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36th International Technical Conference on Circuits/systems, computers and Communications, ITC-CSCC 2021

作者： Jeon, Hobeom Yoon, Youngwoo Kim, Dohyung University of Science and Technology Department of Computer Software Korea Republic of Electronics and Telecommunications Research Institute Intelligent Robotics Research Division Korea Republic of

ISBN: (纸本)9781665435536

In this paper, we describe lightweight 2D human pose estimation for a fitness coaching system. To achieve real-time inference speed on mobile devices, we propose the online pose distillation learning strategy that trains large teacher networks and small student networks simultaneously. The proposed lightweight model requires significantly lower computation as well as shows competitive pose estimation accuracy on the COCO keypoint detection dataset. In addition, the model was fine-tuned on a fitness dataset for the fitness coaching application where various fitness postures were accessed. © 2021 IEEE.

关键词： Distillation

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Deep Generative Models for Constructing Synthetic Populations in Public Health Applications

Deep Generative Models for Constructing Synthetic Population...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Nathan Farmer Arash Mahyari Ashok Sirinivasan Department of Computer Science University of West Florida Pensacola FL USA Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA

There is a growing acknowledgment of the significance of social determinants of health in shaping fair public health policies. These determinants, reflected in emerging data streams like mobility and social media data, are increasingly integral to public health models. However, privacy concerns impede broad access to sensitive data, because even non-identifiable data are susceptible to deductive disclosure. To address this, synthetic populations trained on such data emerge as a privacy-conscious solution, offering the added benefit of exploring various "what-if" scenarios. This paper delves into the application of Generative Adversarial Networks (GANs) to craft synthetic populations that tackle privacy issues linked to social determinants of health data. Utilizing the 2019 PUMS dataset for Florida, the study trains the GAN to produce diverse and adaptable synthetic populations. An autoencoder manages the mix of continuous and categorical variables, ensuring the generated data aligns with categorical ranges. The results, assessed by comparing marginal distributions and employing tSNE, underscore the GAN's efficacy in balancing privacy and data utility. The significance of this work lies in identifying the promise of GANs in generating synthetic populations to advance health research.

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

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AG3D: Learning to Generate 3D Avatars from 2D Image Collections

AG3D: Learning to Generate 3D Avatars from 2D Image Collecti...

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International Conference on computer Vision (ICCV)

作者： Zijian Dong Xu Chen Jinlong Yang Michael J. Black Otmar Hilliges Andreas Geiger Department of Computer Science ETH Zürich University of Tübingen Max Planck Institute for Intelligent Systems Tübingen

While progress in 2D generative models of human appearance has been rapid, many applications require 3D avatars that can be animated and rendered. Unfortunately, most existing methods for learning generative models of 3D humans with diverse shape and appearance require 3D training data, which is limited and expensive to acquire. The key to progress is hence to learn generative models of 3D avatars from abundant unstructured 2D image collections. However, learning realistic and complete 3D appearance and geometry in this under-constrained setting remains challenging, especially in the presence of loose clothing such as dresses. In this paper, we propose a new adversarial generative model of realistic 3D people learned from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator and integrating an efficient, flexible, articulation module. To improve realism, we train our model using multiple discriminators while also integrating geometric cues in the form of predicted 2D normal maps. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance. We validate the effectiveness of our model and the importance of each component via systematic ablation studies.

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Performance Assessment for Stochastic Anomaly Detectors in Industrial Alarm systems

Performance Assessment for Stochastic Anomaly Detectors in I...

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International Conference on Control and Automation (ICCA)

作者： Jing Zhou Jun Shang Tongwen Chen Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Department of Control Science & Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In recent years, stochastic detectors have gained prominence in networked systems for anomaly detection. These detectors have demonstrated advantages over their traditional counterparts, particularly in safe-guarding against data integrity attacks targeting state estimation. Despite these advancements, the impact of the detector on alarm performance-such as alarm-triggering rates at normal conditions-remains under-explored, especially in scenarios where delay timers are applied to the raw alarm sequence. This study delves into the monitoring of a correlated Gaussian process variable using stochastic detectors. An explicit formula for the alarm performance is given, highlighting how it is influenced by the duration of delay timers. The efficacy of the proposed approach is validated through numerical examples and a simplified process model.

