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Devider: Long-Read Reconstruction of Many Diverse Haplotypes 29th

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Devider: Long-Read Reconstruction of Many Diverse Haplotype...

29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Shaw, Jim Boucher, Christina Yu, Yun William Noyes, Noelle Li, Heng Department of Data Science Dana-Farber Cancer Institute BostonMA02215 United States Department of Biomedical Informatics Harvard Medical School BostonMA02215 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States Department of Veterinary Population Medicine University of Minnesota St. PaulMN55421 United States

ISBN: (纸本)9783031902512

Reconstructing haplotypes through sequencing of a mixture of similar sequences is a fundamental problem. Long-read sequencing technologies can connect distant alleles to disentangle similar haplotypes, but handling elevated sequencing error rates requires specialized techniques. We present devider, an algorithm for haplotyping small sequences—such as viruses or genes—from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. Benchmarking on synthetic mixtures of antimicrobial resistance (AMR) genes showed that devider recovered 83% of haplotypes, 23% points higher than the next best method. On real PacBio and Nanopore datasets, devider recapitulates previously known results in seconds, disentangling a bacterial community with >10 strains and an HIV-1 co-infection dataset. We used devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering a history of recombination for diverse AMR gene haplotypes and showcasing devider ’s ability to unveil ecological signals for heterogeneous mixtures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammals

Temporal resolution enhancement in quantum-dot laser neurons due to ground state quenching effects

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Temporal resolution enhancement in quantum-dot laser neurons...

Optical Fiber Communication Conference, OFC 2020

作者： Sarantoglou, George Skontranis, Menelaos Bogris, Adonis Mesaritakis, Charis Department of Information and Communication Systems Engineering University of the Aegean Karlovassi-Samos83200 Greece University of West Attica Department of Informatics and Computer Engineering Ag. Spyridonos Egaleo12243 Greece

We present experimental results for an all-optical quantum-dot neuron, biased to a ground-state quenching regime alongside emission from the excited state. This regime, allows reduction of the temporal width of spikes... 详细信息

ISBN: (纸本)9781943580712

关键词： Semiconductor quantum dots

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Farzaneh Kazemzadeh Sanaz Afsharian Ali Asghar Safaei Mitra Mirzarezaee Department of Computer Engineering Science and Research branch Islamic Azad University Tehran Iran English department Damavand Branch Islamic Azad University Tehran Iran Department of Medical Informatics Faculty of Medical Sciences Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781665476249

Following the invention of the Internet and its expansion among all the people of the world, many software products, sites, games, and even hardware was presented by scientists, programmers, and manufacturers, whose use today is one of the essential and irreplaceable needs. It has become part of people’s daily lives. Along with all the ways and means of using the Internet, social networks also spread among people with great power and speed. Today, most people are familiar with social networks and use them differently according to their needs. One of the ways to spread information is using social networks. Through social networks, many users can be informed about information such as news, advertisement, and many other things. This diffusion of information among users has been investigated by the problem of influence maximization to provide a solution to spread the diffusion in the social network. JACCIM algorithm, after detecting communities and selecting candidate nodes from overlapping and non-overlapping nodes based on topology criteria including Jaccard coefficient, has selected a set of k nodes. This collection has been able to maximize the diffusion of information in the network. With experiments on the proposed algorithm, comparing it with some other algorithms, and checking the results, the proposed algorithm has shown a significant impact on its execution time.

关键词： Technological innovation Runtime Social networking (online) Network topology Signal processing algorithms Signal processing Software

Enhancing UAV Autonomous Navigation in Indoor Environments Using Reinforcement Learning and Convolutional Neural Networks

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Enhancing UAV Autonomous Navigation in Indoor Environments U...

International Symposium on Intelligent Systems and informatics (SISY)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Eniko Nagy Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Apaczai Csere Janos Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Obuda University Hungary Szechenyi University Gyor Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Obuda University Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems.

关键词： Indoor navigation Refining Reinforcement learning Autonomous aerial vehicles Indoor environment Convolutional neural networks Intelligent systems informatics Autonomous robots Global Positioning System

Multiple object trackers in opencv: A benchmark

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arXiv 2021年

作者： Dardagan, Nada Brdanin, Adnan Džigal, Džemil Akagic, Amila Department of Informatics Alpen-Adria-Universität Klagenfurt Klagenfurt am Wörthersee Austria Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Object tracking is one of the most important and fundamental disciplines of computer Vision. Many computer Vision applications require specific object tracking capabilities, including autonomous and smart vehicles, video surveillance, medical treatments, and many others. The OpenCV as one of the most popular libraries for computer Vision includes several hundred computer Vision algorithms. Object tracking tasks in the library can be roughly clustered in single and multiple object trackers. The library is widely used for real-time applications, but there are a lot of unanswered questions such as when to use a specific tracker, how to evaluate its performance, and for what kind of objects will the tracker yield the best results? In this paper, we evaluate 7 trackers implemented in OpenCV against the MOT20 dataset. The results are shown based on Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) metrics. Copyright © 2021, The Authors. All rights reserved.

关键词： Security systems

Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare

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Analyzing the Impact of Personalization on Fairness in Feder...

IEEE International Conference on Healthcare informatics (ICHI)

作者： Tongnian Wang Kai Zhang Jiannan Cai Yanmin Gong Kim-Kwang Raymond Choo Yuanxiong Guo Department of Information Systems and Cyber Security The University of Texas at San Antonio San Antonio USA School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston USA School of Civil and Environmental Engineering and Construction Management The University of Texas at San Antonio San Antonio USA Department of Electrical and Computer Engineering The University of Texas at San Antonio San Antonio USA

Machine learning (ML) models have the potential to generate biased outcomes, which may exacerbate existing health disparities. With privacy regulations leading to data silos in health data, federated learning (FL) has emerged as a promising solution for this issue by enabling collaborative ML without patient data sharing. Personalization within FL aims to handle performance degradation that arises due to heterogeneous data distributions across organizations. However, the relationship between personalized FL and fairness remains unclear. This paper aims to investigate and analyze the potential impact of personalized FL on fairness for healthcare. Our analysis is expected to have significant implications for fairness in federated learning and healthcare.

