Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions

Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.
Model-Agnostic Counterfactual Explanations of Recommendations

Explanations for algorithmically generated recommendations is an important requirement for transparent and trustworthy recommender systems. When the internal recommendation model is not inherently interpretable (e.g., most contemporary systems are complex and opaque), or when access to the system is not available (e.g., recommendation as a service), explanations have to be generated post-hoc, i.e., after the system is trained. In this common setting, the standard approach is to provide plausible interpretations of the observed outputs of the system, e.g., by building a simple surrogate model that is inherently interpretable, and explaining that model. This however has several drawbacks. First, such explanations are not truthful, as they are rationalizations of the observed inputs and outputs constructed by another system. Second, there are privacy concerns, as to train a surrogate model, one has to know the interactions from users other than the one who seeks an explanation. Third, such explanations may not be scrutable and actionable, as they typically return weights for items or other users that are difficult to comprehend, and hard to act upon so to improve the quality of one’s recommendations. In this work, we present a model-agnostic explanation mechanism that is truthful, private, scrutable, and actionable. The key idea is to provide counterfactual explanations, defined as those small changes to the user’s interaction history that are responsible for observing the recommendation output to be explained. Without access to the internal recommendation model, finding concise counterfactual explanations is a hard search problem. We propose several strategies that seek to efficiently extract concise explanations under constraints. Experimentally, we show that these strategies are more efficient and effective than exhaustive and random search.
Graph Networks as Learnable Engines for Relations Inference of Interacting Financial Systems

Although the heterogeneous of financial markets is attracting interest both among scholars and practitioners, however, attention was almost exclusively given to networks in which all individuals were treated indifference, while neglecting all the extra information about the context-related or temporal-spatial properties of the interactions under study. Here introduces a new learnable relation inference model—based on graph networks—which implements an inference for entity- and relation-centric representations of multilayer, dynamical systems. The results show that as a learnable model, the approach supports accurate predictions from real and simulated data.
Research on the Application of BERT in Mongolian-Chinese Neural Machine Translation

In recent years, the research of neural networks has brought new solutions to machine translation. The application of sequence-to-sequence model has made a qualitative leap in the performance of machine translation. The training of neural machine translation model depends on large-scale bilingual parallel corpus, the size of corpus directly affects the performance of neural machine translation. Under the guidance of BERT (Bidirectional Encoder) model to calculate the semantic similarity degree for the extension of training corpus in this paper. The scores of two sentences were calculated by using dot product and cosine similarity, and then the sentences with high scores were expanded to the training corpus with a scale of 540,000 sentence pairs. Finally, Transformer was used to train the Mongolian and Chinese neural machine translation system, which was 0.91 percentage points higher than the BLEU value in the baseline experiment.
The tiebreaking space of constructive heuristics for the permutation flowshop minimizing makespan

There has been intensive research on tiebreakers for insertion-based constructive heuristics in the permutation flow shop scheduling problem when the objective is to minimize the makespan. In this paper we analyze the space of all possible tie-breaking rules for the most common insertion orders, and evaluate the efficacy of existing tiebreakers based on this analysis. We find that an optimal tie breaker would produce results that are about 1% better than current best tie breakers. We propose a constructive heuristic based on a truncated cyclic best-first search in the space of all tie breakers and show that it closely approximates the solutions that such an optimal tie breaker can achieve.
Adaptive scenario subset selection for min-max black-box continuous optimization

We handle min-max black-box optimization problems in which the scenario variable to be maximized is discrete and the design variable to be minimized is a continuous vector. To reduce the number of objective function calls, which are assumed to be computationally expensive, we propose an approach that samples a subset of scenarios at each iteration to approximate the worst-case objective function and apply the covariance matrix adaptation evolution strategy to the approximated worst-case objective function. In addition, we develop an adaptation mechanism for the probability of sampling each scenario. Moreover, we introduce the notion of support scenarios to characterize min-max optimization problems with discrete scenario variables and design test problems with various characteristics of support scenarios. Empirical evaluations reveal that the proposed approach learns to sample the set of support scenarios, being more efficient than sampling all the scenarios, especially when the available scenarios outnumber the support scenarios.
Culture and Health Belief Model: Exploring the Determinants of Physical Activity Among Saudi Adults and the Moderating Effects of Age and Gender

