Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple and Efficient Lagrange Multiplier Based Mixed Finite Element for Gradient Damage

A novel finite element formulation for gradient-regularized damage models is presented which allows for the robust, efficient, and mesh-independent simulation of damage phenomena in engineering and biological materials. The paper presents a Lagrange multiplier based mixed finite element formulation for finite strains. Thereby, no numerical stabilization or penalty parameters are required. On the other hand, no additional degrees of freedom appear for the Lagrange multiplier which is achieved through a suitable FE-interpolation scheme allowing for static condensation. In contrast to competitive approaches from the literature with similar efficiency, the proposed formulation does not require cross-element information and thus, a straightforward implementation using standard element routine interfaces is enabled. Numerical tests show mesh-independent solutions, robustness of the solution procedure for states of severe damage and under cyclic loading conditions. It is demonstrated that the computing time of the gradient damage calculations exceeds the one of purely elastic computations only by an insignificant amount. Furthermore, an improved convergence behavior compared to alternative approaches is shown.
Pruning Very Deep Neural Network Channels for Efficient Inference

—In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhances the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5 × speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2 × speed-up respectively, which is signiﬁcant. Our code has been made publicly available.
The nature of the giant diffuse non-thermal source in the A3411-A3412 complex

VLA deep radio images at 1.4 GHz in total intensity and polarization reveal a di use nonthermal source in the interacting clusters A3411 ‐ A3412. Moreover a small-size low power radio halo at the center of the merging cluster A3411 is found. We present here new optical and X-ray data and discuss the nature and properties of the di use non-thermal source. We suggest that the giant di use radio source is related to the presence of a large scale filamentary structure and to multiple mergers in the A3411‐A3412 complex.
Approximating Flexible Graph Connectivity via Räcke Tree based Rounding

Flexible graph connectivity is a new network design model introduced by Adjiashvili. It has seen several recent algorithmic advances. Despite these, the approximability even in the setting of a single-pair $(s,t)$ is poorly understood. In our recent work, we raised the question of whether there is poly-logarithmic approximation for the survivable network design version (Flex-SNDP) when the connectivity requirements are fixed constants. In this paper, we adapt a powerful framework for survivable network design recently developed by Chen, Laekhanukit, Liao, and Zhang to give an affirmative answer to the question. The framework of is based on R\"acke trees and group Steiner tree rounding. The algorithm and analysis also establishes an upper bound on the integrality gap of an LP relaxation for Flex-SNDP.
EIGER. II. First Spectroscopic Characterization of the Young Stars and Ionized Gas Associated with Strong Hβ and [O iii] Line Emission in Galaxies at z = 5–7 with JWST

We present emission-line measurements and physical interpretations for a sample of 117 [O iii] emitting galaxies at z = 5.33–6.93, using the first deep JWST/NIRCam wide-field slitless spectroscopic observations. Our 9.7 hr integration is centered upon the z = 6.3 quasar J0100+2802—the first of six fields targeted by the EIGER survey—and covers λ = 3–4 μm. We detect 133 [O iii] doublets, but close pairs motivated by their small scale clustering excess. The galaxies are characterized by a UV luminosity M UV ∼ −19.6 (−17.7 to −22.3), stellar mass ∼108 (106.8−10.1) M ⊙, Hβ and [O iii]4960+5008 EWs ≈ 850 Å (up to 3000 Å), young ages, a highly excited interstellar medium, and low dust attenuations. These high EWs are very rare in the local universe, but we show they are ubiquitous at z ∼ 6 based on the measured number densities. The stacked spectrum reveals Hγ and [O iii]4364, which shows that the galaxies are typically dust- and metal-poor (E (B − V) = 0.1, 12+log(O/H)=7.4 ) with a high electron temperature (2 × 104 K) and a production efficiency of ionizing photons (ξ ion = 1025.3 Hz erg−1). We further show the existence of a strong mass–metallicity relation. The properties of the stars and gas in z ∼ 6 galaxies conspire to maximize the [O iii] output from galaxies, yielding an [O iii] luminosity density at z ≈ 6 that is significantly higher than that at z ≈ 2. Thus, [O iii] emission-line surveys with JWST prove a highly efficient method to trace the galaxy density in the Epoch of Reionization.
Relevance of financial development and fiscal stability in dealing with disasters in Emerging Economies

