Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining Language Switching and Cognitive Control Through the Adaptive Control Hypothesis

Increasing evidence suggests that language switching is a distinct form of bilingual language control that engages cognitive control. The most relevant and widely discussed framework is the Adaptive Control Hypothesis. This theoretical framework identifies language switching to be a key aspect of bilingual language control. It proposes that bilinguals’ engagement in three different types of interactional contexts (single-language context, dual-language context, and dense code-switching context) confers adaptive effects on cognitive control processes. These contexts differ in the presence of both languages and how language control is exercised. The model makes predictions about behavioral outcomes associated with these contexts. This study is a novel attempt to test for the model’s assumptions, predictions, and its interactional contexts. It seeks to examine the relationship between language switching behaviors, reported bilingual interactional contexts, and verbal and non-verbal cognitive control through this theoretical framework. Seventy-four English–Mandarin young adult bilinguals were measured on their self-reported engagements in the different interactional contexts and production of word and sentential language switches through experimental language switching tasks (alternating, semi-cued, and uncued switching). Cognitive control processes in verbal and non-verbal goal maintenance, interference control, selective response inhibition, and task engagement and disengagement were measured. Overall, partial support for the model was observed. Higher reported engagement in the dual-language context was positively but not uniquely related to cognitive engagement and disengagement on verbal tasks. Non-verbal goal maintenance and interference control, on the other hand, were related to uncued inter-sentential language switching. However, the distinction of the model’s three interactional contexts might not be evident in a multilingual society, as findings suggest that there is fluidity in bilinguals’ interactional contexts. Current findings reveal the complex interaction of language switching with distinct domains and cognitive control processes. This study is significant in testing an influential bilingual language control model.
Teacher readiness to adopt game-based mobile learning with augmented reality

Mobile augmented reality (AR) games have potential in Education, as they can enhance learning experiences, by offering to learners contextualized information. This study aim is to reveal teachers’ readiness to adopt these strategies, after training. Hence, this is a case study of two editions of a workshop under the EduPARK project, designed to support teachers’ adoption of strategies involving game-based mobile learning with AR. The workshops required collaborative exploration of the EduPARK app and educational resource planning. Data collected from individual questionnaires and focus group interviews were analysed through content analysis, descriptive statistics, and computing of the Educational Value Scale and of the System Usability Scale. The 45 teacher-trainees consider they are ready to integrate mobile AR games in their practices, but they are foreseeing difficulties that need to be addressed. This work presents a typology of teacher training that, according to the participants, is successful in promoting their readiness to adopt innovative practices.
Protective effects of Flacourtia indica on doxorubicin-induced cardiotoxicity in rats

This study examined the cardioprotective activity of an ethanolic extract of Flacourtia indica (FI) against doxorubicin (DOX)-induced myocardial infarction (MI) in rats. Different phytoconstituents were identified by gas chromatography-mass spectroscopy. DOX is a chemotherapeutic agent which produces free oxygen radicals that result in serious dose-limiting cardiotoxicity. A DOX dose of 20 mg kg−1 body weight is used to bring significant changes in biochemical parameters, endogenous antioxidants, and moderate necrosis in the heart. The pretreatment with FI at two doses (250 and 500 mg kg−1) to DOX-treated rats significantly prevented the altered biochemical parameters such as serum marker enzymes serum glutamate-pyruvate transaminase, serum glutamate oxaloacetate transaminase, creatine phosphokinase, and lactate dehydrogenase, lipid profile such as low-density lipoprotein, very low-density lipoprotein, triglycerides, high-density lipoprotein, total cholesterol, and antioxidant parameters such as superoxide dismutase, glutathione, catalase, glutathione peroxidase, and malondialdehyde to near normal level. Serum urea, uric acid, and alkaline phosphate which are increased on DOX administration registered near normal values on pretreatment with FI. In conclusion, these data suggest that the ethanol extract of FI can prevent heart damage by DOX-induced MI in rats and this is likely mediated through its antioxidant activities.
Identification of Xiphinema index in an Austrian vineyard

Material and Methods: For the isolation of nematodes in a vineyard in Burgenland soil samples including fine roots were collected in the rhizosphere of two Vitis vinifera cvs 'Neuburger' (2 and 25 years old) and 'Zweigelt' (5 years old), showing symptoms of GFLV infection. Roots were visually analyzed for nematode feeding symptoms. Leaf samples of the corresponding host plants were collected for GFLV testing by ELISA, using a commercially available kit (Bioreba, Switzerland). Nematodes were ex
CrossBERT: A Triplet Neural Architecture for Ranking Entity Properties

