Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithm 1029: Encapsulated Error, a Direct Approach to Evaluate Floating-Point Accuracy

Floating-point numbers represent only a subset of real numbers. As such, floating-point arithmetic introduces approximations that can compound and have a significant impact on numerical simulations. We introduce encapsulated error, a new way to estimate the numerical error of an application and provide a reference implementation, the Shaman library. Our method uses dedicated arithmetic over a type that encapsulates both the result the user would have had with the original computation and an approximation of its numerical error. We thus can measure the number of significant digits of any result or intermediate result in a simulation. We show that this approach, although simple, gives results competitive with state-of-the-art methods. It has a smaller overhead, and it is compatible with parallelism, making it suitable for the study of large-scale applications.
Symmetric pattern models: a latent variable approach to item non‐response in attitude scales

This paper proposes a new approach to the treatment of item non‐response in attitude scales. It combines the ideas of latent variable identification with the issues of non‐response adjustment in sample surveys. The latent variable approach allows missing values to be included in the analysis and, equally importantly, allows information about attitude to be inferred from non‐response. We present a symmetric pattern methodology for handling item non‐response in attitude scales. The methodology is symmetric in that all the variables are given equivalent status in the analysis (none is designated a ‘dependent’ variable) and is pattern based in that the pattern of responses and non‐responses across individuals is a key element in the analysis. Our approach to the problem is through a latent variable model with two latent dimensions: one to summarize response propensity and the other to summarize attitude, ability or belief. The methodology presented here can handle binary, metric and mixed (binary and metric) manifest items with missing values. Examples using both artificial data sets and two real data sets are used to illustrate the mechanism and the advantages of the methodology proposed.
Automatic CME front edge detection from STEREO white‐light coronagraph images

The coronagraph images captured by a Solar Terrestrial Relations Observatory (STEREO) Ahead/Behind (A/B) spacecraft allow tracking of a coronal mass ejection (CME) from two different viewpoints and reconstructing its propagation in three‐dimensional space. The reconstruction can be done using a triangulation technique that requires a CME front edge location. There are currently no robust automatic CME front edge detection methods that can be integrated with the triangulation technique. In this paper, we propose a novel automatic method to detect the front edge of the CME using STEREO coronagraph 2 red‐colored Red, Green, Blue color model images. Our method consists of two modules: preprocessing and classification. The preprocessing module decomposes each coronagraph image into its three channels and uses only the red channel image for CME segmentation. The output of the preprocessing module is a set of segmented running‐difference binary images which is fed into the classification module. These images are then transformed into polar coordinates followed by CME front edge detection based on the distance that CME travels in the field of view. The proposed method was validated against a manual method using total 56 CME events, 28 from STEREO A and 28 from STEREO B, captured in the period from 1 January 2008 to 16 August 2009. The results show that the proposed method is effective for CME front edge detection. The proposed method is useful in quantitative CME processing and analysis and will be immediately applicable to assist automatic triangulation method for real‐time space weather forecasting.
SPSA vs Simplex in statistical machine translation optimization

Most statistical machine translation systems are combinations of various models and tuning scaling factors is an important step. However, this optimisation problem is hard because the objective function has many local minima and the available algorithms cannot achieve a global optimum. Consequently, optimisations starting from different initial settings can converge to fairly different solutions. We present tuning experiments with the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, and compare them with the widely used downhill simplex method. With IWSLT 2005 Chinese‐English data, both methods showed similar performance, but SPSA was more robust to the choice of initial settings. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Complex length coordinates for quasi-fuchsian groups

Deformation spaces of quasi-Fuchsian groups provide the simplest nontrivial examples of deformation spaces of Kleinian groups. Their understanding is of interest with respect to the study of more general Kleinian groups. On the other hand, these spaces contain subspaces isomorphic to Teichmuller spaces, and are often useful for the study of properties of Teichmuller space. A recent example of this is the study of the Teichmuller space of the punctured torus by Keen and Series [KS].
An Intelligent Method for Test Data Generation Based on Optimized Interval Arithmetic

Path-oriented test data generation is in essence a Constraint Satisfaction Problem solved by search strategies, among which backtracking algorithms are widely used. In this paper, the backtracking algorithm Branch & Bound is introduced to generate path-oriented test data automatically. A model based on state space search is proposed to construct the search tree dynamically. Aiming at the programs containing constraints of strongly related variables even equalities, the static analysis technique interval arithmetic is optimized for the precise judgment of the assignment to each variable. The analysis on conflict is made accurate via distance for further domain reduction, thus ensuring the precise direction of the next search step. Experiments show that the proposed method outperformed other methods used in static test data generation. Specifically, it produces excellent results when variables are strongly related even when they are in equalities, and generation time increases stably and linearly with the increment of number of expressions including both equalities and inequalities.
Numerical Analysis of Three Types of Cavitation Surge in Cascade

