul R. Genssler Gloria Sepanta Hussam Amrouch Mehdi Tahoori Institute of Computer Engineering Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Chair of Semiconductor Test and Reliability (STAR) University of Stuttgart Stuttgart Germany Chair of AI Processor Design Technical University of Munich (TUM) Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany

FPGAs have become a popular choice for machine learning acceleration for both cloud and edge devices. While traditional neural networks show impressive performance in classification tasks, Hyperdimensional Computing (HDC) is rapidly emerging as a promising novel machine learning approach for its hardware-friendly inference. In HDC, classes are embedded into high-dimensional vectors during training, and inputs can be classified by computing similarity metrics between class-vectors during inference. HDC inference is especially promoted in terms of its resiliency against errors, attributed to the large inherent redundancy. In this work, we perform a thorough experimental investigation of the fault resiliency of various FPGA-based machine learning implementations under different aspects of stress, comparing HDC with classical neural network approaches. We explore both the amount of faulty classifications as well as system crashes while subjecting the designs to timing stress using overclocking, voltage stress with excessive switching activity, and thermal stress.

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OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

TechRxiv

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TechRxiv 2023年

作者： Babaians, Edwin Gorla, Praveen Ayvaşık, Serkut Plachý, Jan Becvar, Zdenek Kellerer, Wolfgang Steinbach, Eckehard School of Computation Information and Technology Technical University of Munich Germany School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Dept. of Telecommunication Engineering Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities – Prague and Munich. We focus on the robot’s ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system’s holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial. © 2023, CC BY-NC-SA.

关键词： 5G mobile communication systems

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Generating Realistic VRU-AV Scenarios via Social Force-Based Gradient Optimization

Generating Realistic VRU-AV Scenarios via Social Force-Based...

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International Conference on Intelligent Transportation

作者： Tingting Long Mengru Zong Chi Zhang Yuehu Liu Shuangxun Ma Li Li National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China School of Electrical Engineering Xi’ an Jiaotong University Xi'an Shaanxi China School of Future Transportation Changan University Xi'an China Department of Automation BNRist Tsinghua University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Validating safe and reliable interaction with vulnerable road users (VRUs) is imperative for autonomous vehicles, especially in simulation testing procedures before on-road applications. However, existing VRU agents are either with predefined routes or with collision-oriented behaviors, which limits the fidelity and diversity of scenarios. Bridging the gap between the simulated world and reality requires realistic VRU behaviors. In this paper, we apply social force to capture natural human behavior, considering how individuals are influenced by their surroundings and adhere to real-world rules. To this end, we propose HybridSFM, an improved social force model designed for characterizing human-vehicle interacting behavior. Specifically, the steering and acceleration of the VRUs are parameterized and integrated with social norms to forecast trajectories. Based on this, we further implement a gradient optimization framework for generating VRU-AV safety-critical scenarios. Firstly, VRUs are positioned randomly around the ego-agent. Differentiable simulation updates states and unrolls trajectories while backpropagation optimizes the actions of VRUs. The modified scenario is further optimized in the subsequent time step until a collision happens. The experiments demonstrate that our approach can enhance realistic safety-critical scenarios at intersection scenarios, facilitating the testing of interaction capabilities for autonomous vehicles.

关键词： Pedestrians Roads Force Trajectory Reliability Scenario generation Optimization Autonomous vehicles Intelligent transportation systems Testing

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Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients

Continuous Risk Estimation of Acute Kidney Failure with Dens...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kai Wu Ee Heng Chen Felix Wirth Keti Vitanova Rüdiger Lange Darius Burschka German Heart Center Munich Munich Germany Department of Computer Engineering Machine Vision and Perception Group TUM School of Computation Information and Technology Technical University of Munich Garching Germany MIRMI - Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

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Revolutionizing Tuberculosis Treatment: Smart Technology Innovations for Mycobacterium tuberculosis Management

Revolutionizing Tuberculosis Treatment: Smart Technology Inn...