Previous studies show that natural disasters decelerate economic growth, and more so in countries with lower financial development. We confirm these results with more recent data. We are the first to show that fiscal stability reduces the negative economic impact of natural disasters in poorer countries, and that catastrophe bonds have the same effect in richer countries.
SDSS J133401.39+331534.3: A NEW SUBARCSECOND GRAVITATIONALLY LENSED QUASAR

The quasar SDSS J133401.39+331534.3 at z = 2.426 is found to be a two-image gravitationally lensed quasar with an image separation of 0. ″833. The object is first identified as a lensed quasar candidate in the Sloan Digital Sky Survey Quasar Lens Search, and then confirmed as a lensed system from follow-up observations at the Subaru and University of Hawaii 2.2 m telescopes. We estimate the redshift of the lensing galaxy to be 0.557 based on absorption lines in the quasar spectra as well as the color of the galaxy. In particular, we observe the system with the Subaru Telescope AO188 adaptive optics with a laser guide star, in order to derive accurate astrometry, which well demonstrates the usefulness of the laser guide star adaptive optics imaging for studying strong lens systems. Our mass modeling with improved astrometry implies that a nearby bright galaxy ∼4″ apart from the lensing galaxy is likely to affect the lens potential.
On the GL(2n) eigenvariety: branching laws, Shalika families and $p$-adic $L$-functions

In this paper, we prove that a GL(2n)-eigenvariety is étale over the (pure) weight space at non-critical Shalika points, and construct multi-variabled p-adic L-functions varying over the resulting Shalika components. Our constructions hold in tame level 1 and Iwahori level at p, and give p-adic variation of L-values (of regular algebraic cuspidal automorphic representations, or RACARs, of GL(2n) admitting Shalika models) over the whole pure weight space. In the case of GL(4), these results have been used by Loeffler and Zerbes to prove cases of the Bloch–Kato conjecture for GSp(4). Our main innovations are: a) the introduction and systematic study of ‘Shalika refinements’ of local representations of GL(2n), evaluating their attached local twisted zeta integrals; and b) the p-adic interpolation of representation-theoretic branching laws for GL(n)×GL(n) inside GL(2n). Using (b), we give a construction of many-variabled p-adic functionals on the overconvergent cohomology groups for GL(2n), interpolating the zeta integrals of (a). We exploit the resulting non-vanishing of these functionals to prove our main arithmetic applications.
Neutrino flavor ratios as diagnostic of solar WIMP annihilation

We consider the neutrino (and antineutrino) flavors arriving at the Earth for neutrinos produced in the annihilation of weakly interacting massive particles (WIMPs) in the sun's core. Solar-matter effects on the flavor propagation of the resulting ≥ GeV neutrinos are studied analytically within a density-matrix formalism. Matter effects, including mass-state level crossings, influence the flavor fluxes considerably. The exposition herein is somewhat pedagogical, in that it starts with adiabatic evolution of single flavors from the sun's center, with θ 13 set to zero, and progresses to fully realistic processing of the flavor ratios expected in WIMP decay, from the sun's core to the Earth. In the fully realistic calculation, nonadiabatic level crossing is included, as are possible nonzero values for θ 13 and the CP-violating phase δ. Because of resonance enhancement in matter, nonzero values of θ 13 even smaller than a degree can noticeably affect flavor propagation. Both normal and inverted neutrino-mass hierarchies are considered. Our main conclusion is that measuring flavor ratios (in addition to energy spectra) of ≥ GeV solar neutrinos can provide discrimination between WIMP models. In particular, we demonstrate the flavor differences at the Earth for neutrinos from the two main classes of WIMP final states, namely W + W ― and 95% bb + 5% τ + τ ― . Conversely, if WIMP properties were to be learned from production in future accelerators, then the flavor ratios of ≥ GeV solar neutrinos might be useful for inferring θ 13 and the mass hierarchy. From the full calculations, we find (and prove) some general features: a flavor-democratic flux produced at the sun's core arrives at the Earth still flavor democratic; for maximal θ 32 but arbitrary θ 21 and θ 13 , the replacement δ → π ― δ leaves the ν e flavor spectra unaltered but interchanges ν μ and ν τ spectra at the Earth; and, only for neutrinos in the inverted hierarchy and antineutrinos in the normal hierarchy is the dependence on the mixing phase δ not suppressed to order δm 2 21 /δm 2 32 .
An Efficient Active Learning Pipeline for Legal Text Classification

Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the annotation requires domain expertise and is thus expensive. Recent works have shown the effectiveness of AL strategies for pre-trained language models. However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire. In addition, pre-trained language models have been shown unstable during fine-tuning with small datasets, and their embeddings are not semantically meaningful. In this work, we propose a pipeline for effectively using active learning with pre-trained language models in the legal domain. To this end, we leverage the available {textit{unlabeled} data in three phases. First, we continue pre-training the model to adapt it to the downstream task. Second, we use knowledge distillation to guide the model’s embeddings to a semantically meaningful space. Finally, we propose a simple, yet effective, strategy to find the initial set of labeled samples with fewer actions compared to existing methods. Our experiments on Contract-NLI, adapted to the classification task, and LEDGAR benchmarks show that our approach outperforms standard AL strategies, and is more efficient. Furthermore, our pipeline reaches comparable results to the fully-supervised approach with a small performance gap, and dramatically reduced annotation cost. Code and the adapted data will be made available.
Will Large-scale Generative Models Corrupt Future Datasets?

Recently proposed large-scale text-to-image generative models such as DALL•E 2 [47], Midjourney [42], and StableDiffusion [51] can generate high-quality and realistic images from users’ prompts. Not limited to the research community, ordinary Internet users enjoy these generative models, and consequently, a tremendous amount of generated images have been shared on the Internet. Meanwhile, today’s success of deep learning in the computer vision field owes a lot to images collected from the Internet. These trends lead us to a research question: "will such generated images impact the quality of future datasets and the performance of computer vision models positively or negatively? " This paper empirically answers this question by simulating contamination. Namely, we generate ImageNet-scale and COCO-scale datasets using a state-of-the-art generative model and evaluate models trained with "contaminated" datasets on various tasks, including image classification and image generation. Throughout experiments, we conclude that generated images negatively affect downstream performance, while the significance depends on tasks and the amount of generated images. The generated datasets and the codes for experiments will be publicly released for future research. Generated datasets and source codes are available from https://github.com/moskomule/dataset-contamination.
The GRB luminosity function: predictions from the internal shock model and comparison with observations

We compute the expected luminosity function of gamma-ray bursts (GRBs) in the context of the internal shock model. We assume that GRB central engines generate relativistic outflows characterized by the respective distributions of injected kinetic power E and contrast in Lorentz factor K = Γ max /Γ min . We find that if the distribution of contrast extends down to values close to unity (i.e. if both highly variable and smooth outflows can exist), then the luminosity function has two branches. At high luminosity it follows the distribution of E while at low luminosity it is close to a power law of slope -0.5. We then examine if existing data can constrain the luminosity function. Using the log N-log P curve, the E p distribution of bright Burst and Transient Source Experiment (BATSE) bursts and the X-ray flash (XRF)/GRB ratio obtained by High Energy Transient Explorer 2 (HETE2), we show that single and broken power laws can provide equally good fits of these data. Present observations are therefore unable to favour one form or the other. However, when a broken power law is adopted they clearly indicate a low-luminosity slope ≃-0.6 ± 0.2, compatible with the prediction of the internal shock model.
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use

Large transformer-based language models, e.g. BERT and GPT-3, outperform previous architectures on most natural language processing tasks. Such language models are first pre-trained on gigantic corpora of text and later used as base-model for finetuning on a particular task. Since the pre-training step is usually not repeated, base models are not up-to-date with the latest information. In this paper, we update RobBERT, a RoBERTa-based state-of-the-art Dutch language model, which was trained in 2019. First, the tokenizer of RobBERT is updated to include new high-frequent tokens present in the latest Dutch OSCAR corpus, e.g. corona-related words. Then we further pre-train the RobBERT model using this dataset. To evaluate if our new model is a plug-in replacement for RobBERT, we introduce two additional criteria based on concept drift of existing tokens and alignment for novel tokens.We found that for certain language tasks this update results in a significant performance increase. These results highlight the benefit of continually updating a language model to account for evolving language use.
The Minimal Feature Removal Problem in Neural Networks

