Miracle-dz/UniFAR-FTUs huggingface dataset:
Global radiation-hydrodynamics simulations of red supergiant stars

The small-scale surface granulation on cool main-sequence stars and white dwarfs influences the overall appearance of these objects only weakly. And it is only indirectly observable by analyzing e.g. line-shapes or temporal fluctuations – except for the Sun. The large-scale and high-contrast convective surface cells and accompanying sound waves on supergiants and low-gravity AGB stars on the other hand have a strong impact on the outer atmospheric layers and are directly detectable by interferometric observations. Necessary to interpret modern observations with their high resolution in frequency, time, and/or space are detailed numerical multi-dimensional time-dependent radiation-hydrodynamical simulations. Local simulations of small patches of convective surface layers and the atmosphere of main-sequence stars have matured over three decades and have reached an impressive level of agreement with observations and also between different computational codes. However, global simulations of the entire convective surface and atmosphere of a red supergiants are considerably more demanding – and limited – and have become available only for about one decade. Still, they show how the surface is shaped by the interaction of small surface granules, that sit on top of large envelope convection cells, and waves, that can travel as shocks into the outer atmosphere. The route to more complete future models will be discussed, that comprise the outer atmosphere of the stars and that could explain some of the little-understood phenomena like chromosphere, molsphere, or wind-formation.
Automatic arrangement for the bass guitar in popular music using principle component analysis

: In popular music, the drums and bass guitar create the rhythm. In 2008, we developed a drum pattern database including thousands of music excerpts to investigate the role of drums. However, no studies have been reported on using a large number of samples to investigate the role of the bass guitar in popular music. Moreover, it has been diﬃcult to generate appropriate phrases on automatic-arrangement systems. We propose a method that we have developed for identifying the bass part using Musical Instrument Digital Interface (MIDI) excerpts. In the method, each one-bar length is identiﬁed as either a bass guitar part or not, and if it is, it is appropriately named a ‘‘bass guitar pattern. ’’ The basic information of the bass guitar pattern, such as onset, interval, and dynamics proﬁles, was extracted from it and a database comprising these proﬁles was constructed in order to extract common features among patterns. We use principal component analysis (PCA) to introduce several automatic-arrangement parameters, including those in the proposed database, and call these parameters ‘‘eigenphrases of the bass guitar. ’’ We propose a method of arranging the bass guitar part to generate an appropriate pattern as a musical phrase by multiplying each of the principal component vectors from the eigenphrase of the bass guitar with relative weights. The method is conﬁrmed to be eﬀective as a means of generating bass guitar parts in a natural rather than an artiﬁcial way.
NUFFT-based fast reconstruction for sparse microwave imaging

Compressive sensing (CS) theory has been applied to sparse microwave imaging in many ways that provide better performance and significantly reduce the sampling rate. However, the computational complexity of reconstruction puts strict constraint on some practical applications with large-scale problems in radar imaging. In this paper, we propose a novel fast reconstruction scheme by realizing the traditional matched filtering with CS technique, which maintains the good performance of matched filtering with a reduced number of observations. Meanwhile, a new sparse basis is formed which offers excellent potential for reducing the computational complexity in reconstruction with fast Fourier transform (FFT) and nonuniform FFT, i.e. NUFFT, where the computational complexity can decrease from to . The feasibility and efficiency of the proposed scheme are validated as well through both numerical simulations and raw data processing results.
Solving Operator Equation Based on Expansion Approach

To date, researchers usually use spectral and pseudospectral methods for only numerical approximation of ordinary and partial differential equations and also based on polynomial basis. But the principal importance of this paper is to develop the expansion approach based on general basis functions (in particular case polynomial basis) for solving general operator equations, wherein the particular cases of our development are integral equations, ordinary differential equations, difference equations, partial differential equations, and fractional differential equations. In other words, this paper presents the expansion approach for solving general operator equations in the form , with respect to boundary condition , where , and are linear, nonlinear, and boundary operators, respectively, related to a suitable Hilbert space, is the domain of approximation, is an arbitrary constant, and is an arbitrary function. Also the other importance of this paper is to introduce the general version of pseudospectral method based on general interpolation problem. Finally some experiments show the accuracy of our development and the error analysis is presented in norm.
An analysis of the large‐scale features of the upper troposphere and the stratosphere in a global, three‐dimensional, general circulation model

The formulation of a 13–Ievel general circulation model of the troposphere and stratosphere is described and an account given of the climatology produced from an annual integration. Emphasis is placed on the interhemispheric differences in the stratosphere and the seasonal variability exhibited by its circulation. Overall, a realistic simulation of various features of the large‐scale tropospheric and stratospheric flow patterns was achieved, although a number of shortcomings are noted.
Stretching of capsules in an elongation flow, a route to constitutive law

