Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Factors Associated with Default from Multi- and Extensively Drug-Resistant Tuberculosis Treatment, Uzbekistan: A Retrospective Cohort Analysis

Background The Médecins Sans Frontières project of Uzbekistan has provided multidrug-resistant tuberculosis treatment in the Karakalpakstan region since 2003. Rates of default from treatment have been high, despite psychosocial support, increasing particularly since programme scale-up in 2007. We aimed to determine factors associated with default in multi- and extensively drug-resistant tuberculosis patients who started treatment between 2003 and 2008 and thus had finished approximately 2 years of treatment by the end of 2010. Methods A retrospective cohort analysis of multi- and extensively drug-resistant tuberculosis patients enrolled in treatment between 2003 and 2008 compared baseline demographic characteristics and possible risk factors for default. Default was defined as missing ≥60 consecutive days of treatment (all drugs). Data were routinely collected during treatment and entered in a database. Potential risk factors for default were assessed in univariate analysis using chi-square test and in multivariate analysis with logistic regression. Results 20% (142/710) of patients defaulted after a median of 6 months treatment (IQR 2.6–9.9). Factors associated with default included severity of resistance patterns (pre-extensively drug-resistant/extensively drug-resistant tuberculosis adjusted odds ratio 0.52, 95%CI: 0.31–0.86), previous default (2.38, 1.09–5.24) and age >45 years (1.77, 1.10–2.87). The default rate was 14% (42/294) for patients enrolled 2003–2006 and 24% (100/416) for 2007–2008 enrolments (p = 0.001). Conclusions Default from treatment was high and increased with programme scale-up. It is essential to ensure scale-up of treatment is accompanied with scale-up of staff and patient support. A successful first course of tuberculosis treatment is important; patients who had previously defaulted were at increased risk of default and death. The protective effect of severe resistance profiles suggests that understanding disease severity or fear may motivate against default. Targeted health education and support for at-risk patients after 5 months of treatment when many begin to feel better may decrease default.
The Acquisition of Word Meaning through Global Lexical Co-occurrences

The acquisition of word meaning has been extensively studied for the last thirty years in the field of language acquisition. However, the question of how children acquire word meaning remains highly controversial today. Recently, a number of computational studies have examined the emergence of lexical representations in connectionist networks or similar statistical systems, suggesting that word meaning can be acquired by the computation of statistical regularities inherent in the input data. In particular, Elman (1990, 1998) showed that categories of nouns and verbs, and subcategories of animates versus inanimates (within nouns), and transitives versus intransitives (within verbs), can emerge from the network’s computing of the lexical co-occurrence properties in the input. Redington, Chater, and Finch (1998) also demonstrated that the use of distributional properties in large-scale speech corpus allows a statistical system to acquire syntactic categories. These studies are in many ways consistent with the empirical approach of distributional analysis of the linguistic input, as advocated by Maratsos and Chalkley (1980), among many others. They have revealed the power of distributional information in the input, and revived an interest in how the child could analyze the linguistic input to derive accurate representations of the syntax and semantics of words. It is along this line of interest that we seek to understand the acquisition of word meanings in this study. As a starting point for our approach, we would like to consider two major hypotheses on how children start the process of lexical acquisition: the semantic bootstrapping hypothesis (Grimshaw, 1981; Pinker, 1984, 1987) and the syntactic bootstrapping hypothesis (Landau & Gleitman, 1985; Gleitman, 1990). The semantic bootstrapping hypothesis assumes that
Development and Characterization of Liposome-Enriched Ketoprofen Liposomal Hydrogels

The purpose of this study was to develop liposome-enriched Ketoprofen liposomal hydrogels and carry out in vitro release profile experiment. The aim was to achieve sustained topical drug delivery for extended time interval from liposomal gels. Phosphatidylcholine, Cholesterol and Ketoprofen were dissolved in chloroform/methanol (2:1, v/v) mixture and subsequently transferred to a flask attached to rotavapor. The liposomes were assessed for particle size and percent drug entrapment. F-7 and F-8 batches were found to be optimized batches having optimum sizes, drug entrapment efficiencies and cumulative drug releases. F-8 batch was further evaluated for stability. The results show that the prepared liposomes of Ketoprofen might turn out to be potential candidates for effective and safe sustained drug delivery thereby resulting in the reduction of dosing frequency.
Distributed load balancing mechanism for detouring schemes of geographic routing in wireless sensor networks

