An emerging view suggests that spatial position is an integral component of working memory (WM), such that non-spatial features are bound to locations regardless of whether space is relevant [1, 2]. For instance, past work has shown that stimulus position is spontaneously remembered when non-spatial features are stored. Item recognition is enhanced when memoranda appear at the same location whe...

Objective(s): There is controversial evidence about the effect of methamphetamine (METH) on spatial memory. We tested the time- dependent effects of METH on spatial short-term (working) and long-term (reference) memory in METH –sensitized and withdrawn rats in the Morris water maze. Materials and Methods: Rats were sensitized to METH (2 mg/kg, daily/5 days, SC). Rats were trained in water maze ...

objective(s)previous studies have shown that vitamin b as well as folate supplementation has been implicated in cognitive and neurodegenerative disorders including alzheimer’s and parkinson's diseases. the aim of present study was to evaluate the effects of folic acid on passive avoidance task and motor coordination in healthy adult male rats.materials and methodsanimals were randomly divi...

We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches. This is in contrast to previous person re-id works, which rely on either single frame based person to person patch matching, or graph based sequence to sequence matching. We show that a progressive/sequential fusion framewor...

Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence. We find that, in fact, an n-gram language model with a simple heuristic gives strong results on this task. Furthermore, we show that a long short-term memory (LSTM) language model is even more effective at recovering order, with our basic model out...

We explored two approaches based on Long Short-Term Memory (LSTM) networks on the Quora duplicate question dataset. The first model uses a Siamese architecture with the learned representations from a single LSTM running on both sentences. The second method uses two LSTMs with the two sentences in sequence, and the second attending on the first (word-by-word attention). Our best model achieved 7...

Recurrent (neural) networks have been deployed as models for learning musical processes, by computational scientists who study processes such as dynamic systems. Over time, more intricate music has been learned as the state of the art in recurrent networks improves. One particular recurrent network, the Long Short-Term Memory (LSTM) network shows promise as a module that can learn long songs, a...

In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can ...

This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM model over t...