Miracle-dz/UniFAR-FTUs huggingface dataset:
The Research Progress of the Influence of Agricultural Activities on Atmospheric Environment in Recent Ten Years: A Review

In recent years, the industrial emission of air pollution has been reduced via a series of measures. However, with the rapid development of modern agriculture, air pollution caused by agricultural activities is becoming more and more serious. Agricultural activities can generate a large amount of air pollutants, such as ammonia, methane, nitrogen oxides, volatile organic compounds, and persistent organic pollutants, the sources of which mainly include farmland fertilization, livestock breeding, pesticide use, agricultural residue burning, agricultural machinery, and agricultural irrigation. Greenhouse gases emitted by agricultural activities can affect regional climate change, while atmospheric particulates and persistent organic pollutants can even seriously harm the health of surrounding residents. With the increasing threat of agricultural air pollution, more and more relevant studies have been carried out, as well as some recommendations for reducing emissions. The emissions of ammonia and greenhouse gases can be significantly reduced by adopting reasonable fertilization methods, scientific soil management, and advanced manure treatment systems. Regarding pesticide use and agricultural residues burning, emission reduction are more dependent on the restriction and support of government regulations, such as banning certain pesticides, prohibiting open burning of straw, and supporting the recycling and reuse of residues. This review, summarizing the relevant research in the past decade, discusses the current situation, health effects, and emission reduction measures of agricultural air pollutants from different sources, in order to provide some help for follow-up research.
OPTH_A_301878 2437..2446

1Department of Ophthalmology, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India; 2Ocular Pharmacology and Pharmacy Division, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India; 3Department of Ocular Microbiology, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India Objective: To compare the safety and efficacy of intrastromal voriconazole (IS-VCZ), amphotericin B (IS-AMB) and natamycin (IS-NTM) as an adjunct to topical natamycin (NTM) in cases of recalcitrant fungal keratitis. Design: Prospective randomized trial. Setting: Tertiary eye centre. Participants: Sixty eyes of 60 patients with microbiologically proven recalcitrant fungal keratitis (ulcer size >2 mm, depth >50% of stroma, and not responding to topical NTM therapy for two weeks) were recruited. Methods: patients were randomized into three groups of 20 eyes, each receiving ISVCZ 50ug/ 0.1 mL, ISAMB, 5ug/0.1 mL and ISNTM 10ug/0.1 mL (prepared aseptically in ocular pharmacology). The patients in all three groups continued topical NTM 5% every four hours until the ulcer healed. Primary outcome measure was time taken till complete clinical resolution of infection, and secondary outcome measure was best corrected visual acuity (BCVA) at six months. Results: All three groups had comparable baseline parameters. The mean duration of healing was significantly better (p=0.02) in the ISNTM group (34±5.2 days) as compared to the ISVCZ group (36.1±4.8 days) and the ISAMB group (39.2±7.2 days). About 95%, 90% and 95% patients healed successfully in the ISVCZ, ISAMB and ISNTM groups, respectively. In terms of healing, deep vascularization was significantly greater in the ISAMB group (55%, p=0.02) when compared to the ISVCZ and ISNTM groups (31% and 26%, respectively). There were fewer repeat injections in the ISNTM group (7/20 vs 8/20 and 9/20 in the ISVCZ and ISNTM groups, respectively). Conclusion: Intrastromal injections are a safe and effective adjunct to conventional therapy in the management of recalcitrant fungal keratitis. ISNTM had a similar visual outcome with faster healing while ISAMB had a higher rate of deep vascularization after healing.
Taxonomic notes on the Rutaceae of Thailand

Nomenclatural problems with the name Atalantia simplicifolia are discussed, and it is shown that A. roxburghiana, often used for it, is illegitimate. Formerly part of Murraya, Bergera is recognized as separate genus for Thailand. Murraya siamensis is reduced to a synonym of Bergera koenigii.
Social representations of globalisation and the functioning of the metasystem in social comparison contexts: evidence from two studies in Portugal

