Miracle-dz/UniFAR-FTUs huggingface dataset:
Wave-driven Mass Loss of Stripped Envelope Massive Stars: Progenitor-dependence, Mass Ejection, and Supernovae

The discovery of rapidly rising and fading supernovae powered by circumstellar interaction has suggested the pre-supernova mass eruption phase as a critical phenomenon in massive star evolution. It is important to understand the mass and radial extent of the circumstellar medium (CSM) from theoretically predicted mass ejection mechanisms. In this work, we study the wave heating process in massive hydrogen-poor stars, running a suite of stellar models in order to predict the wave energy and pre-explosion timescale of surface energy deposition. We survey stellar models with main-sequence progenitor masses from 20–70 M ⊙ and metallicity from 0.002–0.02. Most of these models predict that less than ∼1047 erg is deposited in the envelope, with the majority of the energy deposited in the last week of stellar evolution. This translates to CSM masses less than ∼10−2 M ⊙ that extend to less than ∼1014 cm, too small to greatly impact the light curves or spectra of the subsequent supernovae, except perhaps during the shock breakout phase. However, a few models predict somewhat higher wave energy fluxes, for which we perform hydrodynamical simulations of the mass ejection process. Radiative transfer simulations of the subsequent supernovae predict a bright but brief shock-cooling phase that could be detected in some Type Ib/c supernovae if they are discovered within a couple days of explosion.
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Field robotics in perceptually-challenging environments require fast and accurate state estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To this end, this paper presents a lightweight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms. Our Direct LiDAR Odometry (DLO) method includes several key algorithmic innovations which prioritize computational efficiency and enables the use of dense, minimally-preprocessed point clouds to provide accurate pose estimates in real-time. This is achieved through a novel keyframing system which efficiently manages historical map information, in addition to a custom iterative closest point solver for fast point cloud registration with data structure recycling. Our method is more accurate with lower computational overhead than the current state-of-the-art and has been extensively evaluated in several perceptually-challenging environments on aerial and legged robots as part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge.
Some remarks on oscillation inequalities

Abstract In this paper, we establish uniform oscillation estimates on $L^p(X)$ with $p\in (1,\infty )$ for the polynomial ergodic averages. This result contributes to a certain problem about uniform oscillation bounds for ergodic averages formulated by Rosenblatt and Wierdl in the early 1990s [Pointwise ergodic theorems via harmonic analysis. Proceedings of Conference on Ergodic Theory (Alexandria, Egypt, 1993) (London Mathematical Society Lecture Notes, 205). Eds. K. Petersen and I. Salama. Cambridge University Press, Cambridge, 1995, pp. 3–151]. We also give a slightly different proof of the uniform oscillation inequality of Jones, Kaufman, Rosenblatt, and Wierdl for bounded martingales [Oscillation in ergodic theory. Ergod. Th. & Dynam. Sys. 18(4) (1998), 889–935]. Finally, we show that oscillations, in contrast to jump inequalities, cannot be seen as an endpoint for r-variation inequalities.
Batch size-invariance for policy optimization

We say an algorithm is batch size-invariant if changes to the batch size can largely be compensated for by changes to other hyperparameters. Stochastic gradient descent is well-known to have this property at small batch sizes, via the learning rate. However, some policy optimization algorithms (such as PPO) do not have this property, because of how they control the size of policy updates. In this work we show how to make these algorithms batch size-invariant. Our key insight is to decouple the proximal policy (used for controlling policy updates) from the behavior policy (used for off-policy corrections). Our experiments help explain why these algorithms work, and additionally show how they can make more efficient use of stale data.
Do Facial Trait Correlates with Roll Call Voting in Parliament? Using fWHR to Study Performance in Politics

