Miracle-dz/UniFAR-FTUs huggingface dataset:
ParaLog: enabling and accelerating online parallel monitoring of multithreaded applications

Instruction-grain lifeguards monitor the events of a running application at the level of individual instructions in order to identify and help mitigate application bugs and security exploits. Because such lifeguards impose a 10-100X slowdown on existing platforms, previous studies have proposed hardware designs to accelerate lifeguard processing. However, these accelerators are either tailored to a specific class of lifeguards or suitable only for monitoring singlethreaded programs. We present ParaLog, the first design of a system enabling fast online parallel monitoring of multithreaded parallel applications. ParaLog supports a broad class of software-defined lifeguards. We show how three existing accelerators can be enhanced to support online multithreaded monitoring, dramatically reducing lifeguard overheads. We identify and solve several challenges in monitoring parallel applications and/or parallelizing these accelerators, including (i) enforcing inter-thread data dependences, (ii) dealing with inter-thread effects that are not reflected in coherence traffic, (iii) dealing with unmonitored operating system activity, and (iv) ensuring lifeguards can access shared metadata with negligible synchronization overheads. We present our system design for both Sequentially Consistent and Total Store Ordering processors. We implement and evaluate our design on a 16 core simulated CMP, using benchmarks from SPLASH-2 and PARSEC and two lifeguards: a data-flow tracking lifeguard and a memory-access checker lifeguard. Our results show that (i) our parallel accelerators improve performance by 2-9X and 1.13-3.4X for our two lifeguards, respectively, (ii) we are 5-126X faster than the time-slicing approach required by existing techniques, and (iii) our average overheads for applications with eight threads are 51% and 28% for the two lifeguards, respectively.
Detection, Segmentation and Recognition of Face and its Features Using Neural Network

Face detection and recognition has been prevalent with research scholars and diverse approaches have been incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images and video image to be used for detection and recognition. This led to newer methods for face detection and recognition to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA), have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize nonlinear faces with an acceptance ratio of more than 90% and execution time of only few seconds.
Operational Challenges in Large Clinical Trials: Examples and Lessons Learned from the Gambia Pneumococcal Vaccine Trial

The requirements for Good Clinical Practice (GCP) in clinical trials are well documented [1], and ethical issues are hotly debated [2]. Operational aspects of trials, however, have received far less attention [3], perhaps due to restrictions on journal space for detailing methods. In low resource settings, however, large trials often face many logistical and organizational obstacles, and thus the practical difficulties in running a trial to GCP standards should not be ignored. Here, we describe the main operational challenges to a randomized, double-blind, placebo-controlled trial of the safety and efficacy of pneumococcal conjugate vaccine among over 17,000 infants in the Gambia. The trial began in August 2000, and after the magnitude of the challenges were recognized, a new senior principal investigator (FTC) and project manager (FGY) were recruited, taking up post in June 2001. We summarize here the major lessons learnt in trial implementation in a resource-poor setting.
Underage Drinking Among Asian American and Pacific Islander Adolescents

This study assessed the prevalence of alcohol abuse and dependence rates among four major ethnic groups of Hawai‘i and examined the relationship among risk factors, protective factors, and demographic variables related to underage drinking. A total of 196 students were administered the Diagnostic Interview Schedule for Children, the American Drug and Alcohol Survey, and the Prevention Planning Survey. Drinking rates for Native Hawaiian students were significantly higher than those for Japanese and Caucasian students. Multiple logistic regression models accounted for 49% of the variance for any alcohol use. Ethnic group differences were found when data were disaggregated for Asian and Pacific Islander students.
Creation of 3D models of chosen historical buildings for supporting knowledge transfer

Usage of modern digital technologies is a characteristic feature of present times. They are used not only in technically oriented areas but also in the humanities, in areas such as History, Museum studies, Education etc. 3D-virtual models of historical buildings, alternatively in combination with elements of gamification, used as a complement to permanent displays seemed to be a suitable element to enrich exhibits of historical buildings. This complements the exhibition lure percepted by visitors. As one of our main objective, these 3D-virtual models of historical buildings can be used in teaching historically oriented classes at primary schools.
Control of discrete linear repetitive processes using non-local previous pass information

