2,892 research outputs found

    Consistency Techniques for Finding an Optimal Relaxation of a Feature Subscription

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    Partial mixture model for tight clustering of gene expression time-course

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    Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored. Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms. Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion

    Collaborative Diagnosis of Over-Subscribed Temporal Plans

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    PhD thesisOver-subscription, that is, being assigned too many tasks or requirements that are too demanding, is commonly encountered in temporal planning problems. As human beings, we often want to do more than we can, ask for things that may not be available, while underestimating how long it takes to perform each task. It is often difficult for us to detect the causes of failure in such situations and then find resolutions that are effective. We can greatly benefit from tools that assist us by looking out for these plan failures, by identifying their root causes, and by proposing preferred resolutions to these failures that lead to feasible plans. In recent literature, several approaches have been developed to resolve such over-subscribed problems, which are often framed as over-constrained scheduling, configuration design or optimal planning problems. Most of them take an all-or-nothing approach, in which over-subscription is resolved through suspending constraints or dropping goals. While helpful, in real-world scenarios, we often want to preserve our plan goals as much possible. As human beings, we know that slightly weakening the requirements of a travel plan, or replacing one of its destinations with an alternative one is often sufficient to resolve an over-subscription problem, no matter if the requirement being weakened is the duration of a deep-sea survey being planned for, or the restaurant cuisine for a dinner date. The goal of this thesis is to develop domain independent relaxation algorithms that perform this type of slight weakening of constraints, which we will formalize as continuous relaxation, and to embody them in a computational aid, Uhura, that performs tasks akin to an experienced travel agent or ocean scientists. In over-subscribed situations, Uhura helps us diagnose the causes of failure, suggests alternative plans, and collaborates with us in order to resolve conflicting requirements in the most preferred way. Most importantly, the algorithms underlying Uhura supports the weakening, instead of suspending, of constraints and variable domains in a temporally flexible plan. The contribution of this thesis is two-fold. First, we developed an algorithmic framework, called Best-first Conflict-Directed Relaxation (BCDR), for performing plan relaxation. Second, we use the BCDR framework to perform relaxation for several different families of plan representations involving different types of constraints. These include temporal constraints, chance constraints and variable domain constraints, and we incorporate several specialized conflict detection and resolution algorithms in support of the continuous weakening of them. The key idea behind BCDR's approach to continuous relaxation is to generalize the concepts of discrete conflicts and relaxations, first introduced by the model-based diagnosis community, to hybrid conflicts and relaxations, which denote minimal inconsistencies and minimal relaxations to both discrete and continuous relaxable constraints

    Continuous relaxation to over-constrained temporal plans

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 165-168).When humans fail to understand the capabilities of an autonomous system or its environmental limitations, they can jeopardize their objectives and the system by asking for unrealistic goals. The objective of this thesis is to enable consensus between human and autonomous system, by giving autonomous systems the ability to communicate to the user the reasons for goal failure and the relaxations to goals that archive feasibility. We represent our problem in the context of temporal plans, a set of timed activities that can represent the goals and constraints proposed by users. Over-constrained temporal plans are commonly encountered while operating autonomous and decision support systems, when user objectives are in conflict with the environment. Over constrained plans are addressed by relaxing goals and or constraints, such as delaying the arrival time of a trip, with some candidate relaxations being preferable to others. In this thesis we present Uhura, a temporal plan diagnosis and relaxation algorithm that is designed to take over-constrained input plans with temporal flexibility and contingencies, and generate temporal relaxations that make the input plan executable. We introduce two innovative approaches within Uhura: collaborative plan diagnosis and continuous relaxation. Uhura focuses on novel ways of satisfying three goals to make the plan relaxation process more convenient for the users: small perturbation, quick response and simple interaction. First, to achieve small perturbation, Uhura resolves over-constrained temporal plans through partial relaxation of goals, more specifically, through the relaxation of schedules. Prior work on temporal relaxations takes an all-or-nothing approach in which timing constraints on goals, such as arrival times to destinations, are completely relaxed in the relaxations. The Continuous Temporal Relaxation method used by Uhura adjusts the temporal bounds of temporal constraints to minimizes the perturbation caused by the relaxations to the goals in the original plan. Second, to achieve quick responses, Uhura introduces Best-first Conflict-directed Relaxation, a new method that efficiently enumerates alternative options in best-first order. The search space of alternative options to temporal planning problems is very large and finding the best one is a NP-hard problem. Uhura empirically demonstrates fast enumeration by unifying methods from minimal relaxation and conflict-directed enumeration methods, first developed for model based diagnosis. Uhura achieves two orders of magnitude improvement in run-time performance relative to state-of-the-art approaches, making it applicable to a larger group of real-world scenarios with complex temporal plans. Finally, to achieve simple interactions, Uhura presents to the user a small set of preferred relaxations in best-first order based on user preference models. By using minimal relaxations to represent alternative options, Uhura simplifies the options presented to the user and reduces the size of its results and improves their expressiveness. Previous work either generates minimal relaxations or full relaxations based on preference, but not minimal relaxations based on preference. Preferred minimal relaxations simplify the interaction in that the users do not have to consider any irrelevant information, and may reach an agreement with the autonomous system faster. Therefore it makes communication between robots and users more convenient and precise. We have incorporated Uhura within an autonomous executive that collaborates with human operators to resolve over-constrained temporal plans. Its effectiveness has been demonstrated both in simulation and in hardware on a Personal Transportation System concept. The average runtime of Uhura on large problems with 200 activities is two order of magnitude lower compared to current approaches. In addition, Uhura has also been used in a driving assistant system to resolve conflicts in driving plans. We believe that Uhura's collaborative temporal plan diagnosis capability can benefit a wide range of applications, both within industrial applications and in our daily lives.by Peng Yu.S.M

