Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation of the turbulent fluid flow through valves based on low mach models

The aim of the present paper is to carry out a group of numerical experiments over the fluid flow through the valve reed, using the CFD&HT code TermoFluids, an unstructure d and parallel object-oriented CFD code for accurate and reliable solving of industrial flows (Lehmkuhl, O. et al. 2007 ) with special attention on incompressible hypothesis against low Mach compressible flow modeling, as a critic al numerical aspect depending on Reynolds number and gap thickness conditions. In all studied cases a multi-dimensional explicit finite volume fractional-step based algorithm extended to simulate low Mach fluxes using a Runge-Kutta/Crank-Nicholson time integration scheme, with a symmetry preserving discretization has been used. When turbulence modeling is needed, an extension of the WALE (Wall Adapting Local Eddy-viscosity) (Nicoud, F. and Ducros, F., 1999) model to non-structured meshes is applied. The pressure equation is solved by means of parallel Fourier Schur decomposition solver which is an efficient direct solver for loosely coupled PC clusters (Borrell, R. et al. 2011). In a two dimensional periodic way the fluid flow is approach ed by two parallel phenomena (an entrance flow through a channel and a free jet through a surface). In that sense, the present paper is focused on the numeri cal simulation model of the fluid flow through the valve reeds, considering a simplified geometry of an axial hole plus a radial diffuser. The numerical results presented are based on a specific geome try – valve diameter D is 3 times orifice diameter d, while s/d ratio is 0.6 – considering high Reynolds number at the entrance as boundary condition. The studied cases show the influence from laminar to turbulent flow from incompressible assumption to lower subsonic conditions and/or chocked flow.
A typology of nurses' interaction with relatives in emergency situations

Background In nursing homes, residents’ relatives represent important sources of support for nurses. However, in the heightened stress of emergency situations, interaction between nurses and relatives can raise ethical challenges. Research objectives The present analysis aimed at elaborating a typology of nurses’ experience of ethical support and challenges in their interaction with relatives in emergency situations. Research design Thirty-three semi-structured interviews and six focus groups were conducted with nurses from different nursing homes in Germany. Data were analysed according to Mayring’s method of qualitative content analysis. Participants and research context Participants were licensed nurses working in nursing homes. Ethical considerations Ethical approval was granted by Ostfalia University of Applied Sciences (02.07.2020) and the Ethics Committee of Hannover Medical School (Nr. 8866_BO_K_2020; 27.01.2020). Interviewees were anonymised and focus group were pseudonymised during transcription. All participants provided written consent. Findings/results In emergency situations, relatives can represent important sources of support for nurses. However, they may also give rise to different challenges, relating to four ethical conflicts: (1) the challenge of meeting the information needs of relatives while providing appropriate care to all residents; (2) the challenge of managing relatives’ demands for hospitalisation when hospitalisation is not deemed necessary by nurses; (3) the challenge of managing relatives’ demands for lifesaving treatment when such treatment contradicts the will of the resident; and (4) the challenge of attempting to initiate hospitalisation when relatives oppose this course of action. Several external factors make these conflicts especially challenging for nurses: fear of legal consequences, a low staffing ratio, and a lack of qualified nursing staff. Conclusions Conflict between nurses and relatives typically revolves around hospitalisation and the initiation of lifesaving treatment. Whether nurses perceive interaction with relatives as supportive or conflictual essentially depends on the quality of the relationship, which may be negatively influenced by a number of external factors.
Transportation into an Entertainment Narrative about the MMR Vaccine: An Investigation of Self-Referencing and Issue-Related Thoughts in Narrative Persuasion

ABSTRACT The current study considers how an entertainment narrative about childhood vaccination influences related attitudes. We consider the role of counterarguing in narrative persuasion by integrating extant research and theory to test cognitive mechanisms of narrative persuasion, namely self-referencing and positive issue-related thoughts. Results of this experiment show that exposure to a television narrative depicting the importance of the measles, mumps, and rubella (MMR) vaccine led to more favorable attitudes toward childhood vaccination as compared to a control group. As expected by narrative persuasion theorizing, transportation into the narrative predicted vaccine attitudes. In contrast to typical theorizing and some empirical results, counterarguing did not mediate that relationship, however, self-referencing and positive issue-related thinking did. Theoretical contributions and suggestions for future research expanding our understanding of issue-related thoughts are discussed.
Better Understanding Wearable Activity Monitor Use and Non-Use Among Older Adults: A Qualitative Study

