Miracle-dz/UniFAR-FTUs huggingface dataset:
Einstein quartic gravity: Shadows, signals, and stability

Using a continued fraction ansatz we obtain an analytic approximation for a spherically symmetric black hole solution to Einsteinian Quartic Gravity (EQG), the next simplest Generalized Quasi-Topological Gravity (GQTG) after Einsteinian Cubic Gravity (ECG). This approximate solution is valid everywhere outside of the horizon and we use it to investigate the orbit of massive test bodies near a black hole, specifically computing the innermost stable circular orbit. Using Shapiro time delay we calculate the constraints on the EQG coupling parameter. Finally we compute the shadow of an EQG black hole and figure out it to be larger than its Einsteinian counterpart in general relativity for the same value of the mass. By applying our results to Sagittarius A* (Sgr A*) at the center of Milky Way we find, similar to ECG black holes, that departures from general relativity are small but distinguishable for EQG black holes.
Denitrification in soil. II. Factors affecting denitrification

1. The factors affecting denitrification in soil have been studied by determining loss of nitrogen from soil under various conditions by total-N analysis. 2. It was found that the rate of denitrification of nitrate in soil was dependent upon various factors such as the pH, temperature and water content of the soil and that, under conditions conducive to denitrification, 80–86% of nitrate-N added to Rothamsted soils was lost by denitrification in 5 days. 3. The rate of denitrification was greatly affected by the pH of the soil. It was very slow at low pH (below 4·8), increased with rise in soil pH and was very rapid at pH 8·0–8·6. 4. The rate of denitrification increased rapidly with rise in temperature from 2° to 25° C. The optimum temperature for denitrification was about 60° C. 5. The degree of water saturation of the soil had a profound influence on the rate of denitrification. Below a certain moisture level practically no denitrification occurred; above this level denitrification increased rapidly with increase in moisture content. The critical moisture level was about 60% of the water-holding capacity of the soil.
Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions

We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a Stay-at-Home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bayesian estimation of model parameters is carried out using Markov Chain Monte Carlo (MCMC) methods. This framework allows us to treat all available input information, including both the previously published parameters of the epidemic and available local data, in a uniform manner. To accurately model deaths as well as demand on the healthcare system, we calibrate our predictions to total and in-hospital deaths as well as hospital and ICU bed occupancy by COVID-19 patients. We apply this model not only to the state as a whole but also its sub-regions in order to account for the wide disparities in population size and density. Without prior information on non-pharmaceutical interventions (NPIs), the model independently reproduces a mitigation trend closely matching mobility data reported by Google and Unacast. Forward predictions of the model provide robust estimates of the peak position and severity and also enable forecasting the regional-dependent results of releasing Stay-at-Home orders. The resulting highly constrained narrative of the epidemic is able to provide estimates of its unseen progression and inform scenarios for sustainable monitoring and control of the epidemic.
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding

We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: being fully controllable and fully annotated with any type of standard or custom-defined ground truth. To reduce the synthetic-to-real domain gap, we introduce a pipeline consisting of the following steps: 1) we render scenes in a context modeled after the real world, 2) we train a human parsing model on the synthetic images, 3) we use the model to estimate segmentation maps for real images, 4) we train a conditional generative adversarial network (cGAN) to learn the inverse mapping -- from a segmentation map to a real image, and 5) given new synthetic segmentation maps, we use the cGAN to generate realistic images. An illustration of our pipeline is presented in Figure 2. We use the generated data to train a multi-task model on the challenging tasks of UV mapping and dense depth estimation. We demonstrate the value of the data generation and the trained model, both quantitatively and qualitatively on the CMU Panoptic Dataset.
Mammalia in the Steppe Biocenose

