Miracle-dz/UniFAR-FTUs huggingface dataset:
An analysis of repeat imprisonment trends in Australia using prisoner census data from 1994 to 2007, Aug 2010

Reducing the number of prisoners who are repeatedly imprisoned is one of the goals of any correctional system. However, while a period of imprisonment may deter some people from re-offending, in others it may foster further criminal behaviour. This paper presents the results of a study based on a longitudinal dataset constructed from 14 successive Prisoner Censuses between 1994 and 2007 to follow, over time, two cohorts of people who were 'released' from prison (where 'release' is a proxy measure derived from the absence of a prisoner's record in a subsequent Prisoner Census). This paper expands on an earlier study by the Australian Bureau of Statistics by using logistic regression models to examine the factors associated with repeat imprisonment and assess whether or not the propensity for reimprisonment has increased over time. This paper also examines trends in criminal career development using descriptive methods, looking at patterns of specialisation, and of movements from one type of offence to another. The study finds that reimprisonment is strongly associated with being young, being Indigenous, or having been previously imprisoned (that is, being a prisoner who had already served time in prison). In all jurisdictions except Queensland, the rate of reimprisonment in recent years was higher than in the mid-1990s.
Smooth pursuit detection based on multiple observers

While many elaborate algorithms to classify eye movements into fixations and saccades exist, detection of smooth pursuit eye movements is still challenging. Smooth pursuits do not occur for the predominantly studied static stimuli; for dynamic stimuli, it is difficult to distinguish small gaze displacements due to noise from smooth pursuit. We propose to improve noise robustness by combining information from multiple recordings: if several people show similar gaze patterns that are neither fixations nor saccades, these episodes are likely smooth pursuits. We evaluated our approach against two baseline algorithms on a hand-labelled subset of the GazeCom data set of dynamic natural scenes, using three different clustering algorithms to determine gaze similarity. Results show that our approach achieves a very substantial increase in precision at improved recall over state-of-the-art algorithms that consider individual gaze traces only.
Diversity Analysis and Design of Space–Time Multiblock Codes for MIMO Systems Equipped With Linear MMSE Receivers

This paper addresses the problem of designing optimum full-symbol-rate linear space-time block codes (STBC) for a multi-input multi-output (MIMO) communication system with M transmitter and N ≥ M receiver antennas and a linear minimum mean square error (MMSE) receiver. By analyzing the detection error probability expression for the optimized STBC, it is shown that for QAM signaling, the maximum diversity gain for such a system is N - M + 1. The minimum probability of error STBC design is then extended to systems in which the transmission spans L independent realizations from a block fading channel model, and a (multiblock) linear MMSE receiver is employed. Necessary and sufficient conditions for the optimality of the code are obtained, and a systematic design method for generating codes that satisfy these conditions is presented. The detection error probability and diversity gain of this optimized linear multiblock transceiver are analyzed. It is proved that the error probability decreases with L, and it is shown numerically that the diversity gain increases with L. Thus, if the corresponding latency can be accommodated, for sufficiently large L an optimally designed multiblock system with a linear receiver can exploit the temporal diversity provided by the block-fading channel and achieve higher diversity gain than that of any single-block system of the same symbol rate with a maximum likelihood (ML) receiver. The optimized multiblock linear system achieves this diversity at a substantially lower computational cost. In fact, the structure of the optimal codes can be exploited to significantly reduce the cost of the multiblock linear receiver.
Population Genetics Inference for Longitudinally-Sampled Mutants Under Strong Selection

Longitudinal allele frequency data are becoming increasingly prevalent. Such samples permit statistical inference of the population genetics parameters that influence the fate of mutant variants. To infer these parameters by maximum likelihood, the mutant frequency is often assumed to evolve according to the Wright–Fisher model. For computational reasons, this discrete model is commonly approximated by a diffusion process that requires the assumption that the forces of natural selection and mutation are weak. This assumption is not always appropriate. For example, mutations that impart drug resistance in pathogens may evolve under strong selective pressure. Here, we present an alternative approximation to the mutant-frequency distribution that does not make any assumptions about the magnitude of selection or mutation and is much more computationally efficient than the standard diffusion approximation. Simulation studies are used to compare the performance of our method to that of the Wright–Fisher and Gaussian diffusion approximations. For large populations, our method is found to provide a much better approximation to the mutant-frequency distribution when selection is strong, while all three methods perform comparably when selection is weak. Importantly, maximum-likelihood estimates of the selection coefficient are severely attenuated when selection is strong under the two diffusion models, but not when our method is used. This is further demonstrated with an application to mutant-frequency data from an experimental study of bacteriophage evolution. We therefore recommend our method for estimating the selection coefficient when the effective population size is too large to utilize the discrete Wright–Fisher model.
Two stages of enteropathogenic Escherichia coli intestinal pathogenicity are up and down-regulated by the epithelial cell differentiation

