Miracle-dz/UniFAR-FTUs huggingface dataset:
Slow Light Rainbow Trapping in a Uniformly Magnetized Gyromagnetic Photonic Crystal Waveguide

We present a hybrid gyromagnetic photonic crystal (GPC) waveguide composed of different GPC waveguide segments possessing various cylinder radii and waveguide widths but biased by a uniform external magnetic field. We demonstrate in frequency and time domains that based on the strong coupling of two counter-propagating topologically protected one-way edge states, the intriguing slow light rainbow trapping (SLRT) of electromagnetic (EM) waves can be achieved, that is, EM waves of different frequencies can be slowed down and trapped at different positions without cross talk and overlap. More importantly, due to the existence of one-way edge states, external EM waves can be non-reciprocally coupled to the SLRT waveguide channel, although the incident position of the EM wave is far away from the waveguide channel. Besides, the frequency range of the slow light states can also be easily regulated by tuning the intensity of an external magnetic field, which is very beneficial to solve the contradiction between slow light and broad bandwidth. Our results can be applied to the design of high-performance photonic devices, such as an optical buffer, optical switch, and optical filter.
Age of First Exposure to Contact and Collision Sports and Later in Life Brain Health: A Narrative Review

A controversial theory proposes that playing tackle football before the age of 12 causes later in life brain health problems. This theory arose from a small study of 42 retired National Football League (NFL) players, which reported that those who started playing tackle football at a younger age performed worse on selected neuropsychological tests and a word reading test. The authors concluded that these differences were likely due to greater exposure to repetitive neurotrauma during a developmentally sensitive maturational period in their lives. Several subsequent studies of current high school and collegiate contact/collision sports athletes, and former high school, collegiate, and professional tackle football players have not replicated these findings. This narrative review aims to (i) discuss the fundamental concepts, issues, and controversies surrounding existing research on age of first exposure (AFE) to contact/collision sport, and (ii) provide a balanced interpretation, including risk of bias assessment findings, of this body of evidence. Among 21 studies, 11 studies examined former athletes, 8 studies examined current athletes, and 2 studies examined both former and current athletes. Although the literature on whether younger AFE to tackle football is associated with later in life cognitive, neurobehavioral, or mental health problems in former NFL players is mixed, the largest study of retired NFL players (N = 3,506) suggested there was not a significant association between earlier AFE to organized tackle football and worse subjectively experienced cognitive functioning, depression, or anxiety. Furthermore, no published studies of current athletes show a significant association between playing tackle football (or other contact/collision sports) before the age of 12 and cognitive, neurobehavioral, or mental health problems. It is important to note that all studies were judged to be at high overall risk of bias, indicating that more methodologically rigorous research is needed to understand whether there is an association between AFE to contact/collision sports and later in life brain health. The accumulated research to date suggests that earlier AFE to contact/collision sports is not associated with worse cognitive functioning or mental health in (i) current high school athletes, (ii) current collegiate athletes, or (iii) middle-aged men who played high school football. The literature on former NFL players is mixed and does not, at present, clearly support the theory that exposure to tackle football before age 12 is associated with later in life cognitive impairment or mental health problems.
Risk Evaluation of Pathogenic Intestinal Protozoa Infection Among Laboratory Macaques, Animal Facility Workers, and Nearby Villagers From One Health Perspective

Background: Previous epidemiological studies have confirmed non-human primates (NHPs) as reservoirs for Cryptosporidium spp. , Giardia intestinalis, and Enterocytozoon bieneusi. It highlights the possibility of interspecies transmission between humans and macaques in laboratory animal facilities. This study aimed to investigate the prevalence of pathogenic intestinal protozoan infections in macaques and humans and to determine the risk of cross-species transmission from One Health view. Materials and Methods: A total of 360 fecal samples, including 310 from the four Macaca mulatta groups, 25 from the facility workers in a laboratory animal facility, and 25 from the villagers nearby in Yongfu country, southeast China, were collected. Nested PCR assays were done for detecting protozoan pathogens from all the specimens. Furthermore, potential risk factors (gender, age, and direct contact) on the occurrence of intestinal protozoa infection among different sub-groups were evaluated. A phylogenetic and haplotype network analysis was conducted to examine the genetic structure and shared patterns of E. bieneusi and Cyclospora cayetanensis. Results: The pathogenic intestinal protozoa were detected in both human and macaque fecal samples. A total of 134 (37.2%) samples were tested positive, which included 113 (36.4%) macaques, 14 (56.0%) facility workers, and 7 (28.0%) villagers, respectively. There was no significant difference in four intestinal protozoa infections between facility workers and villagers (χ2 = 2.4, P > 0.05). However, the positive rate of pathogenic intestinal protozoa in the facility workers, who had direct contact with macaques, was significantly higher [odds ratio (OR) = 0.31, 95% confidence interval (CI): 0.09–1.00, P < 0.05). Thirty-three ITS genotypes of E. bieneusi were identified, including five known genotypes (PigEBITS7, Peru8, Henan V, D, and CM1) and six novel genotypes (MEB1–6). Seven haplotypes were identified in the network analysis from C. cayetanensis-positive samples. Meanwhile, a phylogenetic and haplotype analysis confirmed the presence of zoonotic subtypes in NHPs and humans. Conclusion: The data collected from this study confirmed a high prevalence of intestinal protozoan infection in humans and macaques. These results warrant workers of such facilities and residents to limit contact with infected animals in order to minimize related health risks. The need for comprehensive strategies to mitigate the risk of zoonotic transmission, especially from a One Health perspective, is recommended.
The Use of Pupillometry in Autobiographical Implicit Association Test