A repetitive process makes a series of sweeps or passes through dynamics defined on a finite duration termed the pass length. The process output is termed the pass profile and when each pass is completed, resetting to the starting location ready for the start of the next one. On any pass the previous pass profile acts as a forcing function on, and hence contributes to, the dynamics of the next one. There has been a considerable volume of profitable work on the development of a control theory for these processes with more recent focus on the design of control laws. The novel contribution of this paper is a new design algorithm which makes more use of available previous pass profile information and reduces the conservativeness present in existing alternatives.
UKF data fusion of odometry and magnetic sensor for a precise indoor localization system of an autonomous vehicle

The precise localization system of autonomous vehicles is one of the important techniques in the research of autonomous driving. In the case of outdoor localization, the Global Positioning System (GPS) sensor is commonly used to attenuate the cumulative error of odometry data occurred from undesirable effects of slippage. However, considering GPS outages, its positioning inaccuracy would not be used as reference information for an accurate indoor localization. Therefore, in this paper, we describe a sensor fusion system using odometry and magnetic sensor data for the precise indoor localization system. The odometry model is based on the Ackerman steering geometry, and directly calculated from encoder information on two rear wheels and one steering. A magnetic sensor is developed to measure magnetic field produced from circular-type magnets located in the ground. Considering the sensor data fusion, Unscented Kalman Filter (UKF) is utilized so that the mean and covariance of the nonlinear system function can be estimated by the Unscented Transformation (UT) with sigma points. The simulation results are presented that demonstrate the validity of the UKF based localization system.
Techniques for the synthesis of reversible Toffoli networks

We present certain new techniques for the synthesis of reversible networks of Toffoli gates, as well as improvements to previous methods. Gate count and technology oriented cost metrics are used. Two new synthesis procedures employing Reed-Muller spectra are introduced and shown to complement earlier synthesis approaches. The previously proposed template simplification method is enhanced through the introduction of a faster and more efficient template application algorithm, an updated classification of the templates, and the addition of new templates of sizes 7 and 9. A resynthesis approach is introduced wherein a sequence of gates is chosen from a network, and the reversible specification it realizes is resynthesized as an independent problem in hopes of reducing the network cost. Empirical results are presented to show that the methods are efficient in terms of the realization of reversible benchmark specifications.
A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies

Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions.
Eltern und Kinder: Der Zürcher Kurzfragebogen zum Erziehungsverhalten (ZKE)

Zusammenfassung. Im Beitrag wird der “Zurcher Kurzfragebogen zur Erfassung des Erziehungsverhaltens“ (ZKE) von Muttern und Vatern aus Sicht der Kinder an einer Stichprobe (N = 877) 11- bis 17jahriger schweizerischer Schulerinnen und Schuler aus dem Kanton Zurich uberpruft. Der Fragebogen erfast in reliabler und valider Weise die Dimensionen “Warme und Unterstutzung“, “Psychologischer Druck“ und “Regeln und Kontrolle“. Im Einklang mit der Forschungsliteratur zur familiaren Sozialisation korrespondierte ein hohes Mas an elterlicher Unterstutzung mit positiven Entwicklungsergebnissen wie einem hoheren Selbstwert, einer hoheren Neigung zu aktiven Bewaltigungsstrategien und einer geringeren Belastung mit externalisierenden und internalisierenden Symptomen. Ein hohes Mas an psychologischem Druck hingegen ging mit einem geringeren Selbstwert, einer starkeren Tendenz zu vermeidendem Coping und einer hoheren Symptombelastung einher. Mit Hilfe der drei Skalen lassen sich daruber hinaus multidimensionale Erziehungst.
Hypoxia Response Elements in the Aldolase A, Enolase 1, and Lactate Dehydrogenase A Gene Promoters Contain Essential Binding Sites for Hypoxia-inducible Factor 1*

