Miracle-dz/UniFAR-FTUs huggingface dataset:
Mindfulness as a Factor in the Relationship between Insecure Attachment Style, Neurotic Personality and Disordered Eating Behavior

Mindfulness, conceptualized as a dispositional trait that differs across individuals, may potentially influence disordered eating behaviors. Previous research has independently identified insecure attachment style and neurotic personality traits as correlates of disordered eating behavior. Thus this current study will investigate whether neurotic personality traits, insecure attachment style and mindfulness predict disordered eating behavior controlling for gender differences. Participants (N = 126) completed the Adult Attachment Scale [1], the Three Factor Eating Questionnaire – Revised 18 [2], The Cognitive and Affective Mindfulness Scale – Revised [3] and the International Personality Item Pool [4]. The results of this cross-sectional study indicated that neurotic personality traits, insecure attachment style and mindfulness were related to disordered eating behaviors. The variance in disordered eating behaviors that was accounted for by neurotic personality traits and insecure attachment style was significantly reduced with the introduction of mindfulness. The results provides preliminary support for the inclusion of mindfulness training in disordered eating behavior interventions for individuals exhibiting an insecure attachment style and neurotic personality traits. Limitations and implications for further research are discussed.
NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination

In this paper, we put forward a system that competed at SemEval-2018 Task 1: “Affect in Tweets”. Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation Coefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670
A temporal deconvolution algorithm for multiecho functional MRI

This work introduces a novel method for the temporal deconvolution of the BOLD signal for multiecho functional MRI (fMRI) data: Multiecho Paradigm Free Mapping (ME-PFM). By solving an inverse problem with the Generalized LASSO, this method obtains voxelwise time-varying estimates of the changes in the transverse relaxation (R) and the net magnetization (So) of the fMRI signal without prior knowledge of the timings of the BOLD responses. Our results with experimental data demonstrate that the maps of R changes obtained with ME-PFM at the times of the stimulus trials closely resemble the maps obtained with a standard approach that is aware of the onsets and durations of the events. Compared with previous deconvolution approaches for BOLD fMRI, this method yields estimates of R changes in physiologically interpretable units, which is particularly relevant for the quantification of the dynamics of the BOLD fMRI signal during tasks and resting state.
Autonomous Multi-Joint Soft Exosuit for Assistance with Walking Overground

Soft exosuits are a new approach for assisting with human locomotion, which applies assistive torques to the wearer through functional apparel. In this paper, we present a new version of autonomous multi-joint soft exosuit for gait assistance, particularly designed for overground walking. The soft exosuit assists with ankle plantarflexion, hip flexion, and hip extension, equally distributing the forces between ankle plantarflexion and hip flexion. A mobile actuation system was developed to generate high assistive forces, and Bowden cables are used to transmit the forces to the exosuit. A sensor harness connects two load cells and three IMU s per leg that are used to measure real-time data for a controller that commands desired force profiles as a function of the walking cycle. In addition, a control adaptation method was developed which adjusts control parameters while walking on irregular surfaces. In preliminary studies, the proposed method substantially improved the force consistency while walking over uneven terrain. Specifically, the number of steps where the peak force deviated from the target force decreased from 100 to 57 out of 250 steps, and RMS error on the peak force decreased from 90.0 N to 76.6 N with respect to 300 N target force. Also, a two-subject case study on country-course walking demonstrated the potential of this soft exosuit to improve human energy economy while walking overground.
Does being on TB treatment predict a higher burden of problems and concerns among HIV outpatients in Kenya? a cross-sectional self-report study

