Miracle-dz/UniFAR-FTUs huggingface dataset:
Insight, psychosis, and depression in Africa: A cross-sectional survey from an inpatient unit in Ghana

Few studies of the relationship of insight to psychopathology have been conducted in non-Western populations. This study examined the relationships between insight and depression, anxiety, and positive and negative symptoms on patients with schizophrenia resident in a psychiatric hospital in Ghana. A sample of 49 participants, (37 men and 12 women), with DSM-IV defined schizophrenia took part in semistructured interviews consisting of the Hamilton Rating Scales for Depression (HAM-D) and Anxiety (HAM-A); the Schedule for the Assessment of Insight – Expanded Version (SAI-E) and the Positive and Negative Syndrome Scale (PANSS). Bivariate correlations between variables were examined and those significantly correlated with an insight domain were included in multiple regression models. Variables associated with the total insight score were age, gender, anxiety symptoms, depression symptoms, and treatment compliance. In the final model, HAM-D positively predicted total SAI-E score, whilst PANSS-pos was negatively associated with total SAI-E score. The results are broadly consistent with those found in Western samples regarding insight and depressive symptoms. Implications of these results for competing theories of insight in psychoses are discussed. Patients able to identify themselves as ill may be aware of their affective symptoms.
Evaluating real-time strategy game states using convolutional neural networks

Real-time strategy (RTS) games, such as Blizzard's StarCraft, are fast paced war simulation games in which players have to manage economies, control many dozens of units, and deal with uncertainty about opposing unit locations in real-time. Even in perfect information settings, constructing strong AI systems has been difficult due to enormous state and action spaces and the lack of good state evaluation functions and high-level action abstractions. To this day, good human players are still handily defeating the best RTS game AI systems, but this may change in the near future given the recent success of deep convolutional neural networks (CNNs) in computer Go, which demonstrated how networks can be used for evaluating complex game states accurately and to focus look-ahead search. In this paper we present a CNN for RTS game state evaluation that goes beyond commonly used material based evaluations by also taking spatial relations between units into account. We evaluate the CNN's performance by comparing it with various other evaluation functions by means of tournaments played by several state-of-the-art search algorithms. We find that, despite its much slower evaluation speed, on average the CNN based search performs significantly better compared to simpler but faster evaluations. These promising initial results together with recent advances in hierarchical search suggest that dominating human players in RTS games may not be far off.
How Damaging is Negative Word of Mouth

The purpose of this exploratory study was to seek evidence of whether word of mouth (WOM) has a significant effect on the attitudes and probability of purchase of potentia l consumers. The study, which partially replicates that of Herr, Kardes and Kim (1991), tested the following two research hypotheses: i. That both positive and negative WOM will affect consumers' probability of purchase and attitudes towards a product. ii. That negative WOM will have a stronger effect than positive WOM on consumers' probability of purchase and attitudes towards a product. The results of this exploratory study indicate that WOM, both positive and negative, is indeed a force that can influence the attitudes and predicted purchase behaviour of consumers. However, due to the lack of significant differences between the experimental groups and the neutral (control) group, this study can not give reliable estimates of the precise magnitude of this effect. Several authors contend that negative WOM is more influential than positive WOM, however, this proposition was not supported by our research.
Beware the Meta-Analysis: Is Multiple Set Training Really Better than Single-Set Training for Muscle Hypertrophy?

Fisher J. Beware the Meta-Analysis: Is Multiple Set Training Really Better than Single Set Training for Muscle Hypertrophy? JEPonline 2012;15(6):23-30. While many individuals perform resistance training with the purpose of increasing the size of their muscles, reviews have not clarified a training prescription for optimizing hypertrophy. A 2010 meta-analysis concluded that multiple set training resulted in greater hypertrophic gains compared to single set training. However, while the use of meta-analysis in exercise physiology is well documented, there is sufficient reason to be cautious when applying a single statistic acquired by combining multiple studies and the relevant effect sizes (ES). This paper reviews the articles included within the 2010 metaanalysis. In particular, the differences in subject training experience, gender, and clinical condition, the training frequency, the intervention duration, the repetition duration used, and the method of measuring hypertrophy, as well as other variables that appear not to have been considered are discussed in this paper. The substantial limitations in comparing or grouping these articles suggest that the meta-analysis cannot be used to support the conclusion that multiple sets produce greater hypertrophic gains than single set training.
Neuropathology of multiple sclerosis