Abstract Soft bio-microcapsules are drops bounded by a thin elastic shell made of cross-linked proteins. Their shapes and their dynamics in flow depend on their membrane constitutive law characterized by shearing and area-dilatation resistance. The deformations of such capsules are investigated experimentally in planar elongation flows and compared with numerical simulations for three bidimensional models: Skalak, neo-Hookean and generalized Hooke. An original cross-flow microfluidic set-up allows the visualization of the deformed shape in the two perpendicular main fields of view. Whatever the elongation rate, the three semi-axis lengths of the ellipsoid fitting the experimental shape are measured up to 180 % of stretching of the largest axis. The geometrical analysis in the two views is sufficient to determine the constitutive law and the Poisson ratio of the membrane without a preliminary knowledge of the shear elastic modulus $G_{s}$ . We conclude that the membrane of human serum albumin capsules obeys the generalized Hooke law with a Poisson ratio of 0.4. The shear elastic modulus is then determined by the combination of numerical and experimental variations of the Taylor parameter with the capillary number.
Biomarkers of oxidative stress in urine and plasma of operators at six Singapore printing centers and their association with several metrics of printer-emitted nanoparticle exposures

Abstract Inhalation of nanoparticles emitted from toner-based printing equipment (TPE), such as laser printers and photocopiers, also known as PEPs, has been associated with systemic inflammation, hypertension, cardiovascular disease, respiratory disorders, and genotoxicity. Global serum metabolomics analysis in 19 healthy TPE operators found 52 dysregulated biomolecules involved in upregulation of inflammation, immune, and antioxidant responses and downregulation of cellular energetics and cell proliferation. Here, we build on the metabolomics study by investigating the association of a panel of nine urinary OS biomarkers reflecting DNA/RNA damage (8OHdG, 8OHG, and 5OHMeU), protein/amino acid oxidation (o-tyrosine, 3-chlorotyrosine, and 3-nitrotyrosine), and lipid oxidation (8-isoprostane, 4-hydroxy nonenal, and malondialdehyde [MDA]), as well as plasma total MDA and total protein carbonyl (TPC), with several nanoparticle exposure metrics in the same 19 healthy TPE operators. Plasma total MDA, urinary 5OHMeU, 3-chlorotyrosine, and 3-nitrotyrosine were positively, whereas o-tyrosine inversely and statistically significantly associated with PEPs exposure in multivariate models, after adjusting for age and urinary creatinine. Urinary 8OHdG, 8OHG, 5OHMeU, and total MDA in urine and plasma had group mean values higher than expected in healthy controls without PEPs exposure and comparable to those of workers experiencing low to moderate levels of oxidative stress (OS). The highest exposure group had OS biomarker values, most notably 8OHdG, 8OHG, and total MDA, that compared to workers exposed to welding fumes and titanium dioxide. Particle number concentration was the most sensitive and robust exposure metric. A combination of nanoparticle number concentration and OS potential of fresh aerosols is recommended for larger scale future studies.
Current density dependence of electromigration failure of submicron width, multilayer Al alloy conductors

Electromigration failure of Al alloy metallization systems is one of the main concerns for integrated circuit reliability as feature sizes are reduced into the submicron regime. One of the most important aspects of electromigration failure is the current density dependence of the failure time. Many studies have been conducted for widths above 1 μm and the consensus of these studies indicates that a current density exponent, n=2, describes microstructure related failure of the metallization. There is very little data available, however, for feature sizes close to 0.5 μm, particularly for metallizations consisting of Al alloys clad by refractory layers. In this letter we investigate the current density dependence of microstructure related failure for narrow (0.6 μm) conductors with multilayer TiN refractory layers, and correlate this dependence with void formation by electromigration. We show that the current density dependence of microstructural failure in such narrow conductors differs significantly from .
Minimax Sequential Tests for Many Composite Hypotheses. II

The problem of sequential testing of many composite hypotheses is considered. Each hypothesis is described by the density function of observations that depends on a parameter from one of disjoint sets. New performance measures for one-sided and multisided sequential tests are proposed and nonasymptotical a priori lower bounds for these measures are proved. Sequential tests are found which use a minimax procedure on parametric sets for sequential likelihood ratio and are asymptotically optimal: the a priori lower bounds for performance measures are attained for these tests. All proofs are in Part II.
Vector Potential Versus Field Intensity

