Miracle-dz/UniFAR-FTUs huggingface dataset:
Discontinuous Galerkin methods: exploiting superconvergence for improved time-stepping

The discontinuous Galerkin (DG) methods are one of the most extensively researched classes of numerical methods for solving partial dfferential equations that display convective or diffusive qualities and have been popularly adopted by the scientific and engineering communities as a method capable of achieving arbitrary orders of accuracy in space. The choice of numerical flux function plays a pivotal role in the successful construction of DG methods and has an intrinsic effect on the superconvergence properties. As an inherent property of the spatial discretisation, superconvergence can only be retained in the solution through a sensitive pairing with a time integrator. The results of the literature and of this work suggest that an improved pairing between the spatial and temporal discretisations is both desirable and possible. We perform analysis of three different but related manifestations of superconvergence: the local, super-accurate points themselves; the subsequent global extraction via the Smoothness-Increasing Accuracy-Conserving (SIAC) filters; and the spectral properties that quantify, in terms of dispersion and dissipation errors, how accurately waves are convected. In order to explore the effect of the numerical flux function on superconvergence, we consider a generalisation of the “natural" upwind choice for a Method of Lines solution to the linear advection equation: the upwind-biased flux. We prove that the method is locally superconvergent at roots of a linear combination of the left- and right-Radau polynomials dependent on the value of a flux parameter and that the use of SIAC filters is still able to draw out the superconvergence information and create a globally smooth and superconvergent solution. In exploring the coupling of DG with a time integrator, we introduce a new scheme to a class of multi-stage multi-derivative methods, following recent incorporation of local DG technologies to recover superconvergence and achieve improved wave propagation properties.
Ultraviolet Line Intensities of Intermediate and High Ions Predicted from Non-equilibrium Ionization Simulations

We predict the UV emission line intensities produced by turbulent mixing layers (TMLs) between hot and warm gas and in high-velocity clouds (HVCs) traveling through hot gas such as that in the extended Galactic halo, circumgalactic medium, and intergalactic space. The calculations include the effects of hydrodynamic mixing, cooling, and non-equilibrium ionization and were done with the FLASH code and the Raymond and Smith code. A variety of mixing-layer and cloud conditions were simulated, resulting in 13 individual models. The intensities of 21 UV emission lines or doublets of 14 ions of carbon, nitrogen, oxygen, and silicon in TMLs and HVCs are tabulated. These ions are C ii, C iii, C iv, N ii, N iii, N iv, N v, O iii, O iv, O v, O vi, Si ii, Si iii, and Si iv. Owing to the relatively small assumed pressures and depths, our predicted intensities are small. However, when properly scaled to account for the pressures and depths of such gas in other places, the predicted intensities can be used to estimate the sensitivity needed for observations of the diffuse UV emission or compared with observations of a variety of astrophysical structures. The ratios of the predicted Si iv, C iv, N v, and O vi intensities are expected to remain invariant when the predictions are scaled to correspond to denser regions. The ratios of the predicted Si iv, C iv, and O vi intensities are compared with those of regions in our Galaxy that were observed by SPEAR/FIMS and the Far Ultraviolet Spectroscopic Explorer and with those predicted for other models, including an analytical model of TMLs and a model with a magnetized thermal conduction front.
Symptoms of eating disorders and low energy availability in recreational active female runners

Objectives This retrospective, cross-sectional study aimed to investigate symptoms of eating disorders (EDs) and low energy availability (LEA) among recreational female runners. Methods Females (18–39 years) (n=89) participating in running group sessions organised by running clubs and companies were recruited via social media and completed an anonymous online survey compromising the Eating Disorder Examination Questionnaire (EDE-Q) and Low Energy Availability in Females Questionnaire (LEAF-Q). An EDE-Q global score ≥2.3 and a LEAF-Q total score ≥8 (in combination with an injury score≥2 and/or menstruation dysfunction score≥4) were used to categorise subjects as having symptoms of EDs and LEA, respectively. Results Among the subjects fulfilling the age criteria (n=85), 18% (n=15) had symptoms of EDs and 19% (n=16) had symptoms of LEA. Of those with symptoms of EDs, 13% (n=2) had concomitant symptoms of LEA. The higher the EDE-Q dietary restraint score, the higher the gastrointestinal problem score (r=0.23, p=0.04), otherwise no other associations were found between EDE-Q global or subscale scores and LEAF-Q scores. Conclusion Our results indicate that symptoms of EDs and LEA are frequent among adult females at all athletic levels, including the recreational level. Hence, to prevent the negative health consequences of EDs and LEA, preventative initiatives are also needed in recreational running communities.
Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution

In recent years, predictive models have been increasingly used by education practitioners and stakeholders to leverage actionable insights to support student success. Usually, model selection (i.e., the decision of which predictive model to use) is largely based on the predictive performance of the models. Nevertheless, it has become important to consider fairness as an integral part of the criteria for model selection. Might a model be unfair towards certain demographic groups? Might it systematically perform poorly for certain demographic groups? Indeed, prior studies affirm this. Which model out of the lot should we choose then? Additionally, prior studies suggest demographic group imbalance in the training dataset to be a source of such unfairness. If so, would the fairness of the predictive models improve if the demographic group distribution in the training dataset becomes balanced? This study seeks to answer these questions. Firstly, we analyze the fairness of 4 of the commonly used state-of-the-art models to predict course success for 3 IT courses in a large public Australian university. Specifically, we investigate if the models serve different demographic groups equally. Secondly, to address the identified unfairnes---supposedly caused by the demographic group imbalance---we train the models on 3 types ofbalanced data and investigate again if the unfairness was mitigated. We found that none of the predictive models wasconsistently fair in all 3 courses. This suggests that model selection decision should be carefully made by both the researchers and stakeholders as the per the requirement of the domain of application. Furthermore, we found that balancing demographic groups (and class labels) is not enough---albeit can be an initial step---to ensure fairness of predictive models in education. An implication of this is that sometimes, the source of unfairness may not be immediately apparent. Therefore, "blindly" attributing the unfairness to demographic group imbalance may cause the unfairness to persist even when the data becomes balanced. We hope that our findings can guide practitioners and relevant stakeholders in making well-informed decisions.
A novel initial weld position identification method for large complex components based on improved YOLOv5

The initial weld position of large complex components has the characteristics of irregular shape and random spatial distribution. Traditional welding robots have technical bottlenecks such as low teaching programming efficiency and difficulty in offline programming in this application scenario. Therefore, at present, the method of "workers piling up" is used to weld large complex components. To overcome these problems, it is necessary and urgent to develop the intelligent initial weld position identification method for large components based on machine vision. Firstly, based on the theoretical background of YOLOv5 algorithm, an initial weld position recognition model for large complex components is established. Secondly, the model is optimized by changing the up-sampling method and fusing the Transformer Self-Attention mechanism. Finally, through the initial weld position detection experiment on the coco dataset, the experimental results show that the detection effect of this model is better than others and the original YOLOv5 model, and the detection speed of a single weld image is 11ms, and the average detection accuracy reaches 92.4%. Under the premise of ensuring the detection speed, the proposed method greatly improves the precision of initial weld position detection, and has better interference immunity and robustness.
Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences

A query performance prediction (QPP) method predicts the effectiveness of an IR system for a given query. While unsupervised approaches have been shown to work well for statistical IR models, it is likely that these approaches would yield limited effectiveness for neural ranking models (NRMs) because the retrieval scores of these models lie within a short range unlike their statistical counterparts. In this work, we propose to leverage a pairwise inference-based NRM's (specifically, DuoT5) output to accumulate evidences on the pairwise believes of one document ranked above the other. We hypothesize that the more consistent these pairwise likelihoods are, the higher is the likelihood of the retrieval to be of better quality, thus yielding a higher QPP score. We conduct our experiments on the TREC-DL dataset leveraging pairwise likelihoods from an auxiliary model DuoT5. Our experiments demonstrate that the proposed method called Pairwise Rank Preference-based QPP (QPP-PRP) leads to significantly better results than a number of standard unsupervised QPP baselines on several NRMs.
BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis

This work presents a new, original document classification dataset, BioSift, to expedite the initial selection and labeling of studies for drug repurposing. The dataset consists of 10,000 human-annotated abstracts from scientific articles in PubMed. Each abstract is labeled with up to eight attributes necessary to perform meta-analysis utilizing the popular patient-intervention-comparator-outcome (PICO) method: has human subjects, is clinical trial/cohort, has population size, has target disease, has study drug, has comparator group, has a quantitative outcome, and an "aggregate" label. Each abstract was annotated by 3 different annotators (i.e., biomedical students) and randomly sampled abstracts were reviewed by senior annotators to ensure quality. Data statistics such as reviewer agreement, label co-occurrence, and confidence are shown. Robust benchmark results illustrate neither PubMed advanced filters nor state-of-the-art document classification schemes (e.g., active learning, weak supervision, full supervision) can efficiently replace human annotation. In short, BioSift is a pivotal but challenging document classification task to expedite drug repurposing. The full annotated dataset is publicly available and enables research development of algorithms for document classification that enhance drug repurposing.
One Blade for One Purpose: Advancing Math Information Retrieval using Hybrid Search

Neural retrievers have been shown to be effective for math-aware search. Their ability to cope with math symbol mismatches, to represent highly contextualized semantics, and to learn effective representations are critical to improving math information retrieval. However, the most effective retriever for math remains impractical as it depends on token-level dense representations for each math token, which leads to prohibitive storage demands, especially considering that math content generally consumes more tokens. In this work, we try to alleviate this efficiency bottleneck while boosting math information retrieval effectiveness via hybrid search. To this end, we propose MABOWDOR, a Math-Aware Bestof-Worlds Domain Optimized Retriever, which has an unsupervised structure search component, a dense retriever, and optionally a sparse retriever on top of a domain-adapted backbone learned by context-enhanced pretraining, each addressing a different need in retrieving heterogeneous data from math documents. Our hybrid search outperforms the previous state-of-the-art math IR system while eliminating efficiency bottlenecks. Our system is available at https://github.com/approach0/pya0.
Hydrus: Improving Personalized Quality of Experience in Short-form Video Services

Traditional approaches to improving users' quality of experience (QoE) focus on minimizing the latency on the server side. Through an analysis of 15 million users, however, we find that for short-form video apps, user experience depends on both response latency and recommendation accuracy. This observation brings a dilemma to service providers since improving recommendation accuracy requires adopting complex strategies that demand heavy computation, which substantially increases response latency. Our motivation is that users' sensitivity to response latency and recommendation accuracy varies greatly. In other words, some users would accept a 20ms increase in latency to enjoy higher-quality videos, while others prioritize minimizing lag above all else. Inspired by this, we present Hydrus, a novel resource allocation system that delivers the best possible personalized QoE by making tradeoffs between response latency and recommendation accuracy. Specifically, we formulate the resource allocation problem as a utility maximization problem, and Hydrus is guaranteed to solve the problem within a few milliseconds. We demonstrate the effectiveness of Hydrus through offline simulation and online experiments in Kuaishou, a massively popular video app with hundreds of millions of users worldwide. The results show that Hydrus can increase QoE by 35.6% with the same latency or reduce the latency by 10.1% with the same QoE. Furthermore, Hydrus can achieve 54.5% higher throughput without a decrease in QoE. In online A/B testing, Hydrus significantly improves click-through rate (CTR) and watch time; it can also reduce system resource costs without sacrificing QoE.
Balanced Topic Aware Sampling for Effective Dense Retriever: A Reproducibility Study

Knowledge distillation plays a key role in boosting the effectiveness of rankers based on pre-trained language models (PLMs); this is achieved using an effective but inefficient large model to teach a more efficient student model. In the context of knowledge distillation for a student dense passage retriever, the balanced topic-aware sampling method has been shown to provide state-of-the-art effectiveness. This method intervenes in the creation of the training batches by creating batches that contain positive-negative pairs of passages from the same topic, and balancing the pairwise margins of the positive and negative passages. In this paper, we reproduce the balanced topic-aware sampling method; we do so for both the dataset used for evaluation in the original work (MS MARCO) and for a dataset in a different domain, that of product search (Amazon shopping queries dataset) to study whether the original results generalize to a different context. We show that while we could not replicate the exact results from the original paper, we do confirm the original findings in terms of trends: balanced topic-aware sampling indeed leads to highly effective dense retrievers. These results partially generalize to the other search task we investigate, product search: although we observe the improvements are less significant compared to MS MARCO. In addition to reproducing the original results and studying how the method generalizes to a different dataset, we also investigate a key aspect that influences the effectiveness of the method: the use of a hard margin threshold for negative sampling. This aspect was not studied in the original paper. With respect to hard margins, we find that while setting different hard margin values significantly influences the effectiveness of the student model, this impact is dataset-dependent -- and indeed, it does depend on the score distributions exhibited by retrieval models on the dataset at hand. Our reproducibility code is available at https://github.com/ielab/TAS-B-Reproduction.
Detecting Learners' Hint Inquiry Behaviors in e-Learning Environment by using Facial Expressions

