Miracle-dz/UniFAR-FTUs huggingface dataset:
Bootstrapping Entity Alignment with Knowledge Graph Embedding

Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.
Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.
GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices

Recent research on grasp detection has focused on improving accuracy through deep CNN models, but at the cost of large memory and computational resources. In this paper, we propose an efficient CNN architecture which produces high grasp detection accuracy in real-time while maintaining a compact model design. To achieve this, we introduce a CNN architecture termed GraspNet which has two main branches: i) An encoder branch which downsamples an input image using our novel Dilated Dense Fire (DDF) modules - squeeze and dilated convolutions with dense residual connections. ii) A decoder branch which upsamples the output of the encoder branch to the original image size using deconvolutions and fuse connections. We evaluated GraspNet for grasp detection using offline datasets and a real-world robotic grasping setup. In experiments, we show that GraspNet achieves superior grasp detection accuracy compared to the stateof-the-art computation-efficient CNN models with real-time inference speed on embedded GPU hardware (Nvidia Jetson TX1), making it suitable for low-powered devices.
Early stage memory impairment, visual hallucinations, and myoclonus combined with temporal lobe atrophy predict Alzheimer’s disease pathology in corticobasal syndrome

ABSTRACT Corticobasal syndrome (CBS) is a typical phenotype of corticobasal degeneration (CBD). However, autopsy series have shown that many CBS cases emerge from various types of non-CBD pathology. We report a 73-year-old Korean man who was clinically diagnosed with CBS whose underlying pathology was Alzheimer’s disease (AD) at autopsy (CBS-AD). This case suggests that early developing memory impairment and myoclonus, severe temporoparietal atrophy, and visual hallucinations may support a more specific prediction of CBS-AD.
Is There a Relationship between Lead Exposure and Aggressive Behavior in Shooters?

Lead exposure has been associated with psycho-neurological disorders. Elevated blood lead levels have been found in shooters. This study assesses the association between the blood lead levels of shooters and their levels of aggression. An analytical cross-sectional study was conducted in Gauteng, South Africa. Participants were recruited from four randomly selected shooting ranges with three randomly selected archery ranges used as a comparison group. A total of 118 (87 shooters and 31 archers) participants were included in the analysis. Aggressiveness was measured using the Buss-Perry Aggression Questionnaire. Shooters had significantly higher blood lead levels (BLL) compared to archers with 79.8% of shooters versus 22.6% of archers found to have a BLL ≥ 5 μg/dL (p < 0.001). Aggression scores were significantly higher in shooters (p < 0.05) except for verbal aggression. In the bivariate and regression analyses, shooters with BLLs ≥ 10 μg/dL were significantly associated with the hostility sub-scale (p = 0.03, adjusted odds ratio (OR) 2.83, 95% confidence interval (CI) 1.103–7.261). Shooters have a significantly higher BLL and aggressiveness compared to archers. However, elevated blood lead levels were significantly associated with hostility only. Interventions need to be put in place to prevent continued exposure and routine screening of populations at risk should be implemented.
In Vitro Model of Neuroinflammation: Efficacy of Cannabigerol, a Non-Psychoactive Cannabinoid

Inflammation and oxidative stress play main roles in neurodegeneration. Interestingly, different natural compounds may be able to exert neuroprotective actions against inflammation and oxidative stress, protecting from neuronal cell loss. Among these natural sources, Cannabis sativa represents a reservoir of compounds exerting beneficial properties, including cannabigerol (CBG), whose antioxidant properties have already been demonstrated in macrophages. Here, we aimed to evaluate the ability of CBG to protect NSC-34 motor neurons against the toxicity induced from the medium of LPS-stimulated RAW 264.7 macrophages. Using MTT assay, we observed that CBG pre-treatment was able to reduce the loss of cell viability induced by the medium of LPS-stimulated macrophages in NSC-34 cells. Indeed, CBG pre-treatment inhibited apoptosis, as shown by the reduction of caspase 3 activation and Bax expression, while Bcl-2 levels increased. Furthermore, CBG pre-treatment counteracted not only inflammation, as demonstrated by the reduction of IL-1β, TNF-α, IFN-γ and PPARγ protein levels assessed by immunocytochemistry, but also oxidative stress in NSC-34 cells treated with the medium of LPS-stimulated RAW 264.7. Indeed, immunocytochemistry showed that CBG pre-treatment reduced nitrotyrosine, SOD1 and iNOS protein levels and restored Nrf-2 levels. All together, these results indicated the neuroprotective effects of CBG, that may be a potential treatment against neuroinflammation and oxidative stress.
Molecular Cloning and Expression Analysis of Lactate Dehydrogenase from the Oriental River Prawn Macrobrachium nipponense in Response to Hypoxia