We present the minimal feature removal problem for neural networks, a combinatorial problem which has interesting potential applications for improving inter-pretability and robustness of neural network predictions. For a given input to a trained neural network, our aim is to compute a smallest set of input features so that the model prediction changes when these features are disregarded by setting them to a given uninformative baseline value. We show that computing such minimal subsets of features is computationally intractable for fully-connected neural networks with ReLU nonlinearities. We show, however, that the problem becomes solvable in polynomial time by a greedy algorithm for monotonic networks. We then show that our tractability result extends seamlessly to more advanced neural network architectures such as convolutional and graph neural networks under suitable monotonicity assumptions.
Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement module that restricts the negative impact of redundant information introduced from the contexts, and thus is endowed with the ability of fixing the locality-aware features to produce refined results. Furthermore, in comprehensive experiments, we demonstrate that our model outperforms other state-of-the-art methods in public benchmarks. Our released codes are available at: https://github.com/liqiokkk/FCtL.
Spatial analysis of physical reservoir computers

Physical reservoir computing is a computational framework that implements spatiotemporal information processing directly within physical systems. By exciting nonlinear dynamical systems and creating linear models from their state, we can create highly energy-efficient devices capable of solving machine learning tasks without building a modular system consisting of millions of neurons interconnected by synapses. To act as an effective reservoir, the chosen dynamical system must have two desirable properties: nonlinearity and memory. We present task agnostic spatial measures to locally measure both of these properties and exemplify them for a specific physical reservoir based upon magnetic skyrmion textures. In contrast to typical reservoir computing metrics, these metrics can be resolved spatially and in parallel from a single input signal, allowing for efficient parameter search to design efficient and high-performance reservoirs. Additionally, we show the natural trade-off between memory capacity and nonlinearity in our reservoir's behaviour, both locally and globally. Finally, by balancing the memory and nonlinearity in a reservoir, we can improve its performance for specific tasks.
Extremely low-loss acoustic phonons in a quartz bulk acoustic wave resonator at millikelvin temperature

Low-loss, high frequency acoustic resonators cooled to millikelvin temperatures are a topic of great interest for application to hybrid quantum systems. When cooled to 20 mK, we show that resonant acoustic phonon modes in a bulk acoustic wave quartz resonator demonstrate exceptionally low loss (with Q-factors of order billions) at frequencies of 15.6 and 65.4 MHz, with a maximum f · Q product of 7.8 × 1016 Hz. Given this result, we show that the Q-factor in such devices near the quantum ground state can be four orders of magnitude better than previously attained. Such resonators possess the low losses crucial for electromagnetic cooling to the phonon ground state, and the possibility of long coherence and interaction times of a few seconds, allowing multiple quantum gate operations.Low-loss, high frequency acoustic resonators cooled to millikelvin temperatures are a topic of great interest for application to hybrid quantum systems. When cooled to 20 mK, we show that resonant acoustic phonon modes in a bulk acoustic wave quartz resonator demonstrate exceptionally low loss (with Q-factors of order billions) at frequencies of 15.6 and 65.4 MHz, with a maximum f · Q product of 7.8 × 1016 Hz. Given this result, we show that the Q-factor in such devices near the quantum ground state can be four orders of magnitude better than previously attained. Such resonators possess the low losses crucial for electromagnetic cooling to the phonon ground state, and the possibility of long coherence and interaction times of a few seconds, allowing multiple quantum gate operations.
Quantum Monte Carlo study of molecular polarization and antiferroelectric ordering in squaric acid crystals

Effects of geometrical frustration and quantum fluctuation are theoretically investigated for the proton ordering in a quasi-two-dimensional hydrogen-bonded system, squaric acid crystal. We elucidate the phase diagram for an effective model, the transverse-field Ising model on a frustrated checkerboard lattice, by using quantum Monte Carlo simulation. A crossover to liquidlike paraelectric state with well-developed molecular polarizations is identified, distinguishably from long-range ordering. Emergence of long-range order from the liquidlike state exhibits peculiar aspects originating from the lifting of quasi-macroscopic degeneracy, such as colossal enhancement of the transition temperature and a vanishingly small anomaly in the specific heat.