Well-known ‘routing hole’ problem of geographic routing is hardly avoided in wireless sensor networks because of various actual geographical environments. Existing geographic routing protocols use perimeter routing strategies to find a detour path around the boundary of holes when they encounter the local minimum during greedy forwarding. However, this solution may lead to uneven energy consumption around the holes since it consumes more energy of the boundary sensors. It becomes more serious when holes appear in most of routing paths in a large-scale sensor network. In this paper, we propose a novel distributed strategy to balance the traffic load on the boundary of holes by virtually changing the sizes of these holes. The proposed mechanism dynamically controls holes to expand and shrink circularly without changing the underlying forwarding strategy. Therefore, it can be applied to most of the existing geographic routing protocols which detour around holes. Simulation results show that our strategy can effectively balance the load around holes, thus prolonging the network life of sensor networks when an existing geographic routing protocol is used as the underlying routing protocol.
Enhancing X3D for advanced MR appliances

In this paper, we explore and discuss X3D as an application description language for advanced mixed reality environments. X3D has been established as an important platform for today's web-based visualization and VR applications. Yet, there are very few examples for augmented reality systems utilizing X3D beyond a simple geometric description format. In order to fulfill the image compositing and synthesis requests of today's augmented reality applications, we propose extensions to X3D, especially with a focus on lighting and realistic rendering.
Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs.
Sparse Q-learning with Mirror Descent

This paper explores a new framework for reinforcement learning based on online convex optimization, in particular mirror descent and related algorithms. Mirror descent can be viewed as an enhanced gradient method, particularly suited to minimization of convex functions in high-dimensional spaces. Unlike traditional gradient methods, mirror descent undertakes gradient updates of weights in both the dual space and primal space, which are linked together using a Legendre transform. Mirror descent can be viewed as a proximal algorithm where the distance generating function used is a Bregman divergence. A new class of proximal-gradient based temporal-difference (TD) methods are presented based on different Bregman divergences, which are more powerful than regular TD learning. Examples of Bregman divergences that are studied include p-norm functions, and Mahalanobis distance based on the covariance of sample gradients. A new family of sparse mirror-descent reinforcement learning methods are proposed, which are able to find sparse fixed points of an l1-regularized Bellman equation at significantly less computational cost than previous methods based on second-order matrix methods. An experimental study of mirror-descent reinforcement learning is presented using discrete and continuous Markov decision processes.
The geometric relationship between Core Vector Machine and Support Vector Machine

Core vector machine (CVM) is an efficient kernel method for large data classification. It has prominent advantages in dealing with large data sets in high-dimensional space. This paper presents a novel geometric framework between CVM and the traditional support vector machine (SVM). We proved theoretically that: (1) In one-class classification, non-training examples on the surface of the exact minimum enclosing ball (MEB) in CVM belong to the optimal separating hyperplane in SVM; (2) In one-class classification, training examples on the surface of the exact MEB in CVM correspond to the support vectors in SVM; (3) In two-class classification, non-training examples on the surface of the exact MEB in CVM belong to the bounding hyperplanes in SVM; (4) In two-class classification, training examples on the surface of the exact MEB in CVM correspond to the support vectors in SVM. Geometric interpretations for points on the (1 + epsiv)-approximate MEB in CVM are presented as well. It is believed that the obtained geometric relationship will be helpful in analyzing CVM and inspiring new classification algorithms.
Fuzzy grey sliding mode control for maximum power point tracking of photovoltaic systems

This paper proposes a fuzzy-grey sliding-mode control (FGSMC) strategy for maximum power point (MPP) tracking in photovoltaic (PV) systems. The problem to be addressed is that the linear reaching law in a previous SMC design has limited reaching speed if control saturation is to be avoided. Hence, a nonlinear reaching law is proposed by means of fuzzy-logic control which employs as inputs the sliding function, and its predicted forward difference provided by a grey-prediction algorithm. Control design is performed on an averaged system model, whereas the performances of FGSMC and SMC are compared in simulation on both the averaged model and the true switching PV system.
Supervised Transfer Sparse Coding

