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SEOE:an option graph based semantically embedding method for prenatal depression detection

Frontiers of computer Science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Supremal and Robust Control against Actuator Jamming Attacks in Cyber-Physical Systems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-13页

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu You, Dan Wisniewski, Remigiusz Bazydlo, Grzegorz Albeshri, Aiiad Institute of Systems Engineering Macau University of Science and Technology Macau China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder injects a jamming signal into an actuator channel. It disrupts the transmission of control commands and prevents an actuator from receiving them. This is termed an actuator jamming attack. In the paper, we first analyze the closed-loop system behavior under such an attack. An attack structure is constructed to illustrate how an intruder exploits a jamming attack to drive a system into unsafe states. Then, we study the supervisory control problem for a system exposed to such an attack. The problem is reduced to a basic supervisory control one in discrete event systems by introducing the concept of dynamically controllable language. A solution to this problem is explored, where we establish an existence condition for a supremal and robust supervisor that is capable of defending against actuator jamming attacks, and design an algorithm to derive it. Finally, the effectiveness of our method is illustrated by an intelligent automated guided vehicle system. IEEE

关键词： Discrete event simulation

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Capacity Bounds for Identification With Effective Secrecy

Capacity Bounds for Identification With Effective Secrecy

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Rosenberger, Johannes Ibrahim, Abdalla Bash, Boulat A. Deppe, Christian Ferrara, Roberto Pereg, Uzi Technical University of Munich Tum School of Computation Information and Technology Germany University of Arizona Electrical and Computer Engineering Department United States Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Helen Diller Quantum Center Israel

ISBN: (纸本)9781665475549

An upper bound to the identification capacity of discrete memoryless wiretap channels is derived under the requirement of semantic effective secrecy, combining semantic secrecy and stealth constraints. A previously established lower bound is improved by applying it to a prefix channel, formed by concatenating an auxiliary channel and the actual channel. The bounds are tight if the legitimate channel is more capable than the eavesdropper's channel. An illustrative example is provided for a wiretap channel that is composed of a point-to-point channel, and a parallel, reversely degraded wiretap channel. A comparison with results for message transmission and for identification with only secrecy constraint is provided. © 2023 IEEE.

关键词： Semantics

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EFFICIENT RECURRENT ARCHITECTURES THROUGH ACTIVITY SPARSITY AND SPARSE BACK-PROPAGATION THROUGH TIME 11

EFFICIENT RECURRENT ARCHITECTURES THROUGH ACTIVITY SPARSITY ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Subramoney, Anand Nazeer, Khaleelulla Khan Schöne, Mark Mayr, Christian Kappel, David Institute for Neural Computation Ruhr University Bochum Germany Royal Holloway University of London United Kingdom Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Technische Universität Dresden Dresden Germany

Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application requirements. The memory and computational requirements arising from propagating the activations of all the neurons at every time step to every connected neuron, together with the sequential dependence of activations, contribute to the inefficiency of training and using RNNs. We propose a solution inspired by biological neuron dynamics that makes the communication between RNN units sparse and discrete. This makes the backward pass with backpropagation through time (BPTT) computationally sparse and efficient as well. We base our model on the gated recurrent unit (GRU), extending it with units that emit discrete events for communication triggered by a threshold so that no information is communicated to other units in the absence of events. We show theoretically that the communication between units, and hence the computation required for both the forward and backward passes, scales with the number of events in the network. Our model achieves efficiency without compromising task performance, demonstrating competitive performance compared to state-of-the-art recurrent network models in real-world tasks, including language modeling. The dynamic activity sparsity mechanism also makes our model well suited for novel energy-efficient neuromorphic hardware. Code is available at https://***/KhaleelKhan/EvNN/. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Recurrent neural networks

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Prompt Baking

arXiv

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arXiv 2024年

作者： Bhargava, Aman Witkowski, Cameron Detkov, Alexander Thomson, Matt Caltech United States University of Toronto Canada Computation & Neural Systems California Institute of Technology United States Department of Electrical and Computer Engineering University of Toronto Canada

Two primary ways to change LLM behavior are prompting and weight updates (e.g. fine-tuning). Prompting LLMs is simple and effective, specifying the desired changes explicitly in natural language, whereas weight updates provide more expressive and permanent behavior changes, specified implicitly via training on large datasets. We present a technique for "baking" prompts into the weights of an LLM. Prompt Baking converts a prompt u and initial weights θ to a new set of weights θu such that the LLM with weights θu behaves like the LLM with weights θ and prompt u. Mathematically, we minimize the KL divergence between Pθ(·|u) and Pθu(·), where P is the LLM’s probability distribution over token sequences. Across all our experiments, we find prompts can be readily baked into weight updates, often in as little as 5 minutes. Baking chain-of-thought prompts improves zero-shot performance on GSM8K, ASDiv, MBPP, ARC-Easy, ARC-Challenge, and CommonsenseQA benchmarks. Baking news headlines directly updates an LLM’s knowledge. And baking instructions & personas alleviates "prompt forgetting" over long sequences, as measured on a Persona Drift benchmark. Furthermore, stopping baking early creates "half-baked" models, allowing for continuous scaling of prompt strength. Baked models retain their sensitivity to further prompting and baking, including re-prompting with the prompt already baked in – thus amplifying the prompt’s strength. Surprisingly, the re-prompted models yield further performance gains in instruction following, as well as math reasoning and coding benchmarks (GSM8K, ASDiv, and MBPP). Taking re-prompting and re-baking to the limit yields a form of iterative self-improvement we call Prompt Pursuit, and preliminary results on instruction following exhibit dramatic performance gains with this technique. Finally, we discuss implications for AI safety, continuous model updating, improving LLM recency, enhancing real-time learning capabilities in LLM-based agents, and metho

