Miracle-dz/UniFAR-FTUs huggingface dataset:
A year in the public life of COVID-19 and Vitamin D: Representation in UK news media and implications for health communications.

Objectives: To investigate how the relationship between vitamin D and COVID-19 has been presented in traditional media sources (UK newspapers) and assess the level of misinformation associated with this issue by comparing newspaper article content to the evidence-based guidelines. Design: Qualitative study Setting: Data were collected over the first year of the pandemic (February 2019-20), from the five most widely read UK based newspapers by searching the Nexis database using keywords vitamin D and COVID. An inductive thematic analysis was carried out on the data to identify themes and subthemes. Quality control of the coding was conducted on a sample of the dataset (20%). Data were also compared to the ground truth identified as the NICE report titled COVID-19 rapid guideline: vitamin D to explore the accuracy of media outputs. Results: The four main themes identified were association of vitamin D with COVID-19, politically informed views, vitamin D deficiency and vitamin D sources. When compared to the ground truth, most of the information from newspaper articles relating to the key findings was correct for each of the findings, except for COVID-19 infection. Conclusions: Although most of the information included in newspaper articles concerning the relationship of vitamin D with COVID-19 was correct, this study highlighted that there was still a notable amount of incorrect information published. In the context of COVID-19, it is imperative that media outputs are accurate and inform the public and front-line health professionals as correct information is a key factor in disease prevention. Future research should focus on the accuracy of media outputs to further investigate health misinformation as an issue in traditional media and how that may affect public health. Attempts should be made to improve journalistic integrity through more rigorous and standardised regulations enforced across all media outlets so that public knowledge on current events is based on evidence rather than conjecture.
The relationship between stiffness, asymmetries and change of direction speed

Change of direction speed (CODS) is an important determinant of performance in many sports. Greater stiffness of the lower limb should be beneficial to CODS, but this had not been well investigated. The purpose of this thesis was to establish the relationship between vertical stiffness, vertical stiffness asymmetries and CODS, with a view to augmenting CODS performance. The pilot study and studies 1-2 sought to determine the most reliable and ecologically valid method to assess stiffness in athletes required to perform changes of direction. The pilot study reported that the use of ultrasonography to determine Achilles tendon stiffness did not demonstrate appropriate reliability for inclusion in subsequent studies. Coefficients of variation (CVs) in excess of 27% were reported during an isometric plantar flexion task. Study 1 reported that CVs for vertical stiffness were lower when assessed during unilateral drop jumping (~7%) than during bilateral drop jumping (~12%) or bilateral hopping (~14%). Study 2 reported that the expression of vertical stiffness (P = 0.033) and vertical stiffness symmetry angle (P = 0.006) was significantly different across three performance tasks: unilateral drop jumping, bilateral drop jumping and bilateral hopping. Asymmetry percentages between compliant and stiff limbs were 5.6% (P < 0.001; d: 0.22), 23.3% (P = 0.001; d = 0.86) and 12.4% (P = 0.001; d = 0.39), respectively. Given the findings of studies 1 and 2, this thesis demonstrated the reliability and validity of a novel method by which to assess vertical stiffness the unilateral drop jump. This task was used in subsequent studies to measure vertical stiffness.
Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver

On-street parking is convenient, but has many disadvantages: on-street spots come at the expense of other road uses such as traffic lanes, transit lanes, bike lanes, or parklets; drivers looking for parking contribute substantially to traffic congestion and hence to greenhouse gas emissions; safety is reduced both due to the fact that drivers looking for spots are more distracted than other road users and that people exiting parked cars pose a risk to cyclists. These social costs may not be worth paying when off-street parking lots are nearby and have surplus capacity. To see where this might be true in downtown Vancouver, we used artificial intelligence techniques to estimate the amount of time it would take drivers to both park on and off street for destinations throughout the city. For on-street parking, we developed (1) a deep-learning model of block-by-block parking availability based on data from parking meters and audits and (2) a computational simulation of drivers searching for an on-street spot. For off-street parking, we developed a computational simulation of the time it would take drivers drive from their original destination to the nearest city-owned off-street lot and then to queue for a spot based on traffic and lot occupancy data. Finally, in both cases we also computed the time it would take the driver to walk from their parking spot to their original destination. We compared these time estimates for destinations in each block of Vancouver's downtown core and each hour of the day. We found many areas where off street would actually save drivers time over searching the streets for a spot, and many more where the time cost for parking off street was small. The identification of such areas provides an opportunity for the city to repurpose valuable curbside space for community-friendly uses more in line with its transportation goals.
Pupillary response to moving stimuli of different speeds

