Miracle-dz/UniFAR-FTUs huggingface dataset:
The KLEVER Survey: spatially resolved metallicity maps and gradients in a sample of 1.2 < z < 2.5 lensed galaxies

We present near-infrared observations of 42 gravitationally lensed galaxies obtained in the framework of the KMOS Lensed Emission Lines and VElocity Review (KLEVER) Survey, a programme aimed at investigating the spatially resolved properties of the ionized gas in 1.2 < z < 2.5 galaxies by means of a full coverage of the YJ, H, and K near-infrared bands. Detailed metallicity maps and gradients are derived for a subsample of 28 galaxies from reconstructed source-plane emission-line maps, exploiting the variety of different emission-line diagnostics provided by the broad wavelength coverage of the survey. About $85 {{\, per\ cent}}$ of these galaxies are characterized by metallicity gradients shallower than $0.05\ \rm dex\, kpc^{-1}$ and $89{{\ \rm per\ cent}}$ are consistent with a flat slope within 3σ ($67{{\ \rm per\ cent}}$ within 1σ), suggesting a mild evolution with cosmic time. In the context of cosmological simulations and chemical evolution models, the presence of efficient feedback mechanisms and/or extended star formation profiles on top of the classical ‘inside-out’ scenario of mass assembly is generally required to reproduce the observed flatness of the metallicity gradients beyond z ∼ 1. Three galaxies with significantly (>3σ) ‘inverted’ gradients are also found, showing an anticorrelation between metallicity and star formation rate density on local scales, possibly suggesting recent episodes of pristine gas accretion or strong radial flows in place. Nevertheless, the individual metallicity maps are characterized by a variety of different morphologies, with flat radial gradients sometimes hiding non-axisymmetric variations on kpc scales, which are washed out by azimuthal averages, especially in interacting systems or in those undergoing local episodes of recent star formation.
MUSE Analysis of Gas around Galaxies (MAGG) – I: Survey design and the environment of a near pristine gas cloud at z ≈ 3.5

We present the design, methods, and first results of the MUSE Analysis of Gas around Galaxies (MAGG) survey, a large programme on the Multi-Unit Spectroscopic Explorer (MUSE) instrument at the Very Large Telescope (VLT), which targets 28 z > 3.2 quasars to investigate the connection between optically thick gas and galaxies at z ∼ 3–4. MAGG maps the environment of 52 strong absorption line systems at z ≳ 3, providing the first statistical sample of galaxies associated with gas-rich structures in the early Universe. In this paper, we study the galaxy population around a very metal poor gas cloud at z ≈ 3.53 towards the quasar J124957.23−015928.8. We detect three Lyα emitters within $\lesssim 200~\rm km~s^{-1}$ of the cloud redshift, at projected separations $\lesssim 185~\rm ~kpc$ (physical). The presence of star-forming galaxies near a very metal-poor cloud indicates that metal enrichment is still spatially inhomogeneous at this redshift. Based on its very low metallicity and the presence of nearby galaxies, we propose that the most likely scenario for this Lyman Limit System (LLS) is that it lies within a filament which may be accreting on to a nearby galaxy. Taken together with the small number of other LLSs studied with MUSE, the observations to date show a range of different environments near strong absorption systems. The full MAGG survey will significantly expand this sample and enable a statistical analysis of the link between gas and galaxies to pin down the origin of these diverse environments at z ≈ 3–4.
New methodology based on energy flux similarity for large-eddy simulation of transitional and turbulent flows

A new methodology based on energy flux similarity is suggested in this paper for large eddy simulation (LES) of transitional and turbulent flows. Existing knowledge reveals that the energy cascade generally exists in transitional and turbulent flows with different distributions, and the characteristic quantity of scale interaction in energy cascade processes is energy flux. Therefore, energy flux similarity is selected as the basic criterion to secure the flow field getting from LES highly similar to the real flow field. Through a priori tests, we find that the energy flux from the tensor-diffusivity (TD) model has high similarity with the real energy flux. Then, we modify the modelled energy flux from the TD model and obtain uniform formulas of energy flux similarity corresponding to different filter widths and locations in the wall-bounded turbulence. To secure the robustness of simulation and the LES results similar to the real flow, we apply the energy flux similarity method (EFSM) to the Smagorinsky model in the LES of compressible turbulent channel flow, compressible flat-plate flow, and flow over a compressible ramp. The a posteriori tests show that, overall, EFSM can better predict these flows than other subgrid-scale models. In the simulation of turbulent channel flow, EFSM can accurately predict the mean stream-wise velocity, Reynolds stress, and affluent coherent structures. In LES of compressible flat-plate flow, EFSM could provide accurate simulation results of the onset of transition and transition peak, skin friction, and mean stream-wise velocity in cases with three different grid scales. Meanwhile, for flow over a compressible ramp, EFSM could correctly describe the process of bypass transition, locations of separation and reattachment in the corner region, and abundant coherent vortex structures, etc.
Binary outflows from young stars: interaction of co-orbital jet and wind

