Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Evaluation of Multi-Round Matrix Multiplication on MapReduce

A common approach in the design of MapReduce algorithms is to minimize the number of rounds. Indeed, there are many examples in the literature of monolithic MapReduce algorithms, which are algorithms requiring just one or two rounds. However, we claim that the design of monolithic algorithms may not be the best approach in cloud systems. Indeed, multi-round algorithms may exploit some features of cloud platforms by suitably setting the round number according to the execution context. In this paper we carry out an experimental study of multi-round MapReduce algorithms aiming at investigating the performance of the multi-round approach. We use matrix multiplication as a case study. We first propose a scalable Hadoop library, named M$_3$, for matrix multiplication in the dense and sparse cases which allows to tradeoff round number with the amount of data shuffled in each round and the amount of memory required by reduce functions. Then, we present an extensive study of this library on an in-house cluster and on Amazon Web Services aiming at showing its performance and at comparing monolithic and multi-round approaches. The experiments show that, even without a low level optimization, it is possible to design multi-round algorithms with a small running time overhead.
Exploring the Genome of a Butyric Acid Producer, Clostridium butyricum INCQS635

ABSTRACT The draft genome sequence of Clostridium butyricum INCQS635 was obtained by means of ion sequencing. The genome provides further insight into the genetic repertoire involved with metabolic pathways related to the fermentation of different compounds and organic solvents synthesis (i.e., butyric acid) with biofuel applications.
Area-Efficient QC-LDPC Decoder Architecture Based on Stride Scheduling and Memory Bank Division

SUMMARY In this paper, an area-efficient decoder architecture is proposed for the quasi-cyclic low-density parity check (QC-LDPC) codes specified in the IEEE 802.16e WiMAX standard. The decoder supports all the code rates and codeword lengths defined in the standard. In order to achieve low area and maximize hardware utilization, the decoder utilizes 4 decoding function units, which is the greatest common divisor of the expansion factors. In addition, the decoder adopts a novel scheduling scheme named stride scheduling, which stores the extrinsic messages in non-sequential order to replace the conventional complex flexible permutation network with simple small-sized cyclic shifters and also minimize the number of memory accesses. To further minimize the complexity, the number of extrinsic memory instances for 24 block columns is reduced to 5 banks by identifying independent sets. All the memory instances used in the decoder are single-port memories which cost less area and price compared to dual-port ones. Finally, the decoding function units have partially parallel structure to make the decoding throughput sufficiently over the requirement of the WiMAX standard. The proposed decoder is synthesized with 49 K equivalent gates and 54,144 bits of memory, and the implementation occupies 0.40 mm 2 in a 65 nm CMOS technology.
A Mean-Risk Model for the Stochastic Traffic Assignment Problem

We embark on an agenda to investigate how stochastic travel times and risk aversion transform the traditional traffic assignment problem and its corresponding equilibrium concepts. Moving from deterministic to stochastic travel times with risk-averse users introduces non-convexities that make the problem more difficult to analyze. For example, even computing a best response of a user to the environment is still of unknown complexity [50, 48]. This paper focuses on equilibrium existence and characterization in the different settings of infinitesimal (non-atomic) vs. atomic users and fixed (exogenous) vs. congestion-dependent (endogenous) variability of travel times. We show that equilibria always exist in three out of the four possible combinations. The exception is the case of atomic users and endogenous variability. Because cost functions are non-additive (i.e., the cost along a path is not a sum of costs over edges of the path as it is assumed in the vast majority of network routing problems), solutions need to be represented as path flows since not all decompositions from edge to path-flows are equivalent. Nevertheless, we show that succinct representations of equilibria and optimal solutions always exist. Finally, we investigate the inefficiencies resulting from the stochastic nature of travel times. We obtain that under exogenous variability of travel times, the worst-case inefficiency of equilibria is exactly the same as when travel time functions are deterministic, meaning that in this case risk-aversion under stochastic travel times does not further degrade a system in the worst-case in relation to users’ self-mindedness.
Forecasting Cultivated Areas And Production Of Wheat In India Using ARIMA Model

A B S T R A C T. Wheat area and production in India data for the period of 1950-51 to 2011-12 were analyzed by time series methods. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were calculated for the data. Appropriate Box-Jenkins Auto Regressive Integrated Moving Average (ARIMA) model was fitted. Validity of the model was tested using standard statistical techniques.ARIMA (1, 1, 1) and ARIMA (1, 1, 0) model were used to forecast area and production in India for five leading years. The results also shows area forecast for the year 2015 to be about 31.46 thousand hectare with upper and lower limit 34.25 and 31.46 thousand hectares respectively. The model also shows Wheat production forecast for the year 2015 to be about 97.73 thousand tonnes with upper and lower limit 107.55 and 87.92 thousand tonnes respectively.
Fisher feature selection for emotion recognition

