Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender Equity and Vertically Transmitted Infections: A Country-Level Analysis Across 153 Countries

Purpose: Gender inequality is a barrier to education toward women and accessibility to health facilities, which are important for preventing vertical transmission. This study was conducted to analyze the impact of gender equity on vertically transmitted infections (hepatitis viruses, human immunodeficiency virus [HIV], and syphilis) using country-level indicators. Methods: The relationship between the Global Gender Gap Index (GGGI), which is indicator of gender equity, and vertical transmission was analyzed. GGGI scores were collected from 153 countries in 2020. Vertical transmission included 10 outcomes for hepatitis viruses, HIV, and syphilis. Generalized linear model (GLM) was used for analyzing the relationship. Other predictors included skilled birth attendant and country income. Results: The median GGGI score was 0.706 (interquartile range, 0.664–0.736). GLM showed that the GGGI score was significantly associated with the incidence of both chronic hepatitis B and C in under 5 years (both p<0.001). For HIV, GGGI score was significantly associated with the pregnant women with unknown HIV status (p=0.001), no early infant diagnosis (p=0.027), and final transmission rate (p=0.005). There was no significant predictor for pregnant women who have not received antiretroviral therapy for prevention of mother-to-child transmission. All syphilis indicators have improved in high-income countries compared to low-income countries. GGGI score had a significant association only with no syphilis screening (p<0.001). Conclusions: A lower GGGI score was associated with higher vertical transmission of hepatitis and HIV. The improvement of gender equity might prevent vertical transmission of these viruses. Further intervention studies are warranted to verify the results.
Concurrent Infection of Fascioliasis andTrypanosomosis and Associated Risk Factors in Local Zebu Breed Cattle of Western Ethiopia

Background A cross-sectional study was conducted from late October 2016 to June 2017, with the primary objective of estimating and analyzing the concurrent occurrence of both fascioliasis and trypanosomosis infections and associated risk factors along the tsetse-infested Didessa river basin. Methods The methodology applied was based on stratified sampling for the parasitological study, with entomological and malacological surveys, including fly dissection. Results The result of variance-ratio testing between trypanosomosis and fascioliasis infections (mean prevalence 0.117±0.322 and 0.283±0.451, respectively), was statistically significant (P[F>f]=0), with higher observed fascioliasis infection (n=147, 28.27%). Severe anemia was observed in trypanosomosis infection, with mean packed cell volume of 19.57 (OR=0.71, P>|z|=0.000), and vast fascioliasis infections identified among cattle with medium and poor body condition in terms of weight (n=91 [32.73%] and n=38 [21.47%]). On entomological study, 578 (62.62%) and 345 (37.38%) female and male Glossina tachinoides fly species were cached, respectively, with overall mean flies/trap/day of 5.19 (n=923). Despite the prevalence of trypanosomosis in infected cattle, of 130 G. tachinoides flies dissected, only three were found to be positive for an infection rate of 2.31%. Malacological study identified three snail species known to maintain fascioliasis: Lymnea truncatula (n=28, 45.16%), Lymnea natalensis (n=23, 37.10%), and Biomphalaria (n=11, 17.74%). Concurrent infection with fascioliasis and trypanosomosis was mainly associated with the co-occurrence of their intermediate host snails and Glossina flies, respectively, with 4.42% (n=23) prevalence. Conclusion This study clearly demonstrated that the former parasite was highly associated with emaciation, whereas the second was responsible for anemia. In future,researchers should focus solely on estimating meat and milk production of local cattle to assess the economic impact of the study parasites.
Increased Levels of Serum Glycosylated Hemoglobin are Associated with Depressive Symptoms in a Population with Cancer (≥49 Years): An Antidepressant-Stratified Analysis

Purpose Patients with cancer tend to have a high prevalence of depressive symptoms. The direct relationship between serum glycosylated hemoglobin (GHb) levels and depressive symptoms in cancer patients is still uncertain. We aimed to evaluate the association with serum GHb levels with depressive symptoms in the population (aged ≥49 years) with cancer. Patients and Methods Longitudinal data in 204 participants with cancer obtained from The Irish LongituDinal Study on Ageing (TILDA) were used to investigate the association of serum GHb levels with depressive symptoms. Results Our results suggested a positive and significant association between serum GHb levels and depression score, independent of age, gender, body mass index (BMI), currently married, education, smoking status, drink alcohol, systolic and diastolic blood pressure (BP), physical activity, self-reported cardiovascular diseases and laboratory measurement in participants with cancer (coefficient =0.141, P<0.001; Model 2) at baseline (wave 1). Higher GHb levels did associate with higher prevalence of depressive symptoms in participants with cancer (OR=2.100, 95% CI 1.105–5.036, P=0.004; Model 2) after adjustment for these same confounding factors in wave 1 was made. Stratified analysis further showed that these significant associations were interfered by antidepressants. Sensitivity analysis showed that higher serum GHb levels in subjects with cancer were linked to higher prevalence of depression events during a follow-up of 4 years. Conclusion Our results found a significant association between elevated serum GHb levels and increased risk of depressive symptoms in the population aged ≥49 years with cancer after confounding factors were adjusted.
Comparing Surgical Site Occurrences in 1 versus 2-stage Breast Reconstruction via Federated EMR Network

