Miracle-dz/UniFAR-FTUs huggingface dataset:
Men’s determinant factors on antenatal care involvement and childbirth place preference in Indonesia: An analysis of the 2012 Indonesia Demographic and Health Survey (IDHS)

Objective: This study aimed to describe the determinant factors of men’s involvement in antenatal care and childbirth place preference in Indonesia. Method: Secondary data from the 2012 Indonesia Demographic and Health Survey was used to identify factors determining men’s presence during antenatal care and childbirth place preference. All factors were analyzed utilizing bivariate and multivariate logistic regression. Result: Men living in urban areas, having a sufficient economy, having higher educational levels, and having more interaction with mass media were more likely present during the antenatal care of their wives. Men in Sulawesi and Maluku showed lower involvement in accompanying wives during antenatal care than those in Papua. In terms of delivery place preference, men in age 15–19 and 35–49 years, living in urban areas, having a more stable economy, reading newspapers or magazines as well as watching television at least once a week, and living in Java and Nusa Tenggara, were more likely having wife giving birth in healthcare facilities. Conclusions: Type of residence, economic situations, lower educational level, and fewer interactions with mass media and electronics are essential barriers to men’s involvement in wives’ antenatal care visits and childbirth place preference in Indonesia. Particular attention should be put to Sulawesi and Maluku to enforce the men and women as those islands underutilized antenatal care services and health facilities for childbirth.
The Impacts of Rail Transit on Property Values and Land Development Intensity: The Case of No.13 Line in Beijing

Theoretical analysis shows that the effect of rail transit on housing price is greater in suburbs than that in the central area. Empirical results validate this point by using the hedonic pricing method and housing transaction data nearby No.13 rail line in Beijing. In suburban areas, the housing prices within 1 km of rail stations are nearly 20% higher than those beyond that distance. However, with the development of surrounding urban transportation networks, the impact of rail transit is declining. Also, rail transit has significant impacts on urban land development intensity. The operation of No.13 Line encourages the land development in the north suburbs of Beijing, and raises the land development intensity around the stations. The FAR of new housing projects decreases by 1.0 every 3.3 km further away from the stations in suburban areas. These empirical results have important policy implications for the TOD strategy, the cost-benefit analysis and value capture strategy of rail transit construction in Chinese cities.
Aspirin and Hemocompatibility Events With a Left Ventricular Assist Device in Advanced Heart Failure: The ARIES-HM3 Randomized Clinical Trial.

IMPORTANCE Left ventricular assist devices (LVADs) enhance quality and duration of life in advanced heart failure. The burden of nonsurgical bleeding events is a leading morbidity. Aspirin as an antiplatelet agent is mandated along with vitamin K antagonists (VKAs) with continuous-flow LVADs without conclusive evidence of efficacy and safety. OBJECTIVE To determine whether excluding aspirin as part of the antithrombotic regimen with a fully magnetically levitated LVAD is safe and decreases bleeding. DESIGN, SETTING, and PARTICIPANTS This international, randomized, double-blind, placebo-controlled study of aspirin (100 mg/d) vs placebo with VKA therapy in patients with advanced heart failure with an LVAD was conducted across 51 centers with expertise in treating patients with advanced heart failure across 9 countries. The randomized population included 628 patients with advanced heart failure implanted with a fully magnetically levitated LVAD (314 in the placebo group and 314 in the aspirin group), of whom 296 patients in the placebo group and 293 in the aspirin group were in the primary analysis population, which informed the primary end point analysis. The study enrolled patients from July 2020 to September 2022; median follow-up was 14 months. Intervention Patients were randomized in a 1:1 ratio to receive aspirin (100 mg/d) or placebo in addition to an antithrombotic regimen. MAIN OUTCOMES AND MEASURES The composite primary end point, assessed for noninferiority (-10% margin) of placebo, was survival free of a major nonsurgical (>14 days after implant) hemocompatibility-related adverse events (including stroke, pump thrombosis, major bleeding, or arterial peripheral thromboembolism) at 12 months. The principal secondary end point was nonsurgical bleeding events. RESULTS Of the 589 analyzed patients, 77% were men; one-third were Black and 61% were White. More patients were alive and free of hemocompatibility events at 12 months in the placebo group (68%) vs those taking aspirin (74%). Noninferiority of placebo was demonstrated (absolute between-group difference, 6.0% improvement in event-free survival with placebo [lower 1-sided 97.5% CI, -1.6%]; P < .001). Aspirin avoidance was associated with reduced nonsurgical bleeding events (relative risk, 0.66 [95% confidence limit, 0.51-0.85]; P = .002) with no increase in stroke or other thromboembolic events, a finding consistent among diverse subgroups of patient characteristics. CONCLUSIONS AND RELEVANCE In patients with advanced heart failure treated with a fully magnetically levitated LVAD, avoidance of aspirin as part of an antithrombotic regimen, which includes VKA, is not inferior to a regimen containing aspirin, does not increase thromboembolism risk, and is associated with a reduction in bleeding events. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04069156.
Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize

Abstract The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus.
Paroxysmal hemicrania and hemicrania continua: Review on pathophysiology, clinical features and treatment

Abstract Background Paroxysmal hemicrania and hemicrania continua are indometacin-sensitive trigeminal autonomic cephalalgias, a terminology which reflects the predominant distribution of the pain, observable cranial autonomic features and shared pathophysiology. Understanding the latter is limited, both by low prevalence and the intricacies of studying brain function, requiring multimodal techniques to glean insights into such disorders. Similarly obscure is the curious response to indometacin. This review will address what is currently known about pathophysiology, the rationale for the current classification and, features which may confound the diagnosis, such as lack of cranial autonomic symptoms and those which are typically associated with migraine such as nausea, photophobia, phonophobia and aura. Despite these characteristics, a dramatic response to indometacin, which is not seen in migraine nor the other trigeminal autonomic cephalalgias , provides the hallmark of the diagnosis. The main clinical differential for paroxysmal hemicrania is based on temporal pattern and lies between cluster headache and short-lasting-neuralgiform headache attacks with tearing or additional cranial autonomic symptoms. For hemicrania continua it is more challenging as the main differential for which the disorder is often treated is migraine. A prior episodic pattern, often days at a time, and the tendency to exacerbation with analgesics will further deflect from the diagnosis. The relevance of this is that there is little overlap in therapeutics between paroxysmal hemicrania and hemicrania continua and other headache disorders and there are limited effective alternatives to indometacin. The most effective are other non-steroidal anti-inflammatory drugs including the newer COX-II inhibitors. Even though early reports suggest that a higher indometacin dose-requirement may herald a secondary precipitating pathology, this does not seem to be the case, with syndrome and response to treatment being similar with the primary disorder. In this context imaging of new onset paroxysmal hemicrania or hemicrania continua and implication of the results will be discussed as will alternative treatment options.
Model uncertainty in measuring the gluon jet fraction at the hadron collider

The work proposes a new method for measuring the gluon jet fraction in jet sample produced at the hadron collider. This method uses model quark/gluon templates - distributions of quark and gluon jets over the jet macro parameter. Within the framework of this method, it is possible to find a model uncertainty of measurement associated with the deviation of model quark/gluon templates from the true ones. The issue of data-motivated corrections to the model quark/gluon templates is discussed.
The gravitational cat on the Cauchy horizon

We show that the stress tensor of a real scalar quantum field on Reissner-Nordstr{\"o}m-deSitter spacetime exhibits correlations over macroscopic distances near the Cauchy horizon. These diverge as the Cauchy horizon is approached and are universal, i.e., state-independent. This signals a breakdown of the semi-classical approximation near the Cauchy horizon. We also investigate the effect of turning on a charge of the scalar field and consider the correlation of the stress tensor between the two poles of the Cauchy horizon of Kerr-de Sitter spacetime.
Structural Parameterizations of Vertex Integrity

The graph parameter vertex integrity measures how vulnerable a graph is to a removal of a small number of vertices. More precisely, a graph with small vertex integrity admits a small number of vertex removals to make the remaining connected components small. In this paper, we initiate a systematic study of structural parameterizations of the problem of computing the unweighted/weighted vertex integrity. As structural graph parameters, we consider well-known parameters such as clique-width, treewidth, pathwidth, treedepth, modular-width, neighborhood diversity, twin cover number, and cluster vertex deletion number. We show several positive and negative results and present sharp complexity contrasts.
Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning

Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available cars to ride requests, rebalancing idle cars to areas of high demand, and charging vehicles to ensure sufficient range. While this problem can be posed as a linear program that optimizes flows over a space-charge-time graph, the size of the resulting optimization problem does not allow for real-time implementation in realistic settings. In this work, we present the E-AMoD control problem through the lens of reinforcement learning and propose a graph network-based framework to achieve drastically improved scalability and superior performance over heuristics. Specifically, we adopt a bi-level formulation where we (1) leverage a graph network-based RL agent to specify a desired next state in the space-charge graph, and (2) solve more tractable linear programs to best achieve the desired state while ensuring feasibility. Experiments using real-world data from San Francisco and New York City show that our approach achieves up to 89% of the profits of the theoretically-optimal solution while achieving more than a 100x speedup in computational time. Furthermore, our approach outperforms the best domain-specific heuristics with comparable runtimes, with an increase in profits by up to 3x. Finally, we highlight promising zero-shot transfer capabilities of our learned policy on tasks such as inter-city generalization and service area expansion, thus showing the utility, scalability, and flexibility of our framework.
An Algorithm for Bichromatic Sorting with Polylog Competitive Ratio

The problem of sorting with priced information was introduced by [Charikar, Fagin, Guruswami, Kleinberg, Raghavan, Sahai (CFGKRS), STOC 2000]. In this setting, different comparisons have different (potentially infinite) costs. The goal is to find a sorting algorithm with small competitive ratio, defined as the (worst-case) ratio of the algorithm's cost to the cost of the cheapest proof of the sorted order. The simple case of bichromatic sorting posed by [CFGKRS] remains open: We are given two sets $A$ and $B$ of total size $N$, and the cost of an $A-A$ comparison or a $B-B$ comparison is higher than an $A-B$ comparison. The goal is to sort $A \cup B$. An $\Omega(\log N)$ lower bound on competitive ratio follows from unit-cost sorting. Note that this is a generalization of the famous nuts and bolts problem, where $A-A$ and $B-B$ comparisons have infinite cost, and elements of $A$ and $B$ are guaranteed to alternate in the final sorted order. In this paper we give a randomized algorithm InversionSort with an almost-optimal w.h. p. competitive ratio of $O(\log^{3} N)$. This is the first algorithm for bichromatic sorting with a $o(N)$ competitive ratio.
BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to"ban"/"pick"a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the"best-of-N"rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner.
Clipped-Objective Policy Gradients for Pessimistic Policy Optimization

To facilitate efficient learning, policy gradient approaches to deep reinforcement learning (RL) are typically paired with variance reduction measures and strategies for making large but safe policy changes based on a batch of experiences. Natural policy gradient methods, including Trust Region Policy Optimization (TRPO), seek to produce monotonic improvement through bounded changes in policy outputs. Proximal Policy Optimization (PPO) is a commonly used, first-order algorithm that instead uses loss clipping to take multiple safe optimization steps per batch of data, replacing the bound on the single step of TRPO with regularization on multiple steps. In this work, we find that the performance of PPO, when applied to continuous action spaces, may be consistently improved through a simple change in objective. Instead of the importance sampling objective of PPO, we instead recommend a basic policy gradient, clipped in an equivalent fashion. While both objectives produce biased gradient estimates with respect to the RL objective, they also both display significantly reduced variance compared to the unbiased off-policy policy gradient. Additionally, we show that (1) the clipped-objective policy gradient (COPG) objective is on average"pessimistic"compared to both the PPO objective and (2) this pessimism promotes enhanced exploration. As a result, we empirically observe that COPG produces improved learning compared to PPO in single-task, constrained, and multi-task learning, without adding significant computational cost or complexity. Compared to TRPO, the COPG approach is seen to offer comparable or superior performance, while retaining the simplicity of a first-order method.
Greedy PIG: Adaptive Integrated Gradients

Deep learning has become the standard approach for most machine learning tasks. While its impact is undeniable, interpreting the predictions of deep learning models from a human perspective remains a challenge. In contrast to model training, model interpretability is harder to quantify and pose as an explicit optimization problem. Inspired by the AUC softmax information curve (AUC SIC) metric for evaluating feature attribution methods, we propose a unified discrete optimization framework for feature attribution and feature selection based on subset selection. This leads to a natural adaptive generalization of the path integrated gradients (PIG) method for feature attribution, which we call Greedy PIG. We demonstrate the success of Greedy PIG on a wide variety of tasks, including image feature attribution, graph compression/explanation, and post-hoc feature selection on tabular data. Our results show that introducing adaptivity is a powerful and versatile method for making attribution methods more powerful.
The category of partial group actions I: quotients, (co)limits and groupoids