In the present study, numerical analysis of a cavitating three-blade cyclic flat-plate cascade was performed considering cavitation surge, which is a type of cavitation instability in pumping machinery. A numerical method employing a uniquely developed gas-liquid two-phase model was applied to solve the unsteady cavitating flow field, where compressibility is considered in the liquid phase of the model. From the numerical results, the surging oscillations by cavitation represented in the present cascade system can be classified into three types of cavitation surge based on the oscillation characteristics and the flow fields. In the first type, oscillation is composed of small-vortex cavitation and large scale pulsation, which correspond to “surge mode oscillation” whose frequency is not affected by cavity volume. The second type of oscillation is composed of sheet cavitation with a re-entrant jet, which corresponds to so-called “cavitation surge. ” The final type of oscillation is subsynchronous rotating cavitation accompanied by pulsation, which is considered as superposition of system and local instability. In addition, the locking phenomenon of break-off frequency of cavitation in the surging oscillations and the mechanism of the pulsation phenomenon accompanied by re-entrant jet in the present cascade were investigated.
On complete curves in moduli space I

Let g denote the moduli space of compact Riemann surfaces of genus g > 3. It is known that g is a non-complete quasi-projective variety that contains many complete curves. This is because the Satake compactification g of g is projective and the boundary \ has co-dimension 2; thus by intersecting with hypersurfaces in sufficiently general position one obtains a complete curve in g passing through any given set of points [8].
Numerical determination of diffraction, slope-, and multiple-diffraction coefficients of impedance wedges by the method of parabolic equation: Space waves

By generalizing the results of Malyuzhinets (1959), Ufimtsev (1965), and Popov (1969), the method of the parabolic equation (PE) can be applied to study the space wave diffraction of a line source by an impedance wedge. The respective diffraction as well as the slope-diffraction coefficients are numerically determined. Contrary to conventional methods, which usually first solve the scattering by a finite body containing the desired diffraction center and then extract the corresponding diffraction coefficient, the PE studies a semi-infinite scattering body and the diffracted field directly. A comparison of PE results with exact ones, as far as available, and uniform geometrical theory of diffraction (UTD) results confirm the accuracy of this method. In addition, a straightforward application of the PE for calculating multiple wedge diffraction eliminates the discontinuities which are typical of the "mechanical" application of the UTD to the same problem. A possible way for combining the PE and the UTD is pointed out. The latter should be of special interest to dealing with wave propagation problems.
SAR observations and modeling of the C-band backscatter variability due to multiscale geometry and soil moisture

The effect of the multiscale surface geometry on the sensitivity of C band synthetic aperture radar (SAR) data to soil moisture is studied. The experimental data consist of C-band SAR images of an agricultural site, including fields with various combinations of three distinct roughness components from small to large scale. The backscatter variability due to surface roughness has been analyzed. The effect of random roughness associated with soil clods is never less than 2 dB, and the effect of a row pattern can be as strong as 10 dB. In addition, the periodic drainage topography induces a backscatter variability due to soil moisture variation and drainage relief. The results indicate that airborne C-band SAR data cannot be easily inverted into soil moisture data. However, with ERS-1 or Radarsat data at an incidence angle of about 20 degrees , the effect of random and periodic roughness can be reduced to about 2 dB if the look angle is less than 50 degrees . >
Bellman equations of risk sensitive control

Risk sensitive control problems are considered. Existence of a nonnegative solution to the Bellman equation of risk sensitive control is shown. The result is applied to prove that no breaking down occurs. Asymptotic behaviour of the nonnegative solution is studied in relation to ergodic control problems and the relationship between the asymptotics and the large deviation principle is noted.
On the evaluation of the integral over the product of two spherical Bessel functions

The integral Il,l’(k,k’)=∫∞0jl (kr)jl’(k’r)r 2 dr, in which the spherical Bessel functions jl(kr) are the radial eigenfunctions of the three‐dimensional wave equation in spherical coordinates, is evaluated in terms of distributions, in particular, step functions and delta functions. It will be shown that the behavior of Il,l’ is very different in the cases l−l’ even (0, ±2, ±4, .) and l−l’ odd (±1, ±3, .). For l−l’ even it is expressed in terms of the delta function, step functions, and Legendre polynomials. For l−l’ odd it is expressed in terms of Legendre functions of the second kind and step functions; no delta functions appear.
Adapting Pre-trained Language Models to Low-Resource Text Simplification: The Path Matters

