Miracle-dz/UniFAR-FTUs huggingface dataset:
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data

Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, often come with many challenges when using classical machine learning models due to a myriad of factors including class imbalance and data heterogeneity (i.e., the complex intra-class variances). To address some of these research gaps, this paper leverages the exciting contrastive learning framework and proposes a novel contrastive regularized clinical classification model. The contrastive loss is found to substantially augment EHR-based prediction: it effectively characterizes the similar/dissimilar patterns (by its "push-and-pull" form), meanwhile mitigating the highly skewed class distribution by learning more balanced feature spaces (as also echoed by recent findings). In particular, when naively exporting the contrastive learning to the EHR data, one hurdle is in generating positive samples, since EHR data is not as amendable to data augmentation as image data. To this end, we have introduced two unique positive sampling strategies specifically tailored for EHR data: a feature-based positive sampling that exploits the feature space neighborhood structure to reinforce the feature learning;and an attribute-based positive sampling that incorporates pre-generated patient similarity metrics to define the sample proximity. Both sampling approaches are designed with an awareness of unique high intra-class variance in EHR data. Our overall framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data with a total of 5,712 patients admitted to a large, urban health system. Specifically, our method reaches a high AUROC prediction score of 0.959, which outperforms other baselines and alternatives: cross-entropy(0.873) and focal loss(0.931).
BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables

Recently, there has been an interest in the research on factual verification and prediction over structured data like tables and graphs. To circumvent any false news incident, it is necessary to not only model and predict over structured data efficiently but also to explain those predictions. In this paper, as the part of the SemEval-2021 Task 9, we tackle the problem of fact verification and evidence finding over tabular data. There are two subtasks, in which given a table and a statement/fact, the subtask A is to determine whether the statement is inferred from the tabular data and the subtask B is to determine which cells in the table provide evidence for the former subtask. We make a comparison of the baselines and state of the art approaches over the given SemTabFact dataset. We also propose a novel approach CellBERT to solve the task of evidence finding, as a form of Natural Language Inference task. We obtain a 3-way F1 score of 0.69 on subtask A and an F1 score of 0.65 on subtask B.
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

In recent years, there are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendation. All these problems remain largely unexplored in the existing literature and make the application of RL challenging. We develop a model-based reinforcement learning framework, called GoalRec. Inspired by the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel disentangled universal value function designed for item recommendation is proposed. It can generalize to various goals that the recommender may have, and disentangle the stochastic environmental dynamics and high-variance reward signals accordingly. As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal. Based on the predicted environmental dynamics, the disentangled universal value function is related to the user's future trajectory instead of a monolithic state and a scalar reward. We demonstrate the superiority of GoalRec over previous approaches in terms of the above three practical challenges in a series of simulations and a real application.
Scaling Scaling Laws with Board Games

The largest experiments in machine learning now require resources far beyond the budget of all but a few institutions. Fortunately, it has recently been shown that the results of these huge experiments can often be extrapolated from the results of a sequence of far smaller, cheaper experiments. In this work, we show that not only can the extrapolation be done based on the size of the model, but on the size of the problem as well. By conducting a sequence of experiments using AlphaZero and Hex, we show that the performance achievable with a fixed amount of compute degrades predictably as the game gets larger and harder. Along with our main result, we further show that the test-time and train-time compute available to an agent can be traded off while maintaining performance.
Hydrodynamic interactions and extreme particle clustering in turbulence

Abstract Expanding recent observations by Hammond & Meng (J. Fluid Mech., vol. 921, 2021, A16), we present a range of detailed experimental data of the radial distribution function (r.d.f.) of inertial particles in isotropic turbulence for different Stokes number, $St$, showing that the r.d. f. grows explosively with decreasing separation r, exhibiting $r^{-6}$ scaling as the collision radius is approached, regardless of $St$ or particle radius $a$. To understand such explosive clustering, we correct a number of errors in the theory by Yavuz et al. (Phys. Rev. Lett., vol. 120, 2018, 244504) based on hydrodynamic interactions between pairs of small, weakly inertial particles. A comparison between the corrected theory and the experiment shows that the theory by Yavuz et al. underpredicts the r.d. f. by orders of magnitude. To explain this discrepancy, we explore several alternative mechanisms for this discrepancy that were not included in the theory and show that none of them are likely the explanation. This suggests new, yet-to-be-identified physical mechanisms are at play, requiring further investigation and new theories.
A matrix math facility for Power ISA(TM) processors

