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arXiv 2023年

作者： Bonnet, Édouard Hodor, Jędrzej Korhonen, Tuukka Masařík, Tomáš Univ Lyon CNRS ENS de Lyon Université Claude Bernard Lyon 1 LIP UMR5668 France Theoretical Computer Science Department Faculty of Mathematics and Computer Science Doctoral School of Exact and Natural Sciences Jagiellonian University Kraków Poland Department of Informatics University of Bergen Norway Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Warszawa Poland

A graph G contains a graph H as an induced minor if H can be obtained from G after vertex deletions and edge contractions. We show that for every k-vertex planar graph H, every graph G excluding H as an induced minor and Kt,t as a subgraph has treewidth at most ∆(G)f(k,t) where ∆(G) denotes the maximum degree of G. Without requiring the absence of a Kt,t subgraph, Korhonen [JCTB’23] has shown the upper bound of kO(1)2∆(G)5 whose dependence in ∆(G) is exponential. Our result partially answers a question of Chudnovsky [Dagstuhl seminar’23] asking whether the treewidth of graphs with ∆(G) = O(log |V (G)|) excluding both a k-vertex planar graph as an induced minor and the biclique Kt,t as a subgraph is in Ok,t(log |V (G)|). We confirm that the treewidth is in this case polylogarithmic in |V (G)|.MSC Codes 05C83 Copyright © 2023, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Detecting Diabetic Retinopathy Using Deep Learning Techniques

Detecting Diabetic Retinopathy Using Deep Learning Technique...

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Intelligent Technologies (CONIT), International Conference on

作者： Sreenivasa Sumadithya Fathima Nihla Latheef Mustafa Sameer Rubell Marion Lincy G Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India Department of Mathematics Indian Institute of Information Technology Kottayam Kottayam India Department of Electronics and Communication Engineering National Institute of Technology Patna Patna India

Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to vision loss. Diabetic Macular Edema (DME) is a related condition where fluid accumulates in the macula, leading to retinal thickening. Early detection of both DR and DME is critical for effective treatment. However, current detection methods are timeconsuming and challenging. In this paper, we propose a novel approach using Capsule Networks and GAN to automatically detect and classify DR and DME using retinal images. We also investigate the correlation between the two conditions and develop a cross-disease grading system using image-level supervision. Our proposed model is tested on two publicly available datasets, Messidor and IDRiD, and achieves promising results. Our approach represents a significant advance in the automated detection of DR and DME using deep learning techniques.

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Detecting a single fault in a deterministic finite automaton

arXiv

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arXiv 2021年

作者： Polański, Artur Lipka, Eryk Institute of Computer Science and Computer Mathematics Jagiellonian University in Cracow Kraków Poland Institute of Mathematics Pedagogical University of Cracow Kraków Poland

MSC Codes 68Q45, 68M15Given a DFA and its implementation with at most one single fault, that we can test on a set of inputs, we provide an algorithm to find a test set that guarantees finding whether the fault exists.... 详细信息

关键词： Fault detection

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Improving Deep Embedded Clustering via Learning Cluster-level Representations 29

Improving Deep Embedded Clustering via Learning Cluster-leve...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yin, Qing Wang, Zhihua Song, Yunya Xu, Yida Niu, Shuai Bai, Liang Guo, Yike Yang, Xian Alliance Manchester Business School The University of Manchester United Kingdom Department of Computer Science Hong Kong Baptist University Hong Kong Shanghai Institute for Advanced Study of Zhejiang University China Shanghai AI Laboratory China Department of Journalism Hong Kong Baptist University Hong Kong Department of Mathematics Hong Kong Baptist University Hong Kong School of Computer and Information Technology Shanxi University China

Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-oriented objectives, which can produce meaningless representations. To alleviate this problem, several improvements have been developed to introduce additional learning objectives in the clustering process, such as models based on contrastive learning. However, existing efforts rely heavily on learning meaningful representations at the instance level. They have limited focus on learning global representations, which are necessary to capture the overall data structure at the cluster level. In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations. Here, we extend the embedded topic modelling approach to introduce reconstruction constraints to help learn cluster-level representations. Experimental results on real-world short text datasets demonstrate that our model produces meaningful clusters. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Cluster analysis