We used qualitative methods to better understand wearable activity monitor (WAM) use and its relationship with physical activity (PA) among older adults. Four focus groups, stratified by current/past (“ever”) WAM users and “never” WAM users, were conducted with 57 participants aged ≥60 years. Data were coded with deductive and inductive approaches. Among ever WAM users, those with low pre-WAM PA levels reported higher PA related to WAM use while those with high pre-WAM PA levels reported they used WAM for tracking and monitoring their activity. Ever WAM users were motivated to increase PA through rewards and social support. Many never WAM users reported that WAM was unnecessary. WAM may be helpful for some older adults to increase their PA, particularly those who are motivated by setting and achieving PA goals. The results could help develop strategies to overcome barriers to WAM adoption and promote WAM use among older adults.
A multitype sticky particle construction of Wasserstein stable semigroups solving one-dimensional diagonal hyperbolic systems with large monotonic data

This article is dedicated to the study of diagonal hyperbolic systems in one space dimension, with cumulative distribution functions, or more generally nonconstant monotonic bounded functions, as initial data. Under a uniform strict hyperbolicity assumption on the characteristic fields, we construct a multitype version of the sticky particle dynamics and obtain existence of global weak solutions by compactness. We then derive a $L^p$ stability estimate on the particle system uniform in the number of particles. This allows to construct nonlinear semigroups solving the system in the sense of Bianchini and Bressan [Ann. of Math. (2), 2005]. We also obtain that these semigroup solutions satisfy a stability estimate in Wasserstein distances of all orders, which encompasses the classical $L^1$ estimate and generalises to diagonal systems the results by Bolley, Brenier and Loeper [J. Hyperbolic Differ. Equ. , 2005] in the scalar case. Our results are obtained without any smallness assumption on the variation of the data, and only require the characteristic fields to be Lipschitz continuous and the system to be uniformly strictly hyperbolic.
Bubbling AdS3

In the light of the recent Lin, Lunin, Maldacena (LLM) results, we investigate 1/2-BPS geometries in minimal (and next to minimal) supergravity in D = 6 dimensions. In the case of minimal supergravity, solutions are given by fibrations of a two-torus T 2 specified by two harmonic functions. For a rectangular torus the two functions are related by a non-linear equation with rare solutions: AdS3 × S3, the pp-wave and the multicenter string. “Bubbling”, i.e. superpositions of droplets, is accommodated by allowing the complex structure of the T 2 to vary over the base. The analysis is repeated in the presence of a tensor multiplet and similar conclusions are reached, with generic solutions describing D1D5 (or their dual fundamental string-momentum) systems. In this framework, the profile of the dual fundamental string-momentum system is identified with the boundaries of the droplets in a two-dimensional plane.
Towards Language-driven Scientific AI

Inspired by recent and revolutionary developments in AI, particularly in language understanding and generation, we set about designing AI systems that are able to address complex scientiﬁc tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange format between scientiﬁc AI and human scientists. In this paper, we identify and discuss some of the main research challenges to accomplish such vision.
A ‘high-hard’ outburst of the black hole X-ray binary GS 1354−64

We study in detail the evolution of the 2015 outburst of GS 1354−64 (BW Cir) at optical, UV and X-ray wavelengths using Faulkes Telescope South/Las Cumbres Observatory Global Telescope Network, Small & Moderate Aperture Research Telescope System and Swift. The outburst was found to stay in the hard X-ray state, albeit being anomalously luminous with a peak luminosity of LX > 0.15 LEdd, which could be the most luminous hard state observed in a black hole X-ray binary. We found that the optical/UV emission is tightly correlated with the X-ray emission, consistent with accretion disc irradiation and/or a jet producing the optical emission. The X-ray spectra can be fitted well with a Comptonization model, and show softening towards the end of the outburst. In addition, we detect a QPO in the X-ray light curves with increasing centroid frequency during the peak and decay periods of the outburst. The long-term optical light curves during quiescence show a statistically significant, slow rise of the source brightness over the 7 years prior to the 2015 outburst. This behaviour as well as the outburst evolution at all wavelengths studied can be explained by the disc instability model with irradiation and disc evaporation/condensation.
Gamma Convergence for the de Gennes-Cahn-Hilliard energy

The degenerate de Gennes-Cahn-Hilliard (dGCH) equation is a model for phase separation which may more closely approximate surface diﬀusion than others in the limit when the thickness of the transition layer approaches zero. As a ﬁrst step to understand the limiting behavior, in this paper we study the Γ–limit of the dGCH energy. We ﬁnd that its Γ–limit is a constant multiple of the interface area, where the constant is determined by the de Gennes coeﬃcient together with the double well potential. In contrast, the transition layer proﬁle is solely determined by the double well potential.
Molecular Gas Kinematics and Star Formation Properties of the Strongly-lensed Quasar Host Galaxy RXS J1131–1231

