Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Cryogenic Material Properties of 3-D-Printed Superconducting Niobium Using a 3-D Lumped Element Microwave Cavity

We present an experimental characterization of the electrical properties of 3-D-printed niobium. The study was performed by inserting a 3-D-printed Nb post inside an aluminum cylindrical cavity, forming a 3-D lumped element reentrant microwave cavity resonator. The resonator was cooled to temperatures below the critical temperature of niobium (9.25 K) and then aluminum (1.2 K) while measuring the quality factors of the electromagnetic resonances. This was then compared with finite element analysis of the cavity and a measurement of the same cavity with an aluminum post of similar dimensions and frequency, to extract the surface resistance of the niobium post. The 3-D-printed niobium exhibited a transition to the superconducting state below 9.25 K, as well as a surface resistance of $3.1\pm 0.54\times 10^{-4}~\Omega $ . This value was comparable to many samples of traditionally machined niobium previously studied without specialized surface treatment. Furthermore, this study demonstrates a simple new method for characterizing the material properties of a relatively small and geometrically simple sample of the superconductor, which could be easily applied to other materials, particularly 3-D-printed materials. Further research and development in additive manufacturing may see the application of 3-D-printed niobium in not only superconducting cavity designs but also in the innovative technology of the future.
Lyα Radiative Transfer: Monte Carlo Simulation of the Wouthuysen–Field Effect

A three-dimensional Monte Carlo Lyα radiative transfer (RT) code, named LaRT, is developed to study the Lyα RT and the Wouthuysen–Field (WF) effect. Using the code, we calculate the line profile of Lyα radiation within the multiphase interstellar medium (ISM), with a particular emphasis on gas at low densities. We show that the WF effect is in action: the central portion of the line profile tends to approach a small slice of the Planck function with a color temperature equal to the kinetic temperature of the gas, even in a system with an optical thickness as low as τ0 ≈ 100–500. We also investigate the effects of the turbulent motion of the ISM on the emergent Lyα spectrum and color temperature. The turbulent motion broadens the emergent spectrum, as generally expected, but the color temperature is not affected by the turbulent motion in typical astrophysical environments. We utilize two multiphase ISM models, appropriate for the vicinity of the Sun, to calculate the 21 cm spin temperature of neutral hydrogen, including excitation via the Lyα resonant scattering. The first ISM model is a simple clumpy model, while the second is a self-consistent magnetohydrodynamics simulation model using the TIGRESS framework. Lyα photons originating from both H ii regions and the collisionally cooling gas are taken into account. We find that the Lyα radiation field is, in general, likely to be strong enough to bring the 21 cm spin temperature of the warm neutral medium close to the kinetic temperature. The escape fraction of Lyα in our ISM models is estimated to be ≈7%–20%.
Black Hole Genealogy: Identifying Hierarchical Mergers with Gravitational Waves

In dense stellar environments, the merger products of binary black hole mergers may undergo additional mergers. These hierarchical mergers are naturally expected to have higher masses than the first generation of black holes made from stars. The components of hierarchical mergers are expected to have significant characteristic spins, imprinted by the orbital angular momentum of the previous mergers. However, since the population properties of first-generation black holes are uncertain, it is difficult to know if any given merger is first-generation or hierarchical. We use observations of gravitational waves to reconstruct the binary black hole mass and spin spectrum of a population including the possibility of hierarchical mergers. We employ a phenomenological model that captures the properties of merging binary black holes from simulations of globular clusters. Inspired by recent work on the formation of low-spin black holes, we include a zero-spin subpopulation. We analyze binary black holes from LIGO and Virgo’s first two observing runs, and find that this catalog is consistent with having no hierarchical mergers. We find that the most massive system in this catalog, GW170729, is mostly likely a first-generation merger, having a 4% probability of being a hierarchical merger assuming a 5 × 105M⊙ globular cluster mass. Using our model, we find that 99% of first-generation black holes in coalescing binaries have masses below M⊙, and the fraction of binaries with near-zero component spins is less than (90% probability). Upcoming observations will determine if hierarchical mergers are a common source of gravitational waves.
A census of young stellar population associated with the Herbig Be star HD 200775