    Mobile Apps for healthy living : segmenting and profiling offer according to user needs

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    Living a healthy lifestyle is becoming increasingly relevant for contemporary society. As a result, health and lifestyle apps are on their way to become the dominant technological tool in supporting such a healthy lifestyle. For that reason, it becomes essential for app developers to understand what exactly consumers consider a healthy lifestyle, in order to design apps that satisfy user needs and ensure enduring success in the health and lifestyle app market. The present study identifies the key activities and goals consumers associate with a healthy lifestyle and assesses to which degree the five health and lifestyle apps, MyFitnessPal, Runtastic, Seven, Sleep Better and Headspace facilitate them. To this end, primary data was obtained by conducting a survey, in which respondents, among other questions, were asked to provide their associations with a healthy lifestyle. The results were compared with qualitative secondary data about the five different health and lifestyle apps. In addition, the apps were matched with a taxonomy of behavioural change processes. The results show that consumers’ understanding of a healthy lifestyle is much more varied and complex than the literature suggests. Moreover, findings showed which parts of a healthy lifestyle are well matched by current apps and which are not. Also, it was seen that current apps should integrate more behaviour change techniques that enhance motivation. In conclusion, these findings are relevant for app developers in designing successful health and lifestyle apps that meet different consumer needs in order to ensure engagement and retention.Viver um estilo de vida saudável é cada vez mais relevante para a sociedade contemporânea. Como resultado, as aplicações de saúde e estilo de vida estão a caminho de se tornarem a ferramenta tecnológica dominante no apoio a um estilo de vida saudável. Por essa razão, torna-se essencial que os desenvolvedores de aplicativos entendam exatamente o que os consumidores consideram um estilo de vida saudável, a fim de criar aplicativos que satisfaçam as necessidades dos usuários e garantam um sucesso duradouro no mercado de aplicativos de saúde e estilo de vida. O presente estudo identifica as principais atividades e objetivos que os consumidores associam a um estilo de vida saudável e avalia até que ponto os cinco aplicativos de saúde e estilo de vida, MyFitnessPal, Runtastic, Seven, Sleep Better e Headspace os facilitam. Para isso, os dados primários foram obtidos por meio da realização de uma pesquisa, na qual os entrevistados, entre outras questões, foram convidados a proporcionar às suas associações um estilo de vida saudável. Os resultados foram comparados com dados secundários qualitativos sobre as cinco diferentes aplicações de saúde e estilo de vida. Além disso, as aplicações foram combinadas com uma taxonomia de processos de mudança comportamental. Os resultados mostram que a compreensão dos consumidores sobre um estilo de vida saudável é muito mais variada e complexa do que a literatura sugere. Além disso, os resultados mostraram quais as partes de um estilo de vida saudável que são bem correspondidas pelas aplicações atuais e quais as que não o são. Além disso, verificou-se que as aplicações atuais devem integrar mais técnicas de mudança de comportamento que aumentem a motivação. Em conclusão, estas conclusões são relevantes para os criadores de aplicações na conceção de aplicações bem-sucedidas de saúde e estilo de vida que satisfaçam as diferentes necessidades dos consumidores, a fim de garantir o seu envolvimento e retenção

