Miracle-dz/UniFAR-FTUs huggingface dataset:
A better edit distance measure allowing for block swaps

Edit Distance, also known as the Levenshtein distance or evolutionary distance, is a concept from information retrieval, and it describes the number of edit operations that have to be made in order to change one string to another. It's one of the most common measures to expose the dissimilarity between two strings. Ordinarily, Edit Distance is based on a character insert, delete and substitution operations. By using these three operators Edit Distance can help us to solve the problem of computing the similarity between two sequences that arise in many areas. However, standard Edit Distance still seems to miss the true relationship between these two similar strings in some cases because of the sequential order of common sub strings. For example, the Edit Distance between "classbook" and "bookclass" is eight, because of the words "book" and "class" is reversed but intuitively the two strings seem much closer. In order to solve this problem, we propose a method for extended Edit Distance, which permits block swap operation. The main contribution in this paper is the method to compute the cut points over a single string, and then allowing block swaps, which move sub strings from one position to another in a string, in order to make common substrings in the right order. Through our experiment, it is revealed that Block Swap Edit Distance can help us to find a better measure for Edit Distance.
Probing the Higgs Portal at the LHC Through Resonant di-Higgs Production

We investigate resonant di-Higgs production as a means of probing extended scalar sectors that include a 125 GeV Standard Model-like Higgs boson. For concreteness, we consider a gauge singlet Higgs portal scenario leading to two mixed doublet-singlet states, h_(1,2). For m(_h_2)>2m(_h_1), the resonant di-Higgs production process pp→h_(2)→h_(1)h_(1) will lead to final states associated with the decaying pair of Standard Model-like Higgs scalars. We focus on h_(2) production via gluon fusion and on the bb^¯τ^(+)τ^(−) final state. We find that discovery of the h_(2) at the LHC may be achieved with ≲100 fb^(−1) of integrated luminosity for benchmark parameter choices relevant to cosmology. Our analysis directly maps onto the decoupling limit of the next-to-minimal supersymmetric Standard Model and more generically onto extensions of the Standard Model Higgs sector in which a heavy scalar produced through gluon-fusion decays to a pair of Standard Model-like Higgs bosons.
Photometric Stereo with Small Angular Variations

Most existing successful photometric stereo setups require large angular variations in illumination directions, which results in acquisition rigs that have large spatial extent. For many applications, especially involving mobile devices, it is important that the device be spatially compact. This naturally implies smaller angular variations in the illumination directions. This paper studies the effect of small angular variations in illumination directions to photometric stereo. We explore both theoretical justification and practical issues in the design of a compact and portable photometric stereo device on which a camera is surrounded by a ring of point light sources. We first derive the relationship between the estimation error of surface normal and the baseline of the point light sources. Armed with this theoretical insight, we develop a small baseline photometric stereo prototype to experimentally examine the theory and its practicality.
Impact of genetics in childhood asthma.

OBJECTIVE To present the most important and recent results of studies on asthma genetics. These data may help general physicians understand the impact of genetics on this complex disorder and how genes and polymorphisms influence asthma and atopy. SOURCES Data were collected from MEDLINE. Genetic association studies were selected from the Genetic Association Database, which is an archive of human genetic association studies of complex diseases and disorders organized by the National Institutes of Health. SUMMARY OF THE FINDINGS Considering the data from several important twin studies on asthma genetics, heritability, which measures the contribution of genetic factors to the variance of asthma, may be estimated in 0.48-0.79. A huge number of genetic association studies have been trying to identify asthma susceptibility genes. The most replicated results in the genetic association studies involve the following five regions of the human genome: 5q31-32, 6p21, 11q12-13, 16p11-12, and 20p13. Only recently a new asthma susceptibility gene (ORMDL3) has been identified by a whole genome association study, considered to be a major determinant for childhood asthma. CONCLUSIONS Genetic contribution to asthma may be estimated ranging from 48 to 79%. Several loci seem to influence asthma susceptibility. Genes located on chromosome 5q (ADRB2, IL13 and IL4) and the recently identified ORMDL3, on chromosome 17, seem to be determinants of childhood asthma. Diagnostics and pharmacogenetics may be the first clinical implication of extensive studies on asthma genetics.
Accurate and Robust Indoor Localization Systems using Ultra-wideband Signals

