• Open Access close
• Conference object close

We present a new method for data sharing across largecollaborations to improve reproducibility and transparency,by creating a Python package that serves as an interface (API) to the multi-omics characterization of tumors from NCI’s CPTAC program.

Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffusion processes over social networks, the underlying phenomena are the result of a complex web of interactions among numerous participants. Here we develop the Linear Influence Model where rather than requiring the knowledge of the social network and then modeling the diffusion by predicting which node will influence which other nodes in the network, we focus on modeling the global influence of a node on the rate of diffusion through the (implicit) network. We model the number of newly infected nodes as a function of which other nodes got infected in the past. For each node we estimate an influence function that quantifies how many subsequent infections can be attributed to the influence of that node over time. A nonparametric formulation of the model leads to a simple least squares problem that can be solved on large datasets. We validate our model on a set of 500 million tweets and a set of 170 million news articles and blog posts. We show that the Linear Influence Model accurately models influences of nodes and reliably predicts the temporal dynamics of information diffusion. We find that patterns of influence of individual participants differ significantly depending on the type of the node and the topic of the information.

Sensitising erbium doped fibres with ytterbium significantly relaxes constraints in pump wavelength and device length. This makes versatile compact lasers and optical amplifiers operating at the important telecommunications window of 1.5µm a reality.

Questo libro raccoglie l’esito di una call, lanciata nell’autunno del 2016, promossa da un gruppo di ricercatori del Dottorato in Paesaggi della Città Contemporanea. Politiche, tecniche e studi visuali, afferenti al Dipartimento di Architettura dell’Università Roma Tre. L’invito, rivolto a studiosi, progettisti, fotografi e artisti in diversi ambiti, è di indagare i caratteri delle molteplici manifestazioni con cui le compresenze – le forme di abitare la città e costruire gli spazi urbani che includono differenti e plurali intenzionalità, spontanee, predisposte o suggerite – si compiono nei luoghi delle città contemporanee e sollecitano l’incontro tra corpi, comportamenti, spazi, tempi e culture differenti. Il libro contiene i contributi selezionati, con una ricca varietà di casi e di riflessioni, rivolti sia a pratiche informali sia a progetti autoriali, a edifici e spazi aperti, esperienze didattiche, di ricerca teorica o applicata, in grado di offrire spunti di riflessione per rispondere ad alcune domande di cui proponiamo l’urgenza.

This paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of a given set of sentence pairs in English. The best run out of three submitted runs of our model achieved a Pearson correlation score of 0.8004 compared to a hidden human annotation of 250 pairs. We used random forest ensemble learning to map an expandable set of extracted pairwise features into a semantic similarity estimated value bounded between 0 and 5. Most of these features were calculated using word embedding vectors similarity to align Part of Speech (PoS) and Name Entities (NE) tagged tokens of each sentence pair. Among other pairwise features, we experimented a classical tf-idf weighted Bag of Words (BoW) vector model but with character-based range of n-grams instead of words. This sentence vector BoW-based feature gave a relatively high importance value percentage in the feature importances analysis of the ensemble learning.