Abstract Pathogens and eucaryotic cells are active partners during the process of pathogenicity. To gain access to enterocytes and to cross the epithelial membrane, many enterovirulent microorganisms interact with the brush border membrane-associated components as receptors. Recent reports provide evidence that intestinal cell differentiation plays a role in microbial pathogenesis. Human enteropathogenic Escherichia coli (EPEC) develop their pathogenicity upon infecting enterocytes. To determine if intestinal epithelial cell differentiation influences EPEC pathogenicity, we examined the infection of human intestinal epithelial cells by JPN 15 (pMAR7) [EAF+ eae+] EPEC strain as a function of the cell differentiation. The human embryonic intestinal INT407 cells, the human colonic T84 cells, the human undifferentiated HT-29 cells (HT-29 Std) and two enterocytic cell lines, HT-29 glc−/+ and Caco-2 cells, were used as cellular models. Cells were infected apically with the EPEC strain and the cell-association and cell-entry were examined by quantitative determination using metabolically radiolabeled bacteria, as well as by light, scanning and tranmission electron microscopy. [EAF+ eae+] EPEC bacteria efficiently colonized the cultured human intestinal cells. Diffuse bacterial adhesion occurred to undifferentiated HT-29 Std and INT407 cells, whereas characteristic EPEC cell clusters were observed on fully differentiated enterocytic HT-29 glc−/+ cells and on colonic crypt T84 cells. As shown using the Caco-2 cell line, which spontaneously differentiates in culture, the formation of EPEC clusters increased as a function of the epithelial cell differentiation. In contrast, efficient cell-entry of [EAF+ eae+] EPEC bacteria occured in recently differentiated Caco-2 cells and decreased when the cells were fully differentiated. Our results provide evidence that the intestinal cell differentiation could play a dual role in EPEC pathogenesis: it up-regulates intestinal cell colonization and down-regulates intestinal cell invasion.
Investigating Efficient Feature Representation Methods and Training Objective for BLSTM-Based Phone Duration Prediction

Accurate modeling and prediction of speech-sound durations are important in generating natural synthetic speech. This paper focuses on both feature and training objective aspects to improve the performance of the phone duration model for speech synthesis system. In feature aspect, we combine the feature representation from gradient boosting decision tree (GBDT) and phoneme identity embedding model (which is realized by the jointly training of phoneme embedded vector (PEV) and word embedded vector (WEV)) for BLSTM to predict the phone duration. The PEV is used to replace the one-hot phoneme identity, and GBDT is utilized to transform the traditional contextual features. In the training objective aspect, a new training objective function which taking into account of the correlation and consistency between the predicted utterance and the natural utterance is proposed. Perceptual tests indicate the proposed methods could improve the naturalness of the synthetic speech, which benefits from the proposed feature representation methods could capture more precise contextual features, and the proposed training objective function could tackle the over-averaged problem for the generated phone durations.
Highly Available Hadoop NameNode Architecture

In past few years Hadoop Distributed File System (HDFS) has been used by many organizations with gigantic data sets and streams of operations on it. HDFS provides distinct features like, high fault tolerance, scalability, etc. The Name Node machine is a single point of failure (SPOF) for a HDFS cluster. If the Name Node machine fails, the system needs to be re-started manually, making the system less available. This paper proposes a highly available architecture and its working principle for the HDFS Name Node against its SPOF utilizing well-known 2-Phase Commit (2PC) Protocol and Election algorithm.
Impact of variability in data on accuracy and diversity of neural network based ensemble classifiers

Ensemble classifiers are very useful tools which can be applied for classification and prediction tasks in many real-world applications. There are many popular ensemble classifier generation techniques including neural network based techniques. However, there are many problems with ensemble classifiers when we apply them to real-world data of different size. This paper presents and investigates an approach for finding the impact of various parameters such as attributes, instances, classes on clusters, accuracy and diversity. The primary aim of this research is to see whether there is any link between these parameters and accuracy and diversity. The secondary aim is to see whether we can find any relationship between number of clusters in ensemble classifier and data variables. A series of experiments has been conducted by using different size of UCI machine learning benchmark datasets and neural network ensemble classifiers.
Using Test Cases Grouping and Iteration Repair to Fix Multi-points Bug

Currently, most automated program repair techniques are based on the test suite to constrain the generation of the patch. However, these techniques are facing an important challenge-how to fix multi-point bugs. A multi-point bug exists in a number of statements, and is always cannot be repaired by mutating a single statement. This paper proposes a method to repair multi-point bugs by grouping failed test cases and iterative repairing. Our experiment on programs that contain multi-point bug presents the effectiveness of our method: we can fix 90% faulty versions, and as a comparison, the selected repair tool can only fix 4.3%, our method can greatly improve the multi-point bug repair capacity of automated repair tools.
Audio-Visual Speech Modeling for Continuous Speech Recognition