Background: TriNetX (TriNetX Inc., Cambridge, Mass.) is a federated electronic medical record network. The TriNetX system conducts customized search queries of over 36 million electronic medical records, and returns results in just minutes. To our group’s knowledge, TriNetX has not been previously used in plastic surgery research. This study aimed to utilize a continuously updated federated network of 36,000,000 electronic medical records (TriNetX) for comparing 90-day postoperative outcomes between prosthetic breast reconstruction techniques. Methods: Using TriNetX, we analyzed the records of approximately 36 million patients in 31 health care organizations. The de-identified records of 18,744,519 women (age 18–9) were retrospectively screened. A cohort of 4747 patients with a diagnosis of malignant neoplasm of the breast, any stage, having undergone mastectomy, and breast reconstruction with tissue expander was compared with a second cohort of 870 patients diagnosed with malignant neoplasm of the breast, any stage, mastectomy, and immediate insertion of breast implant following mastectomy. Surgical site occurrences occurring within 90 days postoperatively were compared using propensity score matching. Results: Propensity score matching resulted in 870 patients in both well-balanced cohorts. There were no statistically significant differences between the balanced cohorts with respect to 90-day surgical site occurrences. Conclusions: TriNetX enables data-driven clinical research such as retrospective cohort comparison. During the 90-day postoperative period, there were fewer complications noted in the single-stage cohort for all outcomes studied; although this comparison was not statistically significant, we believe it demonstrates a clinically significant finding that single-stage direct-to-implant is at least as safe as the more complicated 2-stage approach.
Dirac Equation on the Kerr–Newman Spacetime and Heun Functions

By employing a pseudoorthonormal coordinate-free approach, the Dirac equation for particles in the Kerr–Newman spacetime is separated into its radial and angular parts. In the massless case to which a special attention is given, the general Heun-type equations turn into their confluent form. We show how one recovers some results previously obtained in literature, by other means.
SLUA: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning

Word alignment is essential for the down-streaming cross-lingual language understanding and generation tasks. Recently, the performance of the neural word alignment models [Zenkel et al. , 2020; Garg et al. , 2019; Ding et al. , 2019 ] has exceeded that of statistical models. However, they heavily rely on sophisticated translation models. In this study, we propose a Super Lightweight Unsupervised word Alignment ( SLUA ) model, in which a bidirectional symmetric attention trained with a contrastive learning objective is introduced, and an agreement loss is employed to bind the attention maps, such that the alignments follow mirror-like symmetry hypothesis. Experimental re-sults on several public benchmarks demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in word alignment while signiﬁcantly reducing the training and decoding time on average. Further ablation analysis and case studies show the superiority of our proposed SLUA. Notably, we recognize our model as a pioneer attempt to unify bilingual word embedding and word alignments. En-couragingly, our approach achieves 16.4 × speedup against GIZA++, and 50 × parameter compression compared with the Transformer-based alignment methods. We will release our code to facilitate the community.
How Pandemic Spread in News: Text Analysis Using Topic Model

COVID-19 pandemic has made tremendous impact on the whole world, both the real world and the media atmosphere. Our research conducted a text analysis using LDA topic model. We first scraped 1127 articles and 5563 comments on SCMP covering COVID-19 from Jan 20 to May 19, then we trained the LDA model and tuned parameters based on the Cv coherence as the model evaluation method. With the optimal model, dominant topics, representative documents of each topic and the inconsistency between articles and comments are analyzed. Some factors of the inconsistency are discussed at last.
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases

Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple, a picture-based decision support system able to help users in the diagnosis of apple diseases. Specifically, this paper addresses the problem of sequentially optimizing for the best diagnosis, leveraging past interactions with the system and their contextual information (i.e. the evidence provided by the users). The problem of learning an online model while optimizing for its outcome is commonly addressed in the literature through a stochastic active learning paradigm - i.e. Contextual Multi-Armed Bandit (CMAB). This methodology interactively updates the decision model considering the success of each past interaction with respect to the context provided in each round. However, this information is very often partial and inadequate to handle such complex decision making problems. On the other hand, human decisions implicitly include unobserved factors (referred in the literature as unobserved confounders) that significantly contribute to the human's final decision. In this paper, we take advantage of the information embedded in the observed human decisions to marginalize confounding factors and improve the capability of the CMAB model to identify the correct diagnosis. Specifically, we propose a Counterfactual Contextual Multi-Armed Bandit, a model based on the causal concept of counterfactual. The proposed model is validated with offline experiments based on data collected through a large user study on the application. The results prove that our model is able to outperform both traditional CMAB algorithms and observed user decisions, in real-world tasks of predicting the correct apple disease.
Datasets and Evaluation for Simultaneous Localization and Mapping Related Problems: A Comprehensive Survey

Simultaneous Localization and Mapping (SLAM) has found an increasing utilization lately, such as self-driving cars, robot navigation, 3D mapping, virtual reality (VR) and augmented reality (AR), etc., empowering both industry and daily life. Although the state-of-the-art algorithms where developers have spared no effort are source of intelligence, it is the datasets that dedicate behind and raise us higher. The employment of datasets is essentially a kind of simulation but profits many aspects – capacity of drilling algorithm hourly, exemption of costly hardware and ground truth system, and equitable benchmark for evaluation. However, as a branch of great significance, still the datasets have not drawn wide attention nor been reviewed thoroughly. Hence in this article, we strive to give a comprehensive and open access review of SLAM related datasets and evaluation, which are scarcely surveyed while highly demanded by researchers and engineers, looking forward to serving as not only a dictionary but also a development proposal. The paper starts with the methodology of dataset collection, and a taxonomy of SLAM related tasks. Then followed with the main portion – comprehensively survey the existing SLAM related datasets by category with our considerate introductions and insights. Furthermore, we talk about the evaluation criteria, which are necessary to quantify the algorithm performance on the dataset and inspect the defects. At the end, we summarize the weakness of datasets and evaluation – which could well result in the weakness of topical algorithms – to promote bridging the gap fundamentally.
Bootstrapping Statistical Inference for Off-Policy Evaluation

Bootstrapping provides a flexible and effective approach for assessing the quality of batch reinforcement learning, yet its theoretical property is less understood. In this paper, we study the use of bootstrapping in off-policy evaluation (OPE), and in particular, we focus on the fitted Q-evaluation (FQE) that is known to be minimax-optimal in the tabular and linear-model cases. We propose a bootstrapping FQE method for inferring the distribution of the policy evaluation error and show that this method is asymptotically efficient and distributionally consistent for off-policy statistical inference. To overcome the computation limit of bootstrapping, we further adapt a subsampling procedure that improves the runtime by an order of magnitude. We numerically evaluate the bootrapping method in classical RL environments for confidence interval estimation, estimating the variance of off-policy evaluator, and estimating the correlation between multiple off-policy evaluators.
STAN: Spatio-Temporal Attention Network for Next Location Recommendation

The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical gridding and model temporal relation with explicit time intervals, while some vital questions remain unsolved. Non-adjacent locations and non-consecutive visits provide non-trivial correlations for understanding a user’s behavior but were rarely considered. To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Attention Network (STAN) for location recommendation. STAN explicitly exploits relative spatiotemporal information of all the check-ins with self-attention layers along the trajectory. This improvement allows a point-to-point interaction between non-adjacent locations and non-consecutive check-ins with explicit spatio-temporal effect. STAN uses a bi-layer attention architecture that firstly aggregates spatiotemporal correlation within user trajectory and then recalls the target with consideration of personalized item frequency (PIF). By visualization, we show that STAN is in line with the above intuition. Experimental results unequivocally show that our model outperforms the existing state-of-the-art methods by 9-17%.
Deep Retinex Network for Estimating Illumination Colors with Self-Supervised Learning

We propose a novel Retinex image-decomposition network that can be trained in a self-supervised manner. The Retinex image-decomposition aims to decompose an image into illumination-invariant and illumination-variant components, referred to as “reflectance” and “shading,” respectively. Although there are three consistencies that the reflectance and shading should satisfy, most conventional work considers only one or two of the consistencies. For this reason, the three consistencies are considered in the proposed network. In addition, by using generated pseudo-images for training, the proposed network can be trained with self-supervised learning. Experimental results show that our network can decompose images into reflectance and shading components. Furthermore, it is shown that the proposed network can be used for white-balance adjustment.
Effects of Layer Freezing when Transferring DeepSpeech to New Languages

