Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Impact of Dust Production from Carbon-rich Wolf–Rayet Binaries

We present a dust spectral energy distribution (SED) and binary stellar population analysis revisiting the dust production rates (DPRs) in the winds of carbon-rich Wolf–Rayet (WC) binaries and their impact on galactic dust budgets. DustEM SED models of 19 Galactic WC “dustars” reveal DPRs of M⊙ yr−1 and carbon dust condensation fractions, χC, between 0.002% and 40%. A large (0.1–1.0 μm) dust grain size composition is favored for efficient dustars where χC ≳ 1%. Results for dustars with known orbital periods verify a power-law relation between χC, orbital period, WC mass-loss rate, and wind velocity consistent with predictions from theoretical models of dust formation in colliding-wind binaries. We incorporated dust production into Binary Population and Spectral Synthesis (BPASS) models to analyze dust production rates from WC dustars, asymptotic giant branch stars (AGBs), red supergiants (RSGs), and core-collapse supernovae (SNe). BPASS models assuming constant star formation (SF) and a coeval 106 M⊙ stellar population were performed at low, Large Magellanic Cloud (LMC)–like, and solar metallicities (Z = 0.001, 0.008, and 0.020). Both constant SF and coeval models indicate that SNe are net dust destroyers at all metallicities. Constant SF models at LMC-like metallicities show that AGB stars slightly outproduce WC binaries and RSGs by factors of 2–3, whereas at solar metallicities WC binaries are the dominant source of dust for ∼60 Myr until the onset of AGBs, which match the dust input of WC binaries. Coeval population models show that, for “bursty” SF, AGB stars dominate dust production at late times (t ≳ 70 Myr).
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

Malware classification is a difficult problem, to which machine learning methods have been applied for decades. Yet progress has often been slow, in part due to a number of unique difficulties with the task that occur through all stages of the developing a machine learning system: data collection, labeling, feature creation and selection, model selection, and evaluation. In this survey we will review a number of the current methods and challenges related to malware classification, including data collection, feature extraction, and model construction, and evaluation. Our discussion will include thoughts on the constraints that must be considered for machine learning based solutions in this domain, and yet to be tackled problems for which machine learning could also provide a solution. This survey aims to be useful both to cybersecurity practitioners who wish to learn more about how machine learning can be applied to the malware problem, and to give data scientists the necessary background into the challenges in this uniquely complicated space.
Optimal Virtual Network Function Deployment for 5G Network Slicing in a Hybrid Cloud Infrastructure

Network virtualization is a key enabler for 5G systems to support the expected use cases of vertical markets. In this context, we study the joint optimal deployment of Virtual Network Functions (VNFs) and allocation of computational resources in a hybrid cloud infrastructure by taking the requirements of the 5G services and the characteristics of the cloud architecture into consideration. The resulting mixed-integer problem is reformulated as an integer linear problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid infrastructure over a standard cloud radio access network consisting only of a central cloud, and show that the proposed mechanism to deploy VNF chains leads to high resource utilization efficiency and large gains in terms of the number of supported VNF chains. To deal with the computational complexity of optimizing a large number of clouds and VNF chains, we propose a simple low-complexity heuristic that attempts to find a feasible VNF deployment solution with a limited number of functional splits. Numerical results indicate that the performance of the proposed heuristic is close to the optimal one when the edge clouds are well dimensioned with respect to the computational requirements of the 5G services.
Atomicity and Factorization of Puiseux Monoids

A Puiseux monoid is an additive submonoid of the nonnegative cone of rational numbers. Although Puiseux monoids are torsion-free rank-one monoids, their atomic structure is rich and highly complex. For this reason, they have been important objects to construct crucial examples in commutative algebra and factorization theory. In 1974 Anne Grams used a Puiseux monoid to construct the first example of an atomic domain not satisfying the ACCP, disproving Cohn's conjecture that every atomic domain satisfies the ACCP. Even recently, Jim Coykendall and Felix Gotti have used Puiseux monoids to construct the first atomic monoids with monoid algebras (over a field) that are not atomic, answering a question posed by Robert Gilmer back in the 1980s. This dissertation is focused on the investigation of the atomic structure and factorization theory of Puiseux monoids. Here we established various sufficient conditions for a Puiseux monoid to be atomic (or satisfy the ACCP). We do the same for two of the most important atomic properties: the finite-factorization property and the bounded-factorization property. Then we compare these four atomic properties in the context of Puiseux monoids. This leads us to construct and study several classes of Puiseux monoids with distinct atomic structure. Our investigation provides sufficient evidence to believe that the class of Puiseux monoids is the simplest class with enough complexity to find monoids satisfying almost every fundamental atomic behavior.
The Pristine Inner Galaxy Survey (PIGS) II: Uncovering the most metal-poor populations in the inner Milky Way

