Miracle-dz/UniFAR-FTUs huggingface dataset:
Rearchitecting MapReduce for Heterogeneous Multicore Processors with Explicitly Managed Memories

This paper presents a new design and an implementation of the runtime system of MapReduce for heterogeneous multicore processors with explicitly managed local memories. We advance the state of the art in runtime support for MapReduce using five instruments: (1) A new multi-threaded, event-driven controller for task instantiation, task scheduling, synchronization, and bulk-synchronous execution of MapReduce stages. The controller improves utilization of control efficient cores, minimizes control overhead in the runtime system, and overlaps task instantiation with task scheduling on compute-efficient cores. (2) An implicit partitioning scheme which eliminates redundant memory copies. (3) An adaptive memory management scheme which combines efficient memory preallocation for applications with statically known output volume with dynamic allocation using runahead tasks for applications with statically unknown output volume. (4) An optimized quick-sort/merge-sort scheme which reduces the critical path length of merge-sort. (5) An optimized execution scheme which avoids redundant data transfers to and from local stores in applications that emit keys with the same value. Put together, these techniques accelerate representative MapReduce workloads by a factor of 1.81x (geometric mean) compared to a reference design that represents the state of the art.
A Quality Framework for Continuous Improvement of E-learning: The E-learning Maturity Model

The E-Learning Maturity Model (eMM) is a quality improvement framework designed to help institutional leaders assess their institution’s e-learning maturity. This paper reviews the eMM, drawing on examples of assessments conducted in New Zealand, Australia, the UK and the USA to show how it helps institutional leaders assess and compare their institution’s capability to sustainably develop, deploy and support e-learning. Resume Le modele de maturite pour l’apprentissage en ligne (E-Learning Maturity Model (eMM)) est un cadre de reference sur l’amelioration de la qualite qui est concu pour aider les leaders institutionnels a evaluer la maturite de leur etablissement en lien avec l’apprentissage en ligne. Cet article passe en revue le eMM, en tirant des exemples d’evaluations qui ont ete menees en Nouvelle-Zelande, en Australie, au Royaume-Uni et aux Etats-Unis afin de montrer comment cela aide les leaders institutionnels a evaluer et a comparer la capacite de leur etablissement a developper de facon durable, a deployer et a soutenir l’apprentissage en ligne.
Dynamics of Economic Well-Being: Poverty 1996-1999. Current Population Reports.

ducted at one point in time, or from annual studies conducted on a different set of people every year. In the 1970s and 1980s, when researchers began analyzing information from longitudinal studies, a dynamic view of poverty began to emerge. The longitudinal data (collected from the same set of people for several years) show that a majority of poor individuals do not remain poor for very long periods of time and a relatively high proportion of people have experienced poverty at one point or another. Poverty is
Pathway to functional studies: pipeline linking phylogenetic footprinting and transcription-factor binding analysis

Identification of transcription-factor binding sites is a critical first step in studying transcriptional regulation of genes. The comparative genomics method of phylogenetic footprinting is based on identifying sequence elements that are conserved across multiple genomes, and, thus, likely to be functional. We have developed a systematic high throughput screening pipeline to first search for conserved motifs using two different phylogenetic footprinting methods (motif-discovery and alignment-based), and then rapid evaluate the motifs as potential transcription-factor binding sites. The results are displayed in an interactive graphical user interface, FactorScan, which integrates three separate complementary databases (conserved-sequence motifs, transcription-factor binding site motifs, TRANSFAC). We applied this pipeline for transcription-factor binding site analysis to the orthologous gene regions of prion-protein family genes from vertebrate lineages, taking account of the gene annotations.
An Approach towards Improving the Lifetime and Security in Wireless Sensor Network

— A Wireless Sensor Network (WSN) is a collection of wireless sensor nodes forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, due to the limited range of each node’s wireless transmissions, it may be necessary for one sensor node to ask for the aid of other sensor nodes in forwarding a packet to its destination, usually the base station. One important issue when designing wireless sensor network is the routing protocol that makes the best use of the severely limited resource presented by WSN, especially the energy limitation. Another important factor is providing as much security to the application as possible. In this project, an energy efficient secure node disjoint multipath routing protocol for wireless sensor networks is proposed. Here, the data packets are transmitted in a secure manner by using the digital signature crypto system. Multipath routing protocols enhance the lifetime of the wireless sensor networks by distributing traffic among multiple paths instead of a single optimal path. The protocol guarantees loop freedom and disjointness of alternate path. We have compared the performance of this protocol with an ad hoc on-demand multipath distance vector routing protocol. To evaluate performance of proposed method, we have used parameters packet delivery ratio, energy consumption, routing overhead and throughput compare to the ad hoc on-demand multipath distance vector routing .
Research on Navigation Model for Ontology-Based Online Administrative Permit