This paper describes a speech recognition system that uses both acoustic and visual speech information to improve recognition performance in noisy environments. The system consists of three components: a visual module; an acoustic module; and a sensor fusion module. The visual module locates and tracks the lip movements of a given speaker and extracts relevant speech features. This task is performed with an appearance-based lip model that is learned from example images. Visual speech features are represented by contour information of the lips and grey-level information of the mouth area. The acoustic module extracts noise-robust features from the audio signal. Finally the sensor fusion module is responsible for the joint temporal modeling of the acoustic and visual feature streams and is realized using multistream hidden Markov models (HMMs). The multistream method allows the definition of different temporal topologies and levels of stream integration and hence enables the modeling of temporal dependencies more accurately than traditional approaches. We present two different methods to learn the asynchrony between the two modalities and how to incorporate them in the multistream models. The superior performance for the proposed system is demonstrated on a large multispeaker database of continuously spoken digits. On a recognition task at 15 dB acoustic signal-to-noise ratio (SNR), acoustic perceptual linear prediction (PLP) features lead to 56% error rate, noise robust RASTA-PLP (relative spectra) acoustic features to 7.2% error rate and combined noise robust acoustic features and visual features to 2.5% error rate.
Identification and expression analyses of chitinase genes in mulberry (Morus L.) plants

In the present study, we identified chitinase genes in the genome of mulberry for the first time. The mulberry genome encodes 20 chitinase genes, which are grouped into two main families and organized into five classes. The genomic structures and phylogenetic relationships of mulberry chitinase genes were analyzed, which provided a genetic basis for understanding the functions of these genes. We further investigated the expression of mulberry chitinase genes in five different tissues including root, bark, bud, flower, and leaf. Our results showed that there was no correlation between the spatial expression patterns of chitinase genes and their classification based on the conserved domains. Furthermore, six mulberry chitinase genes were used to detect transcriptional differences in their response to insect wounding, fungal infection, and biochemical elicitors. We found that different mulberry chitinase genes were induced by insect wounding and fungal infection, suggesting that these chitinases help the plant to cope with the challenges from insects and fungi.
Effect of curcumin, the active constituent of turmeric, on penicillin-induced epileptiform activity in rats

Objective: Curcumin is a major constituent of turmeric and has many biological functions such as anticancer and anti-inflammatory effects. The present study was conducted to investigate the effects of curcumin and diazepam in separate and combined treatments on penicillin-induced seizures in rats. Materials and Methods: In urethane-anesthetized rats, epileptiform activity was induced by intracortical (i.c.) administration of penicillin (200 IU, 1 µl), and frequency and amplitude of spike waves were analyzed using electrocorticographic recordings. Results: Intraperitoneal (i.p.) injections of curcumin at doses of 100 and 200 mg/kg, and intracerebroventricular (i.c.v.) injection of diazepam at a dose of 5 µg significantly (p<0.05) reduced both frequency and amplitude of spike waves. Co-administrations of curcumin (50 mg/kg, i.p.) with diazepam (5 µg, i.c.v) enhanced the antiepileptic effect of diazepam (5 µg, i.c.v). Conclusion: The results suggested that both curcumin and diazepam suppressed penicillin-induced epileptiform activity. A potentiation effect was observed between curcumin and diazepam in reducing penicillin-induced seizures.
Reproducibility of Results in Preclinical Studies: A Perspective From the Bone Field

The biomedical research enterprise—and the public support for it—is predicated on the belief that discoveries and the conclusions drawn from them can be trusted to build a body of knowledge which will be used to improve human health. As in all other areas of scientific inquiry, knowledge and understanding grow by layering new discoveries upon earlier ones. The process self‐corrects and distills knowledge by discarding false ideas and unsubstantiated claims. Although self‐correction is inexorable in the long‐term, in recent years biomedical scientists and the public alike have become alarmed and deeply troubled by the fact that many published results cannot be reproduced. The chorus of concern reached a high pitch with a recent commentary from the NIH Director, Francis S. Collins, and Principal Deputy Director, Lawrence A. Tabak, and their announcement of specific plans to enhance reproducibility of preclinical research that relies on animal models. In this invited perspective, we highlight the magnitude of the problem across biomedical fields and address the relevance of these concerns to the field of bone and mineral metabolism. We also suggest how our specialty journals, our scientific organizations, and our community of bone and mineral researchers can help to overcome this troubling trend. © 2014 American Society for Bone and Mineral Research.
Using Dabigatran in Patients With Stroke: A Practical Guide for Clinicians

