Miracle-dz/UniFAR-FTUs huggingface dataset:
LyricSIM: A novel Dataset and Benchmark for Similarity Detection in Spanish Song LyricS

In this paper, we present a new dataset and benchmark tailored to the task of semantic similarity in song lyrics. Our dataset, originally consisting of 2775 pairs of Spanish songs, was annotated in a collective annotation experiment by 63 native annotators. After collecting and refining the data to ensure a high degree of consensus and data integrity, we obtained 676 high-quality annotated pairs that were used to evaluate the performance of various state-of-the-art monolingual and multilingual language models. Consequently, we established baseline results that we hope will be useful to the community in all future academic and industrial applications conducted in this context.
The Surface Composition of Six Newly Discovered Chemically Peculiar Stars. Comparison to the HgMn Stars mu Lep and beta Scl and the Superficially Normal B Star nu Cap

We report on a detailed abundance study of six bright, mostly southern, slowly rotating late B-stars: HD 1279 (B8III), HD 99803 (B9V), HD 123445 (B9V), HD 147550 (B9V), HD 171961 (B8III), and HD 202671 (B5II/III), hitherto reported as normal stars. We compare them to the two classical HgMn stars μ Lep and β Scl, and to the superficially normal star ν Cap. In the spectra of the six stars, the Hg ii line at 3984 Å line is clearly seen and numerous lines of P, Ti, Mn, Fe, Ga, Sr, Y, and Zr appear to be strong absorbers. A comparison of newly acquired and archival spectra of these objects with a grid of synthetic spectra for selected unblended lines reveals large overabundances of P, Ti, Cr, Mn, Sr, Y, Zr, Ba, Pt, and Hg, and underabundances of He, Mg, Sc, and Ni. The effective temperatures, surface gravities, low projected rotational velocities, and the peculiar abundance patterns of the six investigated stars show that they are new chemically peculiar stars, mostly new HgMn stars, and are reclassified as such. The evolutionary status of these stars has been inferred, and their ages and masses estimated. The two most massive objects, HD 1279 and HD 202671, might have evolved away from the main sequence recently, the other stars are main-sequence objects. HD 99803A is a sharp lined HgMn star with grazing eclipses. From TESS and MASCARA photometry, we determine an orbital period of P orb = 26.12022 ± 0.00004 day.
How Should We Support Designing Privacy-Friendly Apps for Children? Using a Research through Design Process to Understand Developers' Needs and Challenges

Mobile apps used by children often make use of harmful techniques, such as data tracking and targeted advertising. Previous research has suggested that developers face several systemic challenges in designing apps that prioritise children's best interests. To understand how developers can be better supported, we used a Research through Design (RtD) method to explore what the future of privacy-friendly app development could look like. We performed an elicitation study with 20 children's app developers to understand their needs and requirements. We found a number of specific technical requirements from the participants about how they would like to be supported, such as having actionable transnational design guidelines and easy-to-use development libraries. However, participants were reluctant to adopt these design ideas in their development practices due to perceived financial risks associated with increased privacy in apps. To overcome this critical gap, participants formulated socio-technical requirements that extend to other stakeholders in the mobile industry, including parents and marketplaces. Our findings provide important immediate and long-term design opportunities for the HCI community, and indicate that support for changing app developers' practices must be designed in the context of their relationship with other stakeholders.
Exact relativistic expressions for polarization of incoherent Thomson scattering

We present a derivation of the degree of polarization for incoherent Thomson scattering (TS) using Mueller matrix formalism. An exact analytic solution is obtained for spectrum-integrated matrix elements. The solution is valid for the full range of incident polarizations, scattering angles, and electron thermal motion from non-relativistic to ultra-relativistic. It is based on a newly developed theoretical model, a finite transit time (FTT) correction to previous theoretical work on TS polarization. The Mueller matrix elements are substantially different from previous calculations without the FTT correction, even to the lowest linear order in Te/mec2≪1. Mathematically, the derivation is a unique example of fully analytical integration of the 3D scattering operator over a relativistic Maxwellian distribution function; experimentally, the results have application to the use of the polarization properties of Thomson scattered light as a method of electron temperature measurement. The results can also be used.
Robust low-rank training via approximate orthonormal constraints