Online administrative permit system is a new window for the government to deal with social affairs, as well as to communication with the public. There is large amount of information inside the government, whether the information can be organized efficiently and transmitted to the public in a rational way determines the success of the website and its ability to serve the public. The study focuses on building navigation model for online administrative permit system. In the process of building ontology, the Skeleton method is explored and some experts are consulted. At last, the relational database is used to store the ontology and implement the online administrative permits system.
A silicon nanocrystals based memory

A new memory structure using threshold shifting from charge stored in nanocrystals of silicon (≊5nm in size) is described. The devices utilize direct tunneling and storage of electrons in the nanocrystals. The limited size and capacitance of the nanocrystals limit the numbers of stored electrons. Coulomb blockade effects may be important in these structures but are not necessary for their operation. The threshold shifts of 0.2–0.4 V with read and write times less than 100’s of a nanosecond at operating voltages below 2.5 V have been obtained experimentally. The retention times are measured in days and weeks, and the structures have been operated in an excess of 109 cycles without degradation in performance. This nanomemory exhibits characteristics necessary for high density and low power.
Above-ground biomass and carbon and nitrogen content of woody species in a subtropical thornscrub parkland

Regression equations were developed to predict above-ground biomass, carbon and nitrogen content from stem and canopy dimensions for 10 shrub species common to subtropical thorn parklands of southern Texas. Projected canopy area yielded slightly more precise estimates of biomass and nutrient concentrations than the sum of stem basal diameters at the soil surface. All such equations were significant (po0.05) and had r values X0.70, except dead wood and large stems of one species. These equations are potentially useful for estimating woody biomass and nutrient content from remotely sensed or field survey data, and in evaluating models of ecosystem biogeochemistry. r 2004 Published by Elsevier Ltd.
Which side are you on? A new Panopticon vs. privacy

Social media and Web 2.0 have enabled internet users to contribute online content, which may be crawled and utilized for a variety of reasons, from personalized advertising to behaviour prediction/profiling. One negative case scenario is the political affiliation profiling. Our hypothesis is that this scenario is nowadays realistic, applicable to social media, and violates civil rights, privacy and freedom. To demonstrate this, we developed a horror story, i.e., a Panopticon method, in order to reveal this threat and contribute in raising the social awareness over it. The Panopticon relies on data/opinion mining techniques; hence it classifies comments, videos and playlists, collected from the popular social medium YouTube. Afterwards, it aggregates these classifications in order to decide over the users' political affiliation. The experimental test case of the Panopticon is an extensive Greek community of YouTube users. In order to demonstrate our case, we performed an extensive graph theoretical and content analysis of the collected dataset and show how and what kind of personal data (e.g. political attitude) can be derived via data mining on publicly available YouTube data. Then, we provide the reader with an analysis of the legal means that are available today, to a citizen or a society as a whole, so as to effectively be prevented from such a threat.
Virtual gateways: enabling connectivity between MAC heterogeneous sensor networks

Over the years many different MAC protocols have been developed for sensor networks, each optimised for a different set of application requirements. Since these MAC protocols are not compatible with each other, there is generally no interoperability between sensor networks. This is a severe problem when connectivity between sensor networks is required. In this paper, we demonstrate the feasibility of using virtual gateways, sensor nodes running multiple MAC protocols simultaneously on top of a single radio interface, to enable connectivity between sensor networks. To this end we have developed MultiMAC, a network stack for TinyOS capable of running multiple MAC protocols simultaneously. We show that the architecture of the MultiMAC stack is flexible and extensible enough to support a wide variety of MAC protocols and that the overhead of this network stack is minimal. We also discuss the results of our initial experiments with a testbed on the impact of using virtual gateways.
Contribution Flavonoid Intake and Colorectal Cancer Risk in Men and Women

1 Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA. 2 Department of Epidemiology, Harvard School of Public Health, Boston, MA. 3 Department of Nutrition, Harvard School of Public Health, Boston, MA. 4 Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA. 5 Department of Adult Oncology, Dana-Farber Cancer Institute, Boston, MA.
Complete Nucleotide Sequences of Bovine as 2-and P-Casein cDNAs : Comparisons with Related Sequences in Other Species

