• USM-SED - A Dataset for Polyphonic Sound Event Detection in Urban Sound Monitoring Scenarios

  This paper introduces a novel dataset for polyphonic sound event detection in urban sound monitoring use-cases. Based on isolated sounds taken from the FSD50k dataset, 20,000 polyphonic soundscapes are synthesized with sounds being randomly positioned in the stereo panorama using different loudness levels. The paper gives a detailed discussion of …

• Honeyboost: Boosting honeypot performance with data fusion and anomaly detection

  With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. Network Anomaly Detection Systems (NADS) ability to identify unusual behavior makes them useful in predicting such attacks. In this paper, we introduce a novel framework to enhance the performance of …

• A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration

  We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is applied to a new suction cup design that contains multiple chambers for air flow. Each chamber connects with its own remote pressure transducer, which enables both absolute and differential pressure measures between …

• Deep Polarization Imaging for 3D shape and SVBRDF Acquisition

  We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictions by coupling polarization imaging with …

• A mollifier approach to regularize a Cauchy problem for the inhomogeneous Helmholtz equation

  The Cauchy problem for the inhomogeneous Helmholtz equation with non-uniform refraction index is considered. The ill-posedness of this problem is tackled by means of the variational form of mollification. This approach is proved to be consistent, and the proposed numerical simulations are quite promising.

• Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design

  Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require …

• Covering Convex Polygons by Two Congruent Disks

  We consider the planar two-center problem for a convex polygon: given a convex polygon in the plane, find two congruent disks of minimum radius whose union contains the polygon. We present an $O(n\log n)$-time algorithm for the two-center problem for a convex polygon, where $n$ is the number of vertices …

• Inverse Scale Space Iterations for Non-Convex Variational Problems Using Functional Lifting

  Non-linear filtering approaches allow to obtain decompositions of images with respect to a non-classical notion of scale. The associated inverse scale space flow can be obtained using the classical Bregman iteration applied to a convex, absolutely one-homogeneous regularizer. In order to extend these approaches to general energies with non-convex data …

• A Generalized Concurrent Rule Construction for Double-Pushout Rewriting

  Double-pushout rewriting is an established categorical approach to the rule-based transformation of graphs and graph-like objects. One of its standard results is the construction of concurrent rules and the Concurrency Theorem pertaining to it: The sequential application of two rules can equivalently be replaced by the application of a concurrent …

• AI Risk Skepticism

  In this work, we survey skepticism regarding AI risk and show parallels with other types of scientific skepticism. We start by classifying different types of AI Risk skepticism and analyze their root causes. We conclude by suggesting some intervention approaches, which may be successful in reducing AI risk skepticism, at …