Miracle-dz/UniFAR-FTUs huggingface dataset:
THE INFLUENCE OF ROTATION AND STELLAR WINDS UPON THE BE PHENOMENON.

A number of rapidly rotating B stars, not previously known as Be stars, were observed spectroscopically at H alpha. These results were then combined with existing data to show that the spectral type of a star and the minimum velocity at which it must rotate in order to become a Be star are related. The trend of this relationship is found to have a natural explanation in terms of stellar winds.
21 Centimeter Study of Spiral Galaxies in the Coma Supercluster. II. Evidence for Ongoing Gas Stripping in Five Cluster Galaxies

High-sensitivity 21 cm observations of 73 galaxies in the Coma supercluster are presented. Seventeen new redshifts are reported. Three galaxies in A1367 and two in the Coma Cluster are found with remarkably asymmetrical H I spectra indicating uneven gas distribution in their disks. This unstable configuration is interpreted as being due to ongoing dynamical gas stripping taking place on time scales shorter than those required for differential rotation to redistribute the gas, i.e., a few times 100 million yr. This suggests that spiral galaxies are continuously supplied to the clusters from the surrounding supercluster. 33 refs.
Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective

Abstract. The “dry gets drier, and wet gets wetter” (DDWW) paradigm has been widely used to summarize the expected trends of the global hydrologic cycle under climate change. However, the paradigm is largely conditioned by choice of different metrics and datasets used and is still comprehensively unexplored from the perspective of terrestrial water storage anomalies (TWSAs). Considering the essential role of TWSAs in wetting and drying of the land system, here we built upon a large ensemble of TWSA datasets, including satellite-based products, global hydrological models, land surface models, and global climate models to evaluate the DDWW hypothesis during the historical (1985–2014) and future (2071–2100) periods under various scenarios with a 0.05 significance level (for trend estimates). We find that 11.01 %–40.84 % (range by various datasets) of global land confirms the DDWW paradigm, while 10.21 %–35.43 % of the area shows the opposite pattern during the historical period. In the future, the DDWW paradigm is still challenged, with the percentage supporting the pattern lower than 18 % and both the DDWW-validated and DDWW-opposed proportion increasing along with the intensification of emission scenarios. We show that the different choices of data sources can reasonably influence the test results up to a 4-fold difference. Our findings will provide insights and implications for global wetting and drying trends from the perspective of TWSA under climate change.
Artisanal and

36 Preserving microbial diversity in food systems is one of the many challenges to be met to 37 achieve food security and quality. Although industrialization led to the selection and spread of specific 38 fermenting microbial strains, there are still ongoing artisanal processes that may allow the conservation 39 of a wider species and genetic diversity. We examined whether the diversity of artisanal practices could 40 lead to an increased level in fungal species diversity for bread making. We used an interdisciplinary 41 participatory research approach including bakers, psycho-sociologists and microbiologists to analyze 42 French bread making practices and describe fungal communities in naturally fermented sourdough of 43 27 bakers and 12 farmer bakers. Bread making practices were classified in two groups: the farmer-like 44 practices group and the artisanal-like practices group. The well-known bakery yeast, Saccharomyces 45 cerevisiae, was dominant (i.e. with a relative abundance over 50%) in only 24% of sourdoughs while 46 other yeast species belonging to the Kazachstania genus were dominant in 54% of sourdoughs. Bread 47 making practices were found to drive the distribution of fungal species across sourdoughs. The most 48 striking bread making practice effect was the occurrence of Kazachstania humilis in sourdoughs made 49 with artisanal-like practices and the occurrence of Kazachstania bulderi in sourdoughs made with 50 farmer-like practices. Phenotypic divergences between sourdough and non-sourdough strains were 51 found for K. humilis but not for K. bulderi . Overall, our results showed that preserving bread making 52 practice diversity allows the preservation of a higher species and phenotypic diversity in microbial 53 communities.
DNA elastic nonlinearities as multiple smooth combinations of soft and hard linear springs

