Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmological Vlasov–Poisson Simulations of Structure Formation with Relic Neutrinos: Nonlinear Clustering and the Neutrino Mass

We present the results of cosmological simulations of large-scale structure formation with massive neutrinos. The phase-space distribution of the cosmic relic neutrinos is followed, for the first time, by directly integrating the six-dimensional Vlasov–Poisson equations. Our novel approach allows us to represent free streaming and clustering of neutrinos, and their gravitational interaction with cold dark matter accurately. We thus obtain solutions for the collisionless dynamics independent of conventional N-body methods. We perform a suite of hybrid N-body/Vlasov simulations with varying the neutrino mass, and systematically examine the dynamical effects of massive neutrinos on large-scale structure formation. Our simulations show characteristic large-scale clustering of the neutrinos and their coherent streaming motions relative to dark matter. The effective local neutrino “temperature” around massive galaxy clusters varies by several percent with respect to the cosmic mean; the neutrinos in clusters can be hotter or colder depending on the neutrino mass. We study a number of statistics of the large-scale structure and of dark matter halos in comparison with those obtained by N-body simulations and/or by perturbation theory. Our simulations mark an important milestone in numerical cosmology, and pave a new way to study cosmic structure formation with massive neutrinos.
Filtration games and potentially projective modules

The notion of a \textbf{$\boldsymbol{\mathcal{C}}$-filtered} object, where $\mathcal{C}$ is some (typically small) collection of objects in a Grothendieck category, has become ubiquitous since the solution of the Flat Cover Conjecture around the year 2000. We introduce the \textbf{$\boldsymbol{\mathcal{C}}$-Filtration Game of length $\boldsymbol{\omega_1}$} on a module, paying particular attention to the case where $\mathcal{C}$ is the collection of all countably presented, projective modules. We prove that Martin's Maximum implies the determinacy of many $\mathcal{C}$-Filtration Games of length $\omega_1$, which in turn imply the determinacy of certain Ehrenfeucht-Fraisse games of length $\omega_1$; this allows a significant strengthening of a theorem of Mekler-Shelah-Vaananen \cite{MR1191613}. Also, Martin's Maximum implies that if $R$ is a countable hereditary ring, the class of \textbf{$\boldsymbol{\sigma}$-closed potentially projective modules}---i.e., those modules that are projective in some $\sigma$-closed forcing extension of the universe---is closed under $<\aleph_2$-directed limits. We also give an example of a (ZFC-definable) class of abelian groups that, under the ordinary subgroup relation, constitutes an Abstract Elementary Class (AEC) with Lowenheim-Skolem number $\aleph_1$ in some models in set theory, but fails to be an AEC in other models of set theory.
The Faint End of the Quasar Luminosity Function at z ∼ 5 from the Subaru Hyper Suprime-Cam Survey

We present the quasar luminosity function at z ∼ 5 derived from the optical wide-field survey data obtained as a part of the Subaru strategic program (SSP) with the Hyper Suprime-Cam (HSC). From a ∼81.8 deg2 area in the Wide layer of the HSC-SSP survey, we selected 224 candidates of low-luminosity quasars at z ∼ 5 by adopting the Lyman-break method down to i = 24.1 mag. Based on our candidates and spectroscopically confirmed quasars from the Sloan Digital Sky Survey (SDSS), we derived the quasar luminosity function at z ∼ 5, covering a wide luminosity range of −28.76 < M1450 < −22.32 mag. We found that the quasar luminosity function is fitted by a double power-law model with a break magnitude of mag. The inferred number density of low-luminosity quasars is lower, and the derived faint-end slope, , is flatter than those of previous studies at z ∼ 5. A compilation of the quasar luminosity function at 4 ≤ z ≤ 6 from the HSC-SSP suggests that there is little redshift evolution in the break magnitude and in the faint-end slope within this redshift range, although previous studies suggest that the faint-end slope becomes steeper at higher redshifts. The number density of low-luminosity quasars decreases more rapidly from z ∼ 5 to z ∼ 6 than from z ∼ 4 to z ∼ 5.
A stochastic algorithm for fault inverse problems in elastic half space with proof of convergence

A general stochastic algorithm for solving mixed linear and nonlinear problems was introduced in [12]. We show in this paper how it can be used to solve the fault inverse problem, where a planar fault in elastic half-space and a slip on that fault have to be reconstructed from noisy surface displacement measurements. With the parameter giving the plane containing the fault denoted by m, we prove that the reconstructed posterior marginal of m is convergent as the number of measurement points and the dimension of the space for discretizing slips increase. Our proof relies on a recent result on the stability of the associated deterministic inverse problem [10], on trace operator theory, and on the existence of exact quadrature rules for a discrete scheme involving the underlying integral operator. The existence of such a discrete scheme was proved by Yarvin and Rokhlin, [16]. Our algorithm models the regularization constant C for the linear part of the inverse problem as a random variable allowing us to sweep through a wide range of possible values. We show in simulations that this is crucial when the noise level is not known. We also show numerical simulations that illustrate the numerical convergence of our algorithm.
The blending region hybrid framework for the simulation of stochastic reaction–diffusion processes

