Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics

Efficient model selection for identifying a suitable pre-trained neural network to a downstream task is a fundamental yet challenging task in deep learning. Current practice requires expensive computational costs in model training for performance prediction. In this paper, we propose a novel framework for neural network selection by analyzing the governing dynamics over synaptic connections (edges) during training. Our framework is built on the fact that back-propagation during neural network training is equivalent to the dynamical evolution of synaptic connections. Therefore, a converged neural network is associated with an equilibrium state of a networked system composed of those edges. To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$. Next, we derive a neural capacitance metric $\beta_{\rm eff}$ as a predictive measure universally capturing the generalization capability of $G_A$ on the downstream task using only a handful of early training results. We carried out extensive experiments using 17 popular pre-trained ImageNet models and five benchmark datasets, including CIFAR10, CIFAR100, SVHN, Fashion MNIST and Birds, to evaluate the fine-tuning performance of our framework. Our neural capacitance metric is shown to be a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.
PromptBERT: Improving BERT Sentence Embeddings with Prompts

We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analysis the drawback of current sentence embedding from original BERT and find that it is mainly due to the static token embedding bias and ineffective BERT layers. Then we propose the first prompt-based sentence embeddings method and discuss two prompt representing methods and three prompt searching methods to make BERT achieve better sentence embeddings . Moreover, we propose a novel unsupervised training objective by the technology of template denoising, which substantially shortens the performance gap between the supervised and unsupervised settings. Extensive experiments show the effectiveness of our method. Compared to SimCSE, PromptBert achieves 2.29 and 2.58 points of improvement based on BERT and RoBERTa in the unsupervised setting.
Interchangeability of biosimilars: A study of expert views and visions regarding the science and substitution

Healthcare systems have reached a critical point regarding the question of whether biosimilar substitution should become common practice. To move the discussion forward, the study objective was to investigate the views of experts from medicines agencies and the pharmaceutical industry on the science underpinning interchangeability of biosimilars. We conducted an empirical qualitative study using semi-structured interviews informed by a cross-disciplinary approach encompassing regulatory science, law, and pharmaceutical policy. In total 25 individuals with experience within biologics participated during September 2018–August 2019. Eight participants were EU national medicines authority regulators, and 17 had pharmaceutical industry background: five from two originator-only companies, four from two companies with both biosimilar and originator products, and eight from seven biosimilar-only companies. Two analysts independently conducted inductive content analysis, resulting in data-driven themes capturing the meaning of the data. The participants reported that interchangeability was more than a scientific question of likeness between biosimilar and reference products: it also pertained to regulatory practices and trust. Participants were overall confident in the science behind exchanging biosimilar products for the reference products via switching, i.e., with physician involvement. However, their opinions differed regarding the scientific risk associated with biosimilar substitution, i.e., without physician involvement. Almost all participants saw no need for additional scientific data to support substitution. Moreover, the participants did not believe that switching studies, as required in the US, were appropriate for obtaining scientific certainty due to their small size. It is unclear why biosimilar switching is viewed as scientifically safer than substitution; therefore, we expect greater policy debate on biosimilar substitution in the near future. We urge European and UK policymakers and regulators to clarify their visions for biosimilar substitution; the positions of these two frontrunners are likely to influence other jurisdictions on the future of biosimilar use.
The impact of skeletal muscle disuse on distinct echo intensity bands: A retrospective analysis