The nucleotide sequences corresponding to bovine crs2and p-casein mRNAs have been determined by cDNA analysis. Both sequences appear to be complete at their 5’ ends. The nucleotide sequence of us2-casein, when compared with the corresponding cavine A sequence, helps to define the boundaries of a large amino acid repeat (-80 residues) whereas comparisons with the nucleotide sequences of rat yand mouse a-casein mRNAs also reveal extensive sequence similarities. An alignment of these four sequences shows that the divergence of their translated regions has been characterized by the duplication and deletion of discrete segments of sequence that probably correspond to exons. A high degree of nucleotide substitution is also found when the four sequences are compared, except for wellconserved leader-peptide and phosphorylation-site sequences and, to a lesser extent, the 5’-untranslated regions. Similar comparison of the bovine and rat p-caseins shows that their divergence has involved a high rate of nucleotide substitution but that no major insertions or deletions of sequence have occurred. The several splice sites that have been defined in the rat P-casein gene are likely to have been conserved in the bovine. The contrasting evolutionary histories of the aand P-casein coding sequences correlate with the distinctive functions of these proteins in the casein micelle system in milk.
Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media

Sentiment analysis is a growing area of research with significant applications in both industry and academia. Most of the proposed solutions are centered around supervised, machine learning approaches and review-oriented datasets. In this article, we focus on the more common informal textual communication on the Web, such as online discussions, tweets and social network comments and propose an intuitive, less domain-specific, unsupervised, lexicon-based approach that estimates the level of emotional intensity contained in text in order to make a prediction. Our approach can be applied to, and is tested in, two different but complementary contexts: subjectivity detection and polarity classification. Extensive experiments were carried on three real-world datasets, extracted from online social Web sites and annotated by human evaluators, against state-of-the-art supervised approaches. The results demonstrate that the proposed algorithm, even though unsupervised, outperforms machine learning solutions in the majority of cases, overall presenting a very robust and reliable solution for sentiment analysis of informal communication on the Web.
An Approach for the Segmentation of Satellite Images using K-means, KFCM, Moving KFCM and Naive Bayes Classifier

This paper presents an improvised Moving kernel based fuzzy C-means(MKFCM) for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure in which first the input image is passed through a median filter to reduce the noise and get a better image fit for segmentation. The pre-processed image is segmented using the Moving KFCM algorithm and classified using Bayesian classifier with kernel Distribution type. KFCM with moving property is used to improve the object segmentation in satellite images. Simulation result show that classification accuracy for different regions using Moving KFCM is better than k-means and KFCM using Naive Bayes classifier with four different kernels
Impact of the routing techniques on the wireless sensor networks performance

The paper analyzes the performance of the wireless sensor network (WSN) from the standpoint of routing algorithm related tos battery-discharge process. Using an experimental WSN implemented with Arduino Uno and XBee modules, measurements are made, of the packet delay and battery-discharge rate. Measurement results are compared with the simulation results obtained in the OMNeT++ simulation environment. The paper provides a short overview of difficulties and challenges commonly encountered in WSN design. The measurement results show that Arduino in its native form is not suitable for the WSN processing unit because its high current value during the sleep mode.
Children’s reading difficulties, language, and reflections on the simple view of reading

ABSTRACT Reading comprehension is a complex task which depends on a range of cognitive and linguistic processes. According to the Simple View of Reading, this complexity can be captured as the product of two sets of skills: decoding and linguistic comprehension. The Simple View explains variance in reading comprehension and provides a good framework to guide the classification of reading disorders. This paper discusses how weaknesses in either or both of components of the Simple View are implicated in children’s reading comprehension difficulties. It concludes with reflections on the strengths and limitations of the Simple View as a theoretical and practical framework to guide our understanding of reading comprehension and its development.
Robust optimization-based multi-loop PID controller tuning: A new tool and an industrial example

Abstract Abstract Modern process plants are highly integrated and as a result, decentralized PID control loops are often strongly interactive. The currently used sequential tuning approach is not only time consuming, but does also not achieve optimal performance of the inherently multivariable control system. This paper describes a method and a software tool which allows a control engineer to calculate optimal PID controller settings for multiloop systems. It is based on the identification of a state space model of the multivariable system, and it uses constrained nonlinear optimization techniques to find the controller parameters. The solution is tailored to the specific control system and PID algorithm to be used. The methodology has been successfully applied in several industrial advanced control projects. The tuning results which have been achieved for interacting PID control loops in the stabilizing section of an industrial Gasoline Treatment Unit at SABIC Petrochemicals are presented.
Postsynaptic injection of calcium‐independent phospholipase A2 inhibitors selectively increases AMPA receptor‐mediated synaptic transmission

