Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Almond Consumption on Metabolic Risk Factors—Glucose Metabolism, Hyperinsulinemia, Selected Markers of Inflammation: A Randomized Controlled Trial in Adolescents and Young Adults

A large percentage of the Indian population has diabetes or is at risk of pre-diabetes. Almond consumption has shown benefits on cardiometabolic risk factors in adults. This study explored the effect of almond consumption on determinants of metabolic dysfunction—blood glucose, lipids, insulin and selected inflammatory markers in adolescents and young adults aged 16–25 years from Mumbai city. This randomized controlled trial was conducted for a period of 90 days on individuals with impaired levels of fasting glucose levels between 100–125 mg/dL (5.6–6.9 mmol/L) and 2-h post-glucose value 140–199 mg/dL (7.8–11.0 mmol/L) and/or fasting insulin (≥15 mIU/ml)/stimulated insulin (≥80 mIU/ml). Of 1,313 individuals screened, 421 met the inclusion criteria, of which 275 consented to participate and 219 completed the trial. The trial was registered with Clinical Trials Registry India (CTRI) CTRI/2018/02/011927. The almonds group (n = 107) consumed 56 g almonds daily, the control group (n = 112) was provided an iso-caloric cereal-pulse based snack. At baseline and endline, blood glucose, insulin, HbA1c, LDL-c, HDL-c, total and ox-cholesterol, triglycerides, hs-CRP, IL-6, TNF-α, adiponectin, leptin were measured and HOMA-IR and FG:FI ratios were calculated. Dietary intakes were assessed. The anthropometric measurements, biochemical markers as well as macronutrient intakes did not differ significantly between the two groups at baseline. Almond consumption significantly decreased HbA1c, total cholesterol and LDL-c. Stimulated insulin decreased post-intervention in both groups, but the decrease was greater in the almonds group. Fasting glucose was reduced post intervention in the controls with no change in the almonds group. FG:FI ratio decreased in the almonds group. TNF-α and IL-6 decreased in the almonds group, while it increased in the control group. Our results showed that almonds reduced HbA1c, LDL-c and total cholesterol levels in just 12 weeks of consumption in these adolescents and young adults who were at risk for developing diabetes. Almonds can be considered as part of food-based strategies for preventing pre-diabetes. Clinical Trial Registration: ClinicalTrials.gov, identifier: CTRI/2018/02/011927.
Early Detection of Temperament Risk Factors: A Comparison of Clinically Referred and General Population Children

Despite an extensive literature on associations between early childhood temperament and behavior problems, most of this evidence is based on general population samples. Hence, relatively little is known about the temperament characteristics of children who have been referred for in- or outpatient treatment of emotional and/or behavioral problems. Whether temperament-to-behavior problems identified in community samples would also be found in samples of clinically referred children is poorly understood. To redress this limitation, we compared temperament attributes of a predominantly preschool-aged sample of children referred for treatment of emotional and/or behavioral disorders (N = 87) with those from a similarly-aged general population sample (N = 85) by using the Integrative Child Temperament Screener (ICTS)—a new nine-item scale to identify clinically significant temperament attributes. Behavioral symptoms in the clinical sample were assessed through diagnostic interviews in combination with the Child Behavior Checklist (CBCL), which was also administered to the general population children. Compared with general population children, referred children exhibited substantially higher scores on all ICTS subscales except behavioral inhibition. Furthermore, areas under the curve analyses showed that discrimination of both groups based on CBCL scales could be improved by adding the ICTS. Overall, the findings fill a long-standing gap in evidence regarding temperament characteristics of children with serious emotional and/or behavioral symptoms and suggest a useful role for the ICTS in assessment, screening, and prevention.
Effects of Altering Mitochondrial Antioxidant Capacity on Molecular and Phenotypic Drivers of Fibrocalcific Aortic Valve Stenosis