Jets from young stellar objects provide insight into the workings of the beating heart at the centre of star forming cores. In some cases, multiple pulsed outflows are detected such as the atomic and molecular jets from a proposed binary system in the T Tauri star HH 30. We investigate here the development and propagation of duelling atomic and molecular outflows stemming from the two stars in co-orbit. We perform a series of numerical experiments with the ZEUS-MP code with enhanced cooling and chemistry modules. The aim of this work is to identify signatures on scales of order 100 AU. The jet sources are off the grid domain and so it is the propagation and interaction from ∼ 20 AU out to 100 AU simulated here. We find that the molecular flow from the orbiting source significantly disturbs the atomic jet, deflecting and twisting the jet and disrupting the jet knots. Regions of high ionisation are generated as the atomic jet rams through the dense molecular outflow. Synthetic images in atomic and molecular lines are presented which demonstrate identifying signatures. In particular, the structure within the atomic jet is lost and Hα may trace the walls of the present CO cavity or where the walls have been recently. These results provide a framework for the interpretation of upcoming high resolution observations.
Remodeling of calcium handling in skeletal muscle through the peroxisome proliferator- activated receptor γ coactivator 1α (PGC-1α): Impact on force, fatigability and fiber type

Serge Summermatter, Raphael Thurnheer, Gesa Santos, Barbara Mosca, Oliver Baum, Susan Treves, Hans Hoppeler, Francesco Zorzato and Christoph Handschin Biozentrum, Department of Pharmacology/Neurobiology, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland Departments of Anesthesia and Biomedicine, Basel University Hospital, Hebelstrasse 20, CH-4031 Basel, Switzerland Department of Experimental and Diagnostic Medicine, University of Ferrara, Via Borsari 46, 44100 Ferrara, Italy Institute of Anatomy, University of Bern, Baltzerstrasse 2, CH-3009 Bern, Switzerland, Running head: Remodeling of muscle calcium handling by PGC-1α
Identification of the parameters of the equation of Richards by the genetic algorithms

In this article it is question of identification of the parameters in the equation of Richards modelling the flow in unsaturated porous medium. The equation considered is strongly nonlinear and is in dimension one of space. The method of inversion developed is based on the genetic algorithms. The direct problem was solved by the finite difference method. The function cost used is built by using the infiltration. A test on real data has been carried. The software used is Matlab. AMS Subject Classification: 35A15, 35R30, 65L09, 65L10, 65L12.
Negative contributions to entropy production

The entropy production in dissipative processes is the essence of thermodynamics and also of the arrow of time. It was recently shown that in dissipation of quantum systems, the entropy production contains two contributions: a classical one and a quantum one. Here we show that for degenerate (or near-degenerate) quantum systems there are additional quantum contributions which, remarkably, can become negative. Furthermore, such negative contributions are related to significant changes in the ongoing thermodynamics. This includes phenomena such as generation of coherences between degenerate energy levels (called horizontal coherences), alteration of energy exchanges and, last but not least, reversal of the natural convergence of the populations toward the thermal equilibrium state. Going further, we establish a complementarity relation between horizontal coherences and population convergence, particularly enlightening for understanding heat flow reversals. Conservation laws of the different types of coherences are derived. Some consequences for thermal machines, fluctuation theorems and resource theory of coherence are suggested.
Age at Marriage, Age at First Birth, and Fertility in Africa

This report derives estimates from national sample surveys conducted in Africa over the past dozen years of the trends in age at first marriage and at first birth. By splicing together cohorts from the earlier World Fertility Survey and the more recent Demographic and Health Surveys, a pattern of rapidly increasing age at marriage and at first birth is depicted for some African countries, while for some others there is evidence of the beginnings of such change. The demographic significance of such changes is explained, and a model of fertility is constructed in which the role of these variables in the association between socioeconomic background factors and reproductive intentions and contraceptive prevalence is described. The units of observation are the provinces or regions of the countries based on a special data bank created for these analyses. The importance of women's education is highlighted, and the trends in educational achievement are reconstructed from these surveys over a 40-year span. The report concludes with some population policy reflections and emphasizes the potential importance of delaying the first birth by increasing the age at marriage. Population policies aimed at reducing fertility should certainly include efforts to raise the age at marriage.
Fault Tolerance of Neural Networks in Adversarial Settings

