Miracle-dz/UniFAR-FTUs huggingface dataset:
Italians Do It … Less. COVID-19 Lockdown Impact on Sexual Activity: Evidence From a Large Representative Sample of Italian Adults

Background To explore how sexual activity was impacted by coronavirus disease 2019 (COVID-19) lockdown measures in the general adult population. Methods A cross-sectional survey was conducted among 6,003 Italian adults aged 18–74 years who were representative of the Italian general population. Study subjects were recruited at the time of the nationwide stay-at-home order (from April 27 to May 3, 2020). We identified characteristics associated with decreased frequency of sex during lockdown, differentiating between cohabiting and non-cohabiting subjects. Results Over one-third (35.3%) of Italians reported to have changed their sexual activity during lockdown (8.4% increased and 26.9% decreased). When focusing on cohabitants (N = 3,949, 65.8%), decreased sexual activity (20.7%) was more frequently reported by men (22.3%; compared to women, multivariable odds ratio 1.23; 95% confidence interval, 1.05–1.44), younger subjects (P for trend <0.001), more educated subjects (P for trend = 0.004), subjects living in smaller houses (P for trend = 0.003), and those reporting longer time spent outdoors before the lockdown (P for trend <0.001). Conclusions COVID-19 lockdown drastically altered people’s day-to-day life and is likely to have impacted lifestyle habits and behavioral risk factors, including sexual attitudes and practice. This is the first national population-level study exploring changes in sexual life in this COVID-19 era. As we report sexual practice to have been affected by lockdown restrictions, we suggest that the mental health, social, and other determinants of these changes are to be explored beyond imposed social distancing.
Bimodal applications of LDH-chitosan nanocomposite: water treatment and antimicrobial activity

Chitosan-LDH (layered doubled hydroxide) composite was prepared for removal heavy metals and antimicrobial activity. It was characterized by Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), Transmission electron microscopy (TEM), Scanning electronic microscope (SEM). Studying various physicochemical parameters such as the effects of pH, adsorbent amount, and contact time for removing heavy metals were performed. The adsorption kinetics and adsorption isotherm model were done to understand the adsorption process. The prepared composite exhibited strong antimicrobial activity against different gram positive (Lactobacillus sakei LMG 2313, Corynebacterium sp, Staphylococcus aureus 5247) and gram negative bacteria (Escherichia coli AO5). Results were recorded in terms of minimum inhibitory concentration (MIC), which is the lowest concentration of antimicrobial/antifungal agent that will inhibit the visible growth of a microorganism after overnight incubation.
QUANTITATIVE ANALYSIS OF ACETAMINOPHEN AND IBUPROFEN MIXTURE IN TABLET UTILIZING CENTERED AVERAGE ON SPECTRUM RATIO BY SPECTROPHOTOMETRIC TECHNIQUE

Objectives: Centered average on spectrum ratio by spectrophotometric technique has been done validated for acetaminophen (ACT) and ibuprofen (IBU) in mixture. The goal of this research is to analyze concurrent determination of ACT and IBU mixture in tablet, using pH 7.2 phosphate buffer and ethanol (91:9) as solvent mixture.Methods: The spectrophotometric technique was made by addition of standard solution from IBU, and the absorption spectrum of each drug has been recorded, which is divided by corresponding concentration as a dividing factor; after that, the centered flat of ratio spectra of ACT and IBU concentrations was determined by calibration graphs with measuring at obtained maximum wavelengths 244 nm for ACT and 221 nm for IBU.Results: The research results showed that linearly correlation acetominophen and IBU are 3.0–9.0 μg/mL and 5.0–13.0 μg/mL, respectively, and linearly regression equations are 1.5800X+0.4809 and 0.0991X+0.0205 with coefficient correlation are 0.9962 and 0.9990. Result of ACT and IBU in local tablet is 100.72±0.12% and 98.82±0.06% with the average of percentage recovery for ACT and IBU is 100.01% and 100.31%, and relative standard deviation value is 0.03% and 0.06% respectively.Conclusion: It can be concluded that centered average on spectrum ratio spectrophotometric technique is a smooth, accelerated, correct, and rigid for the analysis of ACT and IBU in mixture.
Refinement of Non-Antibiotic Spray Programs for Fire Blight Control in Organic Pome Fruit.

