Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Unsupervised Domain Translation

Domain Translation is the problem of finding a meaningful correspondence between two domains. Since in a majority of settings paired supervision is not available, much work focuses on Unsupervised Domain Translation (UDT) where data samples from each domain are unpaired. Following the seminal work of CycleGAN for UDT, many variants and extensions of this model have been proposed. However, there is still little theoretical understanding behind their success. We observe that these methods yield solutions which are approximately minimal w.r. t. a given transportation cost, leading us to reformulate the problem in the Optimal Transport (OT) framework. This viewpoint gives us a new perspective on Unsupervised Domain Translation and allows us to prove the existence and uniqueness of the retrieved mapping, given a large family of transport costs. We then propose a novel framework to efficiently compute optimal mappings in a dynamical setting. We show that it generalizes previous methods and enables a more explicit control over the computed optimal mapping. It also provides smooth interpolations between the two domains. Experiments on toy and real world datasets illustrate the behavior of our method.
Neural networks grown and self-organized by noise

Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can 'grow' a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct a convolutional pooling layer, a key constituent of convolutional neural networks (CNN's). Our approach is inspired by the mechanisms employed by the early visual system to wire the retina to the lateral geniculate nucleus (LGN), days before animals open their eyes. The key ingredients for robust self-organization are an emergent spontaneous spatiotemporal activity wave in the first layer and a local learning rule in the second layer that 'learns' the underlying activity pattern in the first layer. The algorithm is adaptable to a wide-range of input-layer geometries, robust to malfunctioning units in the first layer, and so can be used to successfully grow and self-organize pooling architectures of different pool-sizes and shapes. The algorithm provides a primitive procedure for constructing layered neural networks through growth and self-organization. Broadly, our work shows that biologically inspired developmental algorithms can be applied to autonomously grow functional 'brains' in-silico.
Association between shift work and being overweight or obese among health care workers in a clinical setting in Medellin, Colombia.

BACKGROUND Shift work is common in health care settings and has been hypothesized as a risk factor for being overweight or obese. We examined the relation between shift work and being overweight or obese, adjusting for stress and lifestyle habits in Colombian health care workers. OBJECTIVE The aim of this study was to assess the association between shift work and being overweight/obese in employees of a health care setting in Medellin, Colombia. METHODS This cross-sectional study was carried out among 200 workers in a health care setting. Participants completed a demographic, occupational, work-related stress and life style questionnaire. Their Body Mass Index (BMI) and waist to hip ratio were also measured. RESULTS The study sample consisted of 160 (80%) females and 40 (20%) males. Mean age was 35.1±9.1 years and mean BMI was 25±3.9. After adjusting for potential confounders, multivariate logistic regression revealed no statistically significant association between being overweight, being obese or waist to hip ratio and shift work; 95% CI OR: 1.08 (0.62-1.89), 1.33 (0.44-3.99) and 1.2 (0.8-1.9), respectively. Day workers were statistically more likely to smoke, work more hours, and have a higher educational level than shift workers. CONCLUSIONS No significant associations between shift work and being overweight/obese were observed in health care workers in a Colombian setting. These findings need to be confirmed through longitudinal studies.
Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation

Noise and domain are important aspects of data quality for neural machine translation. Existing research focus separately on domain-data selection, clean-data selection, or their static combination, leaving the dynamic interaction across them not explicitly examined. This paper introduces a “co-curricular learning” method to compose dynamic domain-data selection with dynamic clean-data selection, for transfer learning across both capabilities. We apply an EM-style optimization procedure to further refine the “co-curriculum”. Experiment results and analysis with two domains demonstrate the effectiveness of the method and the properties of data scheduled by the co-curriculum.
Implication of the Superstring theory on Astrophysics and Cosmology

