Miracle-dz/UniFAR-FTUs huggingface dataset:
experimentally induced asthma and alter pulmonary dendritic cell phenotype and function

The role of natural CD4 � CD25 � regulatory T (T reg) cells in the control of allergic asthma remains poorly understood. We explore the impact of T reg cell depletion on the allergic response in mice susceptible (A/J) or comparatively resistant (C3H) to the development of allergen-induced airway hyperresponsiveness (AHR). In C3H mice, anti-CD25‐mediated T reg cell depletion before house dust mite treatment increased several features of the allergic diathesis (AHR, eosinophilia, and IgE), which was concomitant with elevated T helper type 2 (Th2) cytokine production. In similarly T reg cell‐depleted A/J mice, we observed a moderate increase in airway eosinophilia but no effects on AHR, IgE levels, or Th2 cytokine synthesis. As our experiments suggested that T reg cell depletion in C3H mice before sensitization was sufficient to enhance the allergic phenotype, we characterized dendritic cells (DCs) in T reg cell‐depleted C3H mice. T reg cell‐depleted mice had increased numbers of pulmonary myeloid DCs with elevated expression of major histocompatibility complex class II, CD80, and CD86. Moreover, DCs from T reg cell‐depleted mice demonstrated an increased capacity to stimulate T cell proliferation and Th2 cytokine production, which was concomitant with reduced IL-12 expression. These data suggest that resistance to allergen-driven AHR is mediated in part by CD4
First Results from SMAUG: The Need for Preventative Stellar Feedback and Improved Baryon Cycling in Semianalytic Models of Galaxy Formation

Semianalytic models (SAMs) are a promising means of tracking the physical processes associated with galaxy formation, but many of their approximations have not been rigorously tested. As part of the Simulating Multiscale Astrophysics to Understand Galaxies project, we compare predictions from the FIRE-2 hydrodynamical “zoom-in” simulations to those from the Santa Cruz SAM run on the same halo merger trees, with an emphasis on the global mass flow cycle. Our study includes 13 halos spanning low-mass dwarfs (Mvir ∼ 1010M⊙ at z = 0), intermediate-mass dwarfs (Mvir ∼ 1011M⊙), and Milky Way–mass galaxies (Mvir ∼ 1012M⊙). The SAM and FIRE-2 predictions agree relatively well with each other in terms of stellar and interstellar medium mass but differ dramatically on circumgalactic medium mass (the SAM is lower than FIRE-2 by ∼3 orders of magnitude for dwarfs). Strikingly, the SAM predicts higher gas accretion rates for dwarfs compared to FIRE-2 by factors of ∼10–100, and this is compensated for with higher mass outflow rates in the SAM. We argue that the most severe model discrepancies are caused by the lack of preventative stellar feedback and the assumptions for halo gas cooling and recycling in the SAM. As a first step toward resolving these model tensions, we present a simple yet promising new preventative stellar feedback model in which the energy carried by supernova-driven winds is allowed to heat some fraction of gas outside of halos to at least the virial temperature such that accretion is suppressed.
Goα labels ON bipolar cells in the tiger salamander retina

By using double‐label immunocytochemistry and confocal microscopy, we studied rod and cone synaptic contacts, photoreceptor‐bipolar cell convergence, and patterns of axon terminal ramification of ON bipolar cells in the tiger salamander retina. An antibody to recoverin, a calcium‐binding protein found in photoreceptors and other retinal neurons in various vertebrates, differentially labeled rods and cones by lightly staining rod cell bodies, axons, and synaptic pedicles and heavily staining cone cell bodies and pedicles. An antibody to Goα labeled most ON bipolar cells, with axon terminals ramified mainly in strata 6–9 and a minor band in stratum 3 of the inner plexiform layer (IPL). Stratum 10 of the IPL was Goα negative, and previous studies showed that axon terminals of rod‐dominated ON bipolar cells are monostratified in that stratum. The axonal morphology of Goα‐positive cells resembled that of the cone‐dominated (DBCC) or mixed rod and cone ON (DBCM) bipolar cells. The Goα‐positive dendritic processes made close contact with all cone pedicles and superficial contact with some rod pedicles, consistent with the idea that Goα subunits are present in DBCCs and DBCMs. The size and density of these cells were analyzed, and their spatial distributions were determined. To our knowledge, this is the first study to characterize photoreceptor inputs and axon terminal morphology of a population of ON bipolar cell with the use of a Goα antibody as an immunomarker in the salamander retina. J. Comp. Neurol. 461:276–289, 2003. © 2003 Wiley‐Liss, Inc.
Policy Gradient Optimization of Thompson Sampling Policies

