Miracle-dz/UniFAR-FTUs huggingface dataset:
Invariance vs. Robustness of Neural Networks

We study the performance of neural network models on random geometric transformations and adversarial perturbations. Invariance means that the model's prediction remains unchanged when a geometric transformation is applied to an input. Adversarial robustness means that the model's prediction remains unchanged after small adversarial perturbations of an input. In this paper, we show a quantitative trade-off between rotation invariance and robustness. We empirically study the following two cases: (a) change in adversarial robustness as we improve only the invariance of equivariant models via training augmentation, (b) change in invariance as we improve only the adversarial robustness using adversarial training. We observe that the rotation invariance of equivariant models (StdCNNs and GCNNs) improves by training augmentation with progressively larger random rotations but while doing so, their adversarial robustness drops progressively, and very significantly on MNIST. We take adversarially trained LeNet and ResNet models which have good $L_\infty$ adversarial robustness on MNIST and CIFAR-10, respectively, and observe that adversarial training with progressively larger perturbations results in a progressive drop in their rotation invariance profiles. Similar to the trade-off between accuracy and robustness known in previous work, we give a theoretical justification for the invariance vs. robustness trade-off observed in our experiments.
Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents

Standard game-theoretic formulations for settings like contextual pricing and security games assume that agents act in accordance with a specific behavioral model. In practice however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent. Existing algorithms heavily depend on the model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents. How do we design learning algorithms that are robust to the presence of arbitrarily irrational agents? We address this question for a number of canonical game-theoretic applications by designing a robust algorithm for the fundamental problem of multidimensional binary search. The performance of our algorithm degrades gracefully with the number of corrupted rounds, which correspond to irrational agents and need not be known in advance. As binary search is the key primitive in algorithms for contextual pricing, Stackelberg Security Games, and other game-theoretic applications, we immediately obtain robust algorithms for these settings. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis, and may be of independent algorithmic interest.
Influence of finite temperature exchange-correlation effects in hydrogen

We use density functional molecular dynamics (DFT-MD) to study the effect of finite temperature exchange-correlation (xc) in Hydrogen. Using the Kohn-Sham approach, the xc energy of the system, $E_{xc}(r_{s})$ is replaced by the xc free energy $f_{xc}(r_{s},\Theta)$ within the local density approximation (LDA) based on parametrized path integral Monte Carlo (PIMC) data for the uniform electron gas (UEG) at warm dense matter (WDM) conditions. We observe insignificant changes in the equation of state (EOS) at the region of metal-insulator transition compared to the regular LDA form, whereas significant changes are observed for T$>$10000 K, i.e., in the important WDM regime. Thus, our results further corroborate the need for temperature-dependent xc functionals for DFT simulations of WDM systems. Moreover, we present the first finite-temperature DFT results for the EOS of Hydrogen in the electron liquid regime up to $r_{s}=14$ and find a drastic impact (about $20\%$) of thermal xc effects, which manifests at lower temperatures compared to WDM. We expect our results to be important for many applications beyond DFT, like quantum hydrodynamics and astrophysical models.
Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization

Real-noise denoising is a challenging task because the statistics of real-noise do not follow the normal distribution, and they are also spatially and temporally changing. In order to cope with various and complex real-noise, we propose a well-generalized denoising architecture and a transfer learning scheme. Specifically, we adopt an adaptive instance normalization to build a denoiser, which can regularize the feature map and prevent the network from overfitting to the training set. We also introduce a transfer learning scheme that transfers knowledge learned from synthetic-noise data to the real-noise denoiser. From the proposed transfer learning, the synthetic-noise denoiser can learn general features from various synthetic-noise data, and the real-noise denoiser can learn the real-noise characteristics from real data. From the experiments, we find that the proposed denoising method has great generalization ability, such that our network trained with synthetic-noise achieves the best performance for Darmstadt Noise Dataset (DND) among the methods from published papers. We can also see that the proposed transfer learning scheme robustly works for real-noise images through the learning with a very small number of labeled data.
Expansivity and unique shadowing

