Miracle-dz/UniFAR-FTUs huggingface dataset:
Striations, integrals, hourglasses, and collapse – thermal instability driven magnetic simulations of molecular clouds

The MHD version of the adaptive mesh refinement (AMR) code, MG, has been employed to study the interaction of thermal instability, magnetic fields and gravity through 3D simulations of the formation of collapsing cold clumps on the scale of a few parsecs, inside a larger molecular cloud. The diffuse atomic initial condition consists of a stationary, thermally unstable, spherical cloud in pressure equilibrium with lower density surroundings and threaded by a uniform magnetic field. This cloud was seeded with 10% density perturbations at the finest initial grid level around n=1.1 cm^{-3} and evolved with self-gravity included from the outset. Several cloud diameters were considered (100 pc, 200 pc and 400 pc) equating to several cloud masses (17,000 Msun, 136,000 Msun and 1.1x10^6 Msun). Low-density magnetic-field-aligned striations were observed as the clouds collapse along the field lines into disc-like structures. The induced flow along field lines leads to oscillations of the sheet about the gravitational minimum and an integral-shaped appearance. When magnetically supercritical, the clouds then collapse and generate hourglass magnetic field configurations with strongly intensified magnetic fields, reproducing observational behaviour. Resimulation of a region of the highest mass cloud at higher resolution forms gravitationally-bound collapsing clumps within the sheet that contain clump-frame supersonic (M~5) and super-Alfvenic (M_A~4) velocities. Observationally realistic density and velocity power spectra of the cloud and densest clump are obtained. Future work will use these realistic initial conditions to study individual star and cluster feedback.
Updated constraints on decaying cold dark matter

In this paper we update the constraints on the simple decaying cold dark matter (DCDM) model with dark radiation (DR) as decay product. We consider two different regimes of the lifetime, i.e. short-lived and long-lived, and use the most recent CMB data from Planck (2018) to infer new constraints on the decay parameters with which we compare the constraints inferred by the previous Planck data (2015). We hereby show that the newest CMB data constrains the fractional amount of DCDM twice as much as the previous data in the long-lived regime, leading to our current best 2σ upper bound of f dcdm < 2.44%. In the short-lived regime, we get a slightly looser 2σ upper bound of f dcdm < 13.1% compared to the previous CMB data. If we include Baryonic Acoustic Oscillations data from BOSS DR-12, the constraints in both the long-lived and the short-lived regimes relax to f dcdm < 2.62% and f dcdm < 1.49%, respectively. We also investigate how this model impacts the Hubble and σ8 tensions, and we find that each of the decay regimes can slightly relieve a different one of the tensions. The model can thus not accommodate both tensions at once, and the improvements on each are not significant. We furthermore improve on previous work by thoroughly analysing the impacts of short-lived DCDM on the radiation density and deriving a mapping between short-lived DCDM and a correction, ΔN eff, to the effective number of massless neutrino species.
Heavy Double Neutron Stars: Birth, Midlife, and Death

Radio pulsar observations probe the lives of Galactic double neutron star (DNS) systems while gravitational waves enable us to study extragalactic DNS in their final moments. By combining measurements from radio and gravitational-wave astronomy, we seek to gain a more complete understanding of DNS from formation to merger. We analyze the recent gravitational-wave binary neutron star mergers GW170817 and GW190425 in the context of other DNS known from radio astronomy. By employing a model for the birth and evolution of DNS, we measure the mass distribution of DNS at birth, at midlife (in the radio), and at death (in gravitational waves). We consider the hypothesis that the high-mass gravitational-wave event GW190425 is part of a subpopulation formed through unstable case BB mass transfer, which quickly merge in ∼10–100 Myr. We find only mild evidence to support this hypothesis and that GW190425 is not a clear outlier from the radio population as previously claimed. If there are fast-merging binaries, we estimate that they constitute 8%–79% of DNS at birth (90% credibility). We estimate the typical delay time between the birth and death of fast-merging binaries to be ≈5–401 Myr (90% credibility). We discuss the implications for radio and gravitational-wave astronomy.
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss

Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature. In this paper, we investigate how L2 normalisation affects the back-propagated descriptor gradients during training. Based on our observations, we propose HyNet, a new local descriptor that leads to state-of-the-art results in matching. HyNet introduces a hybrid similarity measure for triplet margin loss, a regularisation term constraining the descriptor norm, and a new network architecture that performs L2 normalisation of all intermediate feature maps and the output descriptors. HyNet surpasses previous methods by a significant margin on standard benchmarks that include patch matching, verification, and retrieval, as well as outperforming full end-to-end methods on 3D reconstruction tasks.
Crop yield estimation using satellite images: comparison of linear and non-linear models