This paper deals with the apparent breakdown of gauge invariance in atomic-level-width formulas. Specifically, it is shown that in the analysis of the measurement od the Lamb shift the use of the $\frac{\stackrel{\ensuremath{\rightarrow}}{\mathrm{p}}\ifmmode\cdot\else\textperiodcentered\fi{}\stackrel{\ensuremath{\rightarrow}}{\mathrm{A}}}{(mc)}$ and the $\stackrel{\ensuremath{\rightarrow}}{\mathrm{E}}\ifmmode\cdot\else\textperiodcentered\fi{}\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}}$ forms of the interaction yields almost identical results for the shape of the resonance curve and equal positions for the line center. This is achieved by adding the contribution of all intermediate states to the basic three-level resonance formula. While the contribution of these intermediate states in the $\stackrel{\ensuremath{\rightarrow}}{\mathrm{E}}\ifmmode\cdot\else\textperiodcentered\fi{}\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}}$ language is negligible, it is nevertheless significant when the $\stackrel{\ensuremath{\rightarrow}}{\mathrm{p}}\ifmmode\cdot\else\textperiodcentered\fi{}\stackrel{\ensuremath{\rightarrow}}{\mathrm{A}}(mc)$ form of the interaction is used. Although this particular example would indicate that the use of the $\stackrel{\ensuremath{\rightarrow}}{\mathrm{E}}\ifmmode\cdot\else\textperiodcentered\fi{}\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}}$ form of the interaction is computationally simpler, it is shown that no general statement to this effect is permissible. A simple counter example is presented within the context of elastic photon scattering off atomic targets.
Asymptotic analysis of stochastic approximation algorithms under violated Kushner-Clark conditions with applications

Motivated by the problem of the asymptotic behavior of temporal-difference learning algorithms with nonlinear function approximation, the local almost sure asymptotic properties of stochastic approximation algorithms are analyzed for violated Kushner-Clark conditions (1978). First, the algorithms with additive noise are analyzed for the case where the noise is state-dependent. The obtained results are then applied to the analysis of the algorithms with nonadditive noise. Using these general results, the analysis of temporal-difference learning algorithms is carried out for the case of a general nonlinear function approximation and under the assumptions allowing the underlying Markov chain to be positive Harris. The general results are also illustrated by an example where the noise is nonadditive, correlated and satisfies strong mixing conditions.
AutoNMT: A Framework to Streamline the Research of Seq2Seq Models

We present AutoNMT, a framework to streamline the research of seq-to-seq models by automating the data pipeline (i.e., file management, data preprocessing, and exploratory analysis), automating experimentation in a toolkit-agnostic manner, which allows users to use either their own models or existing seq-to-seq toolkits such as Fairseq or OpenNMT, and finally, automating the report generation (plots and summaries). Furthermore, this library comes with its own seq-to-seq toolkit so that users can easily customize it for non-standard tasks.
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning

Accurate chemical sensors are vital in medical, military, and home safety applications. Training machine learning models to be accurate on real world chemical sensor data requires performing many diverse, costly experiments in controlled laboratory settings to create a data set. In practice even expensive, large data sets may be insufficient for generalization of a trained model to a real-world testing distribution. Rather than perform greater numbers of experiments requiring exhaustive mixtures of chemical analytes, this research proposes learning approximations of complex exposures from training sets of simple ones by using single-analyte exposure signals as building blocks of a multiple-analyte space. We demonstrate this approach to synthetic sensor responses surprisingly improves the detection of out-of-distribution obscured chemical analytes. Further, we pair these synthetic signals to targets in an information-dense representation space utilizing a large corpus of chemistry knowledge. Through utilization of a semantically meaningful analyte representation spaces along with synthetic targets we achieve rapid analyte classification in the presence of obscurants without corresponding obscured-analyte training data. Transfer learning for supervised learning with molecular representations makes assumptions about the input data. Instead, we borrow from the natural language and natural image processing literature for a novel approach to chemical sensor signal classification using molecular semantics for arbitrary chemical sensor hardware designs.
The effect of swirl on jets and wakes: Linear instability of the Rankine vortex with axial flow

The effect of swirl on jets and wakes is investigated by analyzing the inviscid spatiotemporal instability of the Rankine vortex with superimposed plug flow axial velocity profile. The linear dispersion relation is derived analytically as a function of two nondimensional control parameters: the swirl ratio S and the external axial flow parameter a (a>−0.5 for jets, a<−0.5 for wakes). For each azimuthal wave number m, there exists a single unstable Kelvin–Helmholtz mode and an infinite number of neutrally stable inertial waveguide modes. Swirl decreases the temporal growth rate of the axisymmetric Kelvin–Helmholtz mode (m=0), which nonetheless remains unstable for all axial wave numbers. For helical modes (m≠0), small amounts of swirl lead to the widespread occurrence of direct resonances between the unstable Kelvin–Helmholtz mode and the inertial waveguide modes. Such interactions generate, in the low wave number range, neutrally stable wave number bands separated by bubbles of instability. As S increases.
On the approximability of Koopman-based operator Lyapunov equations