With the growth of model and data sizes, a broad effort has been made to design pruning techniques that reduce the resource demand of deep learning pipelines, while retaining model performance. In order to reduce both inference and training costs, a prominent line of work uses low-rank matrix factorizations to represent the network weights. Although able to retain accuracy, we observe that low-rank methods tend to compromise model robustness against adversarial perturbations. By modeling robustness in terms of the condition number of the neural network, we argue that this loss of robustness is due to the exploding singular values of the low-rank weight matrices. Thus, we introduce a robust low-rank training algorithm that maintains the network's weights on the low-rank matrix manifold while simultaneously enforcing approximate orthonormal constraints. The resulting model reduces both training and inference costs while ensuring well-conditioning and thus better adversarial robustness, without compromising model accuracy. This is shown by extensive numerical evidence and by our main approximation theorem that shows the computed robust low-rank network well-approximates the ideal full model, provided a highly performing low-rank sub-network exists.
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.
On the Convergence of Coordinate Ascent Variational Inference

As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming more and more popular for approximating intractable posterior distributions in large-scale Bayesian models due to its comparable efficacy and superior efficiency. Several recent works provide theoretical justifications of VI by proving its statistical optimality for parameter estimation under various settings; meanwhile, formal analysis on the algorithmic convergence aspects of VI is still largely lacking. In this paper, we consider the common coordinate ascent variational inference (CAVI) algorithm for implementing the mean-field (MF) VI towards optimizing a Kullback--Leibler divergence objective functional over the space of all factorized distributions. Focusing on the two-block case, we analyze the convergence of CAVI by leveraging the extensive toolbox from functional analysis and optimization. We provide general conditions for certifying global or local exponential convergence of CAVI. Specifically, a new notion of generalized correlation for characterizing the interaction between the constituting blocks in influencing the VI objective functional is introduced, which according to the theory, quantifies the algorithmic contraction rate of two-block CAVI. As illustrations, we apply the developed theory to a number of examples, and derive explicit problem-dependent upper bounds on the algorithmic contraction rate.
Inconsistencies on the Hubble tension from the ages of the oldest astrophysical objects

Recently different cosmological measurements have shown a tension in the value of the Hubble constant, H 0 . Assuming the Λ CDM model, the Planck satellite mission has inferred the Hubble constant from the cosmic microwave background (CMB) anisotropies to be H 0 = 67 . 4 ± 0 . 5 km s − 1 Mpc − 1 . On the other hand, low redshift measurements such as those using Cepheid variables and supernovae Type Ia (SNIa) have obtained a significantly larger value. For instance, Riess et al. reported H 0 = 73 . 04 ± 1 . 04 km s − 1 Mpc − 1 , which is 5 σ apart of the prediction from Planck observations. This tension is a major problem in cosmology nowadays, and it is not clear yet if it comes from systematic effects or new physics. The use of new methods to infer the Hubble constant is therefore essential to shed light on this matter. In this paper, we discuss using the age of the oldest astrophysical objects (OAO) to probe the Hubble tension. We show that, although this data can provide additional information, the method can also artificially introduce a tension.
4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks

Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (personal avatar representation, LiDAR in autonomous or assisted driving). In many cases, such volume of data is transmitted, thus requiring that proper compression tools are applied to either reduce the resolution or the bandwidth. In this paper, we propose a new solution for upscaling and restoration of time-varying 3D video point clouds after they have been heavily compressed. Our model consists of a specifically designed Graph Convolutional Network that combines Dynamic Edge Convolution and Graph Attention Networks for feature aggregation in a Generative Adversarial setting. We present a different way to sample dense point clouds with the intent to make these modules work in synergy to provide each node enough features about its neighbourhood in order to later on generate new vertices. Compared to other solutions in the literature that address the same task, our proposed model is capable of obtaining comparable results in terms of quality of the reconstruction, while using a substantially lower number of parameters (\simeq 300KB), making our solution deployable in edge computing devices.
Does it pay to optimize AUC?