Indoor localization systems that are accurate and robust with respect to propagation channel conditions are still a technical challenge today. In particular, for systems based on range measurements from radio signals, non-line-of-sight (NLOS) situations can result in large position errors. In this paper, we address these issues using measurements in a representative indoor environment. Results show that conventional tracking schemes using high- and a low-complexity ranging algorithms are strongly impaired by NLOS conditions unless a very large signal bandwidth is used. Furthermore, we discuss and evaluate the performance of multipath-assisted indoor navigation and tracking (MINT), that can overcome these impairments by making use of multipath propagation. Across a wide range of bandwidths, MINT shows superior performance compared to conventional schemes, and virtually no degradation in its robustness due to NLOS conditions.
Reasoning with Modular Ontologies for Context-Aware Applications

Journal of Research and Practice in Information Technology, Vol. 46, No. 4, November 2014 Copyright© 2014, Australian Computer Society Inc. General permission to republish, but not for profi t, all or part of this material is granted, provided that the JRPIT copyright notice is given and that reference is made to the publication, to its date of issue, and to the fact that reprinting privileges were granted by permission of the Australian Computer Society Inc.
Inflammatory Stress and Idiosyncratic Hepatotoxicity: Hints from Animal Models

Adverse drug reactions (ADRs) present a serious human health problem. They are major contributors to hospitalization and mortality throughout the world (Lazarou et al., 1998; Pirmohamed et al., 2004). A small fraction (less than 5%) of ADRs can be classified as “idiosyncratic. ” Idiosyncratic ADRs (IADRs) are caused by drugs with diverse pharmacological effects and occur at various times during drug therapy. Although IADRs affect a number of organs, liver toxicity occurs frequently and is the primary focus of this review. Because of the inconsistency of clinical data and the lack of experimental animal models, how IADRs arise is largely undefined. Generation of toxic drug metabolites and induction of specific immunity are frequently cited as causes of IADRs, but definitive evidence supporting either mechanism is lacking for most drugs. Among the more recent hypotheses for causation of IADRs is that inflammatory stress induced by exogenous or endogenous inflammagens is a susceptibility factor. In this review, we give a brief overview of idiosyncratic hepatotoxicity and the inflammatory response induced by bacterial lipopolysaccharide. We discuss the inflammatory stress hypothesis and use as examples two drugs that have caused IADRs in human patients: ranitidine and diclofenac. The review focuses on experimental animal models that support the inflammatory stress hypothesis and on the mechanisms of hepatotoxic response in these models. The need for design of epidemiological studies and the potential for implementation of inflammation interaction studies in preclinical toxicity screening are also discussed briefly.
Characteristics of rheumatoid arthritis and its association with major comorbid conditions: cross-sectional study of 502 649 UK Biobank participants