To investigate the pupillary response to moving stimuli of different speeds and the influence of different luminance environments, 28 participants with normal or corrected-to-normal vision were included. The participants were required to track moving optotypes horizontally, and their pupils were recorded on video with an infrared camera. Stimuli of different speeds from 10 to 60 degree per seconds were presented in low (0.01 cd/m2) and moderate (30 cd/m2) luminance environments. Experiment 1 demonstrated that the motion stimuli induced pupil dilation in a speed-dependent pattern. The pupil dilation increased as the speed increased, and the pupil dilation gradually increased, then reached saturation. Experiment 2 showed that a stimulus targeting the rod- or cone-mediated pathway could induce pupil dilation in a similar speed-dependent pattern. The absolute but not relative pupil dilation in the cone paradigm was significantly larger than that in the rod paradigm. As the speed increased, the pupil dilation in the cone paradigm reached saturation at speed slower than the rod paradigm. Motion stimuli induced pupil dilation in a speed-dependent pattern, and as the motion speed increased, the pupil dilation gradually increased and reached saturation. The speed required to reach saturation in the cone paradigm was slower than in the rod paradigm.
Comprehensive validation of fasting-based and oral glucose tolerance test–based indices of insulin secretion against gold standard measures

Introduction With pre-diabetes and diabetes increasingly recognized as heterogeneous conditions, assessment of beta-cell function is gaining clinical importance to identify disease subphenotypes. Our study aims to comprehensively validate all types of surrogate indices based on oral glucose tolerance test (OGTT) and fasting measurements in comparison with gold standard methods. Research design and methods The hyperglycemic clamp extended with glucagon-like peptide 1 (GLP-1) infusion and intravenous glucose tolerance test (IVGTT), as well as OGTT, was performed in two well-phenotyped cohorts. The gold standard–derived indices were compared with surrogate insulin secretion markers, derived from fasting state and OGTT, using both Pearson’s and Spearman’s correlation coefficients. The insulin-based and C-peptide-based indices were analyzed separately in different groups of glucose tolerance and the entire cohorts. Results The highest correlation coefficients were found for area under curve (AUC) (I0-30)/AUC (G0-30), I30/G30, first-phase Stumvoll and Kadowaki model. These indices have high correlation coefficients with measures obtained from both insulin and C-peptide levels from IVGTT and hyperglycemic clamp. AUC (I0-120)/AUC (G0-120), BIGTT-AIR0-60-120, I30/G30, first-phase Stumvoll and AUC (I0-30)/AUC (G0-30) demonstrated the strongest association with incretin-stimulated insulin response. Conclusions We have identified glucose-stimulated and GLP-1-stimulated insulin secretion indices, derived from OGTT and fasting state, that have the strongest correlation with gold standard measures and could be potentially used in future researches and clinical practice.
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation

Pre-trained language models have demonstrated superior performance in various natural language processing tasks. However, these models usually contain hundreds of millions of parameters, which limits their practicality because of latency requirements in real-world applications. Existing methods train small compressed models via knowledge distillation. However, performance of these small models drops significantly compared with the pre-trained models due to their reduced model capacity. We propose MoEBERT, which uses a Mixture-of-Experts structure to increase model capacity and inference speed. We initialize MoEBERT by adapting the feed-forward neural networks in a pre-trained model into multiple experts. As such, representation power of the pre-trained model is largely retained. During inference, only one of the experts is activated, such that speed can be improved. We also propose a layer-wise distillation method to train MoEBERT. We validate the efficiency and efficacy of MoEBERT on natural language understanding and question answering tasks. Results show that the proposed method outperforms existing task-specific distillation algorithms. For example, our method outperforms previous approaches by over 2% on the MNLI (mismatched) dataset. Our code is publicly available at https://github.com/SimiaoZuo/MoEBERT.
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

Entity retrieval—retrieving information about entity mentions in a query—is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-art entity retrievers struggle to retrieve rare entities for ambiguous mentions due to biases towards popular entities. Incorporating knowledge graph types during training could help overcome popularity biases, but there are several challenges: (1) existing type-based retrieval methods require mention boundaries as input, but open-domain tasks run on unstructured text, (2) type-based methods should not compromise overall performance, and (3) type-based methods should be robust to noisy and missing types. In this work, we introduce TABi, a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks. TABi leverages a type-enforced contrastive loss to encourage entities and queries of similar types to be close in the embedding space. TABi improves retrieval of rare entities on the Ambiguous Entity Retrieval (AmbER) sets, while maintaining strong overall retrieval performance on open-domain tasks in the KILT benchmark compared to state-of-the-art retrievers. TABi is also robust to incomplete type systems, improving rare entity retrieval over baselines with only 5% type coverage of the training dataset. We make our code publicly available.
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors

Given a multivariate big time series, can we detect anomalies as soon as they occur? Many existing works detect anomalies by learning how much a time series deviates away from what it should be in the reconstruction framework. However, most models have to cut the big time series into small pieces empirically since optimization algorithms cannot afford such a long series. The question is raised: do such cuts pollute the inherent semantic segments, like incorrect punctuation in sentences? Therefore, we propose a reconstruction-based anomaly detection method, MissGAN, iteratively learning to decode and encode naturally smooth time series in coarse segments, and ﬁnding out a ﬁner segment from low-dimensional representations based on HMM. As a result, learning from multi-scale segments, MissGAN can reconstruct a meaningful and robust time series, with the help of adversarial regularization and extra conditional states. MissGAN does not need labels or only need labels of normal instances, making it widely applicable. Experiments on industrial datasets of real water network sensors show our MissGAN outperforms the baselines with scalability. Besides, we use a case study on the CMU Motion dataset to demonstrate that our model can well distinguish unexpected gestures from a given conditional motion.
A Survey on Efficient Processing of Similarity Queries over Neural Embeddings