The Autobiographical Implicit Association Test (aIAT) is a reaction time-based methodology to assess one's recognition of the truth value of propositions about an autobiographical episode. This study introduced pupillometry to examine its utility as an additional measure of aIAT. Participants blindly chose one of two cards and memorized it. They then underwent the aIAT to assess the cards they chose. The pupil diameter was larger in the block in which sentences related to the chosen card shared the same response key with sentences describing false events than the block in which sentences related to the chosen card shared the same response key with true-event sentences. Although preliminary, pupil measurement also yielded high efficiency in discriminating the chosen card. These results indicate that pupillometry can be used as a measure of aIAT.
“Microbial Wars” in a Stirred Tank Bioreactor: Investigating the Co-Cultures of Streptomyces rimosus and Aspergillus terreus, Filamentous Microorganisms Equipped With a Rich Arsenal of Secondary Metabolites

Microbial co-cultivation is an approach frequently used for the induction of secondary metabolic pathways and the discovery of novel molecules. The studies of this kind are typically focused on the chemical and ecological aspects of inter-species interactions rather than on the bioprocess characterization. In the present work, the co-cultivation of two textbook producers of secondary metabolites, namely Aspergillus terreus (a filamentous fungus used for the manufacturing of lovastatin, a cholesterol-lowering drug) and Streptomyces rimosus (an actinobacterial producer of an antibiotic oxytetracycline) in a 5.5-L stirred tank bioreactor was investigated in the context of metabolic production, utilization of carbon substrates and dissolved oxygen levels. The cultivation runs differed in terms of the applied co-culture initiation strategy and the composition of growth medium. All the experiments were performed in three bioreactors running in parallel (corresponding to a co-culture and two respective monoculture controls). The analysis based upon mass spectrometry and liquid chromatography revealed a broad spectrum of more than 40 secondary metabolites, including the molecules identified as the oxidized derivatives of rimocidin and milbemycin that were observed solely under the conditions of co-cultivation. S. rimosus showed a tendency to dominate over A. terreus, except for the runs where S. rimosus was inoculated into the already developed bioreactor cultures of A. terreus. Despite being dominated, the less aggressive strain still had an observable influence on the production of secondary metabolites and the utilization of substrates in co-culture. The monitoring of dissolved oxygen levels was evaluated as a fast approach of identifying the dominant microorganism during the co-cultivation process.
The Clinical and MRI Effect of TNF-α Inhibitors in Spondyloarthritis Patients With Hip Involvement: A Real-World Observational Clinical Study

Objectives Hip involvement is an important cause of disability and poor prognosis in patients with spondyloarthritis (SpA). Tumor necrosis factor (TNF)-α inhibitor treatment has been demonstrated to be effective in SpA patients with hip arthritis; however, quantitative assessment using MRI in long-term follow-up needs further application and observation. Methods A total of 239 patients were involved in this study. Methotrexate and sulfasalazine were given as basic treatment. In total, 165 patients received TNF-α inhibitors plus basic treatment, and 74 received basic treatment only, as controls. Clinical symptoms were assessed at baseline and at weeks 12, 24, and 52. MRI performances of hip arthritis, including bone marrow edema (BME) and synovitis, were quantitatively assessed using the Hip Inflammation MRI Scoring System (HIMRISS). Results The clinical values of erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Harris hip score, and Ankylosing Spondylitis Disease Activity Score (ASDAS)-ESR in both groups showed significant clinical remission at week 52 (p < 0.001). However, the change in disease activity levels at week 52 in the control group was significantly worse than in the TNF-α inhibitor group. At week 52, MRI showed a significant remission trend in the TNF-α inhibitor group versus baseline, and total HIMRISS scores were significantly decreased (26.49 ± 10.37 vs. 20.59 ± 9.41, p < 0.001); the control group only had slight improvement (p < 0.05). Conclusions TNF-α inhibitors could significantly improve clinical and MRI manifestations of hip involvement in patients with SpA. Quantitative MRI assessment combined with clinical assessment can be used to accurately evaluate the treatment effect of TNF-α in SpA patients with hip involvement to help guide targeted treatment.
Comparison of EuroQol-5D-3L and Short Form-6D Utility Scores in Family Caregivers of Colorectal Cancer Patients: A Cross-Sectional Survey in China

