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arXiv 2023年

作者： Kreymer, Shay Singer, Amit Bendory, Tamir The School of Electrical Engineering Tel Aviv University Tel Aviv Israel The Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

A single-particle cryo-electron microscopy (cryoEM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional molecular structure at unknown locations, taken under unknown viewing directions. All existing cryo-EM algorithmic pipelines first locate and extract the projection images, and then reconstruct the structure from the extracted images. However, if the molecular structure is small, the signal-to-noise ratio (SNR) of the data is very low, and thus accurate detection of projection images within the micrograph is challenging. Consequently, all standard techniques fail in low-SNR regimes. To recover molecular structures from measurements of low SNR, and in particular small molecular structures, we devise a stochastic approximate expectation-maximization algorithm to estimate the three-dimensional structure directly from the micrograph, bypassing locating the projection images. We corroborate our computational scheme with numerical experiments, and present successful structure recoveries from simulated noisy measurements. © 2023, CC BY.

关键词： Molecular structure

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Towards new realities: Implications of personalized online layers in our daily lives

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i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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External control of a genetic toggle switch via Reinforcement Learning

External control of a genetic toggle switch via Reinforcemen...

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European Control Conference (ECC)

作者： Sara Maria Brancato Francesco De Lellis Davide Salzano Giovanni Russo Mario Di Bernardo Department of Electrical Engineering and ICT University of Naples Federico II Italy Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Italy

We use a learning-based strategy to stabilize a synthetic toggle switch via an external control approach, adopting a sim-to-real paradigm to overcome the data efficiency problem that would render the algorithm infeasible for practical use in synthetic biology. Here, the policy is learnt via training on a simple model capturing the main dynamical features of a toggle switch and is then validated in-silico using stochastic agent-based simulations. Our in-silico experiments confirm the viability of the approach suggesting its potential use for in-vivo control implementation in microfluidics.

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Energy-Efficient Transmission in Wireless Networks Using Resnet with Gravitational Search Algorithm 2

Energy-Efficient Transmission in Wireless Networks Using Res...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajakumari, S. Hc, Kantharaju Ahmad, Shadab Sharma, Rupa Rani Sudha, S. Ramesh, T. R.M.D. Engineering College Department of Mathematics India Vemana Institute of Technology Department of Ai & Ml Karnataka Bengaluru India Samara University Department of Electrical and Computer Engineering Afar region Ethiopia G.L Bajaj Institute of Technology and Management Department of Applied Science Uttar Pradesh Greater Noida India Sri Ranganathar Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India R.M.K Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350371055

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly. © 2024 IEEE.

关键词： Energy Efficiency Gravitational Search Algorithm Residual Networks Transmission Optimization

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Study and prediction of prostate cancer using fuzzy inference system

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Materials Today: Proceedings 2022年 56卷 157-164页

作者： Boadh, Rahul Aarya, Deo Datta Dahiya, Mamta Rathee, Rakesh Rathee, Sarita Kumar, Ajay Jain, Sarika Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of the Mathematics Acharya Narendra Dev College University of Delhi India Department of Computer Science & Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India University Institute of Engineering & Technology Maharshi Dayanand University Haryana 124001 Rohtak India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India

The essential driver of passing among men on the planet is prostate disease. In the chief stage, for early identification of the prostate disease by applying Fuzzy Inference System (FIS) has been depicted in this study. The age of the patient, Prostate Volume (PV), % Free PSA (FPSA) and Prostate Specific Antigen (PSA) are utilized as input parameters in FIS for computing the risk factor of the prostate cancer. In this study Mamdani method is used for evaluation and analysis of the Prostate Cancer Risk (PCR). The present FIS additionally required the base estimation decreases horribleness lessens dismalness, mortality and more dependable from other system. It can likewise be considered in medicine to decrease manual assignments and human mistake. This review advocate that, this FIS might consequence on work on the productivity to recognized the distinctive kind of the malignant growths just as lessening the expense of care of the patients. © 2022

关键词： Risk assessment

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Variational design of sensory feedback for powerstroke-recovery systems

arXiv

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arXiv 2024年

作者： Yu, Zhuojun Thomas, Peter J. Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Department of Mathematics Applied Mathematics and Statistics Department of Biology Department of Electrical Control and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

Although the raison d’etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke-recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate – such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance-sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator (HCO)-motor system, we observe that the excitation-inhibition property of feedback mechanisms determines the sensitivity pattern while the activation-inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation sy

关键词： Efficiency

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BRAIN-TO-TEXT DECODING WITH CONTEXT-AWARE NEURAL REPRESENTATIONS AND LARGE LANGUAGE MODELS

arXiv

引用

arXiv 2024年

作者： Li, Jingyuan Le, Trung Fan, Chaofei Chen, Mingfei Shlizerman, Eli Department of Electrical and Computer Engineering University of Washington United States Department of Computer Science Stanford University United States Department of Applied Mathematics Department of Electrical and Computer Engineering University of Washington United States

Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. In this work, we propose the use of diphone - an acoustic representation that captures the transitions between two phonemes - as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art Phoneme Error Rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned Large Language Models (LLMs), our method yields a Word Error Rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Brain mapping

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On a Probabilistic Approach for Inverse Data-Driven Optimal Control

arXiv

引用

arXiv 2023年

作者： Garrabé, Émiland Jesawada, Hozefa Del Vecchio, Carmen Russo, Giovanni The Department of Engineering University of Sannio Benevento Italy The Department of Information and Electrical Engineering & Applied Mathematics University of Salerno Italy

We consider the problem of estimating the possibly non-convex cost of an agent by observing its interactions with a nonlinear, non-stationary and stochastic environment. For this inverse problem, we give a result that allows to estimate the cost by solving a convex optimization problem. To obtain this result we also tackle a forward problem. This leads to the formulation of a finite-horizon optimal control problem for which we show convexity and find the optimal solution. Our approach leverages certain probabilistic descriptions that can be obtained both from data and/or from first-principles. The effectiveness of our results, which are turned in an algorithm, is illustrated via simulations on the problem of estimating the cost of an agent that is stabilizing the unstable equilibrium of a pendulum. © 2023, CC BY.

关键词： Stochastic systems

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