991 resultados para CONTENT-ADDRESSABLE-MEMORY


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The goal of this study was to propose a new functional magnetic resonance imaging (fMRI) paradigm using a language-free adaptation of a 2-back working memory task to avoid cultural and educational bias. We additionally provide an index of the validity of the proposed paradigm and test whether the experimental task discriminates the behavioural performances of healthy participants from those of individuals with working memory deficits. Ten healthy participants and nine patients presenting working memory (WM) deficits due to acquired brain injury (ABI) performed the developed task. To inspect whether the paradigm activates brain areas typically involved in visual working memory (VWM), brain activation of the healthy participants was assessed with fMRIs. To examine the task's capacity to discriminate behavioural data, performances of the healthy participants in the task were compared with those of ABI patients. Data were analysed with GLM-based random effects procedures and t-tests. We found an increase of the BOLD signal in the specialized areas of VWM. Concerning behavioural performances, healthy participants showed the predicted pattern of more hits, less omissions and a tendency for fewer false alarms, more self-corrected responses, and faster reaction times, when compared with subjects presenting WM impairments. The results suggest that this task activates brain areas involved in VWM and discriminates behavioural performances of clinical and non-clinical groups. It can thus be used as a research methodology for behavioural and neuroimaging studies of VWM in block-design paradigms.

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Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

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The last decade has witnessed a major shift towards the deployment of embedded applications on multi-core platforms. However, real-time applications have not been able to fully benefit from this transition, as the computational gains offered by multi-cores are often offset by performance degradation due to shared resources, such as main memory. To efficiently use multi-core platforms for real-time systems, it is hence essential to tightly bound the interference when accessing shared resources. Although there has been much recent work in this area, a remaining key problem is to address the diversity of memory arbiters in the analysis to make it applicable to a wide range of systems. This work handles diverse arbiters by proposing a general framework to compute the maximum interference caused by the shared memory bus and its impact on the execution time of the tasks running on the cores, considering different bus arbiters. Our novel approach clearly demarcates the arbiter-dependent and independent stages in the analysis of these upper bounds. The arbiter-dependent phase takes the arbiter and the task memory-traffic pattern as inputs and produces a model of the availability of the bus to a given task. Then, based on the availability of the bus, the arbiter-independent phase determines the worst-case request-release scenario that maximizes the interference experienced by the tasks due to the contention for the bus. We show that the framework addresses the diversity problem by applying it to a memory bus shared by a fixed-priority arbiter, a time-division multiplexing (TDM) arbiter, and an unspecified work-conserving arbiter using applications from the MediaBench test suite. We also experimentally evaluate the quality of the analysis by comparison with a state-of-the-art TDM analysis approach and consistently showing a considerable reduction in maximum interference.

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Accepted in 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia 2015), Amsterdam, Netherlands.

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This paper analyzes several natural and man-made complex phenomena in the perspective of dynamical systems. Such phenomena are often characterized by the absence of a characteristic length-scale, long range correlations and persistent memory, which are features also associated to fractional order systems. For each system, the output, interpreted as a manifestation of the system dynamics, is analyzed by means of the Fourier transform. The amplitude spectrum is approximated by a power law function and the parameters are interpreted as an underlying signature of the system dynamics. The complex systems under analysis are then compared in a global perspective in order to unveil and visualize hidden relationships among them.

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Poster presented in Redes de Veiculos nas sociedades do futuro (RVSF 2015). 3, Jun, 2015. Castelo Branco, Portugal.

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Abstract: Preferential flow and transport through macropores affect plant water use efficiency and enhance leaching of agrochemicals and the transport of colloids, thereby increasing the risk for contamination of groundwater resources. The effects of soil compaction, expressed in terms of bulk density (BD), and organic carbon (OC) content on preferential flow and transport were investigated using 150 undisturbed soil cores sampled from 15 × 15–m grids on two field sites. Both fields had loamy textures, but one site had significantly higher OC content. Leaching experiments were conducted in each core by applying a constant irrigation rate of 10 mm h−1 with a pulse application of tritium tracer. Five percent tritium mass arrival times and apparent dispersivities were derived from each of the tracer breakthrough curves and correlated with texture, OC content, and BD to assess the spatial distribution of preferential flow and transport across the investigated fields. Soils from both fields showed strong positive correlations between BD and preferential flow. Interestingly, the relationships between BD and tracer transport characteristics were markedly different for the two fields, although the relationship between BD and macroporosity was nearly identical. The difference was likely caused by the higher contents of fines and OC at one of the fields leading to stronger aggregation, smaller matrix permeability, and a more pronounced pipe-like pore system with well-aligned macropores.