Let $f\colon X\to X$ be a continuous function on a compact metric space. We show that shadowing is equivalent to backwards shadowing and two-sided shadowing when the map $f$ is onto. Using this we go on to show that, for expansive surjective maps the properties shadowing, two-sided shadowing, s-limit shadowing and two-sided s-limit shadowing are equivalent. We show that $f$ is positively expansive and has shadowing if and only if it has unique shadowing (i.e.\ each pseudo-orbit is shadowed by a unique point), extending a result implicit in Walter's proof that positively expansive maps with shadowing are topologically stable. We use the aforementioned result on two-sided shadowing to find an equivalent characterisation of shadowing and expansivity and extend these results to the notion of $n$-expansivity due to Morales.
A Balancing Weight Framework for Estimating the Causal Effect of General Treatments

In observational studies, weighting methods that directly optimize the balance between treatment and covariates have received much attention lately; however these have mainly focused on binary treatments. Inspired by domain adaptation, we show that such methods can be actually reformulated as specific implementations of a discrepancy minimization problem aimed at tackling a shift of distribution from observational to interventional data. More precisely, we introduce a new framework, Covariate Balance via Discrepancy Minimization (CBDM), that provably encompasses most of the existing balancing weight methods and formally extends them to treatments of arbitrary types (e.g., continuous or multivariate). We establish theoretical guarantees for our framework that both offer generalizations of properties known when the treatment is binary, and give a better grasp on what hyperparameters to choose in non-binary settings. Based on such insights, we propose a particular implementation of CBDM for estimating dose-response curves and demonstrate through experiments its competitive performance relative to other existing approaches for continuous treatments.
Fingerprints of giant planets in the composition of solar twins

The Sun shows a $\sim 10$% depletion in refractory elements relative to nearby solar twins. It has been suggested that this depletion is a signpost of planet formation. The exoplanet statistics are now good enough to show that the origin of this depletion does not arise from the sequestration of refractory material inside the planets themselves. This conclusion arises because most sun-like stars host close-in planetary systems that are on average more massive than the Sun's. Using evolutionary models for the protoplanetary discs that surrounded the young Sun and solar twins we demonstrate that the origin of the depletion likely arises due to the trapping of dust exterior to the orbit of a forming giant planet. In this scenario a forming giant planet opens a gap in the gas disc, creating a pressure trap. If the planet forms early enough, while the disc is still massive, the planet can trap $\gtrsim 100 $M$_\oplus$ of dust exterior to its orbit, preventing the dust from accreting onto the star in contrast to the gas. Forming giant planets can create refractory depletions of $\sim 5-15$%, with the larger values occurring for initial conditions that favour giant planet formation (e.g. more massive discs, that live longer). The incidence of solar-twins that show refractory depletion matches both the occurrence of giant planets discovered in exoplanet surveys and "transition" discs that show similar depletion patterns in the material that is accreting onto the star.
Distributed Algorithms for Composite Optimization: Unified and Tight Convergence Analysis

—We study distributed composite optimization over networks: agents minimize a sum of smooth (strongly) convex functions–the agents’ sum-utility–plus a nonsmooth (extended-valued) convex one. We propose a general uniﬁed algorithmic framework for such a class of problems and provide a uniﬁed convergence analysis leveraging the theory of operator splitting. Distinguishing features of our scheme are: (i) When the agents’ functions are strongly convex, the algorithm converges at a linear rate, whose dependence on the agents’ functions and network topology is decoupled , matching the typical rates of centralized optimization; the rate expression is sharp and improves on existing results; (ii) When the objective function is convex (but not strongly convex), similar separation as in (i) is established for the coefﬁcient of the proved sublinear rate; (iii) The algorithm can adjust the ratio between the number of communications and computations to achieve a rate (in terms of computations) independent on the network connectivity; and (iv) A by-product of our analysis is a tuning recommendation for several existing (non accelerated) distributed algorithms yielding the fastest provably (worst-case) convergence rate. This is the ﬁrst time that a general distributed algorithmic framework applicable to composite optimization enjoys all such properties.
Dark Energy Survey Year 1 Results: Cosmological constraints from cluster abundances and weak lensing

