Soft quantification in statistical relational learning

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation inference algorithm for it. To the best of our knowledge, is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy

Neural Models for Information Retrieval without Labeled Data

Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or \emph{weakly supervised} solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, in our weak supervision approach, neural models are trained on a large set of noisy and biased training data obtained from external resources, existing models, or heuristics. We first introduce relevance-based embedding models that learn distributed representations for words and queries. We show that the learned representations can be effectively employed for a set of IR tasks, including query expansion, pseudo-relevance feedback, and query classification. We further propose a standalone learning to rank model based on deep neural networks. Our model learns a sparse representation for queries and documents. This enables us to perform efficient retrieval by constructing an inverted index in the learned semantic space. Our model outperforms state-of-the-art retrieval models, while performing as efficiently as term matching retrieval models. We additionally propose a neural network framework for predicting the performance of a retrieval model for a given query. Inspired by existing query performance prediction models, our framework integrates several information sources, such as retrieval score distribution and term distribution in the top retrieved documents. This leads to state-of-the-art results for the performance prediction task on various standard collections. We finally bridge the gap between retrieval and recommendation models, as the two key components in most information systems. Search and recommendation often share the same goal: helping people get the information they need at the right time. Therefore, joint modeling and optimization of search engines and recommender systems could potentially benefit both systems. In more detail, we introduce a retrieval model that is trained using user-item interaction (e.g., recommendation data), with no need to query-document relevance information for training. Our solutions and findings in this dissertation smooth the path towards learning efficient and effective models for various information retrieval and related tasks, especially when large-scale training data is not available

Data analytics for mobile traffic in 5G networks using machine learning techniques

This thesis collects the research works I pursued as Ph.D. candidate at the Universitat Politecnica de Catalunya (UPC). Most of the work has been accomplished at the Mobile Network Department Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). The main topic of my research is the study of mobile network traffic through the analysis of operative networks dataset using machine learning techniques. Understanding first the actual network deployments is fundamental for next-generation network (5G) for improving the performance and Quality of Service (QoS) of the users. The work starts from the collection of a novel type of dataset, using an over-the-air monitoring tool, that allows to extract the control information from the radio-link channel, without harming the users’ identities. The subsequent analysis comprehends a statistical characterization of the traffic and the derivation of prediction models for the network traffic. A wide group of algorithms are implemented and compared, in order to identify the highest performances. Moreover, the thesis addresses a set of applications in the context mobile networks that are prerogatives in the future mobile networks. This includes the detection of urban anomalies, the user classification based on the demanded network services, the design of a proactive wake-up scheme for efficient-energy devices.Esta tesis recoge los trabajos de investigación que realicé como Ph.D. candidato a la Universitat Politecnica de Catalunya (UPC). La mayor parte del trabajo se ha realizado en el Centro Tecnológico de Telecomunicaciones de Catalunya (CTTC) del Departamento de Redes Móviles. El tema principal de mi investigación es el estudio del tráfico de la red móvil a través del análisis del conjunto de datos de redes operativas utilizando técnicas de aprendizaje automático. Comprender primero las implementaciones de red reales es fundamental para la red de próxima generación (5G) para mejorar el rendimiento y la calidad de servicio (QoS) de los usuarios. El trabajo comienza con la recopilación de un nuevo tipo de conjunto de datos, utilizando una herramienta de monitoreo por aire, que permite extraer la información de control del canal de radioenlace, sin dañar las identidades de los usuarios. El análisis posterior comprende una caracterización estadística del tráfico y la derivación de modelos de predicción para el tráfico de red. Se implementa y compara un amplio grupo de algoritmos para identificar los rendimientos más altos. Además, la tesis aborda un conjunto de aplicaciones en el contexto de redes móviles que son prerrogativas en las redes móviles futuras. Esto incluye la detección de anomalías urbanas, la clasificación de usuarios basada en los servicios de red demandados, el diseño de un esquema de activación proactiva para dispositivos de energía eficiente.Postprint (published version

Helical Packing Regulates Structural Transitions In Bax

Apoptosis is essential for development and the maintenance of cellular homeostasis and is frequently dysregulated in disease states. Proteins of the BCL-2 family are key modulators of this process and are thus ideal therapeutic targets. In response to diverse apoptotic stimuli, the pro-apoptotic member of BCL-2 family, BAX, redistributes from the cytosol to the mitochondria or endoplasmic reticulum and primes cells for death. The structural changes that enable this lethal protein to transition from a cytosolic form to a membrane-bound form remain poorly understood. Elucidating this process is a necessary step in the development of BAX as a novel therapeutic target for the treatment of cancer, as well as autoimmune and neurodegenerative disorders. A three-part study, utilizing computational modeling and biological assays, was used to examine how BAX, and similar proteins, transition to membranes. The first part tested the hypothesis that the C-terminal α9 helix regulates the distribution and activity of BAX by functioning as a molecular switch to trigger conformational changes that enable the protein to redistribute from the cytosol to mitochondrial membrane. Computational analysis, tested in biological assays, revealed a new finding: that the α9 helix can dock into a hydrophobic groove of BAX in two opposite directions – in a self-associated, forward orientation and a previously, unknown reverse orientation that enables dimerization and apoptosis. Peptides, made to mimic the α9-helix, were able to induce the mitochondrial translocation of BAX, but not when key residues in the hydrophobic groove were mutated. Such findings indicate that the α9 helix of BAX can function as a molecular switch to mediate occupancy of the hydrophobic groove and regulate the membrane-binding activity of BAX. This new discovery contributes to the understanding of how BAX functions during apoptosis and can lead to the design of new therapeutic approaches based on manipulating the occupancy of the hydrophobic groove. The second and third parts of the study used computational modeling to examine how the helical stability of proteins relates to their ability to functionally transition. Analysis of BAX, as a prototypical transitioning protein, revealed that it has a broad variation in the distribution of its helical interaction energy. This observation led to the hypothesis tested, that proteins which undergo 3D structural transitions during execution of their function have broad variations in the distribution of their helical interaction energies. The result of this study, after examination of a large group of all-alpha proteins, was the development of a novel, predictive computational method, based on measuring helical interactions energies, which can be used to identify new proteins that undergo structural transitioning in the execution of their function. When this method was used to examine transitioning in other members the BCL-2 family, a strong agreement with the published experimental findings resulted. Further, it was revealed that the binding of a ligand, such as a small peptide, to a protein can have significant stabilizing or destabilizing influences that impact upon the activation and function of the protein. This computational analysis thus contributes to a better understanding of the function and regulation of the BCL-2 family members and also offers the means by which peptide mimics that modulate protein activity can be designed for testing in therapeutic endeavors