The dependence of the animal world on the surrounding environment has long been an object of scientific investigation. Concerning MIammalia, we possess at present much data showing how these animals are, in many cases, influenced by one or another specific peculiarity of the locations in which they dwell. Temperature, moisture, altitude above the sea-level, character of the soil, etc., put a peculiar stamp on the organization and the mode (f life of mammals, either directly, or else by means of the vegetation which is always highly sensitive to the slightest variation in the environmental conditions. The peculiar characters of the steppe mammalia, resulting from a protracted evolution in wide plains, with low-growing herbaceous vegetation, are so well known that it seems needless to enumerate them. The sandy and buff colors, characteristic of the antelopes, the foxes, the marmots, the suslike, the around squirrels, the voles, the jerboa. etc., harmonize admirably with the golden gray tints of the dry plains. We know about periodic migrations of the herbivorous mammals, owing to the summer drying up of the steppes vegetation. For the rodents, the time of awakening from hibernation corresponds to the appearance of the young vegetation; that of rearing the young coincides with the period of bloom in the vegetation, and lastly, hibernation in the autumn begins with the decay of the nutritive plants. All these facts bear solely upon one side of the question the significance of the steppe vegetation for the mammals dwelling in its midst. They furnish no answer to the second part of the question. viz., whether these animals themselves play any part in the transformation of the environment and whether their vital activity contributes in any way to change the aspect of the steppes. J. Grinnell ('23), in his interesting work, "The Burrowing Rodents of California as agents in soil formation," has shown that the results of the activity of burrowing rodents, belonging to a small group of the mammalian fauna, are considerable, and deserve serious attention. He concludes thus: " On wild land the burrowing rodent is one of the necessary factors in the system of natural wellbeing. " The present article is partially a contribution to the work of Grinnell, and its object is to show the importance of Mammalia in the life of the steppes that extend in a wide belt through eastern Europe and central Asia. My own investigations upon this subject were made in southern Russia and northern Mongolia. during the expedition of the Russian Academy of Sciences (July to October, 1926). of which I had the pleasure of being a member.
The Abutilon Mosaic Virus (AbMV) in Different Leaf Tissues of Several Host Species of Malvaceae

Cytological changes occurring after infection by Abutilon Mosaic Virus (AbMV) have been investigated in the host plants Abutilon sellovianum Rgl. , Malva parviflora L. , Malva silvestris L. , and Sida micrantha Schr. Two types of virus-like inclusions were found in different tissues: particles of 16–18 nm in diameter and elongated filaments with 6–8 nm in diameter. The particles are detectable in the nuclei (Abutilon sellovianum, Malva spec.) and cytoplasm (Malva spec.) of cells in the bundle area, and the filaments accumulate in the nuclei (Sida micrantha) and the plastids (Malva spec.) of leaf mesophyll cells.
Convex Optimization of Electric Vehicle Powertrains based on Geometric Programming

The powertrain of battery electric vehicles can be optimized to maximize the travel distance for a given amount of stored energy in the traction battery. We present a systematic optimization approach to derive optimal powertrain designs for battery electric vehicles that is based on geometric programming. The benefits of this approach are a rapid solution time, a global optimal solution, and the ability to derive local sensitivities in the optimal design point with no extra cost. The presented approach is suitable for the evaluation of a complete drive cycle, as this is commonly done in powertrain system design, or for usage in a stochastic approach, where multiple scenarios are sampled from a given probability density function. The latter is useful, to account for the uncertainty in the driving loads and it can generate solutions that are optimal in an average sense for a high variety of vehicles and drive conditions. For the powertrain model we use a gearbox with one selectable gear ratio and an electric motor model that is based on an efficiency map representation. We can easily model a continuously variable transmission to show beneficial energy savings based on this additional degree of freedom. Furthermore, we show approaches to add further component designs, as for instance a multi-gear gearbox in the optimization model.
ETHOS – an effective parametrization and classification for structure formation: the non-linear regime at z ≳ 5

We propose two effective parameters that fully characterise galactic-scale structure formation at high redshifts ($z\gtrsim5$) for a variety of dark matter (DM) models that have a primordial cutoff in the matter power spectrum. Our description is within the recently proposed ETHOS framework and includes standard thermal Warm DM (WDM) and models with dark acoustic oscillations (DAOs). To define and explore this parameter space, we use high-redshift zoom-in simulations that cover a wide range of non-linear scales from those where DM should behave as CDM ($k\sim10\,h\,{\rm Mpc}^{-1}$), down to those characterised by the onset of galaxy formation ($k\sim500\,h\,{\rm Mpc}^{-1}$). We show that the two physically motivated parameters $h_{\rm peak}$ and $k_{\rm peak}$, the amplitude and scale of the first DAO peak, respectively, are sufficient to parametrize the linear matter power spectrum and classify the DM models as belonging to effective non-linear structure formation regions. These are defined by their relative departure from Cold DM ($k_{\rm peak}\rightarrow\infty$) and WDM ($h_{\rm peak}=0$) according to the non-linear matter power spectrum and halo mass function. We identify a region where the DAOs still leave a distinct signature from WDM down to $z=5$, while a large part of the DAO parameter space is shown to be degenerate with WDM. Our framework can then be used to seamlessly connect a broad class of particle DM models to their structure formation properties at high redshift without the need of additional $N$-body simulations.
Nitrogen Availability in an Organic Potato Crop Following 3-Year Transition under Contrasting Farming Systems