Atrial fibrillation (AF) is 1 of the most common causes of cardioembolic strokes accounting for at least 100 000 strokes each year in the United States. Strokes due to AF tend to be larger and more severe than other types of ischemic strokes with a 30-day mortality rate up to 24%. 1 Anticoagulation with warfarin can reduce the risk of stroke in patients with moderate- and high-risk AF by ≥65%,2 yet the use of warfarin is limited by a number of factors, including concerns about bleeding, the need for frequent international normalized ratio (INR) monitoring as well as significant and common food and drug interactions.3–6 In October 2010, the Food and Drug Administration (FDA) approved the direct thrombin inhibitor dabigatran for primary and secondary stroke prevention (and systemic emboli prevention) in patients with AF.7 It is used as a fixed dose medication in most circumstances (150 mg twice a day if creatinine clearance is >30 mL/min or 75 mg twice a day if creatinine clearance is 15–30 mL/min) and does not require routine blood testing nor does it have any important interactions with most foods and medications. Dabigatran is not a substrate, inducer, or inhibitor of the cytochrome P450 enzymes.8 It has the potential to greatly expand clinicians' and patients' willingness to use anticoagulation in patients with high-risk AF. Often there is a gap between a new medication being approved and its widespread use. This is still true when one considers the underuse of warfarin in patients with AF, although there are a number of reasons for this finding. Patients with AF are often cared for by various physicians and other healthcare professionals in a variety of specialty and primary care areas and settings. For example, 25% of patients with AF first present with …
Nemesis Team Description 2009

In our study, we tried to develop our teams in such a way that machine learning techniques and advanced artificial intelligence tools have the main role in improving skills and increasing team performance. We consider soccer simulation platform as an uncertain and dynamic environment, so we develop learning algorithms according to this important feature and agent's partial observability.
Characterization, modeling, and optimization of FinFET MOS varactors

For the first time, nMOS varactors fabricated in FinFET technology were characterized and modeled at microwave frequency. The RF analysis is carried out as a function of the fin width. It is shown that the fin width has nearly no impact on the tuning range of the device, but on the quality factor (Q). Best Q's are obtained for wide fin devices, mainly due to the reduction of the series resistances, and a higher intrinsic conductance
Learning imitation strategies using cost-based policy mapping and task rewards

Learning by imitation represents a powerful approach for efficient learning and low-overhead programming. An important part of the imitation process is the mapping of observations to an executable control strategy. This is particularly important if the capabilities of the imitating and the demonstrating agent differ significantly. This paper presents an approach that addresses this problem by optimizing a cost function. The result is an executable strategy that as closely as possible resembles the observed effects of the demonstrator on the environment. To ensure that the imitating agent replicates the important aspects of the observed task, a learning component is introduced which learns the appropriate cost function from rewards obtained while executing the imitation strategy. The performance of this approach is illustrated within the context of a simulated multi-agent environment.
Real-time Visualization of Unstructured Volumetric CFD Data Sets on GPUs

Real-time visualization and analysis of scalar and vector fields from massive volumetric data sets require efficient rendering techniques. Irregularly structured Computational Fluid Dynamics (CFD) cells have to be tetrahedralized prior to rendering on the Programmable Graphics Unit (GPU). The cells are organized within an octree providing fast search in the spatial, but also in the scalar function domain. Identification of the cells being part of the resulting isosurface is possible using the hierarchical data structure. Taking into account only relevant cells for rendering reduces time consumption and overhead caused by data transfers. The gained speedup depends on the distribution of values and cells in 3D space. Cross-section of the desired isosurface with a cell leads to the nodes being used for interpolation per polygon. Interpolation, pseudocoloring and shading is done on the GPU using vertex and fragment shaders. Shifting tasks from CPU to the GPU increases the framerate and enables customized shading. The spatial impression of the resulting isosurfaces is increased using Phong shading.
A Corpus Investigation of Syntactic Embedding in Pirahã

The Pirahã language has been at the center of recent debates in linguistics, in large part because it is claimed not to exhibit recursion, a purported universal of human language. Here, we present an analysis of a novel corpus of natural Pirahã speech that was originally collected by Dan Everett and Steve Sheldon. We make the corpus freely available for further research. In the corpus, Pirahã sentences have been shallowly parsed and given morpheme-aligned English translations. We use the corpus to investigate the formal complexity of Pirahã syntax by searching for evidence of syntactic embedding. In particular, we search for sentences which could be analyzed as containing center-embedding, sentential complements, adverbials, complementizers, embedded possessors, conjunction or disjunction. We do not find unambiguous evidence for recursive embedding of sentences or noun phrases in the corpus. We find that the corpus is plausibly consistent with an analysis of Pirahã as a regular language, although this is not the only plausible analysis.