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arXiv 2023年

作者： Yu, Haojun Li, Youcheng Wu, QuanLin Zhao, Ziwei Chen, Dengbo Wang, Dong Wang, Liwei National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Center of Data Science Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Yizhun Medical AI Co. Ltd Beijing China Guangdong China

During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC) [15]. To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. We release the code, checkpoints, and high-quality labels of the CVA-BUS dataset [9] in https://***/HaojunYu1998/UltraDet. © 2023, CC BY-NC-ND.

关键词： Object detection

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An Empirical Study of Super-Resolution on Low-Resolution Micro-Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Zhou, Ling Wang, Mingpei Huang, Xiaohua Zheng, Wenming Mao, Qirong Zhao, Guoying Macau University of Science and Technology School of Computer Science and Engineering 999078 China Nanjing Institute of Technology School of International Education Oulu School Jiangsu Nanjing211167 China Southeast University Key Laboratory of Child Development and Learning Science Ministry of Education Nanjing210096 China Southeast University Research Center for Learning Science China Southeast University School of Biological Science and Medical Engineering Jiangsu Nanjing210096 China Jiangsu University School of Computer Science and Communication Engineering Jiangsu Zhenjiang212013 China University of Oulu Center for Machine Vision and Signal Analysis 90014 Finland

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution (SR) techniques for enhancing the quality of LR images and videos, few study has focused on investigate SR for improving LR MER. The scarcity of investigation can be attributed to the inherent difficulty in capturing the subtle motions of micro-expressions, even in original-resolution MER samples, which becomes even more challenging in LR samples due to the loss of distinctive features. Furthermore, a lack of systematic benchmarking and thorough analysis of SR-assisted MER methods has been noted. This paper tackles these issues by conducting a series of benchmark experiments that integrate both SR and MER methods, guided by an in-depth literature survey. Specifically, we employ seven cutting-edge state-of-the-art (SOTA) MER techniques and evaluate their performance on samples generated from 22 SOTA SR techniques, thereby addressing the problem of SR in MER. Through our empirical study, we uncover the primary challenges associated with SR-assisted MER and identify avenues to tackle these challenges by leveraging recent advancements in both SR and MER methodologies. Our analysis provides insights for progressing toward more efficient SR-assisted MER. © 2010-2012 IEEE.

关键词： Optical resolving power

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Portable tracker for neurophysiological research of sport shooting

Portable tracker for neurophysiological research of sport sh...

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Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics

作者： Antipov, V.M. Badarin, A.A. Grubov, V.V. Kazantsev, V.B. Hramov, A.E. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Laboratory of Advanced Methods for High-Dimensional Data Analysis Lobachevsky State University of Nizhni Novgorod 23 Gagarin ave. Nizhny Novgorod603950 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia Neurotechnology Deparment Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod603022 Russia

ISBN: (数字)9781510653702

ISBN: (纸本)9781510653696

In this work we present the development process of a wireless portable module. It is developed to record various characteristics during sport shooting, such as automatic detection of the moment of shot and barrel movement during aiming, taking into account the peculiarities of its use in neurophysiological research. We propose an approach allowing to synchronize devices in a wireless local network with high accuracy (synchronization accuracy was 2 ms), as well as a method of logging the recorded data at a sampling rate of up to 2 kHz on an onboard flash drive. © 2022 SPIE.

关键词： Neurophysiology

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Natural Convection Optimizer: A Novel Physics-Based Metaheuristic Optimization Algorithm

SSRN

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SSRN 2024年

作者： Liang, Kang Yipeng, Wang Jiayi, Chen Krakhmalev, Oleg Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Foreign Languages and CulturalCommunication Shanghai Polytechnic University Shanghai201209 China School of Economics and Management Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University under the Government of the Rus-sian Federation 4‐th Veshnyakovsky Passage 4 Moscow109456 Russia

In this paper, Natural Convection Optimizer Algorithm (NCvO) is proposed as a novel physical-inspired metaheuristic optimization algorithm. It is a novel physics-inspired metaheuristic optimization algorithm based on natural convection phenomena in fluid dynamics. By simulating the heat transfer process in fluids, NCvO achieves a balanced exploration and exploitation of complex search spaces. We validate NCvO through three experiments: solving the CEC 2017 benchmark in 10 dimensions and comparing results with well-established metaheuristics using statistical analyses like Wilcoxon and Friedman tests. NCvO demonstrates superior performance in terms of average fitness values and objective function evaluations, establishing its potential as a robust optimization tool. © 2024, The Authors. All rights reserved.