The Area Under the ROC Curve (AUC) is an important model metric for evaluating binary classifiers, and many algorithms have been proposed to optimize AUC approximately. It raises the question of whether the generally insignificant gains observed by previous studies are due to inherent limitations of the metric or the inadequate quality of optimization. To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in R2, which runs in O(n+n- log n+n-) where n+ and n- are the number of positive and negative samples respectively. Furthermore, it can be naturally extended to Rd in O(n+n-d-1 log (n+n-)) by recursively calling AUC-opt in lower-dimensional spaces. We prove the problem is NP-complete when d is not fixed, reducing from the open hemisphere problem. Compared with other methods, experiments show that AUC-opt achieves statistically significant improvements between 17 to 40 in R2 and 4 to 42 in R3 of 50 t-SNE training datasets. However, generally, the gain proves insignificant on most testing datasets compared to the best standard classifiers. Similar observations are found for nonlinear AUC methods under real-world datasets.
Chiral Anomaly Beyond Fermionic Paradigm

Two-dimensional magnets have manifested themselves as promising candidates for quantum devices. We here report that the edge and strain effects during the device fabrication with two-dimensional honeycomb ferromagnets such as CrX$_3$ (X=Cl, I, Br) and CrXTe$_3$ (X=Si, Ge) can be characterized by a (1+1)-dimensional magnon chiral anomaly beyond the fermionic paradigm. In the presence of zigzag edges, a pair of chiral bulk-edge magnon bands appear and cause an imbalance of left- and right-chirality magnons when subjected to nonuniform temperature or magnetic fields. In the presence of a uniaxial strain, the bulk Dirac magnons are broken into chiral magnon pseudo-Landau levels, resulting in a magnon chiral anomaly observable through a negative strain-resistivity of the magnetic dipole and heat. Our work demonstrates a chiral anomaly with (quasi)particles obeying non-fermionic statistics and will be instructive in understanding anomalous magnon transport.
Adoption and implication of the Biased-Annotator Competence Estimation (BACE) model into COVID-19 vaccine Twitter data: Human annotation for latent message features

Traditional quantitative content analysis approach (human coding method) has weaknesses, such as assuming all human coders are equally accurate once the intercoder reliability for training reaches a threshold score. We applied the Biased-Annotator Competence Estimation (BACE) model (Tyler, 2021), which draws on Bayesian modeling to improve human coding. An important contribution of this model is it takes each coder's potential biases and reliability into consideration and treats the"true"label of each message as a latent parameter, with quantifiable estimation uncertainties. In contrast, in conventional human coding, each message will receive a fixed label without estimates for measurement uncertainties. In this extended abstract, we first summarize the weaknesses of conventional human coding; and then apply the BACE model to COVID-19 vaccine Twitter data and compare BACE with other statistical models; finally, we discuss how the BACE model can be applied to improve human coding of latent message features.
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks are demonstrated on three data sets, particularly at the early stages of the battery lifetime.
Longitudinal Factor Analysis and Measurement Invariance of Sense of Coherence and General Self-Efficacy in Adolescence

Sense of coherence (SOC) and General Self-efficacy (GSE) are trait-like self-regulatory attributes, supposedly benefitting health. Previous data on their factorial validity and longitudinal stability in adolescent samples have been inconclusive. The present study examined the factor structure, measurement invariance (MI), and stability coefficients of SOC and GSE among German adolescents in a longitudinal design over the course of nine years from age 15 to age 24. Results supported the factorial validity of both scales. GSE parameters were invariant up to the level of strict invariance, whereas for SOC partial scalar and strict invariance were attainable after modifications. Here we document reliability, validity, and factor mean changes of the SOC and GSE scales from adolescence to young adulthood. Interindividual differences in SOC were moderately stable. Though this implies limited sensitivity to intraindividual developmental changes, it qualifies SOC for long-term predictions. GSE was conspicuously less stable, raising questions about its long-term criterion validity.
Developing A Visual-Interactive Interface for Electronic Health Record Labeling: An Explainable Machine Learning Approach

Labeling a large number of electronic health records is expensive and time consuming, and having a labeling assistant tool can significantly reduce medical experts' workload. Nevertheless, to gain the experts' trust, the tool must be able to explain the reasons behind its outputs. Motivated by this, we introduce Explainable Labeling Assistant (XLabel) a new visual-interactive tool for data labeling. At a high level, XLabel uses Explainable Boosting Machine (EBM) to classify the labels of each data point and visualizes heatmaps of EBM's explanations. As a case study, we use XLabel to help medical experts label electronic health records with four common non-communicable diseases (NCDs). Our experiments show that 1) XLabel helps reduce the number of labeling actions, 2) EBM as an explainable classifier is as accurate as other well-known machine learning models outperforms a rule-based model used by NCD experts, and 3) even when more than 40% of the records were intentionally mislabeled, EBM could recall the correct labels of more than 90% of these records.
The Association Between Sarcopenia and Diabetes: From Pathophysiology Mechanism to Therapeutic Strategy