Introduction To characterise the detailed phenotypic and comorbid characteristics of participants with rheumatoid arthritis (RA) in the large population-based UK Biobank, thereby enabling future longitudinal analyses. Methods We undertook a cross-sectional study using baseline data from the unique UK Biobank resource (n=502 649). RA was based on self-report, and type of medication was used as a proxy measure of valid diagnosis. Participants with and without RA were compared in terms of sociodemographic, lifestyle and other disease-related risk factors. Logistic regression models were used to determine whether participants with RA were more likely to report comorbid conditions, and whether this varied by RA severity. The models were adjusted for potential confounders and lifestyle risk factors. Results At baseline, 5657 (1.13%) eligible UK Biobank participants reported RA of whom 2849 (0.57%) had medically treated RA (median duration=10 years). Prevalence was significantly higher among female, South Asian and socioeconomically deprived participants. Participants with RA were significantly more likely to report diabetes (covariate-adjusted OR 1.18, 95% CI 1.06 to 1.32, p<0.01), hypertension (OR 1.19, 95% CI 1.21 to 1.27, p<0.001) and cardiovascular disease (OR 1.52, 95% CI 1.39 to 1.67, p<0.001). Conclusions UK Biobank provides extensive data concerning RA population-level comorbidity and risk factors. The frequency, distribution and characteristics of participants reporting RA in UK Biobank are largely consistent with other studies. It provides a unique opportunity to interrogate biomarkers, genetic data, detailed imaging and linkage to clinical records at the population level across primary and secondary care.
Person-Organisation Fit and Employee Proactivity: Do Shared Values Matter?

For many years, researchers have theorised that congruence between individuals and their work environment should contribute to various positive outcomes for individuals and organisations. In this study we examined the effects of value congruence on employee proactivity. Polynomial regression analyses on data collected from a sample of public sector employees (N = 3574) provided some support for the hypothesis that fit between an individual’s work values and the organisation’s values predict proactive acts designed to achieve greater individual job, team or organisational effectiveness. we predict proactive performance. To we present a brief overview of the proactive work performance construct. Next we theorise as to value congruence may be expected to influence proactivity. We then test the a value-congruence hypothesis using data collected from a organisation. This study expands on the by applying P-E fit theory to a new
What is Intrinsic Motivation? A Typology of Computational Approaches

Intrinsic motivation, centrally involved in spontaneous exploration and curiosity, is a crucial concept in developmental psychology. It has been argued to be a crucial mechanism for open-ended cognitive development in humans, and as such has gathered a growing interest from developmental roboticists in the recent years. The goal of this paper is threefold. First, it provides a synthesis of the different approaches of intrinsic motivation in psychology. Second, by interpreting these approaches in a computational reinforcement learning framework, we argue that they are not operational and even sometimes inconsistent. Third, we set the ground for a systematic operational study of intrinsic motivation by presenting a formal typology of possible computational approaches. This typology is partly based on existing computational models, but also presents new ways of conceptualizing intrinsic motivation. We argue that this kind of computational typology might be useful for opening new avenues for research both in psychology and developmental robotics.
Strategies in the treatment of acute myeloid leukemia.

The term acute myeloid leukemia (AML) collectively refers to a mixture of distinct diseases that differ with regard to their pathogenetic evolution, genetic abnormalities, clinical features, response to therapy and prognosis. Cytogenetic and molecular analyses have been instrumental in identifying disease entities among the mixed bag of AML subtypes, which are currently catalogued as suboptimally defined categories with widely different prognoses. These classifications are mainly based upon cytogenetic knowledge. They provide leads in clinical decision-making, e.g. with regard to treatment choice. The disclosure of genetic abnormalities may also offer potential targets for treatment intervention. Today such specific interventions into the moleculr intracellular derangements of leukemic cells are only available for exceptional genetically defined entities of AML, such as acute promyelocytic leukemia with the translocation t(15;17). The microarray technology for analyzing differences in gene expression among clinical specimens of leukemia, advances in protein technology, the use of clinically relevant animal models, the development of drug design design technology and the use of appropriate cellular in vitro systems, promise to accelerate our understanding of AML, pathogenesis as well our ability to recognize specific AML disease entities in the near future. With this perspective in mind, what are current and emerging strategies in AML therapy?
Extracellular volume and glomerular filtration rate in children with chronic kidney disease.

