203 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Online Resource Sharing via Dynamic Max-Min Fairness: Efficiency, Robustness and Non-Stationarity

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    We study the allocation of shared resources over multiple rounds among competing agents, via a dynamic max-min fair (DMMF) mechanism: the good in each round is allocated to the requesting agent with the least number of allocations received to date. Previous work has shown that when an agent has i.i.d. values across rounds, then in the worst case, she can never get more than a constant strictly less than 11 fraction of her ideal utility -- her highest achievable utility given her nominal share of resources. Moreover, an agent can achieve at least half her utility under carefully designed `pseudo-market' mechanisms, even though other agents may act in an arbitrary (possibly adversarial and collusive) manner. We show that this robustness guarantee also holds under the much simpler DMMF mechanism. More significantly, under mild assumptions on the value distribution, we show that DMMF in fact allows each agent to realize a 1o(1)1 - o(1) fraction of her ideal utility, despite arbitrary behavior by other agents. We achieve this by characterizing the utility achieved under a richer space of strategies, wherein an agent can tune how aggressive to be in requesting the item. Our new strategies also allow us to handle settings where an agent's values are correlated across rounds, thereby allowing an adversary to predict and block her future values. We prove that again by tuning one's aggressiveness, an agent can guarantee Ω(γ)\Omega(\gamma) fraction of her ideal utility, where γ[0,1]\gamma\in [0, 1] is a parameter that quantifies dependence across rounds (with γ=1\gamma = 1 indicating full independence and lower values indicating more correlation). Finally, we extend our efficiency results to the case of reusable resources, where an agent might need to hold the item over multiple rounds to receive utility

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Learning Reserve Prices in Second-Price Auctions

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    This paper proves the tight sample complexity of Second-Price Auction with Anonymous Reserve, up to a logarithmic factor, for each of all the value distribution families studied in the literature: [0,1]-bounded, [1,H]-bounded, regular, and monotone hazard rate (MHR). Remarkably, the setting-specific tight sample complexity poly(?^{-1}) depends on the precision ? ? (0, 1), but not on the number of bidders n ? 1. Further, in the two bounded-support settings, our learning algorithm allows correlated value distributions. In contrast, the tight sample complexity ??(n) ? poly(?^{-1}) of Myerson Auction proved by Guo, Huang and Zhang (STOC 2019) has a nearly-linear dependence on n ? 1, and holds only for independent value distributions in every setting. We follow a similar framework as the Guo-Huang-Zhang work, but replace their information theoretical arguments with a direct proof

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    LIPIcs, Volume 258, SoCG 2023, Complete Volume

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    LIPIcs, Volume 258, SoCG 2023, Complete Volum

