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5th International Conference on Biomedical Engineering, IBIOMED 2024

作者： Hansun, Seng Argha, Ahmadreza Alinejad-Rokny, Hamid Liaw, Siaw-Teng Celler, Branko G. Marks, Guy B. School of Clinical Medicine University of New South Wales Woolcock Institute of Medical Research Sydney Australia Universitas Multimedia Nusantara Tangerang Indonesia University of New South Wales Sydney Australia University of New South Wales BioMedical Machine Learning Lab Sydney Australia School of Population Health University of New South Wales Sydney Australia School of Electrical Engineering and Telecommunications University of New South Wales Biomedical Systems Research Laboratory Sydney Australia

ISBN: (纸本)9798350389265

This study evaluates the effectiveness of a novel ensemble transfer learning (TL) method for tuberculosis (TB) detection. We employed the recently introduced ConvNeXt convolutional-based deep learning architecture as the backbone model. Two TL approaches were applied at intermediate layers of the pre-trained model (PTM), specifically ConvNeXt-Tiny, Small, and Base variants: Hard Cut, where features from an intermediate layer are directly passed to the classifier, and Soft Cut, where features from an intermediate layer are fine-tuned before classification. Experimental results on two widely used TB datasets-the Montgomery County (MC) and the Shenzhen (SZ) datasets demonstrate that the proposed ensemble TL method enhances detection performance. The Soft Cut approach yielded accuracy improvements of approximately 11.46% (Tiny), 8.71% (Small), 3.58% (Base) for MC, and 11.18% (Tiny), 10.27% (Small), 6.65% (Base) for SZ compared to the direct method, compared to the direct method. © 2024 IEEE.

关键词： Contrastive learning

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Protein codes promote selective subcellular compartmentalization

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Science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. Mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

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Development of Modern Forecasting Models 16

Development of Modern Forecasting Models

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Shuvalov, Konstantin Akhobadze, Gurami Malekova, Victoria Mikhailov, Alexey Levchenko, Kiril Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of Large-Scale System Development Moscow Russia V. A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia Financial University under the Government of the Russian Federation Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

ISBN: (纸本)9798350337907

关键词： Forecasting

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Forecasting Financial Time Series Based on the Ensemble Method 15

Forecasting Financial Time Series Based on the Ensemble Meth...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Ivanyuk, Vera Tsvirkun, Anatoly Soloviev, Vladimir Feklin, Vadim Sunchalina, Anna Kravchenko, Oleg Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia Laboratory of Management of the Development of Large-scale Systems V.A. Trapeznikov Institute of Control Sciences of Ras Moscow Russia

The study considers methods for forecasting financial time series. We construct an ensemble forecast based on the linear forecast, ETS forecast, and neural forecast methods. Thereupon, we evaluate the quality of the f... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Time series

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Investor Risk Profile Determination Model

Investor Risk Profile Determination Model

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market.

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Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent 37

Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Sto...

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Zhu, Lingjiong Gürbüzbalaban, Mert Raj, Anant Şimşekli, Umut Dept. of Mathematics Florida State University United States Dept. of Management Science and Information Systems Rutgers Business School United States Center for Statistics and Machine Learning Princeton University United States Coordinated Science Laboratory University of Illinois Urbana-Champaign United States Inria Paris CNRS Ecole Normale Supérieure PSL Research University France

ISBN: (纸本)9781713899921

Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various properties of optimization algorithms, the analysis of each case typically required a different proof technique with significantly different mathematical tools. In this study, we make a novel connection between learning theory and applied probability and introduce a unified guideline for proving Wasserstein stability bounds for stochastic optimization algorithms. We illustrate our approach on stochastic gradient descent (SGD) and we obtain time-uniform stability bounds (i.e., the bound does not increase with the number of iterations) for strongly convex losses and non-convex losses with additive noise, where we recover similar results to the prior art or extend them to more general cases by using a single proof technique. Our approach is flexible and can be generalizable to other popular optimizers, as it mainly requires developing Lyapunov functions, which are often readily available in the literature. It also illustrates that ergodicity is an important component for obtaining time-uniform bounds - which might not be achieved for convex or non-convex losses unless additional noise is injected to the iterates. Finally, we slightly stretch our analysis technique and prove time-uniform bounds for SGD under convex and non-convex losses (without additional additive noise), which, to our knowledge, is novel. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Additive noise