ABSTRACT Tuberculosis illness is associated with uncertain outcomes, and has high prevalence among people living with HIV. The new World Health Organization’s End TB strategy specifies person-centred symptom management and psychosocial support alongside treatment within its pillars and components. There is a paucity of research to inform an effective care response in Kenya in terms of self-reported outcomes. We aimed to measure the three day period intensity of problems and concerns (physical, psychological, social and spiritual), and identify predictors of problems and concerns, among HIV patients attending outpatient care. We conducted a cross-sectional self-report quantitative study among adult (aged at least 18 years) patients with confirmed HIV diagnosis, and aware of their diagnosis and attending HIV outpatient care in Kenya. Multi-dimensional palliative care problems and concerns were collected using African Palliative Outcome Scale (APOS). Ordinal logistic regression assessed the association of multi-dimensional problems and concerns controlling for demographic variables (age, gender, education and wealth) and clinical variables (WHO clinical stage, HIV treatment status, TB treatment status, and CD4 count). We recruited N = 400 participants. N = 61 (15.64%) were on TB treatment. The items with worst score responses were help and advice to plan for the future (52.5%), ability to share feelings (46.25%), at peace (30.75%) pain (27%) and life worthwhile (18.75%). TB treatment status was associated with lower (worse) score for APOS total score (odds ratio .59, 95% CI .36 to .99; P = 0.046) and factor 3(existential and spiritual wellbeing: .55, .32 to .92; p = 0.023). Interestingly higher CD4 count was predictive of lower (worse) factor 3 outcomes (existential and spiritual wellbeing: .84, 95% CI 73 to .97; p = 0.014). This study informs the new WHO End TB policy with novel data on specific clinical needs. This calls for holistic symptom assessment, person-centred care and holistic management to respond positively to the End TB strategy.
Parallel and Gradual Genome Erosion in the Blattabacterium Endosymbionts of Mastotermes darwiniensis and Cryptocercus Wood Roaches

Abstract Almost all examined cockroaches harbor an obligate intracellular endosymbiont, Blattabacterium cuenoti. On the basis of genome content, Blattabacterium has been inferred to recycle nitrogen wastes and provide amino acids and cofactors for its hosts. Most Blattabacterium strains sequenced to date harbor a genome of ∼630 kbp, with the exception of the termite Mastotermes darwiniensis (∼590 kbp) and Cryptocercus punctulatus (∼614 kbp), a representative of the sister group of termites. Such genome reduction may have led to the ultimate loss of Blattabacterium in all termites other than Mastotermes. In this study, we sequenced 11 new Blattabacterium genomes from three species of Cryptocercus in order to shed light on the genomic evolution of Blattabacterium in termites and Cryptocercus. All genomes of Cryptocercus-derived Blattabacterium genomes were reduced (∼614 kbp), except for that associated with Cryptocercus kyebangensis, which comprised 637 kbp. Phylogenetic analysis of these genomes and their content indicates that Blattabacterium experienced parallel genome reduction in Mastotermes and Cryptocercus, possibly due to similar selective forces. We found evidence of ongoing genome reduction in Blattabacterium from three lineages of the C. punctulatus species complex, which independently lost one cysteine biosynthetic gene. We also sequenced the genome of the Blattabacterium associated with Salganea taiwanensis, a subsocial xylophagous cockroach that does not vertically transmit gut symbionts via proctodeal trophallaxis. This genome was 632 kbp, typical of that of nonsubsocial cockroaches. Overall, our results show that genome reduction occurred on multiple occasions in Blattabacterium, and is still ongoing, possibly because of new associations with gut symbionts in some lineages.
The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels

With the overall increase in the elderly population comes additional, necessary medical needs and costs. Medicare is a U.S. healthcare program that provides insurance, primarily to individuals 65 years or older, to of-ﬂoad some of the ﬁnancial burden associated with medical care. Even so, healthcare costs are high and continue to increase. Fraud is a major contributor to these in-ﬂating healthcare expenses. Our paper provides a comprehensive study leveraging machine learning methods to detect fraudulent Medicare providers. We use publicly available Medicare data and provider exclusions for fraud labels to build and assess three different learners. In order to lessen the impact of class imbalance, given so few actual fraud labels, we employ random un-dersampling creating four class distributions. Our re-sults show that the C4.5 decision tree and logistic regression learners have the best fraud detection performance, particularly for the 80:20 class distribution with average AUC scores of 0.883 and 0.882, respectively, and low false negative rates. We successfully demonstrate the efﬁcacy of employing machine learning with random undersampling to detect Medicare fraud.
Alternative male morphs solve sperm performance/longevity trade-off in opposite directions