Metabolic adaption to hypoxic stress in crustaceans implies a shift from aerobic to anaerobic metabolism. Lactate dehydrogenase (LDH) is a key enzyme in glycolysis in prawns. However, very little is known about the role of LDH in hypoxia inducible factor (HIF) pathways of prawns. In this study, full-length cDNA of LDH (MnLDH) was obtained from the oriental river prawn Macrobrachium nipponense, and was characterized. The full-length cDNA is 2267-bp with an open reading frame of 999 bp coding for a protein of 333 amino acids with conserved domains important for function and regulation. Phylogenetic analysis showed that MnLDH is close to LDHs from other invertebrates. Quantitative real-time PCR revealed that MnLDH is expressed in various tissues with the highest expression level in muscle. MnLDH mRNA transcript and protein abundance in muscle, but not in hepatopancreas, were induced by hypoxia. Silencing of hypoxia-inducible factor 1 (HIF-1) α or HIF-1β subunits blocked the hypoxia-dependent increase of LDH expression and enzyme activity in muscle. A series of MnLDH promoter sequences, especially the full-length promoter, generated an increase in luciferase expression relative to promoterless vector; furthermore, the expression of luciferase was induced by hypoxia. These results demonstrate that MnLDH is probably involved a HIF-1-dependent pathway during hypoxia in the highly active metabolism of muscle.
CAGAN: Consistent Adversarial Training Enhanced GANs

Generative adversarial networks (GANs) have shown impressive results, however, the generator and the discriminator are optimized in finite parameter space which means their performance still need to be improved. In this paper, we propose a novel approach of adversarial training between one generator and an exponential number of critics which are sampled from the original discriminative neural network via dropout. As discrepancy between outputs of different sub-networks of a same sample can measure the consistency of these critics, we encourage the critics to be consistent to real samples and inconsistent to generated samples during training, while the generator is trained to generate consistent samples for different critics. Experimental results demonstrate that our method can obtain state-of-the-art Inception scores of 9.17 and 10.02 on supervised CIFAR-10 and unsupervised STL-10 image generation tasks, respectively, as well as achieve competitive semi-supervised classification results on several benchmarks. Importantly, we demonstrate that our method can maintain stability in training and alleviate mode collapse.
Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection

Subset selection is a fundamental problem in many areas, which aims to select the best subset of size at most $k$ from a universe. Greedy algorithms are widely used for subset selection, and have shown good approximation performances in deterministic situations. However, their behaviors are stochastic in many realistic situations (e.g., large-scale and noisy). For general stochastic greedy algorithms, bounded approximation guarantees were obtained only for subset selection with monotone submodular objective functions, while real-world applications often involve non-monotone or non-submodular objective functions and can be subject to a more general constraint than a size constraint. This work proves their approximation guarantees in these cases, and thus largely extends the applicability of stochastic greedy algorithms.
Enhancing Semantic Representations of Bilingual Word Embeddings with Syntactic Dependencies

Cross-lingual representation is a technique that can both represent different languages in the same latent vector space and enable the knowledge transfer across languages. To learn such representations, most of existing works require parallel sentences with word-level alignments and assume that aligned words have similar Bag-of-Words (BoW) contexts. However, due to differences in grammar structures among different languages, the contexts of aligned words in different languages may appear at different positions of the sentence. To address this issue of different syntactics across different languages, we propose a model of bilingual word embeddings integrating syntactic dependencies (DepBiWE) by producing dependency parse-trees which encode the accurate relative positions for the contexts of aligned words. In addition, a new method is proposed to learn bilingual word embeddings from dependency-based contexts and BoW contexts jointly. Extensive experimental results on a real world dataset clearly validate the superiority of the proposed model DepBiWE on various natural language processing (NLP) tasks.
R³Net: Recurrent Residual Refinement Network for Saliency Detection