Development of models for crop yield prediction using remote sensing allows accurate, reliable and timely estimations over large areas. articularly, this information is necessary to ensure the adequacy of a nation’s food supply as well as to aid policy makers and farmers. In Argentina, soybean (Glycine max (L.) Merr. ) and corn (Zea mays L.) are the most important crops. The goal of this research was to develop and evaluate linear and non-linear models to estimate crop yield from satellite data. Particularly, we proposed and applied those models to obtain soybean and corn yield in the central region of Cordoba (Argentina) using Landsat and SPOT images. The models were designed taking into account all or some bands included in the images from one or both satellites. Results showed that models provided a good fit when all images are used, being superior the accuracy obtained by neural networks (NN). For soybean, the best estimation presented a coefficient of determination equal to 0.90 with NN and 0.82 with multiple linear regression models, and for corn 0.92 and 0.88, respectively. This study concludes that Landsat and SPOT images can be effectively used to predict, in early to mid-season crop growth stages, corn and soybean yield.
Bond-weighted tensor renormalization group

We propose an improved tensor renormalization group (TRG) algorithm, the bond-weighted TRG (BTRG). In BTRG, we generalize the conventional TRG by introducing bond weights on the edges of the tensor network. We show that BTRG outperforms the conventional TRG and the higher-order tensor renormalization group with the same bond dimension, while its computation time is almost the same as that of TRG. Furthermore, BTRG can have non-trivial fixed-point tensors at an optimal hyperparameter. We demonstrate that the singular value spectrum obtained by BTRG is invariant under the renormalization procedure in the case of the two-dimensional Ising model at the critical point. This property indicates that BTRG performs the tensor contraction with high accuracy while keeping the scale-invariant structure of tensors.
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance

Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, optimization, and machine learning. Despite its popularity, theoretical guarantees of this method, especially its finite-time performance, are mostly achieved for the linear case while the results for the nonlinear counterpart are very sparse. Motivated by the classic control theory for singularly perturbed systems, we study in this article the asymptotic convergence and finite-time analysis of the nonlinear two-time-scale stochastic approximation. Under some fairly standard assumptions, we provide a formula that explicitly characterizes the rate of convergence of the main iterates to the desired solutions. In particular, we show that the mean square error generated by the method convergences to zero at a rate <inline-formula><tex-math notation="LaTeX">${\mathcal O}(1/k^{2/3})$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the number of iterations. The key idea in our analysis is to properly choose the two step sizes to characterize the coupling between the fast and slow time-scale iterates.
Planothidium sheathii, a new monoraphid diatom species from rivers in California, USA

Planothidium sheathii sp. nov., a new species from rivers in California, USA, is described based on light and scanning electron microscopy. P. sheathii has broadly lanceolate to elliptic valves with obtusely rounded apices and a unique combination of morphological features, as follows: 1) cavum with broad hood aperture that flares out at the valve margin with open borders fused with the neighboring virgae on SV, 2) multiseriate striae, composed of five to six rows of same sized areolae on both valves, interrupted at valve mantle junction, and 3) areolae groups on valve mantle, with rounded areolae on RV, and rounded and elongated areolae on SV. Valve morphology of P. sheathii is discussed in comparison with similar freshwater taxa, from which the new species is clearly distinct.
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks

We present Cyclic-Permutation Invariant Neural Networks, a novel class of neural networks (NNs) designed to be invariant to phase shifts of period-folded periodic sequences by means of ‘symmetry padding’. In the context of periodic variable star light curves, initial phases are exogenous to the physical origin of the variability and should thus be immaterial to the downstream inference application. Although previous work utilizing NNs commonly operated on period-folded light curves, no approach to date has taken advantage of such a symmetry. Across three different data sets of variable star light curves, we show that two implementations of Cyclic-Permutation Invariant Networks—iTCN and iResNet—consistently outperform state-of-the-art non-invariant baselines and reduce overall error rates by between 4 to 22 per cent. Over a 10-class OGLE-III sample, the iTCN/iResNet achieves an average per-class accuracy of 93.4 per cent/93.3 per cent, compared to recurrent NN/random forest accuracies of 70.5 per cent/89.5 per cent in a recent study using the same data. Finding improvement on a non-astronomy benchmark, we suggest that the methodology introduced here should also be applicable to a wide range of science domains where periodic data abounds.
Observational insights on the origin of giant low surface brightness galaxies

