Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching for Converging Flows of Atomic Gas onto a Molecular Cloud

We present new observations of [C ii] 2 P 3/2 → 2 P 1/2 fine structure line emission from an isolated molecular cloud using the upGREAT instrument on board SOFIA. These data are analyzed together with archival CO J=1–0 and H i 21 cm emission spectra to investigate the role of converging atomic gas flows in the formation of molecular clouds. Bright [C ii] emission is detected throughout the mapped area that likely originates from photodissociation regions excited by UV radiation fields produced by newborn stars within the cloud. Upon spatial averaging of the [C ii] spectra, we identify weak [C ii] emission within velocity intervals where the H i 21 cm line is brightest; these are blueshifted relative to velocities of the CO and bright [C ii] emission by 4 km s−1. The brightness temperatures, velocity dispersions, and alignment with H i 21 cm velocities connect this [C ii] emission component to the cold, neutral atomic gas of the interstellar medium, known as the cold, neutral medium (CNM). We propose that this CNM feature is an accretion flow onto the farside of the existing molecular cloud. The mass infall rate is 3.2 × 10−4 M ⊙ yr−1. There is no direct evidence of a comparable redshifted component in the [C ii] or H i 21 cm spectral lines that would indicate the presence of a converging flow.
MEE: A Novel Multilingual Event Extraction Dataset

Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE. To foster future research in this direction, our dataset will be publicly available.
Comment on ``Band structure engineering of graphene by strain: First-principles calculations''

In their first-principles calculations of the electronic band structure of graphene under uniaxial strain, Gui, Li, and Zhong, [Phys. Rev. B 78, 075435 (2008)] have found opening of band gaps at the Fermi level. This finding is in conflict with the tight-binding description of graphene which is closed gap for small strains. In this Comment, we present first-principles calculations which refute the claim that strain opens band gaps in graphene.
M Subdwarf Research. III. Spectroscopic Diagnostics for Breaking Parameter Degeneracy

To understand the parameter degeneracy of M subdwarf spectra at low resolution, we assemble a large number of spectral features in the wavelength range 0.6–2.5 μm with band strength quantified by narrowband indices. Based on the index trends of BT-Settl model sequences, we illustrate how the main atmospheric parameters (T eff, log g, [M/H], and [α/Fe]) affect each spectral feature differently. Furthermore, we propose a four-step process to determine the four parameters sequentially, which extends the basic idea proposed by Jao et al. Each step contains several spectral features that break the degeneracy effect when determining a specific stellar parameter. Finally, the feasibility of each spectroscopic diagnostic with different spectral quality is investigated. The result is resolution-independent down to R ∼ 200.
Switching Attention in Time-Varying Environments via Bayesian Inference of Abstractions

Motivated by the goal of endowing robots with a means for focusing attention in order to operate reliably in complex, uncertain, and time-varying environments, we consider how a robot can (i) determine which portions of its environment to pay attention to at any given point in time, (ii) infer changes in context (e.g., task or environment dynamics), and (iii) switch its attention accordingly. In this work, we tackle these questions by modeling context switches in a time-varying Markov decision process (MDP) framework. We utilize the theory of bisimulation-based state abstractions in order to synthesize mechanisms for paying attention to context-relevant information. We then present an algorithm based on Bayesian inference for detecting changes in the robot's context (task or environment dynamics) as it operates online, and use this to trigger switches between different abstraction-based attention mechanisms. Our approach is demonstrated on two examples: (i) an illustrative discrete-state tracking problem, and (ii) a continuous-state tracking problem implemented on a quadrupedal hardware platform. These examples demonstrate the ability of our approach to detect context switches online and robustly ignore task-irrelevant distractors by paying attention to context-relevant information.
A Model of Seasonal Savanna Dynamics

We introduce a mathematical model of savanna vegetation dynamics. The usual approach of nonequilibrium ecology is extended by including the impact of wet and dry seasons. We present and rigorously analyze a model describing a mixed woodland-grassland ecosystem with stochastic environmental noise in the form of vegetation biomass losses manifesting fires. Both, the probability of ignition and the strength of these losses depend on the current season (as well as vegetation growth rates etc.). Formally it requires an introduction and analysis of a system that is a piecewise deterministic Markov process with parameters switching between given constant periods of time. We study the long time behavior of time averages for such processes.
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling

Training Graph Neural Networks, on graphs containing billions of vertices and edges, at scale using minibatch sampling poses a key challenge: strong-scaling graphs and training examples results in lower compute and higher communication volume and potential performance loss. DistGNN-MB employs a novel Historical Embedding Cache combined with compute-communication overlap to address this challenge. On a 32-node (64-socket) cluster of $3^{rd}$ generation Intel Xeon Scalable Processors with 36 cores per socket, DistGNN-MB trains 3-layer GraphSAGE and GAT models on OGBN-Papers100M to convergence with epoch times of 2 seconds and 4.9 seconds, respectively, on 32 compute nodes. At this scale, DistGNN-MB trains GraphSAGE 5.2x faster than the widely-used DistDGL. DistGNN-MB trains GraphSAGE and GAT 10x and 17.2x faster, respectively, as compute nodes scale from 2 to 32.
DeepG2P: Fusing Multi-Modal Data to Improve Crop Production

Agriculture is at the heart of the solution to achieve sustainability in feeding the world population, but advancing our understanding on how agricultural output responds to climatic variability is still needed. Precision Agriculture (PA), which is a management strategy that uses technology such as remote sensing, Geographical Information System (GIS), and machine learning for decision making in the ﬁeld, has emerged as a promising approach to enhance crop production, increase yield, and reduce water and nutrient losses and environmental impacts. In this context, multiple models to predict agricultural phenotypes, such as crop yield, from genomics (G), environment (E), weather and soil, and ﬁeld management practices (M) have been developed. These models have traditionally been based on mechanistic or statistical approaches. However, AI approaches are intrinsically well-suited to model complex interactions and have more recently been developed, outperforming classical methods. Here, we present a Natural Language Processing (NLP)-based neural network architecture to process the G, E and M inputs and their interactions. We show that by modeling DNA as natural language, our approach performs better than previous approaches when tested for new environments and similarly to other approaches for unseen seed varieties.
Palm Vein Recognition via Multi-task Loss Function and Attention Layer

With the improvement of arithmetic power and algorithm accuracy of personal devices, biological features are increasingly widely used in personal identification, and palm vein recognition has rich extractable features and has been widely studied in recent years. However, traditional recognition methods are poorly robust and susceptible to environmental influences such as reflections and noise. In this paper, a convolutional neural network based on VGG-16 transfer learning fused attention mechanism is used as the feature extraction network on the infrared palm vein dataset. The palm vein classification task is first trained using palmprint classification methods, followed by matching using a similarity function, in which we propose the multi-task loss function to improve the accuracy of the matching task. In order to verify the robustness of the model, some experiments were carried out on datasets from different sources. Then, we used K-means clustering to determine the adaptive matching threshold and finally achieved an accuracy rate of 98.89% on prediction set. At the same time, the matching is with high efficiency which takes an average of 0.13 seconds per palm vein pair, and that means our method can be adopted in practice.
Encrypted Operator Computing: a novel scheme for computation on encrypted data

We introduce a new approach to computation on encrypted data -- Encrypted Operator Computing (EOC) -- as an alternative to Fully Homomorphic Encryption (FHE). Given a plaintext vector $|{x}\rangle$, $x\in \{0,1\}^n$, and a function $F(x)$ represented as an operator $\hat F$, $\hat F\;|{x}\rangle = |{F(x)}\rangle$, the EOC scheme is based on obfuscating the conjugated operator (circuit) $\hat{F}^E = \hat E\;\hat F\;\hat{E}^{-1}$ that implements computation on encrypted data, $\hat E |{x}\rangle$. The construction of EOC hinges on the existence of a two-stage NC$^1$ reversible-circuit-based IND-CCA2 cipher $\hat{E} = \hat{N} \hat{L}$, where $\hat{L}$ and $\hat{N}$ represent, respectively, linear and non-linear NC$^1$ tree-structured circuits of 3-bit reversible gates. We make and motivate security assumptions about such a NC$^1$ cipher. Furthermore, we establish the polynomial complexity of the obfuscated circuit, the evaluator $O(\hat{F}^E)$, by proving that: (a) conjugation of each gate of $F$ with $\hat{L}$ yields a polynomial number of gates; and (b) the subsequent conjugation with $\hat{N}$ yields a polynomial number of ``chips,'' $n$-input/$n$-output reversible functions, with outputs expressed as polynomial-sized ordered Binary Decision Diagrams (OBDDs). The security of individual chips is connected to the notion of Best Possible Obfuscators which relies on poly-size OBDDs and the fact that OBDDs are normal forms that expose the functionality but hide the gate implementation of the chip. We conjecture that the addition of random pairs of NOTs between layers of $\hat{N}$ during the construction of $F^E$, a device analogous to the AddRoundKey rounds of AES, ensures the security of the the evaluator. We also present a generalization to asymmetric encryption.
SVD Factorization for Tall-and-Fat Matrices on Parallel Architectures