Metal-poor stars are important tools for tracing the early history of the Milky Way, and for learning about the first generations of stars. Simulations suggest that the oldest metal-poor stars are to be found in the inner Galaxy. Typical bulge surveys, however, lack low metallicity ([Fe/H] < -1.0) stars because the inner Galaxy is predominantly metal-rich. The aim of the Pristine Inner Galaxy Survey (PIGS) is to study the metal-poor and very metal-poor (VMP, [Fe/H] < -2.0) stars in this region. In PIGS, metal-poor targets for spectroscopic follow-up are selected from metallicity-sensitive CaHK photometry from the CFHT. This work presents the ~250 deg^2 photometric survey as well as intermediate-resolution spectroscopic follow-up observations for ~8000 stars using AAOmega on the AAT. The spectra are analysed using two independent tools: ULySS with an empirical spectral library, and FERRE with a library of synthetic spectra. The comparison between the two methods enables a robust determination of the stellar parameters and their uncertainties. We present a sample of 1300 VMP stars -- the largest sample of VMP stars in the inner Galaxy to date. Additionally, our spectroscopic dataset includes ~1700 horizontal branch stars, which are useful metal-poor standard candles. We furthermore show that PIGS photometry selects VMP stars with unprecedented efficiency: 86%/80% (lower/higher extinction) of the best candidates satisfy [Fe/H] < -2.0, as do 80%/63% of a larger, less strictly selected sample. We discuss future applications of this unique dataset that will further our understanding of the chemical and dynamical evolution of the innermost regions of our Galaxy.
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping

Accurate depth estimation remains an open problem for robotic manipulation; even state of the art techniques including structured light and LiDAR sensors fail on reflective or transparent surfaces. We address this problem by training a neural network model to estimate depth from RGB-D images, using labels from physical interactions between a robot and its environment. Our network predicts, for each pixel in an input image, the z position that a robot’s end effector would reach if it attempted to grasp or poke at the corresponding position. Given an autonomous grasping policy, our approach is self-supervised as end effector position labels can be recovered through forward kinematics, without human annotation. Although gathering such physical interaction data is expensive, it is necessary for training and routine operation of state of the art manipulation systems. Therefore, this depth estimator comes for free while collecting data for other tasks (e.g., grasping, pushing, placing). We show our approach achieves significantly lower root mean squared error than traditional structured light sensors and unsupervised deep learning methods on dif cult, industry-scale jumbled bin datasets.
Revisiting the Formation of HeH+ in the Planetary Nebula NGC 7027

From four independent calculations using three different theoretical approaches, rate coefficients for the formation of HeH+ via the radiative association of He+ and H were computed. Good agreement is found between our new calculations and prior results obtained two decades ago for kinetic temperatures between ∼800 and 20,000 K. This finding is inconsistent with a recent claim in the literature of a wide variation in published values and establishes the robustness of our knowledge of this process for the formation of HeH+. The implications of the current results to the first detection of HeH+ and its modeled abundance in the planetary nebula NGC 7027 are discussed.
Self-Supervised Domain Mismatch Estimation for Autonomous Perception

Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various perception modules. In this work we choose, exemplarily, a semantic segmentation to be monitored, and propose an autoencoder, trained in a self-supervised fashion on the very same training data as the semantic segmentation to be monitored. While the autoencoder’s image reconstruction performance (PSNR) during online inference shows already a good predictive power w.r. t. semantic segmentation performance, we propose a novel domain mismatch metric DM as the earth mover’s distance between a pre-stored PSNR distribution on training (source) data, and an online-acquired PSNR distribution on any inference (target) data. We are able to show by experiments that the DM metric has a strong rank order correlation with the semantic segmentation within its functional scope. We also propose a training domain-dependent threshold for the DM metric to define this functional scope.
Cogradient Descent for Bilinear Optimization

Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure. One reason lies in the insufficient training due to the asynchronous gradient descent, which results in vanishing gradients for the coupled variables. In this paper, we introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem, based on a theoretical framework to coordinate the gradient of hidden variables via a projection function. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent to facilitate the optimization procedure. Our algorithm is applied to solve problems with one variable under the sparsity constraint, which is widely used in the learning paradigm. We validate our CoGD considering an extensive set of applications including image reconstruction, inpainting, and network pruning. Experiments show that it improves the state-of-the-art by a significant margin.
Signatures of secondary acceleration in neutrino flares

