Miracle-dz/UniFAR-FTUs huggingface dataset:
Sample Complexity of Probability Divergences under Group Symmetry

We rigorously quantify the improvement in the sample complexity of variational divergence estimations for group-invariant distributions. In the cases of the Wasserstein-1 metric and the Lipschitz-regularized $\alpha$-divergences, the reduction of sample complexity is proportional to an ambient-dimension-dependent power of the group size. For the maximum mean discrepancy (MMD), the improvement of sample complexity is more nuanced, as it depends on not only the group size but also the choice of kernel. Numerical simulations verify our theories.
Distributional constrained reinforcement learning for supply chain optimization

This work studies reinforcement learning (RL) in the context of multi-period supply chains subject to constraints, e.g., on production and inventory. We introduce Distributional Constrained Policy Optimization (DCPO), a novel approach for reliable constraint satisfaction in RL. Our approach is based on Constrained Policy Optimization (CPO), which is subject to approximation errors that in practice lead it to converge to infeasible policies. We address this issue by incorporating aspects of distributional RL into DCPO. Specifically, we represent the return and cost value functions using neural networks that output discrete distributions, and we reshape costs based on the associated confidence. Using a supply chain case study, we show that DCPO improves the rate at which the RL policy converges and ensures reliable constraint satisfaction by the end of training. The proposed method also improves predictability, greatly reducing the variance of returns between runs, respectively; this result is significant in the context of policy gradient methods, which intrinsically introduce significant variance during training.
Fractal compressed sensing imaging with sparse difference based on fractal and entropy recognition

Abstract Compressed sensing (CS) is a topic of great interest in many research fields, especially image processing. However, in the traditional CS framework, one disadvantage is that the computational cost of sparse representation (SR) is too high to meet basic application requirements. Another is that l1-norm minimisation, as the object function of CS recovery, is unsuitable for the approximation of image details. Therefore, this paper presents a novel fractal CS (FCS) framework for digital imaging. The FCS framework is basically as follows: first, the sparse difference (SD) is used to solve the hard problem of sparse representation; then, acquisition of SD is based on the results of classification under a combined fractal and entropy feature space; finally, fractal minimisation is used instead of l1-norm minimisation as the object function to realise high-quality CS recovery of image details. Several experiments show the feasibility and dependability of the FCS imaging framework.
Four modes waveguide resonator filters

Usually the two slowest modes are used for shaping the passband of waveguide resonator filters. Small waveguide widths and high impedance values are the consequence for filters having a relatively wide passband. Therefore we propose to apply not only the 1st and 2nd but also the 3rd and 4th mode. Calculated and experimental examples for using symmetric (1st and 3rd) or antisymmetric (2nd and 4th) modes alone are presented. Furthermore, a filter involving all four modes is described. Beside the advantage of steep skirts this filter type shows a strong frequency dependence of the group delay. For the calculation of waveguide mode velocities and profiles and of the filter transmission properties a one component bulk wave mode described in a brief manner is used
The MUSE Ultra Deep Field (MUDF). III. Hubble Space Telescope WFC3 Grism Spectroscopy and Imaging

We present extremely deep Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) observations of the MUSE Ultra Deep Field. This unique region of the sky contains two quasars at z ≈ 3.22 that are separated by only ∼500 kpc, providing a stereoscopic view of gas and galaxies in emission and absorption across ∼10 billion years of cosmic time. We have obtained 90 orbits of HST WFC3 G141 near-infrared grism spectroscopy of this field in a single pointing, as well as 142 hr of optical spectroscopy with the Very Large Telescope Multi Unit Spectroscopic Explorer (MUSE). The WFC3 (F140W, F125W, and F336W) and archival WFPC2 (F702W and F450W) imaging provides five-filter photometry that we use to detect 3375 sources between z ≈ 0–6, including 1536 objects in a deep central pointing with both spectroscopic and photometric coverage. The F140W and F336W mosaics reach exceptional depths of m AB ≈ 28 and 29, respectively, providing near-infrared and rest-frame ultraviolet information for 1580 sources, and we reach 5σ continuum detections for objects as faint as m AB ≈ 27 in the grism spectra. The extensive wavelength coverage of MUSE and WFC3 allows us to measure spectroscopic redshifts for 419 sources, down to galaxy stellar masses of log(M/M ⊙) ≈7 at z ≈ 1–2. In this publication, we provide the calibrated HST data and source catalogs as High Level Science Products for use by the community, which includes photometry, morphology, and redshift measurements that enable a variety of studies aimed at advancing our models of galaxy formation and evolution in different environments.
ResMem: Learn what you can and memorize the rest