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Intelligent Control of Power Converters using Reinforcement Learning

Intelligent Control of Power Converters using Reinforcement ...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： R. Sathya Barna Krishna Sudharani R S. Rukmani Devi Pramodkurnar H. Kulkarni Rohini. G M. Sindhu Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Panimalar Engineering College Poonamallee Tamil Nadu India Department of Computer Science SIMATS Saveetha College of Liberal Arts and Sciences Deemed to be University Chennai Tamil Nadu India Department of Electronics and Telecommunications Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra India Department of Electrical and Electronics Engineering S.A. Engineering College Thiruverkadu Tamil Nadu India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. This research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. This research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. The explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. This research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. The report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems.

关键词： Renewable energy sources Uncertain systems Uncertainty Power system stability Deep reinforcement learning Safety Time-varying systems

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Multi-feature and Multi-branch Action Segmentation Framework for Modeling Long-Short-Term Dependencies

Multi-feature and Multi-branch Action Segmentation Framework...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Junkun Hong Yitian Long Yueyi Luo Qianqian Qi Jun Long School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University Nashville Tennessee United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Pioneer efforts have been dedicated to action segmentation that predicts what step is occurring in a video frame. Existing studies focus on improving the accuracy of video segmentation, but neglect the temporal continuity of intersegments and semantic consistency of intra-segments, which are necessary for developing computer-assisted systems. Meanwhile, Temporal Convolutional Networks have shown good performance in action segmentation tasks, but their high layers tend to lose fine-grained information and impact the results. Toward this end, we devise a multi-feature and multi-branch action segmentation framework for modeling long-term and short-term dependencies. Specifically, we present a multi-feature fusion to enhance temporal video representation and design a multi-branch predictor for extracting both segment-level and frame-level information. We justify our framework over three datasets and experimental results demonstrate its superiority, especially in Edit and F1 metrics, which means our framework is more applicable to computer-assisted systems.

关键词： Measurement Accuracy Computational modeling Aggregates Semantics Feature extraction Data mining

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Quantifying White Matter Hyperintensity and Brain Volumes in Heterogeneous Clinical and Low-Field Portable MRI

Quantifying White Matter Hyperintensity and Brain Volumes in...

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IEEE International Symposium on Biomedical Imaging

作者： Pablo Laso Stefano Cerri Annabel Sorby-Adams Jennifer Guo Farrah Mateen Philipp Goebl Jiaming Wu Peirong Liu Hongwei Bran Li Sean I. Young Benjamin Billot Oula Puonti Gordon Sze Sam Payabavash Adam DeHavenon Kevin N. Sheth Matthew S. Rosen John Kirsch Nicola Strisciuglio Jelmer M. Wolterink Arman Eshaghi Frederik Barkhof W. Taylor Kimberly Juan Eugenio Iglesias Massachusetts General Hospital Harvard Medical School USA Electrical Engineering Mathematics and Computer Science University of Twente The Netherlands Pioneer Centre for AI University of Copenhagen Denmark Centre for Medical Image Computing University College London UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA Danish Research Centre for Magnetic Resonance Denmark Yale School of Medicine USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH (ρ=.85) and hippocampal volumes (ρ=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://***/fswiki/WMH-SynthSeg.