    Information Retrieval Based on DOM Trees

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    [ES] Desde hace varios años, la cantidad de información disponible en la web crece de manera exponencial. Cada día se genera una gran cantidad de información que prácticamente de inmediato está disponible en la web. Los buscadores e indexadores recorren diariamente la web para encontrar toda esa información que se ha ido añadiendo y así, ponerla a disposición del usuario devolviéndola en los resultados de las búsquedas. Sin embargo, la cantidad de información es tan grande que debe ser preprocesada con anterioridad. Dado que el usuario que realiza una búsqueda de información solamente está interesado en la información relevante, no tiene sentido que los buscadores e indexadores procesen el resto de elementos de las páginas web. El procesado de elementos irrelevantes de páginas web supone un gasto de recursos innecesario, como por ejemplo espacio de almacenamiento, tiempo de procesamiento, uso de ancho de banda, etc. Se estima que entre el 40% y el 50% del contenido de las páginas web son elementos irrelevantes. Por eso, en los últimos 20 años se han desarrollado técnicas para la detección de elementos tanto relevantes como irrelevantes de páginas web. Este objetivo se puede abordar de diversas maneras, por lo que existen técnicas diametralmente distintas para afrontar el problema. Esta tesis se centra en el desarrollo de técnicas basadas en árboles DOM para la detección de diversas partes de las páginas web, como son el contenido principal, la plantilla, y el menú. La mayoría de técnicas existentes se centran en la detección de texto dentro del contenido principal de las páginas web, ya sea eliminando la plantilla de dichas páginas o detectando directamente el contenido principal. Las técnicas que proponemos no sólo son capaces de realizar la extracción de texto, sino que, bien por eliminación de plantilla o bien por detección del contenido principal, son capaces de aislar cualquier elemento relevante de las páginas web, como por ejemplo imágenes, animaciones, videos, etc. Dichas técnicas no sólo son útiles para buscadores y rastreadores, sino que también pueden ser útiles directamente para el usuario que navega por la web. Por ejemplo, en el caso de usuarios con diversidad funcional (como sería una ceguera) puede ser interesante la eliminación de elementos irrelevantes para facilitar la lectura (o escucha) de las páginas web. Para hacer las técnicas accesibles a todo el mundo, las hemos implementado como extensiones del navegador, y son compatibles con navegadores basados en Mozilla o en Chromium. Además, estas herramientas están públicamente disponibles para que cualquier persona interesada pueda acceder a ellas y continuar con la investigación si así lo deseara.[CA] Des de fa diversos anys, la quantitat d'informació disponible en la web creix de manera exponencial. Cada dia es genera una gran quantitat d'informació que immediatament es posa disponible en la web. Els cercadors i indexadors recorren diàriament la web per a trobar tota aqueixa informació que s'ha anat afegint i així, posar-la a la disposició de l'usuari retornant-la en els resultats de les cerques. No obstant això, la quantitat d'informació és tan gran que aquesta ha de ser preprocessada. Atés que l'usuari que realitza una cerca d'informació solament es troba interessat en la informació rellevant, no té sentit que els cercadors i indexadors processen la resta d'elements de les pàgines web. El processament d'elements irrellevants de pàgines web suposa una despesa de recursos innecessària, com per exemple espai d'emmagatzematge, temps de processament, ús d'amplada de banda, etc. S'estima que entre el 40% i el 50% del contingut de les pàgines web són elements irrellevants. Precisament per això, en els últims 20 anys s'han desenvolupat tècniques per a la detecció d'elements tant rellevants com irrellevants de pàgines web. Aquest objectiu es pot afrontar de diverses maneres, per la qual cosa existeixen tècniques diametralment diferents per a afrontar el problema. Aquesta tesi se centra en el desenvolupament de tècniques basades en arbres DOM per a la detecció de diverses parts de les pàgines web, com són el contingut principal, la plantilla, i el menú. La majoria de tècniques existents se centren en la detecció de text dins del contingut principal de les pàgines web, ja siga eliminant la plantilla d'aquestes pàgines o detectant directament el contingut principal. Les tècniques que hi proposem no sols són capaces de realitzar l'extracció de text, sinó que, bé per eliminació de plantilla o bé per detecció del contingut principal, són capaços d'aïllar qualsevol element rellevant de les pàgines web, com per exemple imatges, animacions, vídeos, etc. Aquestes tècniques no sols són útils per a cercadors i rastrejadors, sinó també poden ser útils directament per a l'usuari que navega per la web. Per exemple, en el cas d'usuaris amb diversitat funcional (com ara una ceguera) pot ser interessant l'eliminació d'elements irrellevants per a facilitar-ne la lectura (o l'escolta) de les pàgines web. Per a fer les tècniques accessibles a tothom, les hem implementades com a extensions del navegador, i són compatibles amb navegadors basats en Mozilla i en Chromium. A més, aquestes eines estan públicament disponibles perquè qualsevol persona interessada puga accedir a elles i continuar amb la investigació si així ho desitjara.[EN] For several years, the amount of information available on the Web has been growing exponentially. Every day, a huge amount of data is generated and it is made immediately available on the Web. Indexers and crawlers browse the Web daily to find the new information that has been added, and they make it available to answer the users' search queries. However, the amount of information is so huge that it must be preprocessed. Given that users are only interested in the relevant information, it is not necessary for indexers and crawlers to process other boilerplate, redundant or useless elements of the web pages. Processing such irrelevant elements lead to an unnecessary waste of resources, such as storage space, runtime, bandwidth, etc. Different studies have shown that between 40% and 50% of the data on the Web are noisy elements. For this reason, several techniques focused on the detection of both, relevant and irrelevant data, have been developed over the last 20 years. The problems of identifying the relevant content of a web page, its template, its menu, etc. can be faced in various ways, and for this reason, there exist completely different techniques to address those problems. This thesis is focused on the development of information retrieval techniques based on DOM trees. Its goal is to detect different parts of a web page, such as the main content, the template, and the main menu. Most of the existing techniques are focused on the detection of text inside the main content of the web pages, mainly by removing the template of the web page or by inferring the main content. The techniques proposed in this thesis do not only extract text by eliminating the template or inferring the main content, but also extract any other relevant information from web pages such as images, animations, videos, etc. Our techniques are not only useful for indexers and crawlers but also for the user browsing the Web. For instance, in the case of users with functional diversity problems (such as blindness), removing noisy elements can facilitate them to read (or listen to) the web pages. To make the techniques broadly accessible to everybody, we have implemented them as browser extensions, which are compatible with Mozilla-based and Chromium-based browsers. In addition, these tools are publicly available, so any interested person can access them and continue with the research if they wish to do so.Alarte Aleixandre, J. (2023). Information Retrieval Based on DOM Trees [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19667

    A review of commercialisation mechanisms for carbon dioxide removal

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    The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally

    Social convergence in times of spatial distancing: The rRole of music during the COVID-19 Pandemic

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