Miracle-dz/UniFAR-FTUs huggingface dataset:
The Rationale for Monitoring Cognitive Function in Multiple Sclerosis: Practical Issues for Clinicians

About half of patients with multiple sclerosis exhibit cognitive impairment which negatively affects their quality of life. The assessment of cognitive function in routine clinical practice is still undervalued, although various tools have been proposed for this reason. In this article, we describe the potential benefits of implementing cognitive assessment tools in routine follow -ups of MS patients. Early detection of changes in cognitive performance may provide evidence of disease activity, could unmask depression or medication side-effects and provide suitable candidates for cognitive rehabilitation. Since apathy and cognitive deficiencies are common presenting symptoms in Progressive Multifocal Leukoencephalopathy, we discuss the utility of frequent monitoring of mental status in multiple sclerosis patients at increased risk. In addition, we propose a relevant algorithm aiming to incorporate a systematic evaluation of cognitive function in every day clinical practice in multiple sclerosis.
Towards Binary-Valued Gates for Robust LSTM Training

Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some information or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal. In this paper, we propose a new way for LSTM training, which pushes the output values of the gates towards 0 or 1. By doing so, we can better control the information flow: the gates are mostly open or closed, instead of in a middle state, which makes the results more interpretable. Empirical studies show that (1) Although it seems that we restrict the model capacity, there is no performance drop: we achieve better or comparable performances due to its better generalization ability; (2) The outputs of gates are not sensitive to their inputs: we can easily compress the LSTM unit in multiple ways, e.g., low-rank approximation and low-precision approximation. The compressed models are even better than the baseline models without compression.
Statistical Analysis and Comparison of Optical Classification of Atmospheric Aerosol Lidar Data

In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models (GLM) and regression tree techniques are used to further analyze the performance of the LIDAR parameter-based aerosol classification methods. The goal of GLM and regression tree analyses is to compare and contrast distinct classification data schemes, and compare the results with the measured aerosol reflection data in the atmosphere. The detailed statistical comparisons and analyses shows that the optical methods adopted in this study for classification and prediction of various harmful aerosol types such as soot, carbon monoxide (CO), sulfates (SOx), and nitrates (NOx) are efficient under appropriate functional distributions. The articleoffers a method for natural ordering of the aerosol types.
A systematic evaluation of recent deep learning architectures for fine-grained vehicle classification

Fine-grained vehicle classification is the task of classifying make, model, and year of a vehicle. This is a very challenging task, because vehicles of different types but similar color and viewpoint can often look much more similar than vehicles of same type but differing color and viewpoint. Vehicle make, model, and year in combination with vehicle color - are of importance in several applications such as vehicle search, re-identification, tracking, and traffic analysis. In this work we investigate the suitability of several recent landmark convolutional neural network (CNN) architectures, which have shown top results on large scale image classification tasks, for the task of fine-grained classification of vehicles. We compare the performance of the networks VGG16, several ResNets, Inception architectures, the recent DenseNets, and MobileNet. For classification we use the Stanford Cars-196 dataset which features 196 different types of vehicles. We investigate several aspects of CNN training, such as data augmentation and training from scratch vs. fine-tuning. Importantly, we introduce no aspects in the architectures or training process which are specific to vehicle classification. Our final model achieves a state-of-the-art classification accuracy of 94.6% outperforming all related works, even approaches which are specifically tailored for the task, e.g. by including vehicle part detections.
A PSF-shape-based beamforming strategy for robust 2D motion estimation in ultrafast data

This paper presents a framework for motion estimation in ultrafast ultrasound data. It describes a novel approach for determining the sampling grid for ultrafast data based on the system’s point-spread-function (PSF). As a consequence, the cross-correlation functions (CCF) used in the speckle tracking (ST) algorithm will have circular-shaped peaks, which can be interpolated using a 2D interpolation method to estimate subsample displacements. Carotid artery wall motion and parabolic blood flow simulations together with rotating disk experiments using a Verasonics Vantage 256 are used for performance evaluation. Zero-degree plane wave data were acquired using an ATL L5-12 (fc = 9 MHz) transducer for a range of pulse repetition frequencies (PRFs), resulting in 0–600 µm inter-frame displacements. The proposed methodology was compared to data beamformed on a conventionally spaced grid, combined with the commonly used 1D parabolic interpolation. The PSF-shape-based beamforming grid combined with 2D cubic interpolation showed the most accurate and stable performance with respect to the full range of inter-frame displacements, both for the assessment of blood flow and vessel wall dynamics. The proposed methodology can be used as a protocolled way to beamform ultrafast data and obtain accurate estimates of tissue motion.
A Multilevel Longitudinal Nested Logit Model for Measuring Changes in Correct Response and Error Types

