Miracle-dz/UniFAR-FTUs huggingface dataset:
Corrigendum: The role of tobacco taxes in starting and quitting smoking: duration analysis of British data

We estimated hazard rates for starting and quitting smoking in which the tax rate on cigarettes was measured as a time-varying covariate. Since completing the analysis an error has come to light in the procedure that was used to map the real tax rate on cigarettes to the year at risk for the analysis of the hazard of starting smoking. This error affects mainly the discussion of the starting models in Section 5.1. It does not affect any of the analysis of the hazard of quitting smoking that is reported in Section 5.2. The effect of correcting this error is to change the estimates of the tax elasticity of starting, as reported in the summary (page 517), Section 1 (page 518) and Section 5.1 (Table 2, pages 533 and 534 andTable 3). In the paper it was reported that the tax elasticitieswere relatively small but that they were statistically significant (page 533). The corrected results show that the estimates are even closer to zero in magnitude and are not statistically significant: the reported point estimate of 0.16 for men is replaced by 0.006 and the estimate of 0.08 for women is replaced by −0:028. The interpretation of the findings reported for the effect of other variables, such as education and parental education, on the hazard of starting, is unchanged by the correction. The correction makes no discernible difference to the figures reported in Section 5.1 or their interpretation; nor are changes required to any part of Section 6 of the paper on ‘policy implications’. Corrected versions of Tables 2 and 3 are given here.
Protein Language Models and Structure Prediction: Connection and Progression

The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks and capture useful information in learning representations for proteins. The past two years have witnessed remarkable success in tertiary protein structure prediction (PSP), including evolution-based and single-sequence-based PSP. It seems that instead of using energy-based models and sampling procedures, protein language model (pLM)-based pipelines have emerged as mainstream paradigms in PSP. Despite the fruitful progress, the PSP community needs a systematic and up-to-date survey to help bridge the gap between LMs in the natural language processing (NLP) and PSP domains and introduce their methodologies, advancements and practical applications. To this end, in this paper, we first introduce the similarities between protein and human languages that allow LMs extended to pLMs, and applied to protein databases. Then, we systematically review recent advances in LMs and pLMs from the perspectives of network architectures, pre-training strategies, applications, and commonly-used protein databases. Next, different types of methods for PSP are discussed, particularly how the pLM-based architectures function in the process of protein folding. Finally, we identify challenges faced by the PSP community and foresee promising research directions along with the advances of pLMs. This survey aims to be a hands-on guide for researchers to understand PSP methods, develop pLMs and tackle challenging problems in this field for practical purposes.
Impulsiveness and energetics in solar flares with and without type II radio bursts - A comparison of hard X-ray characteristics for over 2500 solar flares

The hard X-ray characteristics of more than 2500 solar flares are used to study the relative size, impulsiveness, and energetics of flares with and without type II radio bursts. A quantitative definition of the hard X-ray impulsiveness is introduced, which may be applied to a large number of events unambiguously. It is found that the flares with type II bursts are generally not significantly larger, more impulsive, or more energetic than those without type II bursts. Also, no evidence is found to suggest a simple classification of the flares as either impulsive or gradual. Because type II bursts are present even in small flares with relatively unimpulsive energy releases, it is concluded that changes in the ambient conditions of the solar atmosphere causing an unusually low Alfven speed may be important in the generation of the shock wave that produces type II radio bursts. 22 references.
Pathwise CVA regressions with oversimulated defaults

We consider the computation by simulation and neural net regression of conditional expectations, or more general elicitable statistics, of functionals of processes (X,Y)$(X,Y)$ . Here an exogenous component Y (Markov by itself) is time‐consuming to simulate, while the endogenous component X (jointly Markov with Y) is quick to simulate given Y, but is responsible for most of the variance of the simulated payoff. To address the related variance issue, we introduce a conditionally independent, hierarchical simulation scheme, where several paths of X are simulated for each simulated path of Y. We analyze the statistical convergence of the regression learning scheme based on such block‐dependent data. We derive heuristics on the number of paths of Y and, for each of them, of X, that should be simulated. The resulting algorithm is implemented on a graphics processing unit (GPU) combining Python/CUDA and learning with PyTorch. A CVA case study with a nested Monte Carlo benchmark shows that the hierarchical simulation technique is key to the success of the learning approach.
Implicit integration of elastoplastic constitutive models for frictional materials with highly non-linear hardening functions