Echo intensity (EI) is a novel tool for assessing muscle quality. EI has traditionally been reported as the mean of the pixel histogram, with 0 and 255 arbitrary units (A.U.) representing excellent and poor muscle quality, respectively. Recent work conducted in youth and younger and older adults suggested that analyzing specific EI bands, rather than the mean, may provide unique insights into the effectiveness of exercise and rehabilitation interventions. As our previous work showed deterioration of muscle quality after knee joint immobilization, we sought to investigate whether the increase in EI following disuse was limited to specific EI bands. Thirteen females (age = 21 yrs) underwent two weeks of left knee immobilization and ambulated via crutches. B-mode ultrasonography was utilized to obtain images of the immobilized vastus lateralis. The percentage of the total number of pixels within bands of 0–50, 51–100, 101–150, 151–200, and 201–255 A.U. was examined before and after immobilization. We also sought to determine if further subdividing the histogram into 25 A.U. bands (i.e., 0–25, 26–50, etc.) would be a more sensitive methodological approach. Immobilization resulted in a decrease in the percentage of pixels within the 0–50 A.U. band (-3.11 ± 3.98%), but an increase in the 101–150 A.U. (2.94 ± 2.64%) and 151–200 A.U. (0.93 ± 1.42%) bands. Analyses of variance on the change scores indicated that these differences were large and significant (%EI0-50 vs. %EI101-150: p < .001, d = 1.243); %EI0-50 vs. %EI151-200: p = . 043, d = 0.831). The effect size for the %EI51-100 versus %EI101-150 comparison was medium/large (d = 0.762), but not statistically significant (p = .085). Further analysis of the 25 A.U. bands indicated that the percentage of pixels within the 25–50 A.U. band decreased (-2.97 ± 3.64%), whereas the 101–125 (1.62 ± 1.47%) and 126–150 A.U. (1.18 ± 1.07%) bands increased. Comparison of the 50 A.U. and 25 A.U. band methods found that 25 A.U. bands offer little additional insight. Though studies are needed to ascertain the factors that may influence specific bands, changes in EI during muscle disuse are not homogeneous across the pixel histogram. We encourage investigators to think critically about the robustness of data obtained from EI histograms, rather than simply reporting the EImean value, in muscle quality research.
Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics

This paper presents a benchmarking study of some of the state-of-the-art reinforcement learning algorithms used for solving two simulated vision-based robotics problems. The algorithms considered in this study include soft actor-critic (SAC), proximal policy optimization (PPO), interpolated policy gradients (IPG), and their variants with Hindsight Experience replay (HER). The performances of these algorithms are compared against PyBullet's two simulation environments known as KukaDiverseObjectEnv and RacecarZEDGymEnv respectively. The state observations in these environments are available in the form of RGB images and the action space is continuous, making them difficult to solve. A number of strategies are suggested to provide intermediate hindsight goals required for implementing HER algorithm on these problems which are essentially single-goal environments. In addition, a number of feature extraction architectures are proposed to incorporate spatial and temporal attention in the learning process. Through rigorous simulation experiments, the improvement achieved with these components are established. To the best of our knowledge, such a benchmarking study is not available for the above two vision-based robotics problems making it a novel contribution in the field.
Fundamental differences in the properties of red and blue quasars: measuring the reddening and accretion properties with X-shooter

We have recently found fundamental differences in the radio properties of red quasars when compared to typical blue quasars. In this paper we use data from the X-shooter spectrograph on the Very Large Telescope, providing spectral coverage from ∼3000–25000 Å, of a sample of 40 red and blue luminous quasars at 1.45 < z < 1.65 to explore the connections between the radio, emission-line, and accretion-disc properties. We fit various dust-extinction curves to the data and find that dust reddening can fully explain the observed colours for the majority of the red quasars in our sample, with moderate extinctions ranging from AV ∼ 0.06–0.7 mags. We confront our spectra with a simple thin accretion-disc model and find this can describe the continua of both the blue and red quasars, once corrected for dust extinction; we also find no significant differences in the accretion properties. We detect ionized outflows in a number of red and blue quasars, but do not find any significant evidence that they are more prevalent in the red quasar population. Overall our findings imply that the radio emission is more closely connected to circumnuclear/ISM opacity rather than accretion disc or outflow differences.
Tidal virialization of dark matter haloes with clustering dark energy

We extend the analysis of Pace et al. [1] by considering the virialization process in the extended spherical collapse model for clustering dark-energy models, i.e., accounting for dark-energy fluctuations. Differently from the standard approach, here virialization is naturally achieved by properly modelling deviations from sphericity due to shear and rotation induced by tidal interactions. We investigate the time evolution of the virial overdensity Δvir in seven clustering dynamical dark energy models and compare the results to the ΛCDM model and to the corresponding smooth dark-energy models. Taking into account all the appropriate corrections, we deduce the abundance of convergence peaks for Rubin Observatory-LSST and Euclid-like weak-lensing surveys, of Sunyaev-Zel'dovich peaks for a Simon Observatory-like CMB survey, and of X-ray peaks for an eROSITA-like survey. Despite the tiny differences in Δvir between clustering and smooth dark-energy models, owing to the large volumes covered by these surveys, five out of seven clustering dark-energy models can be statistically distinguished from ΛCDM. The contribution of dark-energy fluctuation cannot be neglected, especially for the Chevallier-Polarski-Limber and Albrecht-Skordis models, provided the instrumental configurations provide high signal-to-noise ratio. These results are almost independent of the tidal virialization model.
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several robustness studies focusing on data sparsity and profile injection attacks. Instead, we propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions - robustness with respect to sub-populations, transformations, distributional disparity, attack, and data sparsity. While there are several libraries that allow users to compare different recommender system models, there is no software library for comprehensive robustness evaluation of recommender system models under different scenarios. As our main contribution, we present a robustness evaluation toolkit, Robustness Gym for RecSys (RGRecSys), that allows us to quickly and uniformly evaluate the robustness of recommender system models.
Rate of force development in the quadriceps of individuals with severe knee osteoarthritis: A preliminary cross-sectional study