We demonstrate an implementation for an approximate rank-k SVD factorization, combining well-known randomized projection techniques with previously known paralel solutions in order to compute steps of the random projection based SVD procedure. We structure the problem in a way that it reduces to fast computation around $k \times k$ matrices computed on a single machine, greatly easing the computability of the problem. The paper is also a tutorial on paralel linear algebra methods using a plain architecture without burdensome frameworks.
Bounds imposed on the sheath velocity of a dense plasma focus by conservation laws and ionization stability condition

Experimental data compiled over five decades of dense plasma focus research are consistent with the snowplow model of sheath propagation, based on the hypothetical balance between magnetic pressure driving the plasma into neutral gas ahead and “wind pressure” resisting its motion. The resulting sheath velocity, or the numerically proportional “drive parameter,” is known to be approximately constant for devices optimized for neutron production over 8 decades of capacitor bank energy. This paper shows that the validity of the snowplow hypothesis, with some correction, as well as the non-dependence of sheath velocity on device parameters, have their roots in local conservation laws for mass, momentum, and energy coupled with the ionization stability condition. Both upper and lower bounds on sheath velocity are shown to be related to material constants of the working gas and independent of the device geometry and capacitor bank impedance.
The global non-linear stability of the Kerr-de Sitter family of black holes

We establish the full global non-linear stability of the Kerr-de Sitter family of black holes, as solutions of the initial value problem for the Einstein vacuum equations with positive cosmological constant, for small angular momenta, and without any symmetry assumptions on the initial data. We achieve this by extending the linear and non-linear analysis on black hole spacetimes described in a sequence of earlier papers by the authors: We develop a general framework which enables us to deal systematically with the diffeomorphism invariance of Einstein's equations. In particular, the iteration scheme used to solve Einstein's equations automatically finds the parameters of the Kerr-de Sitter black hole that the solution is asymptotic to, the exponentially decaying tail of the solution, and the gauge in which we are able to find the solution; the gauge here is a wave map/DeTurck type gauge, modified by source terms which are treated as unknowns, lying in a suitable finite-dimensional space.
Complete history of the observable 21 cm signal from the first stars during the pre-reionization era

We present the first complete calculation of the history of the inhomogeneous 21-cm signal from neutral hydrogen during the era of the first stars. We use hybrid computational methods to capture the large-scale distribution of the first stars, whose radiation couples to the neutral hydrogen emission, and to evaluate the 21-cm signal from z ~ 15-35. In our realistic picture large-scale fluctuations in the 21-cm signal are sourced by the inhomogeneous density field and by the Ly-alpha and X-ray radiative backgrounds. The star formation is suppressed by two spatially varying effects: negative feedback provided by the Lyman-Werner radiative background, and supersonic relative velocities between the gas and dark matter. Our conclusions are quite promising: we find that the fluctuations imprinted by the inhomogeneous Ly-alpha background in the 21-cm signal at z ~ 25 should be detectable with the Square Kilometer Array.
Below-ground traits mediate tree survival in a tropical dry forest restoration

Reforestation is one of our most promising natural climate solutions, and one that addresses the looming biodiversity crisis. Tree planting can catalyse forest community reassembly in degraded landscapes where natural regeneration is slow, however, tree survival rates vary remarkably across projects. Building a trait-based framework for tree survival could streamline species selection in a way that generalizes across ecosystems, thereby increasing the effectiveness of the global restoration movement. We investigated how traits mediated seedling survival in a tropical dry forest restoration, and how traits were coordinated across plant structures. We examined growth and survival of 14 species for 2 years and measured six below-ground and 22 above-ground traits. Species-level survival ranged widely from 7.8% to 90.1%, and a model including growth rate, below-ground traits and their interaction explained more than 73% of this variation. A strong interaction between below-ground traits and growth rate indicated that selecting species with fast growth rates can promote establishment, but this effect was most apparent for species that invest in thick fine roots and deep root structures. Overall, results emphasize the prominent role of below-ground traits in determining early restoration outcomes, and highlight little above- and below-ground trait coordination, providing a path forward for tropical dry forest restoration efforts. This article is part of the theme issue ‘Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration’.
Bubble dynamics and atomization in evaporating polymeric droplets