Emotion Recognition is one of the major areas of Affective Computing. Affective Computing is intended to reduce the communication gap between human and machine. Many research works in this area are focus on how to make a machine properly response to different mood of human. In this research, we propose Fisher Feature Selection (F-Score) for Emotion Recognition of Thai Speech to classify 4 different emotions from Thai utterance: Sad, Angry, Happy and Fear. The essence of our work lies on the inherit difficulties of Thai Language properties that intonation, articulation and accentuation can made different meanings. The proposed method is divided into two steps: First step, the human sound is extracted to get the 14 dominant features using F-Score Feature Selection. Then, in step two, two different learning networks are used to compare the classification performance. The results show that with the use of F-Score Feature Selection as a feature selection method to combine with Back Propagation Neural Network as a learning network offers a distinctive recognition rate of 95.13%.
IPA for continuous Petri nets

Abstract Recently there has been a considerable interest in the application of Infinitesimal Perturbation Analysis (IPA) to continuous queues, where its sample derivatives (gradients) were shown to be unbiased for a large class of systems. This paper extends the investigation to a class of hybrid Petri nets, where the special algebraic structure of continuous transitions yields simple algorithms for the IPA derivatives. We derive such algorithms for the performance functions of throughput and average workload, and show them to be model-free and easily computable from the sample paths.
A Formal Analysis of the Fault-Detecting Ability of Testing Methods

Several relationships between software testing criteria, each induced by a relation between the corresponding multisets of subdomains, are examined. The authors discuss whether for each relation R and each pair of criteria, C/sub 1/ and C/sub 2/, R(C/sub 1/, C/sub 2/) guarantees that C/sub 1/ is better at detecting faults than C/sub 2/ according to various probabilistic measures of fault-detecting ability. It is shown that the fact that C/sub 1/ subsumes C/sub 2/ does not guarantee that C/sub 1/ is better at detecting faults. Relations that strengthen the subsumption relation and that have more bearing on fault-detecting ability are introduced. >
Location Privacy in Buildings: A 3-Dimensional K-Anonymity Model

Privacy protection has recently received considerable attention in location-based services. In this paper, we show that most of the existing k-anonymity location cloaking algorithms are concerned only and cannot effectively prevent location-dependent attacks when users' locations have height information. Therefore, adopting the three dimensional location information, we propose a new clique-based cloaking algorithm, called 3d Clique Cloak, to defend against location leaks in indoor environment. The main idea is to expand the MBV (minimum bounding volume) to a three-dimensional space, thus for a user who initiated location services can find k-anonymity cloaking set in the three-dimensional space. The efficiency and effectiveness of the proposed 3d Clique Cloak algorithm are validated by series of carefully designed experiments.
Inclusion and Functionalization of Polymers with Cyclodextrins: Current Applications and Future Prospects

The numerous hydroxyl groups available in cyclodextrins are active sites that can form different types of linkages. They can be crosslinked with one another, or they can be derivatized to produce monomers that can form linear or branched networks. Moreover, they can form inclusion complexes with polymers and different substrates, modifying their physicochemical properties. This review shows the different applications using polymers with cyclodextrins, either by forming inclusion complexes, ternary complexes, networks, or molecularly imprinted polymers (MIPs). On one hand, the use of cyclodextrins enhances the properties of each polymer, and on the other the use of polymers decreases the amount of cyclodextrins required in different formulations. Both cyclodextrins and polymers contribute synergistically in several applications such as pharmacological, nutritional, environmental, and other industrial fields. The use of polymers based on cyclodextrins is a low cost easy to use potential tool with great future prospects.
Migrating Traditional Database-Based Systems onto Semantic Layer

Across various domains, the ontologies are learned from relational models of databases for the purpose of integrating data to a semantic layer. Through mappings among ontologies, new semantic applications can visit these integrated data with semantic query language like SPARQL. However, the traditional systems are still running on databases with SQL. In this paper we analyze the translations from basic relational algebra operations to SPARQL queries under an ontology learning approach. The result shows that SPARQL is relational completeness in the scenario. So the database-based application could be migrated from relational database onto semantic layer by replacing SQLs with SPARQL queries.
Factors regulating the transcriptional elongation activity of RNA polymerase II

The synthesis of mature and functional messenger RNA by eukaryotic RNA polymerase II (Pol II) is a complex, multistage process requiring the cooperative action of many cellular proteins. This process, referred to collectively as the transcription cycle, proceeds via five stages: preinitiation, initiation, promoter clearance, elongation, and termination. During the past few years, fundamental studies of the elongation stage of transcription have demonstrated the existence of several families of Pol II elongation factors governing the activity of Pol II. It is now clear that the elongation stage of transcription is a critical stage for the regulation of gene expression. In fact, two of these elongation factors, ELL and elongin, have been implicated in human cancer. This article will review the proteins involved in the regulation of the elongation stage of transcription by Pol II, describing the recent experimental findings that have propelled vigorous research on the properties and function of the elongating RNA polymerase II. —Shilatifard, A. Factors regulating the transcriptional elongation activity of RNA polymerase II. FASEB J. 12, 1437–1446 (1998)
SPATIAL STRUCTURE OF LAND VALUES IN NAIROBI, KENYA

