Drug use is provoked by the presentation of drug-associated cues, even following long periods of abstinence. Disruption of these learned associations would therefore limit relapse susceptibility. Drug-associated memories are susceptible to long-term disruption during retrieval and shortly after, during memory reconsolidation. Recent evidence reveals that retrieval and reconsolidation are depend...

The vast majority of research in information retrieval is done using English collections and topics. This raises questions about the effectiveness of retrieval strategies for other languages. To examine this issue, we focus on document retrieval in nine European languages. In particular, we investigate the effectiveness of language-dependent approaches to document retrieval, such as stemming an...

We study information retrieval (IR) on Turkish texts using a large-scale test collection that contains 408,305 documents and 72 ad hoc queries. We examine the effects of several stemming options and query-document matching functions on retrieval performance. We show that a simple word truncation approach, a word truncation approach that uses language dependent corpus statistics, and an elaborat...

Morphine’s effects on learning and memory processes are well known to depend on synaptic plasticity in the hippocampus. Whereas the role of the hippocampus in morphine-induced amnesia and state-dependent learning is established, the biochemical and molecular mechanisms underlying these processes are poorly understood. The present study intended to investigate whether administration of morphine ...

Several studies have demonstrated the mechanisms involved in memory persistence after learning. However, little is known about memory persistence after retrieval. In this study, a protein synthesis inhibitor, anisomycin, was infused into the basolateral amygdala of mice 9.5 h after retrieval of contextual conditioned fear. Anisomycin attenuated fear memory after 7 d, but not after 2 d. In contr...

We investigates the effectiveness of language-dependent approaches to document retrieval, such as stemming and decompounding, and constrast them with language-independent approaches, such as character n-gramming. In order to reap the benefits of more than one type of approach, we also consider the effectiveness of the combination of both types of approaches. We focus on document retrieval in ni...

Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...

In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN). The objective is to heavily prune convolutional edges while maintaining retrieval performance. To this end, we introduce both data-independent and data-dependent heuristics to prune convolutional edges, and evaluate their performance across various c...

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...