Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms

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Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. However, existing methods often count all objects in th ...

Many challenges of modern graph processing stem from the fact that graphs have become massive and change frequently. Numerous techniques have been developed to address those issues, with graph cut sparsification offering to replace original graphs with th ...

Global climate warming is leading to more frequent and severe extreme weather events, with alpine regions like the European Alps being particularly vulnerable. This thesis explores future changes in extreme weather dynamics in the southeastern Swiss Alps a ...

We consider the problem of making a multi-agent system (MAS) resilient to Byzantine failures through replication. We consider a very general model of MAS, where randomness can be involved in the behavior of each agent. We propose the first universal scheme ...

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create inequalities or amplify existing biases. In this paper, we analyze the fairnes ...

In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequ ...

Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...

Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best-known algorithm for k-means in Euclidean space with a provable guarantee is a simple local ...

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

An instance of colorful k-center consists of points in a metric space that are colored red or blue, along with an integer k and a coverage requirement for each color. The goal is to find the smallest radius ρ such that there exist balls of radius ρ around ...

Many natural and man-made signals can be described as having a few degrees of freedom relative to their size due to natural parameterizations or constraints; examples include bandlimited signals, collections of signals observed from multiple viewpoints in ...

Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between th ...

Key performance indicators in engineering problems include but are not limited to financial, operational, management and environmental factors, which are significantly affected by aspects such as seasonality, fouling, economic climate, production rates, su ...

The organization of connectivity in neuronal networks is fundamental to understanding the activity and function of neural networks and information processing in the brain. Recent studies show that the neocortex is not only organized in columns and layers b ...

We introduce a simple and general approach to the problem of clustering structures from atomic trajectories of chemical reactions in solution. By considering distance metrics which are invariant under permutation of identical atoms or molecules, we demonst ...

Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms

Chattez avec Graph Search

Learning to Count from Pseudo-Labeled Segmentation

Graph sparsification, sketching and adaptivity

Future Changes in Dynamics of Extreme Precipitation and Dry Periods in the Swiss Alps and Consequences for Hydropower Production

Byzantine-Resilient Multi-Agent System

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling

Matching Seqlets: An Unsupervised Approach for Locality Preserving Sequence Matching

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Graph Laplacian Mixture Model

Fair Colorful k-Center Clustering

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

A methodology for creating sequential multi-period base-case scenarios for large data sets

Computing the size and number of neuronal clusters in local circuits

Structural cluster analysis of chemical reactions in solution