Giant low surface brightness galaxies (gLSBGs) with dynamically cold stellar discs reaching the radius of 130 kpc challenge currently considered galaxy formation mechanisms. We analyse new deep long-slit optical spectroscopic observations, archival optical images, and published Hi and optical spectroscopic data for a sample of seven gLSBGs, for which we performed mass modelling and estimated the parameters of dark matter haloes assuming the Burkert dark matter density profile. Our sample is not homogeneous by morphology, parameters of stellar populations, and total mass, however, six of seven galaxies sit on the high-mass extension of the baryonic Tully–Fisher relation. In UGC 1382, we detected a global counterrotation of the stellar high surface brightness (HSB) disc with respect to the extended LSB disc. In UGC 1922 with signatures of a possible merger, the gas counterrotation is seen in the inner disc. Six galaxies host active galactic nuclei, three of which have the estimated black hole masses substantially below those expected for their (pseudo-)bulge properties suggesting poor merger histories. Overall, the morphology, internal dynamics, and low star formation efficiency in the outer discs indicate that the three formation scenarios shape gLSBGs: (i) a two-stage formation when an HSB galaxy is formed first and then grows an LSB disc by accreting gas from an external supply; (ii) an unusual shallow and extended dark matter halo; (iii) a major merger with fine-tuned orbital parameters and morphologies of the merging galaxies.
Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings

Social media produces large amounts of contents every day. To help users quickly capture what they need, keyphrase prediction is receiving a growing attention. Nevertheless, most prior efforts focus on text modeling, largely ignoring the rich features embedded in the matching images. In this work, we explore the joint effects of texts and images in predicting the keyphrases for a multimedia post. To better align social media style texts and images, we propose: (1) a novel Multi-Modality Multi-Head Attention (M3H-Att) to capture the intricate cross-media interactions; (2) image wordings, in forms of optical characters and image attributes, to bridge the two modalities. Moreover, we design a unified framework to leverage the outputs of keyphrase classification and generation and couple their advantages. Extensive experiments on a large-scale dataset newly collected from Twitter show that our model significantly outperforms the previous state of the art based on traditional attention networks. Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.
CharBERT: Character-aware Pre-trained Language Model

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile.In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning. We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously.
Learning Deformable Tetrahedral Meshes for 3D Reconstruction

3D shape representations that accommodate learning-based 3D reconstruction are an open problem in machine learning and computer graphics. Previous work on neural 3D reconstruction demonstrated benefits, but also limitations, of point cloud, voxel, surface mesh, and implicit function representations. We introduce Deformable Tetrahedral Meshes (DefTet) as a particular parameterization that utilizes volumetric tetrahedral meshes for the reconstruction problem. Unlike existing volumetric approaches, DefTet optimizes for both vertex placement and occupancy, and is differentiable with respect to standard 3D reconstruction loss functions. It is thus simultaneously high-precision, volumetric, and amenable to learning-based neural architectures. We show that it can represent arbitrary, complex topology, is both memory and computationally efficient, and can produce high-fidelity reconstructions with a significantly smaller grid size than alternative volumetric approaches. The predicted surfaces are also inherently defined as tetrahedral meshes, thus do not require post-processing. We demonstrate that DefTet matches or exceeds both the quality of the previous best approaches and the performance of the fastest ones. Our approach obtains high-quality tetrahedral meshes computed directly from noisy point clouds, and is the first to showcase high-quality 3D tet-mesh results using only a single image as input. Our project webpage: https://nv-tlabs.github. io/DefTet/
SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions

This paper addresses the problem of optimizing partition functions in a stochastic learning setting. We propose a stochastic variant of the bound majorization algorithm that relies on upper-bounding the partition function with a quadratic surrogate. The update of the proposed method, that we refer to as Stochastic Partition Function Bound (SPFB), resembles scaled stochastic gradient descent where the scaling factor relies on a second order term that is however different from the Hessian. Similarly to quasi-Newton schemes, this term is constructed using the stochastic approximation of the value of the function and its gradient. We prove sub-linear convergence rate of the proposed method and show the construction of its low-rank variant (LSPFB). Experiments on logistic regression demonstrate that the proposed schemes significantly outperform SGD. We also discuss how to use quadratic partition function bound for efficient training of deep learning models and in non-convex optimization.
Error- and Tamper-Tolerant State Estimation for Discrete Event Systems Under Cost Constraints

