Miracle-dz/UniFAR-FTUs huggingface dataset:
Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, e.g., from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales depend on training in a specific task and/or are an inherent property of neurons, and whether more fine-grained hierarchies of timescales exist within specific cortical regions. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition. Significance statement Recent studies have demonstrated that neural responses exhibit dynamics with different timescales that follow a certain order or hierarchy across cortical areas. While the hierarchy of timescales is consistent across different tasks, it is unknown if these timescales emerge only after training or if they represent inherent properties of neurons. To answer this question, we estimated multiple timescales in neural response across five subregions of the monkeys’ lateral prefrontal cortex before and after training on a working-memory task. Our results provide evidence for fine-grained gradients related to certain neural dynamics. Moreover, we show that these timescales depend on and can be modulated by training in a cognitive task, and contribute to encoding of task-relevant information at single-cell and population levels.
Distributionally Robust Machine Learning with Multi-source Data

Classical machine learning methods may lead to poor prediction performance when the target distribution differs from the source populations. This paper utilizes data from multiple sources and introduces a group distributionally robust prediction model defined to optimize an adversarial reward about explained variance with respect to a class of target distributions. Compared to classical empirical risk minimization, the proposed robust prediction model improves the prediction accuracy for target populations with distribution shifts. We show that our group distributionally robust prediction model is a weighted average of the source populations' conditional outcome models. We leverage this key identification result to robustify arbitrary machine learning algorithms, including, for example, random forests and neural networks. We devise a novel bias-corrected estimator to estimate the optimal aggregation weight for general machine-learning algorithms and demonstrate its improvement in the convergence rate. Our proposal can be seen as a distributionally robust federated learning approach that is computationally efficient and easy to implement using arbitrary machine learning base algorithms, satisfies some privacy constraints, and has a nice interpretation of different sources' importance for predicting a given target covariate distribution. We demonstrate the performance of our proposed group distributionally robust method on simulated and real data with random forests and neural networks as base-learning algorithms.
Wife Battering in Canada: The Vicious Circle

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Contextual Predictive Mutation Testing

Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale to large systems and projects. One promising recent approach to tackling this scalability problem uses machine learning to predict whether the tests will detect the synthetic bugs, without actually running those tests. However, existing predictive mutation testing approaches still misclassify 33% of detection outcomes on a randomly sampled set of mutant-test suite pairs. We introduce MutationBERT, an approach for predictive mutation testing that simultaneously encodes the source method mutation and test method, capturing key context in the input representation. Thanks to its higher precision, MutationBERT saves 33% of the time spent by a prior approach on checking/verifying live mutants. MutationBERT, also outperforms the state-of-the-art in both same project and cross project settings, with meaningful improvements in precision, recall, and F1 score. We validate our input representation, and aggregation approaches for lifting predictions from the test matrix level to the test suite level, finding similar improvements in performance. MutationBERT not only enhances the state-of-the-art in predictive mutation testing, but also presents practical benefits for real-world applications, both in saving developer time and finding hard to detect mutants that prior approaches do not.
Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations

Accurate determination of thermospheric neutral density holds crucial importance for satellite drag calculations. The problem is twofold and involves the correct estimation of the quiet time climatology and storm time variations. In this work, neutral density estimations from two empirical and three physics‐based models of the ionosphere‐thermosphere are compared with the neutral densities along the Challenging Micro‐Satellite Payload satellite track for six geomagnetic storms. Storm time variations are extracted from neutral density by (1) subtracting the mean difference between model and observation (bias), (2) setting climatological variations to zero, and (3) multiplying model data with the quiet time ratio between the model and observation. Several metrics are employed to evaluate the model performances. We find that the removal of bias or climatology reveals actual performance of the model in simulating the storm time variations. When bias is removed, depending on event and model, storm time errors in neutral density can decrease by an amount of 113% or can increase by an amount of 12% with respect to error in models with quiet time bias. It is shown that using only average and maximum values of neutral density to determine the model performances can be misleading since a model can estimate the averages fairly well but may not capture the maximum value or vice versa. Since each of the metrics used for determining model performances provides different aspects of the error, among these, we suggest employing mean absolute error, prediction efficiency, and normalized root mean square error together as a standard set of metrics for the neutral density.
Depression, anxiety and quality of life in stroke survivors and their family caregivers: A pilot study using an actor/partner interdependence model