Research has shown that people recognize and select leaders based on their facial appearance. However, considering the correlation between the performance of leaders and their facial traits, empirical findings are mixed. This paper adds to the debate by focusing on two previously understudied aspects of facial traits among political leaders: (i) previous studies have focused on electoral success and achievement drive of politicians omitting their actual daily performance after elections; (ii) previous research has analyzed individual politicians omitting the context of social circumstances which potentially influence their performance. We address these issues by analyzing Ukrainian members of parliament (MPs) who voted for bills in six consecutive Verkhovna Rada starting from Rada 4 (2002-06) to Rada 9 (2019-Present) to study politicians' performance, which is defined as co-voting or cooperation between MPs in voting on the same bill. In simple words, we analyze whether politicians tend to follow leaders when voting. This ability to summon the votes of others is interpreted as better performance. To measure performance, we proposed a generic methodology named Feature Importance For Measuring Performance (FIMP) that can be used in various scenarios. Using FIMP, our data suggest that MPs vote has no impact from their colleagues with higher or lower facial width-to-height ratio (fWHR), a popular measure of the facial trait.
SPEC: Seeing People in the Wild with an Estimated Camera

Due to the lack of camera parameter information for in-the-wild images, existing 3D human pose and shape (HPS) estimation methods make several simplifying assumptions: weak-perspective projection, large constant focal length, and zero camera rotation. These assumptions often do not hold and we show, quantitatively and qualitatively, that they cause errors in the reconstructed 3D shape and pose. To address this, we introduce SPEC, the first in-the-wild 3D HPS method that estimates the perspective camera from a single image and employs this to reconstruct 3D human bodies more accurately. First, we train a neural network to estimate the field of view, camera pitch, and roll given an input image. We employ novel losses that improve the calibration accuracy over previous work. We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose. SPEC is more accurate than the prior art on the standard benchmark (3DPW) as well as two new datasets with more challenging camera views and varying focal lengths. Specifically, we create a new photorealistic synthetic dataset (SPEC-SYN) with ground truth 3D bodies and a novel in-the-wild dataset (SPEC-MTP) with calibration and high-quality reference bodies. Code and datasets are available for research purposes at https://spec.is. tue.mpg. de/.
Empirical Understanding of Deletion Privacy: Experiences, Expectations, and Measures

Social platforms are heavily used by individuals to share their thoughts and personal information. However, due to regret over time about posting inappropriate social content, embarrassment, or even life or relationship changes, some past posts might also pose serious privacy concerns for them. To cope with these privacy concerns, social platforms offer deletion mechanisms that allow users to remove their contents. Quite naturally, these deletion mechanisms are really useful for removing past posts as and when needed. However, these same mechanisms also leave the users potentially vulnerable to attacks by adversaries who specifically seek the users' damaging content and exploit the act of deletion as a strong signal for identifying such content. Unfortunately, today user experiences and contextual expectations regarding such attacks on deletion privacy and deletion privacy in general are not well understood. To that end, in this paper, we conduct a user survey-based exploration involving 191 participants to unpack their prior deletion experiences, their expectations of deletion privacy, and how effective they find the current deletion mechanisms. We find that more than 80% of the users have deleted at least a social media post, and users self-reported that, on average, around 35% of their deletions happened after a week of posting. While the participants identified the irrelevancy (due to time passing) as the main reason for content removal, most of them believed that deletions indicate that the deleted content includes some damaging information to the owner. Importantly, the participants are significantly more concerned about their deletions being noticed by large-scale data collectors (e.g., the government) than individuals from their social circle. Finally, the participants felt that popular deletion mechanisms are not very effective in protecting the privacy of those deletions.
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks

We present a new versatile building block for deep point cloud processing architectures that is equally suited for diverse tasks. This building block combines the ideas of spatial transformers and multi-view convolutional networks with the efficiency of standard convolutional layers in two and three-dimensional dense grids. The new block operates via multiple parallel heads, whereas each head differentiably rasterizes feature representations of individual points into a low-dimensional space, and then uses dense convolution to propagate information across points. The results of the processing of individual heads are then combined together resulting in the update of point features. Using the new block, we build architectures for both discriminative (point cloud segmentation, point cloud classification) and generative (point cloud inpainting and image-based point cloud reconstruction) tasks. The resulting architectures achieve state-of-the-art performance for these tasks, demonstrating the versatility of the new block for point cloud processing.
Few-Shot Text Generation with Natural Language Instructions

Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined with regular learning from examples, this idea yields impressive few-shot results for a wide range of text classification tasks. It is also a promising direction to improve data efficiency in generative settings, but there are several challenges to using a combination of task descriptions and example-based learning for text generation. In particular, it is crucial to find task descriptions that are easy to understand for the pretrained model and to ensure that it actually makes good use of them; furthermore, effective measures against overfitting have to be implemented. In this paper, we show how these challenges can be tackled: We introduce GenPET, a method for text generation that is based on pattern-exploiting training, a recent approach for combining textual instructions with supervised learning that only works for classification tasks. On several summarization and headline generation datasets, GenPET gives consistent improvements over strong baselines in few-shot settings.
Mechanical Performance of Steam-Cured Self-Compacting Concrete Incorporating Silica Fume and Limestone Powder

In this paper, influence of heat treatment on evolution of mechanical strengths at early age, less than 24hours of self-compacting concretes containing limestone powder and silica fume as fine materials was investigated experimentally. Two compositions of self-compacting concrete have been studied; the first is elaborated with silica fume addition and the second with limestone powder, each mixture were prepared with a constant water/binder ratio of 0.39. Concrete samples were either cured in water at (23±1°C), or steam cured at 65°C maximum temperature over six hours (6h) curing period. Tests of mechanical strengths were performed on specimens cooled down slowly to room temperature after heating.The obtained results show that all self-compacting mixtures exhibited satisfying fresh properties and check EFNARC specifications of self-compacting concrete (slump flow diameter higher than 650mm, L-box ratio higher than 80% and sieve stability less than 17%). Mechanical strengths of concrete containing limestone addition are slightly lower than those of concrete based on silica fume at all ages. Moreover, heat treatment generates an improvement of compressive and flexural strength. Interesting compressive strengths are obtained. At 24 hours, after heat treatment, the strengths are already greater than 35 MPa. The values are 37 MPa and 40 MPa for self-compacting concrete containing limestone powder and silica fume respectively compared to 40 MPa and 46 MPa obtained at 7 days for the corresponding non-heat treated concretes. Compressive strength gain of SCCs mixtures with limestone powder and with silica fume, undergoing heat treatment at the age of 24hours is 85% and 75% respectively compared to SCCs mixtures cured in water.
Studies on the Performance of Two Dimensional AlSi as the Anodes of Li Ion Battery

Recently, two-dimensional (2D) materials have been rapidly developed and they provided a wide application on the anode of the batteries, reducing the adverse effect of traditional ion batteries including low capacity, short cycle life, low charging rate and poor safety mainly coming from the use of graphite anode. The current report investigates the anode performances of AlSi, a new 2D material exfoliated from NaAlSi, for Li ion batterys (LIBs) through density functional theory (DFT) calculations and gives quantitative discussions on the Li ion valences, binding energies and open-circuit voltages of 2D AlSi anode. The results indicate that 2D AlSi performs great as a novel anode due to the moderate adhesion to Li and low barrier for ion diffusion. Furthermore, our research results illustrate a broad application prospect on the new anode inventions as well as reducing useless consumption on the batteries by the practice of AlSi anode.
Hematology of Wild Lake Erie Watersnakes (Nerodia sipedon insularum) with Natural Ophidiomycosis

Abstract Habitat loss, human persecution, and infectious diseases all threaten declining reptile populations. Lake Erie watersnakes (Nerodia sipedon insularum, LEWS), once classified as an endangered species in part due to human persecution, have recovered to stable population levels but have been observed with a high prevalence of ophidiomycosis. Strategies are needed to mitigate the current disease threat, including assessing overall wellness. Hematologic analysis provides information about the presence of inflammation and infection and thus informs health-based conservation efforts, but has not been previously performed in LEWS. The objective of this study was to evaluate hematologic parameters in LEWS and identify differences based on ophidiomycosis status. Blood was drawn from wild-caught snakes at nine sites in 2018 and 2019 and complete blood counts were performed in 180 individuals. For apparently healthy snakes, packed cell volume was significantly higher in males (median = 32.5%) compared to females (median = 26.5%; P = 0.03). Animals classified as having possible or apparent ophidiomycosis, or those with skin lesions, had a relative azurophilia and lymphopenia compared to individuals classified as negative or Ophidiomyces present, or those without skin lesions (P < 0.01). This is the first study to investigate hematology in a free-ranging population of LEWS and will serve as a baseline for future investigations that aim to improve conservation efforts through population health monitoring.
Evolutionary Large-Scale Multi-Objective Optimization: A Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.
Properties of critical and subcritical second order self-adjoint linear equations

