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	Author	G. Gasbarri; Matias Bilkis; E. Roda Salichs; J. Calsamiglia
	Title	Sequential hypothesis testing for continuously-monitored quantum systems			Type	Journal Article
	Year	2024	Publication	Quantum	Abbreviated Journal
	Volume	8	Issue	1289	Pages
	Keywords
	Abstract	We consider a quantum system that is being continuously monitored, giving rise to a measurement signal. From such a stream of data, information needs to be inferred about the underlying system's dynamics. Here we focus on hypothesis testing problems and put forward the usage of sequential strategies where the signal is analyzed in real time, allowing the experiment to be concluded as soon as the underlying hypothesis can be identified with a certified prescribed success probability. We analyze the performance of sequential tests by studying the stopping-time behavior, showing a considerable advantage over currently-used strategies based on a fixed predetermined measurement time.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	xxxx			Approved	no
	Call Number	Admin @ si @ GBR2024			Serial	3847
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	Author	Luca Ginanni Corradini; Simone Balocco; Luciano Maresca; Silvio Vitale; Matteo Stefanini
	Title	Anatomical Modifications After Stent Implantation: A Comparative Analysis Between CGuard, Wallstent, and Roadsaver Carotid Stents			Type	Journal Article
	Year	2023	Publication	Journal of Endovascular Therapy	Abbreviated Journal
	Volume	30	Issue	1	Pages	18-24
	Keywords	Ginanni Corradini L, Balocco S, Maresca L, Vitale S, Stefanini M.
	Abstract	Abstract Purpose: Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS). Materials and Methods: The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression. Results: For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS. Conclusion: Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents. Level of Evidence: Level 4, Case Series.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	xxx			Approved	no
	Call Number	Admin @ si @ GBM2023			Serial	4006
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	Author	Mingyi Yang; Fei Yang; Luka Murn; Marc Gorriz Blanch; Juil Sock; Shuai Wan; Fuzheng Yang; Luis Herranz
	Title	Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks			Type	Journal Article
	Year	2024	Publication	IEEE Transactions on Circuits and Systems for Video Technology	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	M Yang, F Yang, L Murn, MG Blanch, J Sock, S Wan, F Yang, L Herranz
	Abstract	Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	xxx			Approved	no
	Call Number	Admin @ si @ YYM2024			Serial	4007
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	Author	Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski
	Title	Revisiting Supervision for Continual Representation Learning			Type	Miscellaneous
	Year	2023	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, recent studies have highlighted the strengths of self-supervised continual representation learning. The improved transferability of representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	xxx			Approved	no
	Call Number	Admin @ si @ MCT2023			Serial	4013
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	Author	Daniel Ponsa; Antonio Lopez
	Title	Seguimiento Visual de Contornos Computerizado			Type	Miscellaneous
	Year	2009	Publication	UAB Divulga, Revista de divulgacion cientifica	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	spreading;ADAS			Approved	no
	Call Number	ADAS @ adas @ PoL2009b			Serial	1270
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	Author	David Geronimo; Antonio Lopez
	Title	Deteccion de Peatones para Sistemas Avanzados de Asistencia al Conductor			Type	Miscellaneous
	Year	2010	Publication	UAB Divulga	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Los sistemas de asistencia al conductor, y particularmente los sistemas de protección de peatones, representan uno de los campos de investigación más activos dedicados a la mejora de la seguridad vial. El mayor desafío es el desarrollo de sistemas a bordo fiables de detección de peatones. En esta revisión del estado de la técnica de la detección de peatones, se divide el problema en diferentes etapas, cada una con responsabilidades propias dentro del sistema. Esta división facilita el posterior análisis y discusión de cada uno de los métodos en la literatura, favoreciendo la comparación entre ellos. Finalmente se discuten los temas más importantes de este campo poniendo especial énfasis en las necesidades actuales y los desafíos futuros.
