Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Analysis of Population Differences in LRRK2 Variant Distribution in Parkinson's Disease

Background: LRRK2 variants have been demonstrated to have distinct distributions in different populations. However, researchers have thus far chosen to focus on relatively few variants, such as R1628P, G2019S, and G2385R. We therefore investigated the relationship between common LRRK2 variants and PD risk in various populations. Methods: Using a set of strict inclusion criteria, six databases were searched, resulting in the selection of 94 articles covering 49,299 cases and 47,319 controls for final pooled analysis and frequency analysis. Subgroup analysis were done for Africans, European/West Asians, Hispanics, East Asians, and mixed populations. Statistical analysis was carried out using the Mantel-Haenszel approach to determine the relationship between common LRRK2 variants and PD risk, with the significance level set at p < 0.05. Results: In the absence of obvious heterogeneities and publication biases among the included studies, we concluded that A419V, R1441C/G/H, R1628P, G2019S, and G2385R were associated with increased PD risk (p: 0.001, 0.0004, < 0.00001, < 0.00001, and < 0.00001, respectively), while R1398H was associated with decreased risk (p: < 0.00001). In East Asian populations, A419V, R1628P, and G2385R increased risk (p: 0.001, < 0.00001, < 0.00001), while R1398H had the opposite effect (p: 0.0005). G2019S increased PD risk in both European/West Asian and mixed populations (p: < 0.00001, < 0.00001), while R1441C/G/H increased risk in European/West Asian populations only (p: 0.0004). Conclusions: We demonstrated that LRRK2 variant distribution is different among various populations, which should inform decisions regarding the development of future genetic screening strategies.
e-Sweet: A Machine-Learning Based Platform for the Prediction of Sweetener and Its Relative Sweetness

Artificial sweeteners (AS) can elicit the strong sweet sensation with the low or zero calorie, and are widely used to replace the nutritive sugar in the food and beverage industry. However, the safety issue of current AS is still controversial. Thus, it is imperative to develop more safe and potent AS. Due to the costly and laborious experimental-screening of AS, in-silico sweetener/sweetness prediction could provide a good avenue to identify the potential sweetener candidates before experiment. In this work, we curate the largest dataset of 530 sweeteners and 850 non-sweeteners, and collect the second largest dataset of 352 sweeteners with the relative sweetness (RS) from the literature. In light of these experimental datasets, we adopt five machine-learning methods and conformational-independent molecular fingerprints to derive the classification and regression models for the prediction of sweetener and its RS, respectively via the consensus strategy. Our best classification model achieves the 95% confidence intervals for the accuracy (0.91 ± 0.01), precision (0.90 ± 0.01), specificity (0.94 ± 0.01), sensitivity (0.86 ± 0.01), F1-score (0.88 ± 0.01), and NER (Non-error Rate: 0.90 ± 0.01) on the test set, which outperforms the model (NER = 0.85) of Rojas et al. in terms of NER, and our best regression model gives the 95% confidence intervals for the R2(test set) and ΔR2 [referring to |R2(test set)- R2(cross-validation)|] of 0.77 ± 0.01 and 0.03 ± 0.01, respectively, which is also better than the other works based on the conformation-independent 2D descriptors (e.g., 2D Dragon) according to R2(test set) and ΔR2. Our models are obtained by averaging over nineteen data-splitting schemes, and fully comply with the guidelines of Organization for Economic Cooperation and Development (OECD), which are not completely followed by the previous relevant works that are all on the basis of only one random data-splitting scheme for the cross-validation set and test set. Finally, we develop a user-friendly platform “e-Sweet” for the automatic prediction of sweetener and its corresponding RS. To our best knowledge, it is a first and free platform that can enable the experimental food scientists to exploit the current machine-learning methods to boost the discovery of more AS with the low or zero calorie content.
What Lies Beneath Trait-Anxiety? Testing the Self-Regulatory Executive Function Model of Vulnerability

Vulnerability to psychological disorder can be assessed with constructs such as trait anxiety and neuroticism which among others are transdiagnostic risk factors. However, trait-anxiety and related concepts have been criticised because they don’t illuminate the etiological mechanisms of psychopathology. In contrast, the metacognitive (S-REF) model offers a framework in which metacognitive knowledge conceptualised in trait terms is part of a core mechanism underlying trait-anxiety and related constructs. The present study therefore set out to explore metacognitions as potential underlying factors in trait-anxiety (the propensity to depression and anxiety). Nine hundred and eighty two participants completed self-report measures of metacognitions and trait-anxiety at time 1, and 425 individuals completed the same measures 8 weeks later. At the cross-sectional level, metacognitions accounted for 83% of the variance in anxiety- and 64% of depression propensity. Furthermore, despite both domains of trait-anxiety showing high stability over time, negative- and positive metacognitive beliefs were significant prospective predictors of both domains of vulnerability. These findings suggests that metacognitive beliefs may be an underlying mechanism of vulnerability attributed to trait-anxiety with the implication that the metacognitive (S-REF) model informs conceptualization of psychological vulnerability, and that metacognitive therapy applications might be employed to enhance psychological resilience.
Does Household Capital Mediate the Uptake of Agricultural Land, Crop, and Livestock Adaptations? Evidence From the Indo-Gangetic Plains (India)

