Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardioprotective Effects of Kalanchoe pinnata Aqueous and Ethanolic Extracts in Wistar Rat

Introduction: Kalanchoe pinnata (Crassulaceae) aqueous extract potential cardioprotective property was revealed recently. This study aimed to confirm cardioprotective effects of aqueous and ethanolic extracts against Isoproterenol (ISO) -induced Myocardial Infarction (MI) in rat. Methods: Forty adult wistar rats were randomly distributed into 8 groups, and then treated for 28 days. Control groups 1-3 received water (10 mL/kg, per os, neutral), ISO (150 mg/kg, sc, negative) and ISO+ propranolol (10 mg/kg, per os, positive). Test groups (4-6) received Kalanchoe pinnata ethanolic extract (50 mg/kg, 100 mg/kg and 200 mg/kg, per os). Cardioprotective test groups 7 and 8 received Kalanchoe pinnata aqueous extract (100 mg/kg, per os) +ISO and ethanolic extract (100 mg/kg, per os) +ISO, respectively. Rats were sacrificed on day 29 and blood collected for determination of serum creatine kinase-MB and Troponin I levels. Hearts were histopathologically analyzed. Results: There was no significant change caused by the ethanolic extract alone on neither the biomarkers level nor the heart architecture. Compared to neutral control, creatine kinase-MB level (16.16 ± 6.07) U/L rose to (96.09 ± 14.07) U/L and (19.43 ± 6.24) U/L in the negative and positive controls, respectively. This biomarker rose from 16.16 U/L to (26.09 ± 4.853) U/L and (63.73 ± 10.97) U/L in aqueous Kalanchoe pinnata (100 mg/kg) +ISO and ethanolic Kalanchoe pinnata (100 mg/kg) +ISO groups, respectively. The increase in Troponin was from (0.0348 ± 0.0157) μg/L (neutral control) to (1.886 ± 0.0002) μg/L in the negative control, (0.9694 ± 0.1303) μg/L in the aqueous Kalanchoe pinnata (100 mg/kg) +ISO and (1.493 ± 0.177) μg/L in the ethanolic Kalanchoe pinnata (100 mg/kg) +ISO. Compared to the negative control, there was a significant decrease of Troponin, to (0.2312 ± 0.1314) μg/L in the positive control. The aqueous extract was more potent than ethanolic extract on infracted heart. Conclusion: We demonstrated that both extracts (100 mg/kg/day, per os) are cardioprotective against ISO-induced MI, but the aqueous extract is more potent.
The vecro hypothesis

We consider three fundamental issues in quantum gravity: (a) the black hole information paradox (b) the unboundedness of entropy that can be stored inside a black hole horizon (c) the relation between the black hole horizon and the cosmological horizon. With help from the small corrections theorem, we convert each of these issues into a sharp conflict. We then argue that all three conflicts can be resolved by the following hypothesis: {\it the vacuum wavefunctional of quantum gravity contains a `vecro' component made of virtual fluctuations of configurations of the same type that arise in the fuzzball structure of black hole microstates}. Further, if we assume that causality holds to leading order in gently curved spacetime, then we {\it must} have such a vecro component in order to resolve the above conflicts. The term vecro stands for `Virtual Extended Compression-Resistant Objects', and characterizes the nature of the vacuum fluctuations that resolve the puzzles. It is interesting that puzzle (c) may relate the role of quantum gravity in black holes to observations in the sky.
An Extended Theoretical Scenario for Classical Cepheids. I. Modeling Galactic Cepheids in the Gaia Photometric System

We present a new extended and detailed set of models for classical Cepheid pulsators at solar chemical composition (Z = 0.02, Y = 0.28) based on a well-tested nonlinear hydrodynamical approach. In order to model the possible dependence on crucial assumptions such as the mass–luminosity relation of central helium burning intermediate-mass stars or the efficiency of superadiabatic convection, the model set was computed by varying not only the pulsation mode and the stellar mass but also the mass–luminosity relation and the mixing length parameter that is used to close the system of nonlinear hydrodynamical and convective equations. The dependence of the predicted boundaries of the instability strip as well as of both light and radial velocity curves on the assumed mass–luminosity and the efficiency of superadiabatic convection is discussed. Nonlinear period–mass–luminosity–temperature, period–radius, and period–mass–radius relations are also computed. The theoretical atlas of bolometric light curves for both the fundamental and first overtone mode has been converted in the Gaia filters G, GBP, and GBR and the corresponding mean magnitudes have been derived. Finally, the first theoretical period–luminosity–color and period–Wesenheit relations in the Gaia filters are provided and the resulting theoretical parallaxes are compared with Gaia Data Release 2 results for both fundamental and first overtone Galactic Cepheids.
Excitons in solids from periodic equation-of-motion coupled-cluster theory.