Task-based Virtual Personal Assistants (VPAs) such as the Google Assistant, Alexa, and Siri are increasingly being adopted for a wide variety of tasks. These tasks are grounded in real-world entities and actions (e.g., book a hotel, organise a conference, or requesting funds). In this work we tackle the task of automatically constructing actionable knowledge graphs in response to a user query in order to support a wider variety of increasingly complex assistant tasks. We frame this as an entity property ranking task given a user query with annotated properties. We propose a new method for property ranking, CrossBERT. CrossBERT builds on the Bidirectional Encoder Representations from Transformers (BERT) and creates a new triplet network structure on cross query-property pairs that is used to rank properties. We also study the impact of using external evidence for query entities from textual entity descriptions. We perform experiments on two standard benchmark collections, the NTCIR-13 Actionable Knowledge Graph Generation (AKGG) task and Entity Property Identification (EPI) task. The results demonstrate that CrossBERT significantly outperforms the best performing runs from AKGG and EPI, as well as previous state-of-the-art BERT-based models. In particular, CrossBERT significantly improves Recall and NDCG by approximately 2-12% over the BERT models across the two used datasets.
Anti-inflammatory effects and mechanisms of usnic acid, a compound firstly isolated from lichen Parmelia saxatilis

Usnic acid (UA) was isolated for the first time from Parmelia saxatilis and was confirmed by physical and spectral evidence. Its anti-inflammatory effect and mechanism were explored on lipopolysaccharide (LPS)-stimulated RAW264.7 cell line. The effects of UA on pro-inflammatory cytokines including tumor necrosis factor-alfa (TNF-α), interleukin-6 (IL-6) and interleukin-1 beta (IL-1β), pro-inflammatory mediators such as nitric oxide (NO), inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) were studied by sandwich enzyme-linked immunosorbent assay (ELISA), real-time polymerase chain reaction (PCR) and Western blot analyses. Similarly, the effect of UA on anti-inflammatory cytokine interleukin-10 (IL-10) and anti-inflammatory mediator heme oxygenase-1 (HO-1) were also studied following the same methods. Furthermore, nuclear factor-κB (NF-κB) was assayed by immunocytochemistry. The results showed that UA has anti-inflammatory effect by down-regulatinng iNOS, COX-2, IL-1β, IL-6 and TNF-α, COX-2 gene expression through the suppression of NF-κB activation and increasing anti-inflammatory cytokine IL-10 and anti-inflammatory mediator HO-1 production. Key words: Parmelia saxatilis, usnic acid, anti-inflammation.
Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances

Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem. Since the widely use of mobile applications, more and more information retrieval services have provided interactive functionality and products. Thus, learning from interaction becomes a crucial machine learning paradigm for interactive IR, which is based on reinforcement learning. With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information retrieval techniques, which could continuously update the information retrieval strategies according to users' real-time feedback, and optimize the expected cumulative long-term satisfaction from users. Our workshop aims to provide a venue, which can bring together academia researchers and industry practitioners (i) to discuss the principles, limitations and applications of DRL for information retrieval, and (ii) to foster research on innovative algorithms, novel techniques, and new applications of DRL to information retrieval.
Fair Classification with Counterfactual Learning

Recent advances in machine learning have led to emerging new approaches to deal with different kinds of biases that exist in the data. On the one hand, counterfactual learning copes with biases in the policy used for sampling (or logging) the data in order to evaluate and learn new policies. On the other hand, fairness-aware learning aims at learning fair models to avoid discrimination against certain individuals or groups. In this paper, we design a counterfactual framework to model fairness-aware learning which benefits from counterfactual reasoning to achieve more fair decision support systems. We utilize a definition of fairness to determine the bandit feedback in the counterfactual setting that learns a classification strategy from the offline data, and balances classification performance versus fairness measure. In the experiments, we demonstrate that a counterfactual setting can be perfectly exerted to learn fair models with competitive results compared to a well-known baseline system.
Is there evidence of increased pup production in northwest Atlantic harp seals, Pagophilus groenlandicus ?

– ICES Journal of Marine Science, 59: 81–92.Photographic and visual surveys of the whelping (pupping) concentrations oﬀ easternNewfoundland (‘‘Front’’) and in the Gulf of St Lawrence (‘‘Gulf’’) were conductedduring March 1994 to determine whether pup production of Northwest Atlantic harpseals has increased since the decline in hunting during the mid-1980s and early 1990s.Photographic counts were corrected for misidentiﬁed pups by comparing multiplereadings of photographs made by two or more readers. Survey estimates were alsocorrected for pups absent from the ice at the time of the survey using the occurrenceof distinct age-related developmental stages. Multiple estimates were available forthree of the ten whelping concentrations. Pup production was estimated to be 446 700(s.e.=57 200) at the Front, 57 600 (s.e.=13 700) in the northern Gulf and 198 600(s.e.=24 200) in the southern Gulf (Magdalen Island) for a total of 702 900(s.e.=63 600). This result is greater than estimates obtained in the late 1970s and early1980s using mark-recapture techniques. The 1994 estimate can be compared directlywith the 1990 estimate of pup production (578 000, s.e.=38 800) which was obtainedusing similar aerial survey methods. The null hypothesis of no increase in pupproduction (one-tail t-test) was rejected (p=0.03) indicating that pup production hasincreased.Keywords: pup production, abundance, harp seals, aerial surveys.Received 27 July 2000; accepted 16 August 2001; published electronically 31 October2001.
The effectiveness and safety of acupuncture for allergic rhinitis