High-energy neutrino flares are interesting prospective counterparts to photon flares since their detection would guarantee the presence of accelerated hadrons within a source, in addition to providing precious information about cosmic-ray acceleration and interactions, thus impacting the subsequent modeling of non-thermal emissions in explosive transients. In these sources, photomeson production can be efficient, producing a large amount of secondary particles, such as charged pions and muons, that decay and produce high-energy neutrinos. Before their decay, secondary particles can experience energy losses and acceleration, which can impact high-energy neutrino spectra and thus affect their detectability. In this work, we focus on the impact of secondary acceleration. We consider a one zone model, characterized mainly by a variability timescaletvar, luminosityLbol, and bulk Lorentz factor Γ. The mean magnetic fieldBis deduced from these parameters. The photon field is modeled by a broken power-law. This generic model allows us to systematically evaluate the maximum energy of high-energy neutrinos in the parameter space of explosive transients and shows that it could be strongly affected by secondary acceleration for a large number of source categories. In order to determine the impact of secondary acceleration on the high-energy neutrino spectrum and, in particular, on its peak energy and flux, we complement these estimates with several case studies. We show that secondary acceleration can increase the maximum neutrino flux and produce a secondary peak at the maximum energy in the case of efficient acceleration. Secondary acceleration could, therefore, enhance the detectability of very-high-energy neutrinos that would be the target of next generation neutrino detectors, such as KM3NeT, IceCube-Gen2, POEMMA, or GRAND.
Corralling Stochastic Bandit Algorithms

We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a corralling algorithm that performs almost as well as the best base algorithm. We give two general algorithms for this setting, which we show benefit from favorable regret guarantees. We show that the regret of the corralling algorithms is no worse than that of the best algorithm containing the arm with the highest reward, and depends on the gap between the highest reward and other rewards. We also provide lower bounds for this problem that further justify our approach.
On Effective Parallelization of Monte Carlo Tree Search

Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construct the search tree, which calls for effective parallelization. However, how to design effective parallel MCTS algorithms has not been systematically studied and remains poorly understood. In this paper, we seek to lay its first theoretical foundations, by examining the potential performance loss caused by parallelization when achieving a desired speedup. In particular, we focus on studying the conditions under which the performance loss (measured in excess regret) vanishes over time. To this end, we propose a general parallel MCTS framework that can be specialized to major existing parallel MCTS algorithms. We derive two necessary conditions for the algorithms covered by the general framework to have vanishing excess regret (i.e. excess regret converges to zero as the total number of rollouts grows). We demonstrate the effectiveness of the necessary conditions by showing that, for depth-2 search trees, the recently developed WU-UCT algorithm satisfies both necessary conditions and has provable vanishing excess regret. Finally, we perform empirical studies to closely examine the necessary conditions under the general tree search setting (with arbitrary tree depth). It shows that the topological discrepancy between the search trees constructed by the parallel and the sequential MCTS algorithms is the main reason for the performance loss.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning

Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applying meta-learning techniques. Although the knowledge of governing partial differential equations (PDE) of the data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems. In this work, we propose a framework, physics-aware meta-learning with auxiliary tasks, whose spatial modules incorporate PDE-independent knowledge and temporal modules utilize the generalized features from the spatial modules to be adapted to the limited data, respectively. The framework is inspired by a local conservation law expressed mathematically as a continuity equation and does not require the exact form of governing equation to model the spatiotemporal observations. The proposed method mitigates the need for a large number of real-world tasks for meta-learning by leveraging spatial information in simulated data to meta-initialize the spatial modules. We apply the proposed framework to both synthetic and real-world spatiotemporal prediction tasks and demonstrate its superior performance with limited observations.
Generalisable Relational Reasoning With Comparators in Low-Dimensional Manifolds

While modern deep neural architectures generalise well when test data is sampled from the same distribution as training data, they fail badly for cases when the test data distribution differs from the training distribution even along a few dimensions. This lack of out-of-distribution generalisation is increasingly manifested when the tasks become more abstract and complex, such as in relational reasoning. In this paper we propose a neuroscience-inspired inductive-biased module that can be readily amalgamated with current neural network architectures to improve out-of-distribution (o.o.d) generalisation performance on relational reasoning tasks. This module learns to project high-dimensional object representations to low-dimensional manifolds for more efficient and generalisable relational comparisons. We show that neural nets with this inductive bias achieve considerably better o.o. d generalisation performance for a range of relational reasoning tasks. We finally analyse the proposed inductive bias module to understand the importance of lower dimension projection, and propose an augmentation to the algorithmic alignment theory to better measure algorithmic alignment with generalisation.
Circularly ordering direct products and the obstruction to left-orderability