Constitutive relations in elastoplasticity may be formulated in a variety of ways, and different update algorithms may be employed to solve the resulting equations. Several implicit integration schemes, although some not widely used, have been suggested in the last years. Among them, the closest point projection method (CPPM) has proven to be an effective and robust integration scheme. In order to gain maximum control of the stress projection, a two-level CPPM iteration scheme is proposed. The hardening variables are fixed during the stress projection onto consequently fixed yield surfaces, and after the stress projection, new values of the hardening variables are calculated defining new yield surfaces. The update of the hardening parameters which, in general, may be highly nonlinear functions, may be determined by a combination of a Picard Iteration (PI) on the hardening variables and an adaptative order inverse interpolation (AOII) on the difference of subsequent iterations of the hardening variables. The integration scheme has been implemented in a general constitutive driver which has been formulated independent of the selected constitutive model and easily linked to finite element codes. A third stress invariant dependent, cone–cap elastoplastic constitutive model, referred to as the MRS–Lade, with a highly non-linear hardening function has been used to show the applicability of the proposed iteration scheme. Error analyses and accuracy assessment are presented along with some representative test results. © 1997 by John Wiley & Sons, Ltd
A Nonrepeating Fast Radio Burst in a Dwarf Host Galaxy

We present the discovery of an as yet nonrepeating fast radio burst (FRB), FRB 20210117A, with the Australian Square Kilometre Array Pathfinder (ASKAP), as a part of the Commensal Real-time ASKAP Fast Transients Survey. The subarcsecond localization of the burst led to the identification of its host galaxy at z = 0.214(1). This redshift is much lower than what would be expected for a source dispersion measure (DM) of 729 pc cm−3, given typical contributions from the intergalactic medium and the host galaxy. Optical observations reveal the host to be a dwarf galaxy with little ongoing star formation—very different to the dwarf host galaxies of the known repeating FRBs 20121102A and 20190520B. We find an excess DM contribution from the host and attribute it to the FRB’s local environment. We do not find any radio emission from the FRB site or host galaxy. The low magnetized environment and the lack of a persistent radio source indicate that the FRB source is older than those found in other dwarf host galaxies, establishing the diversity of FRB sources in dwarf galaxy environments. We find our observations to be fully consistent with the “hypernebula” model, where the FRB is powered by an accretion jet from a hyperaccreting black hole. Finally, our high time resolution analysis reveals burst characteristics similar to those seen in repeating FRBs. We encourage follow-up observations of FRB 20210117A to establish any repeating nature.
Maxitive monetary risk measures on subsets of $L^0$: worst-case risk assessment and sharp large deviations

In risk analysis, worst-case risk assessments are performed using maxitive monetary risk measures. In this article, we investigate maxitive monetary risk measures on subsets of \( L^0 \). We prove that a monetary risk measure is maxitive and continuous from below if and only if it is a penalized worst-case risk measure. We characterize the usual worst-case risk measure as the unique maxitive and law-invariant monetary risk measure. We apply the results to large deviation theory by providing a general criterion to establish a sharp large deviation estimate of a sequence of probability measures. Additionally, we prove new limit results for sequences of distorted expectations.
SparsePose: Sparse-View Camera Pose Regression and Refinement

Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation often fail when only a few images are available because they rely on the ability to robustly identify and match visual features between image pairs. While these methods can work robustly with dense camera views, capturing a large set of images can be time-consuming or impractical. We propose SparsePose for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also demonstrate our pipeline for high-fidelity 3D reconstruction using only 5–9 images of an object. Project webpage: https://sparsepose.github. io/
SNAC: Speaker-Normalized Affine Coupling Layer in Flow-Based Architecture for Zero-Shot Multi-Speaker Text-to-Speech

Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a speech sample with the voice characteristic of an unseen speaker. The main challenge of ZSM-TTS is to increase the overall speaker similarity for unseen speakers. One of the most successful speaker conditioning methods for flow-based multi-speaker text-to-speech (TTS) models is to utilize the functions which predict the scale and bias parameters of the affine coupling layers according to the given speaker embedding vector. In this letter, we improve on the previous speaker conditioning method by introducing a speaker-normalized affine coupling (SNAC) layer which allows for unseen speaker speech synthesis in a zero-shot manner leveraging a normalization-based conditioning technique. The newly designed coupling layer explicitly normalizes the input by the parameters predicted from a speaker embedding vector while training, enabling an inverse process of denormalizing for a new speaker embedding at inference. The proposed conditioning scheme yields the state-of-the-art performance in terms of the speech quality and speaker similarity in a ZSM-TTS setting.
Randomized block subsampling Kaczmarz-Motzkin method

By introducing a subsampling strategy, we propose a randomized block Kaczmarz-Motzkin method for solving linear systems. Such strategy not only determines the block size, but also combines and extends two famous strategies, i.e., randomness and greed, and hence can inherit their advantages. Theoretical analysis shows that the proposed method converges linearly in expectation to the least-Euclidean-norm solution. Several numerical examples are reported to verify the efficiency and feasibility of the new method.
Entanglement probabilities of polymers: a white noise functional approach

The entanglement probabilities for a highly flexible polymer to wind n times around a straight polymer are evaluated using white noise analysis. To introduce the white noise functional approach, the one-dimensional random walk problem is taken as an example. The polymer entanglement scenario, viewed as a random walk on a plane, is then treated and the entanglement probabilities are obtained for a magnetic flux confined along the straight polymer, and a case where an entangled polymer is subjected to the potential V = ḟ(s)ϑ. In the absence of the magnetic flux and the potential V, the entanglement probabilities reduce to a result obtained by Wiegel.
The Growth Rates and Densities of Ice Crystals between −3°C and −21°C

Abstract Ice crystals were grown in a supercooled cloud at temperatures ranging from −3°C to −21°C for periods from 30–40 s to 150–180 s. When the axial dimensions at a given time were examined as a function of temperature, there was a marked maximum along the a axis at −15°C and a secondary broader maximum along the c axis at −6°C. The growth of the axial dimensions can he adequately represented by a linear function of time. A power function of time was fitted to the crystal mass growth measurements; these show a sharp maximum at −15°C and a secondary broader maximum at −7°C. Crystal bulk densities estimated from the masses and axial dimensions vary with temperature in a complicated way, with a minimum of about 0.4 Mg m−2 at −5 and − 17°C, and a maximum of 0.92 Mg m−2(pure rice) at and appear to he independent of time.
Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier–Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods.
Attractors: persistence, and density of their basins