A combination of the sparse coding and transfer learning techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from different underlying distributions, i.e., belong to different domains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small number of them. In this paper, we explore such possibility and show how a small number of labeled data in the target domain can significantly leverage classification accuracy of the state-of-the-art transfer sparse coding methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.
Longitudinal cohort study of childhood IQ and survival up to age 76

Abstract Objectives: To test the association between childhood IQ and mortality over the normal human lifespan. Design: Longitudinal cohort study. Setting: Aberdeen. Subjects: All 2792 children in Aberdeen born in 1921 and attending school on 1 June 1932 who sat a mental ability test as part of the Scottish mental survey 1932. Main outcome measure: Survival at 1 January 1997. Results: 79.9% (2230) of the sample was traced. Childhood mental ability was positively related to survival to age 76 years in women (P<0.0001) and men (P<0.0001). A 15 point disadvantage in mental ability at age 11 conferred a relative risk of 0.79 of being alive 65 years later (95% confidence interval 0.75 to 0.84); a 30 point disadvantage reduced this to 0.63 (0.56 to 0.71). However, men who died during active service in the second world war had a relatively high IQ. Overcrowding in the school catchment area was weakly related to death. Controlling for this factor did not alter the association between mental ability and mortality. Conclusion: Childhood mental ability is a significant factor among the variables that predict age at death. What is already known on this topic People in deprived conditions tend to have more illness and die younger The reasons for this inequality in health are not fully established What this study adds IQ at age 11 years was significantly associated with survival up to 76 years in an Aberdeen cohort The association was unaffected by adjustment for overcrowding Men with high IQ were more likely to die in active service in the second world war
Face Tracking and Recognition via Incremental Local Sparse Representation

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. We first develop a robust face tracking algorithm based on the local sparse appearance. This sparse representation model exploits both partial and spatial information of the face based on a covariance pooling method. Following in the face recognition stage, with the employment of a novel template update strategy, our recognition algorithm adapts the template to appearance change and reduces the influence of occlusion and illumination variation. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database. Our proposed method produces a high face recognition results on over 93% of all videos. The tracking results on challenging videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. On the challenging data set in which faces are undergo occlusion and illumination variation, our proposed method also consistently demonstrates a high recognition rate.
Investigation of the transferability of hydrological models and a method to improve model calibration

Abstract. In order to find a model parameterization such that the hydrological model performs well even under different conditions, appropriate model performance measures have to be determined. A common performance measure is the Nash Sutcliffe efficiency. Usually it is calculated comparing observed and modelled daily values. In this paper a modified version is suggested in order to calibrate a model on different time scales simultaneously (days up to years). A spatially distributed hydrological model based on HBV concept was used. The modelling was applied on the Upper Neckar catchment, a mesoscale river in south western Germany with a basin size of about 4000 km2. The observation period 1961-1990 was divided into four different climatic periods, referred to as "warm", "cold", "wet" and "dry". These sub periods were used to assess the transferability of the model calibration and of the measure of performance. In a first step, the hydrological model was calibrated on a certain period and afterwards applied on the same period. Then, a validation was performed on the climatologically opposite period than the calibration, e.g. the model calibrated on the cold period was applied on the warm period. Optimal parameter sets were identified by an automatic calibration procedure based on Simulated Annealing. The results show, that calibrating a hydrological model that is supposed to handle short as well as long term signals becomes an important task. Especially the objective function has to be chosen very carefully.
PDEs in Computer Graphics

Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task. In this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking. Our method is based on a simple premise: neighboring pixels in space-time that have similar intensities should have similar colors. We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input. CR Categories: I.4. 9 [Image Processing and Computer Vision]: Applications;
Development of a human-like motor nerve model to simulate the diseases effects on muscle tension for neurologic examination training