Fire blight-susceptible, certified organic pome fruit is produced currently on 9,000 ha in the Pacific Northwest region of the United States with acreage continuing to expand in spite of a 2014 prohibition on antibiotics as allowable materials for infection suppression. Non-antibiotic practices for fire blight suppression mirror conventional management, but the full bloom to petal fall period when antibiotics are typically sprayed for fire blight control continues to receive research scrutiny owing to drawbacks and weaknesses of alternative materials. As solitary treatments, effective non-antibiotic materials (e.g., a yeast biocontrol, soluble coppers, and alum) raise the risk of a crop-value reducing, phytotoxic response termed fruit russeting. Conversely, materials with less russeting risk (e.g., Bacillus-based biorationals) are less effective for fire blight control. Spray programs using a combination of materials applied from mid-bloom to petal fall have the potential to provide high levels of protection with reduced russeting risk. In orchard trials, the effects of non-antibiotic spray programs on epiphytic population size of Erwinia. amylovora in flowers, yeast biocontrol population size, floral pH, infection suppression, and fruit russeting revealed strategies for sequencing sprays of non-antibiotic materials. The yeast biocontrol, Blossom Protect (Aureobasidium pullulans), sprayed at 70% bloom was an important contributor to fire blight suppression as was the soluble copper material, Previsto, when applied at full bloom. Choice of material for the petal fall spray timing was important to fruit russeting risk but apparently less important to overall infection incidence. Consequently, treatment programs of Blossom Protect at 70% bloom, a soluble copper at full bloom, and a Bacillus-based biorational at petal fall best balances the quality of infection suppression with risk of fruit russeting.
Body fat percentage in adolescents is a better predictor of exercise effect on adiposity changes: a 4-year follow-up study.

BACKGROUND Exercise is generally recognized as beneficial to prevent obesity; however, it is not clear which indicator can better reflect the benefits, especially in adolescents. The aim of this study was to describe the effects of exercise on body mass and fat indexes and to clarify the significance of different indexes in clinical use. METHODS We conducted a prospective cohort study involving 1,941 freshmen from 2014, followed-up biennially until 2018. Various body mass and fat indexes, including weight, height, waist and hip circumference and body fat percentage (BFP), were measured. Physical activity and other variables were collected by questionnaire. All study participants were divided into two groups according to the frequency and intensity of exercise. RESULTS Compared with the low frequency and intensity exercise group, the high frequency and intensity exercise group had a lower increase in BFP during the 4-year follow-up, and no significant differences were observed in the changes of other indexes between the groups. Even after adjusting, the high frequency and intensity exercise group still exhibited a higher likelihood of reducing BFP. CONCLUSIONS High frequency and intensity exercise provides benefits for reducing BFP. No other body mass or fat indexes showed any association. BFP could be a much more sensitive indicator to detect and control obesity in adolescents.
Study of Salicylic Acid Influence on Seedling Growth and Nitrogen Metabolism in Watermelon (Citrullus lanatus L.)

Salicylic acid is involved in the regulation of metabolic activity and defense mechanism in plants under various stress conditions. Present study was conducted to determine the effects of salicylic acid (10 to 500 μM) on seedling growth, development and nitrogen use efficiency in watermelon (Citrullus lanatus L.) plants with or without nitrogen nutrient. Salicylic acid increased contents of chlorophyll, total non-structural carbohydrate and total nitrogen, as well as nitrate assimilation through the induction of nitrate reductase (EC 1.6.6.1) activity in isolated watermelon cotyledons. Accumulation of salicylic acid was two-fold higher in cotyledons without nitrate supply in comparison to that with nitrate supply. Further 50 μM of SA induced enhancement in seed germination and growth characteristics. However higher salicylic acid concentrations inhibited above physiological characteristics. Results show that, field application of salicylic acid need optimum physiological concentration (e.g., 50 μM) to increase nitrogen use efficiency particularly during germination and seedling growth.
Causal ImageNet: How to discover spurious features in Deep Learning?

A key reason for the lack of reliability of deep neural networks in the real world is their heavy reliance on spurious input features that are causally unrelated to the true label. Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to co-occur with the object but not a part of it (e.g., attribute “fingers” for class “band aid”). Traditional methods for discovering spurious features either require extensive human annotations (thus, not scalable), or are useful on specific models. In this work, we introduce a scalable framework to discover a subset of spurious and causal visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision. Our methodology is based on this key idea: to identify spurious or causal visual attributes used in model predictions, we identify spurious or causal neural features (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature). We then show that these neural feature annotations generalize extremely well to many more images without any human supervision. We use the activation maps for these neural features as the soft masks to highlight spurious or causal visual attributes. Using this methodology, we introduce the Causal Imagenet dataset containing causal and spurious masks for a large set of samples from Imagenet. We assess the performance of several popular Imagenet models and show that they rely heavily on various spurious features in their predictions. 1 ar X iv :2 11 0. 04 30 1v 2 [ cs . L G ] 6 N ov 2 02 1
Two-Stage Least Squares with a Randomly Right-Censored Outcome