This article presents a multilevel longitudinal nested logit model for analyzing correct response and error types in multilevel longitudinal intervention data collected under a pretest–posttest, cluster randomized trial design. The use of the model is illustrated with a real data analysis, including a model comparison study regarding model complexity and cluster bias. Two substantive research questions regarding the intervention effect on correct response probability and error patterns are investigated using the proposed model. The recovery of item parameters for the proposed model using two sample size conditions is examined via a simulation study. The accuracy of the parameter estimates is comparable with those found in previous studies for the same family of models, except for the intercept parameters of correct responses. Finally, the impact of ignoring cluster membership in the model on the parameter estimation is also studied by fitting a single-level model to multilevel data. Ignoring cluster membership in the model adversely affects the estimation of intercept parameters in correct and error responses.
A New Online Calibration Method Based on Lord’s Bias-Correction

Online calibration technique has been widely employed to calibrate new items due to its advantages. Method A is the simplest online calibration method and has attracted many attentions from researchers recently. However, a key assumption of Method A is that it treats person-parameter estimates θ ^ s (obtained by maximum likelihood estimation [MLE]) as their true values θ s , thus the deviation of the estimated θ ^ s from their true values might yield inaccurate item calibration when the deviation is nonignorable. To improve the performance of Method A, a new method, MLE-LBCI-Method A, is proposed. This new method combines a modified Lord’s bias-correction method (named as maximum likelihood estimation-Lord’s bias-correction with iteration [MLE-LBCI]) with the original Method A in an effort to correct the deviation of θ ^ s which may adversely affect the item calibration precision. Two simulation studies were carried out to explore the performance of both MLE-LBCI and MLE-LBCI-Method A under several scenarios. Simulation results showed that MLE-LBCI could make a significant improvement over the ML ability estimates, and MLE-LBCI-Method A did outperform Method A in almost all experimental conditions.
Nursing Home Implementation of Health Information Technology: Review of the Literature Finds Inadequate Investment in Preparation, Infrastructure, and Training

Health information technology (HIT) is increasingly adopted by nursing homes to improve safety, quality of care, and staff productivity. We examined processes of HIT implementation in nursing homes, impact on the nursing home workforce, and related evidence on quality of care. We conducted a literature review that yielded 46 research articles on nursing homes’ implementation of HIT. To provide additional contemporary context to our findings from the literature review, we also conducted semistructured interviews and small focus groups of nursing home staff (n = 15) in the United States. We found that nursing homes often do not employ a systematic process for HIT implementation, lack necessary technology support and infrastructure such as wireless connectivity, and underinvest in staff training, both for current and new hires. We found mixed evidence on whether HIT affects staff productivity and no evidence that HIT increases staff turnover. We found modest evidence that HIT may foster teamwork and communication. We found no evidence that the impact of HIT on staff or workflows improves quality of care or resident health outcomes. Without initial investment in implementation and training of their workforce, nursing homes are unlikely to realize potential HIT-related gains in productivity and quality of care. Policy makers should consider creating greater incentives for preparation, infrastructure, and training, with greater engagement of nursing home staff in design and implementation.
Monge blunts Bayes: Hardness Results for Adversarial Training

The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an adversary which carries out local modifications within prescribed balls. None however has so far questioned the broader picture: how to frame a resource-bounded adversary so that it can be severely detrimental to learning, a non-trivial problem which entails at a minimum the choice of loss and classifiers. We suggest a formal answer for losses that satisfy the minimal statistical requirement of being proper. We pin down a simple sufficient property for any given class of adversaries to be detrimental to learning, involving a central measure of "harmfulness" which generalizes the well-known class of integral probability metrics. A key feature of our result is that it holds for all proper losses, and for a popular subset of these, the optimisation of this central measure appears to be independent of the loss. When classifiers are Lipschitz -- a now popular approach in adversarial training --, this optimisation resorts to optimal transport to make a low-budget compression of class marginals. Toy experiments reveal a finding recently separately observed: training against a sufficiently budgeted adversary of this kind improves generalization.
The United Kingdom Primary Immune Deficiency (UKPID) registry 2012 to 2017