Neurologic examination takes an important role in the physical examination. By now, several methods have been carried out for medical training which bring the benefits for trainee to master the skills and accumulate experiences. However, because of the limits of these methods, the training effectiveness is limited. With the developments of technology, more and more simulators have been launched to improve medical training effectiveness. However, most of these simulators only focus on mimicking the symptoms, not simulating the pathology of diseases. In this paper, we propose an improved motor nerve model which is used in the elbow robot named WKE-2(Waseda Kyotokagaku Elbow Robot No.2). This robot is designed to simulate the disorders of motor nerves to give the trainee a full training on the elbow examination. The motor nerve model mimics the real motor nerve structure. And the functions of each part are designed from analysis of the real functions of each organ. In this robot, the effects of motor nerve system, and various symptoms related to the examination of elbow force, biceps tendon reflex, involuntary action are simulated. Making use of this robot, a systematic training on the skills as well as the understanding of knowledge is provided. Finally, several experiments are performed to verify our proposed system. The experimental results lead to the consideration that the approach is worth following in further research.
Kernel-based Regularized Neighbourhood Preserving Embedding in face recognition

Face images always have significant intra-class variations due to different poses, illuminations and facial expressions. These variations trigger substantial deviation from the linearity assumption of data structure, which is essential in formulating linear dimension reduction technique. In this paper, we present a kernel based regularized graph embedding dimension reduction technique, known as kernel-based Regularized Neighbourhood Preserving Embedding (KRNPE) to address this problem. KRNPE first exploits kernel function to unfold the nonlinear intrinsic facial data structure. Neighbourhood Preserving Embedding, a graph embedding based linear dimension reduction technique, is then regulated based on Adaptive Locality Preserving Regulation Model, established in [7] to enhance the locality preserving capability of the projection features, leading to better discriminating capability and generalization performance. Experimental results on PIE and FERET face databases validate the effectiveness of KRNPE.
Requirements traceability in the model-based testing process

Automated test case and test driver generation from a precise behaviour UML model is an emerging approach for software functional validation. This innovative approach for validation testing makes it possible to ensure the functional coverage of the test suite and to automate the production of executable test scripts. This paper presents an approach to automatically produce the traceability matrix from requirements to test cases, as part of the test generation process. This approach is embedded in the LEIRIOS Test Designer technology, and has been used for several real-life applications in eTransactions and Enterprise IT areas. This paper introduces the approach to annotating the UML model and using it to generate the traceability matrix. It also discusses some lessons learnt from our experience using requirements traceability as part of a Model-Based Testing process.
Validity of the French form of the Somatosensory Amplification Scale in a Non-Clinical Sample

The SomatoSensory Amplification Scale (SSAS) is a 10-item self-report instrument designed to assess the tendency to detect somatic and visceral sensations and experience them as unusually intense, toxic and alarming. This study examines the psychometric properties of a French version of the SSAS in a non-clinical population and, more specifically, explores its construct, convergent and discriminant validities. The SSAS was completed by 375 university students, together with measures of somatization propensity (SCL-90-R somatization subscale) and trait anxiety (STAI Y form). The results of principal component and confirmatory factor analyses suggest that the French version of the SSAS evaluates essentially a single, robust factor (Somatosensory amplification) and two kinds of somatic sensitivity (Exteroceptive sensitivity and Interoceptive sensitivity). Somatosensory amplification correlated with somatization tendency and anxiety propensity. These results encourage further investigations in French of the determinants and consequences of somatosensory amplification, and its use as a therapeutic strategy.
How to nudge in Situ: designing lambent devices to deliver salient information in supermarkets

There are a number of mobile shopping aids and recommender systems available, but none can be easily used for a weekly shop at a local supermarket. We present a minimal, mobile and fully functional lambent display that clips onto any shopping trolley handle, intended to nudge people when choosing what to buy. It provides salient information about the food miles for various scanned food items represented by varying lengths of lit LEDs on the handle and a changing emoticon comparing the average miles of all the products in the trolley against a social norm. When evaluated in situ, the lambent handle display nudged people to choose products with fewer food miles than the items they selected using their ordinary shopping strategies. People also felt guilty when the average mileage of the contents of their entire shopping trolley was above the social norm. The findings are discussed in terms of how to provide different kinds of product information that people care about, using simple lambent displays.