This note develops a simple two-stage least squares (2SLS) procedure to estimate the causal effect of some endogenous regressors on a randomly right censored outcome in the linear model. The proposal replaces the usual ordinary least squares regressions of the standard 2SLS by weighted least squares regressions. The weights correspond to the inverse probability of censoring. We show consistency and asymptotic normality of the estimator. The estimator exhibits good finite sample performances in simulations.
Antiferromagnetic spin fluctuations and superconductivity in 
NbRh2B2
 and 
TaRh2B2
 with a chiral crystal structure

We report the $^{11}$B nuclear magnetic resonance (NMR) measurements on non-centrosymmetric superconductors NbRh$_2$B$_2$ (superconducting transition temperature $T_c$ = 7.8 K) and TaRh$_2$B$_2$ ($T_c$ = 5.9 K) with a chiral crystal structure. The nuclear spin-lattice relaxation rate $1/T_1$ shows no coherence peak below $T_{\rm c}$, which suggests unconventional nature of the superconductivity. In the normal state, $1/T_1T$ increases with decreasing temperature $T$ at low temperatures below $T$ = 200 K for TaRh$_2$B$_2$ and $T$ = 15 K for NbRh$_2$B$_2$, while the Knight shift remains constant. These results suggest the presence of antiferromagnetic spin fluctuations in both compounds. The stronger spin fluctuations in TaRh$_2$B$_2$ compared to NbRh$_2$B$_2$ is discussed in the context of spin-orbit coupling.
Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration

Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterranean Challenge, by providing roboticists the opportunity to support civilian and military first responders in complex and high-risk underground scenarios. The subterranean domain presents a handful of challenges, such as limited communication, diverse topology and terrain, and degraded sensing. Team MARBLE proposes a solution for autonomous exploration of unknown subterranean environments in which coordinated agents search for artifacts of interest. The team presents two navigation algorithms in the form of a metric-topological graph-based planner and a continuous frontier-based planner. To facilitate multi-agent coordination, agents share and merge new map information and candidate goal points. Agents deploy communication beacons at different points in the environment, extending the range at which maps and other information can be shared. Onboard autonomy reduces the load on human supervisors, allowing agents to detect and localize artifacts and explore autonomously outside established communication networks. Given the scale, complexity, and tempo of this challenge, a range of lessons was learned, most importantly, that frequent and comprehensive field testing in representative environments is key to rapidly refining system performance.
Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand–protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.
Teaching Robots to Grasp Like Humans: An Interactive Approach

—This work investigates how the intricate task of grasping may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly ﬂawed, particularly at moments when multiple aspects (i.e., end-effector movement, orientation, and gripper width) have to be demonstrated at once. Rather than training a person to provide better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task. The presented framework learns the desired movement dynamics based on the current Cartesian position with Gaussian Processes (GP), resulting in a reactive, time-invariant policy. Using GPs also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization. The experimental evaluation of the framework is carried out on a Franka-Emika Panda. Tests were performed to determine i) the framework’s effectiveness in successfully learning how to quickly grasp an object, ii) ease of policy correction to environmental changes (i.e., different object shapes and mass), and iii) the framework’s usability for non-expert users.
Bismuth kagome sublattice distortions by quenching and flux pinning in superconducting 
RbBi2

The properties of RbBi2, a 4.15 K superconductor, were investigated using magnetic field, pressure and neutron diffraction. Under hydrostatic pressure, an almost 50 % reduction of Tc is observed, linked to a low Debye temperature estimated at 165 K. The resistivity and magnetic susceptibility were measured on quenched and slow-cooled polycrystalline samples. The resistivity follows a low temperature power-law dependence in both types of samples, while the diamagnetic susceptibility, χ, is dependent on the sample cooling history. Slow-cooled samples have a χ = −1 while quenched samples have χ < −1 due to grain size differences. Evidence of the effects of the cooling rate is also discerned from the local structure, obtained by neutron diffraction and the pair density function analysis. Slow-cooled samples have structurally symmetric Bi hexagons, in contrast to quenched samples in which disorder is manifested in periodic distortions of the Bi hexagonal rings of the kagome sublattice. Disorder may lead to flux pinning that reduces vortex mobility, but Tc remains unaffected by the cooling rate.
A proposal to extract and enhance four-Majorana interactions in hybrid nanowires

