Miracle-dz/UniFAR-FTUs huggingface dataset:
The pursuit of the Hubble Constant using Type II Supernovae

The use of multiple independent methods with their own systematic uncertainties is crucial for resolving the ongoing tension between local and distant measurements of the Hubble constant ($H_{0}$). While type Ia supernovae (SNe Ia) have historically been the most widely used distance indicators, recent studies have shown that type II supernovae (SNe II) can provide independent measurements of extragalactic distances with different systematic uncertainties. Unlike SNe Ia, the progenitors of SNe II are well understood, arising from the explosion of red supergiants in late-type galaxies via core-collapse. While SNe II do not exhibit the same level of uniformity in peak luminosity as SNe Ia, their differences can be calibrated using theoretical or empirical methods. Overall, this chapter presents a comprehensive overview of the use of SNe II as extragalactic distance indicators, with a particular focus on their application to measuring $H_0$ and addressing the Hubble tension. We describe the underlying theory of each method, discuss the challenges associated with them, including uncertainties in the calibration of the supernova absolute magnitude, and present a comprehensive list of the most updated Hubble constant measurements.
Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as"Therapeutic or Preventive Procedure. "Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts.
Cross-Domain Policy Adaptation via Value-Guided Data Filtering

Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. For example, a robot learns the policy in a simulator, but when it is deployed in the real world, the dynamics of the environment may be different. Given the source and target domain with dynamics mismatch, we consider the online dynamics adaptation problem, in which case the agent can access sufficient source domain data while online interactions with the target domain are limited. Existing research has attempted to solve the problem from the dynamics discrepancy perspective. In this work, we reveal the limitations of these methods and explore the problem from the value difference perspective via a novel insight on the value consistency across domains. Specifically, we present the Value-Guided Data Filtering (VGDF) algorithm, which selectively shares transitions from the source domain based on the proximity of paired value targets across the two domains. Empirical results on various environments with kinematic and morphology shifts demonstrate that our method achieves superior performance compared to prior approaches.
Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds

Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms. Follow-the-regularized-leader (FTRL) has recently emerged as one of the most promising approaches for obtaining various types of adaptivity in bandit problems. Aiming to further generalize this adaptivity, we develop a generic adaptive learning rate, called stability-penalty-adaptive (SPA) learning rate for FTRL. This learning rate yields a regret bound jointly depending on stability and penalty of the algorithm, into which the regret of FTRL is typically decomposed. With this result, we establish several algorithms with three types of adaptivity: sparsity, game-dependency, and best-of-both-worlds (BOBW). Despite the fact that sparsity appears frequently in real problems, existing sparse multi-armed bandit algorithms with $k$-arms assume that the sparsity level $s \leq k$ is known in advance, which is often not the case in real-world scenarios. To address this issue, we first establish $s$-agnostic algorithms with regret bounds of $\tilde{O}(\sqrt{sT})$ in the adversarial regime for $T$ rounds, which matches the existing lower bound up to a logarithmic factor. Meanwhile, BOBW algorithms aim to achieve a near-optimal regret in both the stochastic and adversarial regimes. Leveraging the SPA learning rate and the technique for $s$-agnostic algorithms combined with a new analysis to bound the variation in FTRL output in response to changes in a regularizer, we establish the first BOBW algorithm with a sparsity-dependent bound. Additionally, we explore partial monitoring and demonstrate that the proposed SPA learning rate framework allows us to achieve a game-dependent bound and the BOBW simultaneously.
Dynamic User Segmentation and Usage Profiling

Usage data of a group of users distributed across a number of categories, such as songs, movies, webpages, links, regular household products, mobile apps, games, etc. can be ultra-high dimensional and massive in size. More often this kind of data is categorical and sparse in nature making it even more difficult to interpret any underlying hidden patterns such as clusters of users. However, if this information can be estimated accurately, it will have huge impacts in different business areas such as user recommendations for apps, songs, movies, and other similar products, health analytics using electronic health record (EHR) data, and driver profiling for insurance premium estimation or fleet management. In this work, we propose a clustering strategy of such categorical big data, utilizing the hidden sparsity of the dataset. Most traditional clustering methods fail to give proper clusters for such data and end up giving one big cluster with small clusters around it irrespective of the true structure of the data clusters. We propose a feature transformation, which maps the binary-valued usage vector to a lower dimensional continuous feature space in terms of groups of usage categories, termed as covariate classes. The lower dimensional feature representations in terms of covariate classes can be used for clustering. We implemented the proposed strategy and applied it to a large sized very high-dimensional song playlist dataset for the performance validation. The results are impressive as we achieved similar-sized user clusters with minimal between-cluster overlap in the feature space (8%) on average). As the proposed strategy has a very generic framework, it can be utilized as the analytic engine of many of the above-mentioned business use cases allowing an intelligent and dynamic personal recommendation system or a support system for smart business decision-making.
A numerical study of the relaxation and breakup of an elongated drop in a viscous liquid