Introduction General Background -. The City of Nairobi is situated at a point where the densely populated agricultural Central Highlands of Kenya merge into the extensive Athi Plains at just above 5,000 feet above sea level. Although founded as a Kenya-Uganda railway encampment some seventy years ago, the City has grown as a national capital to become the largest centre in the urban system of Kenya. A post-industrial and colonially-generated city, Nairobi has become the hub and core area of the country, a focal point for communication networks and the national nuclei for Kenya’s economic, social and political organisations (Fig. 1). According to the 1969 census, Nairobi’s population stood at about half-million people compared with a figure of 266,795 in 1962. Of the half-million inhabitants, an overwhelming majority (83.04 %) are Africans while the rest comprise Asians (1 3. I9 %) and Europeans
Prediction of free word associations based on Hebbian learning

An associative lexical net whose weights are computed on the basis of the co-occurrences of words using Hebb's rule has been built. The co-occurrences of word pairs are determined by shifting a window over a large body of text. To estimate the associative response to a given stimulus word, the corresponding node is activated and its activity is propagated in the net. The proposed model assumes that words with high activities after propagation correspond to the associative responses of human subjects. These predictions have been tested and confirmed using the association norms collected by Russel and Jenkins. <<ETX>>
Effects of Two Sublethal Concentrations of Mercury Chloride on the Morphology and Metallothionein Activity in the Liver of Zebrafish (Danio rerio)

Mercury (Hg) is a highly hazardous pollutant widely used in industrial, pharmaceutical and agricultural fields. Mercury is found in the environment in several forms, elemental, inorganic (iHg) and organic, all of which are toxic. Considering that the liver is the organ primarily involved in the regulation of metabolic pathways, homeostasis and detoxification we investigated the morphological and ultrastructural effects in Danio rerio liver after 96 h exposure to two low HgCl2 concentrations (7.7 and 38.5 μg/L). We showed that a short-term exposure to very low concentrations of iHg severely affects liver morphology and ultrastructure. The main effects recorded in this work were: cytoplasm vacuolization, decrease in both lipid droplets and glycogen granules, increase in number of mitochondria, increase of rough endoplasmic reticulum and pyknotic nuclei. Pathological alterations observed were dose dependent. Trough immunohistochemistry, in situ hybridization and real-time PCR analysis, the induction of metallothionein (MT) under stressor conditions was also evaluated. Some of observed alterations could be considered as a general response of tissue to heavy metals, whereas others (such as increased number of mitochondria and increase of RER) may be considered as an adaptive response to mercury.
Influence spread maximization in social networks

The present subject matter relates to maximizing spread of influence in social networks. In an example implementation, a benefit of selection value for each node in a social network is computed. A node is selected into a set of nodes, for which the benefit of selection value is maximum. The benefit of selection value of each immediate successor node of the selected node is reduced by a factor of one minus the degree of influence of the selected node on a respective immediate successor node of the selected node. The benefit of selection value of each immediate predecessor node of the selected node is reduced at least by a value of the degree of influence of a respective immediate predecessor node on the selected node. A next node from remaining nodes for which the benefit of selection value is maximum is selected into the set of nodes.
Biofilm Formation and Motility Depend on the Nature of the Acinetobacter baumannii Clinical Isolates

Acinetobacter baumannii is a nosocomial pathogen involved in various infections ranging from minor soft-tissue infections to more severe infections such as ventilator-associated pneumonia and bacteremia. The severity and the type of infections depend on the genetic and phenotypic variations of the strains. In this study, we compared the extent of biofilm formation and motility displayed by 60 multidrug-resistant A. baumannii clinical strains isolated from blood and sputum samples from patients from Southern India. Our results showed that isolates from the sputum samples formed significantly more robust biofilm compared to the blood isolates. On the other hand, we observed that the blood isolates were more motile than the sputum isolates. To the best of our knowledge, this is the first study that systematically evaluated the correlation between these two phenotypic traits and the nature of the isolates.
Global pattern analysis and classification of dermoscopic images using textons

