Automatic Text Summarization Approaches to Speed up Topic Model Learning Process

The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for companies and scientists. Methods that represent a textual document by a topic representation are widely used in Information Retrieval (IR) to process big data such as Wikipedia articles. One of the main difficulty in using topic model on huge data collection is related to the material resources (CPU time and memory) required for model estimate. To deal with this issue, we propose to build topic spaces from summarized documents. In this paper, we present a study of topic space representation in the context of big data. The topic space representation behavior is analyzed on different languages. Experiments show that topic spaces estimated from text summaries are as relevant as those estimated from the complete documents. The real advantage of such an approach is the processing time gain: we showed that the processing time can be drastically reduced using summarized documents (more than 60\% in general). This study finally points out the differences between thematic representations of documents depending on the targeted languages such as English or latin languages.Comment: 16 pages, 4 tables, 8 figure

Space-Time Separation During Obstacle-Avoidance Learning in Monkeys

Is the movement duration time known before we move? To answer this question, a new experimental paradigm is introduced that for the first time monitors the acquisition of a new motor skill in rhesus monkeys. Straight reaches were interleaved with reaches around physical obstacles that elicited a different path geometry. Curved and longer spatial paths were immediately resolved and consistent over months of training. A new temporal strategy separately evolved over repetitions from multiple to a single velocity peak. We propose that the obstacle-avoidance spatial paths were resolved before motion execution and used as reference in the computation of the new dynamics. Path conservation from the first trial occurred both at the hand and at the joint angle levels, whereas the speed profile dramatically changed over time. The spatial solution required no learning and was anticipated by the spontaneous repositioning of the initial arm posture. The learning was in the temporal domain, involving the adjustment of the speed during the motion's first impulse. Within the movement initiation, the partial distance traveled by the hand up to the first velocity peak was finely tuned under a constant time. For a given space location, the time of the first impulse remained robust to learning, but significantly shifted for different targets and obstacle configurations. Differences in the temporal-related parameters across time provided a clear distinction between learning and automatic behavior

Minimum of η/s\eta/s and the phase transition of the Linear Sigma Model in the large-N limit

We reexamine the possibility of employing the viscosity over entropy density ratio as a diagnostic tool to identify a phase transition in hadron physics to the strongly coupled quark-gluon plasma and other circumstances where direct measurement of the order parameter or the free energy may be difficult. It has been conjectured that the minimum of eta/s does indeed occur at the phase transition. We now make a careful assessment in a controled theoretical framework, the Linear Sigma Model at large-N, and indeed find that the minimum of eta/s occurs near the second order phase transition of the model due to the rapid variation of the order parameter (here the sigma vacuum expectation value) at a temperature slightly smaller than the critical one.Comment: 22 pages, 19 figures, v2, some references and several figures added, typos corrected and certain arguments clarified, revised for PR

HIV-1 Vpr Causes Synaptodendritic Damage in Neurons

HIV weakens the immune system by infecting and destroying T-cells, leaving the body vulnerable to infection and the development of AIDS. Conventional treatments for HIV, such as combined anti-rectroviral therapy (cART), fail to prevent the development of HIV-associated neurocognitive disorder (HAND). Neurological dysfunction has been directly related to the invasion of HIV in the central nervous system (CNS). HIV produces neurotoxic proteins, such as the Viral Protein R (Vpr), which contribute to HAND. Astrocytes are the most abundant cells in the brain and an important HIV target. We hypothesize that astrocytes expressing Vpr will cause neuronal damage in our co-culture system. Primary astrocytes were transfected with Vpr plasmid or control (pEGFP or mock) using electroporation. Astrocytes were then co-cultured with cortical neurons. At 48 and 72 hours we collected the primary astrocytes to confirm the Vpr expression via western blot analysis. We then measured structural damage in the neurons using immunofluorescence for cytoskeletal (MAP2, f-actin) and synaptic (synaptophysin) damage. Preliminary results showed strong staining of filamentous actin and MAP2 with weak detection of synaptophysin. The positive control for neurotoxicity (2.8µM acrylamide) showed substantial damage to the cellular structure. Results for Vpr expression are pending. After confirming that the immunofluorescence assays are working with our controls, we expect to detect any synaptodendritic damage in the neurons caused by Vpr in our upcoming experiments

L0-regularization induces subregular biases in LSTMs

Ongoing work attempts to identify the formal language patterns in natural language. In phonology, recent work has identified the subregular languages as a good candidate (Heinz, 2018). However, there remain few explanations for the source of this bias. This abstract proposes a means of investigating formal language learnability. We propose using a variant of minimum description length (MDL) as defined on LSTMs with varying priors on LSTM size. We will show its utility on a test case from Heinz and Idsardi (2013) and Rawski et al. (2017)

An Evaluation of Customer Relationship Management (CRM) Practices among Agribusiness Firms

Customer Relationship Management (CRM) has received much attention in the business press as a management process to enhance firm performance. This research highlights differences between groups of respondents who believe their firm's CRM program is performing at a high level, as compared to those not satisfied with the performance of their CRM initiative. Cluster analysis was used to develop a taxonomy of respondents based on their perceived CRM performance. The resulting clusters are then profiled on both demographic variables as well as a core set of activities/behaviors to better understand key differences in the CRM programs of agribusinesses.customer relationship management (CRM), marketing, strategy, information technology, cluster analysis., Agribusiness,