Abstract Diabetes and sarcopenia are emerging as serious public health issues. Sarcopenia, an age-related disorder characterized by loss of skeletal muscle mass and function, is recognized as a new complication in elderly patients with type 2 diabetes mellitus (T2DM). Type 2 diabetes is characterized by insulin resistance, chronic inflammation, accumulation of advanced glycation products and increased oxidative stress, which can negatively affect skeletal muscle mass, strength and function leading to sarcopenia. There is a mutual interrelationship between T2DM and sarcopenia in light of pathophysiology mechanism and long-term outcome. T2DM will accelerate the decline of muscle mass and function, which will in turn lead to glucose metabolism disorders, reduced physical activity and the risk of diabetes. However, the specific mechanism involved has not been thoroughly studied. Therefore, this review aims to explore the pathophysiology and therapeutic strategy related to sarcopenia and diabetes and provide insight for future investigations, which is of great significance for improving the quality of life in the elderly with diabetes and concurrently reducing the incidence of related complications.
Mettl3 Mediated m6A Methylation Involved in Epithelial-Mesenchymal Transition by Targeting SOCS3/STAT3/SNAI1 in Cigarette Smoking-Induced COPD

Purpose Persistent inflammation and epithelial-mesenchymal transition are essential pathophysiological processes in chronic obstructive pulmonary disease (COPD) and involve airway remodeling. m6A methylation modification was discovered to play an important role in various diseases. Nevertheless, the regulatory role of m6A methylation has not yet been investigated in cigarette smoking-induced COPD. The study aims to explore the regulatory role of m6A methylation in cigarette smoking-induced COPD. Patients and Methods In this study, two Gene Expression Omnibus (GEO) datasets were first utilized to analyze the expression profiles of m6A RNA methylation regulators in COPD. We then established a cell model of COPD by exposing human bronchial epithelial cells (HBECs) to cigarette smoke extract (CSE) in vitro and detected the expression of m6A writer Mettl3 and EMT phenotype markers. RNA interference, cycloleucine, RT-qPCR, western blot, MeRIP-sequencing, and cell migration assay were performed to investigate the potential effect of Mettl3 on the EMT process in CSE-induced HBECs. Results Our results showed that Mettl3 expression was significantly elevated in cigarette smoking-induced COPD patients and in a cellular model of COPD. Furthermore, Mettl3 silence and cycloleucine treatment inhibited the EMT process of HBECs caused by CSE. Mechanically, Mettl3 silence weakens the m6A methylation of SOCS3 mRNA to enhance the protein expression of SOCS3, inhibiting CSE-induced SOCS3/STAT3/SNAI1 signaling and EMT processes in HBECs. Conclusion Our study inferred that Mettl3-mediated m6A RNA methylation modification modulates CSE-induced EMT by targeting SOCS3 mRNA and ultimately serves as a crucial regulator in the emergence of COPD. This conclusion reinforces the regulatory role of m6A methylation in COPD.
The Hybrids Algorithm Based on Fuzzy Cognitive Map for Fuzzy Time Series Prediction

Fuzzy logic relationship groups are crucial to fuzzy time series prediction. In generally, fuzzy logic relationship groups can be constructed by manually mining fuzzy logic relationship between adjacent data in time series. However, for the large-scale or long-term time series, the way of manually constructing fuzzy logical relationship groups is difficult and infeasible. In this paper, a hybrids prediction algorithm based on Fuzzy Cognitive Map (FCM) is proposed, in which fuzzy c-means clustering algorithm is used to construct the framework of FCM and genetic algorithm (GA) is applied to learn weights of FCM. Finally, a fully learned fuzzy cognitive map is used to represent, store fuzzy logic relationships of fuzzy time series and realize prediction. A benchmark time series — the enrollments of University of Alberta time series is applied to validate the feasibility and effectiveness of the proposed algorithm, whose results show that the proposed prediction algorithm based on FCM is effective and can obtain the satisfactory prediction precision. It is a potential virtue that the proposed algorithm can automatically process the prediction problem of the large-scale or long-term time series.