Background: While a small number of studies suggest that oxidative stress has an influential role in fibrocalcific aortic valve disease (FCAVD), the roles of specific antioxidant enzymes in progression of this disease remain poorly understood. Here, we focused on selectively altering mitochondrial-derived oxidative stress—which has been shown to alter progression of a myriad of age-associated diseases—on the progression of molecular and phenotypic drivers of FCAVD. Methods: We generated low-density lipoprotein receptor-deficient, Apolipoprotein B100-only mice (LA) that were either haploinsufficient for MnSOD (LA-MnSOD+/−) or genetically overexpressing MnSOD (LA-MnSODTg/0). After 6 months of Western diet feeding, mice underwent echocardiography to assess valvular and cardiac function and tissues were harvested. Quantitative-RT PCR, immunohistochemistry, and histopathology were used to measure changes in molecular pathways related to oxidative stress, calcification, and fibrosis. Results: While reductions in MnSOD increased oxidative stress, there was not an overt phenotypic effect of MnSOD deficiency on valvular and cardiac function in LA-MnSOD+/− mice. While markers of canonical bone morphogenetic protein signaling tended to increase in valve tissue from LA-MnSOD+/− (e.g., p-SMAD1/5/8 and osterix), we did not observe statistically significant increases in osteogenic signaling. We did, however, observe highly significant reductions in expression of osteopontin, which were associated with significant increases in calcium burden in LA-MnSOD+/− mice. Reciprocally, genetically increasing MnSOD did not preserve valve function in LA-MnSODTg/0, but we did observe slight reductions in p-SMAD1/5/8 levels compared to their non-transgenic littermates. Interestingly, overexpression of MnSOD dramatically increased expression of osteopontin in valve tissue from LA-MnSODTg/0 mice, but was not sufficient to attenuate calcium burden when compared to their LA-MnSOD0/0 littermates. Conclusions: Collectively, this study demonstrates that maintenance of mitochondrial antioxidant capacity is important in preventing accelerated disease progression in a mouse model of FCAVD, but that effectively altering mitochondrial antioxidant capacity as a monotherapeutic approach to slow key histopathological and molecular drivers of FCAVD remains biologically and therapeutically challenging.
Retinal Involvement in COVID-19: Results From a Prospective Retina Screening Program in the Acute and Convalescent Phase

Objective: To detect retinal involvement in coronavirus disease 2019 (COVID-19) patients in acute and convalescent phase by their fundus screening. Methods: In a prospective, cross-sectional, observational study (July–November 2020), 235 patients (142 acute and 93 convalescent phase) underwent fundus screening in a tertiary care center in North India. For convalescent phase, “hospitalized” patients (73) were screened at least 2 weeks after hospital discharge, and “home-isolated” patients (20) were screened 17 days after symptom onset/COVID-19 testing. Results: None in acute phase showed any retinal lesion that could be attributed exclusively to COVID-19. Five patients (5.38%) in convalescent phase had cotton wool spots (CWSs) with/without retinal hemorrhage, with no other retinal finding, and no visual symptoms, seen at a median of 30 days from COVID-19 diagnosis. Conclusions: CWSs (and retinal hemorrhages) were an incidental finding in COVID-19, detected only in the convalescent phase. These patients were much older (median age = 69 years) than the average age of our sample and had systemic comorbidities (diabetes mellitus, hypertension, etc.). We propose the term “COVID-19 retinopathy” to denote the presence of CWSs at the posterior pole, occasionally associated with intraretinal hemorrhages, in the absence of ocular inflammation in patients with a history of COVID-19 disease.
Identification of Suicidal Ideation in the Canadian Community Health Survey—Mental Health Component Using Deep Learning

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide. Methods: Using the Canadian Community Health Survey—Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime SI and SI over the last 12 months. From 582 possible parameters we produced 96- and 21-feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data. Results: For lifetime SI, the 96 feature model had an Area under the receiver operating curve (AUC) of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature. Discussion: Although further study is required to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images

We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not possible to train a denoiser with the standard discriminative training or with the recently developed Noise2Noise (N2N) training; the former requires the underlying clean image for the given noisy image, and the latter requires two independently realized noisy image pair for a clean image. To that end, we propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise) method that first learns a generative model that can 1) simulate the noise in the given noisy images and 2) generate a rough, noisy estimates of the clean images, then 3) iteratively trains a denoiser with subsequently synthesized noisy image pairs (as in N2N), obtained from the generative model. In results, we show the denoiser trained with our GAN2GAN achieves an impressive denoising performance on both synthetic and real-world datasets for the blind denoising setting; it almost approaches the performance of the standard discriminatively-trained or N2N-trained models that have more information than ours, and it significantly outperforms the recent baseline for the same setting, \textit{e.g.}, Noise2Void, and a more conventional yet strong one, BM3D. The official code of our method is available at https://github.com/csm9493/GAN2GAN.
Is Attention Better Than Matrix Decomposition?

