Theoretical computer science ≈ Theoretical computer science
View article: Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Open
Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to detect and avoid. When detecting individual functionality-specifi…
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On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper) Open
In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the emergence of Machine Learning techniques, such a problem can n…
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Graph Attention Networks Open
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or the…
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Semi-Supervised Classification with Graph Convolutional Networks Open
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional archi…
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Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads Open
The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long re…
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Quantum Computing in the NISQ era and beyond Open
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in …
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Dynamic Graph CNN for Learning on Point Clouds Open
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have lon…
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Using Embeddings to Improve Named Entity Recognition Classification with Graphs Open
Richer information has potential to improve performance of NLP (Natural Language Processing) tasks such as Named Entity Recognition. A linear sequence of words can be enriched with the sentence structure, as well as their syntactic structu…
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W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis Open
This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, bina…
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Graph neural networks: A review of methods and applications Open
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a mode…
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Inductive Representation Learning on Large Graphs Open
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in th…
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition Open
Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power …
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The ORCA quantum chemistry program package Open
In this contribution to the special software-centered issue, the ORCA program package is described. We start with a short historical perspective of how the project began and go on to discuss its current feature set. ORCA has grown into a r…
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Open
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerg…
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Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments Open
Hi-C experiments explore the 3D structure of the genome, generating terabases of data to create high-resolution contact maps. Here, we introduce Juicer, an open-source tool for analyzing terabase-scale Hi-C datasets. Juicer allows users wi…
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Software update: The <span>ORCA</span> program system—Version 5.0 Open
Version 5.0 of the ORCA quantum chemistry program suite was released in July 2021. ORCA 5.0 represents a major improvement over all previous versions of ORCA and features (1) highly improved performance, (2) increased numerical robustness,…
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UpSetR: an R package for the visualization of intersecting sets and their properties Open
Motivation Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is neede…
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Formal Semantics for Kolmogorov-Arnold Network Representations of Operational Games Open
This paper develops a formal semantic framework for Kolmogorov-Arnold Network (KAN) representations of operational games. Operational games, as generalizations of strategic and dynamic games, provide powerful tools for modeling complex mul…
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Geometric Deep Learning: Going beyond Euclidean data Open
Many scientific fields study data with an underlying structure that is a\nnon-Euclidean space. Some examples include social networks in computational\nsocial sciences, sensor networks in communications, functional networks in\nbrain imagin…
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Neural Message Passing for Quantum Chemistry Open
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already…
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T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction Open
Accurate and real-time traffic forecasting plays an important role in the\nIntelligent Traffic System and is of great significance for urban traffic\nplanning, traffic management, and traffic control. However, traffic forecasting\nhas alwa…
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Heterogeneous Graph Attention Network Open
Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for h…
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Axiomatic Attribution for Deep Networks Open
We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attributio…
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OpenMM 7: Rapid development of high performance algorithms for molecular dynamics Open
OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Tho…
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Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting Open
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existin…
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agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update Open
The agriGO platform, which has been serving the scientific community for >10 years, specifically focuses on gene ontology (GO) enrichment analyses of plant and agricultural species. We continuously maintain and update the databases and acc…
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Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning Open
Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local v…
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Probabilistic programming in Python using PyMC3 Open
Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known a…
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications Open
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In th…
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Understanding the Impact of Value Selection Heuristics in Scheduling Problems Open
It has been observed that value selection heuristics have less impact than other heuristic choices when solving hard combinatorial optimization (CO) problems. It is often thought that this is because more time is spent on unsatisfiable sub…