Abstract We investigate the interaction of an aqueous polymeric droplet with a tuneable continuous laser in an acoustically levitated environment. The effect of laser irradiation intensity and polymeric concentration on various spatio-temporal parameters is unearthed using high-speed shadowgraphy and theoretical scaling analysis. We observe four temporal phases: droplet evaporation, vapour bubble growth followed by membrane inflation, bubble/membrane rupture through hole nucleation and droplet breakup. During the initial droplet evaporation phase, concentration build-up at the droplet surface beyond a critical limit leads to the formation of a skin layer. It is revealed that, at a given location inside the droplet, hot spots occur, and the maximum temperature at the hot spots scales linearly with irradiation intensity until a bubble nucleates. The low-intensity laser interaction leads to symmetric membrane inflation that eventually forms holes at droplet poles and cracks on the shell surface. On the contrary, high intensity causes early bubble nucleation followed by asymmetric membrane inflation that eventually ruptures through multiple hole formation. Furthermore, the growth and rupture of the membrane is followed by a catastrophic breakup of the droplet. Two dominant atomization modes are reported at significantly high irradiation intensities: stable sheet collapse and unstable sheet breakup. The evolution of droplets into a stable/unstable sheet follows universally observed ligament and hole dynamics. A regime map is shown to describe the influence of polymer concentration and irradiation intensity on the strength and mode of droplet atomization.
Finite-size scaling of the stochastic susceptible-infected-recovered model

The critical behavior of the stochastic susceptible-infected-recovered model on a square lattice is obtained by numerical simulations and finite-size scaling. The order parameter as well as the distribution in the number of recovered individuals is determined as a function of the infection rate for several values of the system size. The analysis around criticality is obtained by exploring the close relationship between the present model and standard percolation theory. The quantity UP , equal to the ratio U between the second moment and the squared first moment of the size distribution multiplied by the order parameter P , is shown to have, for a square system, a universal value 1.0167(1) that is the same as for site and bond percolation, confirming further that the SIR model is also in the percolation class. PACS numbers: 05.70. Ln, 05.50. +q, 05.65. +b Finite-size scaling of the stochastic susceptible-infected-recovered model 2
Algorithms for entanglement renormalization

We describe an iterative method to optimize the multiscale entanglement renormalization ansatz for the low-energy subspace of local Hamiltonians on a D -dimensional lattice. For translation-invariant systems the cost of this optimization is logarithmic in the linear system size. Specialized algorithms for the treatment of infinite systems are also described. Benchmark simulation results are presented for a variety of one-dimensional systems, namely, Ising, Potts, XX, and Heisenberg models. The potential to compute expected values of local observables, energy gaps, and correlators is investigated.
THE SPITZER C2D SURVEY OF NEARBY DENSE CORES. XI. INFRARED AND SUBMILLIMETER OBSERVATIONS OF CB130

We present new observations of the CB130 region composed of three separate cores. Using the Spitzer Space Telescope, we detected a Class 0 and a Class II object in one of these, CB130-1. The observed photometric data from Spitzer and ground-based telescopes are used to establish the physical parameters of the Class 0 object. Spectral energy distribution fitting with a radiative transfer model shows that the luminosity of the Class 0 object is 0.14–0.16 L☉, which is low for a protostellar object. In order to constrain the chemical characteristics of the core having the low-luminosity object, we compare our molecular line observations to models of lines including abundance variations. We tested both ad hoc step function abundance models and a series of self-consistent chemical evolution models. In the chemical evolution models, we consider a continuous accretion model and an episodic accretion model to explore how variable luminosity affects the chemistry. The step function abundance models can match observed lines reasonably well. The best-fitting chemical evolution model requires episodic accretion and the formation of CO2 ice from CO ice during the low-luminosity periods. This process removes C from the gas phase, providing a much improved fit to the observed gas-phase molecular lines and the CO2 ice absorption feature. Based on the chemical model result, the low luminosity of CB130-1 is explained better as a quiescent stage between episodic accretion bursts rather than being at the first hydrostatic core stage.
Effects of Early ASL Exposure on Fingerspelling Accuracy in Deaf Adults

Abstract:D/deaf children of Deaf parents reportedly begin learning finger-spelling as young as thirteen months old, but deaf children born to hearing, nonsigning parents lack natural access to the native (spoken) language of their families, often exhibiting later language development. Forty-four deaf adults participated in a fingerspelling test of decoding-encoding pseudowords. The participants offered self-reported demographic information, including age at initial American Sign Language (ASL) exposure and parent hearing status. Analysis of variance using post hoc grouping of participants revealed significantly higher scores on the fingerspelling test among participants with early ASL exposure (p = .000; ηp2 = 0.45), and those with two D/deaf parents (p = .002; ηp2 = 0.23). Development and refinement of a fingerspelling test to be used with both D/deaf and hearing, native, and developing signers should benefit professionals working with D/deaf and hearing children and their D/deaf or hearing parents.