Background Depression and anxiety are common in stroke survivors as well as their family caregivers. However, it is not known whether each person’s emotional distress contributes to their partner’s quality of life (QOL). Objective This study aimed to examine the effect of depression and anxiety on QOL in stroke survivor-caregiver dyads using dyadic analysis technique - the Actor-Partner Interdependence Model (APIM). Methods This was a cross-sectional pilot study with a total of 30 participating dyads (30 stroke survivors and 30 family caregivers) from Hospital Rehabilitasi Cheras, Kuala Lumpur, Malaysia. This pilot study was conducted over a period of 3 months, between December 2014 and February 2015. Depression and anxiety were assessed using the Hospital Anxiety and Depression Scale (HADS). QOL was assessed using the Short Form-12 Health Survey (SF-12). All analyses were carried out using IBM SPSS version 22. Dyadic data were analysed using multilevel modelling (MLM). Results Depression was uniquely associated with an individual’s own QOL. Survivors and caregivers with higher depression had poorer physical component summary (PCS) scores and mental component summary (MCS) scores. Stroke survivor’s depression exerted a significant actor effect on their PCS (b=−1.42, p=0.001) and MCS (b=−1.52, p<0.001). Caregiver’s depression exerted a significant actor effect on their PCS (b=−2.53, p<0.001) and MCS (b=−1.51, p=0.004). Caregivers’ anxiety negatively influenced their own MCS (b=−0.58, p=0.031). Furthermore, depression exerted a significant partner effect on PCS in stroke survivors (b=−1.19, p=0.003). Caregivers’ depression was also related to their stroke survivors’ poorer QOL, particularly PCS. Conclusion The findings suggest that depression affects the QOL of both stroke survivors and caregivers, not only emotionally but also physically. This dyadic study also has evidence pointing to depression in caregivers and its association with stroke survivors’ physical QOL.
Improving African Language Identification with Multi-task Learning

We present AfroLID-v 2 . 0 , a multi-task neural language identification toolkit for 517 African languages and varieties. The languages that make up AfroLID-v 2 . 0 belong to 14 language families spoken across 50 African countries. To ensure robustness of AfroLID-v 2 . 0 , we employ a multi-domain, multi-script dataset. Compared to a previous version of the tool (AfroLID), AfroLID-v 2 . 0 is trained with a multi-task learning objective exploiting language family information. That is, AfroLID-v 2 . 0 performs language identification as the main task and language family identification as an auxiliary task. We demonstrate how our multi-task learning setup yields better performance compared to all previous work, allowing AfroLID-v 2 . 0 to reach a 96 . 44 F 1 on our blind test set. Language identification is a core technology in NLP, and we hope that AfroLID-v 2 . 0 will be a valuable contribution to multilingual NLP in general and African NLP in particular.
PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL KECK RESEARCH SYMPOSIUM IN GEOLOGY April 2013

1. ABSTRACT Molluscan bivalve shells record the elemental concentrations of the water in which they grow. Much is still unknown about trace element variations within freshwater bivalve shells. To better understand the relationship between shell and water concentrations, trace element water samples were collected weekly from the Columbus Zoo and Aquarium Freshwater Mussel Conservation and Research Center and the adjacent O’Shaughnessy Reservoir in 2010. Several specimens of the freshwater bivalve Lampsilis cardium were collected in April, September, and December 2010. Trace elements over entire ontogenetic cycles, δ 18 O shell values, water trace element values, and several environmental factors show evidence for possible controls on shell chemistry. It seems likely that complicated processes not indicated in these environmental data sets, and probably unrelated to environmental conditions, control Mg/Ca shell incorporation. Similarly, Mn/Ca concentrations do not vary directly with any of the parameters tested, however, ontogeny does seem to affect this element. Sr/Ca ratios within these shells exhibit a positive correlation with Sr/Ca ratios of the water in which they lived, and a negative correlation with turbidity. Values for Ba/Ca suggest a low background with peak interruptions, possibly due to variations in growth rate, as indicated by δ 18 O comparisons, and/or chlorophyll-a concentrations.
Confidence response times: Challenging postdecisional models of confidence

