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International Symposium on Problems of Redundancy in Information and Control Systems (RED)

作者： Elena Egorova Ilya Vorobyev Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia 127051

In this paper a new lower bound on the rate of IPP set systems with traceability property is proved. The new bound outperforms the previously known bounds.

ISBN: (数字)9781728119441

ISBN: (纸本)9781728119458

In this paper a new lower bound on the rate of IPP set systems with traceability property is proved. The new bound outperforms the previously known bounds.

关键词： Decoding Redundancy Encryption Control systems Hamming distance

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

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Towards Understanding of eSports Athletes' Potentialities: The Sensing System for data Collection and Analysis

Towards Understanding of eSports Athletes' Potentialities: T...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Alexander Korotin Nikita Khromov Anton Stepanov Andrey Lange Evgeny Burnaev Andrey Somov Center for Computational and Data-Intensive Science and Engineering (CDISE) Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

eSports is a developing multidisciplinary research area. At present, there is a lack of relevant data collected from real eSports athletes and lack of platforms which could be used for the data collection and further analysis. In this paper, we present a sensing system for enabling the data collection from professional athletes. Also, we report on the case study about collecting and analyzing the gaze data from Monolith professional eSports team specializing in Counter-Strike: Global Offensive (CS:GO) discipline. We perform a comparative study on assessing the gaze of amateur players and professional athletes. The results of our work are vital for ensuring eSports data collection and the following analysis in the scope of scouting or assessing the eSports players and athletes.

关键词： Games Sensors Measurement data collection Weapons Mice Affective computing

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Trivariate Lifted Codes with Disjoint Repair Groups

Trivariate Lifted Codes with Disjoint Repair Groups

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International Symposium on Problems of Redundancy in Information and Control Systems (RED)

作者： Nikita Polyanskii Ilya Vorobyev Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia 121205

ISBN: (数字)9781728119441

ISBN: (纸本)9781728119458

Guo, Kopparty, and Sudan introduced the notion of lifted Reed-Solomon codes in the context of locally correctable codes. We continue the study of lifted codes with the application for so-called t-disjoint-repair-group property (t-DRGP) codes. A code is said to have the t-DRGP if every symbol in a codeword from the code has t mutually disjoint recovering sets of coordinates in the codeword. In some parameter regimes, our proposed t-DRGP codes based on lifted codes have lower redundancy than previously known t-DRGP codes.

关键词： Redundancy Upper bound Control systems Maintenance engineering Reed-Solomon codes Frequency modulation

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Sensors and Game Synchronization for data Analysis in eSports

Sensors and Game Synchronization for Data Analysis in eSport...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Anton Stepanov Andrey Lange Nikita Khromov Alexander Korotin Evgeny Burnaev Andrey Somov Center for Computational and Data-Intensive Science and Engineering (CDISE) Skolkovo Institute of Science and Technology Moscow Russia

eSports industry has greatly progressed within the last decade in terms of audience and fund rising, broadcasting, networking and hardware. Since the number and quality of professional team has evolved too, there is a reasonable need in improving skills and training process of professional eSports athletes. In this work, we demonstrate a system able to collect heterogeneous data (physiological, environmental, video, telemetry) and guarantying synchronization with 10 ms accuracy. In particular, we demonstrate how to synchronize various sensors and ensure post synchronization, i.e. logged video, a so-called demo file, with the sensors data. Our experimental results achieved on the CS:GO game discipline show up to 3 ms accuracy of the time synchronization of the gaming computer.

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The forgotten document-oriented database management systems: An overview and benchmark of native XML DODBMSes in comparison with JSON DODBMSes

arXiv

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arXiv 2021年

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Darmont, Jérôme Pedersen, Torben Bach Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania Department of Computer Science Aarhus University Aarhus Denmark Université de Lyon ERIC UR 3083 Lyon 2 France Center for Data Intensive Systems Aalborg University Aalborg Denmark

In the current context of Big data, a multitude of new NoSQL solutions for storing, managing, and extracting information and patterns from semi-structured data have been proposed and implemented. These solutions were developed to relieve the issue of rigid data structures present in relational databases, by introducing semi-structured and flexible schema design. As current data generated by different sources and devices, especially from IoT sensors and actuators, use either XML or JSON format, depending on the application, database technologies that store and query semi-structured data in XML format are needed. Thus, Native XML databases, which were initially designed to manipulate XML data using standardized querying languages, i.e., XQuery and XPath, were rebranded as NoSQL Document-Oriented databases Systems. Currently, the majority of these solutions have been replaced with the more modern JSON based database Management Systems. However, we believe that XML-based solutions can still deliver performance in executing complex queries on heterogeneous collections. Unfortunately nowadays, research lacks a clear comparison of the scalability and performance for database technologies that store and query documents in XML versus the more modern JSON format. Moreover, to the best of our knowledge, there are no Big data-compliant benchmarks for such database technologies. In this paper, we present a comparison for selected Document-Oriented database Systems that either use the XML format to encode documents, i.e., BaseX, eXist-db, and Sedna, or the JSON format, i.e., MongoDB, CouchDB, and Couchbase. To underline the performance differences we also propose a benchmark that uses a heterogeneous complex schema on a large DBLP corpus. © 2021, CC BY.