Saliency detection is a fundamental yet challenging task in computer vision, aiming at highlighting the most visually distinctive objects in an image. We propose a novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image. Our RRBs learn the residual between the intermediate saliency prediction and the ground truth by alternatively leveraging the low-level integrated features and the high-level integrated features of a fully convolutional network (FCN). While the low-level integrated features are capable of capturing more saliency details, the high-level integrated features can reduce non-salient regions in the intermediate prediction. Furthermore, the RRBs can obtain complementary saliency information of the intermediate prediction, and add the residual into the intermediate prediction to refine the saliency maps. We evaluate the proposed R^3Net on five widely-used saliency detection benchmarks by comparing it with 16 state-of-the-art saliency detectors. Experimental results show that our network outperforms our competitors in all the benchmark datasets.
EDI Adoption and Implementation: An Examination of Perceived Operational and Strategic Benefits, and Controls

In recent years, Electronic Data Interchange (EDI) has revolutionized the way in which businesses conduct their trading activities. Even though the popularity and potential attached to EDI is growing rapidly, knowledge regarding the nature of EDI benefits and EDI control practices is very limited. This paper reports the results of a survey of EDI users that explores these key implementation issues. This study focuses on organizational factors that are associated with EDI adoption and implementation. Findings indicate that organizations experience both operational and strategic benefits from EDI. Customer‐initiated EDI users recognized slightly greater EDI strategic benefits than did other users. Also, long‐time users recognized both strategic and operational benefits in greater proportions than did more recent users, and smaller firms more often cited better customer service and convenience (as strategic and operational benefits, respectively) from implementing EDI. An examination of control practices rev.
Finance and Time Use Implications of Team Documentation for Primary Care: A Microsimulation

PURPOSE To estimate the conditions under which team documentation—having a staff member enter history, place orders, and guide patients—would be financially viable at primary care practices, accounting for implementation costs. METHODS We applied a validated microsimulation model of practice costs, revenues, and time use to data from 643 US primary care practices. We estimated critical threshold values for time saved from routine visits that would need to be redirected to new visits to avoid net revenue losses under: (1) a clerical documentation assistant (CDA) strategy where a scribe assists with recordkeeping; and (2) an advanced team-based care (ATBC) strategy where medical assistants perform history, documentation, counseling, and order entry. RESULTS Using a fee-for-service model, we estimated that physicians would need to save 3.5 (95% CI, 3.3-3.7) minutes/encounter under a CDA strategy and 7.4 (95% CI, 4.3-10.5) minutes/encounter under an ATBC strategy to prevent net revenue losses. The redirected time would be expected to add 317 visit slots per year under CDA strategy, and 720 under ATBC strategy. Using a capitated payment model, physicians would need to empanel at least 127 (95% CI, 70-187) more patients under CDA and 227 (95% CI, 153-267) under ATBC to prevent revenue losses. Additional patient visits expected would be 279 (95% CI, 140-449) additional visit slots per year under CDA and 499 (95% CI, 454-641) under ATBC. CONCLUSIONS Financial viability of team documentation under fee-for-service payment may require more physician time to be reallocated to patient encounters than under a capitated payment model.
Effectiveness of Market-Level Biosecurity at Reducing Exposure of Poultry and Humans to Avian Influenza: A Systematic Review and Meta-Analysis

Background In this study, we aimed to identify the effect of market-level risk factors on avian influenza (AI) infection in poultry and humans and generate evidence that will inform AI prevention and control programs at live bird markets (LBMs). Methods We performed a systematic literature review in both English and Chinese search engines. We estimated the pooled odds ratios of biosecurity indicators relating to AI infections at market level using a quality effects (QE) meta-analysis model. Results Biosecurity measures effective at reducing AI market contamination and poultry infection at LBMs include smaller market size, selling single poultry species and separating different species, performing cleaning and disinfection and market closures, ban on overnight storage, and sourcing poultry from local areas. Our meta-analysis indicates that higher risk of exposure to AI infection occurs in workers at retail LBMs, female workers, and those who contact ducks, conduct cleaning, slaughtering, defeathering, or evisceration. Conclusions The most effective strategies to reduce AI market contamination identified in this study should target larger LBMs that are located at noncentral city areas and sell and slaughter multispecies of live poultry. Live bird market workers directly involved in cleaning and poultry processing tasks should participate in occupational health and safety programs.
ACV-tree: A New Method for Sentence Similarity Modeling