The simulation of stochastic reaction–diffusion systems using fine-grained representations can become computationally prohibitive when particle numbers become large. If particle numbers are sufficiently high then it may be possible to ignore stochastic fluctuations and use a more efficient coarse-grained simulation approach. Nevertheless, for multiscale systems which exhibit significant spatial variation in concentration, a coarse-grained approach may not be appropriate throughout the simulation domain. Such scenarios suggest a hybrid paradigm in which a computationally cheap, coarse-grained model is coupled to a more expensive, but more detailed fine-grained model, enabling the accurate simulation of the fine-scale dynamics at a reasonable computational cost. In this paper, in order to couple two representations of reaction–diffusion at distinct spatial scales, we allow them to overlap in a ‘blending region’. Both modelling paradigms provide a valid representation of the particle density in this region. From one end of the blending region to the other, control of the implementation of diffusion is passed from one modelling paradigm to another through the use of complementary ‘blending functions’ which scale up or down the contribution of each model to the overall diffusion. We establish the reliability of our novel hybrid paradigm by demonstrating its simulation on four exemplar reaction–diffusion scenarios.
The Reference Condition: A Comparison of Multimetric and Multivariate Approaches to Assess Water-Quality Impairment Using Benthic Macroinvertebrates

Traditional methods of establishing control sites in field-oriented biomonitoring studies of water quality are limited. The reference-condition approach offers a powerful alternative because sites serve as replicates rather than the multiple collections within sites that are the replicates in traditional designs using inferential statistics. With the reference-condition approach, an array of reference sites characterises the biological condition of a region; a test site is then compared to an appropriate subset of the reference sites, or to all the reference sites with probability weightings. This paper compares the procedures for establishing reference conditions, and assesses the strengths and deficiencies of multimetric (as used in the USA) and multivariate methods (as used in the UK, Canada, and Australia) for establishing water-quality status. A data set of environmental measurements and macroinvertebrate collections from the Fraser River, British Columbia, was used in the comparison. Precision and accuracy of the 2 multivariate methods tested (AUStralian RIVer Assessment Scheme: AusRivAS, BEnthic Assessment of SedimenT: BEAST) were consistently higher than for the multimetric assessment. Classification by ecoregion, stream order, and biotic group yielded precisions of 100% for the AusRivAS, 80-100% for the BEAST, and 40-80% for multimetrics; and accuracies of 100%, 100%, and 38-88%, respectively. Multimetrics are attractive because they produce a single score that is comparable to a target value and they include ecological information. However, not all information collected is used, metrics are often redundant in a combination index, errors can be compounded, and it is difficult to acquire current procedures. Multivariate methods are attractive because they require no prior assumptions either in creating groups out of reference sites or in comparing test sites with reference groups. However, potential users may be discouraged by the complexity of initial model construction. The complementary emphases in the multivariate methods examined (presence / absence in AusRivAS cf. abundance in BEAST) lead us to recommend that they be used together, and in conjunction with, multimetric studies.
C-Arm Non-Circular Orbits: Geometric Calibration, Image Quality, and Avoidance of Metal Artifacts

Metal artifacts present a frequent challenge to cone-beam CT (CBCT) in image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy. Recent advances in mobile C-arm systems have enabled 3D imaging capacity with non-circular orbits. We extend a previously proposed metal artifacts avoidance (MAA) method to reduce the influence of metal artifacts by prospectively defining a non-circular orbit that avoids metal-induced biases in projection domain. Accurate geometric calibration is an important challenge to accurate 3D image reconstruction for such orbits. We investigate the performance of interpolation-based calibration from a library of circular orbits for any non-circular orbit. We apply the method to non-circular scans acquired for MAA, which involves: (i) coarse 3D localization of metal objects via only two scout views using an end-to-end trained neural network; (ii) calculation of the metal-induced x-ray spectral shift for all possible views; and (iii) identification of the non-circular orbit that minimizes the variations in spectral shift. Non-circular orbits with interpolation-based geometric calibration yielded reasonably accurate 3D image reconstruction. The end-to-end neural network accurately localized metal implants with just two scout views even in complex anatomical scenes, improving Dice coefficient by ~42% compared to a more conventional cascade of separately trained U-nets. In a spine phantom with pedicle screw instrumentation, non-circular orbits identified by the MAA method reduced the magnitude of metal "blomming" artifacts (apparent width of the screw shaft) in CBCT reconstructions by ~70%. The proposed imaging and calibration methods present a practical means to improve image quality in mobile C-arm CBCT by identifying non-circular scan protocols that improve sampling and reduce metal-induced biases in the projection data.
Stochastic Optimal Control for Multivariable Dynamical Systems Using Expectation Maximization