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Preface–Introduction to the Special Issue for Chinese-Russian Workshop on Biophotonics and Biomedical Optics

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Journal of Innovative Optical Health Sciences 2024年第5期17卷 1-3页

作者： Tingting Yu Dan Zhu Valery V.Tuchin Britton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics Advanced Biomedical Imaging FacilityWuhan National Laboratory for Optoelectronics–Advanced Biomedical Imaging FacilityHuazhong University of Science and TechnologyWuhan 430074China Department of Optics and Biophotonics Science Medical CenterSaratov State UniversitySaratov 410012Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and ControlFRC“Saratov Scientic Centre of the Russian Academy of Sciences”Saratov 410028Russia Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State UniversityTomsk 634050Russia

The Chinese-Russian Workshop on Biophotonics and Biomedical Optics 2023 was held online twice on 18–21 September and 25–26 September *** bilateral workshop brought together both Russian and Chinese scientists,engineers,and clinical researchers from a variety of disciplines engaged in applying optical science,photonics,and imaging technologies to problems in biology and *** the workshops,two plenary lectures and twenty invited presentations were *** special issue selects some papers from both Russian and Chinese sides,consisting of one review and seven original research articles.

关键词： Russian photonics Biomedical

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A Neurobehavioral Evaluation of the Efficacy of 1mA Longitudinal, Anodal TDCS on Multitasking and Transfer Performance

A Neurobehavioral Evaluation of the Efficacy of 1mA Longitud...

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IEEE International Conference on systems, Man and Cybernetics

作者： Akash K Rao Shashank Uttrani Darshil Shah Vishnu K Menon Arnav Bhavsar Shubhajit Roy Chowdhury Ramsingh Negi Varun Dutt Manipal Academy of Higher Education Applied Cognitive Science Laboratory Indian Institute of Technology Mandi School of Computing and Electrical Engineering Indian Institute of Technology Mandi Cognitive Control and Machine Learning Group at the Institute of Nuclear Medicine and Allied Sciences Defence Research and Development Organization

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS.

关键词： Training Atmospheric measurements Memory management Switches Multitasking Particle measurements Dynamic scheduling control systems Transcranial direct current stimulation Cybernetics

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Analyzing Aerial 3D Maps to Guide a Ground Vehicle to Complement the Regions Not Visible from Above

Analyzing Aerial 3D Maps to Guide a Ground Vehicle to Comple...

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International Conference on Advanced Robotics (ICAR)

作者： Barnabás Bugár-Mészáros András L. Majdik Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control (HUN-REN SZTAKI) Budapest Hungary Department of Material Handling and Logistics Systems Budapest University of Technology and Economics Budapest

The detailed reconstruction of complex unknown environments is a challenging task in several robotic applications. Multi-robot systems combining the advantages of aerial and ground vehicles are often used for this purpose, where the different perspectives of sensors are leveraged to reduce the proportion of uncovered areas. Since the lower speed of ground robots in cluttered environments compared to aerial vehicles can significantly limit the efficiency of the exploration, in this paper we propose a cooperative mapping system in which only the necessary regions are assigned to the ground vehicle. These regions are selected based on the structural analysis of the 3D model built by an aerial robot and a global path is computed connecting them which only passes traversable parts of the environment. A ground 3D map is also created and the two models are fused when the robot reaches the last waypoint of the path. We tested our solution in multiple simulated environments to verify its functionality.

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ActMAD: Activation Matching to Align Distributions for Test-Time-Training

arXiv

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arXiv 2022年

作者： Mirza, Muhammad Jehanzeb Soneira, Pol Jané Lin, Wei Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Institute of Control Systems KIT Germany Christian Doppler Laboratory for Semantic 3D Computer Vision

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture- and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance. © 2022, CC BY.

关键词： Chemical activation

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