Miracle-dz/UniFAR-FTUs huggingface dataset:
Profile and outcome of acute kidney injury in critically ill children admitted to pediatric intensive care unit

Background: Acute kidney injury (AKI) is defined as an abrupt onset of renal dysfunction resulting from injurious endogenous or exogenous processes characterized by a decrease in glomerular filtration rate (GFR) and an increase in serum creatinine. AKI is common in critically ill children and early diagnosis is important for better outcome in these children.Methods: This was a prospective observational study. Critically ill infants and children of either sex and in age group between >28 days to 12 years admitted to pediatric intensive care unit (PICU) were included. Serum creatinine and estimated creatinine clearance (eCC) were used to and patients classified as AKI on pRIFLE criteria either at admission or subsequently during the hospital stay. AKI cases were further classified into risk, injury or failure category on the day of development of AKI and the maximum pRIFLE stage reached during PICU stay was noted. Detailed data regarding the treatment received and use of nephrotoxic drugs, inotropic support, mechanical ventilation, dialysis and total length of stay in PICU in all was noted. Outcome of the subjects were observed for survival or mortality.Results: Total 343 subjects were enrolled in the study. During the study 27.1% patients developed AKI according to pRIFLE staging. In AKI category 60.21% reached maximum risk category, 21.5% reached maximum injury category, 18.28% reached maximum failure category. Amongst AKI subjects 64.52% had infectious etiology. Multiorgan dysfunction, encephalopathy, shock, metabolic acidosis, hypertension, mechanical ventilation and nephrotoxic drugs administration were more associated with AKI and was statistically significant.Conclusions: Pediatric modification of RIFLE criteria is sensitive index to detect AKI at earliest in critically ill children for early intervention leading to better outcome.
Use of Sound Symbolism in Sentiment Classification

In linguistics, sound symbolism is an idea that the vocal sounds of certain words carry meaning in themselves. This paper focuses on the sound symbolism of onomatopoeic words and demonstrates the close relationship between sound symbolism and sentiment polarity. Because onomatopoeic words imitate the sounds they represent, they can help us better understand the sentiment of a sentence when utilizing sound sym-bolism. Therefore, we modeled sound symbolism with N-gram-based features and applied the model to a series of sentiment classiﬁcation tasks. The experimental results show that this method with sound symbolism signiﬁcantly outperformed the baseline method without sound symbolism, which eﬀectively demonstrates that a close relationship exists between sound symbolism and sentiment polarity.
Mechanisms of synaptic and intrinsic modulation of granule cells in the rat olfactory bulb.

By Richard Todd Pressler Mitral cell activity during olfactory behavior varies with respect to spatial location in the olfactory bulb, and temporally evolves during olfaction (Kay and Laurent, 1999). The main inhibitory input onto mitral cells originates from granule cells, axonless interneurons in the olfactory bulb, which sculpt and pattern mitral cell output through dendrodendritic inhibitory synapses (Price and Powell, 1970b). This dendrodendritic microcircuit can mediate recurrent inhibition onto mitral cells (Jahr et al., 1980), as well as lateral inhibition between mitral cells (Isaacson and Strowbridge, 1998). Additionally, this microcircuit plays a large part in generating gamma-frequency oscillations in the olfactory bulb during olfactory behavior (Lagier et al., 2004). Little data exists on the modulatory inputs onto granule cells from other olfactory bulb interneurons and from brain regions outside of the olfactory bulb. Previous anatomical studies suggest that granule cells receive GABAergic inputs from other olfactory bulb interneurons (Price and Powell, 1970b). Additionally, granule cells receive significant cholinergic input from the horizontal limb of the diagonal band (Price, 1969).
Drug policy research at the Centre for Alcohol and Drug Research

The Centre for Alcohol and Drug Research (CRF) has since its foundation in 1991 had a strong tradition for research in drug control. However, researchers at CRF have also started to study drug policy not only from a control perspective but also from a perspective of health and welfare issues. From 2005, CRF has developed a particular interest in how welfare policies related to drug issues come into being and how they are implemented in practice in different welfare institutions. These studies, in opposition to more established drug policy studies based primarily on quantitative and statistical data, use a broader variety of empirical data collected using qualitative interviews and ethnographic observations. The article investigates the development of drug policy studies at CRF and discusses the theoretical and analytical implications of this development. The development is related to, first that the organization of the Danish drug field has changed and a variety of new social and health initiatives have emerged, necessitating a thorough investigation; and, second that more anthropologists and sociologists have been employed at CRF, complementing researchers trained primarily in legal studies.
Preventing reverse engineering of native and managed programs

