TagBook: A Semantic Video Representation without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.Comment: accepted for publication as a regular paper in the IEEE Transactions on Multimedi

Effectiveness of Data Enrichment on Categorization: Two Case Studies on Short Texts and User Movements

The widespread diffusion of mobile devices, e.g., smartphones and tablets, has made possible a huge increment in data generation by users. Nowadays, about a billion users daily interact on online social media, where they share information and discuss about a wide variety of topics, sometimes including the places they visit. Furthermore, the use of mobile devices makes available a large amount of data tracked by integrated sensors, which monitor several users’ activities, again including their position. The content produced by users are composed of few elements, such as only some words in a social post, or a simple GPS position, therefore a poor source of information to analyze. On this basis, a data enrichment process may provide additional knowledge by exploiting other related sources to extract additional data. The aim of this dissertation is to analyze the effectiveness of data enrichment for categorization, in particular on two domains, short texts and user movements. We de- scribe the concept behind our experimental design where users’ content are represented as abstract objects in a geometric space, with distances representing relatedness and similarity values, and contexts representing regions close to the each object where it is possibile to find other related objects, and therefore suitable as data enrichment source. Regarding short texts our research involves a novel approach on short text enrichment and categorization, and an extensive study on the properties of data used as enrich- ment. We analyze the temporal context and a set of properties which characterize data from an external source in order to properly select and extract additional knowledge related to textual content that users produce. We use Twitter as short texts source to build datasets for all experiments. Regarding user movements we address the problem of places categorization recognizing important locations that users visit frequently and intensively. We propose a novel approach on places categorization based on a feature space which models the users’ movement habits. We analyze both temporal and spa- tial context to find additional information to use as data enrichment and improve the importance recognition process. We use an in-house built dataset of GPS logs and the GeoLife public dataset for our experiments. Experimental evaluations on both our stud- ies highlight how the enrichment phase has a considerable impact on each process, and the results demonstrate its effectiveness. In particular, the short texts analysis shows how news articles are documents particularly suitable to be used as enrichment source, and their freshness is an important property to consider. User Movements analysis demonstrates how the context with additional data helps, even with user trajectories difficult to analyze. Finally, we provide an early stage study on user modeling. We exploit the data extracted with enrichment on the short texts to build a richer user profile. The enrichment phase, combined with a network-based approach, improves the profiling process providing higher scores in similarity computation where expectedCo-supervisore: Ivan ScagnettoopenDottorato di ricerca in Informaticaope

Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T immunotherapy

Chimeric antigen receptor (CAR) T-cell therapy has produced remarkable anti-tumor responses in patients with B-cell malignancies. However, clonal kinetics and transcriptional programs that regulate the fate of CAR-T cells after infusion remain poorly understood. Here we perform TCRB sequencing, integration site analysis, and single-cell RNA sequencing (scRNA-seq) to profile CD8+ CAR-T cells from infusion products (IPs) and blood of patients undergoing CD19 CAR-T immunotherapy. TCRB sequencing shows that clonal diversity of CAR-T cells is highest in the IPs and declines following infusion. We observe clones that display distinct patterns of clonal kinetics, making variable contributions to the CAR-T cell pool after infusion. Although integration site does not appear to be a key driver of clonal kinetics, scRNA-seq demonstrates that clones that expand after infusion mainly originate from infused clusters with higher expression of cytotoxicity and proliferation genes. Thus, we uncover transcriptional programs associated with CAR-T cell behavior after infusion.Published versio