关键词： Optimization

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Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference machines

arXiv

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arXiv 2023年

作者： Adam, Alexandre Perreault-Levasseur, Laurence Hezaveh, Yashar Welling, Max Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Microsoft Research AI4Science

Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the Recurrent Inference machine (RIM) to simultaneously reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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KnowMAN: Weakly supervised multinomial adversarial networks

arXiv

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arXiv 2021年

作者： März, Luisa Asgari, Ehsaneddin Braune, Fabienne Zimmermann, Franziska Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Nlp Expert Center Data:Lab Volkswagen Ag Munich Germany

The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. © 2021, CC BY.

关键词： Computational linguistics

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: Quantum Artificial Intelligence for the Automotive Industry

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KI - Künstliche Intelligenz 2024年第4期38卷 351-359页

作者： Stollenwerk, Tobias Bhattacharya, Somtapa Cattelan, Michele Ciani, Alessandro Compostella, Gabriele Headley, David Klepsch, Johannes Klusch, Matthias Leder, Markus Macaluso, Antonio Michielsen, Kristel Nabok, Dmytro Papanikolaou, Anestis Rausch, Alexander Schumann, Marco Skolik, Andrea Yarkoni, Sheir Wilhelm, Frank K. Institute for Quantum Computing Analytics (PGI-12) Forschungszentrum Jülich Jülich Germany Volkswagen Data:Lab Volkswagen Group Munich Germany Machine Learning Research Lab Volkswagen Group Munich Germany Mercedes-Benz AG Stuttgart Germany BMW AG München Germany German Research Center for Artificial Intelligence (DFKI) Saarbrüecken Germany Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany Robert Bosch GmbH Corporate Research Renningen Germany

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S$^\text{3}$Attention: Improving Long Sequence Attention With Smoothed Skeleton Sketching

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IEEE Journal of Selected Topics in Signal Processing 2024年第6期18卷 985-996页

作者： Xue Wang Tian Zhou Jianqing Zhu Jialin Liu Kun Yuan Tao Yao Wotao Yin Rong Jin HanQin Cai Alibaba Group Bellevue WA USA Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Statistics and Data Science University of Central Florida Orlando FL USA Center for Machine Learning Research Peking University Beijing China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China Meta Menlo Park CA USA Department of Statistics and Data Science and the Department of Computer Science University of Central Florida Orlando FL USA

Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S $^{3}$ Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S $^{3}$ Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S $^{3}$ Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S $^{3}$ Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures.

关键词： Computational modeling Complexity theory Transformers Task analysis Skeleton Kernel Noise

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Beyond Gaussian Noise: A Generalized Approach to Likelihood analysis with non-Gaussian Noise

arXiv

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arXiv 2023年

作者： Legin, Ronan Adam, Alexandre Hezaveh, Yashar Levasseur, Laurence Perreault Department of Physics Université de Montréal Montréal Canada Ciela Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Mila Quebec Artificial Intelligence Institute Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function of complex, high-dimensional, non-Gaussian, and anisotropic noise. This is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the Cosmic Microwave Background, gravitational waves, gravitational lensing, and exoplanets. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope. © 2023, CC BY.

关键词： Gaussian distribution

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DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Ai...

引用

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

作者： Iosif Tsangko Andreas Triantafyllopoulos Michael Müller Hendrik Schröter Björn W. Schuller EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany CHI – Chair of Health Informatics Technical University of Munich Germany MCML – Munich Center for Machine Learning Munich Germany WS Audiology Research and Development Erlangen Germany GLAM – Group on Language Audio & Music Imperial College London UK MDSI – Munich Data Science Institute Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a ‘one-size-fits-all’ approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mitigate this. These recordings can be offloaded outside the hearing aid, thus improving performance while adding minimal computational overhead. We introduce these principles to the DFN model, thus proposing the DFingerNet (DFiN) model, which shows superior performance on various benchmarks inspired by the DNS Challenge.

关键词： Performance evaluation Adaptation models Computational modeling Noise Noise reduction Computer architecture Speech enhancement Fingerprint recognition Hearing aids Recording

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