Physical inactivity is a significant risk factor for many non-communicable diseases such as heart disease, diabetes, and evidence shows that physical inactivity is one of the highest risk factors for death globally. Research has shown that theory-driven persuasive interventions are more effective at promoting behaviour change than generic ones. However, research on the determinants of physical activity and the moderating effect of age and gender among non-Western culture is limited. To close this gap, we conduct a large-scale study of the determinants of physical activity among 217 participants from Saudi Arabia using the extended Health Belief Model (HBM), a commonly applied behavioural model in health interventions design. We also assessed for the moderating effect of age and gender. Our findings show that Social influence, Cue to action and Perceived severity are the strongest determinants of physical activity in Saudi adults. We map these determinants to their corresponding persuasive strategies that can be used in operationalizing them in persuasive applications for promoting physical activity. Finally, we discuss the implication of our findings and offer design guidelines for persuasive interventions that appeal to both a broad audience and tailored to a particular group depending on their gender and age group.
Applying Neural Network to Reconstruction of Phylogenetic Tree

Reconstruction of phylogenetic tree from biological sequences is a fundamental step in molecular biology, but it is computationally exhausting. Our goal is to use neural network to learn the heuristic strategy of phylogenetic tree reconstruction algorithm. We propose an attention model to learn heuristic strategies for constructing circular ordering related to phylogenetic trees. We use alignment-free K-mer frequency vector representation to represent biological sequences and use unlabeled sequence data sets to train attention model through reinforcement learning. Comparing with traditional methods, our approach is alignment-free and can be easily extended to large-scale data with computational efficiency. With the rapid growth of public biological sequence data, our method provides a potential way to reconstruct phylogenetic tree.
NOTCH Signaling in Aortic Valve Development and Calcific Aortic Valve Disease

NOTCH intercellular signaling mediates the communications between adjacent cells involved in multiple biological processes essential for tissue morphogenesis and homeostasis. The NOTCH1 mutations are the first identified human genetic variants that cause congenital bicuspid aortic valve (BAV) and calcific aortic valve disease (CAVD). Genetic variants affecting other genes in the NOTCH signaling pathway may also contribute to the development of BAV and the pathogenesis of CAVD. While CAVD occurs commonly in the elderly population with tri-leaflet aortic valve, patients with BAV have a high risk of developing CAVD at a young age. This observation indicates an important role of NOTCH signaling in the postnatal homeostasis of the aortic valve, in addition to its prenatal functions during aortic valve development. Over the last decade, animal studies, especially with the mouse models, have revealed detailed information in the developmental etiology of congenital aortic valve defects. In this review, we will discuss the molecular and cellular aspects of aortic valve development and examine the embryonic pathogenesis of BAV. We will focus our discussions on the NOTCH signaling during the endocardial-to-mesenchymal transformation (EMT) and the post-EMT remodeling of the aortic valve. We will further examine the involvement of the NOTCH mutations in the postnatal development of CAVD. We will emphasize the deleterious impact of the embryonic valve defects on the homeostatic mechanisms of the adult aortic valve for the purpose of identifying the potential therapeutic targets for disease intervention.
The Pharmacoresistant Epilepsy: An Overview on Existant and New Emerging Therapies

Epilepsy is one of the most common neurological chronic disorders, with an estimated prevalence of 0. 5 – 1%. Currently, treatment options for epilepsy are predominantly based on the administration of symptomatic therapy. Most patients are able to achieve seizure freedom by the first two appropriate drug trials. Thus, patients who cannot reach a satisfactory response after that are defined as pharmacoresistant. However, despite the availability of more than 20 antiseizure medications (ASMs), about one-third of epilepsies remain drug-resistant. The heterogeneity of seizures and epilepsies, the coexistence of comorbidities, and the broad spectrum of efficacy, safety, and tolerability related to the ASMs, make the management of these patients actually challenging. In this review, we analyze the most relevant clinical and pathogenetic issues related to drug-resistant epilepsy, and then we discuss the current evidence about the use of available ASMs and the alternative non-pharmacological approaches.
Invariant Set Distributed Explicit Reference Governors for Provably Safe On-Board Control of Nano-Quadrotor Swarms