The region surrounding the well-known reflection nebula, NGC 7023, illuminated by a Herbig Be star, HD 200775, located in the dark cloud L1174 is studied in this work. Based on the distances and proper motion values from Gaia DR2 of 20 previously known young stellar object candidates, we obtained a distance of 335$\pm$11 pc to the cloud complex L1172/1174. Using polarization measurements of the stars projected on the cloud complex, we show additional evidence for the cloud to be at $\sim$ 335 pc distance. Using this distance and proper motion values of the YSO candidates, we searched for additional comoving sources in the vicinity of HD 200775 and found 20 new sources which show low infrared excess emission and are of age $\sim$ 1 Myr. Among these, 10 YSO candidates and 4 newly identified comoving sources are found to show X-ray emission. Three of the four new sources for which we have obtained optical spectra show H$\alpha$ in emission. About 80% of the total sources are found within $\sim$ 1 pc distance from HD 200775. Spatial correlation of some of the YSO candidates with the Herschel dust column density peaks suggests that star formation is still active in the region and may have been triggered by HD 200775.
EFFECT OF HYDROCOLLOIDS ON ORAL VISCOSITY AND BASIC TASTE INTENSITIES

Abstract. This study determined the effects of low concentrations of five food hydrocolloids on the taste intensities of aqueous solutions of sucrose, citric acid, sodium chloride, saccharin, and caffeine. The effects of the taste compounds on both oral and Brookfield viscosities were also measured. In general, sourness of citric acid and bitterness of caffeine were suppressed, while sweetness of saccharin was enhanced. Among the basic tastes, sourness was affected the most and saltiness the least. Except for sucrose, modification of taste intensity was independent of viscosity, and appeared to be related to the physicochemical properties of the hydrocolloid and the taste compound. About 16 cps were needed to reduce significantly the sweetness of sucrose. The taste compounds altered oral and physical viscosities differentially depending on the specific gum/taste combination. Generally, viscosity was reduced by the addition of all taste compounds except sucrose which increased the physical viscosity.
The AVA-Kinetics Localized Human Actions Video Dataset

This paper describes the AVA-Kinetics localized human actions video dataset. The dataset is collected by annotating videos from the Kinetics-700 dataset using the AVA annotation protocol, and extending the original AVA dataset with these new AVA annotated Kinetics clips. The dataset contains over 230k clips annotated with the 80 AVA action classes for each of the humans in key-frames. We describe the annotation process and provide statistics about the new dataset. We also include a baseline evaluation using the Video Action Transformer Network on the AVA-Kinetics dataset, demonstrating improved performance for action classification on the AVA test set. The dataset can be downloaded from this https URL
Polar or nonpolar? That is not the question for perovskite solar cells

Abstract Perovskite solar cells (PSC) are promising next generation photovoltaic technologies, and there is considerable interest in the role of possible polarization of organic-inorganic halide perovskites (OIHPs) in photovoltaic conversion. The polarity of OIHPs is still hotly debated, however. In this review, we examine recent literature on the polarity of OIHPs from both theoretical and experimental points of view, and argue that they can be both polar and nonpolar, depending on composition, processing and environment. Implications of OIHP polarity to photovoltaic conversion are also discussed, and new insights gained through research efforts. In the future, integration of a local scanning probe with global macroscopic measurements in situ will provide invaluable microscopic insight into the intriguing macroscopic phenomena, while synchrotron diffractions and scanning transmission electron microscopy on more stable samples may ultimately settle the debate.
The larval stages of Maja squinado (Herbst, 1788) (Crustacea: Brachyura: Majidae) reared in the laboratory

The zoeal and megalopal morphology of Maja squinado is described from laboratory-reared material, and compared with the larval stages of other spider crabs belonging to the subfamily Majinae. Characters which may facilitate separation of the zoeas and megalopas of these species are listed. Differences between Notomithrax and Eurynome aspera larvae are tabulated. The importance of detailed descriptions and illustrations of larvae based on meticulous observation is emphasized.
Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging

Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global information dissemination and load imbalance across uneven sample lengths. State-of-the-art decentralized optimizers mitigate the problem, but require more iterations to achieve the same accuracy as their globally-communicating counterparts. We present Wait-Avoiding Group Model Averaging (WAGMA) SGD, a wait-avoiding stochastic optimizer that reduces global communication via subgroup weight exchange. The key insight is a combination of algorithmic changes to the averaging scheme and the use of a group allreduce operation. We prove the convergence of WAGMA-SGD, and empirically show that it retains convergence rates similar to Allreduce-SGD. For evaluation, we train ResNet-50 on ImageNet; Transformer for machine translation; and deep reinforcement learning for navigation at scale. Compared with state-of-the-art decentralized SGD variants, WAGMA-SGD significantly improves training throughput (e.g., 2.1× on 1,024 GPUs for reinforcement learning), and achieves the fastest time-to-solution (e.g., the highest score using the shortest training time for Transformer).
Jet efficiencies and black hole spins in jetted quasars

The mechanisms responsible for the production of relativistic jets from supermassive black holes (SMBHs) accreting at near-Eddington rates are not well-understood. Simple theoretical expectations indicate that SMBHs in quasars accrete via thin discs which should produce at most very weak jets. This is contradicted by observations of powerful jets in flat-spectrum radio quasars (FSRQs). We use gamma-ray luminosities observed with the \emph{fermi} Large Area Telescope as a proxy of the jet power for a population of 154 FSRQs. Assuming typical quasar accretion rates and using black hole mass measurements from a variety of methods, we find a mean jet production efficiency of about 10 per cent for FSRQs, with values as high as 222 per cent. We find that this is consistent with FSRQs hosting moderately thin, magnetically arrested accretion discs around rapidly spinning black holes (BHs). Modeling our observations using general relativistic magnetohydrodynamic (GRMHD) simulations of jets from thin discs, we find an average lower limit of $a_* = 0.59$ for the SMBH spins of FSRQs, with tendency for the spins to decrease as the black hole mass increases. Our results are consistent with the merger-driven evolution of SMBHs. 3 per cent of the sample cannot be explained by current GRMHD models of jet production from Kerr BHs due to the high efficiencies. Along the way, we find a correlation between BH masses and $L_\gamma$ which may be an useful mass estimator in blazar gamma-ray studies.
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning

In order to simulate human language capacity, natural language processing systems must complement the explicit information derived from raw text with the ability to reason about the possible causes and outcomes of everyday situations. Moreover, the acquired world knowledge should generalise to new languages, modulo cultural differences. Advances in machine commonsense reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks. Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We benchmark a range of state-of-the-art models on this novel dataset, revealing that current methods based on multilingual pretraining and zero-shot fine-tuning transfer suffer from the curse of multilinguality and fall short of performance in monolingual settings by a large margin. Finally, we propose ways to adapt these models to out-of-sample resource-lean languages where only a small corpus or a bilingual dictionary is available, and report substantial improvements over the random baseline. XCOPA is available at this http URL.
Posterior Consistency of Bayesian Inverse Regression and Inverse Reference Distributions

We consider Bayesian inference in inverse regression problems where the objective is to infer about unobserved covariates from observed responses and covariates. We establish posterior consistency of such unobserved covariates in Bayesian inverse regression problemsunder appropriate priors in a leave-one-out cross-validation setup. We relate this to posterior consistency of inverse reference distributions (Bhattacharya (2013)) for assessing model adequacy. We illustrate our theory and methods with various examples of Bayesian inverse regression, along with adequate simulation experiments.
M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M3VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M3VSNet establishes the state-of-the-arts unsupervised method and achieves better performance than previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks & Temples benchmark with effective improvement.
Universal Adversarial Attacks with Natural Triggers for Text Classification

Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifiers. Despite being successful, the word sequences produced in such attacks are often ungrammatical and can be easily distinguished from natural text. We develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems when added to benign inputs. We leverage an adversarially regularized autoencoder (ARAE) to generate triggers and propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss. Our attacks effectively reduce model accuracy on classification tasks while being less identifiable than prior models as per automatic detection metrics and human-subject studies. Our aim is to demonstrate that adversarial attacks can be made harder to detect than previously thought and to enable the development of appropriate defenses.
Apparent reconsolidation interference without generalized amnesia

Memories remain dynamic after consolidation, and when reactivated, they can be rendered vulnerable to various pharmacological agents that disrupt the later expression of memory (i.e., amnesia). Such drug-induced post-reactivation amnesia has traditionally been studied in AAA experimental designs, where a memory is initially created for a stimulus A (be it a singular cue or a context) and later reactivated and tested through exposure to the exact same stimulus. Using a contextual fear conditioning procedure in rats and midazolam as amnestic agent, we recently demonstrated that drug-induced amnesia can also be obtained when memories are reactivated through exposure to a generalization stimulus (GS, context B) and later tested for that same generalization stimulus (ABB design). However, this amnestic intervention leaves fear expression intact when at test animals are instead presented with the original training stimulus (ABA design) or a novel generalization stimulus (ABC design). The underlying mechanisms of post-reactivation memory malleability and of MDZ-induced amnesia for a generalization context remain largely unknown. Here, we evaluated whether, like typical CS-mediated (or AAA) post-reactivation amnesia, GS-mediated (ABB) post-reactivation amnesia displays key features of a destabilization-based phenomenon. We first show that ABB post-reactivation amnesia is critically dependent on prediction error at the time of memory reactivation and provide evidence for its temporally graded nature. In line with the known role of GluN2B-NMDA receptor activation in memory destabilization, we further demonstrate that pre-reactivation administration of ifenprodil, a selective antagonist of GluN2B-NMDA receptors, prevents MDZ-induced ABB amnesia. In sum, our data reveal that ABB MDZ-induced post-reactivation amnesia exhibits the hallmark features of a destabilization-dependent phenomenon. Implication of our findings for a reconsolidation-based account of post-reactivation amnesia are discussed.
Revisiting Memory-Efficient Incremental Coreference Resolution

We explore the task of coreference resolution under fixed memory by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Our algorithm creates explicit representations for each entity, where given a new sentence, spans are proposed and subsequently scored against each entity representation, leading to emergent clusters. Our approach is end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts.
AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge from multiple tasks; however, they suffer from catastrophic forgetting and difficulties in dataset balancing. To address these shortcomings, we propose AdapterFusion, a new two stage learning algorithm that leverages knowledge from multiple tasks. First, in the knowledge extraction stage we learn task specific parameters called adapters, that encapsulate the task-specific information. We then combine the adapters in a separate knowledge composition step. We show that by separating the two stages, i.e., knowledge extraction and knowledge composition, the classifier can effectively exploit the representations learned from multiple tasks in a non-destructive manner. We empirically evaluate AdapterFusion on 16 diverse NLU tasks, and find that it effectively combines various types of knowledge at different layers of the model. We show that our approach outperforms traditional strategies such as full fine-tuning as well as multi-task learning. Our code and adapters are available at AdapterHub.ml.
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations

In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
Behaviuoral changes associated with the population cycle of Antechinus stuartii (Marsupialia)

The behaviour of field populations of A. stuartii was monitored in relation to their demography by diad testing. Marked and unmarked pairs were tested in a field observation cage. Two measures of activity and the frequency of 10 behaviour categories were recorded for each test. Stepwise multiple regression analysis showed the sex of the animal and time of year to be the best predictors of aggression parameters. The frequencies of behaviour categories suggest that males and females behave quite differently. Males show a trend of increasing aggressiveness over the annual cycle whereas females do not. Repeated laboratory testing of males in 'mating condition' exacerbates aggression as their weight and general appearance deteriorate. It is suggested that the externally synchronized life cycle programs the aggression increase, and this causes physical deterioration, rather than the reverse.