Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the Effects of Test Media in Ground-Based Propulsion Testing

This paper discusses the progress of work which began in mid-2004 sponsored by the Office of the Secretary of Defense (OSD) Test & Evaluation/Science & Technology (T&E/S&T) Program. The purpose of the work is to improve the state of the art of CFD capabilities for predicting the effects of the test media on the flameholding characteristics in scramjet engines. The program has several components including the development of advance algorithms and models for simulating engine flowpaths as well as a fundamental experimental and diagnostic development effort to support the formulation and validation of the mathematical models. The paper will provide details of current work involving the development of phenomenological models for Reynolds averaged Navier-Stokes codes, large-eddy simulation techniques and reduced-kinetics models. Experiments that will provide data for the modeling efforts will also be described, along with with the associated nonintrusive diagnostics used to collect the data.
Neural Architecture Search on Efficient Transformers and Beyond

Recently, numerous efficient Transformers have been proposed to reduce the quadratic computational complexity of standard Transformers caused by the Softmax attention. However, most of them simply swap Softmax with an efficient attention mechanism without considering the customized architectures specially for the efficient attention. In this paper, we argue that the handcrafted vanilla Transformer architectures for Softmax attention may not be suitable for efficient Transformers. To address this issue, we propose a new framework to find optimal architectures for efficient Transformers with the neural architecture search (NAS) technique. The proposed method is validated on popular machine translation and image classification tasks. We observe that the optimal architecture of the efficient Transformer has the reduced computation compared with that of the standard Transformer, but the general accuracy is less comparable. It indicates that the Softmax attention and efficient attention have their own distinctions but neither of them can simultaneously balance the accuracy and efficiency well. This motivates us to mix the two types of attention to reduce the performance imbalance. Besides the search spaces that commonly used in existing NAS Transformer approaches, we propose a new search space that allows the NAS algorithm to automatically search the attention variants along with architectures. Extensive experiments on WMT' 14 En-De and CIFAR-10 demonstrate that our searched architecture maintains comparable accuracy to the standard Transformer with notably improved computational efficiency.
Ever heard of ethical AI? Investigating the salience of ethical AI issues among the German population

Building and implementing ethical AI systems that benefit the whole society is cost-intensive and a multi-faceted task fraught with potential problems. While computer science focuses mostly on the technical questions to mitigate social issues, social science addresses citizens' perceptions to elucidate social and political demands that influence the societal implementation of AI systems. Thus, in this study, we explore the salience of AI issues in the public with an emphasis on ethical criteria to investigate whether it is likely that ethical AI is actively requested by the population. Between May 2020 and April 2021, we conducted 15 surveys asking the German population about the most important AI-related issues (total of N=14,988 respondents). Our results show that the majority of respondents were not concerned with AI at all. However, it can be seen that general interest in AI and a higher educational level are predictive of some engagement with AI. Among those, who reported having thought about AI, specific applications (e.g., autonomous driving) were by far the most mentioned topics. Ethical issues are voiced only by a small subset of citizens with fairness, accountability, and transparency being the least mentioned ones. These have been identified in several ethical guidelines (including the EU Commission's proposal) as key elements for the development of ethical AI. The salience of ethical issues affects the behavioral intentions of citizens in the way that they 1) tend to avoid AI technology and 2) engage in public discussions about AI. We conclude that the low level of ethical implications may pose a serious problem for the actual implementation of ethical AI for the Common Good and emphasize that those who are presumably most affected by ethical issues of AI are especially unaware of ethical risks. Yet, once ethical AI is top of the mind, there is some potential for activism.
Geometric relative entropies and barycentric Rényi divergences