Bourgeois and parasitic males produce sperm maximizing either performance or endurance, according to divergent spawning roles. Males pursuing alternative reproductive tactics have been predicted to face a trade-off between maximizing either swimming performance or endurance of their sperm. However, empirical evidence for this trade-off is equivocal, which may be due to simplistic assumptions. In the shell-brooding cichlid fish Lamprologus callipterus, two Mendelian male morphs compete for fertilization by divergent means: Bourgeois nest males ejaculate sperm, on average, about six times farther from the unfertilized ova than do parasitic dwarf males. This asymmetry is opposite to the usual situation, in which bourgeois males typically benefit from superior fertilization opportunities, suggesting that nest males’ sperm should persist longer than dwarf male sperm. The assumed trade-off between sperm swimming performance and longevity predicts that, in turn, sperm of dwarf males should outperform that of nest males in swimming efficiency. Measurement of sperm performance and endurance reveals that dwarf male spermatozoa swim straighter initially than those of nest males, but their motility declines earlier and their velocity slows down more abruptly. Nest male sperm survives longer, which relates to a larger sperm head plus midpiece, implying more mitochondria. Thus, the trade-off between sperm performance and endurance is optimized in opposite directions by alternative male morphs. We argue that the relative success of alternative sperm performance strategies can be influenced strongly by environmental factors such as the time window between gamete release and fertilization, and the position of gamete release. This is an important yet little understood aspect of gametic adaptations to sperm competition.
Deep Probabilistic Programming for Financial Modeling

Deep probabilistic programming (DPP) combines three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Many applications in trading and risk management involve uncertainty quantification such as portfolio optimization and risk estimation. DPP provides a general purpose Bayesian framework for fitting high dimensional models to large datasets. Wielded correctly, it provides a richer set of statistical properties and more explanatory power over deterministic models such as deep learning. Furthermore, increasing low-cost compute power made available through many-core processors, can be readily exploited by open-source DPP packages such as Edward. This short positional article provides a brief and informal background on deep probabilistic programming and discusses its implications for trading, risk management and financial stability modeling.
An automated cirrus classification

Abstract. Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model–observation comparisons and leading to improved parametrisations of cirrus cloud processes.
Changes In Mastery 1 Running Head : CHANGES IN MASTERY Work-Induced Changes in Feelings of Mastery

Past theory and research indicate that conditions of work can have lasting effects on job incumbents. Karasek and Theorell (1990), for example, proposed that workers’ feelings of mastery increase with levels of job demands and job control, and that these effects are mediated by the process of active learning. To test these propositions, 657 school teachers completed scales assessing job demands, control, active learning and mastery on two occasions, eight months apart. As hypothesized, job control predicted change in mastery, an effect that was mediated by active learning. Job demands had a weaker effect on change in mastery. The demands-mastery relationship was moderated by job control, such that under conditions of high but not low control, increasing job demands were associated with gains in mastery. The findings partially support Karasek and Theorell’s predictions regarding the main, interactive and mediated effects of job conditions on employee mastery.
Handwritten tifinagh character recognition using deep learning architectures

Tifinagh-IRCAM is the official alphabet of the Amazigh language which is widely used in North Africa. Being normalized recently in 2001, Tifinagh remains a young alphabet and, accordingly, its handwritten character recognition is a young field of research. In this context, we propose two deep learning approaches for handwritten Tifinagh character recognition: Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs). Both networks are trained and tested on the AMHCD handwritten character database. DBNs achieve an accuracy of 95.47% while CNNs outperform existent methods with an accuracy of 98.25%.
Template‐Based Synthesis of Aluminum Nitride Hollow Nanofibers Via Plasma‐Enhanced Atomic Layer Deposition

Aluminum nitride (AlN) hollow nanofibers were synthesized via plasma-enhanced atomic layer deposition using sacrificial electrospun polymeric nanofiber templates having different average fiber diameters (~70, ~330, and ~740 nm). Depositions were carried out at 200°C using trimethylaluminum and ammonia precursors. AlN-coated nanofibers were calcined subsequently at 500°C for 2 h to remove the sacrificial polymeric nanofiber template. SEM studies have shown that there is a critical wall thickness value depending on the template's average fiber diameter for AlN hollow nanofibers to preserve their shapes after the template has been removed by calcination. Best morphologies were observed for AlN hollow nanofibers prepared by depositing 800 cycles (corresponding to ~69 nm) on nanofiber templates having ~330 nm average fiber diameter. TEM images indicated uniform wall thicknesses of ~65 nm along the fiber axes for samples prepared using templates having ~70 and ~330 nm average fiber diameters. Synthesized AlN hollow nanofibers were polycrystalline with a hexagonal crystal structure as determined by high-resolution TEM and selected area electron diffraction. Chemical compositions of coated and calcined samples were studied using X-ray photoelectron spectroscopy (XPS). High-resolution XPS spectra confirmed the presence of AlN.
Self-Reported Sleep Quality Modulates Amygdala Resting-State Functional Connectivity in Anxiety and Depression