We study the use of policy gradient algorithms to optimize over a class of generalized Thompson sampling policies. Our central insight is to view the posterior parameter sampled by Thompson sampling as a kind of pseudo-action. Policy gradient methods can then be tractably applied to search over a class of sampling policies, which determine a probability distribution over pseudo-actions (i.e., sampled parameters) as a function of observed data. We also propose and compare policy gradient estimators that are specialized to Bayesian bandit problems. Numerical experiments demonstrate that direct policy search on top of Thompson sampling automatically corrects for some of the algorithm's known shortcomings and offers meaningful improvements even in long horizon problems where standard Thompson sampling is extremely effective.
PARTIAL RECOVERY FOR TOP-K RANKING: OPTIMALITY OF MLE AND SUB-OPTIMALITY OF SPECTRAL METHOD

Given partially observed pairwise comparison data generated by the Bradley-Terry-Luce (BTL) model, we study the problem of top-k ranking. That is, to optimally identify the set of top-k players. We derive the mini-max rate with respect to a normalized Hamming loss. This provides the ﬁrst result in the literature that characterizes the partial recovery error in terms of the proportion of mistakes for top-k ranking. We also derive the optimal signal to noise ratio condition for the exact recovery of the top-k set. The maximum likelihood estimator (MLE) is shown to achieve both optimal partial recovery and optimal exact recovery. On the other hand, we show another popular algorithm, the spectral method, is in general sub-optimal. Our re-sults complement the recent work by [7] that shows both the MLE and the spectral method achieve the optimal sample complexity for exact recovery. It turns out the leading constants of the sample complexity are different for the two algorithms. Another contribution that may be of independent interest is the analysis of the MLE without any penalty or regularization for the BTL model. This closes an important gap between theory and practice in the literature of ranking.
Customized selection indices for dairy bulls in Australia

Customized sire selection indices were developed for Australian dairy bulls in order to promote more objective use of estimated breeding values in commercial breeding programmes. It was assumed that the breeding goal for commercial dairy farmers is the profitability of a bull's progeny. Seven characteristics of the progeny were identified as having a major impact on profitability and were included in the breeding objective: milk, fat and protein yield, survival, body weight, milking speed and temperament. Traits in the selection indices used to predict profit were milk, fat and protein yield, survival, milking speed, temperament, size, overall type and fore teat placement. Size was included because of its correlation with body weight, and overall type and front teat placement because of their correlation with survival. To avoid double counting the benefits of milk production traits, temperament and milking speed, the survival trait in the objective was defined as survival independent of voluntary culling for these traits. Customization of the breeding objective was achieved by adjusting the economic weights for traits in the objective to take account of important characteristics of farmers' herds, the milk payment system under which they operate and make allowance for their own value judgements. An assessment of the impact of customization suggested that, even though there is a wide range in the economic weights that are applicable in different areas of Australia, there would be little loss of efficiency in using a single national index. However, customization is still believed to be desirable given that it is likely that a substantial proportion of farmers will be reluctant to use a national index, especially in those states which have quotas and focus on the liquid milk market. The algorithms described in the paper have been incorporated into a user-friendly microcomputer program called $electabull which is now commercially available to farmers.
Spin and density self-ordering in dynamic polarization gradients fields

We study the zero-temperature quantum phase diagram for a two-component Bose-Einstein condensate in an optical cavity. The two atomic spin states are Raman coupled by two transverse orthogonally-polarized, blue detuned plane-wave lasers inducing a repulsive cavity potential. For weak pump the lasers favor a state with homogeneous density and predefined uniform spin direction. When one pump laser is polarized parallel to the cavity mode polarization, the photons coherently scattered into the resonator induce a polarization gradient along the cavity axis, which mediates long-range density-density, spin-density, and spin-spin interactions. We show that the coupled atom-cavity system implements central aspects of the $t$-$J$-$V$-$W$ model with a rich phase diagram. At the mean-field limit we identify at least four qualitatively distinct density- and spin-ordered phases including ferro- and anti-ferromagnetic order along the cavity axis, which can be controlled via the pump strength and detunings. Nondestructive observation of amplitude and phase of the emitted fields bears strong signatures of the realized phase and allows for real-time observation of the underlying dynamics.
Realizing the Frenkel-Kontorova model with Rydberg-dressed atoms