Detecting and classifying global dermoscopic patterns are crucial steps for detecting melanocytic lesions from non-melanocytic ones. An important stage of melanoma diagnosis uses pattern analysis methods such as 7-point check list, Menzies method etc. In this paper, we present a novel approach to investigate texture analysis and classification of 5 classes of global lesion patterns (reticular, globular, cobblestone, homogeneous, and parallel pattern) in dermoscopic images. Our statistical approach models the texture by the joint probability distribution of filter responses using a comprehensive set of the state of the art filter banks. This distribution is represented by the frequency histogram of filter response cluster centers called textons. We have also examined other two methods: Joint Distribution of Intensities (JDI) and Convolutional Restricted Boltzmann Machine (CRBM) to learn the pattern specific features to be used for textons. The classification performance is compared over the Leung and Malik filters (LM), Root Filter Set (RFS), Maximum Response Filters (MR8), Schmid, Laws and our proposed filter set as well as CRBM and JDI. We analyzed 375 images of the 5 classes of the patterns. Our experiments show that the joint distribution of color (JDC) in the L*a*b* color space outperforms the other color spaces with a correct classification rate of 86.8%.
Adaptive backstepping-based neural decentralized control for stochastic switched systems

This paper addresses the problem of decentralized adaptive neural output-feedback control for a class of uncertain switched nonlinear stochastic large-scale systems. A new adaptive neuarl output-feedback controller design method is developed under the framework of backstepping recursive design, adaptive control, and dynamic surface control technique. It is proved that the proposed controller can ensure that all signals of the resulting closed-loop system are the semi-globally uniformly ultimately mean square bounded.
D-brane couplings, RR fields and Clifford multiplication

Non-trivial configurations of Yang-Mills fields and gravitational backgrounds induce charges on Dp-branes that couple them to lower and higher RR potentials. We show that these couplings can be described in a systematic and coordinate independent way by using Clifford multiplication. In the minimal formulation, D-brane charges and RR potentials combine into bispinors of an SO(1,9) which is defined with a flat metric and does not coincide with the space-time Lorentz group. In a non-minimal formulation, the RR potentials combine into SO(10,10) spinors while the space of charges is formally enlarged to construct SO(10,10) bispinors. The formalism suggests that the general form of the gravitational contribution to the D-brane charges is not modified, though the replacement of wedge product by Clifford multiplication gives rise to new couplings, consistent with T-duality.
Comparing classifiers for pronunciation error detection

Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs linear-discriminant analysis (LDA)), a classifier based on cepstral coefficients in combination with LDA, and one based on confidence measures (the so-called Goodness Of Pronunciation scores). The best results were obtained for the two LDA classifiers which produced accuracy levels of about 85-93%.
Adaptive Baseline Enhances EM-Based Policy Search: Validation in a View-Based Positioning Task of a Smartphone Balancer

EM-based policy search methods estimate a lower bound of the expected return from the histories of episodes and iteratively update the policy parameters using the maximum of a lower bound of expected return, which makes gradient calculation and learning rate tuning unnecessary. Previous algorithms like Policy learning by Weighting Exploration with the Returns, Fitness Expectation Maximization, and EM-based Policy Hyperparameter Exploration implemented the mechanisms to discard useless low-return episodes either implicitly or using a fixed baseline determined by the experimenter. In this paper, we propose an adaptive baseline method to discard worse samples from the reward history and examine different baselines, including the mean, and multiples of SDs from the mean. The simulation results of benchmark tasks of pendulum swing up and cart-pole balancing, and standing up and balancing of a two-wheeled smartphone robot showed improved performances. We further implemented the adaptive baseline with mean in our two-wheeled smartphone robot hardware to test its performance in the standing up and balancing task, and a view-based approaching task. Our results showed that with adaptive baseline, the method outperformed the previous algorithms and achieved faster, and more precise behaviors at a higher successful rate.
Neural activation to emotional faces in adolescents with autism spectrum disorders.

BACKGROUND Autism spectrum disorders (ASD) involve a core deficit in social functioning and impairments in the ability to recognize face emotions. In an emotional faces task designed to constrain group differences in attention, the present study used functional MRI to characterize activation in the amygdala, ventral prefrontal cortex (vPFC), and striatum, three structures involved in socio-emotional processing in adolescents with ASD. METHODS Twenty-two adolescents with ASD and 20 healthy adolescents viewed facial expressions (happy, fearful, sad and neutral) that were briefly presented (250 ms) during functional MRI acquisition. To monitor attention, subjects pressed a button to identify the gender of each face. RESULTS The ASD group showed greater activation to the faces relative to the control group in the amygdala, vPFC and striatum. Follow-up analyses indicated that the ASD relative to control group showed greater activation in the amygdala, vPFC and striatum (p < .05 small volume corrected), particularly to sad faces. Moreover, in the ASD group, there was a negative correlation between developmental variables (age and pubertal status) and mean activation from the whole bilateral amygdala; younger adolescents showed greater activation than older adolescents. There were no group differences in accuracy or reaction time in the gender identification task. CONCLUSIONS When group differences in attention to facial expressions were limited, adolescents with ASD showed greater activation in structures involved in socio-emotional processing.