Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association Between Hypoglycemic Agents and Clinical Outcomes of COVID-19 in Patients with Diabetes: A Systematic Review and Meta-Analysis

Abstract Background: During the current Coronavirus Disease 2019 (COVID-19) pandemic, diabetic patients face disproportionately more. Anti-inflammatory effects of hypoglycemic agents have been reported, and their beneficial or harmful effects in patients with diabetes and COVID-19 remain controversial. Purpose: This study was performed to clarify this association. Data Sources: Relevant literature was searched on China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, Chinese periodical service platform VIP Database, Sinomed (China Biology Medicine, CBM), MedRxiv, PubMed, ScienceDirect, Web of Science, Ovid Databases (LWW), Springer Link, Wiley Online Library, Oxford Academic, Nature Press Group, Cochrane Library and BMJ Evidence-Based Medicine up to November 14, 2020. Study Selection: Only observational studies of hypoglycemic agents vs. drugs or therapy without hypoglycemic agents in adult diabetic patients with COVID-19 were included. Data Extraction: Data of death and poor composite outcomes were extracted. Data Synthesis: The pooled effects were calculated using the fixed-effects or random-effects models based on heterogeneity assessment. Limitation: Most studies were retrospective cohort studies with relative weak capability to verify causality. Conclusion: Home use of metformin might be beneficial in decreasing mortality in diabetic patients infected with SARS-CoV-2. There is insufficient evidence to conclude that metformin and other hypoglycemic agents are associated with poor composite outcomes. More prospective studies, especially RCTs are needed. Registration-PROSPERO: CRD42020221951.
Self‐Discharge and Passivation Phenomena in Lead‐Acid Batteries during Storage

During storage of partially wet-charged and dry-charged lead-acid batteries, processes take place which lead to self-discharge and passivation of the plates. In the case of partially wet-charged batteries, PbSO/sub 4/ crystals form in the separators during storage. When the battery is subjected to charge and overcharge they transform into Pb and PbO/sub 2/ and cause short circuits between the plates and the failure of the battery. Self-discharge reactions are presented for the two kinds of plates. The capacity loss during storage is reversed upon charge of the battery. The passivation phenomena are determined by the structure of the two kinds of plates. The passivation of the positive plate manifests itself by an abrupt fall of its discharge potential. It is assumed that this is due to the formation of a nonstoichiometric oxide following a reaction between the grid and PbO/sub 2/ of the corrosion layer. 13 refs.
Precise limits on the charge-
2/3
 
U1
 vector leptoquark

Arvind Bhaskar,1, ∗ Diganta Das,1, 2, † Tanumoy Mandal,3, ‡ Subhadip Mitra,1, § and Cyrin Neeraj1, ¶ 1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India 2Department of Physics and Astrophysics, University of Delhi, Delhi 110 007, India 3Indian Institute of Science Education and Research Thiruvananthapuram, Vithura, Kerala, 695 551, India (Dated: August 16, 2021)
Easy-GT: Open-Source Software to Facilitate Making the Ground Truth for White Blood Cells Nucleus

The nucleus of white blood cells (WBCs) plays a significant role in their detection and classification. Appropriate feature extraction of the nucleus is necessary to fit a suitable artificial intelligence model to classify WBCs. Therefore, designing a method is needed to segment the nucleus accurately. There should be a comparison between the ground truths distinguished by a hematologist and the detected nuclei to evaluate the performance of the nucleus segmentation method accurately. It is a timeconsuming and tedious task for experts to establish the ground truth manually. This paper presents an intelligent open-source software called Easy-GT to create the ground truth of WBCs’ nucleus faster and easier. This software first detects the nucleus by employing a new Otsu’s thresholding-based method with a dice similarity coefficient (DSC) of 95.42 %; the hematologist can then create a more accurate ground truth, using the designed buttons to modify the threshold value. This software can speed up ground truth’s forming process more than six times. Keyword: White blood cells’ nucleus; Ground truth; segmentation; otsu’s algorithm; open-source software;
Predicting the Mechanical Properties of Fibrin Using Neural Networks Trained on Discrete Fiber Network Data