    From Social Data Mining to Forecasting Socio-Economic Crisis

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    Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

    New perspectives on cost partitioning for optimal classical planning

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    Admissible heuristics are the main ingredient when solving classical planning tasks optimally with heuristic search. There are many such heuristics, and each has its own strengths and weaknesses. As higher admissible heuristic values are more accurate, the maximum over several admissible heuristics dominates each individual one. Operator cost partitioning is a well-known technique to combine admissible heuristics in a way that dominates their maximum and remains admissible. But are there better options to combine the heuristics? We make three main contributions towards this question: Extensions to the cost partitioning framework can produce higher estimates from the same set of heuristics. Cost partitioning traditionally uses non-negative cost functions. We prove that this restriction is not necessary, and that allowing negative values as well makes the framework more powerful: the resulting heuristic values can be exponentially higher, and unsolvability can be detected even if all component heuristics have a finite value. We also generalize operator cost partitioning to transition cost partitioning, which can differentiate between different contexts in which an operator is used. Operator-counting heuristics reason about the number of times each operator is used in a plan. Many existing heuristics can be expressed in this framework, which gives new theoretical insight into their relationship. Different operator-counting heuristics can be easily combined within the framework in a way that dominates their maximum. Potential heuristics compute a heuristic value as a weighted sum over state features and are a fast alternative to operator-counting heuristics. Admissible and consistent potential heuristics for certain feature sets can be described in a compact way which means that the best heuristic from this class can be extracted in polynomial time. Both operator-counting and potential heuristics are closely related to cost partitioning. They offer a new look on cost-partitioned heuristics and already sparked research beyond their use as classical planning heuristics

    Contributions to presence-based systems for deploying ubiquitous communication services