Objective: To compare the EuroQol-5D-3L (EQ-5D-3L) and the Short Form-6D (SF-6D) utility scores in family caregivers (FCs) of colorectal cancer (CRC) patients. Method: This study was performed on FCs of CRC patients from three primary cancer centers in the capital city of the Heilongjiang province. The participants (FCs) who were enrolled, filled the EQ-5D-3L, along with the SF-6D questionnaire. Two tools were compared for their distribution, discriminant validity, agreement, and convergent validity along with known-groups validity. Result: Two hundred ninety-two FCs of CRC patients were enrolled. The score distribution of the SF-6D along with the EQ-5D-3L were not normal. A ceiling impact was seen in 31.8% of the FCs for EQ-5D-3L; however, none for the SF-6D. Good associations (Spearman’s rho = 0.622, p < 0.01) and intraclass correlation coefficient (ICC 0.637 and average ICC 0.778) between the two scores were observed. The EQ-5D-3L yielded higher utility scores in contrast with the SF-6D in the better health subclass. The SF-6D distinguished better between excellent and good health statuses, with better effect size and relative efficiency statistics. Both tools showed good known-groups validity. Conclusion: The utility scores of SF-6D were remarkably lower relative to that of the EQ-5D-3L, but the difference may be clinically insignificant. However, the SF-6D may be superior because of the lack of ceiling impact. SF-6D exhibited a better convergent validity along with discrimination validity of excellent health condition and improved known-groups validity efficiency.
Input or Output: Effects of Explanation Focus on the Perception of Explainable Recommendation with Varying Level of Details

In this paper, we shed light on two important design choices in explainable recommender systems (RS) namely, explanation focus and explanation level of detail. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the input (user model) and output (recommendations), with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study to investigate the relationship between explanation focus and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation aims. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation focus. Consequently, we provided some suggestions to support the effective design of explanations in RS.
Gradient Boosted Trees for Identification of Complex Words in Context

Determining whether a particular word is complex in a given context is an important task for modern NLP, as the presence of complex words may hinder smooth communication. The present work focuses on developing a binary classifier for predicting the complexity of a target word. A set of 51 features, pertaining to eight different classes, has been identified for the said purpose. Four different classifiers have been used, and their performance is compared. CatBoost registered the best performance when tested on CWI2016 dataset, and for the News and Wikinews categories for CWI2018 dataset. In fact, the CatBoost system supersedes the top performers for the 2016 and 2018 contests for the above-mentioned cases. The optimal feature subsets for the datasets are obtained using recursive feature elimination.
What Makes a Query Semantically Hard?

Traditional Information Retrieval (IR) models, also known as lexical models, are hindered by the semantic gap, which refers to the mismatch between different representations of the same underlying concept. To address this gap, semantic models have been developed. Semantic and lexical models exploit complementary signals that are best suited for different types of queries. For this reason, these model categories should not be used interchangeably, but should rather be properly alternated dependingonthequery. Therefore,itisimportanttoidentifyquerieswherethesemanticgapisprominentandthussemantic models prove effective. In this work, we quantify the impact of using semantic or lexical models on different queries, and we show that the interaction between queries and model categories is large. Then, we propose a labeling strategy to classify queries into semantically hard or easy, and we deploy a prototype classifier to discriminate between them.
Open Data Interface (ODI) for secondary school education

Advancements in data technology provide easy access to open data sets that can act as raw material to promote data competencies in the education domain. In this study, we investigate how open data can be used to develop digital and data literacy skills among secondary school students (ages 11 – 15). Using qualitative and quantitative research methods, we identify how data collection and analysis can be integrated into school education using openly available data sets. We also test the usability of a platform for secondary schools that allows teachers to identify and use open data sets when teaching school subjects such as math, science, and geography. The results suggest that open data have the potential to advance the digital and data skills essential for future generations, as well as enable them to understand their surroundings by using real data sets as part of their subjects. The results also demonstrate that the Open Data Interface helps educators utilize open educational data sets in schools.
Combining Lexical and Neural Retrieval with Longformer-based Summarization for Effective Case Law Retrieval