We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset. Our analysis uses the same shear and source photometric redshifts estimates as were used in the DES combined probes analysis. Our analysis results in surprisingly low values for S-8 = sigma(8)(Omega(m)/0.3)(0.5) = 0.65 0.04, driven by a low matter density parameter, Omega(m) = 0.179(-0.038)(+0.031), with sigma(8) - Omega(m) posteriors in 2.4 sigma tension with the DES Y1 3x2pt results, and in 5.6 sigma with the Planck CMB analysis. These results include the impact of post-unblinding changes to the analysis, which did not improve the level of consistency with other data sets compared to the results obtained at the unblinding. The fact that multiple cosmological probes (supernovae, baryon acoustic oscillations, cosmic shear, galaxy clustering and CMB anisotropies), and other galaxy cluster analyses all favor significantly higher matter densities suggests the presence of systematic errors in the data or an incomplete modeling of the relevant physics. Cross checks with x-ray and microwave data, as well as independent constraints on the observable -mass relation from Sunyaev-Zeldovich selected clusters, suggest that the discrepancy resides in our modeling of the weak lensing signal rather than the cluster abundance. Repeating our analysis using a higher richness threshold (lambda >= 30) significantly reduces the tension with other probes, and points to one or more richness -dependent effects not captured by our model.
New Constraints on the Supranuclear Equation of State and the Hubble Constant from Nuclear Physics -- Multi-Messenger Astronomy

The multi-messenger analysis of neutron-star mergers is not only of astrophysical interest, it also allows studying the behavior of supranuclear dense matter, probing the principles of general relativity, and measuring the expansion rate of the Universe. We perform a multi-messenger analysis of the gravitational-wave signal GW170817 and its electromagnetic counterparts AT2017gfo and GRB170817A, as well as the gravitational-wave signal GW190425. By incorporating information from the NICER observation of PSR J0030+0451, the radio observations of PSR J0740+6620, PSR J0348+4032, PSR J1614-2230, and nuclear-theory computations using chiral effective field theory, we provide new constraints on the neutron-star equation of state. Within our analysis, we determine the radius of a $1. 4M_\odot$ NS to be $R_{1. 4M_\odot}=11.74^{+0.98}_{-0.79}\ \rm km$ at $90\%$ confidence and estimate the Hubble constant to be $H_0=66.2^{+4.4}_{-4.2}\ \rm km /Mpc/s$ at $1\sigma$ uncertainty. The same multi-messenger-analysis techniques will be applicable for future observations, improving our understanding of the most extreme objects in the Universe.
Equitable Allocations of Indivisible Chores

In fair division, equitability dictates that each participant receives the same level of utility. In this work, we study equitable allocations of indivisible goods among agents with additive valuations. While prior work has studied (approximate) equitability in isolation, we consider equitability in conjunction with other well-studied notions of fairness and economic efficiency. We show that the Leximin algorithm produces an allocation that satisfies equitability up to any good and Pareto optimality. We also give a novel algorithm that guarantees Pareto optimality and equitability up to one good in pseudopolynomial time. Our experiments on real-world preference data reveal that approximate envy-freeness, approximate equitability, and Pareto optimality can often be achieved simultaneously.
Menstrual Dysfunction in Athletes: An Energetic Challenge

&NA; Intense training programs that are insufficiently fueled by energy intake may lead to fatigue, impaired performance, and a broad spectrum of menstrual cycle disturbances. The effects of acute reduced energy availability and chronic energy deficiency on reproductive function, specifically luteinizing hormone secretion, will be examined, as well as possible recovery strategies.
Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units

Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how information flows between hidden states). However, the vanilla gates in RNN (e.g., the input gate in LSTM) suffer from the problem of gate undertraining, which can be caused by various factors, such as the saturating activation functions, the gate layouts (e.g., the gate number and gating functions), or even the suboptimal memory state etc. Those may result in failures of learning gating switch roles and thus the weak performance. In this paper, we propose a new gating mechanism within general gated recurrent neural networks to handle this issue. Specifically, the proposed gates directly short connect the extracted input features to the outputs of vanilla gates, denoted as refined gates. The refining mechanism allows enhancing gradient back-propagation as well as extending the gating activation scope, which can guide RNN to reach possibly deeper minima. We verify the proposed gating mechanism on three popular types of gated RNNs including LSTM, GRU and MGU. Extensive experiments on 3 synthetic tasks, 3 language modeling tasks and 5 scene text recognition benchmarks demonstrate the effectiveness of our method.
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Bandit learning algorithms typically involve the balance of exploration and exploitation. However, in many practical applications, worst-case scenarios needing systematic exploration are seldom encountered. In this work, we consider a smoothed setting for structured linear contextual bandits where the adversarial contexts are perturbed by Gaussian noise and the unknown parameter $\theta^*$ has structure, e.g., sparsity, group sparsity, low rank, etc. We propose simple greedy algorithms for both the single- and multi-parameter (i.e., different parameter for each context) settings and provide a unified regret analysis for $\theta^*$ with any assumed structure. The regret bounds are expressed in terms of geometric quantities such as Gaussian widths associated with the structure of $\theta^*$. We also obtain sharper regret bounds compared to earlier work for the unstructured $\theta^*$ setting as a consequence of our improved analysis. We show there is implicit exploration in the smoothed setting where a simple greedy algorithm works.
Learning Light Field Angular Super-Resolution via a Geometry-Aware Network