关键词： Automation Data integrity Detectors Gaussian processes Delays Numerical models State estimation

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OCET: One-Dimensional Convolution Embedding Transformer for Stock Trend Prediction 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yang, Peng Fu, Lang Zhang, Jian Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://***/langgege-cqu/OCET. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Representation Learning of Multivariate Time Series using Attention and Adversarial Training

arXiv

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arXiv 2024年

作者： Scharwächter, Leon Otte, Sebastian Neuro-Cognitive Modeling Group Department of Computer Science University of Tübingen Germany Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany

A critical factor in trustworthy machine learning is to develop robust representations of the training data. Only under this guarantee methods are legitimate to artificially generate data, for example, to counteract imbalanced datasets or provide counterfactual explanations for blackbox decision-making systems. In recent years, Generative Adversarial Networks (GANs) have shown considerable results in forming stable representations and generating realistic data. While many applications focus on generating image data, less effort has been made in generating time series data, especially multivariate signals. In this work, a Transformer-based autoencoder is proposed that is regularized using an adversarial training scheme to generate artificial multivariate time series signals. The representation is evaluated using t-SNE visualizations, Dynamic Time Warping (DTW) and Entropy scores. Our results indicate that the generated signals exhibit higher similarity to an exemplary dataset than using a convolutional network approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Shaving weights with occam's razor: Bayesian sparsification for neural networks using the marginal likelihood 24

Shaving weights with occam's razor: Bayesian sparsification ...

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

作者： Rayen Dhahri Alexander Immer Betrand Charpentier Stephan Günnemann Vincent Fortuin Department of Computer Science Technical University of Munich and Munich Center for Machine Learning Department of Computer Science ETH Zürich and Max Planck Institute for Intelligent Systems Tübingen Pruna AI Munich Department of Computer Science Technical University of Munich and Munich Center for Machine Learning and Helmholtz AI Munich

ISBN: (纸本)9798331314385

Neural network sparsification is a promising avenue to save computational time and memory costs, especially in an age where many successful AI models are becoming too large to naïvely deploy on consumer hardware. While much work has focused on different weight pruning criteria, the overall sparsifiability of the network, i.e., its capacity to be pruned without quality loss, has often been overlooked. We present Sparsifiability via the Marginal likelihood (SpaM), a pruning framework that highlights the effectiveness of using the Bayesian marginal likelihood in conjunction with sparsity-inducing priors for making neural networks more sparsifiable. Our approach implements an automatic Occam's razor that selects the most sparsifiable model that still explains the data well, both for structured and unstructured sparsification. In addition, we demonstrate that the pre-computed posterior precision from the Laplace approximation can be re-used to define a cheap pruning criterion, which outperforms many existing (more expensive) approaches. We demonstrate the effectiveness of our framework, especially at high sparsity levels, across a range of different neural network architectures and datasets.

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Continuous-Time Damping-Based Mirror Descent for a Class of Non-Convex Multi-Player Games with Coupling Constraints

Continuous-Time Damping-Based Mirror Descent for a Class of ...

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International Conference on Control and Automation (ICCA)

作者： Guanpu Chen Kun Cao Karl Henrik Johansson Yiguang Hong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Control Science and Engineering Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

We study the computation of the global generalized Nash equilibrium (GNE) for a class of non-convex multi-player games, where players' actions are subject to both local and coupling constraints. Due to the non-convex payoff functions, we employ canonical duality to reformulate the setting as a complementary problem. Under given conditions, we reveal the relation between the stationary point and the global GNE. According to the convex-concave properties within the complementary function, we propose a continuous-time mirror descent to compute GNE by generating functions in the Bregman divergence and the damping-based design. Then, we devise several Lyapunov functions to prove that the trajectory along the dynamics is bounded and convergent.

关键词： Couplings Damping Automation Games Nash equilibrium Trajectory Mirrors

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Body Schema in Autonomous Agents

Journal of Artificial Intelligence and Consciousness

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Journal of Artificial Intelligence and Consciousness 2021年第1期8卷 113-145页

作者： Neemeh, Zachariah A. Kronsted, Christian Kugele, Sean Franklin, Stan Department of Philosophy and Institute for Intelligent Systems The University of Memphis 3720 Alumni Avenue Memphis 38152 TN United States Computer Science Department and Institute for Intelligent Systems The University of Memphis 3720 Alumni Avenue Memphis 38152 TN United States

A body schema is an agent's model of its own body that enables it to act on affordances in the environment. This paper presents a body schema system for the Learning intelligent Decision Agent (LIDA) cognitive architecture. LIDA is a conceptual and computational implementation of Global Workspace Theory, also integrating other theories from neuroscience and psychology. This paper contends that the 'body schema' should be split into three separate functions based on the functional role of consciousness in Global Workspace Theory. There is (1) an online model of the agent's effectors and effector variables (Current Body Schema), (2) a long-term, recognitional storage of embodied capacities for action and affordances (Habitual Body Schema), and (3) "dorsal"stream information feeding directly from early perception to sensorimotor processes (Online Body Schema). This paper then discusses how the LIDA model of the body schema explains several experiments in psychology and ethology. © 2021 World Scientific Publishing Company.

关键词： Body Schema Embodied Cognition Global Workspace Theory LIDA

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