We present an ab initio study of electronically excited states of three-dimensional solids using Gaussian-based periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD). The explicit use of translational symmetry, as implemented via Brillouin zone sampling and momentum conservation, is responsible for a large reduction in cost. Our largest system studied, which samples the Brillouin zone using 64 k-points (a 4×4×4 mesh) corresponds to a canonical EOM-CCSD calculation of 768 electrons in 640 orbitals. We study eight simple main-group semiconductors and insulators, with direct singlet excitation energies in the range of 3 to 15 eV. Our predicted excitation energies exhibit a mean signed error of 0.24 eV and a mean absolute error of 0.27 eV when compared to experiment. Although this error is similar to that found for EOM-CCSD applied to molecules, it may also reflect the role of vibrational effects, which are neglected in the calculations. Our results support recently proposed revisions of experimental optical gaps for AlP and cubic BN. We furthermore calculate the energy of excitons with nonzero momentum and compare the exciton dispersion of LiF with experimental data from inelastic X-ray scattering. By calculating excitation energies under strain, we extract hydrostatic deformation potentials in order to quantify the strength of interactions between excitons and acoustic phonons. Our results indicate that coupled-cluster theory is a promising method for the accurate study of a variety of exciton phenomena in solids.
RTM: Blockchain That Support Revocable Transaction Model

In many typical application scenarios, it is necessary to revoke the incorrect account operations caused by user mis-operation, financial fraud, illegal hacking, etc. Unfortunately, users often blur the lines between the concept of "transaction state revocable" and "business status revocable", which result in revocable transaction not universally supported in blockchain systems at present. In this work, we propose GateChain , a blockchain that support revocable transaction model (RTM) on distributed ledger. Specifically, based on the state-of-the-art blockchain technologies, GateChain can safely withdraw the account status change operations by leveraging an improved account model and extra designed transaction types. On that basis, GateChain exploit the characteristics of functional completeness, easy to deployment and lower complexity.
Adversarial Training for Aspect-Based Sentiment Analysis with BERT

Aspect-Based Sentiment Analysis (ABSA) studies the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize better can be laborious and time-consuming. As an alternative, similar data to the real-world examples can be produced artificially through an adversarial process which is carried out in the embedding space. Although these examples are not real sentences, they have been shown to act as a regularization method which can make neural networks more robust. In this work, we fine- tune the general purpose BERT and domain specific post-trained BERT (BERT-PT) using adversarial training. After improving the results of post-trained BERT with different hyperparameters, we propose a novel architecture called BERT Adversarial Training (BAT) to utilize adversarial training for the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis. The proposed model outperforms the general BERT as well as the in-domain post-trained BERT in both tasks. To the best of our knowledge, this is the first study on the application of adversarial training in ABSA. The code is publicly available on a GitHub repository at https://github.com/IMPLabUniPr/Adversarial-Training-for-ABSA
Revisitation of time delay interferometry combinations that suppress laser noise in LISA

With the purpose of understanding how time delay interferometry (TDI) combinations can best be used for the characterization of LISA instrumental noise, we revisit their laser frequency noise cancellation properties. We have developed an algorithm to search for all possible combinations that suppress noise at the same level as the X, Y and Z classical combinations. The algorithm calculates delays using symbolic formulas that explicitly include velocities and accelerations of satellites up to the relevant order. In addition, once a combination has been identified, delays are verified by solving numerically the relevant equations using Keplerian orbits and Shapiro delay corrections. We find that the number of combinations that suppress the noise is larger than what was reported in the literature. In particular we find that some combinations that were thought to only partly suppress the noise, in reality do suppress it at the same level of accuracy as the basic X, Y and Z combinations.
Assessing the calibration of subdistribution hazard models in discrete time

The generalization performance of a risk prediction model can be evaluated by its calibration, which measures the agreement between predicted and observed outcomes on external validation data. Here, we propose methods for assessing the calibration of discrete time‐to‐event models in the presence of competing risks. Specifically, we consider the class of discrete subdistribution hazard models, which directly relate the cumulative incidence function of one event of interest to a set of covariates. We apply the methods to a prediction model for the development of nosocomial pneumonia. Simulation studies show that the methods are strong tools for calibration assessment even in scenarios with a high censoring rate and/or a large number of discrete time points.
Further Combinatorics and Applications of Two-Toned Tilings

Integer compositions, integer partitions, Fibonacci numbers, and generalizations of these have recently been shown to be interconnected via two-toned tilings of horizontal grids. In this article, we present refinements of two-toned tilings, describe functions which analyze them, and apply these to generalizations of integer compositions and partitions which interpolate between the two.
Gaia’s detectability of black hole–main sequence star binaries formed in open clusters

Black hole–main sequence star (BH–MS) binaries are one of the targets of future data releases of the astrometric satellite Gaia. They are supposed to be formed in two main sites: a galactic field and star clusters. However, previous work has never predicted the number of BH–MS binaries originating in the latter sites. In this paper we estimate the number of BH–MS binaries formed in open clusters and detectable with Gaia based on the results of N-body simulations. By considering interstellar extinction in the Milky Way and observational constraints, we predict that ∼10 BH–MS binaries are observable. We also find that chemical abundance patterns of companion MSs will help us to identify the origin of the binaries as star clusters. Such MSs are not polluted by outflows of the BH progenitors, such as stellar winds and supernova ejecta. Chemical anomalies might be a good test to confirm the origin of binaries with relatively less massive MSs (≲5 M⊙), short orbital periods (∼1.5 yr), and higher eccentricities (e ≳0.1).
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values

We present GradientDICE for estimating the density ratio between the state distribution of the target policy and the sampling distribution in off-policy reinforcement learning. GradientDICE fixes several problems of GenDICE (Zhang et al., 2020), the state-of-the-art for estimating such density ratios. Namely, the optimization problem in GenDICE is not a convex-concave saddle-point problem once nonlinearity in optimization variable parameterization is introduced to ensure positivity, so any primal-dual algorithm is not guaranteed to converge or find the desired solution. However, such nonlinearity is essential to ensure the consistency of GenDICE even with a tabular representation. This is a fundamental contradiction, resulting from GenDICE's original formulation of the optimization problem. In GradientDICE, we optimize a different objective from GenDICE by using the Perron-Frobenius theorem and eliminating GenDICE's use of divergence. Consequently, nonlinearity in parameterization is not necessary for GradientDICE, which is provably convergent under linear function approximation.
Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks

Graphs can be used to represent and reason about real world systems and a variety of metrics have been devised to quantify their global characteristics. An important property is robustness to failures and attacks, which is relevant for the infrastructure and communication networks that power modern society. Prior work on making topological modifications to a graph (such as adding edges) in order to increase robustness is typically based on local and spectral properties or a shallow search since robustness is expensive to compute directly. Such strategies, however, are necessarily suboptimal. In this work, we present RNet-DQN, a solution that uses Reinforcement Learning to address the problem of improving the robustness of graphs in the presence of random and targeted removals of nodes. In particular, the approach relies on changes in the estimated robustness as a reward signal and Graph Neural Networks for representing states. Experiments on synthetic graphs show that this approach can deliver performance superior to existing methods while being much cheaper to evaluate and generalizing to out-of-sample graphs, including graphs larger than those used during training. The approach is readily applicable to optimizing other global structural properties of graphs.
Prospects of joint detections of neutron star mergers and short GRBs with Gaussian structured jets

GW170817 was the first ever joint detection of gravitational waves (GW) from a binary neutron star (BNS) merger with the detections of short γ-ray burst (SGRB) counterparts. Analysis of the multiband afterglow observations of over more than a year revealed that the outflow from the merger end product was consistent with structured relativistic jet models with the core of the jet narrowly collimated to half-opening angles ∼5○. In this work, assuming that all the BNS mergers produce Gaussian structured jets with properties as inferred for GW170817, we explore the prospects of joint detections of BNS mergers and prompt γ-ray emission, expected during the current and upcoming upgrades of LIGO–Virgo–KAGRA detectors. We discuss three specific observational aspects: 1) the distribution of detected binary inclination angles, 2) the distance reach, and 3) the detection rates. Unlike GW-only detections, the joint detections are greatly restricted at large inclination angles, due to the structure of the jets. We find that at lower inclination angles (say below 20○), the distance reach as well as the detection rates of the joint detections are limited by GW detectability while at larger inclinations (say above 20○), they are limited by the γ-ray detectability.
A Study of Fitness Landscapes for Neuroevolution

Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have never been used to study the performance of machine learning algorithms on unseen data, and they have never been applied to neuroevolution. This paper aims at filling both these gaps, applying for the first time fitness landscapes to neuroevolution and using them to infer useful information about the predictive ability of the method. More specifically, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations to evolve them. To characterize fitness landscapes, we study autocorrelation and entropic measure of ruggedness. The results show that these measures are appropriate for estimating both the optimization power and the generalization ability of the considered neuroevolution configurations.
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.
A cryogenic silicon interferometer for gravitational-wave detection

The detection of gravitational waves from compact binary mergers by LIGO has opened the era of gravitational wave astronomy, revealing a previously hidden side of the cosmos. To maximize the reach of the existing LIGO observatory facilities, we have designed a new instrument able to detect gravitational waves at distances 5 times further away than possible with Advanced LIGO, or at greater than 100 times the event rate. Observations with this new instrument will make possible dramatic steps toward understanding the physics of the nearby Universe, as well as observing the Universe out to cosmological distances by the detection of binary black hole coalescences. This article presents the instrument design and a quantitative analysis of the anticipated noise floor.
Do We Need Word Order Information for Cross-lingual Sequence Labeling

Most of the recent work in cross-lingual adaptation does not consider the word order variances in different languages. We hypothesize that cross-lingual models that fit into the source language word order might fail to handle target languages whose word orders are different. To test our conjecture, we build an order-agnostic model for cross-lingual sequence labeling tasks. Our model does not encode the word order information of the input sequences, and the predictions for each token are based on the attention on the whole sequence. Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models.
Piecewise Flat Metrics and Quantum Gravity

We introduce a physical piecewise linear metric associated to a Regge triangulation of a smooth 4-manifold. We describe the basic properties of the corresponding geometry in the cases of the Euclidean and the Minkowski signature. In the Minkowski case, we describe the Regge action and how to define the corresponding path integral for the casual triangulations. We also discus the Regge path integral for a triangulation associated to the Friedman-Lemaitre-Robertson-Walker cosmological model and briefly study the corresponding wavefunctions, namely the Hartle-Hawking and the Vilenkin wavefunction.
Peregrine: Network Localization and Navigation with Scalable Inference and Efficient Operation

Location-aware networks will enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. One promising solution for ubiquitous localization is network localization and navigation (NLN), where devices form a network that cooperatively localizes itself, reducing the infrastructure needed for accurate localization. This paper introduces a real-time NLN system named Peregrine, which combines distributed NLN algorithms with commercially available ultra-wideband (UWB) sensing and communication technology. The Peregrine software application, for the first time, integrates three NLN algorithms to jointly perform the tasks of localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of low-cost, compact devices that comprise a microprocessor and a commercial UWB radio. This paper presents the design of the Peregrine system and characterizes the performance impact of each algorithmic component. Indoor experiments validate that our approach to realizing NLN is both reliable and scalable, and maintains sub-meter-level accuracy even in challenging indoor scenarios.
Effect of Dietary Supplementation of Aspergillus Xylanase on Broiler Chickens Performance

The effect of Aspergillus xylanase (ASXYL) supplementation to maize-soybean diets on serum aspartate aminotransferase, serum alanine aminotransferase, microbial examination, growth traits, carcass characteristics and meat quality traits of broiler chickens was investigated. Three hundred one-day-old mixed sex “Cobb 500” broiler chicks were allotted to 5 dietary treatments with 5 replicates of 12 birds each. The treatments include, ASXYL0 (0 g/kg), ASXYL10 (1 g/kg), ASXYL15 (1.5 g/kg), ASXYL20 (2 g/kg) and ASXYL25 (2.5 g/kg). Birds fed ASXYL20 had the highest (P 0.05) was observed on the population of Bifidobacteria and Escherichia coli in both ileum and caecum of broilers at the end of the feeding trial. It was concluded that dietary ASXYL20 produced a much improved body weight and retail cut yields. Again, the bio-markers showed that ASXYLimproved the health status of broiler chicken and the tested enzyme influenced a positive intestinal environment.