1,537 research outputs found

    Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization

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    A deeper understanding of video activities extends beyond recognition of underlying concepts such as actions and objects: constructing deep semantic representations requires reasoning about the semantic relationships among these concepts, often beyond what is directly observed in the data. To this end, we propose an energy minimization framework that leverages large-scale commonsense knowledge bases, such as ConceptNet, to provide contextual cues to establish semantic relationships among entities directly hypothesized from video signal. We mathematically express this using the language of Grenander's canonical pattern generator theory. We show that the use of prior encoded commonsense knowledge alleviate the need for large annotated training datasets and help tackle imbalance in training through prior knowledge. Using three different publicly available datasets - Charades, Microsoft Visual Description Corpus and Breakfast Actions datasets, we show that the proposed model can generate video interpretations whose quality is better than those reported by state-of-the-art approaches, which have substantial training needs. Through extensive experiments, we show that the use of commonsense knowledge from ConceptNet allows the proposed approach to handle various challenges such as training data imbalance, weak features, and complex semantic relationships and visual scenes.Comment: Accepted to WACV 201

    Balmer line shifts in quasars

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    We offer a broad review of Balmer line phenomenology in type 1 active galactic nuclei, briefly sum- marising luminosity and radio loudness effects, and discussing interpretation in terms of nebular physics along the 4D eigenvector 1 sequence of quasars. We stress that relatively rare, peculiar Balmer line profiles (i.e., with large shifts with respect to the rest frame or double and multiple peaked) that start attracted attentions since the 1970s are still passable of multiple dynamical interpretation. More mainstream objects are still not fully understood as well, since competing dynamical models and geometries are possible. Further progress may come from inter-line comparison across the 4D Eigenvector 1 sequence.Comment: Accepted for publication in Astrophysics and Space Science, Special Issue on Line Shifts in Astrophysics and Laboratory Plasm

    Diagnostic imaging for tracheobronchomalacia patients

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    El camp de la visió per computador va adquirint cada any més i més importància en tot el món, des del seu ús per a crear cotxes no tripulats, fins a ajudar a professionals mèdics en la realització de tasques difícils i dures. Una d'aquestes tasques consisteix en detectar si un pacient pateix de traqueobroncomalacia, una malaltia que fa que les parets de la tràquea i els bronquis es col·lapsin al exhalar. Aquest projecte pretén donar una estimació en el nivell de la obertura de les vies respiratòries de la persona que pateix traqueobroncomalacia, i dels pacients sans, i així donar un diagnòstic per a la malaltia utilitzant algorismes de segmentació existents o creant-ne de nous, i usant les característiques obtingudes dels diversos fotogrames de les broncoscòpies dels pacients. S'usaran varis algorismes de segmentació basats en la segmentació per regions, per bores i la segmentació per binarització, juntament amb la informació obtinguda durant el procés, per calcular i avaluar la evolució del grau de estenosi de la tràquea.The computer vision field is gaining, every year, more and more importance across the whole world, from being used in order to create self-driving cars, to helping medics accomplishing difficult and arduous tasks. One of these tasks is to detect if a patient suffers from tracheobronchomalacia, a condition which causes the walls of the trachea and bronchi to collapse when exhaling. This project aims to give an estimation on the degree of aperture of the airways for people suffering from tracheobronchomalacia, and healthy users, and in so giving a diagnosis for the disease using existing or new segmentation algorithms, and features gathered from the various frames of the patients' bronchoscopies. We will use various segmentation algorithms based on regional, edge and binarization segmentation, together with information gathered during the process, in order to calculate and evaluate the evolution of the stenosis degree of the trachea

    Analyzing Tag Semantics Across Collaborative Tagging Systems

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    The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance

    An Efficient Bandit Algorithm for Realtime Multivariate Optimization

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    Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions. Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.Comment: KDD'17 Audience Appreciation Awar

    Interoperable services based on activity monitoring in ambient assisted living environments

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    Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

    Comparison of Positivity in Two EpidemicWaves of COVID-19 in Colombia with FDA

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    We use the functional data methodology to examine whether there are significant differences between two waves of contagion by COVID-19 in Colombia between 7 July 2020 and 20 July 2021. A pointwise functional t-test is initially used, then an alternative statistical test proposal for paired samples is presented, which has a theoretical distribution and performs well in small samples. Our statistical test generates a scalar p-value, which provides a global idea about the significance of the positivity curves, complementing the existing punctual tests, as an advantage.Government of Andalusia (Spain) PID2020-113961GB-I00Ministry of Science and Innovation, Spain (MICINN) Spanish GovernmentEuropean Commission CEX2020-001105-M/AEI/10.13039/501100011033.Junta de Andaluci
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