Fibrin is a structural protein key for processes such as wound healing and thrombus formation. At the macroscale, fibrin forms a gel and the overall mechanical response is dictated by the mechanics of a microscale fiber network. Hence, accurate description of fibrin gels can be achieved using representative volume elements (RVE) that explicitly model the discrete fiber networks of the microscale. These RVE models, however, cannot be efficiently used to model the macroscale due to the challenges and computational demands of coupling the microand macroscale models. Here, we propose the use of an artificial, fully connected neural network (FCNN) to efficiently capture the behavior of the RVE models. The FCNN was trained on 1100 discrete fiber networks subjected to 121 biaxial deformations. The stress data from the RVE, together with the total energy on the fibers and the condition of incompressibility of the surrounding matrix, were used to determine the derivatives of an unknown strain energy function with respect to the deformation invariants. During training, the loss function was modified to ensure convexity of the strain energy function and symmetry of its Hessian. An invariant representation is compatible with standard finite element codes. Thus, a general FCNN model was coded into a user material subroutine (UMAT) in the commercial software Abaqus. The UMAT implementation takes in the structure and parameters of an arbitrary FCNN as material parameters from the input file. The inputs to the FCNN include the first two isochoric invariants of the deformation, and the FCNN outputs are the derivatives of the strain energy with respect to the isochoric invariants. Second derivatives of the strain energy function are automatically calculated inside the UMAT through back-propagation. In this work, the FCNN trained on the discrete fiber network data was used in finite element simulations of fibrin gels using our UMAT. We anticipate that this work will enable further integration of machine learning tools with standard computational mechanics tools such as finite element analysis. It will also improve computational modeling of biological materials characterized by a multiscale structure.
Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo ’ s third observing run

We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO ’ s and Advanced Virgo ’ s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density Ω GW ≤ 5 . 8 × 10 − 9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20 – 76.6 Hz; Ω GW ð f Þ ≤ 3 . 4 × 10 − 9 at 25 Hz for a power-law GWB with a spectral index of 2 = 3 (consistent with expectations for compact binary coalescences), in the band 20 – 90.6 Hz; and Ω GW ð f Þ ≤ 3 . 9 × 10 − 10 at 25 Hz for a spectral index of 3, in the band 20 – 291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2 = 3 , and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z ≳ 2 than can be achieved with individually resolved mergers alone.
Interpolating Classifiers Make Few Mistakes

This paper provides elementary analyses of the regret and generalization of minimum-norm interpolating classifiers (MNIC). The MNIC is the function of smallest Reproducing Kernel Hilbert Space norm that perfectly interpolates a label pattern on a finite data set. We derive a mistake bound for MNIC and a regularized variant that holds for all data sets. This bound follows from elementary properties of matrix inverses. Under the assumption that the data is independently and identically distributed, the mistake bound implies that MNIC generalizes at a rate proportional to the norm of the interpolating solution and inversely proportional to the number of data points. This rate matches similar rates derived for margin classifiers and perceptrons. We derive several plausible generative models where the norm of the interpolating classifier is bounded or grows at a rate sublinear in $n$. We also show that as long as the population class conditional distributions are sufficiently separable in total variation, then MNIC generalizes with a fast rate.
Modeling Context in Answer Sentence Selection Systems on a Latency Budget

Answer Sentence Selection (AS2) is an efficient approach for the design of open-domain Question Answering (QA) systems. In order to achieve low latency, traditional AS2 models score question-answer pairs individually, ignoring any information from the document each potential answer was extracted from. In contrast, more computationally expensive models designed for machine reading comprehension tasks typically receive one or more passages as input, which often results in better accuracy. In this work, we present an approach to efficiently incorporate contextual information in AS2 models. For each answer candidate, we first use unsupervised similarity techniques to extract relevant sentences from its source document, which we then feed into an efficient transformer architecture fine-tuned for AS2. Our best approach, which leverages a multi-way attention architecture to efficiently encode context, improves 6% to 11% over non-contextual state of the art in AS2 with minimal impact on system latency. All experiments in this work were conducted in English.
Strongly lensed cluster substructures are not in tension with ΛCDM

Strong gravitational lensing observations can test structure formation models by constraining the masses and concentrations of subhaloes in massive galaxy clusters. Recent work has concluded that cluster subhaloes are more abundant and/or concentrated than predicted by ΛCDM simulations; this finding has been interpreted as a arising from unidentified issues with simulations or an incorrect understanding of the nature of dark matter. We test these hypotheses by comparing observed subhalo masses and maximum circular velocities vmax to predictions from the high resolution Hydrangea galaxy cluster simulation suite, which is based on the successful EAGLE galaxy formation model. The simulated subhalo mass distribution and mass–vmax relation agrees well with observations, due to the presence of baryons during tidal stripping. Similar agreement is found for the lower-resolution Illustris-TNG300 simulation. In combination, our results suggest that the abundance and concentration of cluster substructures are not in tension with ΛCDM, but may provide useful constraints for the refinement of baryon physics models in simulations.
Outperforming classical estimation of Post-Newtonian parameters of Earth's gravitational field using quantum metrology

