Miracle-dz/UniFAR-FTUs huggingface dataset:
Scattered data quasi‐interpolation on spheres

This paper studies the construction and approximation of quasi‐interpolation for spherical scattered data. First of all, a kind of quasi‐interpolation operator with Gaussian kernel is constructed to approximate the spherical function, and two Jackson type theorems are established. Second, the classical Shepard operator is extended from Euclidean space to the unit sphere, and the error of approximation by the spherical Shepard operator is estimated. Finally, the compact supported kernel is used to construct quasi‐interpolation operator for fitting spherical scattered data, where the spherical modulus of continuity and separation distance of scattered sampling points are employed as the measurements of approximation error. Copyright © 2014 John Wiley & Sons, Ltd.
Green magnetite (Fe3O4): Unusual optical Mie scattering and magnetic isotropy of submicron-size hollow spheres

Magnetite hollow spheres with a diameter of 650 nm and shell thickness of 40 nm exhibit an unexpected green color, in contrast with usual magnetite solids appearing black. This unusual green color is interpreted in terms of the Mie scattering on the inhomogeneous and low-density structure of the hollow spheres with a characteristic diameter that is comparable to the wavelength of the visible light. Further, thin films of the hollow spheres exhibit no two-dimensional shape anisotropy, reflecting the isotropic structure of individual particles. These results indicate the potential advantages in achieving unique physical properties through constructing hollow structures.
An information flow among industry sectors in the Korean stock market

We investigate the information flow among 22 industry sectors in the Korean stock market by using the symbolic transfer entropy (STE) method. We consider the daily index of 22 industry sectors in the Korean Composite Stock Price Index (KOSPI) from January 3, 2000 to March 30, 2012. We measure the degree of asymmetry in the information flow and the amount of information flow among the industry sectors before, during, and after the subprime crisis in order to analyze how to relate them to the market crisis. We find that the amount of information flow and the number of connectedness during the financial crisis in the Korean stock market are higher than those before and after the market crisis. In addition, we find the role of the insurance sector, which is related to risk management, increases as information source after the crisis.
Experimental design and performance analysis of alumina coatings deposited by a detonation spray process

The increasing demands for high-quality coatings has made it inevitable that the surface coating industry would put more effort into precisely controlling the coating process. Statistical design of experiments is an effective method for finding the optimum spray parameters to enhance thermal spray coating properties. In the present investigation, an attempt is made to produce high-quality alumina (Al2O3) coatings by optimizing the detonation spray process parameters following a (L16-24) factorial design approach. The process parameters that were varied include the fuel ratio, carrier gas flow rate, frequency of detonations and spray distance. The coating characteristics were quantified with respect to roughness, hardness and porosity. The performance of the coatings was quantitively evaluated using erosion, abrasion and sliding wear testing. Through statistical analysis of the experimental results, performed by the ANOVA method, the significance of each process parameter together with an optimal variable combination was obtained for the desired coating attributes. Confirmation experiments were conducted to verify the optimal spray parameter combination, which clearly showed the possibility of producing high-quality Al2O3 coatings.
The REBMIX Algorithm for the Multivariate Finite Mixture Estimation

The article extends the REBMIX to multivariate data. Random variables may follow normal, lognormal, or Weibull parametric families and should be independent within components. The initial weights and component parameters are not required. Preprocessing of observations folows the histogram, Parzen window, or k-nearest neighbor approach. The number of components, weights, and component parameters are gained iteratively by using information measures of the distance, such as the total of positive relative deviations and the information criterion. The number of classes or the number of the nearest neighbors can be optimized, as well. The REBMIX software is available on http://www.fs. uni-lj.si/lavek.
Product partition latent variable model for multiple change-point detection in multivariate data

The product partition model (PPM) is a well-established efficient statistical method for detecting multiple change points in time-evolving univariate data. In this article, we refine the PPM for the purpose of detecting multiple change points in correlated multivariate time-evolving data. Our model detects distributional changes in both the mean and covariance structures of multivariate Gaussian data by exploiting a smaller dimensional representation of correlated multiple time series. The utility of the proposed method is demonstrated through experiments on simulated and real datasets.
Ab initio theory of magnetic-field-induced odd-frequency two-band superconductivity in MgB 2