This article provides a theory for provably safe and computationally efficient distributed constrained control, and describes an application to a swarm of nano-quadrotors with limited on-board hardware and subject to multiple state and input constraints. We provide a formal extension of the explicit reference governor framework to address the case of distributed systems. The efficacy, robustness, and scalability of the proposed theory is demonstrated by an extensive experimental validation campaign and a comparative simulation study on single and multiple nano-quadrotors. The control strategy is implemented in real-time on-board palm-sized unmanned erial vehicles, and achieves safe swarm coordination without relying on any offline trajectory computations.
Implementation of Delayed Cord Clamping for 3 Min During Term Cesarean Sections Does Not Influence Maternal Blood Loss

Background: To assess maternal safety outcomes after a local protocol adjustment to change the interval of cord clamping to 3 min after term cesarean section. Design, Setting, and Patients: A retrospective cohort study in a tertiary referral hospital (Erasmus MC, Rotterdam). We included pregnant women who gave birth at term after cesarean section. A cohort (Nov 2016–Oct 2017) prior to the protocol implementation was compared to a cohort after its implementation (Nov 2017–Nov 2018). The study population covered 789 women (n = 376 pre-cohort; n = 413 post-cohort). Interventions: Implementation of a local protocol changing the interval of cord clamping to 3 min in all term births. Main outcome measures: Primary outcomes were the estimated maternal blood loss and the occurrence of postpartum hemorrhage (blood loss >1,000 ml). Secondary outcomes included both maternal as well as neonatal outcomes. Results: Estimated maternal blood loss was not significantly different between the pre-cohort and post-cohort (400 mL [300–600] vs. 400 mL [300–600], p = 0.52). The incidence of postpartum hemorrhage (26 [6.9%] vs. 35 (8.5%), OR 1.24, 95% CI 0.73–2.11) and maternal blood transfusion (9 [2%] vs. 13 (3%), OR 1.33, 95% CI 0.56–3.14) were not different. Hemoglobin change was significantly higher in the post-cohort (−0.8 mmol/L [−1.3 to −0.5] vs. −0.9 mmol/L [−1.4 to −0.6], p = 0.01). In the post-cohort, neonatal hematocrit levels were higher (51 vs. 55%, p = 0.004) and need for phototherapy was increased (OR 1.95, 95% CI 0.99–3.84). Conclusion: Implementation of delayed cord clamping for 3 min in term cesarean sections was not associated with increased maternal bleeding complications.
Case of Asperger's Syndrome and Lesion of the Right Amygdala: Deficits in Implicit and Explicit Fearful Face Recognition

Introduction: Studies of brain-damaged patients revealed that amygdala lesions cause deficits in the processing and recognition of emotional faces. Patients with autism spectrum disorders (ASD) have similar deficits also related to dysfunctions of the limbic system including the amygdala. Methods: We investigated a male patient who had been diagnosed with Asperger's syndrome. He also presented with a lesion of the right mesial temporal cortex, including the amygdala. We used functional magnetic resonance imaging (fMRI) to investigate neuronal processing during a passive viewing task of implicit and explicit emotional faces. Clinical assessment included a facial emotion recognition task. Results: There was no amygdala activation on both sides during the presentation of masked emotional faces compared to the no-face control condition. Presentation of unmasked happy and angry faces activated the left amygdala compared to the no-face control condition. There was no amygdala activation in response to unmasked fearful faces on both sides. In the facial emotion recognition task, the patient biased positive and neutral expressions as negative. Conclusions: This case report describes a male patient with right amygdala damage and an ASD. He displayed a non-response of the amygdala to fearful faces and tended to misinterpret fearful expressions. Moreover, a non-reactivity of both amygdalae to emotional facial expressions at an implicit processing level was revealed. It is discussed whether the deficient implicit processing of facial emotional information and abnormalities in fear processing could contribute and aggravate the patient's impairments in social behavior and interaction.
Horizontally Acquired Polysaccharide-Synthetic Gene Cluster From Weissella cibaria Boosts the Probiotic Property of Lactiplantibacillus plantarum

