Miracle-dz/UniFAR-FTUs huggingface dataset:
Deprescribing interventions for gabapentinoids in adults: A scoping review

The emerging issue of rising gabapentinoid misuse is being recognized alongside the lack of current evidence supporting the safe and effective deprescribing of gabapentinoids. This scoping review aimed to assess the extent and nature of gabapentinoid deprescribing interventions in adults, either in reducing dosages, or prescribing of, gabapentinoids. Electronic databases were searched on 23 February 2022 without restrictions. Eligible studies included randomized, non‐randomized and observational studies that assessed an intervention aimed at reducing/ceasing the prescription/use of a gabapentinoid in adults for any indication in a clinical setting. The research outcomes investigated the type of intervention, prescribing rates, cessations, patient outcomes and adverse events. Extracted outcome data were categorized as either short (≤3 months), intermediate (>3 but <12 months) or long (≥12 months) term. A narrative synthesis was conducted. The four included studies were conducted in primary and acute care settings. Interventions were of dose‐reducing protocols, education and/or pharmacological‐based approaches. In the randomized trials, gabapentinoid use could be ceased in at least one third of participants. In the two observational trials, gabapentinoid prescribing rates decreased by 9%. Serious adverse events and adverse events specifically related to gabapentinoids were reported in one trial. No study included patient‐focused psychological interventions in the deprescribing process, nor provided any long‐term follow‐up. This review highlights the lack of existing evidence in this area. Due to limited available data, our review was unable to make any firm judgements on the most effective gabapentinoid deprescribing interventions in adults, highlighting the need for more research in this area.
Quality-adjusted survival time without symptoms or toxicity of acalabrutinib with or without obinutuzumab in patients with treatment-naive chronic lymphocytic leukemia

Abstract Acalabrutinib monotherapy (A) and acalabrutinib plus obinutuzumab (A + O) demonstrated improved efficacy and safety versus chlorambucil plus obinutuzumab (C + O) among treatment-naive patients with chronic lymphocytic leukaemia (CLL) in the ELEVATE-TN trial. The relative risk-benefit at a median follow-up of 47 months was assessed using Quality-adjusted Time Without Symptoms and Toxicity (Q-TWiST) methodology. Patient data were partitioned into 3 states: time with toxicity (TOX); time without symptoms or toxicity (TWiST); and time after relapse (REL). Mean Q-TWiST was estimated by summing the mean time in each state, multiplied by its respective utility weight. Patients receiving A or A + O experienced significantly longer Q-TWiST versus C + O when toxicity was defined as grade 3–4 adverse events (AEs) (41.79 vs 34.56 months; 42.07 vs 34.56 months) and grade 2–4 AEs (35.07 vs 30.64 months; 34.21 vs 30.64 months). Overall, patients with treatment-naive CLL treated with A or A + O experienced significant gains in Q-TWiST compared with C + O.
Feasibility of telerehabilitation for heart failure patients inaccessible for outpatient rehabilitation

Despite strong recommendations, outpatient cardiac rehabilitation is underused in chronic heart failure (CHF) patients. Possible barriers are frailty, accessibility, and rural living, which may be overcome by telerehabilitation. We designed a randomized, controlled trial to evaluate the feasibility of a 3‐month real‐time, home‐based telerehabilitation, high‐intensity exercise programme for CHF patients who are either unable or unwilling to participate in standard outpatient cardiac rehabilitation and to explore outcomes of self‐efficacy and physical fitness at 3 months post‐intervention.
Access and adherence to the most recent recommendations regarding resumption of activities after a mild traumatic brain injury

ABSTRACT Objectives 1) To examine access and adherence to the Berlin (2016) recommendations for resuming physical and intellectual activities after mild traumatic brain injury (mTBI) (including an exploration of barriers and facilitators). 2) To assess post-mTBI symptoms in relation to recommendation adherence. Method 73 participants who sustained a mTBI completed an online survey with questions about access and adherence to recommendations and validated measures of symptoms. Results Almost all participants had received recommendations from a health professional after their mTBI. Two thirds of recommendations reported had at least moderate correspondence with the Berlin (2016) recommendations. The vast majority of participants reported weak or partial adherence to these recommendations and only 15.7% reported complete adherence. Overall, adherence to recommendations explained a significant portion of the variance in the severity and number of unresolved post-mTBI symptoms. The most common barriers were: being in a critical period for school or work, pressure to return to work or school, screen use, and presence of symptoms. Conclusions Sustained efforts are required to disseminate appropriate recommendations after mTBI. Clinicians should support patients in eliminating barriers to recommendation adherence, as greater adherence may facilitate recovery.
Modelling ASthma TrEatment Responses (MASTER): Effect of individual patient characteristics on the risk of exacerbation in moderate or severe asthma: A time‐to‐event analysis of randomized clinical trials