We propose a method to realize the Frenkel-Kontorova model using an array of Rydberg-dressed atoms. Our platform can be used to study this model with a range of realistic interatomic potentials. In particular, we concentrate on two types of interaction potentials: A springlike potential and a repulsive long-range potential. We numerically calculate the phase diagram for such systems and characterize the Aubry-type and commensurate-incommensurate phase transitions. Experimental realizations of this system are also discussed.
Impact of contamination factors on the COVID-19 evolution in Senegal

In this article, we perform an analysis of COVID-19 on one of the South Saharan countries (hot zone), the Senegal (West Africa). Many questions remain unanswered: why the African continent is not very contaminated compared to other continents. Factors of cross immunity, temperature, population density, youth, etc. are taken into account for an analysis of the contamination factors. Numerical simulations are carried out for a prediction over the coming week.
Inference in Bayesian additive vector autoregressive tree models

Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This linearity assumption might be overly restrictive and could have a deleterious impact on forecasting accuracy. As a solution, we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher. Since controlling for heteroscedasticity is key for producing precise density forecasts, our model allows for stochastic volatility in the errors. Using synthetic and real data, we demonstrate the advantages of our methods. For Eurozone data, we show that our nonparametric approach improves upon commonly used forecasting models and that it produces impulse responses to an uncertainty shock that are consistent with established findings in the literature.
Theory-Inspired Path-Regularized Differential Network Architecture Search

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly reveals the impact of skip connections to fast network optimization and its competitive advantage over other types of operations in DARTS. Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during the search. Experimental results on image classification tasks validate its advantages. Codes and models will be released.
Assessment of the importance of seed cutting, in-season cultivation, and the passage of row equipment in the spread of PVY° in potatoes

Grower concerns, coupled with recent reports, have suggested that potato virus Y° (PVY°) may be spread as the result of mechanical transmission following the cutting of infected tubers during seed multiplication, or through vine abrasion caused by the passage of row equipment used during spraying operations and post-emergence tillage. We present the results of a series of greenhouse and field experiments (on the potato cultivars ‘Shepody’, ‘Russet Norkotah’, ‘Russet Burbank’, ‘Jemseg’, ‘Kennebec’, and ‘Superior’) to examine this possibility. Our results provide no evidence to support the view that mechanical transmission of PVY° occurs through the use of (hand or mechanical) cutting knives. Statistical analysis of the spatial pattern of in-field virus spread in the highly susceptible cultivar ‘Shepody’ did not show a significant clumping of infected plants within plot rows, arguing, in this case, that the passage of farm equipment and associated tillage operations are of no practical significance in the epidemiology of PVY° under growing conditions currently found in the Maritime provinces.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the nondifferentiable nature of spiking neuronal functions, the standard error backpropagation algorithm is not directly applicable to SNNs. In this work, we propose a tandem learning framework that consists of an SNN and an artificial neural network (ANN) coupled through weight sharing. The ANN is an auxiliary structure that facilitates the error backpropagation for the training of the SNN at the spike-train level. To this end, we consider the spike count as the discrete neural representation in the SNN and design an ANN neuronal activation function that can effectively approximate the spike count of the coupled SNN. The proposed tandem learning rule demonstrates competitive pattern recognition and regression capabilities on both the conventional frame- and event-based vision datasets, with at least an order of magnitude reduced inference time and total synaptic operations over other state-of-the-art SNN implementations. Therefore, the proposed tandem learning rule offers a novel solution to training efficient, low latency, and high-accuracy deep SNNs with low computing resources.
Path integral based convolution and pooling for graph neural networks

Graph neural networks (GNNs) extend the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose path integral-based GNNs (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix that we call the maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus leading to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics that we propose to boost applications of GNN in physical sciences.
Large image datasets: A pyrrhic win for computer vision?