We frame the problem of text simplification from a task and domain adaptation perspective, where neural language models are pre-trained on large-scale corpora and then adapted to new tasks in different domains through limited training examples. We investigate the performance of two popular vehicles of task and domain adaptation: meta-learning and transfer learning (in particular fine-tuning), in the context of low-resource text simplification that involves a diversity of tasks and domains. We find that when directly adapting a Web-scale pre-trained language model to low-resource text simplification tasks, fine-tuning based methods present a competitive advantage over meta-learning approaches. Surprisingly, adding an intermediate stop in the adaptation path between the source and target, an auxiliary dataset and task that allow for the decomposition of the adaptation process into multiple steps, significantly increases the performance of the target task. The performance is however sensitive to the selection and ordering of the adaptation strategy (task adaptation vs. domain adaptation) in the two steps. When such an intermediate dataset is not available, one can build a “pseudostop” using the target domain/task itself. Our extensive analysis serves as a prelim-inary step towards bridging these two popular paradigms of few-shot adaptive learning and towards developing more structured solutions to task/domain adaptation in a novel setting.
Non-local effects on the multiplication characteristics of thin GaAs p-i-n and n-i-p diodes

Electron and hole multiplication characteristics have been measured on a series of GaAs p-i-n and n-i-p diodes in which the nominal i-region thicknesses, w, range from 1 /spl mu/m to 25 nm. Using the conventional analysis, where ionization is assumed to be uniform across the device, the effective electron and hole ionization coefficients (/spl alpha/ and /spl beta/ respectively) have been deduced. The results obtained from the w=1 /spl mu/m and 0.5 /spl mu/m structures agree with published data which was derived from thick devices. However, those observed in the thinner structures show device width dependence. By implementing semi-analytical techniques to solve Boltzmann's equation and to interpret these results, dead space effects are seen to reduce /spl alpha/ and /spl beta/ in short devices at low multiplication values but overshoot effects compensate when the electric field is increased.
Frequency scaling of excess hysteresis losses

The existence of excess hysteresis losses is discussed. It is emphasized that the excess hysteretic losses are caused by some intrinsic nonlinear dynamics underlying bistable (multistable) behavior associated with hysteresis. By using the mathematical formalism of the Preisach model of hysteresis and some known, results on nonlinear bistable dynamics, it is demonstrated that for small frequencies excess hysteretic losses are increased with frequency as /spl omega//sup (2/3)/. The universality of this result is stressed through numerical experiments.
Guided waves on sinusoidally-modulated reactance surfaces

A rigorous solution is derived for the propagation characteristics and field distributions of waves guided by a plane surface which possesses a surface reactance modulated sinusoidally in the propagation direction. The explicit field amplitudes and the determinantal equation for the propagation wavenumber are expressed in a continued fraction form which is rapidly convergent for all values of modulation. Numerical results are obtained for both surface wave (modal) and leaky wave (nonmodal) solutions. The relevance of these studies to high-gain modulated surface-wave antennas is discussed.
Space interferometry mission (SIM): overview and current status

The Space Interferometry Mission (SIM) will be the first in-space, long-baseline Michelson Stellar Interferometer. SIM will perform precision astrometry at the micro-arcsecond accuracy level, which will be used to characterize planetary systems around stars within about ten parsecs of Earth and address a number of other key astrophysics projects. This paper provides a broad overview of the SIM Mission. Topics covered include: the science objectives, key top level requirements, how the mission will be implemented (technical and programmatic), technology development status, an assessment of where the project is today, and prognosis for the future.
RUIN PROBABILITIES UNDER AN OPTIMAL INVESTMENT AND PROPORTIONAL REINSURANCE POLICY IN A JUMP DIFFUSION RISK PROCESS

Abstract In this paper, we consider an insurance company whose surplus (reserve) is modeled by a jump diffusion risk process. The insurance company can invest part of its surplus in n risky assets and purchase proportional reinsurance for claims. Our main goal is to find an optimal investment and proportional reinsurance policy which minimizes the ruin probability. We apply stochastic control theory to solve this problem. We obtain the closed form expression for the minimal ruin probability, optimal investment and proportional reinsurance policy. We find that the minimal ruin probability satisfies the Lundberg equality. We also investigate the effects of the diffusion volatility parameter, the market price of risk and the correlation coefficient on the minimal ruin probability, optimal investment and proportional reinsurance policy through numerical calculations.
Analyzing Twitter Users' Behavior Before and After Contact by the Internet Research Agency

Social media platforms have been exploited to conduct election interference in recent years. In particular, the Russian-backed Internet Research Agency (IRA) has been identified as a key source of misinformation spread on Twitter prior to the 2016 presidential election. The goal of this research is to understand whether general Twitter users changed their behavior in the year following first contact from an IRA account. We compare the before and after behavior of contacted users to determine whether there were differences in their mean tweet count, the sentiment of their tweets, and the frequency and sentiment of tweets mentioning @realDonaldTrump or @HillaryClinton. Our results indicate that users overall exhibited statistically significant changes in behavior across most of these metrics, and that those users that engaged with the IRA generally showed greater changes in behavior.
Detecting Out-of-Distribution via an Unsupervised Uncertainty Estimation for Prostate Cancer Diagnosis