SMSM: a similarity measure for trajectory stops and moves

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2019.Medidas de similaridade são a base para a maioria dos métodos de mineração de dados e extração de conhecimento. Na área de trajetórias de objetos móveis, por muitos anos a pesquisa em similaridade de trajetórias focou nas trajetórias brutas, considerando somente a informação de espaço e tempo. Com o enriquecimento das trajetórias com informações semânticas, como o nome e a categoria dos locais visitados, meio de transporte utilizado durante o movimento, o nome das ruas percorridas, etc, emergiu a necessidade por medidas de similaridade que suportem espaço, tempo e semântica. Apesar de algumas medidas de similaridade para trajetórias lidarem com todas estas dimensões, elas consideram somente os locais onde o objeto móvel faz paradas, denominados stops, ignorando o movimento que ocorre entre as paradas, denominado move. Acredita-se que, para algumas aplicações, o movimento entre os stops é tão importante quanto o stop em si, e ele deve ser levado em consideração na análise da similaridade, como em sistemas de transporte público, turismo, planejamento urbano, entre outros. Nesta dissertação é proposta a medida Similarity Measure for trajectory Stops and Moves (SMSM), um nova medida de similaridade para trajetórias semânticas que considera tanto os stops quanto os moves. O SMSM é avaliado em três conjuntos de dados: (i) um conjunto de dados de trajetórias sintéticas criadas com o gerador de trajetórias semânticas Hermoupolis, (ii) um conjunto de trajetórias reais de táxis do projeto CRAWDAD, e (iii) o conjunto de dados de trajetórias reais chamado Geolife, com trajetórias de pessoas na cidade de Pequim. Os resultados mostram que o SMSM supera as medidas de similaridade do estado da arte desenvolvidas tanto para trajetórias brutas quanto semânticas.Abstract : For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, using information as the name and type of the visited places, the transportation mean, the name of the streets, etc, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only the places where the moving object stays for a certain time, called stop, ignoring the movement between stops. We claim that, for some applications, as traffic management systems, urban planning, public transportation, etc, the movement between stops is as important as the stops, and it must be considered in the similarity analysis. In this thesis we propose the similarity measure called Similarity Measure for trajectory Stops and Moves(SMSM), a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset of taxis from the CRAWDAD project, and (iii) the Geolife trajectory dataset, with raw trajectories of persons around Beijing. The results show that SMSM overcomes state-of-the-art measures developed for both raw and semantic trajectories

Structural investigation of nucleophosmin interaction with the tumor suppressor Fbw7γ

Nucleophosmin (NPM1) is a multifunctional nucleolar protein implicated in ribogenesis, centrosome duplication, cell cycle control, regulation of DNA repair and apoptotic response to stress stimuli. The majority of these functions are played through the interactions with a variety of protein partners. NPM1 is frequently overexpressed in solid tumors of different histological origin. Furthermore NPM1 is the most frequently mutated protein in acute myeloid leukemia (AML) patients. Mutations map to the C-terminal domain and lead to the aberrant and stable localization of the protein in the cytoplasm of leukemic blasts. Among NPM1 protein partners, a pivotal role is played by the tumor suppressor Fbw7γ, an E3-ubiquitin ligase that degrades oncoproteins like c-MYC, cyclin E, Notch and c-jun. In AML with NPM1 mutations, Fbw7γ is degraded following its abnormal cytosolic delocalization by mutated NPM1. This mechanism also applies to other tumor suppressors and it has been suggested that it may play a key role in leukemogenesis. Here we analyse the interaction between NPM1 and Fbw7γ, by identifying the protein surfaces implicated in recognition and key aminoacids involved. Based on the results of computational methods, we propose a structural model for the interaction, which is substantiated by experimental findings on several site-directed mutants. We also extend the analysis to two other NPM1 partners (HIV Tat and CENP-W) and conclude that NPM1 uses the same molecular surface as a platform for recognizing different protein partners. We suggest that this region of NPM1 may be targeted for cancer treatment

Phenotypic robustness can increase phenotypic variability after non-genetic perturbations in gene regulatory circuits

Non-genetic perturbations, such as environmental change or developmental noise, can induce novel phenotypes. If an induced phenotype confers a fitness advantage, selection may promote its genetic stabilization. Non-genetic perturbations can thus initiate evolutionary innovation. Genetic variation that is not usually phenotypically visible may play an important role in this process. Populations under stabilizing selection on a phenotype that is robust to mutations can accumulate such variation. After non-genetic perturbations, this variation can become a source of new phenotypes. We here study the relationship between a phenotype's robustness to mutations and a population's potential to generate novel phenotypic variation. To this end, we use a well-studied model of transcriptional regulation circuits. Such circuits are important in many evolutionary innovations. We find that phenotypic robustness promotes phenotypic variability in response to non-genetic perturbations, but not in response to mutation. Our work suggests that non-genetic perturbations may initiate innovation more frequently in mutationally robust gene expression traits.Comment: 11 pages, 5 figure