This article deals with the state estimation problem in discrete-event systems modeled with nondeterministic finite automata, partially observed via a sensor measuring unit whose measurements (reported observed symbols) may be vitiated by a malicious attacker. The attacks considered in this article include arbitrary deletions, insertions, or substitutions of observed symbols by taking into account a bounded number of attacks or, more generally, a total cost constraint (assuming that each deletion, insertion, or substitution bears a positive cost to the attacker). We provide a characterization of the sequences of symbols that match the received sequence of possibly corrupted symbols, and subsequently use them to recursively perform tamper-tolerant state estimation subject to cost constraints. Each step of the recursive state estimation procedure has complexity of <inline-formula><tex-math notation="LaTeX">$O(|X||\Sigma |C)$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$|X|$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$|\Sigma |$</tex-math></inline-formula>) is the number of states (events) of the given finite automaton and <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula> is the maximum total cost that is allowed for all deletions, insertions, and substitutions.
Optimal measurement of field properties with quantum sensor networks.

We consider a quantum sensor network of qubit sensors coupled to a field f ( x ; θ ) analytically parameterized by the vector of parameters θ . The qubit sensors are fixed at positions x 1, …, x d . While the functional form of f ( x ; θ ) is known, the parameters θ are not. We derive saturable bounds on the precision of measuring an arbitrary analytic function q( θ ) of these parameters and construct the optimal protocols that achieve these bounds. Our results are obtained from a combination of techniques from quantum information theory and duality theorems for linear programming. They can be applied to many problems, including optimal placement of quantum sensors, field interpolation, and the measurement of functionals of parametrized fields.
The impact of tillage intensity and meteorological conditions on soil temperature, moisture content and CO2 effluxin maize and spring barley cultivation

With a worldwide increase in the use of sustainable tillage systems, it is important to ascertain their long-term effects on soil properties and greenhouse gas emissions. However, there are not many long-term experiments of this type, and they are conducted in very different climatic and soil conditions. To fill a knowledge gap in this field of study, a long-term, stationary field experiment was set up at Aleksandras Stulginskis University’s Experimental Station (54°52ʹ57 N lat., 23°50ʹ51 E long.) in 1988. The current paper presents the experimental data from the 2015– 2016 period. The soil of the experimental site is Epieutric Endocalcaric Endogleyic Planosol (PL.gln-can-eup) with a texture of loam on heavy loam. The topsoil layer’s characteristics are as follows: pHKCL 6.6–7.0, available phosphorus (P2O5) content 131.1–206.7 mg kg -1, available potassium (K2O) content 72.0–126.9 mg kg -1, humus content 1.68%. This study set out to investigate the effects of long-term application of different tillage systems and meteorological conditions on soil temperature, moisture content and soil surface carbon dioxide (CO2) efflux in the stands of maize and spring barley. The experiment included the following primary tillage methods differing in intensity: 1) conventional ploughing (CP) at a 23–25 cm depth (control treatment), 2) shallow ploughing (SP) at a depth of 12–15 cm, 3) deep cultivation (DC) at a depth of 23–25 cm, 4) shallow cultivation (SC) at a depth of 12–15 cm and 5) no tillage (NT) (direct drilling). The findings of the study suggest that the soil surface CO2 efflux depended on the amount of rainfall during the crop growing season. In a dry year 2016, the soil CO2 efflux was lower than that in a wet year, the differences between the tillage treatments were more distinct, with the least flux being from the NT treatment. No significant differences among the tillage treatments were determined in a wet year 2015. The soil temperature depended on the tillage intensity and the weather conditions during the crop growing season. A lower soil temperature was recorded in the reduced tillage treatments compared with conventional tillage treatments. A negative strong correlation was established between the soil surface CO2 efflux and soil temperature (y = 13.93867 + 0.303x; r = −0.96, P < 0.05). The tillage methods of different intensity did not have significant effect on the moisture content in the soil surface layer; however, in a dry year, the highest moisture content was determined in the no-tillage treatment, while in a wet year the differences were negligible.
Learning Causal Semantic Representation for Out-of-Distribution Prediction

Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output. To address the problem, we propose a Causal Semantic Generative model (CSG) based on a causal thought where the two factors are modeled separately, and develop methods to learn it on a single training domain and predict in a test domain without (OOD generalization) or with unsupervised data (domain adaptation). We prove that under proper conditions CSG identifies the semantic factor by fitting training data, and this semantic identification guarantees the boundedness of OOD generalization error and the success of adaptation. The methods and theory are based on the invariance principle of causal generative mechanisms, which is more fundamental and general than inference invariance. The methods come from a novel design for both efficient learning and easy prediction, following the first principle of variational Bayes and the graphical structure of CSG. Empirical study demonstrates the improved test accuracy for OOD generalization and domain adaptation.