Abstract Background: Allergic rhinitis (AR) is one of the most common allergic disorders globally. Several systematic reviews and meta-analyses have reported the effectiveness of acupuncture for patients with AR. However, the evidence has not been systematically synthesized. This overview aims to map, synthesize, and assess the reliability of evidence generated from these systematic reviews (SRs) and meta-analyses of acupuncture for AR. Methods: A Comprehensive literature search will be conducted through the PubMed, Embase, the Cochrane Library of Systematic Reviews, the China National Knowledge Infrastructure Database, Wanfang Database, Chinese Biomedical Literature Database, and Chinese Scientific Journal Database from inception until January 2020. Additionally, the PROSPERO database and the reference list of included studies will be searched for unpublished, ongoing, or recently completed SRs and meta-analyses. The reviewers will identify reviews independently and extract data according to the methodological guidelines for overviews provided by the Cochrane Collaboration. The risk of bias will be assessed based on the Risk of Bias in Systematic Reviews. The methodological and reporting quality of the included reviews will be assessed using the Assessing the Methodological Quality of Systematic Reviews (V.2) tool and the Preferred Reporting Items for Systematic Review and Meta-Analyses statement. The outcomes of interest include total nasal symptom score, rhinoconjunctivitis quality-of-life questionnaire, immunoglobulin E, visual analog scale, laboratory examination, and side effects. The quality of evidence of outcomes will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation. The evidence will be synthesized where appropriate based on patient subgroups and outcomes. Ethics and dissemination: Ethical approval is not required for overviews. We plan to publish results in peer-reviewed journals and present at international and national academic, clinical, and patient conferences. Results: The results will be published in a peer-reviewed journal. Conclusion: This overview will provide comprehensive evidence of acupuncture for patients with AR. PROSPERO registration number: CRD42019140756.
TUNEL assay—Standardized method for testing sperm DNA fragmentation

Sperm DNA integrity is important for normal functions such as fertilization, implantation, pregnancy and fetal development. Sperm DNA fragmentation (SDF) is more common in infertile men and may be responsible for poor reproductive function. Although there are a number of tests available to measure SDF, the terminal deoxynucleotidyl transferase‐mediated deoxyuridine triphosphate‐nick end labelling TUNEL) assay using flow cytometry is becoming more popular to measure the sperm DNA fragmentation. It is a direct test that measures both single‐ and double‐ DNA strand breaks. In this review, we describe the protocol, quality control and measurement of sperm DNA fragmentation using a benchtop flow cytometer. We also briefly discuss the factors that can affect the results, challenges and clinical implications of TUNEL in assessing male infertility.
Gender differences in risk taking behaviors for Chlamydia trachomatis

Abstract Chlamydia trachomatis (CT) is a global problem. We compared the risk taking behaviors for CT infection between men and women. Adults (2299 females, 5559 males) were administered the Risk Behavior Assessment. In women, CT was associated with candidiasis, in men with gonorrhea, genital warts, and syphilis. Risk factors for both genders were trading sex for money, use of marijuana for women, and use of Ecstasy and Viagra for men. Those with CT had higher risk perception for HIV infection and were more likely to obtain HIV testing. Patient teaching and concurrent testing for HIV and CT are imperative.
Aerodynamic performance of flexible flapping wings deformed by slack angle

Wing flexibility is unavoidable for flapping wing flyers to ensure a lightweight body and for higher payload allowances on board. It also effectively minimizes the inertia force from high-frequency wingbeat motion. However, related studies that attempt to clarify the essence of wing flexibility remain insufficient. Here, a parametric study of a flexible wing was conducted as part of the effort to build an aerodynamic model and analyze its aerodynamic performance. The quasi-steady modeling was adopted with experimentally determined translational forces. These forces were determined from 84 flexible wing cases while varying the angle of attack at the wing root α r and the flexibility parameter, slack angle θ S, with 19 additional rigid wing cases. This study found α r for optimum lift generation to exceed 45° irrespective of θ S. The coefficient curves were well-fitted with a cubed-sine function. The model was rigorously validated with various wing kinematics, giving a good estimation of the experimental results. The estimated error was less than 5%, 6%, and 8% for the lift, drag, and moment, respectively, considering fast to moderate wing kinematics. The study was extended to analyze the pure aerodynamic performance of the flexible wing. The most suitable wing for a flapping-wing micro-aerial vehicle wing design with a simple vein structure was found to be the 5° slack-angled wing. The inference from this study further shows that a small amount of deformation is needed to increase the lift, as observed in natural flyers. Thus, wing deformation could allow living flyers to undertake less pitching motion in order to reduce the mechanical power and increase the efficiency of their wings.
Visual Intents vs. Clicks, Likes, and Purchases in E-commerce