Hypoxia-inducible factor 1 (HIF-1) is a basic helix-loop-helix transcription factor which is expressed when mammalian cells are subjected to hypoxia and which activates transcription of genes encoding erythropoietin, vascular endothelial growth factor, and other proteins that are important for maintaining oxygen homeostasis. Previous studies have provided indirect evidence that HIF-1 also regulates transcription of genes encoding glycolytic enzymes. In this paper we characterize hypoxia response elements in the promoters of the ALDA, ENO1, and Ldha genes. We demonstrate that HIF-1 plays an essential role in activating transcription via these elements and show that although absolutely necessary, the presence of a HIF-1 binding site alone is not sufficient to mediate transcriptional responses to hypoxia. Analysis of hypoxia response elements in the ENO1 and Ldha gene promoters revealed that each contains two functionally-essential HIF-1 sites arranged as direct and inverted repeats, respectively. Our data establish that functional hypoxia-response elements consist of a pair of contiguous transcription factor binding sites at least one of which contains the core sequence 5′-RCGTG-3′ and is recognized by HIF-1. These results provide further evidence that the coordinate transcriptional activation of genes encoding glycolytic enzymes which occurs in hypoxic cells is mediated by HIF-1.
Quality of group communication as a determinant of group product

This study examined the relationship between quality of communication and the product of the small group. The purposes of the study were (1) to manipulate the quality of communication in problem‐solving groups, (2) to determine whether groups actually experienced communication of varying quality, and, if so, (3) to determine whether groups experiencing high quality communication produced a high quality product. Quality of communication was measured by the Leathers Feedback Rating Instrument. Quality of product was measured by a new Productivity Rating Instrument developed for this study. Results showed a strong, positive relationship between quality of communication and group product.
A Tear in the Social Fabric: Communities Dealing with Socially Generated Crises

The purpose of the study was to develop knowledge about demands and experiences relating to management of socially generated crises in local communities. Interviews were conducted in four municipalities with experiences of such situations, e.g., widely publicized murders, suicides or cases of sexual abuse. A modified grounded theory analysis of the interviews identified six central themes. Two themes pertained to the actual event and its consequences; two concerned the management of the crisis; and two themes focused on reactions and needs among those involved. Basic tensions were identified between confidentiality and openness, between support and accountability and between empathy and distancing. Similarities and differences in relation to management of other kinds of crises are discussed.
Learning and recognition of objects inspired by early cognition

In this paper, we present a unifying approach for learning and recognition of objects in unstructured environments through exploration. Taking inspiration from how young infants learn objects, we establish four principles for object learning. First, early object detection is based on an attention mechanism detecting salient parts in the scene. Second, motion of the object allows more accurate object localization. Next, acquiring multiple observations of the object through manipulation allows a more robust representation of the object. And last, object recognition benefits from a multi-modal representation. Using these principles, we developed a unifying method including visual attention, smooth pursuit of the object, and a multi-view and multi-modal object representation. Our results indicate the effectiveness of this approach and the improvement of the system when multiple observations are acquired from active object manipulation.
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

We consider multi-class classification where the predictor has a hierarchical structure that allows for a very large number of labels both at train and test time. The predictive power of such models can heavily depend on the structure of the tree, and although past work showed how to learn the tree structure, it expected that the feature vectors remained static. We provide a novel algorithm to simultaneously perform representation learning for the input data and learning of the hierarchical predictor. Our approach optimizes an objective function which favors balanced and easily-separable multi-way node partitions. We theoretically analyze this objective, showing that it gives rise to a boosting style property and a bound on classification error. We next show how to extend the algorithm to conditional density estimation. We empirically validate both variants of the algorithm on text classification and language modeling, respectively, and show that they compare favorably to common baselines in terms of accuracy and running time.
Influence of iron on Corynebacterium renale-induced pyelonephritis in a rat experimental model

Growth of Corynebacterium renale in vitro on low-iron medium (1.34 micron) was only slightly less than that on high-iron media (7.16 and 9.85 micron). However, studies on C. renale-induced pyelonephritis using the rat as an experimental model revealed that C. renale cultivated in high-iron media was capable of producing pyelonephritis, but when grown on low-iron medium, these bacteria were noninfective. This apparent avirulence of the bacteria cultivated on low levels of iron could be reversed by injecting the rats intramuscularly with ferric ammonium citrate.
Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: A walk counting approach