S ince the first clinico-pathological descriptions of multiple sclerosis (MS), neuropathologists, neurolo-gists, immunologists, geneticists and scientists have worked together in an attempt to clarify the pathogenesis of this enigmatic disease. Multiple sclerosis is a complex condition and there is no consensus as to whether it represents a single disease or a series of diseases with a common final pathway. For this reason systematic pathological analysis of brains affected by demyelination is still important to provide new insight into the pathogenesis of the disease. This review is the result of experience gained from examining brains donated by patients with MS to the UK Multiple Sclerosis Tissue Bank (UKMSTB) at the Imperial College of London. Brief historical overview Descriptions of cases of putative MS date back as early as the Middle Ages but it was in the 19th century when doctors definitively recognised MS as a distinct disease. The first pathological report was published by Jean-Martin Charcot, Professor of Neurology at the University of Paris in 1868 in the Leçons du mardi. 2 He examined a young woman who presented with tremor, slurred speech and abnormal eye movements. When she died, Charcot had the chance to study her brain and document the characteristic scars he termed 'plaques' coining the definition of 'la sclerose en plaques'. He also delineated the clinical diagnostic criteria with the triad of nystagmus, intention tremor and scanning speech which, though not specific, are still helpful in recognising the disease. In 1916 James Dawson at the University of Edinburgh wrote the first detailed microscopic description of the brains of MS patients, documenting perivascular inflammation and myelin disruption. Since this time, MS research has grown relentlessly with the discovery of abnormalities in spinal fluid in 1919, the discovery of oligodendrocytes made by Ranvier in 1928 and with the observation in animal models in 1935 that injection of brain tissue produces an MS-like illness, called experimental allergic encephalomyelitis. Definition of multiple sclerosis Multiple sclerosis belongs to the complex group of disorders of the central nervous system characterised by destruction of myelin (Table 1). Given the clinical and pathological heterogeneity of MS, there is no conclusive definition of the disease and the one suggested by Poser 11 of an " inflammatory demyelinating disease of the central nervous system in which the particular myelinoclastic sequence, regardless of the clinical course and the pheno-typic manifestation is determined by the individual endowment and which results …
Phenotypic and Haplotype Diversity among Tetraploid and Hexaploid Wheat Accessions with Potentially Novel Insect Resistance Genes for Wheat Stem Sawfly

Haplotype diversity underlying Qss.msub. 3BL solid‐stem QTL present among solid‐stem wheat landraces. Frequency and diversity of haplotypes vary among ploidy levels and geographical regions. New haplotypes potentially containing novel resistance alleles will augment WSS resistance. KASP markers can be used for marker assisted selection of the solid‐stem Qss.msub. 3BL allele.
Neural Sensitivity to Interaural Time Differences: Beyond the Jeffress Model

Interaural time differences (ITDs) are a major cue for localizing the azimuthal position of sounds. The dominant models for processing ITDs are based on the Jeffress model and predict neurons that fire maximally at a common ITD across their responsive frequency range. Such neurons are indeed found in the binaural pathways and are referred to as “peak-type. ” However, other neurons discharge minimally at a common ITD (trough-type), and others do not display a common ITD at the maxima or minima (intermediate-type). From recordings of neurons in the auditory cortex of the unanesthetized rabbit to low-frequency tones and envelopes of high-frequency sounds, we show that the different response types combine to form a continuous axis of best ITD. This axis extends to ITDs well beyond that allowed by the head width. In Jeffress-type models, sensitivity to large ITDs would require neural delay lines with large differences in path lengths between the two ears. Our results suggest instead that sensitivity to large ITDs is created with short delay lines, using neurons that display intermediate- and trough-type responses. We demonstrate that a neuron's best ITD can be predicted from (1) its characteristic delay, a rough measure of the delay line, (2) its characteristic phase, which defines the response type, and (3) its best frequency for ITD sensitivity. The intermediate- and trough-type neurons that have large best ITDs are predicted to be most active when sounds at the two ears are decorrelated and may transmit information about auditory space other than sound localization.
DSGE Model-Based Forecasting of Non-Modelled Variables

This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model-generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, we apply our approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, the unemployment rate, and housing starts along with predictions for the seven variables that have been used to estimate the DSGE model.
From deep to superficial categorization with increasing expertise