Sentence similarity modeling lies at the core of many natural language processing applications, and thus has received much attention. Owing to the success of word embeddings, recently, popular neural network methods have achieved sentence embedding, obtaining attractive performance. Nevertheless, most of them focused on learning semantic information and modeling it as a continuous vector, while the syntactic information of sentences has not been fully exploited. On the other hand, prior works have shown the benefits of structured trees that include syntactic information, while few methods in this branch utilized the advantages of word embeddings and another powerful technique ? attention weight mechanism. This paper makes the first attempt to absorb their advantages by merging these techniques in a unified structure, dubbed as ACV-tree. Meanwhile, this paper develops a new tree kernel, known as ACVT kernel, that is tailored for sentence similarity measure based on the proposed structure. The experimental results, based on 19 widely-used datasets, demonstrate that our model is effective and competitive, compared against state-of-the-art models.
Improving Postdischarge Outcomes in Acute Heart Failure

The global burden that acute heart failure (AHF) carries has remained unchanged over the past several decades (1). European registries (2–5) showed that 1-year outcome rates remain unacceptably high (Table 1) and confirm that hospitalization for AHF represents a change in the natural history of the disease process(6). As patients hospitalized for HF have a bad prognosis, it is crucial to utilize hospitalization as an opportunity to: 1) assess the individual components of the cardiac substrate; 2) identify and treat comorbidities; 3) identify early, safe endpoints of therapy to facilitate timely hospital discharge and outpatient follow-up; and 4) implement and begin optimization guideline-directed medical therapies (GDMTs). As outcomes are influenced by many factors, many of which are incompletely understood, a systematic approach is proposed that should start with admission and continues through post-discharge (7).
Strategyproof and fair matching mechanism for union of symmetric m-convex constraints

We identify a new class of distributional constraints defined as a union of symmetric M-convex sets, which can represent a wide range of real-life constraints in two-sided matching settings. Since M-convexity is not closed under union, a union of symmetric M-convex sets does not belong to this well-behaved class of constraints. Consequently, devising a fair and strategyproof mechanism to handle this new class is challenging. We present a novel mechanism for it called Quota Reduction Deferred Acceptance (QRDA), which repeatedly applies the standard Deferred Acceptance mechanism by sequentially reducing artificially introduced maximum quotas. We show that QRDA is fair and strategyproof when handling a union of symmetric M-convex sets, which extends previous results obtained for a subclass of the union of symmetric M-convex sets: ratio constraints. QRDA always yields a weakly better matching for students than a baseline mechanism called Artificial Cap Deferred Acceptance (ACDA). We also experimentally show that QRDA outperforms ACDA in terms of nonwastefulness.
The Relative Age Effect in under-18 basketball: Effects on performance according to playing position

The Relative Age Effect (RAE) in sport is defined as the age difference in the same sport group. This chronological difference implies a different level of sport experience and performance due to developmental and maturational processes. The aim of the present study was to analyze the relative age effect in elite u-18 basketball according to playing positions. The variables analyzed were: date of birth, playing position and technical-tactical performance indicators in basketball (TTPI). A descriptive analysis was carried out to characterize the sample, a lineal regression was used to analyze the influence of the date of birth on basketball performance and finally an ANOVA and discriminant analysis were performed to identify the differences among different playing positions. The results show the existence of the RAE in the top European Under-18 basketball competition (S1 = 67%; S2 = 33%). Performance indicators which are predictors of the birth quarter (p< .05) were only found in the small forwards (defensive rebounds (β: -.463) and fouls received (β: -.140)) and in the centers (three point throws scored (β: -.321) and tried (β: .342)). These results may be of use for coaches and clubs when recruiting players for each playing position. Older forwards and centers are capable of performing at a higher level. Therefore, clubs have to sign up those players born at the beginning of the year.