In product-to-product search and recommendation, the product image often plays a pivotal role for the user to determine the relevance of that product. The present study investigates the relationship between the users' visual intents (in terms of colour, texture and material, and design) and the amount of user feedback (namely, clicks, likes, and purchases) using real product data and crowdsourcing. Through the analysis, we found that visual relevance (i.e., relevance of a target product with respect to a particular visual intent) correlates with the amount of user feedback, and that visual relevance can be the cause of user feedback.
AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems

Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF) used in recommender systems. When few ratings are available, CF models typically fail to provide satisfactory recommendations for cold-start users or to display cold-start items on users' top-N recommendation lists. Data imputation has been a popular choice to deal with such problems in the context of CF, filling empty ratings with inferred scores. Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models. Notably, AR-CF not only directly tackles the cold-start problems, but is also effective in improving overall recommendation qualities. Via extensive experiments on real-world datasets, AR-CF is shown to (1) significantly improve the accuracy of recommendation for cold-start users, (2) provide a meaningful number of the cold-start items to display in top-N lists of users, and (3) achieve the best accuracy as well in the basic top-N recommendations, all of which are compared with recent state-of-the-art methods.
How UMass-FSD Inadvertently Leverages Temporal Bias

First Story Detection describes the task of identifying new events in a stream of documents. The UMass-FSD system is known for its strong performance in First Story Detection competitions. Recently, it has been frequently used as a high accuracy baseline in research publications. We are the first to discover that UMass-FSD inadvertently leverages temporal bias. Interestingly, the discovered bias contrasts previously known biases and performs significantly better. Our analysis reveals an increased contribution of temporally distant documents, resulting from an unusual way of handling incremental term statistics. We show that this form of temporal bias is also applicable to other well-known First Story Detection systems, where it improves the detection accuracy. To provide a more generalizable conclusion and demonstrate that the observed bias is not only an artefact of a particular implementation, we present a model that intentionally leverages a bias on temporal distance. Our model significantly improves the detection effectiveness of state-of-the-art First Story Detection systems.
Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments

Event representative learning aims to embed news events into continuous space vectors for capturing syntactic and semantic information from text corpus, which is benefit to event-driven quantitative investments. However, the financial market reaction of events is also influenced by the lead-lag effect, which is driven by internal relationships. Therefore, in this paper, we present a knowledge graph-based event embedding framework for quantitative investments. In particular, we first extract structured events from raw texts, and construct the knowledge graph with the mentioned entities and relations simultaneously. Then, we leverage a joint model to merge the knowledge graph information into the objective function of an event embedding learning model. The learned representations are fed as inputs of downstream quantitative trading methods. Extensive experiments on real-world dataset demonstrate the effectiveness of the event embeddings learned from financial news and knowledge graphs. We also deploy the framework for quantitative algorithm trading. The accumulated portfolio return contributed by our method significantly outperforms other baselines.
CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems

Since it can effectively address the problem of sparsity and cold start of collaborative filtering, knowledge graph (KG) is widely studied and employed as side information in the field of recommender systems. However, most of existing KG-based recommendation methods mainly focus on how to effectively encode the knowledge associations in KG, without highlighting the crucial collaborative signals which are latent in user-item interactions. As such, the learned embeddings underutilize the two kinds of pivotal information and are insufficient to effectively represent the latent semantics of users and items in vector space. In this paper, we propose a novel method named Collaborative Knowledge-aware Attentive Network (CKAN) which explicitly encodes the collaborative signals by collaboration propagation and proposes a natural way of combining collaborative signals with knowledge associations together. Specifically, CKAN employs a heterogeneous propagation strategy to explicitly encode both kinds of information, and applies a knowledge-aware attention mechanism to discriminate the contribution of different knowledge-based neighbors. Compared with other KG-based methods, CKAN provides a brand-new idea of combining collaborative information with knowledge information together. We apply the proposed model on four real-world datasets, and the empirical results demonstrate that CKAN significantly outperforms several compelling state-of-the-art baselines.