An experimental study of task design expertise is reported wherein a set of 12 mathematics tasks were sorted by specialist designers of mathematics tasks and by experienced mathematics teachers without specialist design experience. Contrary to the frequent finding of increasing conceptual depth with increasing expertise, conceptual depth did not differ between groups. Teachers sorted on the basis of mathematical content earlier than designers, and were more specific in their content-based categories. Designers produced more sorts than teachers and were more individualistic in their sorting. These findings suggest that domain expertise does not necessarily impair creative problem solving, as has been suggested in other studies. Instead, expertise includes the ability to shift perspectives with respect to the domain.
Time to Amyloid Positivity and Preclinical Changes in Brain Metabolism, Atrophy, and Cognition: Evidence for Emerging Amyloid Pathology in Alzheimer's Disease

Background: Aβ pathology is associated with longitudinal changes of brain metabolism, atrophy, and cognition, in cognitively healthy elders. However, Aβ information is usually measured cross-sectionally and dichotomized to classify subjects as Aβ-positive or Aβ-negative, making it difficult to evaluate when brain and cognitive changes occur with respect to emerging Aβ pathology. In this study, we use longitudinal Aβ information to combine the level and rate of change of Aβ to estimate the time to Aβ-positivity for each subject and test this temporal proximity to significant Aβ pathology for associations with brain structure, metabolism, and cognition. Methods: In 89 cognitively healthy elders with up to 10 years of follow-up, we estimated the points at which rates of fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to the time to Aβ-positivity. Points of initial acceleration in rates of decline were estimated using mixed-effects models with penalized regression splines. Results: Acceleration of rates of FDG PET were observed to occur 20+ years before the conventional threshold for Aβ-positivity. Subtle signs of cognitive dysfunction were observed 10+ years before Aβ-positivity. Conclusions: Aβ may have subtle associations with other hallmarks of Alzheimer's disease before Aβ biomarkers reach conventional thresholds for Aβ-positivity. Therefore, we propose that emerging Aβ pathology occurs many years before cognitively healthy elders reach the current threshold for Aβ positivity (preclinical AD). To allow prevention in the earliest disease stages, AD clinical trials may be designed to also include subjects with Aβ biomarkers in the sub-threshold range.
Dynamic Network Plaid: A Tool for the Analysis of Dynamic Networks

Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram.
How Do Humans Assess the Credibility on Web Blogs: Qualifying and Verifying Human Factors with Machine Learning

The purpose of this paper is to understand the factors involved when a human judges the credibility of information and to develop a classification model for weblogs, a primary source of information for many people. Considering both computational and human-centered approaches, we conducted a user study designed to consider two cognitive procedures--(1) visceral, behavioral and (2) reflective assessments--in the evaluation of information credibility. The results of the 80-participant study highlight that human cognitive processing varies according to an individual's purpose and that humans consider the structures and styles of content in their reflective assessments. We experimentally proved these findings through the development and analysis of classification models using 16,304 real blog posts written by 2,944 bloggers. Our models yield greater accuracy and efficiency than the models with well-known best features identified in prior research
Consensus of multi-agent systems based on controllability under leader-follower topology

In this paper, a necessary and sufficient condition for the controllability of multi-agent systems under the switched topology is proposed. The connection between consensus and controllability of multi-agent systems under both the fixed and switched topology is discussed. The results show that the multi-agent systems can reach to a consensus if the system is controllable. The simulations verify the results.
OPTIMIZED REPRESENTATIONS OF SIGNALS USING BAYESIAN DECOMPOSITION TREES

Tree-structured dictionaries of orthonormal bases (wavelet packets/Malvar's wa-velets) provide a natural framework to answer the problem of nding a \best repre-sentation" of both deterministic and stochastic signals. These classical dictionaries, in turn, may be extended to more general decomposition trees by considering node-varying decomposition operators. In this paper, we reformulate the \best basis" search in these generalized dictionaries as a model selection problem and present a Bayesian approach where the sequence of optimal operators, and subsequently the optimal basis, are considered as model stochastic parameters. Non-homogeneous priors are introduced on the signal transformations and make it compelling to resort to the recently developed reversible jump Markov chain Monte Carlo algorithms. Applications to signal denoising in non-necessarily Gaussian i.i. d. situations are subsequently presented.
X10 and APGAS at Petascale