We introduce a new method to efficiently approximate the number of infections resulting from a given initially infected node in a network of susceptible individuals. Our approach is based on counting the number of possible infection walks of various lengths to each other node in the network. We analytically study the properties of our method, in particular demonstrating different forms for SIS and SIR disease spreading (e.g., under the SIR model our method counts self-avoiding walks). In comparison to existing methods to infer the spreading efficiency of different nodes in the network (based on degree, k-shell decomposition analysis and different centrality measures), our method directly considers the spreading process and, as such, is unique in providing estimation of actual numbers of infections. Crucially, in simulating infections on various real-world networks with the SIR model, we show that our walks-based method improves the inference of the effectiveness of nodes over a wide range of infection rates compared to existing methods. We also analyse the trade-off between estimate accuracy and computational cost, showing that the better accuracy here can still be obtained at a comparable computational cost to other methods.
A survey of Campylobacter species shed in faeces of beef cattle using polymerase chain reaction.

A polymerase chain reaction (PCR)-based survey of campylobacters associated with faeces collected from 382 beef cattle was undertaken. To ensure the removal of PCR inhibitors present in faeces and determine if adequate extraction was achieved, faeces were seeded with internal control DNA (i.e., DNA designed to amplify with the Campylobacter genus primer set, but provide a smaller amplicon) before the extraction procedure. In only two samples (0.5%) were the internal control or Campylobacter genus amplicons not detected. In the remaining 380 faecal samples, Campylobacter DNA was detected in 83% of the faecal samples (80% of the faecal samples were positive for Campylobacter genus DNA, and 3% of the samples were negative for Campylobacter genus DNA but positive for DNA of individual species). The most frequently detected species was Campylobacter lanienae (49%), a species only recently connected to livestock hosts. Campylobacter jejuni DNA was detected in 38% of the faecal samples, and Campylobacter hyointestinalis and Campylobacter coli DNA were detected in 8% and 0.5% of the samples, respectively. Campylobacter fetus DNA was not detected. Twenty-four percent of the faecal samples contained DNA of at least two species of Campylobacter. Of these samples, the majority (81%) contained DNA of C. jejuni and C. lanienae. The results of this study indicate that beef cattle commonly release a variety of Campylobacter species into the environment and may contribute to the high prevalence of campylobacteriosis in humans inhabiting areas of intensive cattle production, such as southern Alberta. Furthermore, this study demonstrates the utility of using PCR as a rapid and accurate method for simultaneously detecting the DNA of a diverse number of Campylobacter species associated with bovine faeces.
Loss Allocation for Weakly Meshed Distribution System Using Analytical Formulation of Shapley Value

A practical loss allocation method should consider size, distance from source, and network usage of its’ participants. The power flow pattern in distribution network has changed over a period due to increased integration of distributed generation (DG) and hence, the equivalent distance and usage of network have changed for the conventional distribution network. The regulators require a fair and robust model for loss allocation which should be able to discriminate between the contributions of multiple participants. The loss allocation techniques proposed earlier in the literature either require additional quantities over and above power flow solution or are specifically developed only for radial distribution networks. This paper presents a cooperative game approach which provides stable solution integrated with appropriate penalties or rewards to participants. The proposed method provides an analytical solution which is solved using Shapley value for radial as well as weakly meshed distribution network. The memory and computational burden with the Shapley value have been overcome by a final analytical formula that satisfies the axioms of fairness. The use of the method is straightforward without any assumptions and requires only network data and power flow solution. The results are illustrated using different scenarios of DG output levels and network topologies and compared with other methods to establish its effectiveness.
Investigation on partial discharge of power cable termination defects using High Frequency Current Transformer

This paper presents partial discharge (PD) investigation of different cable termination defects. The medium voltage power cables as rated of 3.6/7 (7.2) kV are applied. The partial discharges pattern of each case is detected by using a commercial High Frequency Current Transformer (HFCT). The simple cases for internal, surface and corona discharge are firstly observed in order to investigate the performance of the HFCT. Then eight different cases studies of cable termination defects are further investigated, which includes non-terminator, voids between XLPE and stress control, 20 mm. overlaps between semiconductor and stress control, particles on XLPE, non-smooth XLPE, needle tip on insulation screen, impropriate cable bending, and proper termination. The results are then compared with the results from a conventional PD diagnosis tool according to IEC 60270 standard. The results of PD detection show that the commercial product can detect the PD waveform and measure the electric charge when it is highly enough. The test can also identify trends toward breakdown and there severity due to improper cable termination defects.