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Smart Devices (ICSD), International Conference on

作者： Atreyi Pramanik Ajay Singh Gouri Rani Shival Dubey Srinivas Aluvala School of Applied and Life Sciences Division of Research and Innovation Uttaranchal University Dehradun India School of Applied and Life Sciences Uttaranchal University Dehradun India Uttaranchal University Institute of Design Optimization and Robotics School of Mechanical Engineering University of Leeds United Kingdom School of Computer Science & Artificial Intelligence SR University

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Tuberculosis remains a significant global problem. The modification in smart technology (ST) invention generates novel chances to reform tuberculosis (TB) controlling. This work explores the potential of ST for filtering MTB diagnostics and therapy. ST includes telemedicine, mobile health uses (mHealth), wearable devices (WD) and data analytical that deliver many options of improving TB control. Telemedicine licenses distant consulting thus empowering medics to access patients in remote areas, offer quick detects as well as recommend treatment. mHealth apps certify that individuals have right of entry to education materials, can remind them when it’s time for medication and offers self-monitoring tools which enhance adherence to the therapy by keeping the patient involved during the course of treatment. WD support real-time (RT) monitoring of heart rates, blood pressure level, physical activity and other vital signs that are important in coming up with modified remedy plans while also detecting response to cure earlier enough. Data analytics combined with AI can examine large amounts of patient data to identify patterns, forecast drug effects and optimize treatment strategies resulting in more efficient individualized care. These novelties through intellectual technologies may be used as solutions for tasks experienced during TB management, leading to improved patient outcomes and a reduction in the TB burden.

关键词： Technological innovation Tuberculosis Telemedicine Medical treatment Licenses Real-time systems Wearable devices Smart devices Medical diagnostic imaging Monitoring

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Improving AlphaFLOW for Efficient Protein Ensembles Generation

arXiv

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arXiv 2024年

作者： Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Shanghai Institute of Materia Medica Chinese Academy of Sciences China

Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFLOW stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFLOW still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFLOW-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFLOW-Lit performs on-par with AlphaFLOW and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFLOW-Lit in enabling faster and more scalable generation of protein ensembles. © 2024, CC BY.

关键词： Proteins

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Digital Twin-empowered Model-Mediated Teleoperation using Multimodality Data with Signed Distance Fields

Digital Twin-empowered Model-Mediated Teleoperation using Mu...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Mads Antonsen Siwen Liu Xiao Xu Eckehard Steinbach Francesco Chinello Qi Zhang DIGIT Group Department of Electrical and Computer Engineering Aarhus University Denmark Chair of Media Technology (LMT) and Munich Institute of Robotics and Machine Intelligence (MIRMI) School of Computation Information and Technology Technical University of Munich Germany Department of Business Development and Technology Aarhus University Denmark

In human-in-the-loop teleoperation systems, the Quality of Task Perception (QoTP) plays an important role, significantly influencing the overall Quality of Experience (QoE). QoTP characterizes the depth of understanding a user possesses regarding remote environments and tasks. To enhance this comprehension, this paper introduces an innovative geometric modeling approach employing Truncated Signed Distance Fields (TSDF) tailored for Model-Mediated Teleoperation (MMT) systems. Our approach combines haptic and visual cues to provide real-time updates to environmental changes. To ensure optimal responsiveness, we devise a model updating scheme that achieves an effective update frequency. Capitalizing on the capabilities of a Digital Twin, the MMT system presented not only uses the existing knowledge of the remote environment but also records its dynamic alterations. Through comprehensive experimentation evaluating both the geometric modeling and the teleoperation framework, our findings underscore the robustness and applicability of the introduced geometric modeling technique.

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NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products

arXiv

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arXiv 2025年

作者： Ding, Yuheng Wang, Yusong Qiang, Bo Yu, Jie Li, Qi Zhou, Yiran Liu, Zhenmin State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing100191 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA98105 United States

Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products research primarily rely on supervised learning approaches designed for specific downstream tasks. However, such one-model-for-a-task paradigm often lacks generalizability and leaves significant room for performance improvement. Additionally, existing molecular characterization methods are not well-suited for the unique tasks associated with natural products. To address these limitations, we have pre-trained a foundation model for natural products based on their unique properties. Our approach employs a novel pretraining strategy that is especially tailored to natural products. By incorporating contrastive learning and masked graph learning objectives, we emphasize evolutional information from molecular scaffolds while capturing side-chain information. Our framework achieves state-of-the-art (SOTA) results in various downstream tasks related to natural product mining and drug discovery. We first compare taxonomy classification with synthesized molecule-focused baselines to demonstrate that current models are inadequate for understanding natural synthesis. Furthermore, by diving into a fine-grained analysis at both the gene and microbial levels, NaFM demonstrates the ability to capture evolutionary information. Eventually, our method is experimented with virtual screening, illustrating informative natural product representations that can lead to more effective identification of potential drug candidates. © 2025, CC BY.