The relaxation and breakup of an elongated droplet in a viscous and initially quiescent fluid is studied by solving the full Navier–Stokes equations using a three-dimensional finite volume method coupled with a moving mesh interface tracking (MMIT) scheme to locate the interface. The two fluids are assumed incompressible and immiscible. The interface is represented as a surface triangle mesh with zero thickness that moves with the fluid. Therefore, the jump and continuity conditions across the interface are implemented directly, without any smoothing of the fluid properties. Mesh adaptations on a tetrahedral mesh are employed to permit large deformation and to capture the changing curvature. Mesh separation is implemented to allow pinch-off. The detailed investigations of the relaxation and breakup process are presented in a more general flow regime compared to the previous works by Stone & Leal (J. Fluid Mech., vol. 198, 1989, p. 399) and Tong & Wang (Phys. Fluids, vol. 19, 2007, 092101), including the flow field of the both phases. The simulation results reveal that the vortex rings due to the interface motion and the conservation of mass play an important role in the relaxation and pinch-off process. The vortex rings are created and collapsed during the process. The effects of viscosity ratio, density ratio and length ratio on the relaxation and breakup are studied. The simulations indicate that the fluid velocity field and the neck shape are distinctly different for viscosity ratios larger and smaller than O(1), and thus a different end-pinching mechanism is observed for each regime. The length ratio also significantly affects the relaxation process and the velocity distributions, but not the neck shape. The influence of the density ratio on the relaxation and breakup process is minimal. However, the droplet evolution is retarded due to the large density of the suspending flow. The formation of a satellite droplet is observed, and the volume of the satellite droplet depends strongly on the length ratio and the viscosity ratio.
Transfer Learning for Power Outage Detection Task with Limited Training Data

Early detection of power outages is crucial for maintaining a reliable power distribution system. This research investigates the use of transfer learning and language models in detecting outages with limited labeled data. By leveraging pretraining and transfer learning, models can generalize to unseen classes. Using a curated balanced dataset of social media tweets related to power outages, we conducted experiments using zero-shot and few-shot learning. Our hypothesis is that Language Models pretrained with limited data could achieve high performance in outage detection tasks over baseline models. Results show that while classical models outperform zero-shot Language Models, few-shot fine-tuning significantly improves their performance. For example, with 10% fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5% accuracy (+8.5%). This has practical implications for analyzing and localizing outages in scenarios with limited data availability. Our evaluation provides insights into the potential of few-shot fine-tuning with Language Models for power outage detection, highlighting their strengths and limitations. This research contributes to the knowledge base of leveraging advanced natural language processing techniques for managing critical infrastructure.
Initial growth of a disturbance in a boundary layer influenced by a circular cylinder wake

Abstract The initial growth of a disturbance induced by a near-wall circular cylinder in a flat-plate boundary layer is experimentally investigated using both particle image velocimetry and hydrogen bubble visualization techniques. The secondary spanwise vortices appear in the near-wall region as a direct response to the outside passing wake vortices, consistent with previous studies on similar models (Pan et al., J. Fluid Mech., vol. 603, 2008, pp. 367–389; Mandal & Dey, J. Fluid Mech., vol. 684, 2011, pp. 60–84). The streamwise variation of the total disturbance energy within the boundary layer shows a two-stage growth, which characterizes the initial transition process: the first exponential growth stage, followed by a region with slower growth rate. It is revealed that these two stages of growth are related to the formation and the destabilization of the secondary vortex in the near-wall region. The technique of dynamic mode decomposition is used to decompose the total disturbance into temporally orthogonalized modes, and it shows that the first growth stage largely results from the increased disturbance at the same frequency as that of the wake vortex shedding, while the second growth stage comprises the disturbance growth in a number of frequencies, especially the lower ones.
New in, old out: Does learning a new language make you forget previously learned foreign languages?

