Miracle-dz/UniFAR-FTUs huggingface dataset:
Participation of Farm Women in Dairy Management Practices in Krishna District of Andhra Pradesh in India

An investigation was conducted to study the participation of farm women in dairy management practices in Krishna district of Andhra Pradesh . A total of 225 farm women were selected based on their land holding capacity. The study revealed that women from small farmer category had more participation in taking animals for pregnancy diagnosis (81.33%) and post calving care (77.33%). Women under landless category were more involved in regularly taking animals for grazing (45.33%), green fodder collection and feeding (68%) and feeding animals with concentrates (88%). Feeding of animals with dry fodder regularly was more in small women dairy farmer category (89.33%) and landless (86.66%) than that in medium (56%) farm women category. Cleaning of animal sheds regularly was high in landless (76%) and small (69.33%) farm women Original Research Article Krishna et al.; AJAEES, 38(11): 206-214, 2020; Article no.AJAEES. 63695 207 than in medium (44%) farm women. Majority of women under landless category were regularly using disinfectants for cleaning of animal sheds (49.33%). Regular involvement in disposing of dung and composting of dung is high in women under landless (69.33%) and small (65.33%) farm women categories. Feeding of Colostrum to the calves regularly is high in small (86.66%) farmwomen category and cleaning of calf after birth is high in landless (84%) women category. Women in landless (77.33%) and small (74.6%) farmer categories are regularly washing the animals before milking. It was observed that 12% of women under medium farmer category and 8% under small farmer category are engaging labour for milking of animals. Women under small (76%) and landless (73.33%) farmer category were regularly taking care of sick animals. Sale of milk regularly through cooperatives was observed to be high in landless (85.33%) and small (90.66%) farm women categories. More number of women under landless (21.33%) were regularly participating in insurance of animals than small (6.66%) and medium (10.66%) farm women categories. More number of medium (9.33%) women dairy farmers were regularly involved in purchase of animals compared to landless (5.33%) and small (1.33%) farm women.
KNOWLEDGE AND AWARENESS ON ROBOTICS ASSISTED SURGERIES AMONG COLLEGE STUDENTS- A SURVEY

Robotic assisted surgeries are types of surgical procedures that involve the usage of robotics systems and it helps the doctors to perform various types of complex procedures with more precision, flexibility and control than is possible with conventional techniques. Robotic surgery is basically associated with a minimum invasive surgery procedure which is performed through tiny incisions. Robotic surgeries can also be used in laparoscopic surgeries and traditional open surgical procedures.The most widely used clinical robotic surgical system includes a camera Prasath et al (2020): Robotics assisted surgeries Nov 2020 Vol. 23 Issue 22 Annals of Tropical Medicine & Public Health http://doi.org/10.36295/ASRO.2020. 232345 arm and mechanical arms with surgical instruments attached to them. The surgeon who performs the surgery controls the arms while seated at a computer console near the operating table. The console gives the surgeon a clear resolution;high-definition, magnified and 3-D view of the surgical site. The surgeon leads other team members who assist during the operation.A self structured questionnaire containing 18 questions was circulated among College students. The questionnaire has covered basic demographic data such as age, gender and about the awareness of robotic assisted surgeries among college students.The survey was taken through online Google forms link.The result was found that most of the students were aware of robotic assisted surgeries and its related technological advancement.This study concludes that there is awareness on different types of robotics assisted surgeries and future research in this field may be essential for the community.Advancement in robotics assisted surgeries helps in advancement in health care industries and improvement in the surgical field.
Running title : Biodiversity and multifunctionality

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/ecy.1499 This article is protected by copyright. All rights reserved. Received Date : 19-Jan-2016 Revised Date : 23-May-2016 Accepted Date : 01-Jun-2016 Article type : Articles
Observational constraints on bimetric gravity

Ghost-free bimetric gravity is a theory of two interacting spin-2 fields, one massless and one massive, in addition to the standard matter particles and fields, thereby generalizing Einstein’s theory of general relativity. To parameterize the theory, we use five observables with specific physical interpretations. We present, for the first time, observational constraints on these parameters that: (i) apply to the full theory, (ii) are consistent with a working screening mechanism (i.e., restoring general relativity locally), (iii) exhibit a continuous, realvalued background cosmology (without the Higuchi ghost). For the cosmological constraints, we use data sets from the cosmic microwave background, baryon acoustic oscillations, and type Ia supernovae. Bimetric cosmology provides a good fit to data even for large values of the mixing angle between the massless and massive gravitons. Interestingly, the best-fit model is a self-accelerating solution where the accelerated expansion is due to the dynamical massive spin-2 field, without a cosmological constant. Due to the screening mechanism, the models are consistent with local tests of gravity such as solar system tests and gravitational lensing by galaxies. We also comment on the possibility of alleviating the Hubble tension with this theory. ar X iv :2 10 1. 08 79 5v 1 [ gr -q c] 2 1 Ja n 20 21
CutLang v2: Advances in a Runtime-Interpreted Analysis Description Language for HEP Data