The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMem) algorithm, a new method that augments an existing prediction model (e.g. a neural network) by fitting the model's residuals with a $k$-nearest neighbor based regressor. The final prediction is then the sum of the original model and the fitted residual regressor. By construction, ResMem can explicitly memorize the training labels. Empirically, we show that ResMem consistently improves the test set generalization of the original prediction model across various standard vision and natural language processing benchmarks. Theoretically, we formulate a stylized linear regression problem and rigorously show that ResMem results in a more favorable test risk over the base predictor.
Sequencing the Entangled DNA of Fractional Quantum Hall Fluids

We introduce and prove the “root theorem”, which establishes a condition for families of operators to annihilate all root states associated with zero modes of a given positive semi-definite k-body Hamiltonian chosen from a large class. This class is motivated by fractional quantum Hall and related problems, and features generally long-ranged, one-dimensional, dipole-conserving terms. Our theorem streamlines analysis of zero-modes in contexts where “generalized” or “entangled” Pauli principles apply. One major application of the theorem is to parent Hamiltonians for mixed Landau-level wave functions, such as unprojected composite fermion or parton-like states that were recently discussed in the literature, where it is difficult to rigorously establish a complete set of zero modes with traditional polynomial techniques. As a simple application, we show that a modified V1 pseudo-potential, obtained via retention of only half the terms, stabilizes the ν=1/2 Tao–Thouless state as the unique densest ground state.
The anisotropy of EHE cosmic rays

The effects of magnetic field turbulence on the anisotropy of galactic cosmic rays of the highest energies are investigated. It is found that, if the cosmic ray flux at energies above 1 EeV is made up predominantly of galactic protons, neither the observed galactic magnetic field turbulence nor possible halo turbulence are sufficient for both the computed anisotropy and galactic flux gradient to agree with observation. The implication is that the origin of these cosmic rays is not within our galaxy.
Twitter Sentiment Analysis of Movie Reviews using Ensemble Features Based Naïve Bayes

Sentiment analysis or opinion mining is one of the latest research topics in the field of information processing. It aims to know whether the polarity of a text-shaped data (document, sentence, paragraph) will lead to positive, negative, or neutral trait. This research used document text about Indonesian movie review which was obtained from Twitter. The method used in this research was Naïve Bayes using Ensemble Features instead of only using Bag of Words Features. There are several types of features were used for this ensemble i.e. Twitter specific features, textual features, part of speech features, and lexicon-based features, and Bag of Words. Experiment Results showed that system f-measure value using Ensemble Features is 0.88. Meanwhile, Bag of Words Features has better performance with 0.94 f-measure value.
Ethnicity as a predictor of outcomes of psychological therapies for anxiety and depression: a retrospective cohort analysis

Abstract Background: Studies on predictors of outcomes of treatment for common mental health disorders (CMDs) in community mental health settings are scarce, and sample sizes are often small. Research on the impact of identifying as a member of an ethnic minority group on treatment outcomes is limited. Aims: To ascertain whether ethnicity is an independent predictor of outcome and the extent to which any association is mediated by other sociodemographic factors. Method: Retrospective observational study of anonymised treatment data collected for routine clinical purposes. Data were analysed from nine Improving Access to Psychological Therapy (IAPT) services from 2009 to 2016. Social functioning, ethnic group, age, gender, occupation and baseline severity of the mental health disorder were analysed as predictors of outcome. Results: Outcomes varied with ethnic group. Levels of occupation, social deprivation, initial morbidity and social functioning varied between ethnic groups at baseline. After adjustment for these factors the impact of ethnicity was attenuated and only some ethnic groups remained as significant independent predictors of treatment outcome. Conclusions: Ethnic minority status is a marker for multiple disadvantages. Some of the differences in outcome seen between ethnic groups may be the result of more general factors present in all ethnic groups but at greater intensity in some ethnic minority groups.
A Finite Difference Method for Unsteady Flow in Variably Saturated Porous Media: Application to a Single Pumping Well