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Recent embedded processor architectures containing multiple heterogeneous cores and non-coherent caches renewed attention to the use of Software Transactional Memory (STM) as a building block for developing parallel applications. STM promises to ease concurrent and parallel software development, but relies on the possibility of abort conflicting transactions to maintain data consistency, which in turns affects the execution time of tasks carrying transactions. Because of this fact the timing behaviour of the task set may not be predictable, thus it is crucial to limit the execution time overheads resulting from aborts. In this paper we formalise a FIFO-based algorithm to order the sequence of commits of concurrent transactions. Then, we propose and evaluate two non-preemptive and one SRP-based fully-preemptive scheduling strategies, in order to avoid transaction starvation.

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The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.

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Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

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The complex interaction between hepatitis C virus infection, iron homeostasis and the response to antiviral treatment remains controversial. The aim of this study was to evaluate the influence of hepatic iron concentration (HIC) on the sustained virological response (SVR) to antiviral therapy in patients with chronic hepatitis C. A total of 50 patients who underwent pretreatment liver biopsy with assessment of HIC by graphite furnace atomic absorption spectroscopy and were subsequently submitted to antiviral treatment with interferon/peginterferon and ribavirin were included in the study. Patients with alcoholism, history of multiple blood transfusion, chronic kidney disease, hemolytic anemia and parenteral iron therapy were excluded. The iron related markers and HIC were compared between those who achieved an SVR and non-responders (NR) patients. The mean age was 45.7 years and the proportion of patients' gender was not different between SVR and NR patients. The median serum iron was 138 and 134 µg/dL (p = 0.9), the median serum ferritin was 152.5 and 179.5 ng/mL (p = 0.87) and the median HIC was 9.9 and 8.2 µmol/g dry tissue (p = 0.51), for SVR and NR patients, respectively. Thus, hepatic iron concentration, determined by a reliable quantitative method, was not a negative predictive factor of SVR in patients with chronic hepatitis C presenting mild to moderate hepatic iron accumulation.

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Introduction: Rhinoscleroma is a rare, chronic, granulomatous disease that most frequently affects the upper respiratory tract, especially the nasal cavity and sometimes extends through the lower respiratory tract. Is associated with Klebsiella rhinoscleromatis, which is endemic in certain geographic regions namely Central America. The pathogenesis and risk factors remain unclear. Clinical case: We report a five years Old Portuguese boy, previously healthy, brought to the Emergency Department with epistaxis, without other accompanying signs or symptoms. The Otorhinolaryngologist (ORL) performed rhinoscopy and identified an intranasal bleeding mass. The MRI revealed an intranasal mass with extension to the ethmoid bone sinus, and performed biopsy. The histopathology was vital, making the diagnosis of Rhinoscleroma. The child had traveled abroad for the first time on vacations a year before to Dominican Republic. The bacteriologic exam identified a Klebsiella spp. sensible to the association of amoxicillin and clavulanic acid. Blood test performed excluded association of immunodeficiency. Since it’s a rare disease genetic study are under course. Monthly evaluation by ORL and pediatrician was performed which documented progressive reduction until total disappearing of the macro and microscopic lesion, and negative bacteriologic exam. Six months of antibiotic therapy were completed without any known secondary effects. The child remained asymptomatic up to the last visit, 3 months following treatment and has shown no evidence of recurrence. Conclusion: Globalization and free transit of people to areas far from origin countries here some rare diseases are endemic brings a new challenge to modern medicine. Sometimes vacations bring more than memories.

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Dissertação para obtenção do Grau de Mestre em Engenharia de Materiais

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High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

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Several researchers have stated that parasites can alter the behavior of their hosts, in order to increase the transmission rate, principally when prey-predator relationships are a reliable way of infection transmission. The aim of this study was to verify the occurrence of changes in anxiety and short-term memory patterns in experimentally infected Mus musculus by Toxocara canis and/or Toxoplasma gondii. Forty male Mus musculus (Balb/c) eight-week-old were divided into four groups of 10 mice each. One group was infected with 300 eggs of Toxocara canis; a second group was submitted to infection with 10 cysts of Toxoplasma gondii; a third group was concomitantly infected with both parasites with the same inoculums and the last group was maintained without infection. The anxiety levels were evaluated using an elevated plus maze and an actometer; the short-term memory was determined by a two-way active avoidance equipment. The determination of anxiety levels were conducted 40 and 70 days after infection and the short-term memory was evaluated 140 days after infection. Mice chronically infected by Toxoplasma gondii showed impaired learning and short-term memory, but no significant differences were found in mice infected by Toxocara canis or concomitantly infected by Toxocara canis and Toxoplasma gondii when compared to non infected mice.