X10 is a high-performance, high-productivity programming language aimed at large-scale distributed and shared-memory parallel applications. It is based on the Asynchronous Partitioned Global Address Space (APGAS) programming model, supporting the same fine-grained concurrency mechanisms within and across shared-memory nodes. We demonstrate that X10 delivers solid performance at petascale by running (weak scaling) eight application kernels on an IBM Power 775 supercomputer utilizing up to 55,680 Power7 cores (for 1.7 Pflop/s of theoretical peak performance). We detail our advances in distributed termination detection, distributed load balancing, and use of high-performance interconnects that enable X10 to scale out to tens of thousands of cores. For the four HPC Class 2 Challenge benchmarks, X10 achieves 41% to 87% of the system's potential at scale (as measured by IBM's HPCC Class 1 optimized runs). We also implement K-Means, Smith-Waterman, Betweenness Centrality, and Unbalanced Tree Search (UTS) for geometric trees. Our UTS implementation is the first to scale to petaflop systems.
Acute stroke: why do some patients arrive in time and others do not?

The aim of this study was to evaluate which factors are associated with early hospital arrival and help-seeking delays in acute stroke. All consecutive eligible patients were interviewed face-to-face within 72 h of admission. Factors associated with early arrival were assessed by univariate and multivariate analysis. The data of 195 patients were analysed. The patients who first called the emergency medical services rather than the family physician arrived earlier (odds ratio 15.9, 95% confidence interval 3.23–78.3, P<0.01). Those who contacted the emergency medical services within 30 min of symptom onset were more likely to receive thrombolysis (odds ratio 6.9, 95% confidence interval 2.6–18.4, P<0.01). The most common reasons for delaying seeking help were the hope for spontaneous recovery and perceiving the elapsed time as insignificant. The patients who call their family physician lose valuable time and their chance for thrombolysis. Many patients probably neglect symptoms because of stroke itself and therefore do not act fast enough.
Understanding reactions to job redesign: A quasi-experimental investigation of the moderating effects of organizational context on perceptions of performance behavior.

Redesigning jobs from a traditional workgroup structure to a semi-autonomous team structure has become increasingly popular, but the impact of such redesigns on employee effectiveness criteria has been mixed. The present longitudinal quasi-experimental study showed that although such a redesign had positive effects on 3 performance behaviors (effort, skill usage, and problem solving), its effectiveness also depended on aspects of the organizational context. In conditions where the organizational reward and feedback and information systems were effective, redesigning work into a semi-autonomous team structure had no discernible effect on performance behaviors. In conditions where these systems were poor, however, such a redesign produced large positive benefits. This suggests that work redesigns that enhance worker autonomy are most effective in contexts where other supportive management systems are absent.
Direct comparison of 129Xe diffusion measurements with quantitative histology in human lungs

Chronic obstructive pulmonary disease (COPD) is an irreversible lung disease characterized by small‐airway obstruction and alveolar‐airspace destruction. Hyperpolarized 129Xe diffusion MRI of lung is a promising biomarker for assessing airspace enlargement, but has yet to be validated by direct comparison to lung histology. Here we have compared diffusion measurements of hyperpolarized (HP) 129Xe in explanted lungs to regionally matched morphological measures of airspace size.
Semi-Local Affine Parts for Object Recognition

This paper proposes a new approach for finding expressive and geometrically invariant parts for modeling 3D objects. The approach relies on identifying groups of local affine regions (image features having a characteristic appearance and elliptical shape) that remain approximately affinely rigid across a range of views of an object, and across multiple instances of the same object class. These groups, termed semi-local affine parts, are learned using correspondence search between pairs of unsegmented and cluttered input images, followed by validation against additional training images. The proposed approach is applied to the recognition of butterflies in natural imagery.
Scoring from Contests

This article presents a new model for scoring alternatives from “contest” outcomes. The model is a generalization of the method of paired comparison to accommodate comparisons between arbitrarily sized sets of alternatives in which outcomes are any division of a fixed prize. Our approach is also applicable to contests between varying quantities of alternatives. We prove that under a reasonable condition on the comparability of alternatives, there exists a unique collection of scores that produces accurate estimates of the overall performance of each alternative and satisfies a well-known axiom regarding choice probabilities. We apply the method to several problems in which varying choice sets and continuous outcomes may create problems for standard scoring methods. These problems include measuring centrality in network data and the scoring of political candidates via a “feeling thermometer. ” In the latter case, we also use the method to uncover and solve a potential difficulty with common methods of rescaling thermometer data to account for issues of interpersonal comparability.