This is the second report of the United Kingdom Primary Immunodeficiency (UKPID) registry. The registry will be a decade old in 2018 and, as of August 2017, had recruited 4758 patients encompassing 97% of immunology centres within the United Kingdom. This represents a doubling of recruitment into the registry since we reported on 2229 patients included in our first report of 2013. Minimum PID prevalence in the United Kingdom is currently 5·90/100 000 and an average incidence of PID between 1980 and 2000 of 7·6 cases per 100 000 UK live births. Data are presented on the frequency of diseases recorded, disease prevalence, diagnostic delay and treatment modality, including haematopoietic stem cell transplantation (HSCT) and gene therapy. The registry provides valuable information to clinicians, researchers, service commissioners and industry alike on PID within the United Kingdom, which may not otherwise be available without the existence of a well‐established registry.
Effects of auditory and audiovisual presentations on anxiety and behavioral changes in children undergoing elective surgery

Background: Preoperative anxiety is a critical issue in children, and associated with postoperative behavioral changes. Aims: The purpose of the current study is to evaluate how audiovisual and auditory presentations about the perioperative period impact preoperative anxiety and postoperative behavioral disturbances of children undergoing elective ambulatory surgery. Materials and Methods: A total of 99 patients between the ages of 5–12, scheduled to undergo outpatient surgery, participated in this study. Participants were randomly assigned to one of three groups; audiovisual group (Group V, n = 33), auditory group (Group A, n = 33), and control group (Group C, n = 33). During the evaluation, the Modified Yale Preoperative Anxiety Scale (M-YPAS) and the posthospitalization behavioral questionnaire (PHBQ) were used. Results: There were no significant differences in demographic characteristics between the groups. M-YPAS scores were significantly lower in Group V than in Groups C and A (P < 0.001 and P < 0.001, respectively). PHBQ scores in Group C were statistically higher than in Groups A and V, but, no statistical difference was found between Groups A and V. Conclusion: Compared to auditory presentations, audiovisual presentations, in terms of being memorable and interesting, may be more effective in reducing children's anxiety. In addition, we can suggest that both methods can be equally effective for postoperative behavioral changes.
c‐Fos marking of identified midbrain neurons coactive after nicotine administration in‐vivo

Despite the reduced life expectancy and staggering financial burden of medical treatment associated with tobacco smoking, the molecular, cellular, and ensemble adaptations associated with chronic nicotine consumption remain poorly understood. Complex circuitry interconnecting dopaminergic and cholinergic regions of the midbrain and mesopontine tegmentum are critical for nicotine associated reward. Yet our knowledge of the nicotine activation of these regions is incomplete, in part due to their cell type diversity. We performed double immunohistochemistry for the immediate early gene and surrogate activity sensor, c‐Fos, and markers for either cholinergic, dopaminergic or GABAergic cell types in mice treated with nicotine. Both acute (0.5 mg/kg) and chronic (0.5 mg/kg/day for 7 days) nicotine strongly activated GABAergic neurons of the interpeduncular nucleus and medial terminal nucleus of the accessory optic tract (MT). Acute but not chronic nicotine also activated small percentages of dopaminergic and other neurons in the ventral tegmental area (VTA) as well as noncholinergic neurons in the pedunculotegmental and laterodorsal tegmental nuclei (PTg/LDTg). Twenty four hours of nicotine withdrawal after chronic nicotine treatment suppressed c‐Fos activation in the MT. In comparison to nicotine, a single dose of cocaine caused a similar activation in the PTg/LDTg but not the VTA where GABAergic cells were strongly activated but dopaminergic neurons were not affected. These results indicate the existence of drug of abuse specific ensembles. The loss of ensemble activation in the VTA and PTg/LDTg after chronic nicotine represents a molecular and cellular tolerance which may have implications for the mechanisms underlying nicotine dependence.
Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus

Abstract This study set out to identify and analyse trends and seasonal variations of monthly global reported cases of the Middle East respiratory syndrome coronavirus (MERS-CoV). It also made a prediction based on the reported and extrapolated into the future by forecasting the trend. Finally, the study assessed contributions of various risk factors in the reported cases. The motivation for this study is that MERS-CoV remains among the list of blueprint priority and potential pandemic diseases globally. Yet, there is a paucity of empirical literature examining trends and seasonality as the available evidence is generally descriptive and anecdotal. The study is a time series analysis using monthly global reported cases of MERS-CoV by the World Health Organisation between January 2015 and January 2018. We decomposed the series into seasonal, irregular and trend components and identified patterns, smoothened series, generated predictions and employed forecasting techniques based on linear regression. We assessed contributions of various risk factors in MERS-CoV cases over time. Successive months of the MERS-CoV cases suggest a significant decreasing trend (P = 0.026 for monthly series and P = 0.047 for Quarterly series). The MERS-CoV cases are forecast to wane by end 2018. Seasonality component of the cases oscillated below or above the baseline (the centred moving average), but no association with the series over time was noted. The results revealed contributions of risk factors such as camel contact, male, old age and being from Saudi Arabia and Middle East regions to the overall reported cases of MERS-CoV. The trend component and several risk factors for global MERS-CoV cases, including camel contact, male, age and geography/region significantly affected the series. Our statistical models appear to suggest significant predictive capacity and the findings may well inform healthcare practitioners and policymakers about the underlying dynamics that produced the globally reported MERS-CoV cases.
The Case for Full-Matrix Adaptive Regularization

Adaptive regularization methods come in diagonal and full-matrix variants. However, only the former have enjoyed widespread adoption in training large-scale deep models. This is due to the computational overhead of manipulating a full matrix in high dimension. In this paper, we show how to make full-matrix adaptive regularization practical and useful. We present GGT, a truly scalable full-matrix adaptive optimizer. At the heart of our algorithm is an efficient method for computing the inverse square root of a low-rank matrix. We show that GGT converges to first-order local minima, providing the first rigorous theoretical analysis of adaptive regularization in non-convex optimization. In preliminary experiments, GGT trains faster across a variety of synthetic tasks and standard deep learning benchmarks.
Phylogeny of the orb-web building spiders (Araneae, Orbiculariae: Deinopoidea, Araneoidea)

Abstract This phylogenetic analysis of 31 exemplar taxa treats the 12 families of Araneoidea (Anapidae, Araneidae, Cyatholipidae, Linyphiidae, Mysmenidae, Nesticidae, Pimoidae, Symphytognathidae, Synotaxidae, Tetragnathidae, Theridiidae, and Theridiosomatidae). The data set comprises 93 characters: 23 from male genitalia, 3 from female genitalia, 18 from cephalothorax morphology, 6 from abdomen morphology, 14 from limb morphology, 15 from the spinnerets, and 14 from web architecture and other behaviour. Criteria for tree choice were minimum length parsimony and parsimony under implied weights. The outgroup for Araneoidea is Deinopoidea (Deinopidae and Uloboridae). The preferred shortest tree specifies the relationships ((Uloboridae, Deinopidae) (Araneidae (Tetragnathidae ((Theridiosomatidae (Mysmenidae (Symphytognathidae, Anapidae))) ((Linyphiidae, Pimoidae) ((Theridiidae, Nesticidae) (Cyatholipidae, Synotaxidae))))))). The monophyly of Tetragnathidae (including metines and nephilines), the symphytognathoids, theridiid-nesticid lineage, and Synotaxidae are confirmed. Cyatholipidae are sister to Synotaxidae, not closely related to either the Araneidae or Linyphiidae, as previously suggested. Four new clades are proposed: the cyatholipoids (Cyatholipidae plus Synotaxidae), the «spineless femur clade» (theridioid lineage plus cyatholipoids), the «araneoid sheet web builders» (linyphioids plus the spineless femur clade), and the «reduced piriform clade» (symphytognathoids plus araneoid sheet web builders). The results imply a coherent scenario for web evolution in which the monophyletic orb gives rise to the monophyletic araneoid sheet, which in turn gives rise to the gumfoot web of the theridiid-nesticid lineage. While the spinning complement of single pairs of glands does not change much over the evolution of the group, multiple sets of glands are dramatically reduced in number, implying that derived araneoids are incapable of spinning many silk fibers at the same time.
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional distributions between the domains, while simultaneously learning to sample from the marginals of each individual domain. The proposed framework consists of multiple generators and a single softmax-based critic, all jointly trained via adversarial learning. From a simple noise source, the proposed framework allows synthesis of draws from the marginals, conditional draws given observations from a subset of random variables, or complete draws from the full joint distribution. Most examples considered are for joint analysis of two domains, with examples for three domains also presented.