The Hong-Ou-Mandel (HOM) effect is analyzed for photons in a modified Mach-Zehnder setup with two particles experiencing different gravitational potentials, which are later recombined using a beam-splitter. It is found that the HOM effect depends directly on the relativistic time dilation between the arms of the setup. This temporal dilation can be used to estimate the γ and β parameters of the parameterized post-Newtonian formalism. The uncertainty in the parameters γ and β are of the order 10−8 − 10−12, depending on the quantum state employed.
Coexistence of intrinsic piezoelectricity and nontrivial band topology in monolayer InXO (X = Se and Te)

The combination of piezoelectricity with other unique properties (like topological insulating phase and intrinsic ferromagnetism) in two-dimensional (2D) materials is much worthy of intensive study. In this work, the piezoelectric properties of 2D topological insulators InXO (X=Se and Te) from monolayer InX (X=Se and Te) with double-side oxygen functionalization are studied by density functional theory (DFT). The large piezoelectric strain coefficients (e.g. $d_{11}$=-13.02 pm/V for InSeO and $d_{11}$=-9.64 pm/V for InTeO) are predicted, which are comparable and even higher than ones of many other familiar 2D materials. Moreover, we propose two strategies to enhance piezoelectric response of monolayer InXO (X=Se and Te). Firstly, the biaxial strain (0.94-1.06) is applied, and the $d_{11}$ (absolute value) is increased by 53\%/56\% for monolayer InSeO/InTeO at 1.06 strain, which is due to increased $e_{11}$ (absolute value) and reduced $C_{11}-C_{12}$. In considered strain range, InXO (X=Se and Te) monolayers are always 2D topological insulators, which confirm the coexistence of piezoelectricity and nontrivial band topology. Secondly, a Janus monolayer $\mathrm{In_2SeTeO_2}$ is designed by replacing the top Se/Te atomic layer in monolayer InSeO/InTeO with Te/Se atoms, which is dynamically and mechanically stable. More excitingly, Janus monolayer $\mathrm{In_2SeTeO_2}$ is also a 2D topological insulator with sizeable bulk gap up to 0.158 eV, confirming the coexistence of intrinsic piezoelectricity and topological nature. The calculated $d_{11}$ is -9.9 pm/V, which is in the middle of ones of InSeO and InTeO monolayers. Finally, the carrier mobilities of monolayer InXO (X=Se and Te) are obtained, which shows a rather pronounced anisotropy between electron and hole, and are almost isotropic between armchair and zigzag directions. Our works imply that it is possible to use the piezotronic effect to control the quantum transport process, ultimately leading to novel device applications in monolayer InXO (X=Se and Te), and can stimulate further experimental works.
Permutations Avoiding Certain Partially-Ordered Patterns

A permutation $\pi$ contains a pattern $\sigma$ if and only if there is a subsequence in $\pi$ with its letters in the same relative order as those in $\sigma$. Partially ordered patterns (POPs) provide a convenient way to denote patterns in which the relative order of some of the letters does not matter. This paper elucidates connections between the avoidance sets of a few POPs with other combinatorial objects, directly answering five open questions posed by Gao and Kitaev in 2019. This was done by thoroughly analysing the avoidance sets and developing recursive algorithms to derive these sets and their corresponding combinatorial objects in parallel, which yielded natural bijections. We also analysed an avoidance set whose simple permutations are enumerated by the Fibonacci numbers and derived an algorithm to obtain them recursively.
An Analytics Framework for Heuristic Inference Attacks against Industrial Control Systems

Industrial control systems (ICS) of critical infrastructure are increasingly connected to the Internet for remote site management at scale. However, cyber attacks against ICS - especially at the communication channels between human-machine interface (HMIs) and programmable logic controllers (PLCs) - are increasing at a rate which outstrips the rate of mitigation. In this paper, we introduce a vendor-agnostic analytics framework which allows security researchers to analyse attacks against ICS systems, even if the researchers have zero control automation domain knowledge or are faced with a myriad of heterogenous ICS systems. Unlike existing works that require expertise in domain knowledge and specialised tool usage, our analytics framework does not require prior knowledge about ICS communication protocols, PLCs, and expertise of any network penetration testing tool. Using ‘digital twin’ scenarios comprising industry-representative HMIs, PLCs and firewalls in our test lab, our framework's steps were demonstrated to successfully implement a stealthy deception attack based on false data injection attacks (FDIA). Furthermore, our framework also demonstrated the relative ease of attack dataset collection, and the ability to leverage well-known penetration testing tools. We also introduce the concept of ‘heuristic inference attacks', a new family of attack types on ICS which is agnostic to PLC and HMI brands/models commonly deployed in ICS. Our experiments were also validated on a separate ICS dataset collected from a cyber-physical scenario of water utilities. Finally, we utilized time complexity theory to estimate the difficulty for the attacker to conduct the proposed packet analyses, and recommended countermeasures based on our findings.
Synthesis and Characterization of High-Affinity, Low-Molecular-Mass Displacers for Anion-Exchange Chromatography

