Miracle-dz/UniFAR-FTUs huggingface dataset:
PAMS: Pattern Aware Memory System for embedded systems

In this paper, we propose a hardware mechanism for embedded multi-core memory system called Pattern Aware Memory System (PAMS). The PAMS supports static and dynamic data structures using descriptors and specialized memory and reduces area, cost, energy consumption and hit latency. When compared with a Baseline Memory System, the PAMS consumes between 3 and 9 times and 1.13 and 2.66 times less program memory for static and dynamic data structures respectively. The benchmarking applications (having static and dynamic data structures) results show that PAMS consumes 20% less hardware resources, 32% less on chip power and achieves a maximum speedup of 52× and 2.9× for static and dynamic data structures respectively. The results show that the PAMS multi-core system transfers data structures up to 4.65× faster than the MicroBlaze baseline system.
Investigation on the Aggregation Behaviors and Filament Morphology of Tau Protein by a Simple 90° Angle Light-Scattering Assay

The in vitro aggregation of tau constructs was monitored by a simple 90° angle light-scattering (LS) approach which was conducted directly on fluorescence instrument. At the optimum incident wavelength (550 nm, unpolarized), the sensitivity of LS was high enough to detect tau aggregation at micromolar range. The nucleation and elongation, different events in the aggregation process of 4RMBD construct (corresponding with the four repeated units of tau Microtubule Binding Domain) could be observed by this approach, as compared with ThS fluorescence assay. The validity of this technique was demonstrated over a range of tau concentrations with different tau filaments. Linear regression of scattering light against concentration yielded the x-intercept, the critical concentrations of tau constructs. The critical concentrations of 4RMBD and its S305N mutant are 5.26 μM and 4.04 μM respectively, indicating point mutation S305N, which is associated with FTDP-17, appear to enhance the heparin-induced tau aggregation in vitro. Furthermore, the slopes of concentration dependence curves, as well as the angle dependence, were discussed based on the filaments morphology examined by electron microscopy and ultrasonication experiment.
DIGITAL BUILDING MAP REFINEMENT FROM KNOWLEDGE-DRIVEN ACTIVE CONTOURS AND VERY HIGH RESOLUTION OPTICAL IMAGERY

We propose a novel approach for digital building map refinement based on the use of knowledge-driven active contours and very high resolution panchromatic optical imagery. This methodology is designed to finely match each building symbolized in an urban Geographical Information System (GIS) database onto its counterpart representation in remote sensing data. This method is GIS mapdriven: GIS data globally registered to the image allows to initialize an active contour near the target building in the image to achieve subsequent refinement. Moreover the digital map provides valuable shape information about the object in the image we aim at matching. This geometric and specific prior knowledge is embedded as a shape constraint into the active contour and enables to overcome urban artifacts issues. Besides, we propose to embed a coarse Digital Surface Model (DSM) as well as a spatio-temporal shape prior constraint within the active contour model. Experimental results carried out over Beijing city area and illustrated in this paper show how these latter contributions improve the robustness and speed of the map refinement process. Map refinement addressed in this paper is becoming an essential issue for urban planning, telecommunications, automobile navigation, crisis and pollution management, which all rely on up-to-date and precise digital maps of a city.
Knowledge, Acceptance and Action of the Public on Dengue-Related Hygiene Practices

This study aims to look at the public's awareness on hygiene care practices related to dengue disease in the community in Selangor. The study is based on knowledge, acceptance and action aspects and involves only 9 districts in Selangor and 447 respondents. The survey was conducted randomly on hygiene care practices. Awareness of the importance of these factors is a new challenge to the management of human resources and individuals involved in training management to plan strategies to make the people unhappy with their surroundings. Hopefully through this study, serious attention will be given by the responsible party to improve the practice of awareness among the people in Selangor and across Malaysia in general.
Sets and negation in a logic data base language (LDL1)

In this paper we extend LDL, a Logic Based Database Language, to include finite sets and negation. The new language is called LDL1. We define the notion of a model and show that a negation-free program need not have a model, and that it may have more than one minimal model. We impose syntactic restriction in order to define a deterministic language. These restrictions allow only layered (stratified) programs. We prove that for any program satisfying the syntactic restrictions of layering, there is a minimal model, and that this model can be constructed in a bottom-up fashion. Extensions to the basic grouping mechanism are proposed. We show that these extensions can be translated into equivalent LDL1 programs. Finally, we show how the technique of magic sets can be extended to translate LDL1 programs into equivalent programs which can often be executed more efficiently
A Wide-Area Measurement Systems-Based Adaptive Strategy for Controlled Islanding in Bulk Power Systems

