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MDTS: automatic complex materials design using Monte Carlo tree search

SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2017年第1期18卷 498-503页

作者： Dieb, Thaer M. Ju, Shenghong Yoshizoe, Kazuki Hou, Zhufeng Shiomi, Junichiro Tsuda, Koji Natl Inst Mat Sci Tsukuba Ibaraki Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba Japan Univ Tokyo Dept Mech Engn Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan

Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python library calledMDTS (Materials Design using Tree Search). Our algorithm employs a Monte Carlo tree search approach, which has shown exceptional performance in computer Go game. Unlike evolutionary algorithms that require user intervention to set parameters appropriately, MDTS has no tuning parameters and works autonomously in various problems. In comparison to a Bayesian optimization package, our algorithm showed competitive search efficiency and superior scalability. We succeeded in designing large SiliconGermanium (Si-Ge) alloy structures that Bayesian optimization could not deal with due to excessive computational cost. MDTS is available at https://***/tsudalab/MDTS. [GRAPHICS] .

关键词： Materials informatics Materials design Monte Carlo tree search Si-Ge alloy interfacial structure python library

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fromage: A library for the study of molecular crystal excited states at the aggregate scale

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2020年第10期41卷 1045-1058页

作者： Rivera, Miguel Dommett, Michael Sidat, Amir Rahim, Warda Crespo-Otero, Rachel Queen Mary Univ London Sch Biol & Chem Sci Dept Chem London England

The study of photoexcitations in molecular aggregates faces the twofold problem of the increased computational cost associated with excited states and the complexity of the interactions among the constituent monomers. A mechanistic investigation of these processes requires the analysis of the intermolecular interactions, the effect of the environment, and 3D arrangements or crystal packing on the excited states. A considerable number of techniques have been tailored to navigate these obstacles;however, they are usually restricted to in-house codes and thus require a disproportionate effort to adopt by researchers approaching the field. Herein, we present the FRamewOrk for Molecular AGgregate Excitations (fromage), which implements a collection of such techniques in a python library complemented with ready-to-use scripts. The program structure is presented and the principal features available to the user are described: geometrical analysis, exciton characterization, and a variety of ONIOM schemes. Each is illustrated by examples of diverse organic molecules in condensed phase settings. The program is available at .

关键词： exciton molecular aggregate ONIOM photochemistry python library

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An algebraic approach to temporal network analysis based on temporal quantities

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SOCIAL NETWORK ANALYSIS AND MINING 2016年第1期6卷 1-22页

作者： Batagelj, Vladimir Praprotnik, Selena Univ Ljubljana FMF Jadranska 19 Ljubljana 1000 Slovenia Univ Ljubljana Fac Elect Engn Trzaska Cesta 25 Ljubljana 1000 Slovenia

In a temporal network, the presence and activity of nodes and links can change through time. To describe temporal networks we introduce the notion of temporal quantities. We define the addition and multiplication of temporal quantities in a way that can be used for the definition of addition and multiplication of temporal networks. The corresponding algebraic structures are semirings. The usual approach to (data) analysis of temporal networks is to transform the network into a sequence of time slices-static networks corresponding to selected time intervals and analyze each of them using standard methods to produce a sequence of results. The approach proposed in this paper enables us to compute these results directly. We developed fast algorithms for the proposed operations. They are available as an open source python library TQ (Temporal Quantities) and a program Ianus. The proposed approach enables us to treat as temporal quantities also other network characteristics such as degrees, connectivity components, centrality measures, Pathfinder skeleton, etc. To illustrate the developed tools we present some results from the analysis of Franzosi's violence network and Corman's Reuters terror news network.

关键词： Temporal network Time slice Temporal quantity Semiring Algorithm Network measures python library Violence Terror

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Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in python

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EPIDEMICS 2018年 25卷 80-88页

作者： Irvine, Michael A. Hollingsworth, T. Deirdre Univ British Columbia Inst Appl Math Vancouver BC Canada Univ Oxford Li Ka Sluing Ctr Hlth Informat & Discovery Big Data Inst Nuffield Dept Med Oxford England

Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community.

关键词： Approximate Bayesian computation Individual-based model Lymphatic filariasis Model fitting python library

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GrASP: A library for Extracting and Exploring Human-Interpretable Textual Patterns 13

GrASP: A Library for Extracting and Exploring Human-Interpre...

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13th International Conference on Language Resources and Evaluation (LREC)

作者： Lertvittayakumjorn, Piyawat Choshen, Leshem Shnarch, Eyal Toni, Francesca Imperial Coll London Dept Comp London England IBM Res Armonk NY USA

ISBN: (纸本)9791095546726

Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available python library for extracting patterns from textual data. The library integrates a first public implementation of the existing GrASP algorithm. It allows users to extract patterns using a number of general-purpose built-in linguistic attributes (such as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged for the original algorithm, as well as domain-specific custom attributes which can be incorporated into the library by implementing two functions. The library is equipped with a web-based interface empowering human users to conveniently explore data via the extracted patterns, using complementary pattern-centric and example-centric views: the former includes a reading in natural language and statistics of each extracted pattern;the latter shows applications of each extracted pattern to training examples. We demonstrate the usefulness of the library in classification (spam detection and argument mining), model analysis (machine translation), and artifact discovery in datasets (SNLI and 20Newsgroups).

关键词： pattern extraction textual data exploration resource tool grasp python library model debugging model analysis data analysis

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OSL-ActionSpotting: A Unified library for Action Spotting in Sports Videos

OSL-ActionSpotting: A Unified Library for Action Spotting in...

