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  1. Free, publicly-accessible full text available July 23, 2025
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  5. Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe deployment of deep neural network (DNN) classifiers. While a myriad of methods have focused on improving the performance of OOD detectors, a critical gap remains in interpreting their decisions. We help bridge this gap by providing explanations for OOD detectors based on learned high-level concepts. We first propose two new metrics for assessing the effectiveness of a particular set of concepts for explaining OOD detectors: 1) detection completeness, which quantifies the sufficiency of concepts for explaining an OOD-detector’s decisions, and 2) concept separability, which captures the distributional separation between in-distribution and OOD data in the concept space. Based on these metrics, we propose an unsupervised framework for learning a set of concepts that satisfy the desired properties of high detection completeness and concept separability, and demonstrate its effectiveness in providing concept-based explanations for diverse off-the-shelf OOD detectors. We also show how to identify prominent concepts contributing to the detection results, and provide further reasoning about their decisions. 
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    Free, publicly-accessible full text available July 3, 2024
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  7. The Frobenius-Perron theory of an endofunctor of a k \Bbbk -linear category (recently introduced in Chen et al. [Algebra Number Theory 13 (2019), pp. 2005–2055]) provides new invariants for abelian and triangulated categories. Here we study Frobenius-Perron type invariants for derived categories of commutative and noncommutative projective schemes. In particular, we calculate the Frobenius-Perron dimension for domestic and tubular weighted projective lines, define Frobenius-Perron generalizations of Calabi-Yau and Kodaira dimensions, and provide examples. We apply this theory to the derived categories associated to certain Artin-Schelter regular and finite-dimensional algebras. 
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  8. Abstract. Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub ( to facilitate further usage and research by the wider academic community.

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  9. We fabricate three-terminal hybrid devices consisting of a semiconductor nanowire segment proximitized by a grounded superconductor and having tunnel probe contacts on both sides. By performing simultaneous tunneling measurements, we identify delocalized states, which can be observed from both ends, and states localized near one of the tunnel barriers. The delocalized states can be traced from zero magnetic field to fields beyond 0.5 T. Within the regime that supports delocalized states, we search for correlated low-energy features consistent with the presence of Majorana zero modes. While both sides of the device exhibit ubiquitous low-energy features at high fields, no correlation is inferred. Simulations using a one-dimensional effective model suggest that the delocalized states, which extend throughout the whole system, have large characteristic wave vectors, while the lower momentum states expected to give rise to Majorana physics are localized by disorder. To avoid such localization and realize Majorana zero modes, disorder needs to be reduced significantly. We propose a method for estimating the disorder strength based on analyzing the level spacing between delocalized states.

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