Sufficient sleep plays an important role in neurocognitive function, yet, problematic sleep is ubiquitous in the general population. It is also frequently predictive of, and concurrent with, internalizing psychopathologies (IPs) such as anxiety and depression suggesting sleep quality is dimensional and transdiagnostic. Along with problematic sleep, IPs are characterized by negative affectivity, therefore, prominent neurobiological models of internalizing conditions involve the amygdala, a region central to emotion. In resting-state studies (independent of sleep considerations), abnormalities in amygdala-frontal functional connectivity are commonly observed suggesting emotion dysregulation may contribute to clinically-relevant phenotypes. In a separate line of research, studies of sleep deprivation, and insomnia disorder suggest sleep loss may alter amygdala-frontal connectivity. Taken together, findings point to shared neurobiology between sleep and emotion systems, however, the impact of sleep quality on the amygdala circuit in anxiety or depression is unclear. Therefore, we evaluated variance in naturalistic sleep quality on amygdala-based circuity in individuals with and without psychiatric illness. Resting-state fMRI data was collected in 87 un-medicated, treatment-seeking adults diagnosed with a primary anxiety disorder (n = 68) or primary depressive disorder (n = 19) in addition to healthy individuals (n = 40). Regression analysis was conducted with bilateral anatomical amygdala as seed regions and self-reported sleep quality was indexed with a validated self-report measure, the Pittsburgh Sleep Quality Index (PSQI). Post-hoc analysis was performed to evaluate whether diagnostic status (primary anxiety, primary depression, healthy) significantly explained functional connectivity results. Whole-brain regression analysis, controlling for anxiety and depression symptoms, revealed worse sleep quality (i.e., higher PSQI total scores) predicted increased left amygdala-subgenual anterior cingulate functional connectivity and reduced connectivity with posterior cerebellar lobe and superior temporal gyrus. For right amygdala, increased coupling with postcentral gyrus corresponded with worse sleep. Post-hoc analysis did not detect a significant relationship between diagnostic status and whole-brain findings. Results expand on previous studies and indicate variance in sleep quality tracks brain pathways involved in cognitive-emotion functions implicated in the neurobiology of IPs that may extend to individuals at risk for clinical anxiety or depression. Altogether, the clinical relevance of identifying phenotypes to improve our understanding of psychopathology may be improved by incorporating sleep quality.
The complexity of splitting necklaces and bisecting ham sandwiches

We resolve the computational complexity of two problems known as Necklace Splitting and Discrete Ham Sandwich, showing that they are PPA-complete. For Necklace Splitting, this result is specific to the important special case in which two thieves share the necklace. We do this via a PPA-completeness result for an approximate version of the Consensus Halving problem, strengthening our recent result that the problem is PPA-complete for inverse-exponential precision. At the heart of our construction is a smooth embedding of the high-dimensional Mobius strip in the Consensus Halving problem. These results settle the status of PPA as a class that captures the complexity of “natural” problems whose definitions do not incorporate a circuit.
Physical exercise increases involvement of motor networks as a compensatory mechanism during a cognitively challenging task

Neuroimaging studies suggest that older adults may compensate for declines in cognitive function through neural compensation and reorganization of neural resources. While neural compensation as a key component of cognitive reserve is an important factor that mediates cognitive decline, the field lacks a quantitative measure of neural compensatory ability, and little is known about factors that may modify compensation, such as physical exercise.
Ranking Scientific Papers and Venues in Heterogeneous Academic Networks by Mutual Reinforcement

Evaluating papers and venues objectively and fairly is a vital and challenging task for scientists, research organizations, and research funding bodies alike. Recently, heterogeneous networks have been used to evaluate papers, authors and venues separately or simultaneously. However, most of the approaches treat all the papers in the citation network equally and ignore the prestige of citing venues and citation time intervals. In this paper, we propose a new framework, MR-Rank, which ranks papers and venues iteratively in a mutually reinforcing way. Several factors including citation time interval, recent performance of a venue, and contribution of a paper to its venue are considered at the same time. Based on the ACL dataset, our experiments show that MR-Rank outperforms other models in terms of ranking effectiveness and efficiency.
Consistency rates and asymptotic normality of the high risk conditional for functional data

Abstract The maximum of the conditional hazard function is a parameter of great importance in seismicity studies, because it constitutes the maximum risk of occurrence of an earthquake in a given interval of time. Using the kernel nonparametric estimates of the first derivative of the conditional hazard function, we establish uniform convergence properties and asymptotic normality of an estimate of the maximum in the context of independence data.