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    Next-Generation Networks (NGNs) will converge the existing fixed and wireless networks. These networks rely on the IMS (IP Multimedia Subsystem), introduced by the 3GPP. The presence service came into being in instant messaging applications. A user¿s presence information consists in any context that is necessary for applications to handle and adapt the user's communications. The presence service is crucial in the IMS to deploy ubiquitous services. SIMPLE is the standard protocol for handling presence and instant messages. This protocol disseminates users' presence information through subscriptions, notifications and publications. SIMPLE generates much signaling traffic for constantly disseminating presence information and maintaining subscriptions, which may overload network servers. This issue is even more harmful to the IMS due to its centralized servers. A key factor in the success of NGNs is to provide users with always-on services that are seamlessly part of their daily life. Personalizing these services according to the users' needs is necessary for the success of these services. To this end, presence information is considered as a crucial tool for user-based personalization. This thesis can be briefly summarized through the following contributions: We propose filtering and controlling the rate of presence publications so as to reduce the information sent over access links. We probabilistically model presence information through Markov chains, and analyzed the efficiency of controlling the rate of publications that are modeled by a particular Markov chain. The reported results show that this technique certainly reduces presence overload. We mathematically study the amount of presence traffic exchanged between domains, and analyze the efficiency of several strategies for reducing this traffic. We propose an strategy, which we call Common Subscribe (CS), for reducing the presence traffic exchanged between federated domains. We compare this strategy traffic with that generated by other optimizations. The reported results show that CS is the most efficient at reducing presence traffic. We analyze the load in the number of messages that several inter-domain traffic optimizations cause to the IMS centralized servers. Our proposed strategy, CS, combined with an RLS (i.e., a SIMPLE optimization) is the only optimization that reduces the IMS load; the others increase this load. We estimate the efficiency of the RLS, thereby concluding that the RLS is not efficient under certain circumstances, and hence this optimization is discouraged. We propose a queuing system for optimizing presence traffic on both the network core and access link, which is capable to adapt the publication and notification rate based on some quality conditions (e.g, maximum delay). We probabilistically model this system, and validate it in different scenarios. We propose, and implement a prototype of, a fully-distributed platform for handling user presence information. This approach allows integrating Internet Services, such as HTTP or VoIP, and optimizing these services in an easy, user-personalized way. We have developed SECE (Sense Everything, Control Everything), a platform for users to create rules that handle their communications and Internet Services proactively. SECE interacts with multiple third-party services for obtaining as much user context as possible. We have developed a natural-English-like formal language for SECE rules. We have enhanced SECE for discovering web services automatically through the Web Ontology Language (OWL). SECE allows composing web services automatically based on real-world events, which is a significant contribution to the Semantic Web. The research presented in this thesis has been published through 3 book chapters, 4 international journals (3 of them are indexed in JCR), 10 international conference papers, 1 demonstration at an international conference, and 1 national conferenceNext-Generation Networks (NGNs) son las redes de próxima generación que soportaran la convergencia de redes de telecomunicación inalámbricas y fijas. La base de NGNs es el IMS (IP Multimedia Subsystem), introducido por el 3GPP. El servicio de presencia nació de aplicaciones de mesajería instantánea. La información de presencia de un usuario consiste en cualquier tipo de información que es de utilidad para manejar las comunicaciones con el usuario. El servicio de presencia es una parte esencial del IMS para el despliegue de servicios ubicuos. SIMPLE es el protocolo estándar para manejar presencia y mensajes instantáneos en el IMS. Este protocolo distribuye la información de presencia de los usuarios a través de suscripciones, notificaciones y publicaciones. SIMPLE genera mucho tráfico por la diseminación constante de información de presencia y el mantenimiento de las suscripciones, lo cual puede saturar los servidores de red. Este problema es todavía más perjudicial en el IMS, debido al carácter centralizado de sus servidores. Un factor clave en el éxito de NGNs es proporcionar a los usuarios servicios ubicuos que esten integrados en su vida diaria y asi interactúen con los usuarios constantemente. La personalización de estos servicios basado en los usuarios es imprescindible para el éxito de los mismos. Para este fin, la información de presencia es considerada como una herramienta base. La tesis realizada se puede resumir brevemente en los siguientes contribuciones: Proponemos filtrar y controlar el ratio de las publicaciones de presencia para reducir la cantidad de información enviada en la red de acceso. Modelamos la información de presencia probabilísticamente mediante cadenas de Markov, y analizamos la eficiencia de controlar el ratio de publicaciones con una cadena de Markov. Los resultados muestran que este mecanismo puede efectivamente reducir el tráfico de presencia. Estudiamos matemáticamente la cantidad de tráfico de presencia generada entre dominios y analizamos el rendimiento de tres estrategias para reducir este tráfico. Proponemos una estrategia, la cual llamamos Common Subscribe (CS), para reducir el tráfico de presencia entre dominios federados. Comparamos el tráfico generado por CS frente a otras estrategias de optimización. Los resultados de este análisis muestran que CS es la estrategia más efectiva. Analizamos la carga en numero de mensajes introducida por diferentes optimizaciones de tráfico de presencia en los servidores centralizados del IMS. Nuestra propuesta, CS, combinada con un RLS (i.e, una optimización de SIMPLE), es la unica optimización que reduce la carga en el IMS. Estimamos la eficiencia del RLS, deduciendo que un RLS no es eficiente en ciertas circunstancias, en las que es preferible no usar esta optimización. Proponemos un sistema de colas para optimizar el tráfico de presencia tanto en el núcleo de red como en la red de acceso, y que puede adaptar el ratio de publicación y notificación en base a varios parametros de calidad (e.g., maximo retraso). Modelamos y analizamos este sistema de colas probabilísticamente en diferentes escenarios. Proponemos una arquitectura totalmente distribuida para manejar las información de presencia del usuario, de la cual hemos implementado un prototipo. Esta propuesta permite la integracion sencilla y personalizada al usuario de servicios de Internet, como HTTP o VoIP, asi como la optimizacón de estos servicios. Hemos desarrollado SECE (Sense Everything, Control Everything), una plataforma donde los usuarios pueden crear reglas para manejar todas sus comunicaciones y servicios de Internet de forma proactiva. SECE interactúa con una multitud de servicios para conseguir todo el contexto possible del usuario. Hemos desarollado un lenguaje formal que parace como Ingles natural para que los usuarios puedan crear sus reglas. Hemos mejorado SECE para descubrir servicios web automaticamente a través del lenguaje OWL (Web Ontology Language)
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