11,931 research outputs found

    Satellite Image Based Cross-view Localization for Autonomous Vehicle

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    Existing spatial localization techniques for autonomous vehicles mostly use a pre-built 3D-HD map, often constructed using a survey-grade 3D mapping vehicle, which is not only expensive but also laborious. This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy, providing a cheaper and more practical way for localization. While the utilization of satellite imagery for cross-view localization is an established concept, the conventional methodology focuses primarily on image retrieval. This paper introduces a novel approach to cross-view localization that departs from the conventional image retrieval method. Specifically, our method develops (1) a Geometric-align Feature Extractor (GaFE) that leverages measured 3D points to bridge the geometric gap between ground and overhead views, (2) a Pose Aware Branch (PAB) adopting a triplet loss to encourage pose-aware feature extraction, and (3) a Recursive Pose Refine Branch (RPRB) using the Levenberg-Marquardt (LM) algorithm to align the initial pose towards the true vehicle pose iteratively. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view. The results demonstrate the superiority of our method in cross-view localization with median spatial and angular errors within 11 meter and 1∘1^\circ, respectively.Comment: Accepted by ICRA202

    Loss minimization yields multicalibration for large neural networks

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    Multicalibration is a notion of fairness that aims to provide accurate predictions across a large set of groups. Multicalibration is known to be a different goal than loss minimization, even for simple predictors such as linear functions. In this note, we show that for (almost all) large neural network sizes, optimally minimizing squared error leads to multicalibration. Our results are about representational aspects of neural networks, and not about algorithmic or sample complexity considerations. Previous such results were known only for predictors that were nearly Bayes-optimal and were therefore representation independent. We emphasize that our results do not apply to specific algorithms for optimizing neural networks, such as SGD, and they should not be interpreted as "fairness comes for free from optimizing neural networks"

    Security and Privacy Problems in Voice Assistant Applications: A Survey

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    Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

    Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

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    This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making

    UniverSeg: Universal Medical Image Segmentation

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    While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project Website: https://universeg.csail.mit.ed

    Comedians without a Cause: The Politics and Aesthetics of Humour in Dutch Cabaret (1966-2020)

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    Comedians play an important role in society and public debate. While comedians have been considered important cultural critics for quite some time, comedy has acquired a new social and political significance in recent years, with humour taking centre stage in political and social debates around issues of identity, social justice, and freedom of speech. To understand the shifting meanings and political implications of humour within a Dutch context, this PhD thesis examines the political and aesthetic workings of humour in the highly popular Dutch cabaret genre, focusing on cabaret performances from the 1960s to the present. The central questions of the thesis are: how do comedians use humour to deliver social critique, and how does their humour resonate with political ideologies? These questions are answered by adopting a cultural studies approach to humour, which is used to analyse Dutch cabaret performances, and by studying related materials such as reviews and media interviews with comedians. This thesis shows that, from the 1960s onwards, Dutch comedians have been considered ‘progressive rebels’ – politically engaged, subversive, and carrying a left-wing political agenda – but that this image is in need of correction. While we tend to look for progressive political messages in the work of comedians who present themselves as being anti-establishment rebels – such as Youp van ‘t Hek, Hans Teeuwen, and Theo Maassen – this thesis demonstrates that their transgressive and provocative humour tends to protect social hierarchies and relationships of power. Moreover, it shows that, paradoxically, both the deliberately moderate and nuanced humour of Wim Kan and Claudia de Breij, and the seemingly past-oriented nostalgia of Alex Klaasen, are more radical and progressive than the transgressive humour of van ‘t Hek, Teeuwen and Maassen. Finally, comedians who present absurdist or deconstructionist forms of humour, such as the early student cabarets, Freek de Jonge, and Micha Wertheim, tend to disassociate themselves from an explicit political engagement. By challenging the dominant image of the Dutch comedian as a ‘progressive rebel,’ this thesis contributes to a better understanding of humour in the present cultural moment, in which humour is often either not taken seriously, or one-sidedly celebrated as being merely pleasurable, innocent, or progressively liberating. In so doing, this thesis concludes, the ‘dark’ and more conservative sides of humour tend to get obscured

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries

    Die akute Appendizitis im Kindes- und Jugendalter: neue diagnostische Verfahren für die prätherapeutische Differenzierung histopathologischer Entitäten zur Unterstützung konservativer Therapiestrategien

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    Hintergrund der hier zusammengefassten Studien war die aktuelle Datenlage, die dafür spricht, dass es sich bei der klinisch unkomplizierten, histopathologisch phlegmonösen und der klinisch komplizierten, histopathologisch gangränösen Appendizitis um unabhängige Entitäten handelt. Diese können unterschiedlichen Therapieoptionen (konservativ vs. operativ) zugeführt werden. Vor diesem Hintergrund war es ein Ziel der Arbeiten zu untersuchen, wie die Formen der akuten Appendizitis im Kindes- und Jugendalter bereits prätherapeutisch unterschieden werden können. Sowohl in der Labordiagnostik (P1 und P2) als auch im Ultraschall (P3) lassen sich Unterschiede zwischen Patient*innen mit unkomplizierter, phlegmonöser und komplizierter (gangränöser und perforierender) Appendizitis aufzeigen. Hierdurch allein kann allerdings aufgrund unzureichender Trennschärfe noch keine ausreichende Entscheidungssicherheit erreicht werden. Mit Verfahren der künstlichen Intelligenz auf Untersucher-unabhängige diagnostische Parameter (P4) konnte die Vorhersagegenauigkeit der akuten Appendizitis weiter gesteigert werden. Interessante Ergebnisse bezüglich der unterschiedlichen Pathomechanismen der beiden inflammatorischen Entitäten ergaben sich durch eine differenzielle Genexpressionsanalyse (P5). In einer Proof-of-Concept-Studie wurden zuvor beschriebene Methoden der künstlichen Intelligenz auf die Genexpressionsdaten angewandt (P6). Hierdurch konnte im Modell eine grundsätzliche Differenzierbarkeit der Entitäten durch die Anwendung der neuen Methode aufgezeigt werden. Ein mittelfristiges Ziel ist es, eine Biomarkersignatur zu definieren, die ihre Aussagekraft durch einen Computeralgorithmus hat. Hierdurch soll eine schnelle Therapieentscheidung ermöglicht werden. Im Idealfall sollte diese Biomarkersignatur sicher, objektiv und einfach zu bestimmen sein sowie eine höhere diagnostische Sicherheit als die bisherige Diagnostik mittels Anamnese, Untersuchung, Laboranalyse und Ultraschall bieten. Langfristiges Ziel von Folgestudien ist die Identifizierung einer Biomarkersignatur mit der bestmöglichen Vorhersagekraft. Hinsichtlich der routinemäßigen klinischen Diagnostik ist die Anwendung von Point-of-Care Devices auf PCR-Basis denkbar. Hier könnte eine limitierte Anzahl von Primern für eine Biomarkersignatur mit hoher Vorhersagekraft zum Einsatz kommen. Der dadurch ermittelte Biomarker würde seine Aussagekraft durch einen einfach anzuwendenden Computeralgorithmus erhalten. Die Kombination aus Genexpressionsanalyse mit Methoden der künstlichen Intelligenz kann somit die Grundlage für ein neues diagnostisches Instrument zur sicheren Unterscheidung unterschiedlicher Appendizitisentitäten darstellen
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