Kiperberg, Michael Preventing Reverse Engineering of Native and Managed Programs Jyvaskyla: University of Jyvaskyla, 2015, 60 p.(+included articles) (Jyvaskyla Studies in Computing ISSN 1456-5390; 228) ISBN 978-951-39-6436-8 (nid.) ISBN 978-951-39-6437-5 (PDF) Finnish summary Diss. One of the important aspects of protecting software from attack, theft of algorithms, or illegal software use is eliminating the possibility of performing reverse engineering. One common method used to deal with these issues is code obfuscation. However, it is proven to be ineffective. Code encryption is a much more effective means of defying reverse engineering, but it requires managing a cryptographic key available to none but the permissible users. The thesis presents a system for managing cryptographic keys in a protected environment and supporting execution of encrypted code. The system has strong security guarantees. In particular, the cryptographic keys are never stored on the target machine, but rather delivered to it from a remote server, upon a successful verification of its authenticity. The keys and the decrypted instructions are protected by a thin hypervisor at all times. The system allows the encryption and execution of both native and Java code. During native code execution, the decrypted instructions are inaccessible to a potentially malicious code. This is achieved by either preventing execution of any other code or by protecting the memory region containing the decrypted instructions during their execution. Java programs, unlike native programs, are not executed directly by the processor, but are interpreted (and sometimes compiled) by the Java Virtual Machine (JVM). Therefore, the JVM will require the cryptographic key to decrypt the encrypted portions of Java code, and there is no feasible way of securing the key inside the JVM. The thesis proposes to implement a Java bytecode interpreter inside the secure environment, governed by a thin hypervisor. This interpreter will run in parallel to the standard JVM, both cooperating to execute encrypted Java programs.
Multi-Channel Fusion Model of Perception System in Lower Extremity Exoskeleton Suits and it’s Implementation

the human exoskeleton suit is a complex servo system characterized by high degree of human-machine coupling, which means that exoskeleton suit would be able to follow or synchronize with the human body movement. The perception system, which is responsible for sensing the wearer's movement and movement intention, plays a key role in realizing human-machine coupling. How to build a perception system in human exoskeleton suit? In the paper, a multi-channel perception model is described, through which the data from multi-channels is gathered, fused, and then wearer’s movement and movement intention are sensed and transmitted to the controller. In order to easily gather the information from various sensors and devices, interface management layer is introduced. Meanwhile, it also can provide the support for redundancy, fault tolerance, and adaptation in dynamic environments. After being gathered, how do the data from heterogeneous sensors be integrated? How would the data been interpreted into accurate and comprehensive movement posture? In the paper, the three methods, which are time correlation, posture perception and movement prediction, are used to implement the data fusion. Meanwhile, a prototype based on this model was implemented in our human exoskeleton suit. In the paper, the layout of sensors, hardware, software, algorithms, and test of the prototype are explained. Keywords-Exoskeleton; Human-machine coupling; Multichannel
Using WinBUGS to study family frailty in child mortality, with an application to child survival in Ivory Coast

This article analyzes the effects of unobserved family heterogeneity in children survival times through a Bayesian approach. We rely on survey data from Ivory Coast and use a proportional hazard model with multiplicative random effect. With such a model, the usual assumption of independence of observations is avoided. The posterior distributions of the parameters are estimated through a Gibbs sampler algorithm using the WinBUGS software. This technique overcomes the possible local convergence problem observed with the commonly used Expectation-Maximization method.
A survey of parallel programming tools

This survey examines 39 parallel programming tools. Focus is placed on those tool capabilites needed for parallel scientific programming rather than for general computer science. The tools are classified with current and future needs of Numerical Aerodynamic Simulator (NAS) in mind: existing and anticipated NAS supercomputers and workstations; operating systems; programming languages; and applications. They are divided into four categories: suggested acquisitions, tools already brought in; tools worth tracking; and tools eliminated from further consideration at this time.
Impact of informative band selection on target detection performance

In this paper, the effect of dimensionality reduction of hyperspectral data on 10 subpixel target detectors is investigated. The genetic algorithm (GA) and wavelet feature extraction methods are used for dimensionality reduction as they maintain physically meaningful bands and physical structure of the spectra, respectively. In the former case, the wrapper method is used to improve subpixel target detectors' results in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Meanwhile, in the latter case, the AUC is used as a criterion to choose the optimum level of wavelet decomposition. Experimental results obtained from a real-world hyperspectral data and a challenging synthetic dataset approved that band selection with the wrapper method is more efficient than using target detection methods without dimensionality reduction, especially in the presence of difficult targets at subpixel level.
Role of fine needle aspiration cytology (FNAC) of testes in male infertility