Knee osteoarthritis (KOA) is a leading cause of knee pain and disability due to irreversible cartilage degeneration. Previous studies have not identified modifiable risk factors for KOA. In this preliminary cross-sectional study, we aimed to test the following hypotheses: individuals with severe KOA would have a significantly lower quadriceps rate of force development (RFD) than individuals with early KOA, and the decrease in quadriceps RFD would be greater than the decrease in maximum quadriceps strength in individuals with severe KOA. The maximum isometric strength of the quadriceps was assessed in individuals with mild (Kellgren and Lawrence [K&L] grade 1–2) and severe KOA (K&L grade 3–4) using a handheld dynamometer. The RFD was analyzed at 200 ms from torque onset and normalized to the body mass and maximum voluntary isometric contraction torque. To test whether the quadriceps RFD was lowered and whether the lower in the quadriceps RFD was greater than the lower in maximum quadriceps strength in individuals with severe knee OA, the Mann–Whitney U-test and analysis of covariance were performed, respectively. The effect size (ES) based on Hedges’ g with a 95% confidence interval (CI) was calculated for the quadriceps RFD and maximum quadriceps strength. Sixty-six participants were analyzed. Individuals with severe KOA displayed significantly lower quadriceps RFD (p = 0.009), the lower being greater than the lower in maximum quadriceps strength (between-group difference, ES: 0.88, -1.07 vs. 0.06, -0.22). Our results suggest that a decreased quadriceps RFD is a modifiable risk factor for progressive KOA. Our finding could help in the early detection and prevention of severe KOA.
Haptic Feedback to Assist Blind People in Indoor Environment Using Vibration Patterns

Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with speech feedback. The vibration feedback could effectively be used as an alternative approach to these mediums. This paper proposes a real-time feedback system specifically designed for blind people to convey information to them based on vibration patterns. The proposed solution has been evaluated through an empirical study by collecting data from 24 blind people through a mixed-mode survey using a questionnaire. Results show the average recognition accuracy for 10 different vibration patterns are 90%, 82%, 75%, 87%, 65%, and 70%.
Fatigue Crack Evaluation with the Guided Wave–Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram

On-line fatigue crack evaluation is crucial for ensuring the structural safety and reducing the maintenance costs of safety-critical systems. Among structural health monitoring (SHM), guided wave (GW)-based SHM has been deemed as one of the most promising techniques. However, the traditional damage index-based method and machine learning methods require manual processing and selection of GW features, which depend highly on expert knowledge and are easily affected by complicated uncertainties. Therefore, this paper proposes a fatigue crack evaluation framework with the GW–convolutional neural network (CNN) ensemble and differential wavelet spectrogram. The differential time–frequency spectrogram between the baseline signal and the monitoring signal is processed as the CNN input with the complex Gaussian wavelet transform. Then, an ensemble of CNNs is trained to jointly determine the crack length. Real fatigue tests on complex lap joint structures were carried out to validate the proposed method, in which several structures were tested preliminarily for collecting the training dataset and a new structure was adopted for testing. The root mean square error of the training dataset is 1.4 mm. Besides, the root mean square error of the evaluated crack length in the testing lap joint structure was 1.7 mm, showing the effectiveness of the proposed method.
Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects.
Harnessing Antitumor CD4+ T Cells for Cancer Immunotherapy