BACKGROUND AND OBJECTIVES Extracellular volume (ECV) is the fluid contained in all noncellular compartments of the body and is a quantity tightly controlled by the kidney. Thus, there is a strong link between ECV and kidney function. DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS The Chronic Kidney Disease in Children (CKiD) study uses injected iohexol to obtain direct measures of GFR. Direct calculation of ECV was viable from GFR studies using descriptors of the disappearance curves. Using linear regression methods on the log-transformed variables, markers of size (height and weight) and biomarkers of kidney disease (serum creatinine, blood urea nitrogen, and cystatin C) were assessed for their relationships with ECV normalized to body weight (ECV/wt). The relationship to hypertension (systolic BP >95th percentile for age, sex, and height) was also assessed. RESULTS Data from 790 iohexol studies with medians for GFR=43.4 ml/min per 1.73 m(2), weight=35 kg, and height=1.4 m were used. The median ECV was 8.6 L, and the median ECV/wt was 0.23 L/kg. ECV was found to be a function of height (m) and weight (kg) according to the relationship ECV=√weight×height. Biomarkers of kidney disease yielded significant relationships with ECV/wt, but the strength of association was small. No significant association between ECV/wt and hypertension was found. CONCLUSIONS ECV has relevance to studies of chronic kidney disease, is related to biomarkers, and can be easily estimated from the square root of weight and height.
Mapping of sheep sensory cortex with a novel microelectrocorticography grid

Microelectrocorticography (µECoG) provides insights into the cortical organization with high temporal and spatial resolution desirable for better understanding of neural information processing. Here we evaluated the use of µECoG for detailed cortical recording of somatosensory evoked potentials (SEPs) in an ovine model. The approach to the cortex was planned using an MRI‐based 3D model of the sheep's brain. We describe a minimally extended surgical procedure allowing placement of two different µECoG grids on the somatosensory cortex. With this small craniotomy, the frontal sinus was kept intact, thus keeping the surgical site sterile and making this approach suitable for chronic implantations. We evaluated the procedure for chronic implantation of an encapsulated µECoG recording system. During acute and chronic recordings, significant SEP responses in the triangle between the ansate, diagonal, and coronal sulcus were identified in all animals. Stimulation of the nose, upper lip, lower lip, and chin caused a somatotopic lateral‐to‐medial, ipsilateral response pattern. With repetitive recordings of SEPs, this somatotopic pattern was reliably recorded for up to 16 weeks. The findings of this study confirm the previously postulated ipsilateral, somatotopic organization of the sheep's sensory cortex. High gamma band activity was spatially most specific in the comparison of different frequency components of the somatosensory evoked response. This study provides a basis for further acute and chronic investigations of the sheep's sensory cortex by characterizing its exact position, its functional properties, and the surgical approach with respect to macroanatomical landmarks. J. Comp. Neurol. 522:3590–3608, 2014. © 2014 Wiley Periodicals, Inc.
Enabling Adaptive Cloud Gaming in an Open-Source Cloud Gaming Platform

We study the problem of optimally adapting ongoing cloud gaming sessions to maximize the gamer experience in dynamic environments. The considered problem is quite challenging because: 1) gamer experience is subjective and hard to quantify; 2) the existing open-source cloud gaming platform does not support dynamic reconfigurations of video codecs; and 3) the resource allocation among concurrent gamers leaves a huge room to optimize. We rigorously address these three challenges by: 1) conducting a crowdsourced user study over the live Internet for an empirical gaming experience model; 2) enhancing the cloud gaming platform to support frame rate and bitrate adaptation on-the-fly; and 3) proposing optimal yet efficient algorithms to maximize the overall gaming experience or ensure the fairness among gamers. We conduct extensive trace-driven simulations to demonstrate the merits of our algorithms and implementation. Our simulation results show that the proposed efficient algorithms: 1) outperform the baseline algorithms by up to 46% and 30%; 2) run fast and scale to large (≤8000 gamers) problems; and 3) achieve the user-specified optimization criteria, such as maximizing average gamer experience or maximizing the minimum gamer experience. The resulting cloud gaming platform can be leveraged by many researchers, developers, and gamers.
An integrated view of cultural perceptions of cancer among Arab people in Israel