Similarity query is the family of queries based on some similarity metrics. Unlike the traditional database queries which are mostly based on value equality, similarity queries aim to find targets"similar enough to"the given data objects, depending on some similarity metric, e.g., Euclidean distance, cosine similarity and so on. To measure the similarity between data objects, traditional methods normally work on low level or syntax features(e.g., basic visual features on images or bag-of-word features of text), which makes them weak to compute the semantic similarities between objects. So for measuring data similarities semantically, neural embedding is applied. Embedding techniques work by representing the raw data objects as vectors (so called"embeddings"or"neural embeddings"since they are mostly generated by neural network models) that expose the hidden semantics of the raw data, based on which embeddings do show outstanding effectiveness on capturing data similarities, making it one of the most widely used and studied techniques in the state-of-the-art similarity query processing research. But there are still many open challenges on the efficiency of embedding based similarity query processing, which are not so well-studied as the effectiveness. In this survey, we first provide an overview of the"similarity query"and"similarity query processing"problems. Then we talk about recent approaches on designing the indexes and operators for highly efficient similarity query processing on top of embeddings (or more generally, high dimensional data). Finally, we investigate the specific solutions with and without using embeddings in selected application domains of similarity queries, including entity resolution and information retrieval. By comparing the solutions, we show how neural embeddings benefit those applications.
Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images

Remote-sensing (RS) Change Detection (CD) aims to detect"changes of interest"from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attributed to the large amounts of annotated data used to train the networks. However, annotating large amounts of remote sensing images is labor-intensive and expensive, particularly with bi-temporal images, as it requires pixel-wise comparisons by a human expert. On the other hand, we often have access to unlimited unlabeled multi-temporal RS imagery thanks to ever-increasing earth observation programs. In this paper, we propose a simple yet effective way to leverage the information from unlabeled bi-temporal images to improve the performance of CD approaches. More specifically, we propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss by constraining the output change probability map of a given unlabeled bi-temporal image pair to be consistent under the small random perturbations applied on the deep feature difference map that is obtained by subtracting their latent feature representations. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD even with access to as little as 10% of the annotated training data. Code available at https://github.com/wgcban/SemiCD
Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.
Interaction of a Relativistic Magnetized Collisionless Shock with a Dense Clump

The interactions between a relativistic magnetized collisionless shock and dense clumps have been expected to play a crucial role in magnetic field amplification and cosmic-ray acceleration. We investigate this process using two-dimensional Particle-In-Cell (PIC) simulations, for the first time, where the clump size is much larger than the gyroradius of the downstream particles. We also perform relativistic magnetohydrodynamic (MHD) simulations for the same condition, to see the kinetic effects. We find that particles escape from the shocked clump along magnetic field lines in the PIC simulations, so that the vorticity is lower than that in the MHD simulations. Moreover, in both the PIC and MHD simulations, the shocked clump quickly decelerates because of relativistic effects. Owing to the escape and the deceleration, the shocked clump cannot amplify the downstream magnetic field in relativistic collisionless shocks. This large-scale PIC simulation opens a new window to understanding large-scale behaviors in collisionless plasma systems.
Efficient Circular Polarized Metamaterial RF Energy Harvester for Wi-Fi Applications

A new metamaterial based, circular polarized electromagnetic energy harvester is presented in this work. The structure, which is multilayer and utilises the well-known to the literature geometry of the spiral split ring resonator, operates at the Wi-Fi frequency band. Based on full electromagnetic analysis, the proposed harvester presents high efficiency (i.e., greater than 90%), adequate angle insensitivity (i.e., 44 deg.) and it is capable of absorbing circular polarised plane waves.
Consideration behavior and design decision making

Over the past decade, design engineering has developed a systematic framework to coordinate with consumer behavior models. Traditional consumer models applied in the past has mainly focused on the preference of compensatory trade-offs in the choice decisions. Recent marketing research has become interested in developing consumer models that are ”representative” in that they reflect realistic human decision processes. One important example is consideration: the process of quickly screening out many available alternatives using non-compensatory rules before trading off the value of different feature combinations. This research investigates the impact of modeling consideration behavior to design engineering, aiming at constructing consideration models that can inform strategic decisions. The study includes several features absent in existing research: quantifying the mis-specifications of the underlying choice process, tailoring survey instruments for particular models, and exploring the models? strategic value on product profitability and design decisions. First, numerical methods are explored to address the discontinuity in the profit-oriented optimization problem introduced by the consideration models. Methods based on complementarity constraints, smoothing functions, and genetic algorithms are implemented and evaluated with a vehicle design case study. Second, a simulation experiment based on synthetic market data compares consideration models and a variety of conventional compensatory choice models in model estimation and design optimization. The simulation finds that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses considerations; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than modeling heterogeneity in compensatory trade-offs. The synthetic experiment framework then further extends the comparison to include the survey design process guided by the different assumptions behind consideration models and traditional choice models. A product line design case study reveals