Anecdotal evidence suggests that learning a new foreign language (FL) makes you forget previously learned FLs. To seek empirical evidence for this claim, we tested whether learning words in a previously unknown L3 hampers subsequent retrieval of their L2 translation equivalents. In two experiments, Dutch native speakers with knowledge of English (L2), but not Spanish (L3), first completed an English vocabulary test, based on which 46 participant-specific, known English words were chosen. Half of those were then learned in Spanish. Finally, participants’ memory for all 46 English words was probed again in a picture naming task. In Experiment 1, all tests took place within one session. In Experiment 2, we separated the English pre-test from Spanish learning by a day and manipulated the timing of the English post-test (immediately after learning vs. 1 day later). By separating the post-test from Spanish learning, we asked whether consolidation of the new Spanish words would increase their interference strength. We found significant main effects of interference in naming latencies and accuracy: Participants speeded up less and were less accurate to recall words in English for which they had learned Spanish translations, compared with words for which they had not. Consolidation time did not significantly affect these interference effects. Thus, learning a new language indeed comes at the cost of subsequent retrieval ability in other FLs. Such interference effects set in immediately after learning and do not need time to emerge, even when the other FL has been known for a long time.
Numerical approach based on the composition of one-step time-integration schemes for highly deformable interfaces

In this work, we propose a numerical approach for simulations of large deformations of interfaces in a level set framework. To obtain a fast and viable numerical solution in both time and space, temporal discretization is based on the composition of one-step methods exhibiting higher orders and stability, especially in the case of stiff problems with strongly oscillatory solutions. Numerical results are provided in the case of ordinary and partial differential equations to show the main features and demonstrate the performance of the method. Convergence properties and efficiency in terms of computational cost are also investigated.
Unsupervised machine learning for identifying phase transition using two-times clustering

In recent years, developing unsupervised machine learning for identifying phase transition is a research direction. In this paper, we introduce a two-times clustering method that can help select perfect configurations from a set of degenerate samples and assign the configuration with labels in a manner of unsupervised machine learning. These perfect configurations can then be used to train a neural network to classify phases. The derivatives of the predicted classification in the phase diagram, show peaks at the phase transition points. The effectiveness of our method is tested for the Ising, Potts, and Blume-Capel models. By using the ordered configuration from two-times clustering, our method can provide a useful way to obtain phase diagrams.
RL + Model-Based Control: Using On-Demand Optimal Control to Learn Versatile Legged Locomotion

This letter presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference motions generated through finite-horizon optimal control, covering a broad range of velocities and gaits. These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability. Furthermore, by utilizing realistic simulation data that captures whole-body dynamics, RL effectively overcomes the inherent limitations in reference motions imposed by modeling simplifications. We validate the robustness and controllability of the RL training process within our framework through a series of experiments. In these experiments, our method showcases its capability to generalize reference motions and effectively handle more complex locomotion tasks that may pose challenges for the simplified model, thanks to RL's flexibility. Additionally, our framework effortlessly supports the training of control policies for robots with diverse dimensions, eliminating the necessity for robot-specific adjustments in the reward function and hyperparameters.
Comparative safety of sodium-glucose co-transporter 2 inhibitors in elderly patients with type 2 diabetes mellitus and diabetic kidney disease: a systematic review and meta-analysis

Abstract The safety of sodium-glucose co-transporter 2 (SGLT2) inhibitors in elderly patients with diabetic kidney disease (DKD) is still controversial. This study aimed to analyze the safety of SGLT2 inhibitors in elderly patients with type 2 diabetes mellitus (T2DM) and DKD. We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library from inception to March 2023. Randomized controlled trials (RCTs) were included. Data including patient characteristics and interesting outcomes were extracted, and the dichotomous data and continuous variables were evaluated using risk ratio (RR) with 95% confidence intervals (CIs) and mean difference (MD) with 95% CIs, respectively. A total of 14 RCTs with 59874 participants were finally included. There were 38,252 males (63.9%) and 21,622 females (36.1%). The patients’ mean age was > 64.6 years. SGLT2 inhibitors could delay the further decline of estimated glomerular filtration rate (eGFR) when eGFR ≥ 60 ml/min/1.73m2 (MD: 2.36; 95%CI [1.15–3.57]). SGLT2 inhibitors in elderly patients with eGFR < 60 ml/min/1.73m2 (RR: 0.86; 95%CI [0.67–1.11]) may have a relatively increased risk of acute kidney injury compared to eGFR ≥ 60 ml/min/1.73m2. SGLT2 inhibitors increased the incidence of genital mycotic infections (RR: 3.47; 95%CI [2.97–4.04]) and diabetic ketoacidosis (RR: 2.25; 95%CI [1.57–3.24]). Except for genital mycotic infections and diabetic ketoacidosis, other adverse reactions were few, indicating that SGLT2 inhibitors are relatively safe for elderly patients with T2DM and DKD. Safety and renoprotection may be diminished when SGLT2 inhibitors are used in elderly patients with eGFR < 60 ml/min/1.73m2.
Pharmacokinetic and toxicological prediction of the chemical constituents of the essential oil of the leaves of Croton heliotropiifolius Kunth