We report observations of CO(J = 2 → 1) and line emission toward the quadruply-lensed quasar RXS J1131−1231 at z = 0.654 obtained using the Plateau de Bure Interferometer (PdBI) and the Combined Array for Research in Millimeter-wave Astronomy (CARMA). Our lens modeling shows that the asymmetry in the double-horned CO(J = 2 → 1) line profile is mainly a result of differential lensing, where the magnification factor varies from ∼3 to ∼9 across different kinematic components. The intrinsically symmetric line profile and a smooth source-plane velocity gradient suggest that the host galaxy is an extended rotating disk, with a CO size of kpc and a dynamical mass of M⊙. We also find a secondary CO-emitting source near RXS J1131−1231, the location of which is consistent with the optically-faint companion reported in previous studies. The lensing-corrected molecular gas masses are Mgas = (1.4 ± 0.3) × 1010 M⊙ and (2.0 ± 0.1) × 109 M⊙ for RXS J1131−1231 and the companion, respectively. We find a lensing-corrected stellar mass of M* = (3 ± 1) × 1010 M⊙ and a star formation rate of SFRFIR = (120 ± 63) M⊙ yr−1, corresponding to a specific SFR and star formation efficiency comparable to z ∼ 1 disk galaxies not hosting quasars. The implied gas mass fraction of ∼18 ± 4% is consistent with the previously observed cosmic decline since z ∼ 2. We thus find no evidence for quenching of star formation in RXS J1131−1231. This agrees with our finding of an elevated ratio of >0.27 % compared to the local value, suggesting that the bulk of its black hole mass is largely in place while its stellar bulge is still assembling.
Exploiting Prompt Learning with Pre-Trained Language Models for Alzheimer’s Disease Detection

Early diagnosis of Alzheimer’s disease (AD) is crucial in facilitating preventive care and to delay further progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Textual embedding features produced by pre-trained language models (PLMs) such as BERT are widely used in such systems. However, PLM domain fine-tuning is commonly based on the masked word or sentence prediction costs that are inconsistent with the back-end AD detection task. To this end, this paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function. Disfluency features based on hesitation or pause filler token frequencies are further incorporated into prompt phrases during PLM fine-tuning. The decision voting based combination among systems using different PLMs (BERT and RoBERTa) or systems with different fine-tuning paradigms (conventional masked-language modelling fine-tuning and prompt-based fine-tuning) is further applied. Mean, standard deviation (std) and the maximum among accuracy scores over 15 experiment runs are adopted as performance measurements for the AD detection system. Mean detection accuracy of 84.20% (with std 2.09%, best 87.5%) and 82.64% (with std 4.0%, best 89.58%) were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers.
The Ionizing Spectra of Extremely Metal-poor O Stars: Constraints from the Only H ii Region in Leo P

Metal-poor, star-forming dwarf galaxies produce extreme nebular emission and likely played a major role in cosmic reionization. Yet, determining their contribution to the high-redshift ionizing photon budget is hampered by the lack of observations constraining the ionizing spectra of individual massive stars more metal-poor than the Magellanic Clouds (20%–50% Z ⊙). We present new Keck Cosmic Web Imager (KCWI) optical integral field unit spectroscopy of the only H ii region in Leo P (3% Z ⊙), which is powered by a single O star. We calculate the required production rate of photons capable of ionizing hydrogen and helium from the observed Hβ and He i λ4471 emission-line fluxes. Remarkably, we find that the ionizing photon production rate and spectral hardness predicted by a tlusty model fit to the stellar spectral energy distribution agrees with our observational measurements within the uncertainties. We then fit Cloudy photoionization models to the full suite of optical emission lines in the KCWI data and show that the shape of the same tlusty ionizing continuum simultaneously matches lines across a wide range of ionization energies. Finally, we detect O iii] and N iii] nebular emission in the Hubble Space Telescope far-ultraviolet spectrum of the Leo P H ii region, and highlight that the rarely observed N iii] emission cannot be explained by our Cloudy models. These results provide the first observational evidence that widely used, yet purely theoretical, model spectra accurately predict the ionizing photon production rate from late-O stars at very low metallicity, validating their use to model metal-poor galaxies both locally and at high redshift.
On D. Peterson's presentation of quantum cohomology of $G/P$

We prove in full generality that the $T$-equivariant quantum cohomology of any flag variety $G/P$ is isomorphic to the coordinate ring of a stratum of the Peterson scheme associated to the Langlands dual group scheme $G^{\vee}$. This result was discovered by Dale Peterson but remains unpublished. Our isomorphism is constructed using Yun-Zhu's isomorphism and Peterson-Lam-Shimozono's homomorphism.
FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes

Quantum Approximate Optimization Algorithm (QAOA) is one of the leading candidates for demonstrating the quantum advantage using near-term quantum computers. Unfortunately, high device error rates limit us from reliably running QAOA circuits for problems with more than a few qubits. In QAOA, the problem graph is translated into a quantum circuit such that every edge corresponds to two 2-qubit CNOT operations in each layer of the circuit. As CNOTs are extremely error-prone, the fidelity of QAOA circuits is dictated by the number of edges in the problem graph. We observe that the majority of graphs corresponding to real-world applications follow a “power-law” distribution, where some hotspot nodes have significantly higher number of connections. We leverage this insight and propose “FrozenQubits” that freezes the hotspot nodes or qubits and intelligently partitions the state-space of the given problem into several smaller sub-spaces, which are then solved independently. The corresponding QAOA sub-circuits are significantly less vulnerable to gate and decoherence errors due to the reduced number of CNOT operations in each sub-circuit. Unlike prior circuit-cutting approaches, FrozenQubits does not require any exponentially complex postprocessing step. Our evaluations with 5,300 QAOA circuits on eight different quantum computers from IBM show that FrozenQubits can improve the quality of solutions by 8.73x on average (and by up to 57x), albeit while utilizing 2x more quantum resources.
Digital twins of physical printing-imaging channel

In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a.k. a. digital twin for anti-counterfeiting applications based on copy detection patterns (CDP). The digital twin is formulated on an information-theoretic framework called Turbo that uses variational approximations of mutual information developed for both encoder and decoder in two-directional information passage. The proposed model generalizes several state-of-the-art architectures such as adversarial autoencoder (AAE) [1], CycleGAN [2] and adversarial latent space auto-encoder (ALAE) [3]. This model can be applied to any type of printing and imaging and it only requires training data consisting of digital templates or artworks that are sent to a printing device and data acquired by an imaging device. Moreover, these data can be paired, unpaired or hybrid paired-unpaired which makes the proposed architecture very flexible and scalable to many practical setups. We demonstrate the impact of various architectural factors, metrics and discriminators on the overall system performance in the task of generation/prediction of printed CDP from their digital counterparts and vice versa. We also compare the proposed system with several state-of-the-art methods used for image-to-image translation applications. The code and extended results of the simulation are publicly available1.
Learning New Tasks from a Few Examples with Soft-Label Prototypes

Existing approaches to few-shot learning in NLP rely on large language models and fine-tuning of these to generalise on out-of-distribution data. In this work, we propose a simple yet powerful approach to"extreme"few-shot learning, wherein models are exposed to as little as 4 examples per class, based on soft-label prototypes that collectively capture the distribution of different classes across the input domain space. Inspired by previous work (Sucholutsky et al., 2021) on univariate or simple multivariate (synthetic) data, we propose a novel approach that is effective on large, high-dimensional and real-world datasets. We learn soft-label prototypes within a neural framework (DeepSLP) and we experimentally demonstrate that it achieves superior performance on 31/48 tested tasks and few-shot settings while closely matching the performance of strong baselines on the rest. We focus on learning previously unseen NLP tasks from very few examples (4, 8, 16) per label and present an in-depth analysis of the effectiveness of our approach.
Model Compression for DNN-Based Text-Independent Speaker Verification Using Weight Quantization

DNN-based models achieve high performance in the speaker veriﬁcation (SV) task with substantial computation costs. The model size is an essential concern in applying models on resource-constrained devices, while model compression for SV models has not been studied extensively in previous works. Weight quantization is exploited to compress DNN-based speaker embedding extraction models in this paper. Uniform and Powers-of-Two quantization are utilized in the experiments. The results on VoxCeleb show that the weight quantization can decrease the size of ECAPA-TDNN and ResNet by 4 times with insigniﬁcant performance decline. The quantized 4-bit ResNet achieves similar performance to the original model with an 8 times smaller size. We empirically show that the performance of ECAPA-TDNN is more sensitive than ResNet to quantization due to the difference in weight distribution. The experiments on CN-Celeb also demonstrate that quantized models are robust for SV in the language mismatch scenario.
DiffusER: Discrete Diffusion via Edit-based Reconstruction

In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revise existing text, which limits their usability in many practical scenarios. We look to address this, with DiffusER (Diffusion via Edit-based Reconstruction), a new edit-based generative model for text based on denoising diffusion models -- a class of models that use a Markov chain of denoising steps to incrementally generate data. DiffusER is not only a strong generative model in general, rivalling autoregressive models on several tasks spanning machine translation, summarization, and style transfer; it can also perform other varieties of generation that standard autoregressive models are not well-suited for. For instance, we demonstrate that DiffusER makes it possible for a user to condition generation on a prototype, or an incomplete sequence, and continue revising based on previous edit steps.