The acquisition of light field images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light field, they always focus on the light field with a small baseline, which is captured by a consumer light field camera. By making full use of the intrinsic \textit{geometry} information of light fields, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. Our model consists of two learnable modules and a physically-based module. Specifically, it includes a depth estimation module for explicitly modeling the scene geometry, a physically-based warping for novel views synthesis, and a light field blending module specifically designed for light field reconstruction. Moreover, we introduce a novel loss function to promote the preservation of the light field parallax structure. Experimental results over various light field datasets including large baseline light field images demonstrate the significant superiority of our method when compared with state-of-the-art ones, i.e., our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48$\times$. In addition, our method preserves the light field parallax structure better.
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data

Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN, proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.
Hours Worked and the U.S. Distribution of Real Annual Earnings 1976–2016

We examine the impact of annual hours worked on annual earnings by decomposing changes in the real annual earnings distribution into composition, structural and hours effects. We do so via a nonseparable simultaneous model of hours, wages and earnings. We provide identification results and estimators of the objects required for the decompositions. Using the Current Population Survey for the survey years 1976-2016, we find that changes in the level of annual hours of work are important in explaining movements in inequality in female annual earnings. This captures the substantial changes in their employment behavior over this period. The impact of hours on males' earnings inequality operates only through the lower part of the earnings distribution and reflects the sensitivity of these workers' annual hours of work to cyclical factors.
DeBayes: a Bayesian method for debiasing network embeddings

As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits. In recent years much progress has been made on ensuring fairness and reducing bias in standard machine learning settings. Yet, for network embedding, with applications in vulnerable domains ranging from social network analysis to recommender systems, current options remain limited both in number and performance. We thus propose DeBayes: a conceptually elegant Bayesian method that is capable of learning debiased embeddings by using a biased prior. Our experiments show that these representations can then be used to perform link prediction that is significantly more fair in terms of popular metrics such as demographic parity and equalized opportunity.
Prophylaxis and treatment of pediculosis with Quassia amarga

A double-blind study against placebo was done in two primary schools of the west of La Pampa, with 105 and 43 pupils respectively. The object of this work was to find out the prophylactic action of the Quassia Amarga in the pediculosis of the human scalp. The number of new infested cases in both groups turned out to be most significant according to the formula SQUARE CHI. No adverse reactions were found after the application of the respective alcoholic solutions. As a result, we conclude that: a) The prophylactic and therapeutic action of the Quassia Amarga in the human pediculosis is confirmed. b) A noticeable reduction in expenses to eradicate permanently this form of parasitation at an official as well as at a community level is of importance.
Status and progress of basic researches of elemene injection for anti-tumor.

Elemene is one of terpenes compound,which extracted from zingiberaceae Wenyujin (curcuma aromatica salisb1). β-elemene is as the main ingredient of elemene injection that also contains a small amount of γ-and δ-elemene and other terpenoids.Through pre-clinical experiments and clinical trials in 1994 elemene injection is an application as a national second-class new drug of anti-tumor.Over the years,further basic researches related to elemene injection,which fully shows the drug for a variety of tumor cells were killed with a significant inhibitory effect,as well as inducing tumor cell apoptosis and differentiation,reversal of multidrug resistance of tumor cells and anti-metastases.Combined with radiotherapy and chemotherapy,it has sensibilization,attenuation improve immunity,and mild toxicities.