Finite difference equations were derived by using the divergence theorem to convert the nonlinear partial differential equation (which approximately describes liquid flow in a variably saturated, elastic porous medium) to an integral equation, and then to integrate around individual mesh volume elements. Original nonlinearity of the differential equation was preserved by keeping saturations and relative conductivities current with hydraulic heads during the iterative matrix solution method. The problem of axisymmetric flow to a water well that penetrates one or more elastic rock units, the upper one of which is unconfmed, provides a convenient vehicle for analysis of the procedural and theoretical study of unconfined and semiconfined flow. Of the three methods tried to solve the matrix equation that resulted from the finite difference equations (which included a form of the direct alternating direction implicit method, the iterative alternating direction implicit method, and the line successive overrelaxation method), the line successive overrelaxation method was the fastest and was selected for use in a general computer program. A comparison with analytical solutions that use Boulton's convolution integral as a velocity boundary condition at the water table for a single aquifer and an aquifer-aquitard system demonstrates close correspondence of the numerical and analytical solutions, even for a case where the water table is lowered appreciably.
How Far Has India Integrated with East Asian Economies? Evidence from International Trade Data

Abstract:Since the 1990s, East Asia—including Northeast and Southeast Asia—has led the world in adopting a task-by-task international division of labour or international production networks (IPNs), the core of which consists of machinery industries. In this regard, how far has India integrated with East Asia? Using international trade data for comparison from multiple perspectives, this article gives an overview of the current position of India with respect to machinery IPNs and information and communication technology (ICT) services. The article shows that India has not yet participated in machinery IPNs in the East Asian region. We argue that ICT services are a source of strength for the Indian economy, and its competitiveness could be utilized effectively by combining new technologies with traditional industries such as manufacturing. India still has huge untapped opportunities for utilizing the mechanics of a new international division of labour to accelerate economic growth, innovation and poverty alleviation. And economic integration with East Asia could work as a trigger to redirect India's industrialization strategies.
The smooth Colonel meets the Reverend

Kernel smoothing techniques have attracted much attention and some notoriety in recent years. The attention is well deserved as kernel methods free researchers from having to impose rigid parametric structure on their data. The notoriety arises from the fact that the amount of smoothing (i.e., local averaging) that is appropriate for the problem at hand is under the control of the researcher. In this study we provide a deeper understanding of kernel smoothing methods for discrete data by leveraging the unexplored links between hierarchical Bayes models and kernel methods for discrete processes. Several potentially useful results are thereby obtained, including bounds on when kernel smoothing can be expected to dominate non-smooth (e.g., parametric) approaches in mean squared error and suggestions for thinking about the appropriate amount of smoothing.
Particle-In-Cell Monte-Carlo Simulation of Capacitive RF Discharges: Comparison with Experimental Data

Particle-in-cell (PIC) simulations with Monte Carlo collision (MCC) are performed in argon capacitive rf discharges for the comparison with experimental data. The electron density and electron temperature along with the electron energy probability function are calculated for various gas pressures. It is shown that the sufficient number of simulation particles is one of the critical conditions for the reliability of PIC-MCC simulation. From the simulation with a large number of superparticles, a good agreement with experimental data measured by Godyak et al. [Phys. Rev. Lett. 65 (1990) 996] is obtained.
Elastic waves in crystals under a bias

Abstract The temperature and stress induced effects on the propagation of elastic waves in anisotropic solids have been described by equations for dynamic fields superposed on a static bias. These equations are derived from the rotationally invariant equations of nonlinear elasticity for small dynamic fields superposed on a linear (infinitesimal) bias. The salient features of such equations of motion written in the reference and intermediate configurations of solids have been reviewed. The definitions of effective elastic constants in the two formulations and their relations with some of the earlier linearly based descriptions have been outlined. It has been shown that a proper description of nonlinear temperature induced effects on wave propagation in terms of either fundamental or effective elastic constants requires temperature derivatives of higher order elastic constants of quartz which are presently not available. Although the existing phenomenological approach based on a linearly based formulation .
Elamipretide Improves ADP Sensitivity in Aged Mitochondria by Increasing Uptake through the Adenine Nucleotide Translocator (ANT)