Controlled islanding is the last countermeasure for a bulk power system when it suffers from severe cascading contingencies. The objective of controlled islanding is to maintain the stability of each island and to keep the total loss of loads of the whole system to a minimum. This paper presents a novel integrated wide-area measurement systems (WAMS)-based adaptive controlled islanding strategy, which depends on the dynamic post-fault trajectories under different failure modes. We first utilize an improved Laplacian eigenmap algorithm (ILEA) to identify the coherent generators and use the slow coherency grouping algorithm to guarantee coherent stability within an island. Using the identification result, we then define the minimum coherent generator virtual nodes to reduce the searching space in a graph and utilize the k-way partitioning (KWP) algorithm to obtain a preliminary partition of the simplified graph. Based on the preliminary partition, we consider the direction of power flow and propose a variable neighborhood heuristic searching algorithm to search the optimal separation surfaces so that the net imbalanced power of islands is minimized. Finally, the bidirectional power flow tracing algorithm and PQ decomposition power flow analysis are utilized to determine the corrective controls within each island. The test results with the New England 39-bus system and the IEEE 118-bus system show that the proposed integrated controlled islanding strategy can automatically adapt to different fault modes through generator coherency identification and effectively group the different coherent generators into different islands.
Matching 2 D & 3 D Articulated Shapes using Eccentricity

Shape matching should be invariant to the typical intra-class deformations present in nature. The majority of shape descriptors are quite complex and not invariant to the deformation or articulation of object parts. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. The eccentricity transform considers the length of the longest geodesics. It is robust with respect to Salt and Pepper noise, and minor segmentation errors, and is stable in the presence of holes. We present a method for 2D and 3D shape matching based on the eccentricity transform. Eccentricity histograms make up descriptors insensitive to rotation, scaling, and articulation. The descriptor is highly compact and the method is straight-forward. Experimental results on established 2D and 3D benchmarks show results comparable to more complex state of the art methods. Properties and results are discussed in detail. Partially supported by the Austrian Science Fund under grants S9103-N13 and P18716-N13. Adrian Ion PRIP, Vienna University of Technology E-mail: ion@prip.tuwien. ac.at
Active learning in the real-world design and analysis of the Nomao challenge

Active Learning is an active area of research in the Machine Learning and Data Mining communities. In parallel, needs for efficient active learning methods are raised in real-world applications. As an illustration, we present in this paper an active learning challenge applied to a real-world application named Nomao. Nomao is a search engine of places. It aggregates information coming from multiple sources on the web to propose complete information related to a place. In this context, active learning is used to efficiently detect data that refer to a same place. The process is called data deduplication. Since it is a real-world application, some additional constraints have to be handled. The main ones are scalability of the proposed method, representativeness of the training dataset, and practicality of the labeling process. The website of the challenge remains open beyond the termination of the challenge as a resource for students and researchers (http://www.nomao.com/labs/challenge) and to share that problem with the community, the whole labeled dataset has been delivered publicly to the UCI Machine Learning Repository http://archive.ics. uci.edu/ml/datasets/Nomao)
Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.
An Autonomous Multicast-tree Creation Algorithm for Wireless Sensor Networks

In this paper, we propose an algorithm to realize a power-aware network topology in wireless sensor networks. The algorithm creates a multicast tree connecting all sensor nodes. In the multicast tree, a sink node should be a root node, and all the data sensed in the sensor nodes are transferred to the sink node using the tree connection. The relay nodes aggregate the received data, and transfer the aggregated data to the sink at an appropriate timing so as to avoid packet collisions. To do this, all the sensor nodes are grouped in clusters. Here, each cluster has a head node, which is automatically selected from among the nodes in the cluster. In addition, our algorithm has an autonomous mechanism that can change the cluster head dynamically based on the remaining energy of each sensor node and the communication quality among the sensor nodes, which are obtained by periodical information exchange between the sensor nodes. This mechanism contributes to extending the lifetime of the sensor network. After describing the algorithm, some experimental results are shown to prove its effectiveness and robustness.
Automatic Acquisition of Matching Patterns for Pattern-Based Parsing on Specific Chinese Text