Farmers in the Indo-Gangetic Plains produce much of the wheat and rice grown in India. However, food production and millions of farm-based livelihoods in this region will continue to be adversely affected by hydro-climatic change and variation, reduced land productivity, and declining groundwater levels. Thus, agricultural adaptations are essential for protecting and improving upon intersecting goals of food security, poverty alleviation, and wellbeing. Household “capital” (e.g., natural, human, financial, physical, and social) is commonly cited as an indicator of livelihood adaptability and innovation. We develop a series of mediated structural equation models to empirically evaluate the validity of capital as a suitable indicator for adaptation and adaptive capacity. These models assess the extent to which household capital mediates the relationship between over 1,000 socio-economically differentiated and randomly selected farm households, and their crop, livestock, and land management adaptations in the states of Haryana and Bihar. Central to our models is a single household capital variable, constructed by aggregating 80 different measurements of households’ i) physical/infrastructural capital, ii) owned or accessed assets, iii) livelihood diversity, iv) ability to reach market or commercialize, v) access to weather information, and vi) social capital. We find household capital is a significant predictor in adopting crop, land management, and livestock-related adaptation strategies across both states. Second, and in certain cases, lower castes and less educated households engaged in fewer agricultural adaptations – an outcome mediated by their lower composition of capital. Further, and across almost all contexts, household capital mediated the effect of owning a greater proportion of land, and the higher uptake of agricultural adaptation activities. While improvements in any capital category can improve adoption, we recommend programs that improve i) access to public and private agricultural infrastructure for lower castes; ii) education and shared knowledge spaces for less-educated households; and iii) the availability of low-interest loans and the more efficient legal transfer of land for agriculturalists owning a smaller proportion of their land. Through this novel and large-scale analysis of household data, we provide short-term and immediate recommendations for more equitable agricultural adaptation in this breadbasket region of northern India.
High-Low Impact Exercise Program Including Pelvic Floor Muscle Exercises Improves Pelvic Floor Muscle Function in Healthy Pregnant Women – A Randomized Control Trial

Background: Pregnancy and high-impact activity are considered as risk factors for pelvic floor dysfunctions, including urinary incontinence. Aim: To investigate whether a structured exercise program, including high- and low-impact aerobics and supported by pelvic floor muscle exercises, improves the neuromuscular activity of the pelvic floor and does not reduce the quality of life in terms of urinary incontinence in healthy pregnant women. Methods: This was a randomized control trial among 97 Caucasian healthy nulliparas in uncomplicated pregnancies (age 30 ± 4 years, 21 ± 5 weeks of gestation; mean ± SD). Women were assessed for pelvic floor muscle functions with surface electromyography (EMG) using vaginal probes and using the Incontinence Impact Questionnaire (IIQ). Only women able to contract pelvic floor muscles and with good quality of life based on IIQ were included for the study. Seventy women in the experimental group took part in a supervised exercise program including high-low impact aerobics and pelvic floor muscle exercises three times a week. Twenty-seven controls did not receive any exercise intervention. After 6 weeks both groups were re-tested with EMG and IIQ. Post- and pre-exercise program changes in each group were analyzed using a repeated-measures ANOVA. Results: Women in the experimental group improved the neuromuscular activity of the pelvic floor in some motor tasks without any adverse outcomes of the intervention. After the exercise program we observed in the experimental group significantly higher EMG amplitude in the pelvic floor muscles during 3-s contractions (p = 0.014). We also noticed a beneficial trend in the increase of neuromuscular activity during 10- and 60-s contractions, but the changes were not statistically significant. The exercising women substantially improved their abilities for relaxation following 3- and 10-s contractions (p = 0.013 and p < 0.001). In controls, we reported no statistically significant improvement in either of the motor tasks. All study participants maintained good quality of life related to urinary incontinence. Conclusion: Prenatal exercise programs that include high- and low-impact aerobics and are supported by pelvic floor muscle exercises should be recommended for pregnant women, especially those who are accustomed to higher exercise intensity before pregnancy. Nevertheless, these recommendations can be directed to continent women who can properly contract pelvic floor muscles. ISRCTN. DOI: 10.1186/ISRCTN92265528: “Pelvic floor muscle training with surface electromyography”, retrospectively registered on the 25th of July, 2016.
A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing

Bike-sharing is a new low-carbon and environment-friendly mode of public transport based on the “sharing economy”. Since 2017, the bike-sharing market has boomed in China’s major cities. Bikes equipped with GPS transmitters are docked along sidewalks that can be easily accessed through smartphone apps. However, this new form of transport has also led to problems, such as illegal parking, vandalism, and theft, each of which presents a major administrative challenge. Further, imbalances in user demand and bike availability need to be overcome to ensure a convenient, flexible service for customers. Hence, predicting a cyclist’s destination could be of great importance to shared-bike operators. In this paper, we propose an innovative deep learning model to predict the most probable destination for each user. The model, called destination prediction network based on spatiotemporal data (DPNst), comprises three steps. First, the data is preprocessed and a pool of likely candidate destinations is generated based on frequent item mining. This candidate set is then used to build the DPNst model: a long short-term memory network learns the user’s behavior; a convolutional neural network learns the spatial relationships between the origin and the candidate destinations; and a fully connected neural network learns the external features. In the final step, DPNst dynamically aggregates the output of the three neural networks based on the given data and generates the predictions. In a series of experiments on real-world stationless bike-sharing data, DPNst returned an F1 score of 42.71% and demonstrated better performance overall than the compared baselines.
Individualized Calibration of Rotation Gain Thresholds for Redirected Walking

Redirected walking allows the exploration of large virtual environments within a limited physical space. To achieve this, redi-rected walking algorithms must maximize the rotation gains applied while remaining imperceptible to the user. Previous research has established population averages for redirection thresholds, including rotation gains. However, these averages do not account for individual variation in tolerance of and susceptibility to redirection. This paper investigates methodologies designed to quickly and accurately calculate rotation gain thresholds for an individual user. This new method is straightforward to implement, requires a minimal amount of space, and takes only a few minutes to estimate a user’s personal threshold for rotation gains. Results from a user study support the wide variability in detection thresholds and indicate that the method of parameter estimation through sequential testing (PEST) is viable for efﬁciently calibrating individual thresholds.
Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster

The paper presents an adaptive system for the control of small satellites’ attitude by using a pyramidal cluster of four variable-speed control moment gyros as actuators. Starting from the dynamic model of the pyramidal cluster, an adaptive control law is designed by means of the dynamic inversion method and a feed-forward neural network-based nonlinear subsystem; the control law has a proportional-integrator component (for the control of the reduced-order linear subsystem) and an adaptive component (for the compensation of the approximation error associated with the function describing the dynamics of the nonlinear system). The software implementation and validation of the new control architecture are achieved by using the Matlab/Simulink environment.
Improved Adversarial Learning for Fair Classification

Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machine learning research. One important paradigm towards this has been providing algorithms for adversarially learning fair classifiers (Zhang et al., 2018; Madras et al., 2018). We formulate the adversarial learning problem as a multi-objective optimization problem and find the fair model using gradient descent-ascent algorithm with a modified gradient update step, inspired by the approach of Zhang et al., 2018. We provide theoretical insight and guarantees that formalize the heuristic arguments presented previously towards taking such an approach. We test our approach empirically on the Adult dataset and synthetic datasets and compare against state of the art algorithms (Celis et al., 2018; Zhang et al., 2018; Zafar et al., 2017). The results show that our models and algorithms have comparable or better accuracy than other algorithms while performing better in terms of fairness, as measured using statistical rate or false discovery rate.
Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample

We present two sampled quasi-Newton methods for deep learning: sampled LBFGS (S-LBFGS) and sampled LSR1 (S-LSR1). Contrary to the classical variants of these methods that sequentially build Hessian or inverse Hessian approximations as the optimization progresses, our proposed methods sample points randomly around the current iterate at every iteration to produce these approximations. As a result, the approximations constructed make use of more reliable (recent and local) information, and do not depend on past iterate information that could be significantly stale. Our proposed algorithms are efficient in terms of accessed data points (epochs) and have enough concurrency to take advantage of parallel/distributed computing environments. We provide convergence guarantees for our proposed methods. Numerical tests on a toy classification problem as well as on popular benchmarking neural network training tasks reveal that the methods outperform their classical variants.
PuppetGAN: Transferring Disentangled Properties from Synthetic to Real Images