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Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses

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arXiv 2021年

作者： Angelopoulos, Constantinos Marios Filios, Gabriel Nikoletseas, Sotiris Raptis, Theofanis P. Bournemouth University Poole United Kingdom Department of Computer Engineering and Informatics University of Patras Greece Greece Institute of Informatics and Telematics National Research Council Pisa Italy

Strawberries are widely appreciated for their characteristic aroma, bright red color, juicy texture, and sweetness. They are, however, among the most sensitive fruits when it comes to the quality of the end product. The recent commercial trends show a rising number of farmers who directly sell their products in the market and are more interested in using smart solutions for a continuous control of the factors that affect the quality of the final product. Cloud-based approaches for smart irrigation have been widely used in the recent years. However, the network traffic, security and regulatory challenges, which come hand in hand with sharing the crop data with third parties outside the edge of the network, lead strawberry farmers and data owners to rely on global clouds and potentially lose control over their data, which are usually transferred to third party data centers. In this paper, we follow a three-step methodological approach in order to design, implement and validate a solution for smart strawberry irrigation in greenhouses, while keeping the corresponding data at the edge of the network: (i) we develop a small-scale smart irrigation prototype solution with off-the-shelf hardware and software equipment, which we test and evaluate on different kinds of plants in order to gain useful insights for larger scale deployments, (ii) we introduce a reference network architecture, specifically targeting smart irrigation and edge data distribution for strawberry greenhouses, and (iii) adopting the proposed reference architecture, we implement a full-scale system in an actual strawberry greenhouse environment in Greece, and we compare its performance against that of conventional strawberries irrigation. We show that our design significantly outperforms the conventional approach, both in terms of soil moisture variation and in terms of water consumption, and conclude by critically appraising the costs and benefits of our approach in the agricultural industry. © 2021, CC

关键词： Fruits

Online algorithms for finding distinct substrings with length and multiple prefix and suffix conditions

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arXiv 2022年

作者： Leonard, Laurentius Inenaga, Shunsuke Bannai, Hideo Mieno, Takuya Department of Information Science and Technology Kyushu University Japan Department of Informatics Kyushu University Japan M&d Data Science Center Tokyo Medical and Dental University Japan Department of Computer and Network Engineering University of Electro-Communications Japan

Let two static sequences of strings P and S, representing prefix and suffix conditions respectively, be given as input for preprocessing. For the query, let two positive integers k1and k2be given, as well as a string T given in an online manner, such that Tirepresents the lengthi prefix of T for 1 ≤ i ≤ |T|. In this paper we are interested in computing the set ansiof distinct substrings w of Tisuch that k1≤ |w| ≤ k2, and w contains some p ∈ P as a prefix and some s ∈ S as a suffix. More specifically, the counting problem is to output |ansi|, whereas the reporting problem is to output all elements of ansi, for each iteration i. Let σ denote the alphabet size, and for a sequence of strings A, ||A|| = Σu∈A|u|. Then, we show that after O((||P|| + ||S||) log σ )-time preprocessing, the solutions for the counting and reporting problems for each iteration up to i can be output in O (|Ti| log σ ) and O (|Ti| log σ + |ansi|) total time. The preprocessing time can be reduced to O (||P|| +||S||) for integer alphabets of size polynomial with regard to ||P|| + ||S||. Our algorithms have possible applications to network traffic classification. © 2022, CC BY-SA.

关键词： Pattern matching

FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer

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arXiv 2023年

作者： Khowaja, Sunder Ali Nkenyereye, Lewis Mujtaba, Ghulam Lee, Ik Hyun Fortino, Giancarlo Dev, Kapal Faculty of Engineering and Technology University of Sindh Sindh Jamshoro76080 Pakistan Department of Computer Science and Information Security Seoul05006 Korea Republic of Department of Computer Science West Virginia University MorgantownWV26506 United States IKLab Inc. Gyeonggi-do Siheung15073 Korea Republic of Department of Mechatronics Tech University of Korea Gyeonggi-do Siheung15073 Korea Republic of Department of Informatics Modeling Electronics and Systems University of Calabria Calabria Italy Department of Computer Science ADAPT Centre Munster Technological University Cork Ireland

With the surge in emerging technologies such as Metaverse, spatial computing, and generative AI, the application of facial style transfer has gained a lot of interest from researchers as well as startups enthusiasts alike. StyleGAN methods have paved the way for transfer-learning strategies that could reduce the dependency on the huge volume of data that is available for the training process. However, StyleGAN methods have the tendency of overfitting that results in the introduction of artifacts in the facial images. Studies, such as DualStyleGAN, proposed the use of multipath networks but they require the networks to be trained for a specific style rather than generating a fusion of facial styles at once. In this paper, we propose a FusIon of STyles (FIST) network for facial images that leverages pre-trained multipath style transfer networks to eliminate the problem associated with lack of huge data volume in the training phase along with the fusion of multiple styles at the output. We leverage pre-trained styleGAN networks with an external style pass that use residual modulation block instead of a transform coding block. The method also preserves facial structure, identity, and details via the gated mapping unit introduced in this study. The aforementioned components enable us to train the network with very limited amount of data while generating high-quality stylized images. Our training process adapts curriculum learning strategy to perform efficient, flexible style and model fusion in the generative space. We perform extensive experiments to show the superiority of FISTNet in comparison to existing state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Learning systems