Globalisation generates intense debate and contradictory positions, being a particularly relevant object of social representations (SR). This article attempts to better understand the SR of globalisation by analysing how the normative social metasystem controls, verifies and selects the material produced by the operative system in contexts of social comparison. Using a multi-method approach, we conducted two studies, with semi-structured interviews (N=30) and a free association questionnaire (N=100), in the Porto Metropolitan Area, Portugal. Results highlight how social comparison contexts trigger arguments in favour of group interests and reveal the existing power relations between groups. The importance of identifying the relations that guide thinking about social objects and the applicability of SR theory for understanding complex, controversial, sociopolitical objects are discussed.
Recovery characteristics of Bt insecticidal protein and relative physiological mechanisms after high temperature stress termination in square of Bt cotton

The experiments were conducted at Key Laboratory of Genetics and Physiology of Yangzhou University. The conventional cultivar Sikang-1 and hybrid cultivar Sikang-3 were used as the experimental materials. The potted cotton at squaring stage was moved to artificial climatic chamber with 38°C for 72 h , and 38°C for 96 h respectively in 2017 and 2018 cotton growth season, and moved to natural conditions, then the squares were collected to measure the Bt protein concentration and nitrogen metabolic physiology at 0, 12, 24, 48, 72, 96 hours respectively after the high temperature termination. The square Bt insecticidal protein contents were able to recover to the corresponding control level, the time for the recovery was in need of longer as the stressed high temperature period increased. In 72 h high temperature treatment, the square Bt insecticidal protein content could recover to the corresponding control level at 72 h for Sikang-1 and at 48 h for Sikang-3. In 96 h high temperature treatment, the square Bt insecticidal protein content could recover to the corresponding control level at 96 h for Sikang-1 and at 72 h for Sikang-3. There were significantly positive correlations of Bt toxin content with soluble protein content, glutamate pyruvate transaminase activity and glutamate oxaloacetate transaminase activity (the correlation coefficients were 0.964**, 0.981**, and 0.971**, respectively), and significantly negative correlations of Bt toxin content with free amino acid contents, and activitied of protease and peptidase (the correlation coefficients were -0.894**, -0.912**, and -0.834**, respectively). Therefore, the recovery degree of Bt insecticidal protein content of cotton square can be predicted according to the duration of high temperature stress, reasonably controlling Helicoverpa armigera and related pests in production.
Zeroth-order randomized block methods for constrained minimization of expectation-valued Lipschitz continuous functions

We consider the minimization of an $L_{0}$-Lipschitz continuous and expectation-valued function, denoted by $f$ and defined as $f(\mathrm{x})\ {\buildrel \triangle\over=}\\mathbb{E}[\tilde{f}(\mathrm{x}, \omega)]$, over a Cartesian product of closed and convex sets with a view towards obtaining both asymptotics as well as rate and complexity guarantees for computing an approximate stationary point (in a Clarke sense). We adopt a smoothing-based approach reliant on minimizing $f_{\eta}$ where $f(\mathrm{x})\ {\buildrel \triangle\over=}\\mathbb{E}_{u}[f(\mathrm{x}+\eta u)],u$ is a random variable defined on a unit sphere, and $\eta > 0$. In fact, it is observed that a stationary point of the $\eta$-smoothed problem is a $2\eta$-stationary point for the original problem in the Clarke sense. In such a setting, we derive a suitable residual function that provides a metric for stationarity for the smoothed problem. By leveraging a zeroth-order framework reliant on utilizing sampled function evaluations implemented in a block-structured regime, we make two sets of contributions for the sequence generated by the proposed scheme. (i) The residual function of the smoothed problem tends to zero almost surely along the generated sequence; (ii) To compute an $\mathrm{x}$ that ensures that the expected norm of the residual of the $\eta$-smoothed problem is within $\epsilon$ requires no greater than $\mathcal{O}(\frac{1}{\eta\epsilon^{2}})$ projection steps and $\mathcal{O}\left(\frac{1}{\eta^{2}\epsilon^{4}}\right)$ function evaluations. These statements appear to be novel with few related results available to contend with general nonsmooth, nonconvex, and stochastic regimes via zeroth-order approaches.
Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction

Multi-pedestrian trajectory prediction is an indispensable element of autonomous systems that safely interact with crowds in unstructured environments. Many recent efforts in trajectory prediction algorithms have focused on understanding social norms behind pedestrian motions. Yet we observe these works usually hold two assumptions, which prevent them from being smoothly applied to robot applications: (1) positions of all pedestrians are consistently tracked, and (2) the target agent pays attention to all pedestrians in the scene. The first assumption leads to biased interaction modeling with incomplete pedestrian data. The second assumption introduces aggregation of redundant surrounding information, and the target agent may be affected by unimportant neighbors or present overly conservative motion. Thus, we propose Gumbel Social Transformer, in which an Edge Gumbel Selector samples a sparse interaction graph of partially detected pedestrians at each time step. A Node Transformer Encoder and a Masked LSTM encode pedestrian features with sampled sparse graphs to predict trajectories. We demonstrate that our model overcomes potential problems caused by the aforementioned assumptions, and our approach outperforms related works in trajectory prediction benchmarks. Code is available at https://github.com/tedhuang96/gst.
Detection and Bulk Properties of the HR 8799 Planets with High-resolution Spectroscopy

Using the Keck Planet Imager and Characterizer, we obtained high-resolution (R ∼ 35,000) K-band spectra of the four planets orbiting HR 8799. We clearly detected H2O and CO in the atmospheres of HR 8799 c, d, and e, and tentatively detected a combination of CO and H2O in b. These are the most challenging directly imaged exoplanets that have been observed at high spectral resolution to date when considering both their angular separations and flux ratios. We developed a forward-modeling framework that allows us to jointly fit the spectra of the planets and the diffracted starlight simultaneously in a likelihood-based approach and obtained posterior probabilities on their effective temperatures, surface gravities, radial velocities, and spins. We measured vsin(i) values of 10.1−2.7+2.8kms−1 for HR 8799 d and 15.0−2.6+2.3kms−1 for HR 8799 e, and placed an upper limit of <14 km s−1 of HR 8799 c. Under two different assumptions of their obliquities, we found tentative evidence that rotation velocity is anticorrelated with companion mass, which could indicate that magnetic braking with a circumplanetary disk at early times is less efficient at spinning down lower-mass planets.
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills

Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning. In this work, we propose to leverage semi-structured tables, and automatically generate at scale question-paragraph pairs, where answering the question requires reasoning over multiple facts in the paragraph. We add a pre-training step over this synthetic data, which includes examples that require 16 different reasoning skills such as number comparison, conjunction, and fact composition. To improve data efficiency, we sample examples from reasoning skills where the model currently errs. We evaluate our approach on three reasoning-focused reading comprehension datasets, and show that our model, PReasM, substantially outperforms T5, a popular pre-trained encoder-decoder model. Moreover, sampling examples based on model errors leads to faster training and higher performance.
Lossless, scalable implicit likelihood inference for cosmological fields

We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two cases where the information content is calculable analytically: Gaussian random fields whose covariance depends on parameters through the power spectrum; and correlated lognormal fields with cosmological power spectra. We compare two inference techniques: i) explicit field-level inference using the known likelihood and ii) implicit likelihood inference with maximally informative summary statistics compressed via Information Maximising Neural Networks (IMNNs). We find that a) summaries obtained from convolutional neural network compression do not lose information and therefore saturate the known field information content, both for the Gaussian covariance and the lognormal cases, b) simulation-based inference using these maximally informative nonlinear summaries recovers nearly losslessly the exact posteriors of field-level inference, bypassing the need to evaluate expensive likelihoods or invert covariance matrices, and c) even for this simple example, implicit, simulation-based likelihood incurs a much smaller computational cost than inference with an explicit likelihood. This work uses a new IMNN implementation in Jax that can take advantage of fully-differentiable simulation and inference pipeline. We also demonstrate that a single retraining of the IMNN summaries effectively achieves the theoretically maximal information, enhancing the robustness to the choice of fiducial model where the IMNN is trained.
Modeling Neutron Star Oscillations in a Fixed General Relativistic Background Including Solid Crust Dynamics