DNA molecules perform biological functions via large elastic deformations and conformational changes in courses of their interactions with the cellular machinery. Adequate characterization of DNA elasticity is accordingly essential to an in‐depth understanding of DNA functions. Because of profound complexity, however, nonlinear features inherent in DNA elasticity remains to be uncovered in a direct, full sense and a unified, explicit characterization over the entire stretch range has been unavailable. Here it is suggested that DNA elastic nonlinearities may be understood by combining linear springs with high compliance and high rigidity. This finding uncovers the inherent geometrical feature in profound nonlinearities of DNA elasticity and leads to a unified, explicit force‐extension formula up to breaking. This formula reveals for the first time a complete set of characteristic constants for DNA elasticity and is shown to provide a unified characterization for both double‐ and single‐stranded DNA molecules. Results are presented in a straightforward, broad sense independent of any substructural details.
A Numerical Study of Circulations Induced by a Dry Salt Lake

Abstract Observations of surface winds in the vicinity of a moderate-sized (about 70 km2) dry salt lake by Tapper suggest that differences in albedo and soil thermal properties between the salt and surrounding sand surfaces may be strong enough to drive a mesoscale thermal circulation. In this paper a numerical mesoscale model is used to investigate disturbances generated by moderate and large-sized (about 7000 km2) lakes. In the latter case, a typical size for the great salt lakes of inland Australia, disturbances with strong horizontal and vertical shear are found at a distance of more than 200 km from the lakeshore by midnight. The separate contribution of albedo and soil property differences to the circulation are assessed, under both dry and moist soil conditions. The possible influence of such circulations on regional climate is discussed and it is suggested that salt lakes may be a source for solitary waves observed in the Australian interior.
Second order inverse born approximation for diffuse optical tomography

Diffuse Optical Tomography (DOT) involves a nonlinear optimization problem to find the tissue optical properties by measuring near-infrared light noninvasively. Many researchers used linearization methods to obtain the optical image in real time. However, the linearization procedure may neglect small but sometimes important regions such as small tumors at an early stage. Therefore, nonlinear optimization methods such as gradient- or Newton- type methods are exploited, resulting in better resolution image than that of linearization methods. But the disadvantage of nonlinear methods is that they need much computation time. To solve this trade-off dilemma between image resolution and computing time, we suggest second order inverse Born expansion algorithm in this paper. It is known that a small perturbation of photon density is represented by Born expansion with respect to the perturbation of optical coefficients, which is an infinite series of integral operators having Robin function kernel. Whereas, inverse Born expansion is an implicit representation of a small perturbation of optical coefficients by an infinite series of the integral operators with respect to the photon density and its perturbation, which is appropriate series expansion for inverse DOT problem. Solving the inverse Born expansion itself and the first order approximation correspond to nonlinear and linear method, respectively. We formulated a second order approximation of the inverse Born expansion explicitly to make numerical implementation possible and showed the convergence order of the proposed method is higher than the linear method.
A pigeon-inspired design for a biomimetic flapping wing

As an effort to explore the potential implementation of wing feather separation and lead-lagging motion to a flapping wing, a biomimetic flapper with separable outer wings has been designed and demonstrated. The artificial wing feather separation is implemented to the biomimetic wing by dividing the wing into inner and outer wings. The features of flapping, lead-lagging and feather separation of the flapper are captured by a high-speed camera for evaluation. The performance of the biomimetic flapper with separable outer wings is compared with that of a flapper with inseparable outer wings in terms of lift and thrust production. For low flapping frequency ranging from 2.47 Hz to 3.90 Hz, the biomimetic flapper shows higher thrust and lift generation capability, which is demonstrated from a series of experiments. The experiments show that the outer parts of the separable wing are able to deform largely resulting smaller amount of drag production during upstroke, while still producing relatively larger lift and thrust during downstroke.
Children cooled for neonatal encephalopathy without cerebral palsy retain healthy resting-state static and dynamic functional connectivity

Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy (HIE), reducing cases of death and severe disability such as cerebral palsy compared to normothermia management. However, when cooled children reach early school-age they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity cause differences in functional connectivity between cooled children and healthy controls. Resting-state fMRI has been used to characterise static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with HIE have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal HIE without cerebral palsy (n = 22, median age [IQR] 7.08 [6.85-7.52] years), and healthy controls matched for age, sex and socioeconomic status (n = 20, median age [IQR] 6.75 [6.48-7.25] years). Using group independent component analysis, we identified 33 intrinsic connectivity networks consistent with those previously reported in children and adults. There were no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses, and found no case-control differences in functional connectivity after FDR correction. To study the time-varying organisation of resting-state networks, we used sliding-window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy, or state functional connectivity matrices. These results show that the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability are comparable to those in healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with HIE beyond the neonatal period, and the first to investigate dynamic functional connectivity in any children with HIE.
How to disagree well: Investigating the dispute tactics used on Wikipedia

Disagreements are frequently studied from the perspective of either detecting toxicity or analysing argument structure. We propose a framework of dispute tactics which unifies these two perspectives, as well as other dialogue acts which play a role in resolving disputes, such as asking questions and providing clarification. This framework includes a preferential ordering among rebuttal-type tactics, ranging from ad hominem attacks to refuting the central argument. Using this framework, we annotate 213 disagreements (3,865 utterances) from Wikipedia Talk pages. This allows us to investigate research questions around the tactics used in disagreements; for instance, we provide empirical validation of the approach to disagreement recommended by Wikipedia. We develop models for multilabel prediction of dispute tactics in an utterance, achieving the best performance with a transformer-based label powerset model. Adding an auxiliary task to incorporate the ordering of rebuttal tactics further yields a statistically significant increase. Finally, we show that these annotations can be used to provide useful additional signals to improve performance on the task of predicting escalation.
Coarse-Grained Effective Hamiltonian via the Magnus Expansion for a Three-Level System

Quantum state processing is one of the main tools of quantum technologies. While real systems are complicated and/or may be driven by non-ideal control, they may nevertheless exhibit simple dynamics approximately confined to a low-energy Hilbert subspace. Adiabatic elimination is the simplest approximation scheme allowing us to derive in certain cases an effective Hamiltonian operating in a low-dimensional Hilbert subspace. However, these approximations may present ambiguities and difficulties, hindering a systematic improvement of their accuracy in larger and larger systems. Here, we use the Magnus expansion as a systematic tool to derive ambiguity-free effective Hamiltonians. We show that the validity of the approximations ultimately leverages only on a proper coarse-graining in time of the exact dynamics. We validate the accuracy of the obtained effective Hamiltonians with suitably tailored fidelities of quantum operations.
Learning Sparsity and Randomness for Data-driven Low Rank Approximation

Learning-based low rank approximation algorithms can signiﬁcantly improve the performance of randomized low rank approximation with sketch matrix. With the learned value and ﬁxed non-zero positions for sketch matrices from learning-based algorithms, these matrices can reduce the test error of low rank approximation signiﬁcantly. However, there is still no good method to learn non-zero positions as well as overcome the out-of-distribution performance loss. In this work, we introduce two new methods Learning Sparsity and Learning Randomness which try to learn a better sparsity patterns and add randomness to the value of sketch matrix. These two methods can be applied with any learning-based algorithms which use sketch matrix directly. Our experiments show that these two methods can improve the performance of previous learning-based algorithm for both test error and out-of-distribution test error without adding too much complexity.
Provable Fairness for Neural Network Models using Formal Verification

Machine learning models are increasingly deployed for critical decision-making tasks, making it important to verify that they do not contain gender or racial biases picked up from training data. Typical approaches to achieve fairness revolve around efforts to clean or curate training data, with post-hoc statistical evaluation of the fairness of the model on evaluation data. In contrast, we propose techniques to \emph{prove} fairness using recently developed formal methods that verify properties of neural network models.Beyond the strength of guarantee implied by a formal proof, our methods have the advantage that we do not need explicit training or evaluation data (which is often proprietary) in order to analyze a given trained model. In experiments on two familiar datasets in the fairness literature (COMPAS and ADULTS), we show that through proper training, we can reduce unfairness by an average of 65.4\% at a cost of less than 1\% in AUC score.
Neural Enhanced Belief Propagation for Multiobject Tracking