Lactiplantibacillus plantarum are probiotic bacteria, maintaining the integrity of the gastrointestinal epithelial barrier, and preventing the infection of pathogenic bacteria. Exopolysaccharides (EPSs) are often involved in the probiotic property of L. plantarum. Here, we identified a new EPS-synthetic gene cluster, cpsWc, carrying 13 genes, laid on a large plasmid in a well-characterized probiotic L. plantarum strain LTC-113. The cpsWc gene cluster was horizontally acquired from Weissella cibaria, enhancing the biofilm formation ability of the host strain and its tolerance to harsh environmental stresses, including heat, acid, and bile. Transfer of cpsWc also conferred the probiotic properties to other L. plantarum strains. Moreover, cpsWc strengthened the adhesion of LTC-113 to intestinal epithelial cells. Both the cpsWc-carrying LTC-113 and its EPSs per se effectively attenuated the LPS-induced pro-inflammatory effect of intestinal epithelial cells, and inhibited the adhesion of pathogenic bacteria, such as S. typhimurium and E. coli by exclusion and competition. The newly identified cpsWc gene cluster emphasized the contribution of mobile EPS-synthetic element on the probiotic activity of L. plantarum, and shed a light on the engineering of probiotic bacteria.
Cross-Sectional Study Protocol for the COVID-19 Impact Survey of Mothers and Their 7–11 Year Old Children in Alberta, Canada

Objectives: Our aim is to understand the effect of the COVID-19 pandemic on families who have been followed longitudinally in two cohorts studied in Alberta, Canada. We will examine household infections during the COVID-19 pandemic, financial impact, domestic violence, substance use, child school and daily life and relationships in the home. We will identify risk and protective factors for maternal mental health outcomes using longitudinal data that can inform policy and government resource allocation in future disasters. Methods: Mothers who are currently participating in two longitudinal studies, Alberta Pregnancy Outcomes and Nutrition (APrON; N = 1,800) and All Our Families (AOF: N = 2,534) were eligible to participate. Mothers were invited to complete the baseline COVID-19 Impact Survey (20–30 min) within 4 months of March 15, 2020, which was when the province of Alberta, Canada, implemented school closures and physical-distancing measures to prevent the spread of COVID-19. Mothers were asked to report on their own, their child's and their family's functioning. Mothers were re-surveyed at 6 months after completion of the initial COVID-19 Impact Survey, and will be re-surveyed again at 12 months. Results: Responses from participants in both cohorts will be examined in harmonized analyses as well as separately. Descriptive, multivariable analysis will be undertaken to examine risk and resiliency over time and factors that predict mental health and well-being. Conclusions: This study will provide timely information on the impact of COVID-19 for Albertan families. It will identify risk and protective factors for mental health and well-being among contemporary urban families supported by a publicly funded health care system to inform allocation of resources to support those most vulnerable during a global pandemic.
Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.
Pituitary Adenylate Cyclase-Activating Polypeptide in Learning and Memory

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a highly conserved neuropeptide that regulates neuronal physiology and transcription through Gs/Gq-coupled receptors. Its actions within hypothalamic, limbic, and mnemonic systems underlie its roles in stress regulation, affective processing, neuroprotection, and cognition. Recently, elevated PACAP levels and genetic disruption of PAC1 receptor signaling in humans has been linked to maladaptive threat learning and pathological stress and fear in post-traumatic stress disorder (PTSD). PACAP is positioned to integrate stress and memory in PTSD for which memory of the traumatic experience is central to the disorder. However, PACAP’s role in memory has received comparatively less attention than its role in stress. In this review, we consider the evidence for PACAP-PAC1 receptor signaling in learning and plasticity, discuss emerging data on sex differences in PACAP signaling, and raise key questions for further study toward elucidating the contribution of PACAP to adaptive and maladaptive fear learning.
Modeling adsorbate‐induced property changes of carbon nanotubes

Because of their potential for chemical functionalization, carbon nanotubes (CNTs) are promising candidates for the development of devices such as nanoscale sensors or transistors with novel gating mechanisms. However, the mechanisms underlying the property changes due to functionalization of CNTs still remain subject to debate. Our goal is to reliably model one possible mechanism for such chemical gating: adsorption directly on the nanotubes. Within a Kohn–Sham density functional theory framework, such systems would ideally be described using periodic boundary conditions. Truncating the tube and saturating the edges in practice often offers a broader selection of approximate exchange–correlation functionals and analysis methods. By comparing the two approaches systematically for NH3 and NO2 adsorbates on semiconducting and metallic CNTs, we find that while structural properties are less sensitive to the details of the model, local properties of the adsorbate may be as sensitive to truncation as they are to the choice of exchange–correlation functional, and are similarly challenging to compute as adsorption energies. This suggests that these adsorbate effects are nonlocal. © 2017 Wiley Periodicals, Inc.