Measurements of the gravitational-wave signals from neutron star mergers allow scientists to learn about the interior of neutron stars and the properties of dense nuclear matter. The study of neutron star mergers is usually performed with computational fluid dynamics codes, mostly in Eulerian but also in Lagrangian formulation such as smoothed particle hydrodynamics (SPH). Codes include our best knowledge of nuclear matter in the form of an equation of state as well as effects of general relativity (GR). However, one important aspect of neutron stars is usually ignored: the solid nature of their crust. The solid matter in the crust is the strongest material known in nature which could lead to a multitude of possible observational effects that have not been studied in dynamical simulations yet. The crust could change the way a neutron star deforms during a merger, leaving an imprint in the gravitational wave signal. It could even shatter during the inspiral, producing a potentially observable electromagnetic signal. Here, we present a first study of the dynamical behavior of neutron stars with a solid crust and fixed GR background with FleCSPH. FleCSPH is a general-purpose SPH code, developed at Los Alamos National Laboratory. It features an efficient algorithm for gravitational interactions via the Fast Multipole Method, which, together with the implemented nuclear equation of state, makes it appropriate for astrophysical applications. The solid material dynamics is described via the elastic-perfectly plastic model with maximum-strain breaking. Despite its simplicity, the model reproduces the stress-strain curve of crustal material as extracted from microphysical simulations very well. We present first tests of our implementation via simulations of neutron star oscillations and give an outlook on our study of the dynamical behavior of the solid crust in neutron star merger events.
The primordial black hole from running curvaton

In light of our previous work \cite{Liu:2019xhn}, we investigate the possibility of the formation of a primordial black hole in the second inflationary process induced by the oscillation of curvaton. By adopting the instability of the Mathieu equation, one could utilize the $\delta$ function to fully describe the power spectrum. Due to the running of curvaton mass, we can simulate the value of abundance of primordial black holes nearly covering all of the mass ranges, in which we have given three special cases. One case could account for the dark matter in some sense since the abundance of a primordial black hole is about $75\%$. At late times, the relic of exponential potential could be approximated to a constant of the order of cosmological constant dubbed as a role of dark energy. Thus, our model could unify dark energy and dark matter from the perspective of phenomenology. Finally, it sheds new light on exploring Higgs physics.
Clustering-based convergence diagnostic for multi-modal identification in parameter estimation of chromatography model with parallel MCMC

Uncertainties from experiments and models render multi-modal difficulties in model calibrations. Bayesian inference and \textsc{mcmc} algorithm have been applied to obtain posterior distributions of model parameters upon uncertainty. However, multi-modality leads to difficulty in convergence criterion of parallel \textsc{mcmc} sampling chains. The commonly applied $\widehat{R}$ diagnostic does not behave well when multiple sampling chains are evolving to different modes. Both partitional and hierarchical clustering methods has been combined to the traditional $\widehat{R}$ diagnostic to deal with sampling of target distributions that are rough and multi-modal. It is observed that the distributions of binding parameters and pore diffusion of particle parameters are multi-modal. Therefore, the steric mass-action model used to describe ion-exchange effects of the model protein, lysozyme, on the \textsc{sp} Sepharose \textsc{ff} stationary phase might not be fully capable in certain experimental conditions, as model uncertainty from steric mass-action would result in multi-modality.
Gradient blow-up for dispersive and dissipative perturbations of the Burgers equation