Algorithmic solutions for multi-object tracking (MOT) are a key enabler for applications in autonomous navigation and applied ocean sciences. State-of-the-art MOT methods fully rely on a statistical model and typically use preprocessed sensor data as measurements. In particular, measurements are produced by a detector that extracts potential object locations from the raw sensor data collected at discrete time steps. This preparatory processing step reduces data flow and computational complexity but may result in a loss of information. State-of-the-art Bayesian MOT methods that are based on belief propagation (BP) systematically exploit graph structures of the statistical model to reduce computational complexity and improve scalability. However, as a fully model-based approach, BP can provide highly suboptimal estimates when there is a mismatch between the statistical model and the true data-generating process. Existing BP-based MOT methods can further only make use of preprocessed measurements. In this paper, we introduce a variant of BP that combines model-based with data-driven MOT. The proposed neural enhanced belief propagation (NEBP) method complements the statistical model of BP by information learned from raw sensor data. This approach conjectures that the learned information can reduce model mismatch and thus improve data association and false alarm rejection. Our NEBP method improves tracking performance compared to model-based methods. At the same time, it inherits the advantages of BP-based MOT, i.e., it scales only quadratically in the number of objects, and it can thus generate and maintain a large number of object tracks. We evaluate the performance of our NEBP approach for MOT on the nuScenes autonomous driving dataset and demonstrate that it can achieve state-of-the-art performance. In particular, an average multi-object tracking accuracy (AMOTA) of 0.683 was obtained and, compared with non-BP-based methods, identity switches (IDS) and track fragments (Frag) were reduced by 23% and 19%, respectively.
CD-TTA: Compound Domain Test-time Adaptation for Semantic Segmentation

Test-time adaptation (TTA) has attracted signiﬁcant attention due to its practical properties which enable the adaptation of a pre-trained model to a new domain with only target dataset during the inference stage. Prior works on TTA assume that the target dataset comes from the same distribution and thus constitutes a single homogeneous domain. In practice, however, the target domain can contain multiple homogeneous domains which are sufﬁciently distinctive from each other and those multiple domains might occur cyclically. Our preliminary investigation shows that domain-speciﬁc TTA outperforms vanilla TTA treating compound domain (CD) as a single one. However, domain labels are not available for CD, which makes domain-speciﬁc TTA not practicable. To this end, we propose an online clustering algorithm for ﬁnding pseudo-domain labels to obtain similar beneﬁts as domain-speciﬁc conﬁguration and accumulating knowledge of cyclic domains effectively. More-over, we observe that there is a signiﬁcant discrepancy in terms of prediction quality among samples, especially in the CD context. This further motivates us to boost its performance with gradient denoising by considering the image-wise similarity with the source distribution. Overall, the key contribution of our work lies in proposing a highly signiﬁcant new task compound domain test-time adaptation (CD-TTA) on semantic segmentation as well as providing a strong baseline to facilitate future works to benchmark.
Flavoured $(g-2)_\mu$ with dark lepton seasoning

As a joint explanation for the dark matter (DM) problem and the muon (g-2)(g−2) anomaly, we propose a simplified model of lepton-flavoured complex scalar DM with couplings to both the left- and right-handed leptons of the Standard Model (SM). Both interactions are governed by the same new flavour-violating coupling matrix \lambdaλ, however we allow for a relative scaling of the coupling strength. The SM is further extended by two fermion representations, transforming as an SU(2)_LSU(2)L doublet and singlet, respectively, and mediating these interactions. The fermions additionally couple to the SM Higgs doublet via a new Yukawa coupling. To study the model’s phenomenology we first investigate constraints from collider searches, flavour experiments, precision tests of the SM, the DM relic density, and direct as well as indirect detection experiments individually. We then perform a combined analysis by demanding that all mentioned constraints are satisfied simultaneously. We use the results of this combined analysis and examine if the model is capable of accommodating the (g-2)_\mu(g−2)μ anomaly within its viable parameter space without introducing fine-tuned lepton masses. For all benchmark scenarios we consider, we find that the central value of \Delta a_\mu^\text{exp}Δaμexp can be reached without generating too large corrections to the lepton masses. We hence conclude that this model qualifies as a viable and attractive lepton-flavoured DM model that at the same time solves the (g-2)_\mu(g−2)μ anomaly.