Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiwavelength View of the Rapidly Evolving SN 2018ivc: An Analog of SN IIb 1993J but Powered Primarily by Circumstellar Interaction

SN 2018ivc is an unusual Type II supernova (SN II). It is a variant of SNe IIL, which might represent a transitional case between SNe IIP with a massive H-rich envelope and SNe IIb with only a small amount of the H-rich envelope. However, SN 2018ivc shows an optical light-curve evolution more complicated than that of canonical SNe IIL. In this paper, we present the results of prompt follow-up observations of SN 2018ivc with the Atacama Large Millimeter/submillimeter Array. Its synchrotron emission is similar to that of SN IIb 1993J, suggesting that it is intrinsically an SN IIb–like explosion of an He star with a modest (∼0.5–1M ⊙) extended H-rich envelope. Its radio, optical, and X-ray light curves are explained primarily by the interaction between the SN ejecta and the circumstellar material (CSM); we thus suggest that it is a rare example (and the first involving the “canonical” SN IIb ejecta) for which the multiwavelength emission is powered mainly by the SN–CSM interaction. The inner CSM density, reflecting the progenitor activity in the final decade, is comparable to that of SN IIb 2013cu, which shows a flash spectral feature. The outer CSM density, and therefore the mass-loss rate in the final ∼200 yr, is higher than that of SN 1993J by a factor of ∼5. We suggest that SN 2018ivc represents a missing link between SNe IIP and SNe IIb/Ib/Ic in the binary evolution scenario.
Taylor dispersion and phase mixing in the non-cutoff Boltzmann equation on the whole space

In this paper we describe the long-time behavior of the non-cutoff Boltzmann equation with soft potentials near a global Maxwellian background on the whole space in the weakly collisional limit (i.e. infinite Knudsen number 1/ν → ∞). Specifically, we prove that for initial data sufficiently small (independent of the Knudsen number), the solution displays several dynamics caused by the phase mixing/dispersive effects of the transport operator v · ∇x and its interplay with the singular collision operator. For x-wavenumbers k with |k| ≫ ν, one sees an enhanced dissipation effect wherein the characteristic decay time-scale is accelerated to O(1/ν 1 1+2s |k| 2s 1+2s ), where s ∈ (0, 1] is the singularity of the kernel (s = 1 being the Landau collision operator, which is also included in our analysis); for |k| ≪ ν, one sees Taylor dispersion, wherein the decay is accelerated to O(ν/ |k|). Additionally, we prove almost-uniform phase mixing estimates. For macroscopic quantities as the density ρ, these bounds imply almost-uniformin-ν decay of (t∇x)ρ in Lx due to Landau damping and dispersive decay. CONTENTS
ESTIMATING THE FIRST-LIGHT TIME OF THE TYPE IA SUPERNOVA 2014J IN M82

The Type Ia supernova (SN Ia) 2014J in M82 (d ≈ 3.5 Mpc) was serendipitously discovered by S. Fossey's group on 2014 January 21 UT and has been confirmed to be the nearest known SN Ia since at least SN 1986G. Although SN 2014J was not discovered until ∼7 days after first light, both the Katzman Automatic Imaging Telescope at Lick Observatory and K. Itagaki obtained several prediscovery observations of SN 2014J. With these data, we are able to constrain the object's time of first light to be January 14.75 UT, only 0.82 ± 0.21 days before our first detection. Interestingly, we find that the light curve is well described by a varying power law, much like SN 2013dy, which makes SN 2014J the second example of a changing power law in early-time SN Ia light curves. A low-resolution spectrum taken on January 23.388 UT, ∼8.70 days after first light, shows that SN 2014J is a heavily reddened but otherwise spectroscopically normal SN Ia.
Geometry-induced reduction of the critical current in superconducting nanowires

Reduction of the critical current in narrow superconducting NbN lines with sharp and rounded bends with respect to the critical current in straight lines was studied at different temperatures.We compare our experimental results with the reduction expected in the framework of the London model and the Ginsburg-Landau model. We have experimentally found that the reduction is significantly less than either model predicts. We also show that in our NbN lines the bends mostly contribute to the reduction of the critical current at temperatures well below the superconducting transition temperature.
Further stable neutron star models from f(R) gravity