The aim of this study was to review published studies on the cultural aspects of screening and care of Arab cancer patients living in Israel. The literature published from the beginning of recording, available up to December 2012, was systematically reviewed. Fifteen studies on perceptions of cancer screening and five studies on different aspects of coping with cancer were identified. Non-attendance of screening for early detection of breast or colorectal cancer was reported to be associated with higher personal barriers (the health belief model) and higher cultural and social barriers. Perceptions of cancer risk, causes, outcomes and personal responsibility were widespread from traditional to more modern biomedical views, and sometimes integrated. Among breast cancer patients, effects of a collective and family centred way of life and strong support by one's spouse were evident, followed by a change towards closer spousal relations. Religious coping strategies were the most prevalent among breast cancer patients and were related to better well-being. An integration of traditional and modern perceptions of cancer, attitudes towards screening and coping with cancer were evident. More research is needed in order to advance a culturally competent care of cancer patients and of interventions that encourage screening for the early detection of cancer.
An Unsupervised Convolutional Feature Fusion Network for Deep Representation of Remote Sensing Images

Unsupervised learning of a convolutional neural network (CNN) is a feasible method to represent and classify remote sensing images, where labeling the observed data to prepare training samples is a highly expensive and time-consuming task. In this letter, we propose an unsupervised convolutional feature fusion network to formulate an easy-to-train but effective CNN representation of remote sensing images. The efficiency and effectiveness are derived from the following two aspects. First, the proposed method trains a deep CNN through unsupervised learning of each CNN layer in a greedy layer-wise manner, which makes the training relatively easy and efficient. Second, the feature fusion strategy in the proposed network can effectively use both the information from individual layers and the important interactions between different layers. As a result, the proposed network requires only several layers to obtain comparable or even better results than very deep networks. The experiments on unsupervised deep representations and the classification of remote sensing images demonstrate the efficiency and effectiveness of the proposed method.
Stacked auto encoder training incorporating printed text data for handwritten bangla numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Bangla is a major language in Indian subcontinent and is the first language of Bangladesh; but unfortunately, study regarding handwritten Bangla numeral recognition (HBNR) is very few with respect to other major languages such as English, Roman etc. Some noteworthy research works have been conducted for recognition of Bangla handwritten numeral using artificial neural network (ANN) as ANN and its various updated models are found to be efficient for classification task. The aim of this study is to develop a better HBNR system and hence investigated deep architecture of stacked auto encoder (SAE) incorporating printed text (SAEPT) method. SAE is a variant of neural networks (NNs) and is applied efficiently for hierarchical feature extraction from its input. The proposed SAEPT contains the encoding of handwritten numeral into printed form in the course of pre-training and finally initializing a multi-layer perceptron (MLP) using these pre-trained weights. Unlike other methods, it does not employ any feature extraction technique. Benchmark dataset with 22000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown to outperform other prominent existing methods achieving satisfactory recognition accuracy.
Towards energy efficient hybrid on-chip Scratch Pad Memory with non-volatile memory

Scratch Pad Memory (SPM), a software-controlled on-chip memory, has been widely adopted in many embedded systems due to its small area and low power consumption. As technology scaling reaches the sub-micron level, leakage energy consumption is surpassing dynamic energy consumption and becoming a critical issue. In this paper, we propose a novel hybrid SPM which consists of non-volatile memory (NVM) and SRAM to take advantage of the ultra-low leakage power consumption and high density of NVM as well as the efficient writes of SRAM. A novel dynamic data allocation algorithm is proposed to make use of the full potential of both NVM and SRAM. According to the experimental results, with the help of the proposed algorithm, the novel hybrid SPM architecture can reduce memory access time by 18.17%, dynamic energy by 24.29%, and leakage power by 37.34% on average compared with a pure SRAM based SPM with the same size area.