In this work we propose a model that enables controlled manipulation of visual attributes of real "target" images (e.g. lighting, expression or pose) using only implicit supervision with synthetic "source" exemplars. Specifically, our model learns a shared low-dimensional representation of input images from both domains in which a property of interest is isolated from other content features of the input. By using triplets of synthetic images that demonstrate modification of the visual property that we would like to control (for example mouth opening) we are able to perform disentanglement of image representations with respect to this property without using explicit attribute labels in either domain. Since our technique relies on triplets instead of explicit labels, it can be applied to shape, texture, lighting, or other properties that are difficult to measure or represent as explicit conditioners. We quantitatively analyze the degree to which trained models learn to isolate the property of interest from other content features with a proof-of-concept digit dataset and demonstrate results in a far more difficult setting, learning to manipulate real faces using a synthetic 3D faces dataset. We also explore limitations of our model with respect to differences in distributions of properties observed in two domains.
`I make up a silly name': Understanding Children's Perception of Privacy Risks Online

Children under 11 are often regarded as too young to comprehend the implications of online privacy. Perhaps as a result, little research has focused on younger kids' risk recognition and coping. Such knowledge is, however, critical for designing efficient safeguarding mechanisms for this age group. Through 12 focus group studies with 29 children aged 6-10 from UK schools, we examined how children described privacy risks related to their use of tablet computers and what information was used by them to identify threats. We found that children could identify and articulate certain privacy risks well, such as information oversharing or revealing real identities online; however, they had less awareness with respect to other risks, such as online tracking or game promotions. Our findings offer promising directions for supporting children's awareness of cyber risks and the ability to protect themselves online.
Learning Schatten-Von Neumann Operators

We study the learnability of a class of compact operators known as Schatten--von Neumann operators. These operators between infinite-dimensional function spaces play a central role in a variety of applications in learning theory and inverse problems. We address the question of sample complexity of learning Schatten-von Neumann operators and provide an upper bound on the number of measurements required for the empirical risk minimizer to generalize with arbitrary precision and probability, as a function of class parameter $p$. Our results give generalization guarantees for regression of infinite-dimensional signals from infinite-dimensional data. Next, we adapt the representer theorem of Abernethy \emph{et al.} to show that empirical risk minimization over an a priori infinite-dimensional, non-compact set, can be converted to a convex finite dimensional optimization problem over a compact set. In summary, the class of $p$-Schatten--von Neumann operators is probably approximately correct (PAC)-learnable via a practical convex program for any $p < \infty$.
Representation transfer for differentially private drug sensitivity prediction

Abstract Motivation Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymization strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published results do not leak too much information about any individual data point. Recent research has reached promising results on differentially private drug sensitivity prediction using gene expression data. Differentially private learning with genomic data is challenging because it is more difficult to guarantee privacy in high dimensions. Dimensionality reduction can help, but if the dimension reduction mapping is learned from the data, then it needs to be differentially private too, which can carry a significant privacy cost. Furthermore, the selection of any hyperparameters (such as the target dimensionality) needs to also avoid leaking private information. Results We study an approach that uses a large public dataset of similar type to learn a compact representation for differentially private learning. We compare three representation learning methods: variational autoencoders, principal component analysis and random projection. We solve two machine learning tasks on gene expression of cancer cell lines: cancer type classification, and drug sensitivity prediction. The experiments demonstrate significant benefit from all representation learning methods with variational autoencoders providing the most accurate predictions most often. Our results significantly improve over previous state-of-the-art in accuracy of differentially private drug sensitivity prediction. Availability and implementation Code used in the experiments is available at https://github.com/DPBayes/dp-representation-transfer.
Crescendo: An Infrastructure-free Ubiquitous Cellular Network-based Localization System

A ubiquitous outdoor localization system that is easy to deploy and works equally well for all mobile devices is highly-desirable. The GPS, despite its high accuracy, cannot be reliably used for this purpose since it is not available on low-end phones nor in areas with low satellite coverage. The application of classical fingerprinting approaches, on the other hand, is prohibited by excessive maintenance and deployment costs. In this paper, we propose Crescendo, a cellular network-based outdoor localization system that does not require calibration or infrastructure support. Crescendo builds on techniques borrowed from computational geometry to estimate the user's location. Specifically, given the network cells heard by the mobile device it leverages the Voronoi diagram of the network sites to provide an initial ambiguity area and incrementally reduces this area by leveraging pairwise site comparisons and visible cell information. Evaluation of Crescendo in both an urban and a rural area using real data shows median accuracies of 152m and 224m, respectively. This is an improvement over classical techniques by at least 18% and 15%, respectively.