Artificial intelligence-based prostate cancer (PCa) detection models have been widely explored to assist clinical diagnosis. However, these trained models may generate erroneous results specifically on datasets that are not within training distribution. In this paper, we propose an approach to tackle this so-called out-of-distribution (OOD) data problem. Specifically, we devise an end-to-end unsupervised framework to estimate uncertainty values for cases analyzed by a previously trained PCa detection model. Our PCa detection model takes the inputs of bpMRI scans and through our proposed approach we identify OOD cases that are likely to generate degraded performance due to the data distribution shifts. The proposed OOD framework consists of two parts. First, an autoencoder-based reconstruction network is proposed, which learns discrete latent representations of in-distribution data. Second, the uncertainty is computed using perceptual loss that measures the distance be-tween original and reconstructed images in the feature space of a pre-trained PCa detection network. The effectiveness of the proposed framework is evaluated on seven independent data collections with a total of 1,432 cases. The performance of pre-trained PCa detection model is significantly improved by excluding cases with high uncertainty.
Signalling pathway crosstalk stimulated by L-proline drives differentiation of mouse embryonic stem cells to primitive-ectoderm-like cells

The amino acid L-proline exhibits novel growth factor-like properties during development - from improving blastocyst development to driving neurogenesis in vitro. Addition of 400 μM L-proline to self-renewal medium drives mouse embryonic stem cells (ESCs) to a transcriptionally distinct pluripotent cell population - early primitive ectoderm-like (EPL) cells - which lies between the naïve and primed states. EPL cells retain expression of pluripotency genes, upregulate primitive ectoderm markers, undergo a morphological change and have increased cell number. These changes are facilitated by a complex signalling network hinging on the Mapk, Fgfr, Pi3k and mTor pathways. We use a factorial experimental design coupled with linear modelling and Bayesian regularised neural networks to understand which signalling pathways are involved in the transition between ESCs and EPL cells, and how they underpin changes in morphology, cell number, apoptosis, proliferation and gene expression. This approach allows for consideration of where pathways work antagonistically or synergistically. Modelling showed that most properties were affected by more than one inhibitor, and each inhibitor blocked specific aspects of differentiation. These mechanisms underpin both progression of stem cells across the in vitro pluripotency continuum and serve as a model for pre-, peri- and post-implantation embryogenesis. Summary Statement L-proline acts as growth factor to modulate phosphorylation of the Mapk, Pi3k, Fgf and mTor signalling pathways to drive embryonic stem cells to primitive ectoderm-like cells.
A Flexible Meta Learning Model for Image Registration

We propose a trainable architecture for affine image registration to produce robust starting points for conventional image registration methods. Learning-based methods for image registration often require networks with many parameters and heavily engineered cost functions and thus are complex and computationally expensive. Despite their success in recent years, these methods often lack the accuracy of classical iterative image registration and struggle with large deformations. On the other hand, iterative methods depend on good initial estimates and tuned hyperparameters. We tackle this problem by combining effective shallow networks and classical optimization algorithms using strategies from the field of meta-learning. The architecture presented in this work incorporates only first-order gradient information of the given registration problems, making it highly flexible and particularly well-suited as an initialization step for classical image registration.
The origin of the magnetic fields in white dwarfs

Magnetic white dwarfs with fields in excess of ∼10 6 G (the high field magnetic white dwarfs; HFMWDs) constitute about ∼10 per cent of all white dwarfs and show a mass distribution with a mean mass of ∼0.93 M ○. compared to ∼0.56 M ○. for all white dwarfs. We investigate two possible explanations for these observations. First, that the initial-final mass relationship (IFMR) is influenced by the presence of a magnetic field and that the observed HFMWDs originate from stars on the main sequence that are recognized as magnetic (the chemically peculiar A and B stars). Secondly, that the IFMR is essentially unaffected by the presence of a magnetic field, and that the observed HFMWDs have progenitors that are not restricted to these groups of stars. Our calculations argue against the former hypothesis and support the latter. The HFMWDs have a higher than average mass because on the average they have more massive progenitors and not because the IFMR is significantly affected by the magnetic field. A requirement of our model is that ∼40 per cent of main-sequence stars more massive than ∼4.5 M ○. must either have magnetic fields in the range of ∼10-100 G, which is below the current level of detection, or generate fields during subsequent stellar evolution towards the white dwarf phase. In the former case, the magnetic fields of the HFMWDs could be fossil remnants from the main-sequence phase consistent with the approximate magnetic flux conservation.