In this paper we investigate problematic practices and consequences of large scale vision datasets (LSVDs). We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example, we perform a cross-sectional model-based quantitative census covering factors such as age, gender, NSFW content scoring, class- wise accuracy, human-cardinality-analysis, and the semanticity of the image class information in order to statistically investigate the extent and subtleties of ethical transgressions. We then use the census to help hand-curate a look-up-table of images in the ImageNet-ILSVRC-2012 dataset that fall into the categories of verifiably pornographic: shot in a non-consensual setting (up-skirt), beach voyeuristic, and exposed private parts. We survey the landscape of harm and threats both the society at large and individuals face due to uncritical and ill-considered dataset curation practices. We then propose possible courses of correction and critique their pros and cons. We have duly open-sourced all of the code and the census meta-datasets generated in this endeavor for the computer vision community to build on. By unveiling the severity of the threats, our hope is to motivate the constitution of mandatory Institutional Review Boards (IRB) for large scale dataset curation.
SN 2018hti: a nearby superluminous supernova discovered in a metal-poor galaxy

SN 2018hti is a Type I superluminous supernova (SLSN~I) with an absolute $g$-band magnitude of $-22.2$ at maximum brightness, discovered in a metal-poor galaxy at a redshift of 0.0612. We present extensive photometric and spectroscopic observations of this supernova, covering the phases from $\sim -35$ days to more than +340 days from the $r$-band maximum. Combining our $BVgri$-band photometry with {\it Swift} UVOT optical/ultraviolet photometry, we calculated the peak luminosity as $\sim 3.5\times10^{44}$ erg s$^{-1}$. Modeling the observed light curve reveals that the luminosity evolution of SN 2018hti can be produced by an ejecta mass of 5.8 $M_\odot$ and a magnetar with a magnetic field of $B=1.8\times10^{13}$~G having an initial spin period of $P_0=1.8$ ms. Based on such a magnetar-powered scenario and a larger sample, a correlation between the spin of the magnetar and the kinetic energy of the ejecta can be inferred for most SLSNe~I, suggesting a self-consistent scenario. Like for other SLSNe~I, the host galaxy of SN 2018hti is found to be relatively faint ($M_{g} = -17.75$ mag) and of low metallicity ($Z=0.3~Z_\odot$), with a star-formation rate of 0.3 $M_\odot$ yr$^{-1}$. According to simulation results of single-star evolution, SN 2018hti could originate from a massive, metal-poor star with a zero-age main sequence (ZAMS) mass of 25--40 $M_\odot$, or from a less massive rotating star with $M_\mathrm{ZAMS} \approx 16$--25 $M_\odot$. For the case of a binary system, its progenitor could also be a star with $M_\mathrm{ZAMS} \gtrsim 25$ $M_\odot$.
Folk medicines in the villages of Ilıca District (Erzurum, Turkey)

The villages of Ilica District in Erzurum Province, Turkey, were surveyed in terms of plants and animals used traditionally for medicinal purposes. Plants were compiled according to their families, botanical names, local names, usages, and applications. A total of 70 plant species belonging to 29 families are used by village people to treat a wide range of complaints, such as hemorrhoids, urinary system infections, stomach disorders, respiratory diseases, skin diseases, rheumatism, and jaundice. In addition, 3 animal species are used for the treatment of some diseases. The collected data were compared with those formerly recorded.
Optimal surface topography for cell adhesion is driven by cell membrane mechanics

Titanium surface treated with titanium oxide nanotubes was used in many studies to quantify the effect of surface topography on cell fate. However, the predicted optimal diameter of nanotubes considerably differs among studies. We propose a model that explain cell adhesion to nanostructured surface by considering deformation energy of cell protrusions into titanium nanotubes and adhesion to surface. The optimal surface topology is defined as a geometry that gives membrane a minimum energy shape. A dimensionless parameter, the cell interaction index, was proposed to describe interplay between the cell membrane bending, intrinsic curvature and strength of cell adhesion. Model simulation show that optimal nanotube diameter ranging from 20 nm to 100 nm (cell interaction index between 0.2 and 1, respectively) is feasible within certain range of parameters describing adhesion and bending energy. The results indicates a possibility to tune the topology of nanostructural surface in order to enhance proliferation and differentation of cells mechanically compatible with given surface geometry while suppress the growth of other mechanically incompatible cells.