We will present the latest developments in CutLang, the runtime interpreter of a recently-developed analysis description language (ADL) for collider data analysis. ADL is a domain-specific, declarative language that describes the contents of an analysis in a standard and unambiguous way, independent of any computing framework. In ADL, analyses are written in human-readable plain text files, separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Adopting ADLs would bring numerous benefits to the LHC experimental and phenomenological communities, ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Since their initial release, ADL and CutLang have been used for implementing and running numerous LHC analyses. In this process, the original syntax from CutLang v1 has been modified for better ADL compatibility, and the interpreter has been adapted to work with that syntax, resulting in the current release v2. Furthermore, CutLang has been enhanced to handle object combinatorics, to include tables and weights, to save events at any analysis stage, to benefit from multi-core/multi-CPU hardware among other improvements. In this contribution, these and other enhancements are discussed in details. In addition, real life examples from LHC analyses are presented together with a user manual.
Massive stars in the Small Magellanic Cloud

Context. The evolution of massive stars depends on several physical processes and parameters. Metallicity and rotation are among the most important, but their quantitative effects are not well understood. Aims. To complement our earlier study on main-sequence stars, we study the evolutionary and physical properties of evolved O stars in the Small Magellanic Cloud (SMC). We focus in particular on their surface abundances to further investigate the efficiency of rotational mixing as a function of age, rotation, and global metallicity. Methods. We analysed the UV and optical spectra of 13 SMC O-type giants and supergiants using the stellar atmosphere code CMFGEN to derive photospheric and wind properties. We compared the inferred properties to theoretical predictions from evolution models. For a more comprehensive analysis, we interpret the results together with those we previously obtained for O-type dwarfs. Results. Most dwarfs of our sample lie in the early phases of the main sequence. For a given initial mass, giants are farther along the evolutionary tracks, which confirms that they are indeed more evolved than dwarfs. Supergiants have higher initial masses and are located past the terminal-age main-sequence in each diagram. We find no clear trend of a mass discrepancy, regardless of the diagram that was used to estimate the evolutionary mass. Surface CNO abundances are consistent with nucleosynthesis from the CNO cycle. Comparisons to theoretical predictions reveal that the initial mixture is important when the observed trends in the N/C versus N/O diagram are to be reproduced. A trend for stronger chemical evolution for more evolved objects is observed. Above about 30 M⊙, more massive stars are on average more chemically enriched at a given evolutionary phase. Below 30 M⊙, the trend vanishes. This is qualitatively consistent with evolutionary models. A principal component analysis of the abundance ratios for the whole (dwarfs and evolved stars) sample supports the theoretical prediction that massive stars at low metallicity are more chemically processed than their Galactic counterparts. Finally, models including rotation generally reproduce the surface abundances and rotation rates when different initial rotational velocities are considered. Nevertheless, for some objects, a stronger braking and/or more efficient mixing is required.
Parabolic velocity profile causes drift of inertial prolate spheroids -- but gravity is stronger

Motion of elongated particles in shear is studied. In applications where particles are much heavier than the carrying ﬂuid, e.g. aerosols, the inﬂuence of particle inertia dominates the particle dynamics. Assuming that the particle only experiences a local linear velocity proﬁle, its rotational and translational motion are independent. However, we show that quadratic terms of the local velocity proﬁle combined with particle inertia cause a lateral drift of prolate spheroidal particles. We ﬁnd that this drift is maximal when particle inertial forces are of the same order of magnitude as viscous forces, and that both extremely light and extremely heavy particles have negligible drift. In the non-inertial case, the particle rotates according to the local linear velocity proﬁle, with each instantaneous orientation corresponding to a velocity that gives zero force on the particle. This results in a translational motion in the ﬂow direction with periodic velocity ﬂuctuations. With added particle inertia, the particle is slow to react to the surrounding ﬂuid motion and the particle will switch between slower and faster rotation compared to the zero-force solution. The ﬁnal motion that gives zero integrated force over a rotational period, is a motion with a lateral drift. We show that this drift is purely an eﬀect of the non-sphericity of the particle and its translational inertia, while rotational inertia is negligible. Finally, although this inertial drift will contribute to the lateral motion of heavy elongated particles in channel ﬂow, sedimentation due to gravity will dominate in any practical application on earth. parabolic velocity proﬁle. In this work we will show that the inﬂuence of particle inertia combined with a quadratic velocity proﬁle will cause a lateral drift even
CSI-Based Localization with CNNs Exploiting Phase Information