There is limited understanding of how clinical and demographic characteristics are associated with exacerbation risk in patients with moderate‐to‐severe asthma, and how these factors correlate with symptom control and treatment response. Here we assess the relationship between baseline characteristics and exacerbation risk during regular dosing with inhaled corticosteroids (ICS) monotherapy or in combination with long‐acting beta2‐agonists (ICS/LABA) in clinical trial patients with varying levels of symptom control, as assessed by the asthma control questionnaire (ACQ‐5).
Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis

Length extrapolation permits training a transformer language model on short sequences that preserves perplexities when tested on substantially longer sequences.A relative positional embedding design, ALiBi, has had the widest usage to date. We dissect ALiBi via the lens of receptive field analysis empowered by a novel cumulative normalized gradient tool. The concept of receptive field further allows us to modify the vanilla Sinusoidal positional embedding to create Sandwich, the first parameter-free relative positional embedding design that truly length information uses longer than the training sequence. Sandwich shares with KERPLE and T5 the same logarithmic decaying temporal bias pattern with learnable relative positional embeddings; these elucidate future extrapolatable positional embedding design.
Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering

The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a novel density-based unsupervised learning method that determines cluster centers by constructing a potential function. In this method, we use the Graph Gradient Descent algorithm to find the centers of clusters. GPU parallelization is utilized for computing potential values. We also conducted experiments on five widely used datasets and evaluated using four indicators. The results show superior performance of the method. Finally, we discuss the influence of $\sigma$ on the experimental results.
Globular clusters in M31, Local Group, and external galaxies

Abstract Throughout most of the Local Group, globular clusters (GCs) remain recognisable as extended objects in ground-based images taken in good seeing conditions. However, studying the full extent of the GC systems is challenging because of the large sky area that needs to be surveyed and recent years have seen dramatic progress in our knowledge of GC populations in nearby galaxies, thanks to large imaging surveys. At the same time, techniques for deriving detailed abundances from integrated-light spectra of GCs are maturing so that detailed comparisons of the chemical composition for GCs in different galaxies can now be made. Such comparisons may shed important light on the properties of proto-galactic fragments that were accreted onto galaxy halos. Nevertheless, our census of Local Group GCs probably remains far from complete, in particular at low luminosities and for very extended clusters.
Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

Short-term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per-horizon-basis, meaning that they lack information about the development of the uncertainty over time or the inter-temporal correlation of forecast errors for different horizons. This information is very important for forecast end-users optimizing time-dependent variables or dealing with multi-period decision-making problems, such as the management and operation of power systems with a high penetration of renewable generation. This paper provides input to these problems by proposing a model based on stochastic differential equations that allows generating predictive densities as well as scenarios for wind power. We build upon a probabilistic model for wind speed and introduce a dynamic power curve. The model thus decomposes the dynamics of wind power prediction errors into wind speed forecast errors and errors related to the conversion from wind speed to wind power. We test the proposed model on an out-of-sample period of 1year for a wind farm with a rated capacity of 21MW. The model outperforms simple as well as advanced benchmarks on horizons ranging from 1 to 24h. Copyright © 2016 John Wiley & Sons, Ltd.
Boundary Attention Mapping (BAM): Fine-grained saliency maps for segmentation of Burn Injuries

Burn injuries can result from mechanisms such as thermal, chemical, and electrical insults. A prompt and accurate assessment of burns is essential for deciding definitive clinical treatments. Currently, the primary approach for burn assessments, via visual and tactile observations, is approximately 60%-80% accurate. The gold standard is biopsy and a close second would be non-invasive methods like Laser Doppler Imaging (LDI) assessments, which have up to 97% accuracy in predicting burn severity and the required healing time. In this paper, we introduce a machine learning pipeline for assessing burn severities and segmenting the regions of skin that are affected by burn. Segmenting 2D colour images of burns allows for the injured versus non-injured skin to be delineated, clearly marking the extent and boundaries of the localized burn/region-of-interest, even during remote monitoring of a burn patient. We trained a convolutional neural network (CNN) to classify four severities of burns. We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images. We demonstrated the effectiveness of our proposed pipeline through extensive experiments and evaluations using two datasets; 1) A larger skin burn image dataset consisting of 1684 skin burn images of four burn severities, 2) An LDI dataset that consists of a total of 184 skin burn images with their associated LDI scans. The CNN trained using the first dataset achieved an average F1-Score of 78% and micro/macro- average ROC of 85% in classifying the four burn severities. Moreover, a comparison between the BAM results and LDI results for measuring injury boundary showed that the segmentations generated by our method achieved 91.60% accuracy, 78.17% sensitivity, and 93.37% specificity.
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition

Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-language labeled data or performing knowledge distillation on target-language unlabeled data. However, these methods may suffer from label noise due to the automatic labeling process. In this paper, we propose CoLaDa, a Collaborative Label Denoising Framework, to address this problem. Specifically, we first explore a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other. We then present an instance-collaboration-based strategy that considers the label consistency of each token’s neighborhood in the representation space for denoising. Experiments on different benchmark datasets show that the proposed CoLaDa achieves superior results compared to previous methods, especially when generalizing to distant languages.
On the Minimax Regret for Online Learning with Feedback Graphs

In this work, we improve on the upper and lower bounds for the regret of online learning with strongly observable undirected feedback graphs. The best known upper bound for this problem is $\mathcal{O}\bigl(\sqrt{\alpha T\ln K}\bigr)$, where $K$ is the number of actions, $\alpha$ is the independence number of the graph, and $T$ is the time horizon. The $\sqrt{\ln K}$ factor is known to be necessary when $\alpha = 1$ (the experts case). On the other hand, when $\alpha = K$ (the bandits case), the minimax rate is known to be $\Theta\bigl(\sqrt{KT}\bigr)$, and a lower bound $\Omega\bigl(\sqrt{\alpha T}\bigr)$ is known to hold for any $\alpha$. Our improved upper bound $\mathcal{O}\bigl(\sqrt{\alpha T(1+\ln(K/\alpha))}\bigr)$ holds for any $\alpha$ and matches the lower bounds for bandits and experts, while interpolating intermediate cases. To prove this result, we use FTRL with $q$-Tsallis entropy for a carefully chosen value of $q \in [1/2, 1)$ that varies with $\alpha$. The analysis of this algorithm requires a new bound on the variance term in the regret. We also show how to extend our techniques to time-varying graphs, without requiring prior knowledge of their independence numbers. Our upper bound is complemented by an improved $\Omega\bigl(\sqrt{\alpha T(\ln K)/(\ln\alpha)}\bigr)$ lower bound for all $\alpha>1$, whose analysis relies on a novel reduction to multitask learning. This shows that a logarithmic factor is necessary as soon as $\alpha
Interpretation of Time-Series Deep Models: A Survey

Deep learning models developed for time-series associated tasks have become more widely researched nowadays. However, due to the unintuitive nature of time-series data, the interpretability problem -- where we understand what is under the hood of these models -- becomes crucial. The advancement of similar studies in computer vision has given rise to many post-hoc methods, which can also shed light on how to explain time-series models. In this paper, we present a wide range of post-hoc interpretation methods for time-series models based on backpropagation, perturbation, and approximation. We also want to bring focus onto inherently interpretable models, a novel category of interpretation where human-understandable information is designed within the models. Furthermore, we introduce some common evaluation metrics used for the explanations, and propose several directions of future researches on the time-series interpretability problem. As a highlight, our work summarizes not only the well-established interpretation methods, but also a handful of fairly recent and under-developed techniques, which we hope to capture their essence and spark future endeavours to innovate and improvise.
Robustness of Quantum Random Walk Search Algorithm in Hypercube when only first or both first and second neighbors are measured

In this work we study the robustness of two modifications of quantum random walk search algorithm on hypercube. In the first previously suggested modification, on each even iteration only quantum walk is applied. And in the second, the closest neighbors of the solution are measured classically. In our approach the traversing coin is constructed by both generalized Householder reflection and an additional phase multiplier and we investigate the stability of the algorithm to deviations in those phases. We have shown that the unmodified algorithm becomes more robust when a certain relation between those phases is preserved. The first modification we study here does not lead to any change in the robustness of quantum random walk search algorithm. However, when a measurement of the first and second neighbors is included, there are some differences. The most important one, in view of our study of the robustness, is an increase in the stability of the algorithm, especially for large coin dimensions.
Invited Paper: Initial Steps Toward a Compiler for Distributed Programs

In the Hydro project we are designing a compiler toolkit that can optimize for the concerns of distributed systems, including scale-up and scale-down, availability, and consistency of outcomes across replicas. This invited paper overviews the project, and provides an early walk-through of the kind of optimization that is possible. We illustrate how type transformations as well as local program transformations can combine, step by step, to convert a single-node program into a variety of distributed design points that offer the same semantics with different performance and deployment characteristics.
Psychological Metrics for Dialog System Evaluation