ABSTRACT The development of new drugs through studies of candidate molecules is a complex undertaking; however, computational or in silico approaches aimed at optimizing molecules with greater development potential are being utilized for predictions of pharmacokinetic properties such as absorption, distribution, metabolism and excretion (ADME) as well as toxicological parameters. The objective of this study was to examine in silico and in vivo pharmacokinetic and toxicological properties of the chemical constituents present in the essential oil of Croton heliotropiifolius Kunth leaves. The following Pubchem platform as well as Software SwissADME and PreADMET software were employed for in silico studies while micronucleus (MN) testing for in vivo determination of mutagenicity, using Swiss adult male Mus musculus mice. In silico findings demonstrated that all chemical constituents presented (1) high oral absorption (2) medium cellular permeability and (3) high blood brain permeability. As for toxicity, these chemical constituents exhibited low to medium risk of occurrence of cytotoxicity. Regarding in vivo evaluation, peripheral blood samples obtained from animals tested with the oil showed no significant differences in number of MN compared to negative controls. Data indicate that further investigations are necessary to corroborate the findings of this study. Our data suggest that essential oil extracted from Croton heliotropiifolius Kunth leaves may serve as a candidate for new drug development.
High-Order Accurate Solutions to the Helmholtz Equation in the Presence of Boundary Singularities

Abstract : Problems of time-harmonic wave propagation arise in important fields of study such as geological surveying, radar detection/evasion, and aircraft design. These often involve high-frequency waves, which demand high-order methods to mitigate the dispersion error. We propose a high-order method for computing solutions to the variable-coefficient inhomogeneous Helmholtz equation in two dimensions on domains bounded by piecewise smooth curves of arbitrary shape with a finite number of boundary singularities at known locations. We utilize compact finite difference (FD) schemes on regular structured grids to achieve high order accuracy due to their efficiency and simplicity, as well as the capability to approximate variable-coefficient differential operators. In this work, a 4th-order compact FD scheme for the variable-coefficient Helmholtz equation on a Cartesian grid in 2D is derived and tested. The well known limitation of finite differences is that they lose accuracy when the boundary curve does not coincide with the discretization grid, which is a severe restriction on the geometry of the computational domain. Therefore, the algorithm presented in this work combines high-order FD schemes with the method of difference potentials (DP), which retains the efficiency of FD while allowing for boundary shapes that are not aligned with the grid without sacrificing the accuracy of the FD scheme.
Geographic variability in limited English proficiency: A cross-cultural study of cognitive profiles

Abstract Objective: This study was designed to evaluate the effect of limited English proficiency (LEP) on neurocognitive profiles. Method: Romanian (LEP-RO; n = 59) and Arabic (LEP-AR; n = 30) native speakers were compared to Canadian native speakers of English (NSE; n = 24) on a strategically selected battery of neuropsychological tests. Results: As predicted, participants with LEP demonstrated significantly lower performance on tests with high verbal mediation relative to US norms and the NSE sample (large effects). In contrast, several tests with low verbal mediation were robust to LEP. However, clinically relevant deviations from this general pattern were observed. The level of English proficiency varied significantly within the LEP-RO and was associated with a predictable performance pattern on tests with high verbal mediation. Conclusions: The heterogeneity in cognitive profiles among individuals with LEP challenges the notion that LEP status is a unitary construct. The level of verbal mediation is an imperfect predictor of the performance of LEP examinees during neuropsychological testing. Several commonly used measures were identified that are robust to the deleterious effects of LEP. Administering tests in the examinee’s native language may not be the optimal solution to contain the confounding effect of LEP in cognitive evaluations.