关键词： Metabolites

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GSA-Gaze: Generative Self-adversarial Learning for Domain Generalized Driver Gaze Estimation

GSA-Gaze: Generative Self-adversarial Learning for Domain Ge...

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International Conference on Intelligent Transportation

作者： Hongcheng Han Zhiqiang Tian Yuying Liu Shengpeng Li Dong Zhang Shaoyi Du National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Faculty of Electronic and Information Engineering School of Software Engineering Xi'an Jiaotong University Xi'an China Faculty of Electronic and Information Engineering School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China

Estimating driver gaze accurately is critical for the human-machine cooperative driving, but the significant facial appearance diversions caused by background, illumination, personal characteristics, etc. pose a challenge to the generalizability of gaze estimation models. In this paper, we propose the generative self-adversarial learning mechanism for generalized gaze estimation that aims to learn general gaze features while eliminating sample-specific features and preventing cross-domain feature over-fitting. Firstly, to reduce information redundancy, the feature encoder is designed based on pyramid-grouped convolution to extract a sparse feature representation from the facial appearance. Secondly, the gaze regression module supervises the model to learn as many gaze-relevant features as possible. Thirdly, the adversarial image reconstruction task prompts the model to eliminate the domain-specific features. The adversarial learning of the gaze regression and the image reconstruction tasks guides the model to learn only general gaze features across domains, preventing cross-domain feature over-fitting, enhancing the domain generalization capability. The results of cross-domain testing of four active gaze datasets prove the effectiveness of the proposed method. The code is available at https://***/HongchengHan/GSA-Gaze

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Modeling and Benchmarking 5nm Ferroelectric FinFET from Room Temperature down to Cryogenic Temperatures

Modeling and Benchmarking 5nm Ferroelectric FinFET from Room...

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IEEE Conference on Nanotechnology

作者： Shivendra Singh Parihar Swetaki Chatterjee Girish Pahwa Yogesh Singh Chauhan Hussam Amrouch Semiconductor Test and Reliability (STAR) University of Stuttgart Germany Department of Electrical Engineering Indian Institute of Technology Kanpur India Department of Electrical Engineering & Computer Sciences University of California Berkeley USA Munich Institute of Robotics and Machine Intelligence Munich Germany AI Processor Design Technical University of Munich (TUM) Germany

The rise in quantum-computing systems, space electronics, and superconducting processors requires compatible cryogenic memories. The stringent operating conditions for these applications put additional constraints on the endurance and reliable operation of such memories. Ferroelectric-Field Effect Transistors (FeFETs) based on ferroelectric properties of the Hafnium Zirconium Oxide (HZO) can be an excellent choice for these systems. This requires a thorough characterization of FeFET at deep cryogenic temperatures. Also, the scalability of the FeFET to lower technology nodes implies a lower area and reduced leakage. In this work, we, therefore, fully characterize the 5 nm node Fe-FinFET from 10 K to 400 K. To this end, the underlying 5 nm node FinFET transistor is calibrated with experimental data from cryogenic temperatures to above-room temperatures. The material parameters of the Ferroelectric layer are also calibrated with reported measurement data. We propose that the reported endurance improvement of the HZO layer at cryogenic temperatures can improve the reliability of the Fe-FinFET. The observed wake-up and fatigue at higher temperatures are also nonexistent at cryogenic temperatures. Although the memory window is reduced at cryogenic temperature compared to room temperature, we can still hold multiple states. This is also verified through our simulations. Lastly, we demonstrate the variability in high and low threshold voltage (V TH ) states due to extrinsic variation sources of the underlying transistor and ferroelectric material parameters. We observe a relatively lower variation at cryogenic temperature.

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