Artificial Intelligence systems require a through assessment of different pillars of trust, namely, fairness, interpretability, data and model privacy, reliability (safety) and robustness against against adversarial attacks. While these research problems have been extensively studied in isolation, an understanding of the trade-off between different pillars of trust is lacking. To this extent, the trade-off between fault tolerance, privacy and adversarial robustness is evaluated for the specific case of Deep Neural Networks, by considering two adversarial settings under a security and a privacy threat model. Specifically, this work studies the impact of the fault tolerance of the Neural Network on training the model by adding noise to the input (Adversarial Robustness) and noise to the gradients (Differential Privacy). While training models with noise to inputs, gradients or weights enhances fault tolerance, it is observed that adversarial robustness and fault tolerance are at odds with each other. On the other hand, ($\epsilon,\delta$)-Differentially Private models enhance the fault tolerance, measured using generalisation error, theoretically has an upper bound of $e^{\epsilon} - 1 + \delta$. This novel study of the trade-off between different elements of trust is pivotal for training a model which satisfies the requirements for different pillars of trust simultaneously.
Carboxymethylglucan in Cosmetics

Carboxymethyl glucan (CM glucan) is a water soluble polysaccharide modified from the insoluble β-(1→3)-glucan by carboxymethylation. CM glucan has biological activities consistent with its insoluble form. Solubility improvement enables application as a biomaterial for pharmaceutical and cosmetic aspects. Microbial formation of CM glucan is mainly from Baker.s yeast. In addition, Brewer.s yeast is now present as the sufficient source for commercial CM glucan. The cosmetic ingredient properties of CM glucan was investigated both in in vitro and clinical studies of which the CM glucan promises as an efficient ingredient offering anti-aging and anti-wrinkle effects in addition to nourishing effect including skin lightening efficacy.
ON-TRAC Consortium End-to-End Speech Translation Systems for the IWSLT 2019 Shared Task

This paper describes the ON-TRAC Consortium translation systems developed for the end-to-end model task of IWSLT Evaluation 2019 for the English→ Portuguese language pair. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Université), LIG (Université Grenoble Alpes), and LIUM (Le Mans Université). A single end-to-end model built as a neural encoder-decoder architecture with attention mechanism was used for two primary submissions corresponding to the two EN-PT evaluations sets: (1) TED (MuST-C) and (2) How2. In this paper, we notably investigate impact of pooling heterogeneous corpora for training, impact of target tokenization (characters or BPEs), impact of speech input segmentation and we also compare our best end-to-end model (BLEU of 26.91 on MuST-C and 43.82 on How2 validation sets) to a pipeline (ASR+MT) approach.
Training DNN IoT Applications for Deployment On Analog NVM Crossbars

A trend towards energy-efficiency, security and privacy has led to a recent focus on deploying deep-neural networks (DNN) on microcontrollers. However, limits on compute and memory resources restrict the size and the complexity of the machine-learning (ML) models deployable in these systems. Computation-In-Memory architectures based on resistive non-volatile memory (NVM) technologies hold great promise of satisfying the compute and memory demands of high-performance and low-power, inherent in modern DNNs. Nevertheless, these technologies are still immature and suffer from both the intrinsic analog-domain noise problems and the inability of representing negative weights in the NVM structures, incurring in larger crossbar sizes with concomitant impact on Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). In this paper, we provide a training framework for addressing these challenges and quantitatively evaluate the circuit-level efficiency gains thus accrued. We make two contributions: Firstly, we propose a training algorithm that eliminates the need for tuning individual layers of a DNN ensuring uniformity across layer-weights and activations. This ensures analog-blocks that can be reused and peripheral hardware substantially reduced. Secondly, using Network Architecture Search (NAS) methods, we propose the use of unipolar-weighted (either all-positive or all-negative weights) matrices/sub-matrices. Weight unipolarity obviates the need for doubling crossbar area leading to simplified analog periphery. We validate our methodology with CIFAR10 and HAR applications by mapping to crossbars using 4-bit and 2-bit devices. We achieve up to 92.91% accuracy (95% floating-point) using 2-bit only-positive weights for HAR. A combination of the proposed techniques leads to 80% area improvement and up to 45% energy reduction.
Sim2real gap is non-monotonic with robot complexity for morphology-in-the-loop flapping wing design

Morphology of a robot design is important to its ability to achieve a stated goal and therefore applying machine learning approaches that incorporate morphology in the design space can provide scope for significant advantage. Our study is set in a domain known to be reliant on morphology: flapping wing flight. We developed a parameterised morphology design space that draws features from biological exemplars and apply automated design to produce a set of high performance robot morphologies in simulation. By performing sim2real transfer on a selection, for the first time we measured the shape of the reality gap for variations in design complexity. We found for the flapping wing that the reality gap changes non-monotonically with complexity, suggesting that certain morphology details narrow the gap more than others, and that such details could be identified and further optimised in a future end-to-end automated morphology design process.
Fully Bayesian imputation model for non-random missing data in qPCR