Motivated by the recent result that left-orderability of a group $G$ is intimately connected to circular orderability of direct products $G \times \mathbb Z/n\mathbb Z$, in this paper we provide necessary and sufficient cohomological conditions that such a direct product be circularly orderable. As a consequence of the main theorem we arrive at a new characterization for the fundamental group of a rational homology 3-sphere to be left-orderable. Our results imply that for mapping class groups of once-punctured surfaces, and other groups whose actions on $S^1$ are cohomologically rigid, the products $G \times \mathbb Z/n\mathbb Z$ are seldom circularly orderable. We also address circular orderability of direct products in general, dealing with the cases of factor groups admitting a bi-invariant circular ordering, and iterated direct products whose factor groups are amenable.
Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database

We study the applicability of tools developed by the computer vision community for features learning and semantic image inpainting to perform data reconstruction of fluid turbulence configurations. The aim is twofold. First, we explore on a quantitative basis, the capability of Convolutional Neural Networks embedded in a Deep Generative Adversarial Model (Deep-GAN) to generate missing data in turbulence, a paradigmatic high dimensional chaotic system. In particular, we investigate their use in reconstructing two-dimensional damaged snapshots extracted from a large database of numerical configurations of 3d turbulence in the presence of rotation, a case with multi-scale random features where both large-scale organised structures and small-scale highly intermittent and non-Gaussian fluctuations are present. Second, following a reverse engineering approach, we aim to rank the input flow properties (features) in terms of their qualitative and quantitative importance to obtain a better set of reconstructed fields. We present two approaches both based on Context Encoders. The first one infers the missing data via a minimization of the L2 pixel-wise reconstruction loss, plus a small adversarial penalisation. The second searches for the closest encoding of the corrupted flow configuration from a previously trained generator. Finally, we present a comparison with a different data assimilation tool, based on Nudging, an equation-informed unbiased protocol, well known in the numerical weather prediction community. The TURB-Rot database, \url{this http URL}, of roughly 300K 2d turbulent images is released and details on how to download it are given.
Basic loci of Coxeter type with arbitrary parahoric level

Abstract Motivated by the desire to understand the geometry of the basic loci in the reduction of Shimura varieties, we study their “group-theoretic models”—generalized affine Deligne–Lusztig varieties—in cases where they have a particularly nice description. Continuing the work of Görtz and He (2015, Cambridge Journal of Mathematics 3, 323–353) and Görtz, He, and Nie (2019, Peking Mathematical Journal 2, 99–154), we single out the class of cases of Coxeter type, give a characterization in terms of the dimension, and obtain a complete classification. We also discuss known, new, and open cases from the point of view of Shimura varieties/Rapoport–Zink spaces.
The Population Prevalence, Associations of Congenital Heart Defect and Mortality Risk for Down’s Syndrome in South Korea Based on National Health Insurance Service (NHIS) Data

Background In the present study, we estimated the population prevalence, associations of congenital heart defect (CHD) and mortality risk for DS using data from National Health Insurance Service (NHIS) and Rare Diseases Registry (RDR). Methods We collected data on subjects with DS who were registered in the RDR between 2010 and 2015. To estimate associations of CHD and mortality risk of DS, the data of DS subjects were compared with 1:5 age- and sex-matched controls. Results In 2015, 2077 individuals with DS were identified out of the total population of 51,574,044 South Koreans and the prevalence was 4.03 per 100,000 persons. The trend of DS population prevalence across 10-year-old intervals showed a peak in the group under the age of 10 years (26.0 per 100,000 persons) and then declined sharply after the age of 20 years (0.98 per 100,000 persons at 30–39 years of age). In subjects with DS, the frequencies of atrial septal defect [odds ratios (OR) =65.9; 95% CI, 84.1–99.1], ventricular septal defect (OR = 88.1, 95% CI, 57.9–134.1), patent ductus arteriosus (OR = 56.9, 95% CI, 40.1–80.8), tetralogy of fallot (OR = 42.1, 95% CI, 19.3–92.3), or atrioventricular septal defect (OR = 510.0, 95% CI, 126.7–999.0) were higher than those of age- and sex-matched controls. The risk of death in patients with DS was significantly higher than that of age- and sex-matched controls [hazard ratio (HR) =41.7, 95% CI 20.0–87.0]. Conclusion In South Korea, the DS population prevalence was 4.03 per 100,000 persons in 2015. The subjects with DS were more likely to accompany CHD and have higher mortality risk than healthy controls.