As a generalized approach in natural language processing, pattern matching is seldom applied in syntactic parsing nowadays. In some applications on short text analysis such as microblog opinion mining, the sentences are characterized by obvious patterns. Thus automatic parsing by pattern matching may be more effective than general syntactic parsing method. This paper puts forward a lightweight algorithm of Matching Pattern (MP) acquisition to achieve the syntactic parsing on some specific Chinese text composed of short clauses. The key points of the algorithm are MP generation based on word/POS sequence and MP selection based on weight ranking of mapping between MP groups and sentence groups. Experiments show that this method performs well on Chinese corpus with the following features: (1) The sentences are mainly composed of short clauses, (2) Most of the clauses can be represented by limited patterns.
Please Scroll down for Article British Journal of Religious Education Identity, Cultural Change, and Religious Education

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Automatically Discovering Semantic Links among Documents and Applications

Automatically discovering semantic links among documents is the basis of developing advanced applications on large-scale documentary resources. This paper proposes an approach to automatically discover semantic links in a given document set. It has the following advantages: (1) It does not rely on any predefined ontology. (2) The semantic link networks and relevant rules automatically evolve. (3) It can adapt to the update of the adopted techniques. Experiments on document sets of different types (scientific papers and Web pages on Dunhuang culture) and different scales show the proposed approach feasible. The approach can be used to automatically construct semantic overlays on large document sets to support advanced applications like various relation queries on documents.
Software components prioritization using OCL formal specification for effective testing

In soft real time system development, testing effort minimization is a challenging task. Earlier research has shown that often a small percentage of components are responsible for most of the faults reported at the later stages of software development. Due to the time and other resource constraints, fault-prone components are ignored during testing activity which leads to compromises on software quality. Thus there is a need to identify fault-prone components of the system based on the data collected at the early stages of software development. The major focus of the proposed methodology is to identify and prioritize fault-prone components of the system using its OCL formal specifications. This approach enables testers to distribute more effort on fault-prone components than non fault-prone components of the system. The proposed methodology is illustrated based on three case study applications.
Efficient shading of indirect illumination applying reflective shadow maps

While global illumination is a crucial issue for most computer graphics applications fostering photo realistic rendering, fast and efficient implementations remain challenging for real-time applications. One approach to approximate indirect illumination is to distribute virtual point lights (VPL) at surfaces that emit indirect light. This distribution may be realized using reflective shadow maps (RSM). One major drawback of this approach is that each surface point has to be illuminated by thousands of VPLs, leading to a performance bottleneck in the shading step. Therefore several approaches trying to reduce the shading costs either by decreasing the number of VPLs or by lowering the surface points to be shaded exist. In our approach we propose a novel indirect shading approximation allowing us to reduce the number of surface points to be shaded to a minimum, still achieving a high image quality. Even complex and animated models can thus be represented by a few dozens of surface points for shading. Furthermore our approach allows graphic artists to intuitively tune the shading by adding or changing surface points without any pre-computations. The approach is very efficient and is completely implemented on the GPU not requiring any high shader profiles. This will even enable almost photo realistic rendering on upcoming handheld devices.
The Skin Microbiome: A Focus on Pathogens and Their Association with Skin Disease

Microbes are present in every environmental niche and live in close association with humans and each other. Members of these communities include bacteria, archaea, viruses, fungi, and protists. In the environment, microbes perform various roles and are essential in agriculture and food production. In humans and animals, microbes provide protection against foreign invaders, educate and stimulate the immune response, produce antimicrobials, aid in digestion, and produce vitamins, among a host of other functions. In the last decade, researchers have actively investigated the impact of microbes and their gene products in human health and disease. This field of research is defined as the human microbiome, which is the totality of the microbes and their genes in and on the human body. To better understand the impact of microbes on health and disease, multiple body sites have been investigated, including skin, lungs, the nasal passage, the oral cavity, and the gastrointestinal and urogenital tracts. The results of these studies have revealed that each body site is home to a unique microbial community. Shifts in microbial communities can result from multiple factors, including environment, genetic variation, lifestyle and hygienic factors, and the immune system, and have been associated with many diseases.
Circulating Tumor Cells: Application as a Biomarker for Molecular Characterization and Predictor of Survival in an All-Comer Solid Tumor Phase I Clinical Study