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IEEE International Workshop on Sport, Technology and Research (IEEE STAR)

作者： Benzakour, Yassine Cabado, Bruno Giancola, Silvio Cioppa, Anthony Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Inst Montefiore Liege Belgium Univ A Coruna Cinfo La Coruna Spain Univ A Coruna CITIC Res Ctr La Coruna Spain KAUST IVUL Thuwal Saudi Arabia

ISBN: (纸本)9798350351460;9798350351453

Action spotting is crucial in sports analytics as it enables the precise identification and categorization of pivotal moments in sports matches, providing insights that are essential for performance analysis and tactical decision-making. The fragmentation of existing methodologies, however, impedes the progression of sports analytics, necessitating a unified codebase to support the development and deployment of action spotting for video analysis. In this work, we introduce OSL-ActionSpotting, a python library that unifies different action spotting algorithms to streamline research and applications in sports video analytics. OSL-ActionSpotting encapsulates various state-of-the-art techniques into a singular, user-friendly framework, offering standardized processes for action spotting and analysis across multiple datasets. We successfully integrated three cornerstone action spotting methods into OSL-ActionSpotting, achieving performance metrics that match those of the original, disparate codebases. This unification within a single library preserves the effectiveness of each method and enhances usability and accessibility for researchers and practitioners in sports analytics. By bridging the gaps between various action spotting techniques, OSL-ActionSpotting significantly contributes to the field of sports video analysis, fostering enhanced analytical capabilities and collaborative research opportunities. The scalable and modularized design of the library ensures its long-term relevance and adaptability to future technological advancements in the domain.

关键词： Video understanding Action spotting Sports Analytics python library Benchmark Algorithms

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Development of AI Educational Datasets library Using Synthetic Dataset Generation Method 5

Development of AI Educational Datasets Library Using Synthet...

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5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kim, Seul Ki Kim, Kwihoon Kim, Taeyoung Korea Natl Univ Educ Dept Comp Educ Cheongju South Korea Korea Natl Univ Educ Dept Artificial Intelligence Convergence Educ Cheongju South Korea

ISBN: (纸本)9781665456456

Recently, as the impact of AI technology on society has increased, education to develop students' AI capabilities has been emphasized. Along with the importance of AI technology, the importance of datasets, which are an axis of technological development, is being emphasized, and many studies on datasets for AI are being conducted. In order to provide meaningful AI education to students from an educational perspective, this paper reconstructs libraries to utilize synthetic dataset generation libraries in different classroom instruction environments and confirms the applicability of educational purposes.

关键词： Artificial Intelligence Education Synthetic data Teaching and learning materials python library

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A Programmatic Approach for Development of the ViewHRV Service Platform with Accurate and Reliable Results 43

A Programmatic Approach for Development of the ViewHRV Servi...

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43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

作者： Shaqiri, E. Gusev, M. Innovat Dooel Skopje North Macedonia Ss Cyril & Methodius Univ Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9789532330991

There are dozens of available packages and libraries that claim to calculate HRV. This paper aims at comparing the results from the calculation of a single array of intervals between normal beats, including the most popular open source python HRV measurement packages available on GitHub. Furthermore, the same array was ran through the Kubios software and compared to the previous results. In order to compare the accuracy of the results, as a benchmark we used the C programs provided by Physionet. The results showed a huge difference in the results with almost all the indices, in fact the simplest measurement that of Standard Deviation of NN Intervals showed to be incorrect in Kubios and in most of the python packages. Results like these are the reason we decided to develop our own package to calculate HRV. Finally, the goal of this paper is to present details on developing a publicly available web service platform ViewHRV with guaranteed precision obtaining accurate and reliable results.

关键词： ECG signal Heart Rate Variability python library Physionet

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TAT-HUM: Trajectory analysis toolkit for human movements in python

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BEHAVIOR RESEARCH METHODS 2024年第4期56卷 4103-4129页

作者： Wang, Xiaoye Michael Welsh, Timothy N. Univ Toronto Fac Kinesiol & Phys Educ Ctr Motor Control Toronto ON Canada

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.

关键词： Human movement analysis Kinematic analysis Trajectory analysis python library Spatial cueing paradigm

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python Package for the Network Operator Implementation and its Application for Group Control System Synthesis Problem 13

Python Package for the Network Operator Implementation and i...

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13th International Symposium on Intelligent Systems (INTELS)

作者： Dotsenko, A. V. Diveev, A. I. Malyuk, Yu. A. Ryndin, D. A. RUDN Univ 6Mikluho Maklaya Str Moscow 117198 Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control 44-2Vavilova Str Moscow 119333 Russia

One of the main problems of symbolic regression methods is how to encode mathematical expressions to make the structure concise and readable. Here we address to the problem of network operator's matrix scarcity, which we resolve using python's standard types. Our approach allows to get rid of zero elements bellow the main diagonal. Moreover, we introduce new parametric variation, which helps us to tune parameters during structural evolving. We provide a description of the network operator and algorithms for finding the optimal solution using the principle of small variations of the basis solution. Our new encoding scheme and the complex functional for the group control task then described. In the experimental part we introduce a new way to apply network operator with a variable number of arguments to the group control system synthesis problem. Along with this we use for the first time individual network operators for each robot. From the experiment we can see that provided tricks lead to results that are not inferior in the case of usual network operator shared by all robots. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： control synthesis python library symbolic regression optimal control mobile robot

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