It is difficult for teachers to track how each student is engaged in a class through e-learning. The current study simulated an e-learning environment by designing a website which contains interactable functions and hint buttons to examine whether the mental state of hint seeking can be estimated from facial features. We currently recruited 9 participants and asked them to solve a problem used in the International Olympiad of Linguistics in 2018. In this study, participants learned alone without discussing with others on a website designed for the experiment. While they were solving the problems, their face images were recorded. We extracted their facial features as Action Unit codes (AUs) by an open-source software, OpenFace. There was a function that provided hints to participants when he or she clicked a button. We attempted to estimate the time to click the button, that is the time when a hint was required, from facial expressions using a machine learning technique. We used LightGBM for the prediction. The model trained by LightGBM classified the state of "seeking hints" and the other states with an accuracy of 85.0%. The SHAP (SHapley Additive exPlanations) values, which are indexes of contribution levels, showed that facial features of "brow lowerer", "lip tightener", and "jaw drop" are the top three important features for the classification. This suggests that it is possible to build a system that detects the mental state of hint-seeking from facial features.
A Unified Formulation for the Frequency Distribution of Word Frequencies using the Inverse Zipf's Law

The power-law approximation for the frequency distribution of words postulated by Zipf has been extensively studied for decades, which led to many variations on the theme. However, comparatively less attention has been paid to the investigation of the case of word frequencies. In this paper, we derive its analytical expression from the inverse of the underlying rank-size distribution as a function of total word count, vocabulary size and the shape parameter, thereby providing a unified framework to explain the nonlinear behavior of low frequencies on the log-log scale. We also present an efficient method based on relative entropy minimization for a robust estimation of the shape parameter using a small number of empirical low-frequency probabilities. Experiments were carried out for a selected set of languages with varying degrees of inflection in order to demonstrate the effectiveness of the proposed approach.
How to Open Science: Analyzing the Open Science Statement Compliance of the Learning @ Scale Conference

There have been numerous efforts documenting the effects of open science in existing papers; however, these efforts typically only consider the author's analyses and supplemental materials from the papers. While understanding the current rate of open science adoption is important, it is also vital that we explore the factors that may encourage such adoption. One such factor may be publishing organizations setting open science requirements for submitted articles: encouraging researchers to adopt more rigorous reporting and research practices. For example, within the education technology discipline, theACM Conference on Learning @ Scale (L@S) has been promoting open science practices since 2018 through a Call For Papers statement. The purpose of this study was to replicate previous papers within the proceedings of L@S and compare the degree of open science adoption and robust reproducibility practices to other conferences in education technology without a statement on open science. Specifically, we examined 93 papers and documented the open science practices used. We then attempted to reproduce the results with invitation from authors to bolster the chance of success. Finally, we compared the overall adoption rates to those from other conferences in education technology. Although the overall responses to the survey were low, our cursory review suggests that researchers at L@S might be more familiar with open science practices compared to the researchers who published in theInternational Conference on Artificial Intelligence in Education (AIED) and theInternational Conference on Educational Data Mining (EDM): 13 of 28 AIED and EDM responses were unfamiliar with preregistrations and 7 unfamiliar with preprints, while only 2 of 7 L@S responses were unfamiliar with preregistrations and 0 with preprints. The overall adoption of open science practices at L@S was much lower with only 1% of papers providing open data, 5% providing open materials, and no papers had a preregistration. All openly accessible work can be found in an Open Science Framework project.
Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval

Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF https://github.com/Rosenberg37/OPRF.
Magnetization of a Single Carbonaceous Grain Obtained by Field-Induced Acceleration

Diamagnetic susceptibility χDIA of single carbonaceous grains were detected by observing their translations induced by field-gradient force in an area of microgravity. Using the above method, χDIA of a small carbonaceous particle is obtained with no interfering signal of the sample holder; it is unnecessary to know the mass of sample. The χDIA values of various materials obtained by the above method agreed fairly well with their published values. By comparing the obtained χDIA value with a list of published values, the material of an unidentified organic grain can be determined without consuming the sample. The principle of magnetic transition is applicable to investigate the magnetic properties of nano-size carbonaceous materials.