Under the spirit of J. Polchinsky's introduction of D-brane in the string target spacetime (or 'bulk', for short), we consider the presence of both closed and open superstrings and coupling of their low-lying modes in the bulk. As a probe to explore the astrophysical and cosmological implications of the superstring theory at larger length scale or in low-energy dynamics, we construct the Yang-Mills monopole black hole solution and investigate its features. Our results exhibit that as a result of intimate interactions among low-lying modes, some cherished features of classical gravitation and cosmology break down. They are the violation of black hole no-hair theorem and the break down of cosmic censorship hypothesis. We learn from this lesson that these implications of fully developed superstring theory in classical gravitation and cosmology largely depart from our conventional wisdom signaling that the most current version of superstring theory is indeed the best candidate for quantum theory of gravitation.
Example-Guided Style-Consistent Image Synthesis From Semantic Labeling

Example-guided image synthesis aims to synthesize an image from a semantic label map and an exemplary image indicating style. We use the term "style" in this problem to refer to implicit characteristics of images, for example: in portraits "style" includes gender, racial identity, age, hairstyle; in full body pictures it includes clothing; in street scenes it refers to weather and time of day and such like. A semantic label map in these cases indicates facial expression, full body pose, or scene segmentation. We propose a solution to the example-guided image synthesis problem using conditional generative adversarial networks with style consistency. Our key contributions are (i) a novel style consistency discriminator to determine whether a pair of images are consistent in style; (ii) an adaptive semantic consistency loss; and (iii) a training data sampling strategy, for synthesizing style-consistent results to the exemplar. We demonstrate the efficiency of our method on face, dance and street view synthesis tasks.
Disk–Jet Coupling in the 2017/2018 Outburst of the Galactic Black Hole Candidate X-Ray Binary MAXI J1535–571

MAXI J1535−571 is a Galactic black hole candidate X-ray binary that was discovered going into outburst in 2017 September. In this paper, we present comprehensive radio monitoring of this system using the Australia Telescope Compact Array, as well as the MeerKAT radio observatory, showing the evolution of the radio jet during its outburst. Our radio observations show the early rise and subsequent quenching of the compact jet as the outburst brightened and then evolved toward the soft state. We constrain the compact jet quenching factor to be more than 3.5 orders of magnitude. We also detected and tracked (for 303 days) a discrete, relativistically moving jet knot that was launched from the system. From the motion of the apparently superluminal knot, we constrain the jet inclination (at the time of ejection) and speed to ≤45° and ≥0.69 c, respectively. Extrapolating its motion back in time, our results suggest that the jet knot was ejected close in time to the transition from the hard intermediate state to soft intermediate state. The launching event also occurred contemporaneously with a short increase in X-ray count rate, a rapid drop in the strength of the X-ray variability, and a change in the type-C quasi-periodic oscillation (QPO) frequency that occurs >2.5 days before the first appearance of a possible type-B QPO.
Joint Effects of Context and User History for Predicting Online Conversation Re-entries

As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage with each other online, we study a challenging problem of re-entry prediction foreseeing whether a user will come back to a conversation they once participated in. We hypothesize that both the context of the ongoing conversations and the users’ previous chatting history will affect their continued interests in future engagement. Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior. We experiment with two large-scale datasets collected from Twitter and Reddit. Results show that our proposed framework with bi-attention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-of-the-art methods from previous work.
Better Character Language Modeling through Morphology

We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint. Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows. We then transfer morphological supervision across languages to improve performance in the low-resource setting.
Near-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games

We study two-player general sum repeated finite games where the rewards of each player are generated from an unknown distribution. Our aim is to find the egalitarian bargaining solution (EBS) for the repeated game, which can lead to much higher rewards than the maximin value of both players. Our most important contribution is the derivation of an algorithm that achieves simultaneously, for both players, a high-probability regret bound of order $\mathcal{O}(\sqrt[3]{\ln T}\cdot T^{2/3})$ after any $T$ rounds of play. We demonstrate that our upper bound is nearly optimal by proving a lower bound of $\Omega(T^{2/3})$ for any algorithm.
Sampled-Data Control of the Stefan System