We develop the anisotropic Eliashberg framework for superconductivity in the presence of an applied magnetic field. Using as input the ab initio calculated electron and phonon band structures and electron-phonon coupling, we solve self-consistently the anisotropic Eliashberg equations for the archetypal superconductor ${\mathrm{MgB}}_{2}$. We find two self-consistent solutions, time-even two-band superconductivity, as well as unconventional time-odd $s$-wave spin triplet two-band superconductivity emerging with applied field. We provide the full momentum, frequency, and spin-resolved dependence and magnetic field-temperature phase diagrams of the time-even and time-odd superconducting pair amplitudes and predict fingerprints of this novel odd-frequency state in tunneling experiments.
Computationally efficient methods for two multivariate fractionally integrated models

Abstract. We discuss two distinct multivariate time‐series models that extend the univariate ARFIMA (autoregressive fractionally integrated moving average) model. We discuss the different implications of the two models and describe an extension to fractional cointegration. We describe algorithms for computing the covariances of each model, for computing the quadratic form and approximating the determinant for maximum likelihood estimation and for simulating from each model. We compare the speed and accuracy of each algorithm with existing methods individually. Then, we measure the performance of the maximum likelihood estimator and of existing methods in a Monte Carlo. These algorithms are much more computationally efficient than the existing algorithms and are equally accurate, making it feasible to model multivariate long memory time series and to simulate from these models. We use maximum likelihood to fit models to data on goods and services inflation in the United States.
A frontal computation scheme for the Schur algorithm to efficiently solve large boundary-element problems

The Schur algorithm provides an approximate inverse for partially specified staircase band matrices. The authors develop a frontal computation scheme for the Schur algorithm that achieves an O(b/sup 2/) space bound, where b is the width of the staircase band. This property allows certain classes of boundary element problems, such as those that occur with VLSI capacitance modeling, to be solved in linear time and using constant memory. Such an application is described and experimental results are given that illustrate the efficiency and practicality of the new algorithm. >
Model reduction based on modal Hankel singular values

This paper presents a model reduction method based on modal coordinates and Modal Hankel Singular Values (MHSV). Model reduction has recently become one of the main topics among many numerical engineers, since a reduced model increases its flexibility and adaptability in synchronizing with other models in many numerical multi-physics applications. On the other hand, a full numerical model from various FEM/BEM tools has pin-point modeling accuracy over their modeling domain. The proposed model reduction method reduces the full size model to a smaller size while maintaining the modeling accuracy. The original model reduction theory is established based on state space model that describes a linear dynamic system as a first order differential matrix equation. The magnitude of each state variable is measured by the Hankel Singular Value (HSV), and the reduced model has state variables with large HSVs. In this paper, the model is described using modal coordinates system instead of state space, since most dynamic system is described as a second order system rather than first order. A modal Hankel singular value for each mode is introduced to measure the magnitude of the mode. A numerical example of coupled acoustic and piezoelectric models is included.
Frailty increases the risk of adverse outcomes among 38,950 UK Biobank participants with prediabetes: A prospective cohort study

Background: We aimed to systematically evaluate the associations of frailty, a simple health indicator, with risks of multiple adverse outcomes in late life among adults with prediabetes. Methods: We evaluated 38,950 adults aged 40-64 years with prediabetes from the baseline survey of the UK Biobank. Frailty was assessed using the frailty phenotype (FP, 0-5), and participants were grouped into non-frail (FP =0), pre-frail (1[≤] FP [≤]2), and frail (FP [≥]3). Multiple health outcomes were ascertained during a median follow-up of 12 years. Cox proportional hazards regression models were used to estimate the associations. Results: At baseline, 49.1% and 5.9% of adults with prediabetes were identified as pre-frail and frail, respectively. Both pre-frailty and frailty were associated with higher risks of multiple adverse outcomes in adults with prediabetes (P for trend <0.001). For instance, compared with their non-frail counterparts, frail participants with prediabetes had a significantly higher risk (P <0.001) of type 2 diabetes mellitus (T2DM) (hazard ratio [HR]: 1.73), diabetes-related microvascular disease (HR: 1.89), cardiovascular disease (HR: 1.66), chronic kidney disease (HR: 1.76), eye disease (HR: 1.31), dementia (HR: 2.03), depression (HR: 3.01), and all-cause mortality (HR: 1.81) in the multivariable-adjusted models. Furthermore, with each 1-point increase in FP score, the risk of these adverse outcomes increased by 10% to 42%. Conclusions: In UK adults with prediabetes, both pre-frailty and frailty are significantly associated with higher risks of multiple adverse outcomes, including T2DM, diabetes-related diseases, and all-cause mortality. Our findings suggest that frailty assessment should be incorporated into the routine care for middle-aged adults with prediabetes, to improve the allocation of healthcare resources and reduce diabetes-related burdens.
Faster finite-difference time-domain method using moving spatial boundaries