We simulate the smallest building block of the Sachdev-Ye-Kitaev (SYK) model, a system of four interacting Majorana modes. We propose a 1D Kitaev chain that has been split into three segments, i.e., two topological segments separated by a non-topological segment in the middle, hosting four Majorana Zero Modes at the ends of the topological segments. We add a non-local interaction term to this Hamiltonian which produces both bilinear (two-body) interactions and a quartic (four-body) interaction between the Majorana modes. We further tune the parameters in the Hamiltonian to reach the regime with a finite quartic interaction strength and close to zero bilinear interaction strength, as required by the SYK model. To achieve this, we map the Hamiltonian from Majorana basis to a complex fermion basis, and extract the interaction strengths using a method of characterization of low-lying energy levels and then finding the differences in energies between odd and even parity levels. We show that the interaction strengths can be tuned using two methods - (i) an approximate method of tuning overlapping Majorana wave functions (without non-local interactions) to a zero energy point followed by addition of a non-local interaction, and (ii) a direct parameter space optimization method using a genetic algorithm. We propose that this model could be further extended to more Majorana modes, and show a 6-Majorana model as an example. Since eigenspectral characterization of one-dimensional nanowire devices can be done via tunneling spectroscopy in quantum transport measurements, this study could be performed in experiment.
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition

Psychoacoustic studies have shown that locally-time reversed (LTR) speech, i.e., signal samples time-reversed within a short segment, can be accurately recognised by human listeners. This study addresses the question of how well a state-of-the-art automatic speech recognition (ASR) system would perform on LTR speech. The underlying objective is to explore the feasibility of deploying LTR speech in the training of end-to-end (E2E) ASR models, as an attempt to data augmentation for improving the recognition performance. The investigation starts with experiments to understand the effect of LTR speech on general-purpose ASR. LTR speech with reversed segment duration of 5 ms - 50 ms is rendered and evaluated. For ASR training data augmentation with LTR speech, training sets are created by combining natural speech with different partitions of LTR speech. The efficacy of data augmentation is confirmed by ASR results on speech corpora in various languages and speaking styles. ASR on LTR speech with reversed segment duration of 15 ms - 30 ms is found to have lower error rate than with other segment duration. Data augmentation with these LTR speech achieves satisfactory and consistent improvement on ASR performance.
Nano Version Control and "Robots of Robots" – Data Driven, Regenerative Production Code

The Corona pandemic highlights the need for sustainable production systems using automation. Traditional automation is fragile and hard to get right. This method converts one hard problem of producing sustainable production code into three simpler problems: data, patterns and working prototypes. By using agent-based simulation and NanoVC repos for agent arbitration, we create a simulated environment for the approach. Patterns developed by people are used to transform working prototypes into templates where data is fed through to create the robots that create the production code. Having two layers of robots allow early implementation choices to be replaced. Robot of Robots encode a legacy of the person that designed them, thus reducing the fragility of the production code.
Dark photon bursts from compact binary systems and constraints

In this work, we consider the burst signal of the dark photon, the hypothetical vector boson of the U (1) B or U (1) B − L gauge group, generated by a compact binary star system. The absence of the signal in the laser interferometer puts bounds on the coupling constant (cid:15) to the ordinary matter. It turns out that if the dark photon is massless, (cid:15) 2 is on the order of 10 − 37 − 10 − 33 at most; in the massive case, the upper bound of (cid:15) 2 is about 10 − 38 − 10 − 31 in the mass range from 10 − 19 eV to 10 − 11 eV. These are the ﬁrst bounds derived from the interferometer observations independent of the assumption of dark photons being dark matter.
Provenance in Temporal Interaction Networks

In temporal interaction networks (TINs), vertices correspond to entities, which exchange data quantities (e.g., money, bytes, messages) over time. We study the problem of con-tinuously tracking the origins of quantities at network vertices, as interactions take place over time. We target applications, such as financial exchange networks, where the selected transferred units at each interaction are not specified. We investigate several quantity selection policies that apply to different application scenarios. For each policy, we propose space- and time-efficient meta-data propagation mechanisms for continuously tracking provenance at vertices. For the hard case of proportional selection policy, we reduce the cost of tracking in practice, by either (i) limiting provenance tracking to a subset of vertices or groups of vertices, or (ii) tracking provenance only for quantities that were generated in the near past or limiting the provenance data in each vertex by a budget constraint. Our experimental evaluation on real datasets demonstrate the efficiency and scalability of our techniques compared to baseline approaches that extend flow computation algorithms to track provenance.