This paper presents results for the sampled-data boundary feedback control to the Stefan problem. The Stefan problem represents a liquid-solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. First, we consider the sampled-data control for the one-phase Stefan problem by assuming that the solid phase temperature is maintained at the equilibrium melting temperature. We apply Zero-Order-Hold (ZOH) to the nominal continuous-time control law developed in [23] which is designed to drive the liquid-solid interface position to a desired setpoint. Provided that the control gain is bounded by the inverse of the upper diameter of the sampling schedule, we prove that the closed-loop system under the sampled-data control law satisfies some conditions required to validate the physical model, and the system's origin is globally exponentially stable in the spatial $L_2$ norm. Analogous results for the two-phase Stefan problem which incorporates the dynamics of both liquid and solid phases with moving interface position are obtained by applying the proposed procedure to the nominal control law for the two-phase problem developed in [30]. Numerical simulation illustrates the desired performance of the control law implemented to vary at each sampling time and keep constant during the period.
Malunion after intramedullary nailing of tibial shaft fractures.

BACKGROUND AND AIMS The purpose of the study was to find out the consequences of malunion of tibial shaft fracture. PATIENTS AND METHODS Sixty-four fractures were treated using an intramedullary nail or an intramedullary compression nail. Malunion was defined as an angular or rotational deformity exceeding five degrees or a shortening exceeding 10 mm. RESULTS The number of malunited fractures was 17. The main reason for malunion was a technical failure occurring in 11 fractures. Patients with malunion had on average twice as many pathological functional findings as patients with sound union (P < 0.05). The mean number of subjective complaints was 5.3 in the malunion group and 3.3 in the sound union group. Among patients less than 45 years of age, those with malunited fractures had to reduce or give up physical exercise more often than those with fractures with sound union (P < 0.05). CONCLUSION Malunion of tibial shaft fracture seems to be especially harmful in distal fractures, in fractures with marked primary displacement, in fractures caused by high energy injury, and among patients less than 45 years of age.
A Generic Acceleration Framework for Stochastic Composite Optimization

In this paper, we introduce various mechanisms to obtain accelerated first-order stochastic optimization algorithms when the objective function is convex or strongly convex. Specifically, we extend the Catalyst approach originally designed for deterministic objectives to the stochastic setting. Given an optimization method with mild convergence guarantees for strongly convex problems, the challenge is to accelerate convergence to a noise-dominated region, and then achieve convergence with an optimal worst-case complexity depending on the noise variance of the gradients. A side contribution of our work is also a generic analysis that can handle inexact proximal operators, providing new insights about the robustness of stochastic algorithms when the proximal operator cannot be exactly computed.
Extrapolation methods for fixed‐point multilinear PageRank computations

Nonnegative tensors arise very naturally in many applications that involve large and complex data flows. Due to the relatively small requirement in terms of memory storage and number of operations per step, the (shifted) higher order power method is one of the most commonly used technique for the computation of positive Z‐eigenvectors of this type of tensors. However, unlike the matrix case, the method may fail to converge even for irreducible tensors. Moreover, when it converges, its convergence rate can be very slow. These two drawbacks often make the computation of the eigenvectors demanding or unfeasible for large problems. In this work, we consider a particular class of nonnegative tensors associated with the multilinear PageRank modification of higher order Markov chains. Based on the simplified topological ε‐algorithm in its restarted form, we introduce an extrapolation‐based acceleration of power method type algorithms, namely, the shifted fixed‐point method and the inner‐outer method. The accelerated methods show remarkably better performance, with faster convergence rates and reduced overall computational time. Extensive numerical experiments on synthetic and real‐world datasets demonstrate the advantages of the introduced extrapolation techniques.
General method for computing the evolution of a scalar field in curved spacetime, with applications to quantum fields. I. Static boundaries