In this paper, we combine lexical and neural ranking models for case law retrieval. In this task, the query is a full case document, and the candidate documents are prior cases that are potentially relevant to the current case. Most documents are longer than 1024 tokens, which makes retrieval and classification with Transformer-based models problematic. We create shorter query documents with different methods: term extraction, noun phrase extraction, entity extraction, and automatic summarization using Longformer-Encoder-Decoder (LED). We then combine the summaries with five different rankingmodels: aBM25ranker,statisticallanguagemodelling,theDeepRelevanceMatchingModel(DRMM),aVanillaBERT ranker, and a Longformer ranker. We optimised all models and combined the best lexical ranker with neural retrieval models using different ensemble classifiers. We evaluate our methods on the retrieval benchmarks from COLIEE’20 and COLIEE’21. We beat state-of-the-art models for case law retrieval with both benchmark sets. Our experiments show the importance of tuning lexical retrieval methods, summarizing query documents, and combining lexical and neural models into one ranker for effective case law retrieval. In addition, training and optimizing our rankers is much faster than passage-level retrieval models (a few hours compared to several days for training).
Investigating Mobile Banking Continuance Intention: A Mixed-Methods Approach

Building on IS research, this study investigates m-banking continuance from an emerging market perspective. Using a mixed-methods approach, the study presents m-banking continuance phenomenon through an integrated model. Study 1 focuses on qualitative interviews of mobile banking users, whereas study 2 empirically tests the conceptual model derived from literature and the results of study 1. Study 1 reveals three additional constructs, perceived ubiquity, perceived autonomy, and perceived security concerns to the existing literature-based constructs. However, the results of study 2—a survey of 390 m-banking users—provide empirical evidence to support the hypotheses drawn in the proposed conceptual model. The results reveal that perceived ubiquity, perceived usefulness, satisfaction, facilitating conditions, perceived security concerns, and trust have emerged as significant direct influencers on m-banking continuance. Moreover, the study offers practical academic and managerial implications regarding m-banking.
Bagging ensemble for deep learning based gender recognition using test-time augmentation on large-scale datasets

: We present a bagging ensemble of convolutional networks in combination with the test-time augmentation technique to improve performance on the cross-dataset gender recognition problem. The bagging ensemble combines the predictions from multiple homogeneous models into the ensemble prediction. Augmentation techniques are often used in the learning phase of the CNNs to improve the generalization ability. On the other hand, test-time augmentation is not a common method used in the testing phase of the learned model. We conducted experiments on models trained using different hyperparameters. We augmented the test data and combine the predictive outputs from these network models. Experiments performed on diverse gender datasets, including Adience, AFAD, CelebA, Gallagher, Genki-4K, IMDb, LFW, Morph, VGGFace2, and Wiki, showed that the use of bagging ensemble of convolutional networks and test-time augmentation outperforms standalone models. We obtained the highest cross-dataset accuracy in the literature on seven out of eleven datasets. For the remaining four datasets we reported the cross-dataset results for the first time. According to our experiments, VGGFace2, IMDb, and CelebA datasets provided the highest cross-dataset classification results for most of the test datasets in the gender recognition problem.
Structuring and Accessing Knowledge for Historical and Streaming Digital Twins

Organisations are intensely developing Digital Twins (DTs) to correctly and eﬃciently answer questions about the history and behaviour of physical systems. However, it is not clear how to construct these DTs starting from the data, information, knowledge, and wisdom available in the organisation. In this work, we present our approach to DT construction which involves a layered knowledge graph (KG) architecture communicating with the organisation’s data repositories. We explain the components and timelines for using the KG to build both historical and streaming DTs, and what kinds of questions can be answered for our
Vicomtech at eHealth-KD Challenge 2021: Deep Learning Approaches to Model Health-related Text in Spanish

This paper describes the participation of the Vicomtech NLP team in the eHealth-KD 2021 shared task about detecting and classifying entities and relations in health-related texts written in Spanish. We participate with two systems. Joint Classifier is a simpliﬁed version of the system that won the main scenario of the previous eHealth-KD edition. It consists of a single end-to-end deep neural network with pre-trained BERT models as the core for the semantic representation of the input texts, that predicts all the output variables—entities and relations—at the same time, modelling the whole problem jointly. The main change w.r. t. the original implementation aﬀects the representation of relations. The Joint Classifier model achieved the ﬁrst position in the main scenario of the competition and ranked second in the rest of the scenarios. The second submitted system, Seq2Seq , uses an approach based on an encoder-decoder model. It transduces the input text into an output sequence by reading the input text. The target sequence is a compact representation of the information contained in the gold-labels of the datasets. This approach showed a promising performance despite not being competitive enough. However, it poses an interesting potential future work.
Evaluating Fault Localization Techniques with Bug Signatures and Joined Predicates

Predicate-based fault localization techniques have an advantage over other approaches by not only determining the location of a fault but also potentially giving the developer additional information to understand it. In this paper, we evaluate the accuracy of predicate-based bug signatures based on the Defects4J benchmark. Additionally, we try to improve the predicate-based approach by extending it with joined predicates, a technique for combining multiple predicates, to extract even more information. To validate our results, we compare our approaches with established spectrum-based fault localization methods.