We introduce the category of partial actions, where the group and the set upon which the group acts can vary. Within this framework, we develop a theory of quotient partial actions and prove that this category is both (co)complete and encompasses the category of groupoids as a full subcategory. In particular, we establish the existence of a pair of functors, denoted as $\Phi : \textbf{Grpd} \to \textbf{ParAct}$ and $\Psi : \textbf{ParAct} \to \textbf{Grpd}$, with the property that $\Psi \Phi \cong 1_{\textbf{Grpd} }$. Next, for a given groupoid $\Gamma$, we provide a characterization of all partial actions that allow the recovery of the groupoid $\Gamma$ through $\Psi$. This characterization is expressed in terms of certain normal subgroups of a universal group constructed from $\Gamma. $ Furthermore, we extend some of these results to the topological case, showing that topological groupoids can always be obtained from (weak) topological partial group action.
Incorporating sufficient physical information into artificial neural networks: a guaranteed improvement via physics-based Rao-Blackwellization

The concept of Rao-Blackwellization is employed to improve predictions of artificial neural networks by physical information. The error norm and the proof of improvement are transferred from the original statistical concept to a deterministic one, using sufficient information on physics-based conditions. The proposed strategy is applied to material modeling and illustrated by examples of the identification of a yield function, elasto-plastic steel simulations, the identification of driving forces for quasi-brittle damage and rubber experiments. Sufficient physical information is employed, e.g., in the form of invariants, parameters of a minimization problem, dimensional analysis, isotropy and differentiability. It is proven how intuitive accretion of information can yield improvement if it is physically sufficient, but also how insufficient or superfluous information can cause impairment. Opportunities for the improvement of artificial neural networks are explored in terms of the training data set, the networks' structure and output filters. Even crude initial predictions are remarkably improved by reducing noise, overfitting and data requirements.
Community Forestry as a Requisite for REDD 1

Community forestry has proven in many countries to be an effective mechanism to enhance the sustainable management of forest resources in many tropical countries around the world. Active involvement of local communities leads to effective management protecting valuable forest resources, which has often been elusive to state management forestry organizations. Under the UNFCCC a new instrument is being developed that will make emission reduction in natural forests eligible for financial compensation. Reducing Emissions from Deforestation and Degradation (REDD) has the potential to generate large amounts of income for forest managers, thereby making sustainable management (more) profitable. There is recognition among the negotiators at the UNFCCC that indigenous groups and local communities need to be involved in the implementation of REDD – and have a meaningful share of the proceeds – but many countries are still debating how to operationalize such involvement. In this paper we will demonstrate how communities can contribute in the collection of basic data on forest properties, in a very cost‐effective manner. This community‐based inventory is not only cost‐effective; it is also addressing many of the technical obstacles that other inventory techniques present, in particular for the assessment of forest degradation. Forest degradation occurs in different forms: not only as the result of selective logging in by commercial parties, but as the result of increasing pressure from local populations for subsistence purposes. The emissions from this second type of degradation have probably been greatly underestimated as they are gradual, on‐going and very widespread, rather than episodic like selective logging. The associated biomass losses do not show up on remotely sensed images. Reversing this trend is possibly the greatest source of emission reductions to be achieved.
Does Differential Privacy Prevent Backdoor Attacks in Practice?

Differential Privacy (DP) was originally developed to protect privacy. However, it has recently been utilized to secure machine learning (ML) models from poisoning attacks, with DP-SGD receiving substantial attention. Nevertheless, a thorough investigation is required to assess the effectiveness of different DP techniques in preventing backdoor attacks in practice. In this paper, we investigate the effectiveness of DP-SGD and, for the first time in literature, examine PATE in the context of backdoor attacks. We also explore the role of different components of DP algorithms in defending against backdoor attacks and will show that PATE is effective against these attacks due to the bagging structure of the teacher models it employs. Our experiments reveal that hyperparameters and the number of backdoors in the training dataset impact the success of DP algorithms. Additionally, we propose Label-DP as a faster and more accurate alternative to DP-SGD and PATE. We conclude that while Label-DP algorithms generally offer weaker privacy protection, accurate hyper-parameter tuning can make them more effective than DP methods in defending against backdoor attacks while maintaining model accuracy.