Organic farming systems often involve livestock and/or forages. However, managing soil nitrogen (N) availability by adjusting soil amendment rates and forage duration in crop rotations is a challenge. A 4-year experiment was conducted to evaluate N availability in the fourth year of organic potato systems with contrasting livestock and forage components. The experiment included three forage levels (0, 1, or 2 years of forage) through 4-year rotations starting with wheat (Triticum aestivum L.) and ending in potato (Solanum tuberosum L.). Within each forage level, three soil amendments (alfalfa meal, composted beef manure, and composted poultry manure) represented nutrient sources from stockless, ruminant, and monogastric systems, respectively. As farmers do, we modified the amendment application rate and timing by taking a system approach to reflect crop rotations. Nitrogen availability was higher in alfalfa meal compared with the composts varied with feedstocks. Nitrogen supply from soil amendments differed in the year of application, with no apparent N carryover effects. With increased forage frequency, soil N supply rates increased in the organic potato season following the 3-year transition period. The pre-experimental pasture with high soil fertility limited differences in plant N uptake among treatments. Nitrogen availability as indicated by potato N uptake was linearly related to cumulative amendment inputs in spite of different amounts of amendment input to different treatments.
A Negative Correlation Strategy for Bracketing in Difference-in-Differences with Application to the Effect of Voter Identification Laws on Voter Turnout

The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID exploits a before and after comparison of the treated and control units to remove the bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel trends assumption is violated. We propose a general identification strategy that leverages two groups of control units whose outcomes relative to the treated units exhibit a negative correlation, and achieves partial identification of the average treatment effect for the treated. The identified set is of a union bounds form that previously developed partial identification inference methods do not apply to. We develop a novel bootstrap method to construct valid confidence intervals for the identified set and parameter of interest when the identified set is of a union bounds form, and we establish the theoretical properties. We develop a simple falsification test and sensitivity analysis. We apply the proposed strategy for bracketing to an application on the effect of voter identification laws in Georgia and Indiana on turnout and find evidence that the laws increased turnout rates.
Automatic Counting of Restricted Dyck Paths via (Numeric and Symbolic) Dynamic Programming

Dyck paths are one of the most important objects in enumerative combinatorics, and there are many papers devoted to counting selected families of Dyck paths. Here we present two approaches for the automatic counting of many such families, using both a "dumb" approach (driven by numeric dynamic programming) that often works in practice, and a "clever" approach, needed for larger problems, driven by "symbolic" dynamic programming. Both approaches are fully automated and implemented in Maple.
Dietary Supplementation with Modified Arabinoxylan Rice Bran (MGN-3) Modulates Inflammatory Responses in Broiler Chickens

The objective of this study was to investigate the effect of dietary supplementation with modified arabinoxylan rice bran (MGN-3) on the immune system and inflammatory response in broiler chickens. The levels of cluster of differentiation 3 (CD3), interleukin (IL)-2 and interferon (IFN)-γ mRNA in the spleen of chickens increased with the supplementation of MGN-3 at 100 ppm in diet, while those expression levels in the foregut did not change. Mitogen-induced proliferation of splenic mononuclear cells (MNC) and blood MNC phagocytosis in chickens fed MGN-3-supplemented diets were significantly greater than in chickens fed a basal diet (control). These results provide the first evidence that the use of dietary MGN-3 supplementation induces the T-cell immune system in chickens. Two hours after Escherichia coli (E. coli) lipopolysaccharide (LPS)-induced immune stimulation, the levels of mRNA encoding pro-inflammatory cytokines, such as IL-2, IFN-γ and tumor necrosis factor-like ligand 1A (TL1A), in the spleen of chickens fed a MGN-3-supplemented diet were significantly lower than those in chickens exposed to other treatments. The levels of toll-like receptor -4 and -7 mRNAs in the foregut of chickens fed MGN-3 supplemented diets were lower than those in control chickens at 2h after injection of LPS. The plasma ceruloplasmin concentration in chickens fed a MGN-3-supplemented diet was significantly lower than in controls at 24h after injection of LPS. These results show that MGN-3 might be useful as an immunomodulator to stimulate T-cells in growing broiler chickens, thereby protecting chickens from disease, particularly colibacillosos, without reducing growth performance.
Interpretable Time-series Classification on Few-shot Samples

Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely Dual Prototypical Shapelet Networks (DPSN) for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity: 1) global overview using representative time series samples, and 2) local highlights using discriminative shapelets. In particular, the generated dual prototypical shapelets consist of representative samples that can mostly demonstrate the overall shapes of all samples in the class and discriminative partial-length shapelets that can be used to distinguish different classes. We have derived 18 few-shot TSC datasets from public benchmark datasets and evaluated the proposed method by comparing with baselines. The DPSN framework outperforms state-of-the-art time-series classification methods, especially when training with limited amounts of data. Several case studies have been given to demonstrate the interpret ability of our model.
Effect of mechanical processing of corn silage on in vitro ruminal fermentation, and in situ bacterial colonization and dry matter degradation

Rumen fermentation and bacterial colonization of mechanically processed corn silage were evaluated in a series of in vitro and in situ experiments using ruminally cannulated cows. Data from rumen-simulated continuous culture (Rusitec) showed that total VFA concentration was increased (P 0.05). Mechanical processing enhanced the in situ rate and extent of dry matter disappearance for corn stems and kernels, respectively (P < 0.05). It also enhanced in situ, the initial attachment of rumen bacteria responsible for corn digestion, as monitored by real-time PCR. The16S rDNA copy numbers of amylolytic and lipolytic bacteria, such as Succinivibrio dextrinosolvens, Ruminobacter amylophilus and Anaerovibrio lipolytica, attached to corn kernels were increased for processed corn silage (P < 0.05), in particular at 2 h after the start of incubation. In addition, corn stemattached cellulolytic and .
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior

Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in context. However, it remains an open question what computational architecture best characterizes the expectations deployed in real time by humans that determine the behavioral signatures of reading. Here we test over two dozen models, independently manipulating computational architecture and training dataset size, on how well their next-word expectations predict human reading time behavior on naturalistic text corpora. We find that across model architectures and training dataset sizes the relationship between word log-probability and reading time is (near-)linear. We next evaluate how features of these models determine their psychometric predictive power, or ability to predict human reading behavior. In general, the better a model's next-word expectations, the better its psychometric predictive power. However, we find nontrivial differences across model architectures. For any given perplexity, deep Transformer models and n-gram models generally show superior psychometric predictive power over LSTM or structurally supervised neural models, especially for eye movement data. Finally, we compare models' psychometric predictive power to the depth of their syntactic knowledge, as measured by a battery of syntactic generalization tests developed using methods from controlled psycholinguistic experiments. Once perplexity is controlled for, we find no significant relationship between syntactic knowledge and predictive power. These results suggest that different approaches may be required to best model human real-time language comprehension behavior in naturalistic reading versus behavior for controlled linguistic materials designed for targeted probing of syntactic knowledge.
Fine structure of the chelicera in the spider Nephila clavata

The fine structural characteristics of the biting apparatus in the orb‐web spider Nephila clavata were studied using scanning electron microscopy. The main biting apparatuses of spiders are the chelicerae and cheliceral fangs in the cephalothorax. The chelicera of N. clavata is that of the jack‐knife (folding knife) type, which is composed of two segments, and has a labidognathous form that moves at right angles to the body axis. Each chelicera bears a hinged fang that folds into a cheliceral groove. The tips of the fangs are quite sharp, and the spider's body is well adapted to driving the fangs into prey. Just below the fang, each side of the cheliceral groove is covered with a total of seven cuticular teeth (four promarginal teeth and three retromarginal teeth) in two rows. The cheliceral fang has a single aperture at the tip of the posterior surface, and the lower margin of the fang which meets the promarginal teeth is a saw‐like groove. Fine structural observation reveals that each fang has a single venom pore, and each cuticular depressive area on the cheliceral groove has two different types of surface pit. Approximately 40 to 50 spiky protrusions were counted at the cheliceral groove, to hold prey tightly.
Phenylalanine ammonia lyase as a precursory enzyme of legume stem lignification

In some instances, lignin content may not be significantly correlated with phenylalanine ammonia lyase (PAL) activity because: (1) PAL is not committed exclusively to lignin, and (2) the time of maximum PAL activity may not coincide with maximum lignin deposition. This study evaluates correlations and timing of PAL activity and lignin deposition during legume stem maturation. Three forage legumes, alfalfa (Medicago sativa L.), birdsfoot trefoil (Lotus corniculatus L.), and red clover (Trifolium pratense L.), were established, and basal stem regrowth was sampled, biweekly, for 10 wk, for dry weight (DW), cell wall (CW), lignin, and PAL analyses. Nonlinear regression of lignin content by the Gompertz function indicated that lignin increased sigmoidally, and PAL activity by the third-order quadratic demonstrated rapid initial increases in activity, followed by decreases, as a function of regrowth days. First derivative of the Gompertz function demonstrated that changes in lignin deposition closely resembled .