We develop a method for computing the evolution of a Klein-Gordon field in a globally hyperbolic spacetime. The method is both of general applicability and particularly useful in certain problems of interest, such as the study of confined quantum fields under small perturbations. Instead of solving the Klein-Gordon equation, we define and formally compute Bogoliubov transformations between spacelike hypersurfaces at different times, which contain themselves the evolution of the field. The method can be applied to classical and quantum fields, both real and complex, although for the most interesting case of quantum fields some discussion on the physical interpretation and unitarily equivalence of quantizations at different regions is required, which we approach on the ground of physical arguments. In the case of fields confined inside cavities, the method allows to easily make quantitative precise predictions on their behaviour under small perturbations of the background geometry and/or the boundary conditions. Therefore, the method provides a crucial tool in the recently growing research area of confined quantum fields in table-top experiments. We also prove its utility in addressing other problems, such as particle creation in a cosmological expansion, thus giving an example of its more general applicability. This article is the first of two articles introducing the method, dedicated to the particular case of spacetimes with static boundaries. A second article will handle the more general case of moving boundaries. PACS numbers: 02.90. +p, 03.65. Pm, 03.70. +k, 04.30. Nk, 04.62. +v Submitted to: Class. Quantum Grav.
Can community pharmacists improve tuberculosis case finding? A mixed methods intervention study in India

Introduction India has the world’s highest burden of tuberculosis (TB). Private retail pharmacies are the preferred provider for 40% of patients with TB symptoms and up to 25% of diagnosed patients. Engaging pharmacies in TB screening services could improve case detection. Methods A novel TB screening and referral intervention was piloted over 18 months, under the pragmatic staggered recruitment of 105 pharmacies in Patna, India. The intervention was integrated into an ongoing public–private mix (PPM) programme, with five added components: pharmacy training in TB screening, referral of patients with TB symptoms for a chest radiograph (CXR) followed by a doctor consultation, incentives for referral completion and TB diagnosis, short message service (SMS) reminders and field support. The intervention was evaluated using mixed methods. Results 81% of pharmacies actively participated in the intervention. Over 132.49 pharmacy person-years of observation in the intervention group, 1674 referrals were made and 255 cases of TB were diagnosed. The rate of registration of symptomatic patients was 62 times higher in the intervention group compared with the control group (95% CI: 54 to 72). TB diagnosis was 25 times higher (95% CI: 20 to 32). Microbiological testing and test confirmation were also significantly higher among patients diagnosed in the intervention group (p<0.001). Perceived professional credibility, patient trust, symptom severity and providing access to a free screening test were seen to improve pharmacists’ engagement in the intervention. Workload, patient demand for over-the-counter medicines, doctor consultation fees and programme documentation impeded engagement. An additional 240 cases of TB were attributed to the intervention, and the approximate cost incurred per case detected due to the intervention was US$100. Conclusions It is feasible and impactful to engage pharmacies in TB screening and referral activities, especially if working within existing public-private mix (PPM) programmes, appealing to pharmacies’ business mindset and among pharmacies with strong community ties.
Cost and cost-effectiveness of a parenting programme to prevent violence against adolescents in South Africa

Introduction This paper presents the costs and cost-effectiveness of ‘Parenting for Lifelong Health: Sinovuyo Teen’, a non-commercialised parenting programme aimed at preventing violence against adolescents in low-income and middle-income countries. Methods The effectiveness of Sinovuyo Teen was evaluated with a cluster randomised controlled trial in 40 villages and peri-urban townships in the Eastern Cape of South Africa from 2015 to 2016. The costs of implementation were calculated retrospectively and models of costs at scale estimated, from the perspective of the programme provider. Cost-effectiveness analysis considers both the cost per incident of abuse averted, and cost per disability-adjusted life year averted. Potential economic benefits from the societal perspective were estimated by developing a framework of possible savings. Results The total implementation cost for Sinovuyo Teen over the duration of the trial was US$135 954, or US$504 per family enrolled. Among the 270 families in the treatment group, an estimated 73 incidents of physical and emotional abuse were averted (95% CI 29 to 118 incidents averted). During the trial, the total cost per incident of physical or emotional abuse averted was US$1837, which is likely to decrease to approximately US$972 if implemented at scale. By comparison, the economic benefits of averting abuse in South Africa are large with an estimated lifetime saving of US$2724 minimum per case. Conclusion Parenting programmes are a cost-effective intervention to prevent the abuse of adolescents by their caregivers in South Africa, when compared with existing violence prevention programmes and cost-effectiveness thresholds based on GDP per capita.