Aging muscle experiences functional decline in part mediated by impaired mitochondrial ADP sensitivity. Elamipretide (ELAM) rapidly improves physiological and mitochondrial function in aging and binds directly to the mitochondrial ADP transporter ANT. We hypothesized that ELAM improves ADP sensitivity in aging leading to rescued physiological function. We measured the response to ADP stimulation in young and old muscle mitochondria with ELAM treatment, in vivo heart and muscle function, and compared protein abundance, phosphorylation, and S-glutathionylation of ADP/ATP pathway proteins. ELAM treatment increased ADP sensitivity in old muscle mitochondria by increasing uptake of ADP through the ANT and rescued muscle force and heart systolic function. Protein abundance in the ADP/ATP transport and synthesis pathway was unchanged, but ELAM treatment decreased protein s-glutathionylation incuding of ANT. Mitochondrial ADP sensitivity is rapidly modifiable. This research supports the hypothesis that ELAM improves ANT function in aging and links mitochondrial ADP sensitivity to physiological function. Graphical Abstract. ELAM Binds Directly to ANT and ATP Synthase and ELAM Treatment Improves ADP Sensitivity, Increases ATP Production, and Improves Physiological Function in Old Muscles. ADP (adenosine diphosphate), ATP (adenosine triphosphate), VDAC (voltage-dependent anion channel), ANT (Adenine nucleotide translocator), H+ (proton), ROS (reactive oxygen species), NADH (nicotinamide adenine dinucleotide), FADH2 (flavin adenine dinucleotide), O2 (oxygen), ELAM (elamipretide), −SH (free thiol), −SSG (glutathionylated protein).
One-sided multifractal analysis and points of non-differentiability of devil's staircases

We examine the multifractal spectra of one-sided local dimensions of Ahlfors regular measures on ${\bf R}$. This brings into a natural context a curious property that has been observed in a number of instances, namely that the Hausdorff dimension of the set of points of non-differentiability of a self-affine ‘devil's staircase’ function is the square of the dimension of the set of points of increase.
Glutaredoxin regulation of primary root growth is associated with early drought stress tolerance in pearl millet

Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millet’s early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modelling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including GWAS and QTL approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re-annotation of one of these genomic regions identified a glutaredoxin-encoding gene PgGRXC9 as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homolog AtROXY19 revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment.
Near-optimal integration of the magnitude information of time and numerosity

Magnitude information is often correlated in the external world, providing complementary information about the environment. As if to reflect this relationship, the perceptions of different magnitudes (e.g., time and numerosity) are known to influence one another. Recent studies suggest that such magnitude interaction is similar to cue integration, such as multisensory integration. Here, we tested whether human observers could integrate the magnitudes of two quantities with distinct physical units (i.e., time and numerosity) as abstract magnitude information. The participants compared the magnitudes of two visual stimuli based on time, numerosity, or both. Consistent with the predictions of the maximum likelihood estimation (MLE) model, the participants integrated time and numerosity in a near-optimal manner; the weights for numerosity increased as the reliability of the numerosity information increased, and the integrated estimate was more reliable than either the time or numerosity estimate. Furthermore, the integration approached a statistical optimum as the temporal discrepancy of the acquisition of each piece of information became smaller. These results suggest that magnitude interaction arises through a similar computational mechanism to cue integration. They are also consistent with the idea that different magnitudes are processed by a generalized magnitude system.
Risk assessment during nest defense against three simulated predators by female northern house wrens ( Troglodytes aedon aedon )

Offspring predation is one of the greatest obstacles to an organism’s reproductive success, but parents vary in the strength of their response to potential predators. One explanation for this variable investment is that defending current offspring has the potential to lower future reproductive success if the predator is also capable of injuring or killing the parent. House wrens ( Troglodytes aedon aedon ) are cavity-nesting songbirds that defend against multiple species of nest predators including small mammals, birds of prey, and snakes. Here, we used three different predator decoys: two nest predators - an eastern chipmunk ( Tamias striatus ) and an eastern ratsnake ( Pantherophis alleghaniensis ) - as well as a predator of both offspring and adults - a juvenile Cooper’s hawk ( Accipiter cooperi ) - to elicit nest defense and test whether females use risk assessment to modulate their antipredator behavior. We found that antipredator behaviors were not significantly different between the two nest predators, which posed a high risk to the nestlings, but lower risk to the parents as neither species frequently captures adult wrens. Females also did not differ significantly in their responses to the snake between years, suggesting that the population-level response might be consistent through time. However, female wrens never dove at or attacked the Cooper’s hawk despite frequently attacking both the snake and chipmunk decoys. Collectively, these results show that female house wrens exhibit plasticity in their antipredator behavior, and they respond to different types of predators in a way that could maximize lifetime fitness while risking the loss of their current offspring.