We consider a class of dispersive and dissipative perturbations of the inviscid Burgers equation, which includes the fractional KdV equation of order $\alpha$, and the fractal Burgers equation of order $\beta$, where $\alpha, \beta \in [0,1)$, and the Whitham equation. For all $\alpha, \beta \in [0,1)$, we construct solutions whose gradient blows up at a point, and whose amplitude stays bounded, which therefore display a"shock-like"singularity. We moreover provide an asymptotic description of the blow-up. To our knowledge, this constitutes the first proof of gradient blow-up for the fKdV equation in the range $\alpha \in [2/3, 1)$, as well as the first description of explicit blow-up dynamics for the fractal Burgers equation in the range $\beta \in [2/3, 1)$. Our construction is based on modulation theory, where the well-known smooth self-similar solutions to the inviscid Burgers equation are used as profiles. A somewhat amusing point is that the profiles that are less stable under initial data perturbations (in that the number of unstable directions is larger) are more stable under perturbations of the equation (in that higher order dispersive and/or dissipative terms are allowed) due to their slower rates of concentration. Another innovation of this article, which may be of independent interest, is the development of a streamlined weighted $L^{2}$-based approach (in lieu of the characteristic method) for establishing the sharp spatial behavior of the solution in self-similar variables, which leads to the sharp H\"older regularity of the solution up to the blow-up time.
Hierarchical Graph Neural Nets can Capture Long-Range Interactions

Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range interactions in graphs. In this paper we study hierarchical message passing models that leverage a multi-resolution representation of a given graph. This facilitates learning of features that span large receptive fields without loss of local information, an aspect not studied in preceding work on hierarchical GNNs. We introduce Hierarchical Graph Net (HGNet), which for any two connected nodes guarantees existence of message-passing paths of at most logarithmic length w.r. t. the input graph size. Yet, under mild assumptions, its internal hierarchy maintains asymptotic size equivalent to that of the input graph. We observe that our HGNet outperforms conventional stacking of GCN layers particularly in molecular property prediction benchmarks. Finally, we propose two benchmarking tasks designed to elucidate capability of GNNs to leverage long-range interactions in graphs.
Lockout: Sparse Regularization of Neural Networks

Many regression and classification procedures fit a parameterized function $f(x;w)$ of predictor variables $x$ to data $\{x_{i},y_{i}\}_1^N$ based on some loss criterion $L(y,f)$. Often, regularization is applied to improve accuracy by placing a constraint $P(w)\leq t$ on the values of the parameters $w$. Although efficient methods exist for finding solutions to these constrained optimization problems for all values of $t\geq0$ in the special case when $f$ is a linear function, none are available when $f$ is non-linear (e.g. Neural Networks). Here we present a fast algorithm that provides all such solutions for any differentiable function $f$ and loss $L$, and any constraint $P$ that is an increasing monotone function of the absolute value of each parameter. Applications involving sparsity inducing regularization of arbitrary Neural Networks are discussed. Empirical results indicate that these sparse solutions are usually superior to their dense counterparts in both accuracy and interpretability. This improvement in accuracy can often make Neural Networks competitive with, and sometimes superior to, state-of-the-art methods in the analysis of tabular data.
First-Principles Study of Alkali and Alkaline Earth Ion Intercalation in Iron Hexacyanoferrate: The Important Role of Ionic Radius

Hexacyanoferrate compounds recently have attracted much interest because of their potential as aqueous and nonaqueous battery cathodes in the application of large-scale energy storage. Here, the feasibility to use iron hexacyanoferrate, Fe[Fe(CN)6], as a cathode candidate for nonaqueous rechargeable batteries is assessed with first-principle calculations. The structural deformation induced by the intercalation of alkali (Li+, Na+, K+, Rb+, Cs+) and alkaline earth (Mg2+, Ca2+, Sr2+, Ba2+) ions into Fe[Fe(CN)6] induces is negligible as compared to other Li-ion battery cathodes. The intercalation is strongly affected by the ionic radius of inserted species. With increasing ionic size, the most stable interstitial site changes from face-centered site to body-centered site. More importantly, the voltages for the intercalation are highly correlated to the ionic radius of the inserted species. The insertion of cations with larger ionic radius happens at higher voltages, and thus provides higher energy density. H.