关键词： XML

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Duplication with transposition distance to the root for q-ary strings

arXiv

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arXiv 2020年

作者： Polyanskii, Nikita Vorobyev, Ilya Institute for Communications Engineering Technical University of Munich Munich80333 Germany Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow121205 Russia Advanced Combinatorics and Complex Networks Lab Moscow Institute of Physics and Technology Dolgoprudny141701 Russia

We study the duplication with transposition distance between strings of length n over a q-ary alphabet and their roots. In other words, we investigate the number of duplication operations of the form x = (abcd) → y = (abcbd), where x and y are strings and a, b, c and d are their substrings, needed to get a q-ary string of length n starting from the set of strings without duplications. For exact duplication, we prove that the maximal distance between a string of length at most n and its root has the asymptotic order n/ log n. For approximate duplication, where a β-fraction of symbols may be duplicated incorrectly, we show that the maximal distance has a sharp transition from the order n/ log n to log n at β = (q - 1)/q. The motivation for this problem comes from genomics, where such duplications represent a special kind of mutation and the distance between a given biological sequence and its root is the smallest number of transposition mutations required to generate the sequence. Copyright © 2020, The Authors. All rights reserved.

关键词： DNA sequences

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Hyena architecture enables fast and efficient protein language modeling

iMetaOmics

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iMetaOmics 2024年第1期2卷

作者： Yiming Zhang Bian Bian Manabu Okumura Department of Information and Communications Engineering School of Engineering Institute of Science Tokyo Yokohama Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Department of Data Science School of Frontier Engineering Kitasato University Sagamihara Japan

The emergence of self-supervised deep language models has revolutionized natural language processing tasks and has recently extended its applications to biological sequence analysis. Traditional language models, primarily based on Transformer architectures, demonstrate substantial effectiveness in various applications. However, these models are inherently constrained by the attention mechanism's quadratic computational complexity, , which limits their efficiency and leads to high computational costs. To address these limitations, we introduce ProtHyena, a novel approach that leverages the Hyena operator in protein language modeling. This innovative methodology alternates between subquadratic long convolutions and element-wise gating operations, which circumvents the constraints imposed by attention mechanisms and reduces computational complexity to subquadratic levels. This enables faster and more memory-efficient modeling of protein sequences. ProtHyena can achieve state-of-the-art results and comparable performance in 8 downstream tasks, including protein engineering (protein fluorescence and stability prediction), protein property prediction (neuropeptide cleavage, signal peptide, solubility, disorder, gene function prediction), protein structure prediction, with only 1.6 M parameters. The architecture of ProtHyena represents a highly efficient solution for protein language modeling, offering a promising avenue for fast and efficient analysis of protein sequences. Novel Architecture: ProtHyena applies Hyena architecture for protein language modeling, outperforming traditional attention-based models with lower computational costs and fewer parameters. State-of-the-Art Performance: Achieves leading results across classification and regression tasks, excelling with diverse protein sequences. Efficiency: Optimized for faster training and lower memory usage, enabling scalable modeling of long protein sequences.

关键词： Hyena architecture protein language model protein pre-training

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Real-Time TCAD: a new paradigm for TCAD in the artificial intelligence era

Real-Time TCAD: a new paradigm for TCAD in the artificial in...

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International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Sanghoon Myung Jinwoo Kim Yongwoo Jeon Wonik Jang In Huh Jaemin Kim Songyi Han Kang-hyun Baek Jisu Ryu Yoon-Suk Kim Jiseong Doh Jae-ho Kim Changwook Jeong Dae Sin Kim Computational Science and Engineering Team Data and Information Technology Center Device Solution Business Samsung Electronics Co. Ltd Korea

ISBN: (数字)9784863487635

ISBN: (纸本)9781728173542

This paper presents a novel approach to enable real-time device simulation and optimization. State-of-the-art algorithms which can describe semiconductor domain are adopted to train deep learning models whose input and output are process condition and doping profile / electrical characteristic, respectively. Our framework enables to update automatically deep learning models by estimating the uncertainty of the model prediction. Our Real-Time TCAD framework is validated on 130nm processes for display driver integration circuit (DDI), and 1) prediction time was 530,000 times faster than conventional TCAD, and time spent for process optimization was reduced by 300,000 times compared to human expert, 2) the model achieved average accuracy of 99% compared to TCAD simulation results, and thus, 3) process development time for DDI was reduced by 8 weeks.

关键词： Deep learning Uncertainty Predictive models Real-time systems Semiconductor process modeling Integrated circuit modeling Optimization

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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