Abstract We discuss critical and subcritical linear second-order difference equations, and we observe several identities and inequalities which such equations satisfy depending on their coefficients. Later, we investigate the limit behaviour depending on the coefficients of solutions and of the sequences which appear when finding said solutions. We will see that certain identity is preserved in limit under weaker assumptions. Finally, we investigate a class of fourth-order linear difference equations and show that they are always 1-critical.
Structure of the Thermally Induced Cross-Link in C-Linked Methyl Ester-Functionalized Polydicyclopentadiene (fPDCPD)

Polydicyclopentadiene (PDCPD) is a durable, highly cross-linked material that is used industrially for the production of automotive body panels among other applications. We recently described a novel C-linked, ester-functionalized polydicyclopentadiene (fPDCPD) that introduces a tunable surface energy and increased Tg, without sacrificing the desirable thermal stability of the parent polymer. In this report, we describe an extensive spectroscopic study aimed at the rigorous characterization of the structure of the cross-link in our thermally cured material. The results from this study rule out the involvement of metathesis-derived interchain couplings and show that the majority of cross-links derive from thermally initiated radical polymerization of the embedded methyl methacrylate motif. We also show that the degree of cross-linking can be tuned by the applied thermal conditions; rapid high-temperature heating freezes the system at a relatively low cross-link density, while longer heating at a lower temp.
Bifurcation scenario for a two-dimensional static airfoil exhibiting trailing edge stall

Abstract We numerically investigate stalling flow around a static airfoil at high Reynolds numbers using the Reynolds-averaged Navier–Stokes equations (RANS) closed with the Spalart–Allmaras turbulence model. An arclength continuation method allows to identify three branches of steady solutions, which form a characteristic inverted S-shaped curve as the angle of attack is varied. Global stability analyses of these steady solutions reveal the existence of two unstable modes: a low-frequency mode, which is unstable for angles of attack in the stall region, and a high-frequency vortex shedding mode, which is unstable at larger angles of attack. The low-frequency stall mode bifurcates several times along the three steady solutions: there are two Hopf bifurcations, two solutions with a two-fold degenerate eigenvalue and two saddle-node bifurcations. This low-frequency mode induces a cyclic flow separation and reattachment along the airfoil. Unsteady simulations of the RANS equations confirm the existence of large-amplitude low-frequency periodic solutions that oscillate around the three steady solutions in phase space. An analysis of the periodic solutions in the phase space shows that, when decreasing the angle of attack, the low-frequency periodic solution collides with the unstable steady middle-branch solution and thus disappears via a homoclinic bifurcation of periodic orbits. Finally, a one-equation nonlinear stall model is introduced to reveal that the disappearance of the limit cycle, when increasing the angle of attack, is due to a saddle-node bifurcation of periodic orbits.
Surfactant transport between foam films

Abstract Surfactant transport from foam film to foam film is an essential (yet poorly understood) aspect of the viscoplastic yielding behaviour of flowing foam. Recent experimental and modelling work by Bussonnière & Cantat (J. Fluid Mech., vol. 922, 2021, A25) has, however, helped to advance understanding of the relevant surfactant transport processes: the significance of that work is described herein.
What Difference Does It Make? Gendered Campaigning in the 2016 Irish General Election

Abstract Possible gender differences in the self-presentation of political candidates have been a recurring research topic for many years. Yet studies that compare large numbers of candidates have mainly used data from the United States. This article uses a unique data set from the run-up to the 2016 general election in Ireland to compare the self-presentation of male and female candidates. The data are based on video statements of almost 90% of the candidates who ran in the election. With its lack of party polarization and recent introduction of a gender quota, Ireland is a particularly interesting case for analyzing possible gender differences in political campaigning. Findings confirm previous research that has found few gender differences in issue priorities but contradict it in other respects, especially regarding differences in stressing political experience and personal background. The results suggest that female candidates saw electoral benefits from conforming to expectations about women as caregivers, but they wished to avoid a stereotype limiting them to this role by also emphasizing their occupational background. Their strong personalization may also indicate an attempt to stress individuality in a context in which the gender quota drew special attention to women as a category.