We propose a new statistical approach to obtain differential gene expression of non-detects in quantitative real-time PCR (qPCR) experiments through Bayesian hierarchical modeling. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute Ct values or obtain direct estimates of relevant model parameters. A typical laboratory does not have the resources to perform experiments with a large number of replicates; therefore, we propose an approach that does not rely on large sample theory. We aim to demonstrate the possibilities that exist for analyzing qPCR data in the presence of non-random missingness through the use of Bayesian estimation. Bayesian analysis typically allows for smaller data sets to be analyzed without losing power while retaining precision. The heart of Bayesian estimation is that everything that is known about a parameter before observing the data (the prior) is combined with the information from the data itself (the likelihood), resulting in updated knowledge about the parameter (the posterior). In this work we introduce and describe our hierarchical model and chosen prior distributions, assess the model sensitivity to the choice of prior, perform convergence diagnostics for the Markov Chain Monte Carlo, and present the results of a real data application.
An Augmented Transformer Architecture for Natural Language Generation Tasks

The Transformer based neural networks have been showing significant advantages on most evaluations of various natural language processing and other sequence-tosequence tasks due to its inherent architecture based superiorities. Although the main architecture of the Transformer has been continuously being explored, little attention was paid to the positional encoding module. In this paper, we enhance the sinusoidal positional encoding algorithm by maximizing the variances between encoded consecutive positions to obtain additional promotion. Furthermore, we propose an augmented Transformer architecture encoded with additional linguistic knowledge, such as the Part-of-Speech (POS) tagging, to boost the performance on some natural language generation tasks, e.g., the automatic translation and summarization tasks. Experiments show that the proposed architecture attains constantly superior results compared to the vanilla Transformer.
Deep Learning for Optoelectronic Properties of Organic Semiconductors

Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excited-state electronic-structure calculations, a computationally daunting task for many OSC applications. In this work, we advocate the use of deep learning to address this challenge and demonstrate that state-of-the-art deep neural networks (DNNs) are capable of predicting the electronic properties of OSCs at an accuracy comparable with the quantum chemistry methods used for generating training data. We extensively investigate the performances of four recent DNNs (deep tensor neural network, SchNet, message passing neural network, and multilevel graph convolutional neural network) in predicting various electronic properties of an important class of OSCs, i.e., oligothiophenes (OTs), including their HOMO and LUMO energies, excited-state energies and associated transition dipole moments. We find that SchNet shows the best performance for OTs of different sizes (from bithiophene to sexithiophene), achieving average prediction errors in the range of 20-80meV compared to the results from (time-dependent) density functional theory. We show that SchNet also consistently outperforms shallow feed-forward neural networks, especially in difficult cases with large molecules or limited training data. We further show that SchNet could predict the transition dipole moment accurately, a task previously known to be difficult for feed-forward neural networks, and we ascribe the relatively large errors in transition dipole prediction seen for some OT configurations to the charge-transfer character of their excited states. Finally, we demonstrate the effectiveness of SchNet by modeling the UV-Vis absorption spectra of OTs in dichloromethane and a good agreement is observed between the calculated and experimental spectra.
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a highly optimized implementation that scales to 27,500 NVIDIA Volta GPUs. We develop a hierarchical scheme based on a multi-player game-theoretic approach for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. Our implementation scales to 4584 nodes on the Summit supercomputer with a 93.1%scaling efficiency, achieving peak and sustained half-precision . rates of 1228 PF/s and 1207 PF/s.
The Central Role of Content Representation in Test Validity

MF01/PCO2 Plus Postage. *Construct Validity; *Content Validity; *Definitions; Item Analysis; Responses; Scoring; Test Construction; *Test Content; Test Items; Test Theory The purpose of this paper is to clarify the seemingly discrepant views of test theorists and test developers about terminology related to the evaluation of test content. The origin and evolution of the concept of content validity are traced, and the concept is reformulated in a way that emphasizes the notion that content domain definition, relevance, and representation are necessary 7.nd fundamental qualities on which all tests should be evaluated. An important area of convergence for test theorists and test developers has been that adequately defining and representing the construct measured is of critical importance. Broad definitions have asserted that content validity is concerned with test and response properties, but narrow definitions limit content validity to investigations of items, tests, and scoring. Future descriptions of test validity must emphasize the important role of content validity in the construction and evaluation of test. Theories and applications of content validity belong within the framework of construct validity. (Contains 1 table, 2 figures, and 69 references.) (SLD) ********************************************************************** * Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** U S DEPARTMENT OF EDUCATION OPce ot Eclocalaym. Researcn and imomvomer. , E DUCA TIONAL RESOURCES INFORMATION