关键词： Zero-shot learning

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An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA 23

An Exploration of Optimal Parameters for Efficient Blind Sou...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Frank, Gwenevere Shirazi, Seyed Yahya Palmer, Jason Cauwenberghs, Gert Makeig, Scott Delorme, Arnaud Electrical and Computer Engineering University of California La Jolla San Diego United States Swartz Center for Computational Neuroscience Institute for Neural Computation University of California La Jolla San Diego United States Statistics West Virginia University Morgantown United States Institute for Neural Computation University of California La Jolla San Diego United States Centre de recherche Cerveau et Cognition Paul Sabatier University Toulouse France

ISBN: (纸本)9798350393118

EEG continues to find a multitude of uses in both neuroscience research and medical practice, and independent component analysis (ICA) continues to be an important tool for analyzing EEG. A multitude of ICA algorithms for EEG decomposition exist, and in the past, their relative effectiveness has been studied. AMICA is considered the benchmark against which to compare the performance of other ICA algorithms for EEG decomposition. AMICA exposes many parameters to the user to allow for precise control of the decomposition. However, several of the parameters currently tend to be set according to"rules of thumb" shared in the EEG community. Here, 70-channel AMICA decompositions are run on data from a collection of participants while varying certain key parameters. The running time and quality of decompositions are analyzed based on two metrics, Pairwise Mutual Information (PMI) and Mutual Information Reduction (MIR), and derived recommendations for selecting parameter values are presented. © 2023 IEEE.

关键词： Blind source separation

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Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving

arXiv

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arXiv 2024年

作者： Yan, Shengchao König, Lukas Burgard, Wolfram Department of Computer Science University of Freiburg Germany Institute for Neural Computation Ruhr University Bochum Germany Department of Engineering University of Technology Nuremberg Germany

Active traffic management with autonomous vehicles offers the potential for reduced congestion and improved traffic flow. However, developing effective algorithms for real-world scenarios requires overcoming challenges related to infinite-horizon traffic flow and partial observability. To address these issues and further decentralize traffic management, we propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles using single-agent reinforcement learning. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability, eliminating the need for predefined agents or agent-specific experience buffers in multi-agent reinforcement learning. Extensive evaluations across various traffic scenarios demonstrate our method’s significant potential in improving traffic flow at critical bottleneck points. Moreover, we address the challenges posed by conservative autonomous vehicle driving behaviors that adhere strictly to traffic rules, showing that our cooperative policy effectively alleviates potential slowdowns without compromising safety. Copyright © 2024, The Authors. All rights reserved.

关键词： Observability

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Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving

Agent-Agnostic Centralized Training for Decentralized Multi-...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shengchao Yan Lukas König Wolfram Burgard Department of Computer Science University of Freiburg Germany Institute for Neural Computation Ruhr University Bochum Germany Department of Engineering University of Technology Nuremberg Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

关键词： Training neural networks Reinforcement learning Safety Observability Vehicle dynamics Driver behavior Autonomous vehicles Intelligent robots

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MiSO: Optimizing brain stimulation to create neural population activity states 38

MiSO: Optimizing brain stimulation to create neural populati...

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38th Conference on neural Information Processing Systems, NeurIPS 2024

作者： Minai, Yuki Soldado-Magraner, Joana Smith, Matthew A. Yu, Byron M. Neuroscience Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Biomedical Engineering Carnegie Mellon University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Center for the Neural Basis of Cognition United States

Brain stimulation has the potential to create desired neural population activity states. However, it is challenging to search the large space of stimulation parameters, for example, selecting which subset of electrodes to be used for stimulation. In this scenario, creating a model that maps the configuration of stimulation parameters to the brain's response can be beneficial. Training such an expansive model usually requires more stimulation-response samples than can be collected in a given experimental session. Furthermore, changes in the properties of the recorded activity over time can make it challenging to merge stimulation-response samples across sessions. To address these challenges, we propose MiSO (MicroStimulation Optimization), a closed-loop stimulation framework to drive neural population activity toward specified states by optimizing over a large stimulation parameter space. MiSO consists of three key components: 1) a neural activity alignment method to merge stimulation-response samples across sessions, 2) a statistical model trained on the merged samples to predict the brain's response to untested stimulation parameter configurations, and 3) an online optimization algorithm to adaptively update the stimulation parameter configuration based on the model's predictions. In this study, we implemented MiSO with a factor analysis (FA) based alignment method, a convolutional neural network (CNN), and an epsilon greedy optimization algorithm. We tested MiSO in closed-loop experiments using electrical microstimulation in the prefrontal cortex of a non-human primate. Guided by the CNN predictions, MiSO successfully searched amongst thousands of stimulation parameter configurations to drive the neural population activity toward specified states. More broadly, MiSO increases the clinical viability of neuromodulation technologies by enabling the use of many-fold larger stimulation parameter spaces. © 2024 neural information processing systems foundation. All right

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Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

Synthesizing Robust Walking Gaits via Discrete-Time Barrier ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Maegan Tucker Kejun Li Aaron D. Ames School of Electrical and Computer Engineering and the Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA Department of Computation and Neural Systems California Institute of Technology Pasadena CA Department of Mechanical and Civil Engineering and the Department of Control and Dynamical Systems California Institute of Technology Pasadena CA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness – the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.

关键词： Legged locomotion Measurement Uncertainty Exoskeletons Robustness State estimation Robots

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