Even though the nature of confidence computations has been the topic of intense interest, little attention has been paid to what confidence response times (cRTs) reveal about the underlying confidence computations. Several previous studies found cRTs to be negatively correlated with confidence in the group as a whole and consequently hypothesized the existence of an intrinsic relationship of cRT with confidence for all subjects. This hypothesis was further used to support postdecisional models of confidence that predict that cRT and confidence should always be negatively correlated. Here we test the alternative hypothesis that cRT is driven by the frequency of confidence responses such that the most frequent confidence ratings are inherently made faster regardless of whether they are high or low. We examined cRTs in three large data sets from the Confidence Database and found that the lowest cRTs occurred for the most frequent confidence rating. In other words, subjects who gave high confidence ratings most frequently had negative confidence–cRT relationships, whereas subjects who gave low confidence ratings most frequently had positive confidence–cRT relationships. In addition, we found a strong across-subject correlation between response time and cRT, suggesting that response speed for both the decision and the confidence rating is influenced by a common factor. Our results show that cRT is not intrinsically linked to confidence and strongly challenge several postdecisional models of confidence.
The potential of Myricitrin, a Flavonoid Compound in Eugenia polyantha from Indonesia, as an Antiviral Drug for SARS-Cov-2 through the Molecular Docking Analysis

A Flavonoid glycoside compound, isolated and identified from E. polyantha as myricitrin, was analyzed as a ligand for its molecular binding activity against SARS-CoV-2 protein (receptor binding domain on Spike/RBD, main protease/nsp5, EndoRNAse, RNA-dependent-RNA-polymerase/RdRp), and its receptor, ACE2, and computationally assessed via molecular docking method. This study aims to determine the potential of myricitrin in E. polyantha from Indonesia as an antiviral drug for SARS-CoV-2 through molecular docking and molecular dynamic simulation analysis. The results showed that the myricitrin had the strongest binding affinity energy towards the three important SARS-CoV-2 proteins, namely endoRNAse, main protease (3CLpro), and RdRp with ∆G values of −9.60 kcal/mol, −8.40 kcal/mol, and −8.30 kcal/mol, respectively. These values are stronger than the comparator ligands of favipiravir (−5.60 kcal/mol), atazanavir (−7.20 kcal/mol), and remdesivir (−7.70 kcal/mol). This indicated that the compound has the potential as an inhibitor against 3CLpro, endoRNAse, and RdRp of SARS-CoV-2 proteins. This result was supported by the prediction made according to the Molprobity and PASS Online web servers, which showed that myricitrin has high bioactivity potential as an enzyme inhibitor (with a score of 0.38) and antiviral (with a score of 0.704).
Neurogenic Dysphagia Systematic Review and Proposal of a Classi ﬁ cation System

Objective Introduction and validation of a phenotypic classi ﬁ cation of neurogenic dysphagia based on ﬂ exible endoscopic evaluation of swallowing (FEES). Methods A systematic literature review was conducted, searching MEDLINE from inception to May 2020 for FEES ﬁ ndings in neurologic diseases of interest. Based on a retrospective analysis of FEES videos in neurologic diseases and considering the results from the review, a classi ﬁ cation of neurogenic dysphagia was developed distinguishing di ﬀ erent phenotypes. The classi ﬁ cation was validated using 1,012 randomly selected FEES videos of patients with various neurologic disorders. Chi-square tests were used to compare the distribution of dysphagia phenotypes between the underlying neurologic disorders.
Metaverso: el futuro de la medicina en un mundo virtual

El metaverso es un espacio virtual que utiliza la Realidad Virtual (RV), la Realidad Aumentada (RA) y la Inteligencia Artificial (IA) capaz de recrear un mundo paralelo a la realidad, en las ciencias de la salud el uso de las tecnologías durante la última década ha aumentado vertiginosamente y los beneficios tanto para los pacientes como para los profesionales son incontables. Tras la implementación de la cuarentena por la COVID-19 el aislamiento social llevo al surgimiento de nuevas herramientas que hicieron que las ciencias de la salud se inmiscuyeran aún más en este mundo digital. Las aplicaciones del metaverso en la medicina van desde el procesamiento de datos, la simulación de ambientes para el intercambio entre grupos de pacientes, hasta la simulación de medios quirúrgicos, procesos diagnósticos, experimentales y la simulación de avatares de médicos o enfermeras capaces de aconsejar a los pacientes según sus condiciones clínicas.
Sleeved CoClustering