Power ISA(TM) Version 3.1 has introduced a new family of matrix math instructions, collectively known as the Matrix-Multiply Assist (MMA) facility. The instructions in this facility implement numerical linear algebra operations on small matrices and are meant to accelerate computation-intensive kernels, such as matrix multiplication, convolution and discrete Fourier transform. These instructions have led to a power- and area-efficient implementation of a high throughput math engine in the future POWER10 processor. Performance per core is 4 times better, at constant frequency, than the previous generation POWER9 processor. We also advocate the use of compiler built-ins as the preferred way of leveraging these instructions, which we illustrate through case studies covering matrix multiplication and convolution.
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based visual features usually represent parts of an image, it is challenging for existing vision-language models to fully understand the semantics from paired natural languages. In this paper, we propose SOHO to "Seeing Out of tHe bOx" that takes a whole image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than region-based approaches. In particular, SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics. It is updated on-the-fly and utilized in our proposed pre-training task Masked Visual Modeling (MVM). We conduct experiments on four well-established vision-language tasks by following standard VLPT settings. In particular, SOHO achieves absolute gains of 2.0% R@1 score on MSCOCO text retrieval 5k test split, 1.5% accuracy on NLVR2 test-P split, 6.7% accuracy on SNLI-VE test split, respectively.
Nanobubble-induced flow of immersed glassy polymer films

We study the free-surface deformation dynamics of an immersed glassy thin polymer film supported on a substrate, induced by an air nanobubble at the free surface. We combine analytical and numerical treatments of the glassy thin film equation, resulting from the lubrication approximation applied to the surface mobile layer of the glassy film, under the driving of an axisymmetric step function in the pressure term accounting for the nanobubble’s Laplace pressure. Using the method of Green’s functions, we derive a general solution for the film profile. We show that the lateral extent of the surface perturbation follows an asymptotic viscocapillary power-law behaviour in time, and that the film’s central height decays logarithmically in time in this regime. This process eventually leads to film rupture and dewetting at finite time, for which we provide an analytical prediction exhibiting explicitly the dependencies in surface mobility, film thickness and bubble size, among others. Finally, using finite-element numerical integration, we discuss how non-linear effects induced by the curvature and film profile can affect the evolution.
Des nanofils Nitrure à la génération piézoélectrique

Les nanofils nitrures, de par leurs proprietes mecaniques et piezoelectriques exceptionnelles, sont des nanomateriaux tres prometteurs pour la realisation de dispositifs piezogenerateurs ultra-compacts et de haute efficacite. Les nanofils de GaN etudies dans cette these sont synthetises par epitaxie par jets moleculaires assistee par plasma. Grâce a des caracterisations employant un AFM resiscope specialement adapte, nous demontrons le fort potentiel des nanofils de GaN dopes de type-n et dopes de type-p pour convertir une energie mecanique en energie electrique. Nous confirmons la correlation entre la polarite des nanofils, leurs dopages et l'etablissement du potentiel piezoelectrique dans les nanostructures. Nous etudions egalement la capacite de recuperation de l'energie generee par les nanofils, qui se fait au travers d'une diode Schottky de dimension nanometrique. De ce nanocontact, resulte une conduction a l'interface metal/semi-conducteur accrue et donc une meilleure collection de l'energie piezogeneree par les nanofils de GaN. Enfin, en nous basant sur les mecanismes de piezoconversion mis en evidence, nous fabriquons un piezogenerateur integrant un reseau de nanofils verticaux de GaN et fonctionnant sous compression. Le prototype delivre une densite de puissance de l'ordre de 12,7 mW/cm3, ce qui le place a l'etat de l'art des piezogenerateurs a base de nanofils nitrures.
Toxicological profile for styrene

DISCLAIMER The use of company or product name(s) is for identification only and does not imply endorsement by the Agency for Toxic Substances and Disease Registry. A Toxicological Profile for Styrene, Draft for Public Comment was released in October 2007. This edition supersedes any previously released draft or final profile. Toxicological profiles are revised and republished as necessary. For information regarding the update status of previously released profiles, contact ATSDR at: FOREWORD This toxicological profile is prepared in accordance with guidelines* developed by the Agency for Toxic Substances and Disease Registry (ATSDR) and the Environmental Protection Agency (EPA). The original guidelines were published in the Federal Register on April 17, 1987. Each profile will be revised and republished as necessary. The ATSDR toxicological profile succinctly characterizes the toxicologic and adverse health effects infonnation for these toxic substances described therein. Each peer-reviewed profile identifies and reviews the key literature that describes a substance's toxicologic properties. Other pertinent literature is also presented, but is described in less detail than the key studies. The profile is not intended to be an exhaustive document; however, more comprehensive sources of specialty information are referenced. The focus of the profiles is on health and toxicologic infonnation; therefore, each toxicological profile begins with a public health statement that describes, in nontechnical language, a substance's relevant toxicological properties. Following the public health statement is infonnation concerning levels of significant human exposure and, where known, significant health effects. The adequacy of infonnation to detennine a substance's health effects is described in a health effects summary. Data needs that are of significance to protection of public health are identified by ATSDR. Each profile includes the following: (A) The examination, summary, and interpretation of available toxicologic infonnation and epidemiologic evaluations on a toxic substance to ascertain the levels of significant human exposure for the substance and the associated acute, subacute, and chronic health effects; (B) A determination of whether adequate infonnation on the health effects of each substance is available or in the process of development to detennine levels of exposure that present a significant risk to human health of acute, subacute, and chronic health effects; and (C) Where appropriate, identification of toxicologic testing needed to identifY the types or levels of exposure that may present significant risk of adverse health effects in humans. The principal audiences for the toxicological profiles are health professionals at the Federal, State, and local levels; …
The evolution of crypsis when pigmentation is physiologically costly G. Moreno–Rueda

The evolution of crypsis when pigmentation is physiologically costly. Predation is one of the main selective forces in nature, frequently selecting for crypsis in prey. Visual crypsis usually implies the deposition of pigments in the integument. However, acquisition, synthesis, mobilisation and maintenance of pigments may be physiologically costly. Here, I develop an optimisation model to analyse how pigmentation costs may affect the evolution of crypsis. The model provides a number of predictions that are easy to test empirically. It predicts that imperfect crypsis should be common in the wild, but in such a way that pigmentation is less than what is required to maximise crypsis. Moreover, optimal crypsis should be closer to “maximal” crypsis as predation risk increases and/or pigmentation costs decrease. The model predicts for intraspecific variation in optimal crypsis, depending on the difference in the predation risk or the costs of pigmentation experienced by different individuals.
Agriculture et effet de serre

Le gaz carbonique (CO2) et d'autres gaz, presents naturellement a l'etat de trace, comme le methane (CH4), ou l’oxyde nitreux (N2O) s'accumulent dans l'atmosphere. En piegeant, le rayonnement infra-rouge emis par la surface terrestre (forcage radiatif, qualifie couramment « d'effet de serre »), ces gaz contribuent a la regulation du climat.
Low REST Expression Indicates a Biomarker of Poor Prognosis in Patients with Renal Cell Carcinoma

It was initially found that neural-restrictive silencer factor/repressor 1-silencing transcription factor (REST) is a transcriptional repressor of neuronal genes in nonneuronal cells. However, it is reported to be abundantly expressed in various types of aggressive cancer cells. In this study, we evaluated the expression patterns of REST in renal cell carcinoma and found that its expression is lower in tumor tissues compared to normal tissues. The chi-square test showed that the low REST expression was closely related to patients' clinicopathologic parameters, including the pathologic stage and survival status. ROC curve showed that REST had excellent clinical diagnostic prospect. In addition, patients with low REST expression had poor over survival (OS) and relapse-free survival (RFS). Univariate and multivariate Cox regression analysis confirmed that the low REST expression was an independent predictor of poor prognosis in renal cell carcinoma. Gene set enrichment analysis identified P53 pathway, reactive oxygen species pathway, glycolysis, DNA repair, cholesterol homeostasis, and MYC targets V2 enriched with low REST expression phenotype. These results suggested that REST may be a novel biomarker for the diagnosis and prognosis of renal cell carcinoma in clinical applications.
Prediction of Mortality in Patients After Oncologic Gastrointestinal Surgery: Comparison of the ASA, APACHE II, and POSSUM Scoring Systems

Scoring systems have been developed to predict the expected mortality and morbidity in surgical procedures. In this study, our aim was to compare the ASA (American Society of Anesthesiologists), APACHE (Acute Physiology and Chronic Health Evaluation) II, POSSUM (Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity) scoring systems as predictors of mortality in patients who underwent gastrointestinal oncologic surgery, followed, and were admitted to the intensive care unit during the postoperative period. We examined the files of 82 patients who underwent oncologic gastrointestinal surgery and followed up in the intensive care units (ICUs). The patients’ APACHE II scores and predicted mortality rates (PMR) according to the APACHE II, POSSUM, and ASA scores were calculated. The receiver operator characteristic (ROC) curve analysis was used when evaluating the performances of the ASA, APACHE, and POSSUM scoring systems in terms of accurate assessment of mortality. Accordingly, the area under the curve (AUC) = 0.5 no distinction, 0.5 <AUC <0.7 discriminative power of the test is statistically not significant, 0.7 <AUC <0.8 acceptable, 0.8 <AUC <0.9 very good and 0.9 <AUC <1 perfect. The evaluations showed that APACHE II had the best performance with 0.81, followed by POSSUM, which had an acceptable level at 0.78. On the other hand, the ASA score was 0.63 and its discriminative power was identified as statistically insignificant. Our results show that the POSSUM and APACHE II scoring systems were better at predicting mortality than the ASA scoring system for the prediction of mortality in the postoperative period. Both the POSSUM and APACHE II scoring systems can be confidently used for the prediction of mortality in patients undergoing operations due to oncologic gastrointestinal diseases.
Review of Pharmacological Properties and Chemical Constituents of Pastinaca sativa

Objectives Pastinaca sativa (parsnip), is a plant with nutritional and medicinal properties which has been used in all over the world and study about it is rare. In Persian Medicine parsnip is named as zardak and has many uses such as laxative, libido enhancer, kidney stone crusher and diuretic. Because the wide traditional usage of parsnip, in this review the composition and pharmacological properties of this plant are discussed. Methods Some data base such as Cochrane, Scopus, PubMed were searched up to 2018 for studies about Pastinaca sativa. In this review study after consider to exclusion criteria, all of the English review and clinical trial were included. Results Finally, 46 articles were selected for extraction data about the parsnip. Data extraction based on these studies the most important active ingredients of parsnip include coumarins, furanocoumarins, polyacetylenes, essential oils and flavonoids. Different studies determined that Pastinaca sativa has pharmacological effects in CNS, respiratory, gastrointestinal, liver, skin, cardiovascular and urogenital diseases. Conclusion The most important active ingredients in Pastinaca sativa are furanocoumarins, flavonoids and polyacetylenes, and it has many pharmacological properties, including anti-inflammatory, antispasmodic, vasodilator, antifungal, antimicrobial and antidepressant. A main mentioned side effect of parsnip is phototoxicity that was usually reported in direct skin contact. However, family and Some properties and compounds of Pastinaca sativa and Daucus carota are similar but carrots are very popular nowadays. Due to abundant active components and few clinical studies of parsnip, more Studies are recommended to evaluate the effects of it.
Management of Patient with Simultaneous Overt Gastrointestinal Bleeding and Myocardial Infarction with ST-Segment Elevation – Priority Endoscopy

Background The current ERC guidelines are the source of many positive changes, reduction of mortality, length of hospitalization and improvement of prognosis of STEMI patients. However, there is a small group of patients whose slight modification in guidelines would further reduce in-hospital mortality and hospitalization costs. These are patients with concomitant STEMI infarction and gastrointestinal bleeding. Methods Two separate methods of treatment were compared in patients with concomitant gastrointestinal bleeding and ST-segment elevation myocardial infarction. The first – traditional approach, in the line with the ESC guidelines, the second innovative, with priority for endoscopy. Results Despite the innovative approach, the patient with endoscopy before PCI was discharged without complication. A patient who has undergone coronary intervention and who has been started on typical antiplatelet therapy prior to gastroenterological diagnosis has died due to massive bleeding. Conclusion For ethical reasons and in connection with the cardiological guidelines of the management of ACS, a study of patients with ASC a high risk of intestinal bleeding, in which endoscopy will have priority, and only later PCI, will probably never be performed. Although, as the described case shows, despite exceeding the 90 minutes time to implement PCI (<120 minutes) in logistic terms such behavior is completely feasible.