The finite-difference time-domain (FD-TD) method is augmented with the moving spatial boundaries which define the computation domain. In cases where the simulated electromagnetic field is zero for some time over part of the spatial domain studied, savings in computation time can be realised. Savings of 50 and 25% have been achieved for transmission and reflection analyses, respectively, in an example concerning coplanar waveguide.
xTrimoABFold: De novo Antibody Structure Prediction without MSA

In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2, a breakthrough in the field of structural biology, provides a solution to predict protein structure based on protein sequences and computationally expensive coevolutionary multiple sequence alignments (MSAs). However, the computational efficiency and undesirable prediction accuracy of antibodies, especially on the complementarity-determining regions (CDRs) of antibodies limit their applications in the industrially high-throughput drug design. To learn an informative representation of antibodies, we employed a deep antibody language model (ALM) on curated sequences from the observed antibody space database via a transformer model. We also developed a novel model named xTrimoABFold to predict antibody structure from antibody sequence based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss. xTrimoABFold outperforms AlphaFold2 and other protein language model based SOTAs, e.g., OmegaFold, HelixFold-Single, and IgFold with a large significant margin (30+\% improvement on RMSD) while performing 151 times faster than AlphaFold2. To the best of our knowledge, xTrimoABFold achieved state-of-the-art antibody structure prediction. Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
The IGRINS YSO Survey. III. Stellar Parameters of Pre-main-sequence Stars in Ophiuchus and Upper Scorpius

We used the Immersion GRating Infrared Spectrometer (IGRINS) to determine fundamental parameters for 61 K- and M-type young stellar objects (YSOs) located in the Ophiuchus and Upper Scorpius star-forming regions. We employed synthetic spectra and a Markov chain Monte Carlo approach to fit specific K-band spectral regions and determine the photospheric temperature (T), surface gravity ( logg ), magnetic field strength (B), projected rotational velocity ( vsini ), and K-band veiling (r K ). We determined B for ∼46% of our sample. Stellar parameters were compared to the results from Taurus-Auriga and the TW Hydrae association presented in Paper I of this series. We classified all the YSOs in the IGRINS survey with infrared spectral indices from Two Micron All Sky Survey and Wide-field Infrared Survey Explorer photometry between 2 and 24 μm. We found that Class II YSOs typically have lower logg and vsini , similar B, and higher K-band veiling than their Class III counterparts. Additionally, we determined the stellar parameters for a sample of K and M field stars also observed with IGRINS. We have identified intrinsic similarities and differences at different evolutionary stages with our homogeneous determination of stellar parameters in the IGRINS YSO survey. Considering logg as a proxy for age, we found that the Ophiuchus and Taurus samples have a similar age. We also find that Upper Scorpius and TWA YSOs have similar ages, and are more evolved than Ophiuchus/Taurus YSOs.
Exploring non-residential technology adoption: an empirical analysis of factors associated with the adoption of photovoltaic systems by municipal authorities in Germany

This research article explores potential influencing factors of solar photovoltaic (PV) system adoption by municipal authorities in Germany in the year 2019. We derive seven hypothesized relationships from the empirical literature on residential PV adoption, organizational technology adoption, and sustainability policy adoption by local governments, and apply a twofold empirical approach to examine them. First, we explore the associations of a set of explanatory variables on the installed capacity of adopter municipalities (N=223) in an OLS model. Second, we use a logit model to analyze whether the identified relationships are also apparent between adopter and non-adopter municipalities (N=423). Our findings suggest that fiscal capacity (measured by per capita debt and per capita tax revenue) and peer effects (measured by the pre-existing installed capacity) are positively associated with both the installed capacity and adoption. Furthermore, we find that institutional capacity (measured by the presence of a municipal utility) and environmental concern (measured by the share of green party votes) are positively associated with municipal PV adoption. Economic factors (measured by solar irradiation) show a significant positive but small effect in both regression models. No evidence was found to support the influence of political will. Results for the role of municipal characteristics are mixed, although the population size was consistently positively associated with municipal PV adoption and installed capacity. Our results support previous studies on PV system adoption determinants and offer a starting point for additional research on non-residential decision-making and PV adoption.
Parasitic-Free Gate: An Error-Protected Cross-Resonance Switch in Weakly Tunable Architectures