A coCluster of a m×n matrix X is a submatrix determined by a subset of the rows and a subset of the columns. The problem of finding coClusters with specific properties is of interest, in particular, in the analysis of microarray experiments. In that case the entries of the matrix X are the expression levels of m genes in each of n tissue samples. One goal of the analysis is to extract a subset of the samples and a subset of the genes, such that the expression levels of the chosen genes behave similarly across the subset of the samples, presumably reflecting an underlying regulatory mechanism governing the expression level of the genes. We propose to base the similarity of the genes in a coCluster on a simple biological model, in which the strength of the regulatory mechanism in sample j is Hj , and the response strength of gene i to the regulatory mechanism is Gi. In other words, every two genes participating in a good coCluster should have expression values in each of the participating samples, whose ratio is a constant depending only on the two genes. Noise in the expression levels of genes is taken into account by allowing a deviation from the model, measured by a relative error criterion. The sleeve-width of the coCluster reflects the extent to which entry i, j in the coCluster is allowed to deviate, relatively, from being expressed as the product GiHj . We present a polynomial-time Monte-Carlo algorithm which outputs a list of coClusters whose sleeve-widths do not exceed a prespecified value. Moreover, we prove that the list includes, with fixed probability, a coCluster which is nearoptimal in its dimensions. Extensive experimentation with synthetic data shows that the algorithm performs well.
Doing Wholeness, Producing Subjects Kinesiological Sensemaking and Energetic Kinship

This article is concerned with the ways in which bodies and subjects are enacted and negotiated in the encounter between client and practitioner within specialized kinesiology - a specific Complementary and Alternative Medical (CAM) practice. In the article we trace the ideas of connections and disconnections, which are conceptualized and practiced within kinesiology. We attempt to come to grips with these specific notions of relatedness through the introduction of the concept ‘energetic kinship’ and to relate them to more general discussions about the nature of subjects, bodies and social identity in late-modern society. We argue that through the particular approach to the body as a prime locus of knowledge on which kinesiological treatment practices are based, kinesiology offers clients an alternative understanding of being-in-the-world. The understanding of the body as the locus of knowledge might, on the one hand, potentially alleviate the individual from the weight of a late-modern focus on self-responsible, rational and autonomous individuality, whilst on the other hand, it simultaneously supports this understanding. Hence, we argue that kinesiology operates with a subject, which is both inherently related AND individual at the same time, and that these particular understandings may be appealing in a world in which health and illness is increasingly seen as a question of individual responsibility. The article was written on the basis of interviews and participant observation among kinesiological teachers and practitioners in Denmark.
Crustal structure of the mid‐ocean ridges: 2. Computed model from gravity and seismic refraction data

Seismic results have demonstrated that the crust under the mid-ocean ridges is not thicker than under the ocean basins. Seismic results also show that the velocity in the mantle immediately under the ridge flanks is normal but that it is low under the axial zones, indicating an ‘anomalous mantle. ’ The free-air gravity anomaly over the entire ridge is close to 0 (within ±50 mgal), and consequently the gradients in the Bouguer anomaly are steep (sometimes approaching 1 mgal/km). In order for gravity and seismic results to agree, the anomalous mantle, which provides compensation in the axial zone of the ridge, is postulated to extend to the flanks below the normal mantle observed by seismic refraction measurements. A model computed on the basis of continuous gravity measurements over the mid-Atlantic ridge and the seismic refraction results given in part 1 is presented. Alternative models that provide compensation at greater depth under the ridge flanks are ruled out by the steep gradients in the Bouguer anomaly. It is shown that a similar model can explain the seismic and gravity data obtained over the east Pacific rise but that a deeper compensation is not impossible because of the smaller gradients in the topography and Bouguer anomaly curves. A lack of correlation between the sharp east Pacific rise heat-flow anomalies and the magnetic and gravity anomalies is also noted.