In this paper, we train Mozilla’s DeepSpeech architecture on German and Swiss German speech datasets and compare the results of diﬀerent training methods. We ﬁrst train the models from scratch on both languages and then improve upon the results by using an English pretrained version of DeepSpeech for weight initialization and experiment with the eﬀects of freezing diﬀerent layers during training. We see that even freezing only one layer already improves the results dramatically.
OV$^{2}$SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications

Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability. In this work, we describe OV$^{2}$SLAM, a fully online algorithm, handling both monocular and stereo camera setups, various map scales and frame-rates ranging from a few Hertz up to several hundreds. It combines numerous recent contributions in visual localization within an efficient multi-threaded architecture. Extensive comparisons with competing algorithms shows the state-of-the-art accuracy and real-time performance of the resulting algorithm. For the benefit of the community, we release the source code: https://github.com/ov2slam/ov2slam.
Model-Augmented Q-learning

In recent years, $Q$-learning has become indispensable for model-free reinforcement learning (MFRL). However, it suffers from well-known problems such as under- and overestimation bias of the value, which may adversely affect the policy learning. To resolve this issue, we propose a MFRL framework that is augmented with the components of model-based RL. Specifically, we propose to estimate not only the $Q$-values but also both the transition and the reward with a shared network. We further utilize the estimated reward from the model estimators for $Q$-learning, which promotes interaction between the estimators. We show that the proposed scheme, called Model-augmented $Q$-learning (MQL), obtains a policy-invariant solution which is identical to the solution obtained by learning with true reward. Finally, we also provide a trick to prioritize past experiences in the replay buffer by utilizing model-estimation errors. We experimentally validate MQL built upon state-of-the-art off-policy MFRL methods, and show that MQL largely improves their performance and convergence. The proposed scheme is simple to implement and does not require additional training cost.
Solving the Hubble tension without spoiling big bang nucleosynthesis

The Hubble parameter inferred from cosmic microwave background observations is consistently lower than that from local measurements, which could hint towards new physics. Solutions to the Hubble tension typically require a sizable amount of extra radiation ∆Neff during recombination. However, the amount of ∆Neff in the early Universe is unavoidably constrained by Big Bang Nucleosynthesis (BBN), which causes problems for such solutions. We present a possibility to evade this problem by introducing neutrino self-interactions via a simple Majoron-like coupling. The scalar is slightly heavier than 1 MeV and allowed to be fully thermalized throughout the BBN era. The rise of neutrino temperature due to the entropy transfer via φ → νν reactions compensates the effect of a large ∆Neff on BBN. Values of ∆Neff as large as 0.7 are in this case compatible with BBN. We perform a fit to the parameter space of the model.
Additive Feature Hashing

The hashing trick is a machine learning technique used to encode categorical features into a numerical vector representation of pre-defined fixed length. It works by using the categorical hash values as vector indices, and updating the vector values at those indices. Here we discuss a different approach based on additive-hashing and the"almost orthogonal"property of high-dimensional random vectors. That is, we show that additive feature hashing can be performed directly by adding the hash values and converting them into high-dimensional numerical vectors. We show that the performance of additive feature hashing is similar to the hashing trick, and we illustrate the results numerically using synthetic, language recognition, and SMS spam detection data.
High-level Approaches to Detect Malicious Political Activity on Twitter

Our work represents another step into the detection and prevention of these ever-more present political manipulation efforts. We, therefore, start by focusing on understanding what the state-of-the-art approaches lack -- since the problem remains, this is a fair assumption. We find concerning issues within the current literature and follow a diverging path. Notably, by placing emphasis on using data features that are less susceptible to malicious manipulation and also on looking for high-level approaches that avoid a granularity level that is biased towards easy-to-spot and low impact cases. We designed and implemented a framework -- Twitter Watch -- that performs structured Twitter data collection, applying it to the Portuguese Twittersphere. We investigate a data snapshot taken on May 2020, with around 5 million accounts and over 120 million tweets (this value has since increased to over 175 million). The analyzed time period stretches from August 2019 to May 2020, with a focus on the Portuguese elections of October 6th, 2019. However, the Covid-19 pandemic showed itself in our data, and we also delve into how it affected typical Twitter behavior. We performed three main approaches: content-oriented, metadata-oriented, and network interaction-oriented. We learn that Twitter's suspension patterns are not adequate to the type of political trolling found in the Portuguese Twittersphere -- identified by this work and by an independent peer - nor to fake news posting accounts. We also surmised that the different types of malicious accounts we independently gathered are very similar both in terms of content and interaction, through two distinct analysis, and are simultaneously very distinct from regular accounts.