In this paper, the synthesis and characterization of various low-molecular-mass anion-exchange displacers is described. A homologous series of displacers based on either triazine or phloroglucinol were synthesized. The displacer molecules were then characterized using the steric mass action (SMA) isotherm model and dynamic affinity lines resulting from this model. In addition to the dynamic affinity analysis, log P descriptors were calculated as well as several other molecular descriptors to characterize these molecules. The displacers were used to screen for their behavior on agarose-based Q Sepharose HP, hydrophilized poly(styrene−divinylbenzene)- (PS−DVB-) based Source 15Q, and poly(methyl methacrylate)-based ToyoPearl Super Q-650S stationary-phase materials. This work demonstrates that aromaticity/hydrophobicity is very important in increasing the affinity of displacers for anion-exchange resins regardless of their backbone chemistry. The results also indicate that a benzene ring is superior to a tria.
Size, shade, or shape? The contribution of galaxies of different types to the star formation history of the Universe from SDSS-IV MaNGA

By fitting stellar populations to the fourth generation of the Sloan Digital Sky Survey (SDSS-IV) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey observations of ∼7000 suitably weighted individual galaxies, we reconstruct the star formation history of the Universe, which we find to be in reasonable agreement with previous studies. Dividing the galaxies by their present-day stellar mass, we demonstrate the downsizing phenomenon, whereby the more massive galaxies hosted the most star formation at earlier times. Further dividing the galaxy sample by colour and morphology, we find that a galaxy’s present-day colour tells us more about its historical contribution to the cosmic star formation history than its current morphology. We show that downsizing effects are greatest among galaxies currently in the blue cloud, but that the level of downsizing in galaxies of different morphologies depends quite sensitively on the morphological classification used, due largely to the difficulty in classifying the smaller low-mass galaxies from their ground-based images. Nevertheless, we find agreement that among galaxies with stellar masses $M_{\star } \gt 6\times 10^{9}\, \mathrm{ M}_{\odot }$, downsizing is most significant in spirals. However, there are complicating factors. For example, for more massive galaxies, we find that colour and morphology are predictors of the past star formation over a longer time-scale than in less massive systems. Presumably this effect is reflecting the longer period of evolution required to alter these larger galaxies’ physical properties, but shows that conclusions based on any single property do not tell the full story.
Integral Representations of Ultraspherical Polynomials II

In the first part, by the first author’s work of 1972, an integral representation for an ultraspherical polynomial of higher index in terms of one of lower index and an infinite series was obtained. While this representation works well from a theoretical point of view, it is not numerically satisfactory as it involves polynomials of high degree, which are numerically unstable. Here we sum this series to obtain an integral, which is numerically tractable.
A two-sex branching process with oscillations: application to predator-prey systems

A two-type two-sex branching process is introduced with the aim of describing the interaction of predator and prey populations with sexual reproduction and promiscuous mating. In each generation and in each species the total number of individuals which mate and produce offspring is controlled by a binomial distribution with size given by this number of individuals and probability of success depending on the density of preys per predator. The resulting model enables us to depict the typical cyclic behaviour of predator-prey systems under some mild assumptions on the shape of the function that characterises the probability of survival of the previous binomial distribution. We present some basic results about fixation and extinction of both species as well as conditions for the coexistence of both of them. We also analyse the suitability of the process to model real ecosystems comparing our model with a real dataset.
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources

We propose a novel hybrid approach to lemmatization that enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. During training, the enhanced lemmatizer learns both to generate lemmas via a sequential decoder and copy the lemma characters from the external candidates supplied during run-time. Our lemmatizer enhanced with candidates extracted from the Apertium morphological analyzer achieves statistically significant improvements compared to baseline models not utilizing additional lemma information, achieves an average accuracy of 97.25% on a set of 23 UD languages, which is 0.55% higher than obtained with the Stanford Stanza model on the same set of languages. We also compare with other methods of integrating external data into lemmatization and show that our enhanced system performs considerably better than a simple lexicon extension method based on the Stanza system, and it achieves complementary improvements w.r. t. the data augmentation method.