Abstract In data science and content-based retrieval, we find many domain-specific techniques that employ a processing pipeline with two fundamental steps. First, entities are represented by some visualizations, while in the second step, visualizations used machine learning model to extract deep features. Deep convolutional neural networks (DCNN) became standard reliable choice. The purpose of ...

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods. key words: C...

In this paper, we investigate the effect of the topic dependent translation model for patent translation. We employ clustering technique to estimate topics in the training corpus and document retrieval to identify the topic fitting to the source sentence. In our experimental evaluation, we investigate the contribution of our topic dependent models to phrase-base Statistical Machine Translation.

Functional neuroimaging and neuropsychological findings suggest that memory retrieval of common and proper names is subserved by different neuro-functional systems but little is known about the topographic localization of neural generators. In the present study brain electrical activity was recorded with a high density electrode montage in healthy young volunteers during lexical retrieval upon ...

Recent research in video retrieval has focused on automated, high-level feature indexing on shots or frames. One of the most important applicationof such indexing is to support precise video retrieval. We report on extensions ofthis semantic indexing on news video retrieval. First, we utilize extensive queryanalysis to relate various high-level features and query terms by matchi...

In this paper we show that during the retrieval process in a binary symmetric Hebb neural network, spatial localized states can be observed when the connectivity of the network is distance-dependent and when a constraint on the activity of the network is imposed, which forces different levels of activity in the retrieval and learning states. This asymmetry in the activity during the retrieval a...

Although it is widely known that memory is enhanced when encoding and retrieval occur in the same state, the impact of elevated stress/arousal is less understood. This study explores mood-dependent memory's effects on visual recognition and recall of material memorized either in a neutral mood or under higher stress/arousal levels. Participants' (N = 60) recognition and recall were assessed whi...

Reconsolidation postulates that reactivation of a memory trace renders it susceptible to disruption by treatments similar to those that impair initial memory consolidation. Despite evidence that implicit, or non-declarative, human memories can be disrupted at retrieval, a convincing demonstration of selective impairment in retrieval of target episodic memories following reactivation is lacking....

The task of entity retrieval becomes increasingly prevalent as more and more structured information about entities is available on the Web in various forms such as documents embedding metadata (RDF, RDFa, Microdata, Microformats). International benchmarking campaigns, e.g., the Text REtrieval Conference or the Semantic Search Challenge, propose entity-oriented search tracks. This reflects the n...