The calcium‐independent form of phospholipase A2 (iPLA2), an enzyme known to generate arachidonic acid (AA), was recently identified as the predominant constitutive phospholipase in the hippocampus. The present study shows that the iPLA2 inhibitor bromoenol lactone, when introduced into hippocampal CA1 pyramidal cells through a patch pipette, generated a dose‐dependent increase in the amplitude of α‐amino‐3‐hydroxy‐5‐methylisoxazole‐4‐propionate (AMPA) receptor‐mediated excitatory postsynaptic currents (EPSCs). The iPLA2 inhibitor by itself interfered with neither paired pulse facilitation nor N‐methyl‐D‐aspartate (NMDA) receptor‐mediated EPSCs, suggesting that its influence on synaptic transmission is postsynaptic in origin and specific to the AMPA subtype of glutamate receptors. Comparable results were obtained with palmitoyl trifluoromethyl ketone, a second structurally distinct iPLA2 inhibitor. The ability of iPLA2 inhibitors to increase AMPA receptor‐mediated currents was also reproduced by MK‐866, an inhibitor recognized to interfere with the generation of 5‐lipoxygenase by‐products of AA. At the biochemical level, we found that AMPA, but not NMDA glutamate receptor subunits, were upregulated in rat brain sections pre‐incubated with the iPLA2 inhibitors. Collectively, these results provide the first experimental evidence that constitutive iPLA2 and/or its metabolites play an important role in the postsynaptic modulation of neurotransmission in CA1 pyramidal cells of the hippocampus. © 2004 Wiley‐Liss, Inc.
World Health Organization International Standard to Harmonize Assays for Detection of Hepatitis E Virus RNA

Nucleic acid amplification technique–based assays are a primary method for the detection of acute hepatitis E virus (HEV) infection, but assay sensitivity can vary widely. To improve interlaboratory results for the detection and quantification of HEV RNA, a candidate World Health Organization (WHO) International Standard (IS) strain was evaluated in a collaborative study involving 23 laboratories from 10 countries. The IS, code number 6329/10, was formulated by using a genotype 3a HEV strain from a blood donation, diluted in pooled human plasma and lyophilized. A Japanese national standard, representing a genotype 3b HEV strain, was prepared and evaluated in parallel. The potencies of the standards were determined by qualitative and quantitative assays. Assay variability was substantially reduced when HEV RNA concentrations were expressed relative to the IS. Thus, WHO has established 6329/10 as the IS for HEV RNA, with a unitage of 250,000 International Units per milliliter.
Minimizing the memory usage with outofcore multifrontal direct solver

In this paper we present the out.of. core multi.fron tal direct solver algorithm for multi.physics problems. The solver is based on the binary elimination tree, with number of leaves equal to the number of finite elements. The solver browses the elimination tree in the post.order, eliminates fully assembled degrees of freedom and merges resulting Schur complement matrices. The solver is intended to minimize the memory usage by dumping out all Schur complements and processed local systems to the disc. The solver algorithm is executed on challenging computational problem involving the linear elasticity coupled with linear accoustics. The history of execution of the solver algorithm is analyzed by measuring the memory usage of the solver as well as the sparsities of the nodal matrices. The memory usage of the solver is compared with the MUMPS solver.
Data-driven Outbreak Detection in Social Networks Group No : 26

In social networks, influence such as information, virus, innovations spreads from node to node through the edges of the networks. Understanding this diffusion process is the key to many important real-world applications, for example marketing strategies, virus and pollution control. The problem of outbreak detection and influence maximization ask about selecting a set of “important” nodes (sensor locations, influential bloggers) in a network, from which influence may outbreak or we can detect influence outbursts. These two problems can be modeled as selecting nodes in order to maximize some measures defined over all the sets of nodes. These measures are usually chosen in terms of the number of influenced nodes, the time passed from outbreak until detection and etc. Most traditional studies [6][8] focus on how to maximize the defined measures effectively and efficiently. Research efforts have been made to cast the original problem into the submodular function maximization where greedy hill climbing algorithms can deliver 63% approximation of the optimal. However, what the previous research failed to capture is that in realworld scenarios multiple influences spread through the network simultaneously, and these spread of influences tend to correlate with content of information being spread. Furthermore, nodes may have specific preferences towards specific contents, so that nodes that are chosen to be the “outbreakers” for one type of information cascades may not be the “outbreakers” for information cascades of another content type. This project addresses how to incorporate content into outbreak detection, which we refer to “data-driven outbreak detection”. We propose an adapted model that allows for modeling outbreak detection with content information, and evaluate our model on real world collaboration network and Twitter network. Moreover, we will use the proposed model and machine learning techniques to train classifiers that predict whether a specific piece of content information will cause an outbreak in the network. Finally, we discuss the pros and cons of incorporating content information into the analysis of information outbreak.