We present metrics for evaluating dialog systems through a psychologically-grounded"human"lens in which conversational agents express a diversity of both states (e.g., emotion) and traits (e.g., personality), just as people do. We present five interpretable metrics from established psychology that are fundamental to human communication and relationships: emotional entropy, linguistic style and emotion matching, agreeableness, and empathy. These metrics can be applied (1) across dialogs and (2) on turns within dialogs. The psychological metrics are compared against seven state-of-the-art traditional metrics (e.g., BARTScore and BLEURT) on seven standard dialog system data sets. We also introduce a novel data set, the Three Bot Dialog Evaluation Corpus, which consists of annotated conversations from ChatGPT, GPT-3, and BlenderBot. We demonstrate that our proposed metrics offer novel information; they are uncorrelated with traditional metrics, can be used to meaningfully compare dialog systems, and lead to increased accuracy (beyond existing traditional metrics) in predicting crowd-sourced dialog judgements. The interpretability and unique signal of our psychological metrics make them a valuable tool for evaluating and improving dialog systems.
Assessment of Anterior Cruciate Ligament Injury Risk Based on Human Key Points Detection Algorithm

This paper aims to detect the potential injury risk of the anterior cruciate ligament (ACL) by proposing an ACL potential injury risk assessment algorithm based on key points of the human body detected using computer vision technology. To obtain the key points data of the human body in each frame, OpenPose, an open source computer vision algorithm, was employed. The obtained data underwent preprocessing and were then fed into an ACL potential injury feature extraction model based on the Landing Error Evaluation System (LESS). This model extracted several important parameters, including the knee flexion angle, the trunk flexion on the sagittal plane, trunk flexion angle on the frontal plane, the ankle knee horizontal distance, and the ankle shoulder horizontal distance. Each of these features was assigned a threshold interval, and a segmented evaluation function was utilized to score them accordingly. To calculate the final score of the participant, the score values were input into a weighted scoring model designed based on the Analytic Hierarchy Process (AHP). The AHP based model takes into account the relative importance of each feature in the overall assessment. The results demonstrate that the proposed algorithm effectively detects the potential risk of ACL injury. The proposed algorithm demonstrates its effectiveness in detecting ACL injury risk, offering valuable insights for injury prevention and intervention strategies in sports and related fields. Code is available at: https://github.com/ZiyuGong-proj/Assessment-of-ACL-Injury-Risk-Based-on-Openpose
Reconstruction, forecasting, and stability of chaotic dynamics from partial data

The forecasting and computation of the stability of chaotic systems from partial observations are tasks for which traditional equation-based methods may not be suitable. In this computational paper, we propose data-driven methods to (i) infer the dynamics of unobserved (hidden) chaotic variables (full-state reconstruction); (ii) time forecast the evolution of the full state; and (iii) infer the stability properties of the full state. The tasks are performed with long short-term memory (LSTM) networks, which are trained with observations (data) limited to only part of the state: (i) the low-to-high resolution LSTM (LH-LSTM), which takes partial observations as training input, and requires access to the full system state when computing the loss; and (ii) the physics-informed LSTM (PI-LSTM), which is designed to combine partial observations with the integral formulation of the dynamical system's evolution equations. First, we derive the Jacobian of the LSTMs. Second, we analyze a chaotic partial differential equation, the Kuramoto-Sivashinsky, and the Lorenz-96 system. We show that the proposed networks can forecast the hidden variables, both time-accurately and statistically. The Lyapunov exponents and covariant Lyapunov vectors, which characterize the stability of the chaotic attractors, are correctly inferred from partial observations. Third, the PI-LSTM outperforms the LH-LSTM by successfully reconstructing the hidden chaotic dynamics when the input dimension is smaller or similar to the Kaplan-Yorke dimension of the attractor. The performance is also analyzed against noisy data. This work opens new opportunities for reconstructing the full state, inferring hidden variables, and computing the stability of chaotic systems from partial data.
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular solution concept of Shapley values extended to stochastic cooperative games, resulting in explanations that are random variables. The GP explanations generated using our approach satisfy similar favorable axioms to standard Shapley values and possess a tractable covariance function across features and data observations. This covariance allows for quantifying explanation uncertainties and studying the statistical dependencies between explanations. We further extend our framework to the problem of predictive explanation, and propose a Shapley prior over the explanation function to predict Shapley values for new data based on previously computed ones. Our extensive illustrations demonstrate the effectiveness of the proposed approach.