Purpose Clinical development of cancer drugs has a low success rate. Prognostic and predictive biomarkers using minimally invasive approaches hold promise for increasing the probability of success by enabling disease characterization, patient selection and early detection of drug treatment effect. Enumeration and molecular characterization of circulating tumor cells (CTC) may address some of these needs, and thus were evaluated for utility in a Phase I solid tumor clinical study. Experimental Design Blood samples for CTC analysis were obtained from 24 cancer patients in a multi-center all-comer Phase I study of MEDI-575, a novel anti-PDGFRα antibody. Samples were taken at screening and analyzed for enumeration of CTC using the CellSearch® platform and for molecular characterization using a novel quantitative RT-PCR assay. Results Fifty-nine percent of the patients showed at least 1 CTC per 7.5 ml of blood at baseline. Progression-free survival (PFS) and overall survival (OS) of patients with 0 CTCs at baseline were longer than PFS and Os for patients with 1-3 and >3 CTCs (8.8 versus 1.4 and 1.3 months PFS, P = 0.02; 9.0 vs 7.4 and 3.5 months OS, P = 0.20, respectively). Patients with 0 CTC showed a greater percentage of stable disease than the other 2 groups with 1-3 and >3 CTCs (57% vs 29% and 0%). The multimarker qRT-PCR method detected CTC in 40% of the patients, and 80% of these patients were positive for pre-selected drug target genes. Conclusion CTC enumeration of patients in an all-comer study is feasible and may allow for patient stratification for PFS and Os to evaluate the clinical response of investigational agents. Gene expression profiling of isolated CTC may provide a means for molecular characterization of selected tumor targets.
CHLAMYDIA PNEUMONIC INFECTION IN CORONARY HEART DISEASE IN BASRAH

Aim: The aim of the present study was to determine the relationship between Chlamydia pneumoniae infection and coronary artery disease with classical risk factors in Basrah patients. Background: Chronic inflammatory diseases might play significant role in the pathogenesis of atherosclerosis. We studied the differences between the percentage of immunoglobulin G against Chlamydia pneumonia among Coronary Heart Disease patients and control subject. Method: Antibody titer against Chlamydia pneumonia by microimmunofluorescence method and C-reactive protein by agglutination method was determined among 225 patients proven Coronary Heart Disease and 180 control subject. Lipid risk and non-lipid risk factors in Coronary Heart Diseases patients was determined. Results: Chlamydia pneumoniae seropositivity 1/64 titer was significantly higher in patients than control group (46.6% versus 25.5%) respectively; P<0.01; odd ratio 2.64; CI (1.62; 3.46). Positive C-reactive protein (≥6mg/I) was significantly higher in patients (53.3 versus 39.4; P<0.001; odd ratio 2.21; CI (1.463.11)), than control group. All percentages of lipid and non-lipid risk factors in patients were significantly higher than control group. Conclusion: We have demonstrated a significant rise in Chlamydia pneumoniae-specific immunoglobulin G and C-reactive protein levels is associated with Coronary Heart Disease.
Facial Emotion Recognition in Adolescents with Bipolar Disorder

Object Bipolar disorder (BD) is associated with abnormalities in emotional competence. One of the main aspects of this competency is emotion recognition which is presented in the face. This study aimed to evaluate facial emotion recognition in adolescents with bipolar disorder when they are free of acute symptoms. Method Thirty patients diagnosed with BD aged 12 to 18 were selected from a large sample of consecutive admitted adolescents in Roozbeh hospital. They were compared with 30 matched normal developing adolescents who were recruited from mainstream schools at Tehran. All participants were evaluated using a facial emotion recognition task. The participants needed to recognize happy, sad, angry and neutral facial expressions The ANOVA was used to analyze the differences between the two groups in terms of emotion recognition variables. Results The patients with BD showed a significant deficit in recognizing emotions in general (p=.01) which was prominent in the angry faces. Their response time to recognize the facial expressions was longer compared to the normal individuals. This difference was significant in recognizing the happy and neutral faces (p<0.05). Conclusion This study suggests an inaccurate recognition of facial expressions in adolescents with BD, particularly for anger, as well as slowness in detecting emotions especially happiness.