As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decomposition (MD) model developed 20 years ago regarding the performance and computational cost for encoding the long-distance dependencies. We model the global context issue as a low-rank recovery problem and show that its optimization algorithms can help design global information blocks. This paper then proposes a series of Hamburgers, in which we employ the optimization algorithms for solving MDs to factorize the input representations into sub-matrices and reconstruct a low-rank embedding. Hamburgers with different MDs can perform favorably against the popular global context module self-attention when carefully coping with gradients back-propagated through MDs. Comprehensive experiments are conducted in the vision tasks where it is crucial to learn the global context, including semantic segmentation and image generation, demonstrating significant improvements over self-attention and its variants.
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When multimodality is not required, our surprising finding is that we do not need probabilistic posterior predictives: deterministic models are on par, in fact they consistently (although non-significantly) outperform their probabilistic counterparts. We also found that heteroscedasticity at training time, perhaps acting as a regularizer, improves predictions at longer horizons. At the methodological side, we design metrics and an experimental protocol which can be used to evaluate the various models, predicting their asymptotic performance when using them on the control problem. Using this framework, we improve the state-of-the-art sample complexity of MBRL on Acrobot by two to four folds, using an aggressive training schedule which is outside of the hyperparameter interval usually considered
Predicting Inductive Biases of Pre-Trained Models

Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then finetuned for a specific task of interest. Recent work attempts to understand why this recipe works and explain when it fails. Currently, such analyses have produced two sets of apparently-contradictory results. Work that analyzes the representations that result from pre-training (via “probing classifiers”) finds evidence that rich features of linguistic structure can be decoded with high accuracy, but work that analyzes model behavior after fine-tuning (via “challenge sets”) indicates that decisions are often not based on such structure but rather on spurious heuristics specific to the training set. In this work, we test the hypothesis that the extent to which a feature influences a model’s decisions can be predicted using a combination of two factors: The feature’s extractability after pre-training (measured using information-theoretic probing techniques), and the evidence available during finetuning (defined as the feature’s co-occurrence rate with the label). In experiments with both synthetic and naturalistic data, we find strong evidence (statistically significant correlations) supporting this hypothesis.
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions

Stochastic optimization has become the workhorse behind many successful machine learning applications, which motivates a lot of theoretical analysis to understand its empirical behavior. As a comparison, there is far less work to study the generalization behavior especially in a non-convex learning setting. In this paper, we study the generalization behavior of stochastic optimization by leveraging the algorithmic stability for learning with β -gradient-dominated objective functions. We develop generalization bounds of the order O (1 / ( nβ )) plus the convergence rate of the optimization algorithm, where n is the sample size. Our stability analysis signiﬁcantly improves the existing non-convex analysis by removing the bounded gradient assumption and implying better generalization bounds. We achieve this improvement by exploiting the smoothness of loss functions instead of the Lipschitz condition in Charles & Papailiopoulos (2018). We apply our general results to various stochastic optimization algorithms, which show clearly how the variance-reduction techniques improve not only training but also generalization. Furthermore, our discussion explains how interpolation helps generalization for highly expressive models.
Online Adversarial Purification based on Self-supervised Learning

Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. In this paper, we combine canonical supervised learning with self-supervised representation learning, and present Self-supervised Online Adversarial Purification (SOAP), a novel defense strategy that uses a self-supervised loss to purify adversarial examples at test-time. Our approach leverages the label-independent nature of self-supervised signals and counters the adversarial perturbation with respect to the self-supervised tasks. SOAP yields competitive robust accuracy against state-of-the-art adversarial training and purification methods, with considerably less training complexity. In addition, our approach is robust even when adversaries are given knowledge of the purification defense strategy. To the best of our knowledge, our paper is the first that generalizes the idea of using self-supervised signals to perform online test-time purification.
QUANTIZATION OF ABELIAN VARIETIES : DISTRIBUTIONAL SECTIONS AND THE TRANSITION FROM KÄHLER TO REAL POLARIZATIONS

We study the dependence of geometric quantization of the standard symplectic torus on the choice of invariant polarization. Real and mixed polarizations are interpreted as degenerate complex structures. Using a weak version of the equations of covariant constancy, and the Weil-Brezin expansion to describe distributional sections, we give a unified analytical description of the quantization spaces for all nonnegative polarizations. The Blattner-Kostant-Sternberg (BKS) pairing maps between half-form corrected quantization spaces for different polarizations are shown to be transitive and related to an action of Sp(2g,R). Moreover, these maps are shown to be unitary.
Using the Absolute Advantage Coe�cient(AAC) to Measure the Strength of Damage Hit by COVID-19 in India on a Growth-Share Matrix

Background: The COVID-19 pandemic occurred and rapidly spread around the world. Some online dashboards have included essential features on a world map. However, only transforming data into visualizations for countries/regions is insu�cient for the public need. This study aims to (1) develop an algorithm for classifying countries/regions into four quadrants inn GSM and (2) design an app for a better understanding of the COVID-19 situation. Methods: We downloaded COVID-19 outbreak numbers daily from the Github website, including 189 countries/regions. A four-quadrant diagram was applied to present the classi�cation of each country/region using Google Maps run on dashboards. A novel presentation scheme was used to identify the most struck entities by observing (1) the multiply infection rate(MIR) and (2) the growth trend in the recent seven days. Four clusters of the COVID-19 outbreak were dynamically classi�ed. An app based on a dashboard aimed at public understanding of the outbreak types and visualizing of the COVID-19 pandemic with Google Maps run on dashboards. The absolute advantage coe�cient(AAC) was used to measure the damage hit by COVID-19 referred to the next two countries severely hit by COVID-19. Results: We found that the two hypotheses were supported: India (i) is in the increasing status as of April 28, 2021, (ii) has a substantially higher ACC(=0.81>0.70), and (iii) has a substantially higher ACC(=0.66<0.70) as of May 17,2021. Conclusion: Four clusters of the COVID-19 outbreak were dynamically classi�ed online on an app making the public understand the outbreak types of COVID-19 pandemic shown on dashboards. The app with GSM and AAC
International Journal of Food Engineering Modeling of Respiration Rate of Capsicum at Various Temperatures

Respiration rate is critical in the design of modified atmosphere/control atmosphere (MA / CA) storage system. Success of these systems is based on correctly mapping of respiration process. Respiration rate of capsicum was studied at 5, 10, 15, 20, and 25 °C using closed system respiration method. Respiration was found to be influenced by temperature and followed Arrhenius law. The generated data were used to develop different types of models, one based on principle of enzyme kinetics, and two based on regression analysis. All developed models were validated at 13°C. The obtained results showed that developed models were valid and represented the respiration process. Model based on principle of enzyme kinetics with Arrhenius type and four-parameter second-order polynomial model showed better agreement with the experimental data than the nonlinear model developed.
S ep 2 00 7 High-order numerical method for the nonlinear Helmholtz equation with material discontinuities in one space dimension

The nonlinear Helmholtz equation (NLH) models the propagat ion of electromagnetic waves in Kerr media, and describes a range of important pheno m a in nonlinear optics and in other areas. In our previous work, we developed a fourth or der method for its numerical solution that involved an iterative solver based on freezin g the nonlinearity. The method enabled a direct simulation of nonlinear self-focusing in t he nonparaxial regime, and a quantitative prediction of backscattering. However, our s imulations showed that there is a threshold value for the magnitude of the nonlinearity, abov e which the iterations diverge. In this study, we numerically solve the one-dimensional NLH using a Newton-type nonlinear solver. Because the Kerr nonlinearity contains abso lute values of the field, the NLH has to be recast as a system of two real equations in order to ap ply Newton’s method. Our numerical simulations show that Newton’s method converges apidly and, in contradistinction with the iterations based on freezing the nonlinearity , enables computations for very high levels of nonlinearity. In addition, we introduce a novel compact finite-volume four th order discretization for the NLH with material discontinuities. Our computations co rroborate the design fourth order convergence of the method. The one-dimensional results of the current paper create a fo undation for the analysis of multi-dimensional problems in the future.
Convergence Analysis of Sparsiﬁed Asynchronous SGD

Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model. In this work we study the performance of asynchronous, distributed settings, when applying sparsiﬁcation, a technique used to reduce communication overheads. In particular, for the ﬁrst time in an asynchronous, non-convex setting, we theoretically prove that, in presence of staleness, sparsiﬁcation does not harm SGD performance: the ergodic convergence rate matches the known result of standard SGD, that is O (cid:16) 1 / √ T (cid:17) . We also carry out an empirical study to complement our theory, and conﬁrm that the eﬀects of sparsiﬁcation on the convergence rate are negligible, when compared to “vanilla” SGD, even in the challenging scenario of an asynchronous, distributed system.