Where do bright ideas occur in our brain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity

Many studies have assessed the neural underpinnings of creativity, failing to find a clear anatomical localization. We aimed to provide evidence for a multi-componential neural system for creativity. We applied a general activation likelihood estimation (ALE) meta-analysis to 45 fMRI studies. Three individual ALE analyses were performed to assess creativity in different cognitive domains (Musical, Verbal, and Visuo-spatial). The general ALE revealed that creativity relies on clusters of activations in the bilateral occipital, parietal, frontal, and temporal lobes. The individual ALE revealed different maximal activation in different domains. Musical creativity yields activations in the bilateral medial frontal gyrus, in the left cingulate gyrus, middle frontal gyrus, and inferior parietal lobule and in the right postcentral and fusiform gyri. Verbal creativity yields activations mainly located in the left hemisphere, in the prefrontal cortex, middle and superior temporal gyri, inferior parietal lobule, postcentral and supramarginal gyri, middle occipital gyrus, and insula. The right inferior frontal gyrus and the lingual gyrus were also activated. Visuo-spatial creativity activates the right middle and inferior frontal gyri, the bilateral thalamus and the left precentral gyrus. This evidence suggests that creativity relies on multi-componential neural networks and that different creativity domains depend on different brain regions

Improving data prefetching efficacy in multimedia applications

The workload of multimedia applications has a strong impact on cache memory performance, since the locality of memory references embedded in multimedia programs differs from that of traditional programs. In many cases, standard cache memory organization achieves poorer performance when used for multimedia. A widely-explored approach to improve cache performance is hardware prefetching, which allows the pre-loading of data in the cache before they are referenced. However, existing hardware prefetching approaches are unable to exploit the potential improvement in performance, since they are not tailored to multimedia locality. In this paper we propose novel effective approaches to hardware prefetching to be used in image processing programs for multimedia. Experimental results are reported for a suite of multimedia image processing programs including MPEG-2 decoding and encoding, convolution, thresholding, and edge chain coding

Dyslexia and Comorbid Dyscalculia: rate of comorbidity and underlying cognitive and learning profile

PURPOSE OF THE STUDY. Children diagnosed with a specific learning disorder (SLD) have four to five times higher chances of developing a comorbid condition. In particular, the high prevalence of comorbid dyscalculia (MD) in children with dyslexia (RD) has been documented. Nevertheless, the exact rate of MD comorbidity and the causes underlying the overlap remain unclear since most research has focused on studying them in isolation. Given the relevance of early identification and evidence-based interventions for further compensation of SLD, there is a need for studies on this matter. The study intended to fill this gap. METHOD. The study was a secondary data analysis of the standardised test scores of 215 neuropsychological assessments administered to grade 1 to 3 schoolchildren in Argentina who had a prior diagnosis of RD. For the purposes of the study, they were classified into 2 groups (RD only and comorbid RDMD). Scores were analyzed using SPSS Statistics to (i) explore the rate of MD comorbidity in children with RD; (ii) contrast the cognitive and learning profiles of the RD and the RDMD group; and (iii) assess the predictive value of each cognitive factor to the development of the RDMD comorbidity. RESULTS AND CONCLUSION. The study found that children with RD developed RDMD at a frequency of 33.5%. There was a significant difference in the two groups' learning and cognitive factors scores, with the comorbid group worst affected in all domains. Among these, verbal working memory, spatial skills, semantic long-term memory and phonological awareness were the most sensitive predictors; together they could account for 35% of the MD comorbidity. These findings are evidence of the high incidence of MD comorbidity in the population with RD and highlight the predictive value of specific cognitive markers

Neighbor cache prefetching for multimedia image and video processing

Cache performance is strongly influenced by the type of locality embodied in programs. In particular, multimedia programs handling images and videos are characterized by a bidimensional spatial locality, which is not adequately exploited by standard caches. In this paper we propose novel cache prefetching techniques for image data, called neighbor prefetching, able to improve exploitation of bidimensional spatial locality. A performance comparison is provided against other assessed prefetching techniques on a multimedia workload (with MPEG-2 and MPEG-4 decoding, image processing, and visual object segmentation), including a detailed evaluation of both the miss rate and the memory access time. Results prove that neighbor prefetching achieves a significant reduction in the time due to delayed memory cycles (more than 97% on MPEG-4 with respect to 75% of the second performing technique). This reduction leads to a substantial speedup on the overall memory access time (up to 140% for MPEG-4). Performance has been measured with the PRIMA trace-driven simulator, specifically devised to support cache prefetching

Patterns of variability in the structure of global value chains: a network analysis

Global Value Chains (GVCs) are a feature of the organization of production in many sectors and countries and they deeply affect international trade patterns. How far the separation of production stages—generating increasingly widespread GVCs—can go, is currently a matter of debate. The main focus of this paper is to investigate GVCs at the country-industry level by modelling them through the construction of a specific network and using network analysis tools. In particular, the aim is to propose a network-based measure of GVCs length to assess whether the structure of GVCs has stretched or shrank over time. Analyzing the evolution of these structures is important to better understand the role played by countries in the production chain, with implications also for their fragility or resilience in presence of external shocks. Our measure allows to consider differently shaped GVCs, and the results show that there are relevant differences among sectors and countries in terms of the evolution of GVCs, especially considering direct or indirect links. Overall, we find a general stability over time of GVCs, confirming the importance of the “relational approach” in GVCs. But the shifts in the geographical patterns of the connections also support the view that firms organizing this complex form of production are ready to grasp better opportunities when they appear in the global markets

An Investigation of Recurrent Neural Architectures for Drug Name Recognition

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand crafted features and domain specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures - the Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.Comment: Accepted for Oral Presentation at LOUHI 2016 : EMNLP 2016 Workshop - The Seventh International Workshop on Health Text Mining and Information Analysis (LOUHI 2016

Application of Virtual Reality in Spatial Memory

: In recent years, virtual reality (VR) has become a widely used tool with a plethora of applications in neuroscience [...]

Bidirectional LSTM-CRF for Clinical Concept Extraction

Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.Comment: This paper "Bidirectional LSTM-CRF for Clinical Concept Extraction" is accepted for short paper presentation at Clinical Natural Language Processing Workshop at COLING 2016 Osaka, Japan. December 11, 201

Performance analysis of MPEG-4 decoder and encoder

© 2002 Croatian Soc. Electronics in Marine-ELMAR. A performance analysis of MPEG-4 encoder and decoder programs on a standard personal computer is presented. The paper first describes the MPEG-4 computational load and discusses related works, then outlines the performance analysis. Experimental results show that while the decoder program can be easily executed in real time, the encoder requires execution times in the order of seconds per frame which call for substantial optimisation to satisfy real-time constraints