Counting pedestrians in video sequences using trajectory clustering

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Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

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Knowledge of a program's input format is essential for effective input generation in fuzzing. Automated input format reverse engineering represents an attractive but challenging approach to learning the format. In this paper, we address several challenges ...

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Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve ...

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Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistica ...

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who ar ...

We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph -structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure ...

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We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...

Counting pedestrians in video sequences using trajectory clustering

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Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

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Fairness and Explainability in Clustering Problems

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Commoning the touristic city. Urban pedestrian routes and the ambiguous politics of exploration

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Transfer learning application of self-supervised learning in ARPES

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Landscapes of DNA Mechanics and Genomes

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke

Neural ADMIXTURE for rapid genomic clustering

Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE

Controlling pedestrian flows with moving walkways

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

Covariance matrices for variance-suppressed simulations

Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots

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