关键词： Atrophy Neuroimaging Image segmentation Multiple sclerosis Image resolution Magnetic resonance imaging White matter

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Alternating mirror descent for constrained min-max games 22

Alternating mirror descent for constrained min-max games

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Andre Wibisono Molei Tao Georgios Piliouras Department of Computer Science Yale University Department of Mathematics Georgia Institute of Technology Engineering Systems and Design Singapore University of Technology and Design

ISBN: (纸本)9781713871088

In this paper we study two-player bilinear zero-sum games with constrained strategy spaces. An instance of natural occurrences of such constraints is when mixed strategies are used, which correspond to a probability simplex constraint. We propose and analyze the alternating mirror descent algorithm, in which each player takes turns to take action following the mirror descent algorithm for constrained optimization. We interpret alternating mirror descent as an alternating discretization of a skew-gradient flow in the dual space, and use tools from convex optimization and modified energy function to establish an O(K-2/3) bound on its average regret after K iterations. This quantitatively verifies the algorithm's better behavior than the simultaneous version of mirror descent algorithm, which is known to diverge and yields an O(K-1/2) average regret bound. In the special case of an unconstrained setting, our results recover the behavior of alternating gradient descent algorithm for zero-sum games which was studied in [2].

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Quantum Algorithms for the Pathwise Lasso

arXiv

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arXiv 2023年

作者： Doriguello, Joao F. Lim, Debbie Pun, Chi Seng Rebentrost, Patrick Vaidya, Tushar HUN-REN Alfréd Rényi Institute of Mathematics Budapest Hungary Centre for Quantum Technologies National University of Singapore Singapore Center for Quantum Computer Science Faculty of Computing University of Latvia Latvia School of Physical and Mathematical Sciences Nanyang Technological University Singapore Department of Computer Science National University of Singapore Singapore

We present a novel quantum high-dimensional linear regression algorithm with an 1-penalty based on the classical LARS (Least Angle Regression) pathwise algorithm. Similarly to available classical numerical algorithms for Lasso, our quantum algorithm provides the full regularisation path as the penalty term varies, but quadratically faster per iteration under specific conditions. A quadratic speedup on the number of features/predictors d is possible by using the simple quantum minimum-finding subroutine from Dürr and Høyer (arXiv’96) in order to obtain the joining time at each iteration. We then improve upon this simple quantum algorithm and obtain a quadratic speedup both in the number of features d and the number of observations n by using the approximate quantum minimum-finding subroutine from Chen and de Wolf (ICALP’23). In order to do so, we construct a quantum unitary based on quantum amplitude estimation to approximately compute the joining times to be searched over by the approximate quantum minimum-finding subroutine. Since the joining times are no longer exactly computed, it is no longer clear that the resulting approximate quantum algorithm obtains a good solution. As another main contribution, we prove, via an approximate version of the KKT conditions and a duality gap, that the LARS algorithm (and therefore our quantum algorithm) is robust to errors. This means that it still outputs a path that minimises the Lasso cost function up to a small error if the joining times are only approximately computed. Furthermore, we show that, when the observations are sampled from a Gaussian distribution, our quantum algorithm’s complexity only depends polylogarithmically on n, exponentially better than the classical LARS algorithm, while keeping the quadratic improvement on d. Moreover, we propose a dequantised version of our quantum algorithm that also retains the polylogarithmic dependence on n, albeit presenting the linear scaling on d from the standard LARS algorit

关键词： Cost functions

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Trees maximizing the number of almost-perfect matchings

arXiv

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arXiv 2022年

作者： Cambie, Stijn McCoy, Bradley Sharma, Gunjan Wagner, Stephan Yap, Corrine Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk Belgium Department of Computer Science James Madison University HarrisonburgVA United States Applied Mathematics Illinois Institute of Technology ChicagoIL United States Institute of Discrete Mathematics TU Graz Graz Austria Department of Mathematics Uppsala University Uppsala Sweden School of Mathematics Georgia Institute of Technology AtlantaGA United States

We characterize the extremal trees that maximize the number of almost-perfect matchings, which are matchings covering all but one or two vertices, and those that maximize the number of strong almost-perfect matchings, which are matchings missing only one or two leaves. We also determine the trees that minimize the number of maximal matchings. We apply these results to extremal problems on the weighted Hosoya index for several choices of vertex-degree-based weight *** Codes 05C05 05C09 05C35 05C05 05C09 05C35 05C05, 05C09, 05C35, 05C70 © 2022, CC BY.

关键词： Forestry

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