Trajectory optimization is a fundamental stochastic optimal control (SOC) problem. This article deals with a trajectory optimization approach for dynamical systems subject to measurement noise that can be fitted into linear time-varying stochastic models. Exact/complete solutions to these kind of control problems have been deemed analytically intractable in literature because they come under the category of partially observable Markov decision processes (MDPs). Therefore, effective solutions with reasonable approximations are widely sought for. We propose a reformulation of stochastic control in a reinforcement learning setting. This type of formulation assimilates the benefits of conventional optimal control procedure, with the advantages of maximum likelihood approaches. Finally, an iterative trajectory optimization paradigm called as SOC—expectation maximization (SOC-EM) is put forth. This trajectory optimization procedure exhibits better performance in terms of reduction in cumulative cost-to-go which is proven both theoretically and empirically. Furthermore, we also provide novel theoretical work which is related to uniqueness of control parameter estimates. Analysis of the control covariance matrix is presented, which handles stochasticity through efficiently balancing exploration and exploitation.
Spatial localization beyond steady states in the neighbourhood of the Takens–Bogdanov bifurcation

Double-zero eigenvalues at a Takens–Bogdanov (TB) bifurcation occur in many physical systems such as double-diffusive convection, binary convection and magnetoconvection. Analysis of the associated normal form, in 1D with periodic boundary condition, shows the existence of steady patterns, standing waves, modulated waves (MW) and travelling waves, and describes the transitions and bifurcations between these states. Values of coefficients of the terms in the normal form classify all possible different bifurcation scenarios in the neighbourhood of the TB bifurcation (Dangelmayr, G. & Knobloch, E. (1987) The Takens–Bogdanov bifurcation with O(2)-symmetry. Phil. Trans. R. Soc. Lond. A, 322, 243-279). In this work we develop a new and simple pattern-forming partial differential equation (PDE) model, based on the Swift–Hohenberg equation, adapted to have the TB normal form at onset. This model allows us to explore the dynamics in a wide range of bifurcation scenarios, including in domains much wider than the lengthscale of the pattern. We identify two bifurcation scenarios in which coexistence between different types of solutions is indicated from the analysis of the normal form equation. In these scenarios, we look for spatially localized solutions by examining pattern formation in wide domains. We are able to recover two types of localized states, that of a localized steady state (LSS) in the background of the trivial state (TS) and that of a spatially localized travelling wave (LTW) in the background of the TS, which have previously been observed in other systems. Additionally, we identify two new types of spatially localized states: that of a LSS in a MW background and that of a LTW in a steady state (SS) background. The PDE model is easy to solve numerically in large domains and so will allow further investigation of pattern formation with a TB bifurcation in one or more dimensions and the exploration of a range of background and foreground pattern combinations beyond SSs.
ON EVOLUTION EQUATIONS FOR LIE GROUPOIDS

Using the calculus of Fourier integral operators on Lie groupoids developped in [18], we study the fundamental solution of the evolution equation (∂ ∂t + iP)u = 0 where P is a self adjoint elliptic order one G-pseudodifferential operator on the Lie groupoid G. Along the way, we continue the study of distributions on Lie groupoids done in [17] by adding the reduced C *-algebra of G in the picture and we investigate the local nature of the regularizing operators of [32].
Molecules and morphology unite Sarcophaga (Stackelbergeola) Rohdendorf and S. (Rohdendorfisca) Grunin within megadiverse Sarcophaga Meigen (sensu lato) (Diptera, Sarcophagidae)

Abstract. The largest genus of the family Sarcophagidae (Insecta, Diptera), Sarcophaga Meigen (sensu lato), has ∼160 subgenera; however, the validity and phylogenetic relationships of these are still unclear, impeding progress in evolutionary studies. This study presents a phylogenetic hypothesis for selected subgenera of Sarcophaga s.l. based on COI sequences (685 bp) for 87 species representing 27 valid subgenera. The subgenera Stackelbergeola Rohdendorf and Rohdendorfisca Grunin are reconsidered in the light of new molecular, morphological and biological data. The female is described for the first time for a representative of both subgenera, and Sarcophaga (Rohdendorfisca) flagellifera (Grunin) is shown to be a parasitoid of tettigoniid grasshoppers. As the male of Sarcophaga (Stackelbergeola) sushkini (Rohdendorf) is insufficiently documented in the literature, a redescription is provided based on material from Xinjiang, thereby providing the first record of this subgenus and species from China. Detailed documentation through photographs, scanning electron microscopy and illustrations of the adult morphology is also provided. The subgenera Stackelbergeola and Rohdendorfisca are shown to be monophyletic, together forming a monophylum supported by molecular and morphological data, and they are placed in a wider phylogenetic context of the megadiverse genus Sarcophaga s.l.
Bag of Tricks for Adversarial Training

