Fast Approximate Spectral Clustering for Dynamic Networks

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How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

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This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

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Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

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Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

We study the performance of Markov chains for the q-state ferromagnetic Potts model on random regular graphs. While the cases of the grid and the complete graph are by now well-understood, the case of random regular graphs has resisted a detailed analysis ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Fast Approximate Spectral Clustering for Dynamic Networks

Chattez avec Graph Search

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Person Re-Identification and its Application to Multi-Object Tracking

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Recovering Static and Time-Varying Communities Using Persistent Edges

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Learning to Reason with Neural Networks: Principles and Methods

Scalable Quantum Algorithms for Noisy Quantum Computers

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

Sequential Reasoning with Socially Caused Beliefs

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Metastability of the Potts Ferromagnet on Random Regular Graphs

Equivariant Neural Architectures for Representing and Generating Graphs

A full characterization of invariant embeddability of unimodular planar graphs

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes