Miracle-dz/UniFAR-FTUs huggingface dataset:
UnICORNN: A recurrent model for learning very long time dependencies

The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gradient problem. To overcome this, we propose a novel RNN architecture which is based on a structure preserving discretization of a Hamiltonian system of second-order ordinary differential equations that models networks of oscillators. The resulting RNN is fast, invertible (in time), memory efficient and we derive rigorous bounds on the hidden state gradients to prove the mitigation of the exploding and vanishing gradient problem. A suite of experiments are presented to demonstrate that the proposed RNN provides state of the art performance on a variety of learning tasks with (very) long-time dependencies.
Rigidity and deformation of discrete conformal structures on polyhedral surfaces

Discrete conformal structure on polyhedral surfaces is a discrete analogue of the conformal structure on smooth surfaces, which includes tangential circle packing, Thurston’s circle packing, inversive distance circle packing and vertex scaling as special cases and generalizes them to a very general context. Glickenstein [40] conjectured the rigidity of discrete conformal structures on polyhedral surfaces, which includes Luo’s conjecture on the rigidity of vertex scaling [57] and Bowers-Stephenson’s conjecture on the rigidity of inversive distance circle packings [6] on polyhedral surfaces as special cases. We prove Glickenstein’s conjecture using a variational principle. We further study the deformation of discrete conformal structures on polyhedral surfaces by combinatorial curvature flows. It is proved that the combinatorial Ricci flow for discrete conformal structures, which is a generalization of Chow-Luo’s combinatorial Ricci flow for circle packings [7] and Luo’s combinatorial Yamabe flow for vertex scaling [57], could be extended to exist for all time and the extended combinatorial Ricci flow converges exponentially fast for any initial data if the discrete conformal structure with prescribed combinatorial curvature exists. This confirms another conjecture of Glickenstein [40] on the convergence of the combinatorial Ricci flow and provides an effective algorithm for finding discrete conformal structures with prescribed combinatorial curvatures. The relationship of discrete conformal structures on polyhedral surfaces and 3dimensional hyperbolic geometry is also discussed. As a result, we obtain some new convexities of the co-volume functions for some generalized 3-dimensional hyperbolic tetrahedra. MSC (2020): 52C25; 52C26
Forbidden intersections for codes

Determining the maximum size of a t$t$ ‐intersecting code in [m]n$[m]^n$ was a longstanding open problem of Frankl and Füredi, solved independently by Ahlswede and Khachatrian and by Frankl and Tokushige. We extend their result to the setting of forbidden intersections, by showing that for any m>2$m>2$ and n$n$ large compared with t$t$ (but not necessarily m$m$ ) that the same bound holds for codes with the weaker property of being (t−1)$(t-1)$ ‐avoiding, that is, having no two vectors that agree on exactly t−1$t-1$ coordinates. Our proof proceeds via a junta approximation result of independent interest, which we prove via a development of our recent theory of global hypercontractivity: we show that any (t−1)$(t-1)$ ‐avoiding code is approximately contained in a t$t$ ‐intersecting junta (a code where membership is determined by a constant number of coordinates). In particular, when t=1$t=1$ , this gives an alternative proof of a recent result of Eberhard, Kahn, Narayanan and Spirkl that symmetric intersecting codes in [m]n$[m]^n$ have size o(mn)$o(m^n)$ .
A Data Augmentation Method by Mixing Up Negative Candidate Answers for Solving Raven's Progressive Matrices

Raven’s Progressive Matrices (RPMs) are frequently-used in testing human’s visual reasoning ability. Recently developed RPM-like datasets and solution models transfer this kind of problems from cognitive science to computer science. In view of the poor generalization performance due to insufﬁcient samples in RPM datasets, we propose a data augmentation strategy by image mix-up, which is generalizable to a variety of multiple-choice problems, especially for image-based RPM-like problems. By focusing on potential functionalities of negative candidate answers, the visual reasoning capability of the model is enhanced. By applying the proposed data augmentation method, we achieve signiﬁcant and consistent improvement on various RPM-like datasets compared with the state-of-the-art models.
An introduction to deep generative modeling

Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high‐dimensional probability distributions using samples. When trained successfully, we can use the DGM to estimate the likelihood of each observation and to create new samples from the underlying distribution. Developing DGMs has become one of the most hotly researched fields in artificial intelligence in recent years. The literature on DGMs has become vast and is growing rapidly. Some advances have even reached the public sphere, for example, the recent successes in generating realistic‐looking images, voices, or movies; so‐called deep fakes. Despite these successes, several mathematical and practical issues limit the broader use of DGMs: given a specific dataset, it remains challenging to design and train a DGM and even more challenging to find out why a particular model is or is not effective. To help advance the theoretical understanding of DGMs, we introduce DGMs and provide a concise mathematical framework for modeling the three most popular approaches: normalizing flows, variational autoencoders, and generative adversarial networks. We illustrate the advantages and disadvantages of these basic approaches using numerical experiments. Our goal is to enable and motivate the reader to contribute to this proliferating research area. Our presentation also emphasizes relations between generative modeling and optimal transport.
Deep Learning for Android Malware Defenses: A Systematic Literature Review

Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing DL techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on DL approaches for Android malware defenses exists. In this article, we conducted a systematic literature review to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014–2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.
Learning Connectivity for Data Distribution in Robot Teams

Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be disseminated among the team. However, in harsh environments with no existing communication infrastructure, robots must form ad-hoc networks, forcing the team to operate in a distributed fashion. To overcome this challenge, we propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN). Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. To do this, agents glean information about the topology of the network from packet transmissions and feed it to a GNN running locally which instructs the agent when and where to transmit the latest state information. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions. Our approach performs favorably compared to industry-standard methods for data distribution such as random flooding and round robin. We also show that the trained policies generalize to larger teams of both static and mobile agents.
Trends in age-standardised prevalence of type 2 diabetes mellitus according to country from 1990 to 2017 and their association with socioeconomic, lifestyle and health indicators: An ecological study

Background There is a need for global public health strategies to effectively curb the ever-growing global diabetes population. This longitudinal ecological study was conducted to elucidate the country-specific trends of the age-standardised prevalence of type 2 diabetes mellitus (T2DM) and their association with socioeconomic, lifestyle and health indicators. Methods Data pertaining to the age-standardised prevalence of T2DM (rates per 100 000) and socioeconomic, lifestyle and health indicators were obtained from several international databases. Data available from 1990 to 2017 for 139 countries with populations of 1 million or greater were analysed, followed by estimation of slopes for T2DM prevalence in each country. The longitudinal association between T2DM and the standardised variables of socioeconomic, lifestyle and health indicators was fitted using a generalised linear mixed-effects model with random intercept for each country and random slope for year. Results The country-specific age-standardised prevalence of T2DM decreased significantly in 9 countries, remained unchanged in 11 countries and increased significantly in 119 countries. The estimated standardised effect of age-standardised education for T2DM was the largest at −524.5 (SE; 109.9), followed by −514.8 (SE; 95.6) for physical activity and 398.8 (SE; 45.8) for body mass index (BMI) (P < 0.0001 for all). Conclusions The factors largely associated with global T2DM prevalence and trends were years of education, followed by physical activity and BMI. This study also provides basic resources for examining public health approaches to curb the increase in global T2DM prevalence.
Passive Immunity Trial for Our Nation (PassITON): study protocol for a randomized placebo-control clinical trial evaluating COVID-19 convalescent plasma in hospitalized adults

Background: Convalescent plasma is being used widely as a treatment for coronavirus disease 2019 (COVID-19). However, the clinical efficacy of COVID-19 convalescent plasma is unclear. Methods: The Passive Immunity Trial for Our Nation (PassITON), is a multicenter, placebo-controlled, blinded, randomized clinical trial being conducted in the United States to provide high-quality evidence on the efficacy of COVID-19 convalescent plasma as a treatment for adults hospitalized with symptomatic disease. Adults hospitalized with COVID-19 with respiratory symptoms for less than 14 days are eligible. Enrolled patients are randomized in a 1:1 ratio to 1 unit (200–399 mL) of COVID-19 convalescent plasma that has demonstrated neutralizing function using a SARS-CoV-2 chimeric virus neutralization assay. Study treatments are administered in a blinded fashion and patients are followed for 28 days. The primary outcome is clinical status 14 days after study treatment as measured on a 7-category ordinal scale assessing mortality, respiratory support, and return to normal activities of daily living. Key secondary outcomes include mortality and oxygen-free days. The trial is projected to enroll 1000 patients and is designed to detect an odds ratio ≤ 0.73 for the primary outcome. Discussion: This trial will provide the most robust data available to date on the efficacy of COVID-19 convalescent plasma for the treatment of adults hospitalized with acute moderate to severe COVID-19. These data will be useful to guide the treatment of COVID-19 patients in the current pandemic and for informing decisions about whether developing a standardized infrastructure for collecting and disseminating convalescent plasma to prepare for future viral pandemics is indicated. Trial Registration: ClinicalTrials.gov: NCT04362176. Date of trial registration: April 24, 2020, https://clinicaltrials.gov/ct2/show/NCT04362176
Evaluating the utility of synthetic COVID-19 case data

Abstract Background Concerns about patient privacy have limited access to COVID-19 datasets. Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner. Objectives Evaluate the utility of synthetic data by comparing analysis results between real and synthetic data. Methods A gradient boosted classification tree was built to predict death using Ontario’s 90 514 COVID-19 case records linked with community comorbidity, demographic, and socioeconomic characteristics. Model accuracy and relationships were evaluated, as well as privacy risks. The same model was developed on a synthesized dataset and compared to one from the original data. Results The AUROC and AUPRC for the real data model were 0.945 [95% confidence interval (CI), 0.941–0.948] and 0.34 (95% CI, 0.313–0.368), respectively. The synthetic data model had AUROC and AUPRC of 0.94 (95% CI, 0.936–0.944) and 0.313 (95% CI, 0.286–0.342) with confidence interval overlap of 45.05% and 52.02% when compared with the real data. The most important predictors of death for the real and synthetic models were in descending order: age, days since January 1, 2020, type of exposure, and gender. The functional relationships were similar between the two data sets. Attribute disclosure risks were 0.0585, and membership disclosure risk was low. Conclusions This synthetic dataset could be used as a proxy for the real dataset.
Role of Oxidative Stress and Reduced Endogenous Hydrogen Sulfide in Diabetic Nephropathy

Purpose Persistent hyperglycemia lead towards depletion of hydrogen sulfide (H2S) resulting in generation of oxidative stress and diabetic nephropathy. The aim of the current study was to explore the antioxidant potential of H2S and captopril, a -SH containing compound in streptozotocin (STZ)-induced diabetic nephropathy. Methods Fifty four Wistar-Kyoto (WKY) rats male (200–250g) were divided into nine groups (n=6) with each group injected once with STZ (60mg/kg i.p) except normal control. After 3 weeks of induction of diabetes, groups were assigned as normal control, diabetic control, diabetic-captopril, diabetic-NaHS, diabetic-captopril-NaHS, diabetic-spironolactone, diabetic-metformin, diabetic-metformin-NaHS and diabetic-vitamin-c. All the animals were served with normal saline (N/S 4mL/kg p.o), captopril (50mg/kg/day p.o), sodium hydrosulfide (NaHS) (56µmol/kg i.p), spironolactone (50mg/kg/day s.c), metformin (500mg/kg/day p.o) and vitamin-c (50mg/kg p.o) on daily basis for next 4 weeks, respectively. Metabolic studies, H2S levels, renal hemodynamics and oxidative stress markers were analyzed at 0, 14 and 28 days followed by histopathological analysis of renal tissues. Results The results showed decreased H2S levels, body weight, sodium to potassium ratio, glutathione (GSH), superoxide dismutase (SOD), total antioxidant assay (T-AOC) with malondialdehyde (MDA) and blood glucose levels significantly increased among diabetic rats. Treatment with captopril, NaHS, metformin, spironolactone and vitamin C showed significant improvement among renal hemodynamics and oxidative stress markers, respectively. But treatment groups like NaHS in combination with captopril and metformin showed more pronounced effects. Conclusion The observations suggest that H2S mediated protective effects on STZ-induced diabetic nephropathy may be associated with reduced oxidative stress via augmenting the antioxidant effect.
A randomised trial of Mycobacterium w in critically ill patients with COVID-19: ARMY-1

Purpose We investigated whether Mycobacterium w (Mw), an immunomodulator, would improve clinical outcomes in coronavirus disease 2019 (COVID-19). Methods We conducted an exploratory, randomised, double-blind, placebo-controlled trial of hospitalised subjects with severe COVID-19 (pulmonary infiltrates and oxygen saturation ≤94% on room air) conducted at four tertiary care centres in India. Patients were randomised 1:1 to receive either 0.3 mL·day−1 of Mw intradermally or a matching placebo for three consecutive days. The primary outcome of the study was the distribution of clinical status assessed on a seven-point ordinal scale ranging from discharged (category 1) to death (category 7) on study days 14, 21, and 28. The co-primary outcome was a change in SOFA (sequential organ failure assessment) score on days 7 and 14 compared to the baseline. The secondary outcomes were 28-day mortality, time to clinical recovery, time to reverse transcription PCR negativity, adverse events, and others. Results We included 42 subjects (22 Mw, 20 placebo). On days 14 (OR 30.4 (95% CI 3.3–276.4)) and 21 (OR 14.9 (95% CI 1.8–128.4)), subjects in the Mw arm had a better clinical status distribution than placebo. There was no difference in the SOFA score change on days 7 and 14 between the two groups. We did not find any difference in the mortality, or other secondary outcomes. We observed no adverse events related to the use of Mw. Conclusions The use of Mw results in better clinical status distribution on days 14 and 21 compared to placebo in critically ill patients with COVID-19. In this exploratory, multicentre, randomised, double-blind, placebo-controlled trial of 42 patients with severe #COVID19, a significantly better distribution of clinical status on days 14 and 21 was found for those randomised to the Mycobacterium w arm https://bit.ly/3q0sjOZ
The Use of Classic Hallucinogens/Psychedelics in a Therapeutic Context: Healthcare Policy Opportunities and Challenges

Abstract Psychedelics or serotonergic hallucinogens are a group of substances that share the agonism of serotonergic 5-HT2A receptors as their main mechanism of action. Its main effects include changes in perception, cognitive process, and mood. Despite being used for centuries by different cultures in ritual contexts, these substances have currently aroused the interest of science and industry for their promising antidepressant, anxiolytic, and anti-addictive effects. Considering this evidence, this article aims to explore some of the possible health policy challenges to integrate these therapeutic tools into broad and heterogeneous health systems. As a main benefit, these substances produce rapid and enduring effects with the administration of single or few doses, which could lead to new treatment possibilities for patients with severe mental disorders resistant to the usual medications. The main challenge is associated with the fact that these substances remain scheduled in most countries and are associated with social stigma related to their recreational use (especially LSD and psilocybin). This situation makes it exceedingly difficult to conduct clinical trials, although international conventions allow such research. Ethically, this could be interpreted as a violation of human rights since thousands of people are prevented from having access to possible benefits. Interestingly, ritual ayahuasca use is more acceptable to the public than the use of psilocybin-containing mushrooms or LSD. The controlled, clinical use of LSD and psilocybin seems to be less criticized and is being explored by the industry. Rigorous scientific evidence coupled with industrial interests (LSD and psilocybin), together with respect for traditional uses (ayahuasca) and international conventions, seems to be the best way for these drugs to be integrated into health systems in the next years. Which highlights the need for an urgent dialogue between science, health system, society, and politics.
Early Study of Tumor Abnormal Protein in Gastric Adenocarcinoma

Objective To study the correlation between tumor abnormal protein (TAP) and tumor markers, blood glucose, uric acid and coagulation function in gastric adenocarcinoma and to evaluate the clinical application of TAP in the diagnosis of gastric adenocarcinoma. Methods A total of 34 nontumor patients and 95 gastric adenocarcinoma patients admitted to the First Affiliated Hospital of Jinzhou Medical University were enrolled in this study. Fresh blood from patients’ fingertips was collected, all blood samples were examined with TAP testing kit, and then searched and measured the condensed particulate matter. Results The comparison of TAP between nontumor patients and gastric adenocarcinoma patients was statistically significant (P<0.05). Bivariate correlation analysis was conducted between TAP and other related tumor markers (alpha-fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19–9 (CA199), carbohydrate antigen 72–4 (CA72-4)), blood glucose, uric acid, and coagulation function-related indicators, and the results showed that the correlation between TAP and CA199, CA72-4, and activated partial prothrombin time was statistically significant. In addition, according to the analysis results, there was no significant difference among TAP and age, height and weight in the tumor population and the nontumor population. Conclusion TAP can be used for the screening and diagnosis of gastric adenocarcinoma, and the effect of TAP combined with other indicators is more significant than TAP alone.