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Unsupervised multivariate time series anomaly detection (UMTSAD) plays a critical role in various domains, including finance, networks, and sensor systems. In recent years, due to the outstanding performance of deep learning in general…

Machine Learning · Computer Science 2025-04-28 Tiange Huang , Yongjun Li

Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly.…

Machine Learning · Computer Science 2024-05-30 Mohamed elShehaby , Aditya Kotha , Ashraf Matrawy

Recent advances in video-language models have enabled powerful applications like video retrieval, captioning, and assembly. However, executing such multi-stage pipelines efficiently on mobile devices remains challenging due to redundant…

Machine Learning · Computer Science 2025-12-22 Kunjal Panchal , Saayan Mitra , Somdeb Sarkhel , Haoliang Wang , Ishita Dasgupta , Gang Wu , Hui Guan

We present MIST, the Molecular Integration Simulation Toolkit, a lightweight and efficient software library written in C++ which provides an abstract in- terface to common molecular dynamics codes, enabling rapid and portable development of…

We present the Code Documentation and Analysis Tool (CoDAT). CoDAT is a tool designed to maintain consistency between the various levels of code documentation, e.g. if a line in a code sketch is changed, the comment that documents the…

Software Engineering · Computer Science 2024-07-17 Paul Attie , Anas Obeidat , Nathaniel Oh , Ian Yelle

A fundamental objective of materials modeling is identifying atomic structures that align with experimental observables. Conventional approaches for disordered materials involve sampling from thermodynamic ensembles and hoping for an…

Materials Science · Physics 2025-09-30 Tigany Zarrouk , Miguel A. Caro

Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in…

Materials Science · Physics 2014-10-14 Venkatesh Botu , Rampi Ramprasad

Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex scientific tasks…

Artificial Intelligence · Computer Science 2025-02-14 Quintina Campbell , Sam Cox , Jorge Medina , Brittany Watterson , Andrew D. White

-Molecular simulations allow the study of properties and interactions of molecular systems. This article presents an improved version of the Adaptive Resolution Scheme that links two systems having atomistic (also called fine-grained) and…

Computational Engineering, Finance, and Science · Computer Science 2017-08-01 Iuliana Marin , Virgil Tudose , Anton Hadar , Nicolae Goga , Andrei Doncescu

Automated text scoring (ATS) tasks, such as automated essay scoring and readability assessment, are important educational applications of natural language processing. Due to their interpretability of models and predictions, traditional…

Computation and Language · Computer Science 2021-04-09 Hitoshi Manabe , Masato Hagiwara

In this paper, we present a nonlinear analysis software toolkit, which can help in biomechanical gait data analysis by implementing various nonlinear statistical analysis algorithms. The toolkit is proposed to tackle the need for an…

Emerging Technologies · Computer Science 2023-11-14 Shifat Sarwar , Aaron Likens , Nick Stergiou , Spyridon Mastorakis

Large language models (LLMs) face significant challenges when processing complex rule systems, as they typically treat interdependent rules as unstructured textual data rather than as logically organized frameworks. This limitation results…

Temperature is a fundamental regulator of chemical and biochemical kinetics, yet capturing nonlinear thermal effects directly from experimental data remains a major challenge due to limited throughput and model flexibility. Recent advances…

Quantitative Methods · Quantitative Biology 2025-12-23 Mamoru Saita , Yutaka Hori

Droplets, sub-millilitre liquid volumes with at least one interface, have traditionally served as compartments for storing, transporting, and delivering materials. Beyond familiar applications in food, coatings, and consumer goods, they…

Soft Condensed Matter · Physics 2025-12-16 Joe Forth , Robert Malinowski , Giorgio Volpe

Computational tools for normal mode analysis, which are widely used in physics and materials science problems, are designed here in a single package called NMscatt (Normal Modes & scattering) that allows arbitrarily large systems to be…

Computational Physics · Physics 2009-11-13 Franci Merzel , Fabien Fontaine-Vive , Mark R. Johnson

The HBT-Analyzer is an universal tool for particle correlations analysis under the ROOT environment. It provides an efficient mixing mechanism, a wide range of correlation and monitoring functions, and a set of cuts that are applicable on…

Data Analysis, Statistics and Probability · Physics 2010-11-19 Piotr Krzysztof Skowronski , for ALICE Collaboration

FFT-based solvers are increasingly used by many researcher groups interested in modelling the mechanical behavior associated to a heterogeneous microstructure. A development is reported here that concerns the viscoelastic behavior of…

Classical Physics · Physics 2020-12-07 Stéphane André , Julien Boisse , Camille Noûs

The dynamics of many atomic systems is controlled by activated events taking place on a time scale which is long compared to that associated with thermal vibrations. This often places problems of interest outside the range of standard…

Materials Science · Physics 2007-05-23 G. T. Barkema , Normand Mousseau

Machine learning (ML) models deployed in healthcare systems must face data drawn from continually evolving environments. However, researchers proposing such models typically evaluate them in a time-agnostic manner, splitting datasets…

Machine Learning · Computer Science 2023-07-21 Helen Zhou , Yuwen Chen , Zachary C. Lipton

OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source…

Computation and Language · Computer Science 2018-05-30 Guillaume Klein , Yoon Kim , Yuntian Deng , Vincent Nguyen , Jean Senellart , Alexander M. Rush