Simple Summary Diverse evidence revealed that CD4+ T cells play an important role in antitumor immunity by promoting or suppressing cytotoxic T cell responses. This review outlines the role of CD4+ T subsets within the tumor microenvironment and summarizes the latest progress regarding their potentials in cancer immunotherapy and methods for improving outcomes in cancer strategies by modulating CD4+ T responses. Abstract Over the past decades, CD4+ T cells have been considered as a supporting actor in the fields of cancer immunotherapy. Until recently, accumulating evidence has demonstrated the critical role of CD4+ T cells during antitumor immunity. CD4+ T cells can either suppress or promote the antitumor cytotoxic CD8+ T cell responses, either in secondary lymphoid organs or in the tumor. In this review, we provide an overview of the multifaceted role of different CD4+ T cell subsets in cancer immune response and their contribution during cancer therapies. Specifically, we focus on the latest progress regarding the impact of CD4+ T cell modulation on immunotherapies and other cancer therapies and discuss the prospect for harnessing CD4+ T cells to control tumor progression and prevent recurrence in patients.
Exploring Factors Associated with Women’s Willingness to Provide Digital Fingerprints in Accessing Healthcare Services: A Cross-Sectional Study in Urban Slums of Bangladesh

Digital fingerprints are increasingly used for patient care and treatment delivery, health system monitoring and evaluation, and maintaining data integrity during health research. Yet, no evidence exists about the use of fingerprinting technologies in maternal healthcare services in urban slum contexts, globally. The present study aimed to explore the recently delivered women’s willingness to give digital fingerprints to community health workers to access healthcare services in the urban slums of Bangladesh and identify the associated factors. Employing a two-stage cluster random sampling procedure, we chose 458 recently delivered women from eight randomly selected urban slums of Dhaka city, Bangladesh. Chi-square tests were performed for descriptive analyses, and binary logistic regression analyses were performed to explore the factors associated with willingness to provide fingerprints. Overall, 78% of the participants reported that they were willing to provide digital fingerprints if that eased access to healthcare services. After adjusting for potential confounders, the sex of the household head, family type, and household wealth status were significantly associated with the willingness to provide fingerprints to access healthcare services. The study highlighted the potentials of using fingerprints for making healthcare services accessible. Focus is needed for female-headed households, women from poor families, and engaging husbands and in-laws in mobile health programs.
Addition of a carboxy-terminal tail to the normally tailless gonadotropin-releasing hormone receptor impairs fertility in female mice

Gonadotropin-releasing hormone (GnRH) is the primary neuropeptide controlling reproduction in vertebrates. GnRH stimulates follicle-stimulating hormone (FSH) and luteinizing hormone (LH) synthesis via a G-protein-coupled receptor, GnRHR, in the pituitary gland. In mammals, GnRHR lacks a C-terminal cytosolic tail (Ctail) and does not exhibit homologous desensitization. This might be an evolutionary adaptation that enables LH surge generation and ovulation. To test this idea, we fused the chicken GnRHR Ctail to the endogenous murine GnRHR in a transgenic model. The LH surge was blunted, but not blocked in these mice. In contrast, they showed reductions in FSH production, ovarian follicle development, and fertility. Addition of the Ctail altered the nature of agonist-induced calcium signaling required for normal FSH production. The loss of the GnRHR Ctail during mammalian evolution is unlikely to have conferred a selective advantage by enabling the LH surge. The adaptive significance of this specialization remains to be determined.
An Investigation on a Plane-Based Dynamic Calibration Method for the Handheld LiDAR Scanner

A plane-based dynamic calibration method had been proposed by the previous study for the GeoSLAM ZEB Horizon handheld LiDAR scanner. Only one preliminary test was presented. Therefore, three datasets in a calibration field were collected in this study on different dates and at different times on the same date to investigate the efficiency of the proposed calibration approach and calibration results. The calibration results for these three datasets showed that all average residuals were closer to 0, and all a posterior unit weight standard deviations of the adjustment were also significantly reduced after calibration. Moreover, the RMSE (root mean square error) of the check planes was improved by about an average of 32.61%, 28.44%, and 14.7%, respectively, for the three datasets. The improvement was highly correlated with the quality of the calibration data. The RMSE differences of all check planes using calibration data collected on different dates and at different times on the same date for calibration was about 1–2 cm and less than 1 mm, respectively. There was no difference in the calibration results, demonstrating the efficiency of the proposed calibration approach and the calibration results during the two different dates.