In this paper we study the use of the Channel State Information (CSI) as fingerprint inputs of a Convolutional Neural Network (CNN) for localization. We examine whether the CSI can be used as a distinct fingerprint corresponding to a single position by considering the inconsistencies with its raw phase that cause the CSI to be unreliable. We propose two methods to produce reliable fingerprints including the phase information. Furthermore, we examine the structure of the CNN and more specifically the impact of pooling on the positioning performance, and show that pooling over the subcarriers can be more beneficial than over the antennas.
The Bessel kernel determinant on large intervals and Birkhoff's ergodic theorem

The Bessel process models the local eigenvalue statistics near 0 of certain large positive definite matrices. In this work, we consider the probability P∃no points in the Bessel process on(0,x1)∪(x2,x3)∪⋯∪(x2g,x2g+1),$$\begin{equation*} {\mathbb{P}\left(\exists \text{ no points in the Bessel process on}\ (0,{x}_{1})\cup ({x}_{2},{x}_{3})\cup \cdots \cup ({x}_{2g},{x}_{2g+1})\right),} \end{equation*}$$where 0
The Privacy-Utility Tradeoff of Robust Local Differential Privacy

We consider data release protocols for data $X=(S,U)$, where $S$ is sensitive; the released data $Y$ contains as much information about $X$ as possible, measured as $\operatorname{I}(X;Y)$, without leaking too much about $S$. We introduce the Robust Local Differential Privacy (RLDP) framework to measure privacy. This framework relies on the underlying distribution of the data, which needs to be estimated from available data. Robust privacy guarantees are ensuring privacy for all distributions in a given set $\mathcal{F}$, for which we study two cases: when $\mathcal{F}$ is the set of all distributions, and when $\mathcal{F}$ is a confidence set arising from a $\chi^2$ test on a publicly available dataset. In the former case we introduce a new release protocol which we prove to be optimal in the low privacy regime. In the latter case we present four algorithms that construct RLDP protocols from a given dataset. One of these approximates $\mathcal{F}$ by a polytope and uses results from robust optimisation to yield high utility release protocols. However, this algorithm relies on vertex enumeration and becomes computationally inaccessible for large input spaces. The other three algorithms are low-complexity and build on randomised response. Experiments verify that all four algorithms offer significantly improved utility over regular LDP.
Breaking the Deadly Triad with a Target Network

The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for the conventional wisdom that a target network stabilizes training. We first propose and analyze a novel target network update rule which augments the commonly used Polyak-averaging style update with two projections. We then apply the target network and ridge regularization in several divergent algorithms and show their convergence to regularized TD fixed points. Those algorithms are off-policy with linear function approximation and bootstrapping, spanning both policy evaluation and control, as well as both discounted and average-reward settings. In particular, we provide the first convergent linear $Q$-learning algorithms under nonrestrictive and changing behavior policies without bi-level optimization.
On Maximum Likelihood Training of Score-Based Generative Models

Score-based generative modeling has recently emerged as a promising alternative to traditional likelihood-based or implicit approaches. Learning in score-based models involves ﬁrst perturbing data with a continuous-time stochastic process, and then matching the time-dependent gradient of the logarithm of the noisy data density—or score function —using a continuous mixture of score matching losses. In this note, we show that such an objec-tive is equivalent to maximum likelihood for certain choices of mixture weighting. This connection provides a principled way to weight the objective function, and justiﬁes its use for comparing diﬀerent score-based generative models. Taken together with previous work, our result reveals that both maximum likelihood training and test-time log-likelihood evaluation can be achieved through parameterization of the score function alone, without the need to explicitly parameterize a density function.
How does COVID-19 change insurance and vaccine demand? Evidence from short-panel data in Japan

In this study, we explored how the coronavirus disease (COVID-19) affected the demand for insurance and vaccines in Japan from mid-March to mid-April 2020. Through independent internet surveys, respondents were asked hypothetical questions concerning the demand for insurance and vaccines for protection against COVID-19. Using the collected short-panel data, after controlling for individual characteristics using the fixed effects model, the key findings, within the context of the pandemic, were as follows: (1) Contrary to extant studies, the demand for insurance by females was smaller than that by their male counterparts;(2) The gap in demand for insurance between genders increased as the pandemic prevailed;(3) The demand for a vaccine by females was higher than that for males;and (4) As COVID-19 spread throughout Japan, demand for insurance decreased, whereas the demand for a vaccine increased.
Differentiable Trust Region Layers for Deep Reinforcement Learning

Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), are based on approximations. Due to those approximations, they violate the constraints or fail to find the optimal solution within the trust region. Moreover, they are difficult to implement, lack sufficient exploration, and have been shown to depend on seemingly unrelated implementation choices. In this work, we propose differentiable neural network layers to enforce trust regions for deep Gaussian policies via closed-form projections. Unlike existing methods, those layers formalize trust regions for each state individually and can complement existing reinforcement learning algorithms. We derive trust region projections based on the Kullback-Leibler divergence, the Wasserstein L2 distance, and the Frobenius norm for Gaussian distributions. We empirically demonstrate that those projection layers achieve similar or better results than existing methods while being almost agnostic to specific implementation choices. (Code: https://git. io/Jt3go)
Transport Properties of Superfluid Phonons in Neutron Stars

We review the effective field theory associated with the superfluid phonons that we use for the study of transport properties in the core of superfluid neutrons stars in their low temperature regime. We then discuss the shear and bulk viscosities together with the thermal conductivity coming from the collisions of superfluid phonons in neutron stars. With regard to shear, bulk, and thermal transport coefficients, the phonon collisional processes are obtained in terms of the equation of state and the superfluid gap. We compare the shear coefficient due to the interaction among superfluid phonons with other dominant processes in neutron stars, such as electron collisions. We also analyze the possible consequences for the r-mode instability in neutron stars. As for the bulk viscosities, we determine that phonon collisions contribute decisively to the bulk viscosities inside neutron stars. For the thermal conductivity resulting from phonon collisions, we find that it is temperature independent well below the transition temperature. We also obtain that the thermal conductivity due to superfluid phonons dominates over the one resulting from electron-muon interactions once phonons are in the hydrodynamic regime. As the phonons couple to the Z electroweak gauge boson, we estimate the associated neutrino emissivity. We also briefly comment on how the superfluid phonon interactions are modified in the presence of a gravitational field or in a moving background.
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric

Temporal Sentence Grounding in Videos (TSGV), \ie, grounding a natural language sentence which indicates complex human activities in a long and untrimmed video sequence, has received unprecedented attentions over the last few years. Although each newly proposed method plausibly can achieve better performance than previous ones, current TSGV models still tend to capture the moment annotation biases and fail to take full advantage of multi-modal inputs. Even more incredibly, several extremely simple baselines without training can also achieve state-of-the-art performance. In this paper, we take a closer look at the existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils to cause unreliable benchmarking. To this end, we propose to re-organize two widely-used TSGV benchmarks (ActivityNet Captions and Charades-STA). Specifically, we deliberately make the ground-truth moment distribution different in the training and test splits, \ie, out-of-distribution (OOD) testing. Meanwhile, we introduce a new evaluation metric "dR@n,IoU@m'' to calibrate the basic IoU scores by penalizing on the bias-influenced moment predictions and alleviate the inflating evaluations caused by the dataset annotation biases such as overlong ground-truth moments. Under our new evaluation protocol, we conduct extensive experiments and ablation studies on eight state-of-the-art TSGV methods. All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV. Our reorganized datsets are available at https://github.com/yytzsy/grounding_changing_distribution.
X-ray emission of Seyfert 2 galaxy MCG-01-24-12

We present a detailed X-ray spectral analysis of the nearby Seyfert 2 galaxy MCG-01-24-12 based on a multi-epoch data set. Data were obtained with different X-ray satellites, namely XMM-Newton, NuSTAR, Swift, and Chandra, and cover different time intervals, from a few days to years. From 2006 to 2013 the source had a 2–10 keV flux of ∼1.5 × 10−11 erg cm−2 s−1, consistent with archival observations based on HEAO and BeppoSAX data, though a 2019 Chandra snapshot caught the source in an extreme low flux state a factor of ∼10 fainter than its historical level. Based on phenomenological and physically motivated models, we find the X-ray spectrum of MCG-01-24-12 to be best modelled by a power-law continuum emission with Γ = 1.76 ± 0.09 with a high energy cut-off at Ec = 70−14+21 keV that is absorbed by a fairly constant column density of NH = (6.3 ± 0.5) × 1022 cm−2. These quantities allowed us to estimate the properties of the hot corona in MCG-01-24-12 for the cases of a spherical or slab-like hot Comptonising plasma to be kTe = 27−4+8 keV, τe = 5.5 ± 1.3 and kTe = 28−5+7 keV, τ = 3.2 ± 0.8, respectively. Finally, despite the short duration of the exposures, possible evidence of the presence of outflows is discussed.
Harder-Narasimhan stratification for the moduli stack of parabolic vector bundles

For every set of parabolic weights, we construct a Harder-Narasimhan stratification for the moduli stack of parabolic vector bundles on a curve. It is based on the notion of parabolic slope, introduced by Mehta and Seshadri. We also prove that the stratification is schematic, that each stratum is complete, and establish an analogue of Behrend’s conjecture for parabolic vector bundles. A comparison with recent Θ-stratification approaches is discussed.