	Address	Bellaterra (Catalonia), Spain
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	spreading;ADAS			Approved	no
	Call Number	ADAS @ adas @ GeL2010a			Serial	1414
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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras
	Title	Aprendiendo a recrear la realidad en 3D			Type	Journal
	Year	2008	Publication	UAB Divulga, Revista de divulgacion cientifica	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	spreading;ADAS			Approved	no
	Call Number	ADAS @ adas @ JSL2008b			Serial	1472
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	Author	David Geronimo; Antonio Lopez
	Title	Sistema de deteccion de peatones			Type	Miscellaneous
	Year	2010	Publication	UAB Divulga	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Durante la próxima década, los sistemas de protección de peatones jugarán un papel fundamental en el reto de mejorar la seguridad viaria. El objetivo principal de estos sistemas, detectar peatones en entornos urbanos, implica procesar imágenes de escenas exteriores desde una plataforma móvil para buscar objetos de aspecto variable como son las personas. Dadas estas dificultades, estos sistemas hacen uso de las últimas técnicas de visión por computador. Esta propuesta consiste en un sistema de tres módulos basado tanto en información 2D como en 3D. El primer módulo utiliza información 3D para hacer una estimación de los parámetros de la carretera y seleccionar regiones de interés que serán analizadas después. El segundo módulo utiliza un clasificador de ventanas 2D para etiquetar las mencionadas regiones como peatón o no peatón. El módulo final vuelve a utilizar de nuevo la información 3D para verificar las regiones clasificadas y, con información 2D, refinar los resultados finales. Los resultados experimentales son positivos tanto en rendimiento como en tiempo de cómputo.
	Address	Bellaterra (Spain)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	spreading;ADAS			Approved	no
	Call Number	ADAS @ adas @ GeL2010b			Serial	1473
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	Author	Angel Sappa; M.A. Garcia
	Title	Aprendiendo a recrear la realidad en 3D			Type	Journal
	Year	2007	Publication	UAB Divulga, Revista de Divulgacion Cientifica	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	spreading			Approved	no
	Call Number	ADAS @ adas @ SaG2007c			Serial	1470
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	Author	Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Using the Average Landmark Vector Method for Robot Homing			Type	Conference Article
	Year	2007	Publication	Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA	Abbreviated Journal
	Volume	163	Issue		Pages	331–338
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978–1–58603–798–7	Medium
	Area		Expedition		Conference	CCIA’07
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ GRL2007			Serial	899
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	Author	Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Comparing Combinations of Feature Regions for Panoramic VSLAM			Type	Conference Article
	Year	2007	Publication	4th International Conference on Informatics in Control, Automation and Robotics	Abbreviated Journal
	Volume		Issue		Pages	292–297
	Keywords
	Abstract
	Address	Angers (France)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICINCO
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ RLA2007			Serial	900
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	Author	Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors			Type	Conference Article
	Year	2008	Publication	IEEE International Conference on Robotics and Automation,	Abbreviated Journal
	Volume		Issue		Pages	538–543
	Keywords
	Abstract
	Address	Pasadena; CA; USA
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICRA
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ RTL2008			Serial	1144
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	Author	Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras
	Title	Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas			Type	Journal Article
	Year	2011	Publication	Journal of Intelligent and Robotic Systems	Abbreviated Journal	JIRC
	Volume	64	Issue	3-4	Pages	625-649
	Keywords
	Abstract	Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments.
	Address
	Corporate Author				Thesis
	Publisher	Springer Netherlands	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0921-0296	ISBN		Medium
	Area		Expedition		Conference
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ RGA2011			Serial	1728
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	Author	Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras
	Title	The IIIA30 MObile Robot Object Recognition Datset			Type	Conference Article
	Year	2011	Publication	11th Portuguese Robotics Open	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones.
	Address	Lisboa
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	Robotica
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ RAV2011			Serial	1777
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	Author	L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip
	Title	Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation			Type	Journal Article
	Year	2016	Publication	Computers & Industrial Engineering	Abbreviated Journal	CIE
	Volume	94	Issue		Pages	93-104
	Keywords	Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning
	Abstract	In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one.
	Address
	Corporate Author				Thesis
	Publisher	PERGAMON-ELSEVIER SCIENCE LTD	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	CIE
	Series Volume		Series Issue		Edition
	ISSN	0360-8352	ISBN		Medium
	Area		Expedition		Conference
	Notes	OR;MV;			Approved	no
	Call Number	Admin @ si @ CFG2016			Serial	2749
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