Fine needle aspiration of the testis, which is minimally traumatic, has questioned the need of a more invasive open biopsy in the evaluation of male infertility. This study aims to evaluate the efficacy of fine needle aspiration cytology (FNAC) as compared to open testicular biopsy in the evalu­ation of male infertility by correlating diagnoses from tes­tis FNA cytology with biopsy histology. We have also studied the necessity of bilateral or unilateral FNACs in the workup of the infertile man. This study was a prospec­tive study of 57 infertile men in whom bilateral testicular fine needle aspiration as well as testicular biopsy was done. The correlation between the 2 methods was 91.9%. Dis­crepancies between cytology and histology were mainly the result of insufficient smears. These data also suggest that bilateral FNACs and biopsies can be restricted to patients in whom there is appreciable difference in tes­ticular size or consistency.
A NOVEL APPROACH FOR MINIMIZING AND PRIORITIZING TEST SUITE

In this paper, two strategies have been used to generate reduced test suite for regression testing. Firstly, a new path selection strategy for regression testing has been proposed which enables the tester to execute the test cases in an order that increases their effectiveness to find faults taking minimum efforts. This strategy has been used to choose an appropriate subset of test cases from a previously run test suite for a software system, based on the information about the modifications made to the system for enhancement. Secondly, a genetic algorithm based technique is also proposed which further minimizes the, prioritized version of test suite and select a new test suite without compromising the fault detection effectiveness of test cases. The objectives of tester are considered on the basis of time available for the regression testing.
Decreasing Physical Activity Levels across Religious Sikh Male South Asian Migrant Population in Kent, UK

Physical activity (PA) plays a crucial role in reducing the risk of non-communicable diseases (NCDs). We investigated intergenerational physical activity level (PAL) among first and second generation Sikh Punjabi male subjects (n = 137), recruited from two Sikh temples in Medway, UK. Employing a cross-sectional survey PA was quantified using the validated Global PA Questionnaire (GPAQ). Data were analysed using SPSS 20 and Epi Info software. Ninety-one per cent of the subjects were classified as overweight. Mean PAL range was sedentary to low levels of PA (1.45–1.60). Comparisons between first and second generation Punjabi male subjects showed that the two groups are equally culpable in not engaging in work-related or recreational PA, but for the second generation this is significantly lower. Low PAL is a contributory factor to increased risk and prevalence of NCDs among this population and a public health concern. Efforts to increase PA in this group should continue.
Data Editing for Semi-Supervised Co-Forest by the Local Cut Edge Weight Statistic Graph

In order to address the large amount of unlabeled training data problem, many semi-supervised algorithms have been proposed. The training data in semi-supervised learning may contain much noise due to the insufficient number of labeled data in training set. Such noise may snowball themselves in the following learning process and thus hurt the generalization ability of the final hypothesis. If such noise could be identified and removed by some strategy, the performance of the semi-supervised algorithms should be improved. However, such useful techniques of identifying and removing noise have been seldom explored in existing semi-supervised algorithms. In this paper, we use the semi-supervised ensemble method “Co-forest” with data editing (we call it CEWS-Co-forest) to improve sparsely labeled medical dataset. The cut edges weight statistic data editing technique is used to actively identify possibly mislabeled examples in the newly-labeled data throughout the co-labeling iterations in Co-forest. The fusion of semi-supervised ensemble method with data editing makes CEWS-co-Forest more robust to the sparsity and the distribution bias of the training data. It further simplifies the design of semi-supervised learning which makes CEWS-co-forest more efficient. An experimental study on several medical data sets shows encouraging results compared with state-of-the-art methods.
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents

We study the problem of safe adaptation: given a model trained on a variety of past experiences for some task, can this model learn to perform that task in a new situation while avoiding catastrophic failure? This problem setting occurs frequently in real-world reinforcement learning scenarios such as a vehicle adapting to drive in a new city, or a robotic drone adapting a policy trained only in simulation. While learning without catastrophic failures is exceptionally difficult, prior experience can allow us to learn models that make this much easier. These models might not directly transfer to new settings, but can enable cautious adaptation that is substantially safer than naı̈ve adaptation as well as learning from scratch. Building on this intuition, we propose risk-averse domain adaptation (RADA). RADA works in two steps: it first trains probabilistic model-based RL agents in a population of source domains to gain experience and capture epistemic uncertainty about the environment dynamics. Then, when dropped into a new environment, it employs a pessimistic exploration policy, selecting actions that have the best worst-case performance as forecasted by the probabilistic model. We show that this simple maximin policy accelerates domain adaptation in a safety-critical driving environment with varying vehicle sizes. We compare our approach against other approaches for adapting to new environments, including meta-reinforcement learning.
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning

Machine learning applications, such as object detection and content recommendation, often require training a single model to predict multiple targets at the same time. Multi-task learning through neural networks became popular recently, because it not only helps improve the accuracy of many prediction tasks when they are related, but also saves computation cost by sharing model architectures and low-level representations. The latter is critical for real-time large-scale machine learning systems. However, classic multi-task neural networks may degenerate significantly in accuracy when tasks are less related. Previous works (Misra et al. 2016; Yang and Hospedales 2016; Ma et al. 2018) showed that having more flexible architectures in multi-task models, either manually-tuned or softparameter-sharing structures like gating networks, helps improve the prediction accuracy. However, manual tuning is not scalable, and the previous soft-parameter sharing models are either not flexible enough or computationally expensive. In this work, we propose a novel framework called SubNetwork Routing (SNR) to achieve more flexible parameter sharing while maintaining the computational advantage of the classic multi-task neural-network model. SNR modularizes the shared low-level hidden layers into multiple layers of subnetworks, and controls the connection of sub-networks with learnable latent variables to achieve flexible parameter sharing. We demonstrate the effectiveness of our approach on a large-scale dataset YouTube8M. We show that the proposed method improves the accuracy of multi-task models while maintaining their computation efficiency.
Unsupervised learning for text-to-speech synthesis

This thesis introduces a general method for incorporating the distributional analysis of textual and linguistic objects into text-to-speech (TTS) conversion systems. Conventional TTS conversion uses intermediate layers of representation to bridge the gap between text and speech. Collecting the annotated data needed to produce these intermediate layers is a far from trivial task, possibly prohibitively so for languages in which no such resources are in existence. Distributional analysis, in contrast, proceeds in an unsupervised manner, and so enables the creation of systems using textual data that are not annotated. The method therefore aids the building of systems for languages in which conventional linguistic resources are scarce, but is not restricted to these languages. The distributional analysis proposed here places the textual objects analysed in a continuous-valued space, rather than specifying a hard categorisation of those objects. This space is then partitioned during the training of acoustic models for synthesis, so that the models generalise over objects’ surface forms in a way that is acoustically relevant. The method is applied to three levels of textual analysis: to the characterisation of sub-syllabic units, word units and utterances. Entire systems for three languages (English, Finnish and Romanian) are built with no reliance on manually labelled data or language-specific expertise. Results of a subjective evaluation are presented.
Detecting Android Locker-Ransomware on Chinese Social Networks

In recent years, an increasing amount of locker-ransomware has been posing a great threat to the Android platform as well as users’ properties. Locker-ransomware blackmails victims for ransom by compulsorily locking the devices. What is worse, a mature locker-ransomware transaction chain has taken shape on Chinese social networks. The effective detection of locker-ransomware is an emergent yet crucial issue. To deal with this issue, in this paper, we are motivated to propose a light-weight and automated method for the detection of locker-ransomware. First, we conduct a thorough survey of the locker-ransomware’s transaction market and perform a comprehensive analysis of locker-ransomware’s behaviors. Second, to cope with the code obfuscation problem, we extract features of both displayed texts and background operations based on the observed behaviors. The fine-grained features are extracted from multiple sources, which can profile locker-ransomware in different aspects. Finally, we employ the ensemble of four machine learning algorithms for detection. The experimental results show that our method outperforms VirusTotal. It achieves the best performance with the detection accuracy of 99.98%.
Disparities and antecedents to cancer prevention information seeking among cancer patients and caregivers attending a Minority-Serving Cancer Center

Abstract Cancer prevention information (CPI) needs are common among cancer patients and their families, but processes of CPI seeking following cancer diagnosis are largely unknown. The goal of this study was to examine antecedents of CPI seeking among a diverse sample of patients and their caregivers. Findings revealed high levels of perceived personal importance of CPI and motivational factors (health consciousness), but almost half of respondents never sought CPI. Non-seekers were more likely to have lower household income and educational attainment. Minority respondents were less likely to seek CPI compared with non-Hispanic Whites, with increased risk among Spanish-speaking Hispanics. However, only educational attainment, health consciousness, and marital status were significant predictors of CPI seeking in the final logistic regression model. These disparities demonstrate the need for communication interventions in healthcare settings.
A Comparison of Two Computer Implemented Psychophysical Procedures Applied to Real-Ear Attenuation Testing (ANSI S12.6-1984)

Abstract : The application of computer technology to acoustical instrumentation has significantly increased the capability and flexibility of modern acoustical laboratories. The need to replace old recording attenuators used in real-ear sound attenuation testing with state of the art instrumentation prompted the combination of a CMOS multiplying D/A converter chip (which can accurately and reliably attenuate an analog signal) and a table-top computer to control the D/A chip. The computer was also used to record the measurement of auditory threshold, perform statistical analysis, and permanently store data. The flexibility of computer technology allowed the choice of psychophysical procedure. Consideration was given to two such procedures, tracking and method of adjustment. This study was undertaken to determine if one of these procedures would produce faster, more accurate results.