In two-qubit gates activated by microwave pulses, by turning pulse on or off, the state of qubits are swapped between entangled or idle modes. In either mode, the presence of stray couplings makes qubits accumulate coherent phase error. However, the error rates in the two modes differ because qubits carry different stray coupling strengths in each mode; therefore, eliminating stray coupling from one mode does not remove it from the other. We propose to combine such a gate with a tunable coupler and show that both idle and entangled qubits can become free from stray couplings. This significantly increases the operational switch fidelity in quantum algorithms. We further propose a weakly-tunable qubit as an optimum coupler to bring the two modes parametrically near each other. This remarkably enhances the tuning process by reducing its leakage.
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization

Knowledge Distillation (KD) has been extensively used for natural language understanding (NLU) tasks to improve a small model's (a student) generalization by transferring the knowledge from a larger model (a teacher). Although KD methods achieve state-of-the-art performance in numerous settings, they suffer from several problems limiting their performance. It is shown in the literature that the capacity gap between the teacher and the student networks can make KD ineffective. Additionally, existing KD techniques do not mitigate the noise in the teacher's output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. We propose a new KD method that addresses these problems and facilitates the training compared to previous techniques. Inspired by continuation optimization, we design a training procedure that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. Our method (Continuation-KD) achieves state-of-the-art performance across various compact architectures on NLU (GLUE benchmark) and computer vision tasks (CIFAR-10 and CIFAR-100).
Regression with Label Differential Privacy

We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained privately, we derive a label DP randomization mechanism that is optimal under a given regression loss function. We prove that the optimal mechanism takes the form of a"randomized response on bins", and propose an efficient algorithm for finding the optimal bin values. We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm.
Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network

Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications. This work presents a novel algorithm for fingerphoto verification using a nested residual block - Finger-NestNet. The proposed Finger-NestNet architecture is designed with three consecutive convolution blocks followed by a series of nested residual blocks to achieve reliable fingerphoto verification. This paper also presents the interpretability of the proposed method using four different visualization techniques that can shed light on the critical regions in the fingerphoto biometrics that can contribute to the reliable verification performance of the proposed method. Extensive experiments are performed on the fingerphoto dataset comprised of 196 unique fingers collected from 52 unique data subjects using an iPhone6S. Experimental results indicate the improved verification of the proposed method compared to six different existing methods with EER = 1.15%.
AliCHI: A Large-scale Multi-modal Dataset and Automated Evaluation Tool for Human-like Dialogue Systems

A well-designed interactive human-like dialogue system is expected to take actions (e.g. smiling) and respond in a pattern similar to humans. However, due to the limitation of single-modality (only speech) or small volume of currently public datasets, most dialogue systems can only respond in speech and cannot take human-like actions. In this work, we build a large-scale multi-modal dataset of human-to-human conversation in a face-to-face fashion, with fine-grained annotations. The raw data in video format contains 635 dialogue sessions, being collected from 200 participants on designed topics and lasting 52 hours in total. Moreover, we manually annotated the verbal and non-verbal behaviors in each dialogue session on their start/end timestamp. Furthermore, we developed a corresponding evaluation tool for human-like dialogue systems to automatically evaluates the accuracy of two basic tasks, turn-taking prediction, and backchannel prediction, on both time and content. We have opened the data, the tools will be released at the conference.
Note on Cyclotomic Units and Gauss Sums in Local Cyclotomic Fields

the Hilbert norm residue symbol at p. There are canonical subgroups Cn and Gn of Un generated by certain cyclotomic units and certain Gauss sums, respectively. (For the definition of Gn , see [8, Section 5.1].) We first show that these subgroups are ``in a general position'' with respect to the above pairing for all n. Namely, we show that the index (Cn , Gn)n in +pn+1 is bounded as n (Theorem 1). Let An be the Sylow p-subgroup of the ideal class group of Kn , and
GPU-accelerated Guided Source Separation for Meeting Transcription

Guided source separation (GSS) is a type of target-speaker extraction method that relies on pre-computed speaker activities and blind source separation to perform front-end enhancement of overlapped speech signals. It was first proposed during the CHiME-5 challenge and provided significant improvements over the delay-and-sum beamforming baseline. Despite its strengths, however, the method has seen limited adoption for meeting transcription benchmarks primarily due to its high computation time. In this paper, we describe our improved implementation of GSS that leverages the power of modern GPU-based pipelines, including batched processing of frequencies and segments, to provide 300x speed-up over CPU-based inference. The improved inference time allows us to perform detailed ablation studies over several parameters of the GSS algorithm -- such as context duration, number of channels, and noise class, to name a few. We provide end-to-end reproducible pipelines for speaker-attributed transcription of popular meeting benchmarks: LibriCSS, AMI, and AliMeeting. Our code and recipes are publicly available: https://github.com/desh2608/gss.