Adversarial training (AT) is one of the most effective strategies for promoting model robustness. However, recent benchmarks show that most of the proposed improvements on AT are less effective than simply early stopping the training procedure. This counter-intuitive fact motivates us to investigate the implementation details of tens of AT methods. Surprisingly, we find that the basic training settings (e.g., weight decay, learning rate schedule, etc.) used in these methods are highly inconsistent, which could largely affect the model performance as shown in our experiments. For example, a slightly different value of weight decay can reduce the model robust accuracy by more than 7%, which is probable to override the potential promotion induced by the proposed methods. In this work, we provide comprehensive evaluations on the effects of basic training tricks and hyperparameter settings for adversarially trained models. We provide a reasonable baseline setting and re-implement previous defenses to achieve new state-of-the-art results.
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention

Textbook Question Answering is a complex task in the intersection of Machine Comprehension and Visual Question Answering that requires reasoning with multimodal information from text and diagrams. For the first time, this paper taps on the potential of transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges this task entails. Rather than training a language-visual transformer from scratch we rely on pre-trained transformers, fine-tuning and ensembling. We add bottom-up and top-down attention to identify regions of interest corresponding to diagram constituents and their relationships, improving the selection of relevant visual information for each question and answer options. Our system ISAAQ reports unprecedented success in all TQA question types, with accuracies of 81.36%, 71.11% and 55.12% on true/false, text-only and diagram multiple choice questions. ISAAQ also demonstrates its broad applicability, obtaining state-of-the-art results in other demanding datasets.
Chemically homogeneous evolution: a rapid population synthesis approach

We explore chemically homogeneous evolution (CHE) as a formation channel for massive merging binary black holes (BBHs). We develop methods to include CHE in a rapid binary population synthesis code, Compact Object Mergers: Population Astrophysics and Statistics (compas), which combines realistic models of binary evolution with cosmological models of the star formation history of the Universe. For the first time, we simultaneously explore conventional isolated binary star evolution under the same set of assumptions. This approach allows us to constrain population properties and make simultaneous predictions about the gravitational-wave detection rates of BBH mergers for the CHE and conventional formation channels. The overall mass distribution of detectable BBHs is consistent with existing gravitational-wave observations. We find that the CHE channel may yield up to ${\sim} 70{{\ \rm per\ cent}}$ of all gravitational-wave detections of BBH mergers coming from isolated binary evolution.
Models of ultraluminous X-ray transient sources

Context. It is now widely accepted that most ultraluminous X-ray sources (ULXs) are binary systems whose large (above 1039 erg s−1) apparent luminosities are explained by super-Eddington accretion onto a stellar-mass compact object. Many of the ULXs, especially those containing magnetized neutron stars, are highly variable; some exhibit transient behaviour. Large luminosities might imply large accretion discs that could be therefore prone to the thermal–viscous instability known to drive outbursts of dwarf novae and low-mass X-ray binary transient sources. Aims. The aim of this paper is to extend and generalize the X-ray transient disc-instability model to the case of large (outer radius larger than 1012 cm) accretion discs and apply it to the description of systems with super-Eddington accretion rates at outburst and, in some cases, super-Eddington mass transfer rates. Methods. We have used our disc-instability-model code to calculate the time evolution of the accretion disc and the outburst properties. Results. We show that, provided that self-irradiation of the accretion disc is efficient even when the accretion rate exceeds the Eddington value, possibly due to scattering back of the X-ray flux emitted by the central parts of the disc on the outer portions of the disc, heating fronts can reach the disc’s outer edge generating high accretion rates. We also provide analytical approximations for the observable properties of the outbursts. We have successfully reproduced the observed properties of galactic transients with large discs, such as V404 Cyg, as well as some ULXs such as M51 XT-1. Our model can reproduce the peak luminosity and decay time of ESO 243-39 HLX-1 outbursts if the accretor is a neutron star. Conclusions. Observational tests of our predicted relations between the outburst duration and decay time with peak luminosity would be most welcome.
Dynamic facial asset and rig generation from a single scan

The creation of high-fidelity computer-generated (CG) characters for films and games is tied with intensive manual labor, which involves the creation of comprehensive facial assets that are often captured using complex hardware. To simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial models, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic textures, as well as secondary facial components (e.g., teeth and eyeballs). Based on a facial database with over 4, 000 scans with pore-level details, varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of a full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with professional production pipelines for facial animation and rendering. We demonstrate a highly robust and effective framework on a wide range of subjects, and showcase high-fidelity facial animations with automatically generated personalized dynamic textures.