An investigation of qualitative features of flows on manifolds, in terms of their attractors and quasi-attractors. A quasi-attractor is any nonempty intersection of attractors. It is shown that quasi-attractors other than attractors occur for a large set of flows. It is also shown that for a generic flow (for each flow in a residual subset of the set of all flows), each attractor "persists" as an attractor of all nearby flows. Similar statements are shown to hold with "quasi-attractor", "chain transitive attractor", and "chain transitive quasi-attractor" in place of "attractor". Finally, the set of flows under which almost all points tend asymptotically to a chain transitive quasi-attractor is characterized in terms of stable sets of invariant sets. 0. Introduction. This paper investigates qualitative properties of attractors of flows on compact manifolds. An attractor of a flow, /, is a nonempty, compact, /-invariant set A that has a neighborhood, U, satisfying C\t>Qf(t, U) = A. The basin of attraction of the attractor A is the set of all points in the manifold that approach A asymptotically under the forward flow. The central concern is to determine when there is a collection of attractors of a given flow such that: (a) Each attractor in the collection is "indecomposable" in some sense. (b) Each of these attractors is "persistent" under perturbations of the flow. (c) The union of the basins of these attractors forms a dense subset in the manifold. The origin of the problem is a conjecture by R. Thorn: Conjecture [T, p. 39]. Given r > 1, there is a set of flows, S, residual in the set of all C flows on M, such that / £ S implies that / has a finite number of structurally stable, topologically transitive attractors whose basins are dense in M. Note that the attractors Thorn is talking about satisfy strong forms of the notions of "indecomposability" (topological transitivity) and "persistence" (structural stability). The primary examples of the exact situation Thom describes are the Axiom A and no cycles flows (see [S] or [N] for definitions), except of course that these flows do not in general form a residual subset. In the time since the original conjecture was made, a number of examples have shown that the conjecture needs Received by the editors October 6, 1980. Portions of the paper were presented in the Special Session on Differentiable Dynamical Systems at the A.M.S. Annual Meeting in San Antonio, January 1980. 1980 Mathematics Subject Classification Primary 58F12; Secondary 54H20, 58F10.
The Roles of Morphology and Environment on the Star Formation Rate–Stellar Mass Relation in COSMOS from 0 < z < 3.5

We investigate the relationship between environment, morphology, and the star formation rate (SFR)–stellar mass relation derived from a sample of star-forming (SF) galaxies (commonly referred to as the “star formation main sequence”, SFMS) in the COSMOS field from 0 < z < 3.5. We constructed and fit the far-UV–far-IR spectral energy distributions of our stellar-mass-selected sample of 111,537 galaxies with stellar and dust emission models using the public packages MAGPHYS and SED3FIT. From the best-fit parameter estimates, we construct the SFR–stellar mass relation as a function of redshift, local environment, NUVrJ color diagnostics, and morphology. We find that the shape of the main sequence derived from our color–color and specific-star-formation-rate-selected SF galaxy population, including the turnover at high stellar mass, does not exhibit an environmental dependence at any redshift from 0 < z < 3.5. We investigate the role of morphology in the high-mass end of the SFMS to determine whether bulge growth is driving the high-mass turnover. We find that SF galaxies experience this turnover independent of bulge-to-total ratio, strengthening the case that the turnover is due to the disk component’s specific SFR evolving with stellar mass rather than bulge growth.
Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in"template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, {\fontfamily{qcr}\selectfont manifold}. The template bank contains $1.8 \times 10^6$ sets of template parameters covering plausible neutron star and black hole systems up to a total mass of $400$ $M_\odot$ with component masses between $1$-$200$ $M_\odot$ and mass ratios between $1$ and $20$ under the assumption that each component object's angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, {\fontfamily{qcr}\selectfont manifold}, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The {\fontfamily{qcr}\selectfont GstLAL} search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly $5$ times as compared to previously reported SVD sorting schemes.
Weisfeiler and Leman Go Relational

Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emerged recently, often outperforming shallow architectures. However, the design of such multi-relational graph neural networks is ad-hoc, driven mainly by intuition and empirical insights. Up to now, their expressivity, their relation to each other, and their (practical) learning performance is poorly understood. Here, we initiate the study of deriving a more principled understanding of multi-relational graph neural networks. Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance. By aligning both architectures with a suitable version of the Weisfeiler-Leman test, we establish under which conditions both models have the same expressive power in distinguishing non-isomorphic (multi-relational) graphs or vertices with different structural roles. Further, by leveraging recent progress in designing expressive graph neural networks, we introduce the $k$-RN architecture that provably overcomes the expressiveness limitations of the above two architectures. Empirically, we confirm our theoretical findings in a vertex classification setting over small and large multi-relational graphs.