Neutron star models in perturbative $f(R)$ gravity are considered with realistic equations of state. In particular, we consider the FPS, SLy and other equations of state and a case of piecewise equation of state for stars with quark cores. The mass-radius relations for $f(R)=R+R(e^{-R/R_{0}}-1)$ model and for $R^2$ models with logarithmic and cubic corrections are obtained. In the case of $R^2$ gravity with cubic corrections, we obtain that at high central densities ($\rho>10\rho_{ns}$, where $\rho_{ns}=2.7\times 10^{14}$ g/cm$^{3}$ is the nuclear saturation density), stable star configurations exist. The minimal radius of such stars is close to $9$ km with maximal mass $\sim 1.9 M_{\odot}$ (SLy equation). A similar situation takes place for AP4 and BSK20 EoS. Such an effect can give rise to more compact stars than in General Relativity. If observationally identified, such objects could constitute a formidable signature for modified gravity at astrophysical level. Another interesting result can be achieved in modified gravity with only a cubic correction. For some EoS, the upper limit of neutron star mass increases and therefore these EoS can describe realistic star configurations (although, in General Relativity, these EoS are excluded by observational constraints).
Andreev spin qubits in multichannel Rashba nanowires

We theoretically analyze the Andreev bound states and their coupling to external radiation in superconductor-nanowire-superconductor Josephson junctions. We provide an effective Hamiltonian for the junction projected onto the Andreev level subspace and incorporating the effects of nanowire multichannel structure, Rashba spin-orbit coupling, and Zeeman field. Based on this effective model, we investigate the dependence of the Andreev levels and the matrix elements of the current operator on system parameters such as chemical potential, nanowire dimensions, and normal transmission. We show that the combined effect of the multichannel structure and the spin-orbit coupling gives rise to finite current matrix elements between odd states having different spin polarizations. Moreover, our analytical results allow to determine the appropriate parameters range for the detection of transitions between even as well as odd states in circuit QED like experiments, which may provide a way for the Andreev spin qubit manipulation.
THE ASTRONOMY OF TWO INDIAN TRIBES: THE BANJARAS AND THE KOLAMS

We report field studies of the astronomical beliefs of two Indian tribes: the Banjaras and the Kolams. The Banjaras are an ancient tribe connected with the gypsies of Europe while the Kolams have been foragers until recently. They share their landscape with each other and also with the Gonds whose astronomy was reported previously (Vahia and Halkare, 2013). The primary profession of the Banjaras was trade, based on the large-scale movement of goods over long distances, but their services were taken over by the railways about one hundred years ago. Since then the Banjaras have begun the long journey to a sedentary lifestyle. Meanwhile, the Kolams were foragers until about fifty years ago when the Government of India began to help them lead a settled life. Here, we compare their astronomical beliefs of the Banjaras and the Kolams, which indicate the strong sense of identity that each community possesses. Our study also highlights their perspective about the sky and its relation to their daily lives. We show that apart from the absolute importance of the data on human perception of the sky, the data also reveal subtle aspects of interactions between physically co-located but otherwise isolated communities as well as their own lifestyles. We also show that there is a strong relationship between profession and perspective of the sky.
Optimal discrete pipe sizing for tree-shaped CO2 networks

Many energy-intensive industries, like the steel industry, plan to switch to renewable energy sources. Other industries, such as the cement industry, have to rely on carbon capture utilization and storage (CCUS) technologies to reduce their production processes' inevitable carbon dioxide (CO2) emissions. However, a new transport infrastructure needs to be established to connect the point of capture and the point of storage or utilization. Given a tree-shaped network transporting captured CO2 from multiple sources to a single sink, we investigate how to select optimal pipeline diameters from a discrete set of diameters. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO2 regarding temperature and pressure changes. We propose an iterative algorithm that splits the problem into two parts: a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and b) the thermophysical modeling, including mixing effects, the Joule-Thomson effect, and the heat exchange with the surrounding environment. We show the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO2 network in Germany.
T-Cyg1-12664: a low-mass chromospherically active eclipsing binary in the Kepler field

The eclipsing binary T-Cyg1-12664 has been observed both spectroscopically and photo- metrically, and the radial velocities of both the components and the ground-based VRI light curves have been obtained. The Kepler R data and radial velocities for the system have been analysed simultaneously. Masses and radii have been obtained as 0.680 ± 0.021 Mand 0.613 ± 0.007 Rfor the primary star and 0.341 ± 0.012 Mand 0.897 ± 0.012 Rfor the secondary star. The distance to the system has been estimated as 127 ± 14 pc. The ob- served wave-like distortion at out-of-eclipse is modelled with two separate spots on the more massive star, which is also confirmed by the Ca II K and H emission lines in its spectra. The locations of the components in the mass-radius and mass-effective temperature planes have been compared with the well-determined low-mass components of eclipsing binaries as well as with the theoretical models. While the radius of the primary star is consistent with the main-sequence stars, the radius of the less massive component appears to be 2.8 times larger than that of the main-sequence models. A comparison of the radii of low-mass stars with the models reveals that the observationally determined radii begin to deviate from the models with a mass of 0.27 M� , and suddenly reach to maximum deviation at a mass of 0.34 M� . Then, the deviations begin to decrease up to the solar mass. The maximum deviation, seen at a mass of about 0.34 M� , is very close to the mass of fully convective stars, as suggested by theoretical studies. A third star in the direction of the eclipsing pair has been detected from our VRI images. The observed infrared excess of the binary most probably arises from this star, which can be radiated mostly in the infrared bands.
DeepE: a deep neural network for knowledge graph embedding

Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow models might suffice for accurately representing the structure of the simple KGs. In this paper, we propose a neural network based model, named DeepE, to address the problem, which stacks multiple building blocks to predict the tail entity based on the head entity and the relation. Each building block is an addition of a linear and a non-linear function. The stacked building blocks are equivalent to a group of learning functions with different non-linear depth. Hence, DeepE allows deep functions to learn deep features, and shallow functions to learn shallow features. Through extensive experiments, we find DeepE outperforms other state-of-the-art baseline methods. A major advantage of DeepE is the robustness. DeepE achieves a Mean Rank (MR) score that is 6%, 30%, 65% lower than the best baseline methods on FB15k-237, WN18RR and YAGO3-10. Our design makes it possible to train much deeper networks on KGE, e.g. 40 layers on FB15k-237, and without scarifying precision on simple relations.
The impact of numerical viscosity in SPH simulations of galaxy clusters

The goal of this paper is to investigate in N-body/SPH hydrodynamical cluster simulations the impact of artificial viscosity on the ICM thermal and velocity field statistical properties. To properly reduce the effects of artificial viscosity, a time-dependent artificial viscosity scheme is implemented in an SPH code in which each particle has its own viscosity parameter, whose time evolution is governed by the local shock conditions. The new SPH code is verified in a number of test problems with known analytical or numerical reference solutions and is then used to construct a large set of N-body/SPH hydrodynamical cluster simulations. These simulations are designed to study in SPH simulations the impact of artificial viscosity on the thermodynamics of the ICM and its velocity field statistical properties by comparing results extracted at the present epoch from runs with different artificial viscosity parameters, cluster dynamical states, numerical resolution, and physical modeling of the gas. Spectral properties of the gas velocity field are investigated by measuring the velocity power spectrum E(k) for the simulated clusters. Over a limited range, the longitudinal component Ec(k) exhibits a Kolgomorov-like scaling ∝k −5/3 , whilst the solenoidal power spectrum component Es(k) is strongly influenced by numerical resolution effects. The dependence of the spectra E(k) on dissipative effects is found to be significant at length scales 256 3 gas particles are necessary to provide a correct description of turbulent spectral properties over a decade in wavenumbers, whilst radial profiles of thermodynamic variables can be reliably obtained using N > 64 3 particles. Finally, simulations in which the gas can cool radiatively are characterized by the presence in the cluster inner regions of high levels of turbulence, generated by the interaction of the compact cool gas core with the ambient medium. These findings strongly support the viability of a turbulent heating model in which radiative losses in the core are compensated by heat diffusion and viscous dissipation of turbulent motion.
Directed Acyclic Outerplanar Graphs Have Constant Stack Number

The stack number of a directed acyclic graph G is the minimum k for which there is a topological ordering of G and a k-coloring of the edges such that no two edges of the same color cross, i.e., have alternating endpoints along the topological ordering. We prove that the stack number of directed acyclic outerplanar graphs is bounded by a constant, which gives a positive answer to a conjecture by Heath, Pemmaraju and Trenk [SIAM J. Computing, 1999]. As an immediate consequence, this shows that all upward outerplanar graphs have constant stack number, answering a question by Bhore et al. [GD 2021] and thereby making significant progress towards the problem for general upward planar graphs originating from Nowakowski and Parker [Order, 1989]. As our main tool we develop the novel technique of directed H-partitions, which might be of independent interest.We complement the bounded stack number for directed acyclic outerplanar graphs by constructing a family of directed acyclic 2-trees that have unbounded stack number, thereby refuting a conjecture by Nöllenburg and Pupyrev [GD 2023].
Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations

Randomized subspace approximation with"matrix sketching"is an effective approach for constructing approximate partial singular value decompositions (SVDs) of large matrices. The performance of such techniques has been extensively analyzed, and very precise estimates on the distribution of the residual errors have been derived. However, our understanding of the accuracy of the computed singular vectors (measured in terms of the canonical angles between the spaces spanned by the exact and the computed singular vectors, respectively) remains relatively limited. In this work, we present bounds and estimates for canonical angles of randomized subspace approximation that can be computed efficiently either a priori or a posterior. Under moderate oversampling in the randomized SVD, our prior probabilistic bounds are asymptotically tight and can be computed efficiently, while bringing a clear insight into the balance between oversampling and power iterations given a fixed budget on the number of matrix-vector multiplications. The numerical experiments demonstrate the empirical effectiveness of these canonical angle bounds and estimates on different matrices under various algorithmic choices for the randomized SVD.
ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation

Over the past few years, AI methods of generating images have been increasing in capabilities, with recent breakthroughs enabling high-resolution, photorealistic"deepfakes"(artificially generated images with the purpose of misinformation or harm). The rise of deepfakes has potential for social disruption. Recent work has proposed using ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge) and attested cameras to verify that images were taken by a camera. ZK-SNARKs allow verification of image transformations non-interactively (i.e., post-hoc) with only standard cryptographic hardness assumptions. Unfortunately, this work does not preserve input privacy, is impractically slow (working only on 128$\times$128 images), and/or requires custom cryptographic arguments. To address these issues, we present zk-img, a library for attesting to image transformations while hiding the pre-transformed image. zk-img allows application developers to specify high level image transformations. Then, zk-img will transparently compile these specifications to ZK-SNARKs. To hide the input or output images, zk-img will compute the hash of the images inside the ZK-SNARK. We further propose methods of chaining image transformations securely and privately, which allows for arbitrarily many transformations. By combining these optimizations, zk-img is the first system to be able to transform HD images on commodity hardware, securely and privately.
Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter

Misinformation spread over social media has become an undeniable infodemic. However, not all spreading claims are made equal. If propagated, some claims can be destructive, not only on the individual level, but to organizations and even countries. Detecting claims that should be prioritized for fact-checking is considered the first step to fight against spread of fake news. With training data limited to a handful of languages, developing supervised models to tackle the problem over lower-resource languages is currently infeasible. Therefore, our work aims to investigate whether we can use existing datasets to train models for predicting worthiness of verification of claims in tweets in other languages. We present a systematic comparative study of six approaches for cross-lingual check-worthiness estimation across pairs of five diverse languages with the help of Multilingual BERT (mBERT) model. We run our experiments using a state-of-the-art multilingual Twitter dataset. Our results show that for some language pairs, zero-shot cross-lingual transfer is possible and can perform as good as monolingual models that are trained on the target language. We also show that in some languages, this approach outperforms (or at least is comparable to) state-of-the-art models.
Large Language Models with Controllable Working Memory

Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), owing to their excellent understanding and generation abilities. Remarkably, what further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. While many downstream applications provide the model with an informational context to aid its performance on the underlying task, how the model's world knowledge interacts with the factual information presented in the context remains under explored. As a desirable behavior, an LLM should give precedence to the context whenever it contains task-relevant information that conflicts with the model's memorized knowledge. This enables model predictions to be grounded in the context, which can then be used to update or correct specific model predictions without frequent retraining. By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge. In this paper, we undertake a first joint study of the aforementioned two properties, namely controllability and robustness, in the context of LLMs. We demonstrate that state-of-the-art T5 and PaLM (both pretrained and finetuned) could exhibit poor controllability and robustness, which do not scale with increasing model size. As a solution, we propose a novel method - Knowledge Aware FineTuning (KAFT) - to strengthen both controllability and robustness by incorporating counterfactual and irrelevant contexts to standard supervised datasets. Our comprehensive evaluation showcases the utility of KAFT across model architectures and sizes.
A CLASSICAL MORPHOLOGICAL ANALYSIS OF GALAXIES IN THE SPITZER SURVEY OF STELLAR STRUCTURE IN GALAXIES (S4G)

The Spitzer Survey of Stellar Structure in Galaxies (S4G) is the largest available database of deep, homogeneous middle-infrared (mid-IR) images of galaxies of all types. The survey, which includes 2352 nearby galaxies, reveals galaxy morphology only minimally affected by interstellar extinction. This paper presents an atlas and classifications of S4G galaxies in the Comprehensive de Vaucouleurs revised Hubble-Sandage (CVRHS) system. The CVRHS system follows the precepts of classical de Vaucouleurs morphology, modified to include recognition of other features such as inner, outer, and nuclear lenses, nuclear rings, bars, and disks, spheroidal galaxies, X patterns and box/peanut structures, OLR subclass outer rings and pseudorings, bar ansae and barlenses, parallel sequence late-types, thick disks, and embedded disks in 3D early-type systems. We show that our CVRHS classifications are internally consistent, and that nearly half of the S4G sample consists of extreme late-type systems (mostly bulgeless, pure disk galaxies) in the range Scd-Im. The most common family classification for mid-IR types S0/a to Sc is SA while that for types Scd to Sm is SB. The bars in these two type domains are very different in mid-IR structure and morphology. This paper examines the bar, ring, and type classification fractions in the sample, and also includes several montages of images highlighting the various kinds of “stellar structures” seen in mid-IR galaxy morphology.