Lyapunov functions play a vital role in the context of control theory for nonlinear dynamical systems. Besides its classical use for stability analysis, Lyapunov functions also arise in iterative schemes for computing optimal feedback laws such as the well-known policy iteration. In this manuscript, the focus is on the Lyapunov function of a nonlinear autonomous finite-dimensional dynamical system which will be rewritten as an infinite-dimensional linear system using the Koopman or composition operator. Since this infinite-dimensional system has the structure of a weak-* continuous semigroup, in a specially weighted $\mathrm{L}^p$-space one can establish a connection between the solution of an operator Lyapunov equation and the desired Lyapunov function. It will be shown that the solution to this operator equation attains a rapid eigenvalue decay which justifies finite rank approximations with numerical methods. The potential benefit for numerical computations will be demonstrated with two short examples.
The amalgamated limit and its topological interpretation

This is a survey on the amalgamated limit, a limit construction for complete Boolean algebras in iterated forcing theory, which generalizes both the direct limit and the two-step amalgamation. We focus in particular on examples of the amalgamated limit from the literature and on the topological amalgamated limit for compact Hausdorff spaces.
Gaussian Process-Gated Hierarchical Mixtures of Experts

In this paper, we propose novel Gaussian process-gated hierarchical mixtures of experts (GPHMEs). Unlike other mixtures of experts with gating models linear in the input, our model employs gating functions built with Gaussian processes (GPs). These processes are based on random features that are non-linear functions of the inputs. Furthermore, the experts in our model are also constructed with GPs. The optimization of the GPHMEs is performed by variational inference. The proposed GPHMEs have several advantages. They outperform tree-based HME benchmarks that partition the data in the input space, and they achieve good performance with reduced complexity. Another advantage is the interpretability they provide for deep GPs, and more generally, for deep Bayesian neural networks. Our GPHMEs demonstrate excellent performance for large-scale data sets, even with quite modest sizes.
Quantum Reference Frames on Finite Homogeneous Spaces

We present an operationally motivated treatment of quantum reference frames in the setting that the frame is a covariant positive operator valued measure (POVM) on a finite homogeneous space, generalising the principal homogeneous spaces studied in previous work. We focus on the case that the reference observable is the canonical covariant projection valued measure on the given space, and show that this gives rise to a rank-one covariant POVM on the group, which can be seen as a system of coherent states, thereby making contact with recent work in the perspective-neutral approach to quantum reference frames.
Causal Inference out of Control: Estimating the Steerability of Consumption

Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inference problem we call the steerability of consumption that abstracts many settings of interest. Focusing on observational designs and exploiting the structure of the problem, we exhibit a set of assumptions for causal identifiability that significantly weaken the often unrealistic overlap assumptions of standard designs. The key novelty of our approach is to explicitly model the dynamics of consumption over time, viewing the platform as a controller acting on a dynamical system. From this dynamical systems perspective, we are able to show that exogenous variation in consumption and appropriately responsive algorithmic control actions are sufficient for identifying steerability of consumption. Our results illustrate the fruitful interplay of control theory and causal inference, which we illustrate with examples from econometrics, macroeconomics, and machine learning.
AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer

Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to real-world deployment. Previous approaches typically handcraft distributions over such parameters (domain randomization), or identify parameters that best match the dynamics of the real environment (system identification). However, there is often an irreducible gap between simulation and reality: attempting to match the dynamics between simulation and reality across all states and tasks may be infeasible and may not lead to policies that perform well in reality for a specific task. Addressing this issue, we propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments -- instead of matching dynamics between simulation and reality. First, we meta-learn an adaptation policy in simulation using reinforcement learning for adjusting the simulation parameter distribution based on the current policy's performance in a target environment. We then perform iterative real-world adaptation by inferring new simulation parameter distributions for policy training, using a small amount of real data. We perform experiments in three robotic tasks: (1) swing-up of linearized double pendulum, (2) dynamic table-top pushing of a bottle, and (3) dynamic scooping of food pieces with a spatula. Our extensive simulation and hardware experiments demonstrate AdaptSim achieving 1-3x asymptotic performance and $\sim$2x real data efficiency when adapting to different environments, compared to methods based on Sys-ID and directly training the task policy in target environments. Website: https://irom-lab.github. io/AdaptSim/
XFL: A High Performace, Lightweighted Federated Learning Framework

This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryption, differential privacy, secure multi-party computation and other security technologies ensuring no leakage of data. XFL provides an abundant algorithms library, integrating a large number of pre-built, secure and outstanding federated learning algorithms, covering both the horizontally and vertically federated learning scenarios. Numerical experiments have shown the prominent performace of these algorithms. XFL builds a concise configuration interfaces with presettings for all federation algorithms, and supports the rapid deployment via docker containers.Therefore, we believe XFL is the most user-friendly and easy-to-develop federated learning framework. XFL is open-sourced, and both the code and documents are available at https://github.com/paritybit-ai/XFL.