We give systematic ways of defining monotone quantum relative entropies and (multi-variate) quantum R\'enyi divergences starting from a set of monotone quantum relative entropies. Despite its central importance in information theory, only two additive and monotone quantum extensions of the classical relative entropy have been known so far, the Umegaki and the Belavkin-Staszewski relative entropies. Here we give a general procedure to construct monotone and additive quantum relative entropies from a given one with the same properties; in particular, when starting from the Umegaki relative entropy, this gives a new one-parameter family of monotone and additive quantum relative entropies interpolating between the Umegaki and the Belavkin-Staszewski ones on full-rank states. In a different direction, we use a generalization of a classical variational formula to define multi-variate quantum R\'enyi quantities corresponding to any finite set of quantum relative entropies $(D^{q_x})_{x\in X}$ and signed probability measure $P$, as $$ Q_P^{\mathrm{b},\mathbf{q}}((\rho_x)_{x\in X}):=\sup_{\tau\ge 0}\left\{\text{Tr}\,\tau-\sum_xP(x)D^{q_x}(\tau\|\rho_x)\right\}. $$ We show that monotone quantum relative entropies define monotone R\'enyi quantities whenever $P$ is a probability measure. With the proper normalization, the negative logarithm of the above quantity gives a quantum extension of the classical R\'enyi $\alpha$-divergence in the 2-variable case ($X=\{0,1\}$, $P(0)=\alpha$). We show that if both $D^{q_0}$ and $D^{q_1}$ are monotone and additive quantum relative entropies, and at least one of them is strictly larger than the Umegaki relative entropy then the resulting barycentric R\'enyi divergences are strictly between the log-Euclidean and the maximal R\'enyi divergences, and hence they are different from any previously studied quantum R\'enyi divergence.
Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe

Electronic and optical excitations in two-dimensional systems are distinctly sensitive to the presence of a moiré superlattice. We used cryogenic transmission electron microscopy and spectroscopy to simultaneously image the structural reconstruction and associated localization of the lowest-energy intralayer exciton in a rotationally aligned WS2-WSe2 moiré superlattice. In conjunction with optical spectroscopy and ab initio calculations, we determined that the exciton center-of-mass wave function is confined to a radius of approximately 2 nanometers around the highest-energy stacking site in the moiré unit cell. Our results provide direct evidence that atomic reconstructions lead to the strongly confining moiré potentials and that engineering strain at the nanoscale will enable new types of excitonic lattices. Description Probing the Moiré lattice The optoelectronic properties of bilayers of two-dimensional materials exhibit a wealth of properties dependent on the twist angle. A direct probe of the excitonic properties and their correlation with the atomic registry has been lacking. Susarla et al. used cryogenic transmission electron microscopy and spectroscopy to simultaneously image the structural reconstruction and the associated spatial localization of the lowest-energy intralayer exciton in a rotationally aligned bilayer heterostructure of tungsten disulfide and tungsten diselenide. The subnanometer spatial resolution allows the real-space mapping of intralayer excitons within a Moiré unit cell and should aid in the development of materials with controlled properties. —ISO Electron microscopy and spectroscopy enable real-space visualization of Moiré excitons in a 2D heterostructure.
Efficient Training of Language Models to Fill in the Middle

We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales. Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default. To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span. We use these ablations to prescribe strong default settings and best practices to train FIM models. We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research.
Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods

Persona and Knowledge dual context open-domain chat is a novel dialogue generation task introduced recently. While Persona and Knowledge is each interesting context of open-domain dialogue, the combination of both has not been well studied. We tackle Persona-Knowledge identification and response generation tasks in this paper. We design an informed data augmentation strategy that is compatible with neural Q&A retrieval models. With the augmented data, we perform permutative Persona-Knowledge evaluation and successive Persona search fine-tuning. Furthermore, we perform dialogue generation with various decoding techniques and illustrate crucial elements. We achieve SOTA across official metrics with 93.99% Grounding accuracy average and 23.62 SacreBLEU score.
Kiloparsec-scale Radio Structure in z ∼ 0.25 Radio-quiet QSOs

We present analysis of a homogeneous, optically selected, volume-limited (0.2 < z < 0.3) sample of 128 radio-quiet quasi-stellar objects (QSOs) recently observed at 6 GHz with the Very Large Array (VLA) in A configuration (∼0.″33 resolution). We compare these new results to earlier (2010–2011) 6 GHz observations with the VLA in C configuration (∼3.″5). While all of these radio-quiet QSOs (RQQs) were unresolved on a 3. ″5 scale (∼14 kpc at z = 0.25), we resolve notable complex subgalactic structures in about half of the RQQs at 0. ″33 resolution (∼1.3 kpc at z = 0.25). By comparison of flux density measurements between the two sets of observations, we demonstrate that significant sub-galactic-scale radio structure is present in at least 70% of the RQQ population and that the central component accounts for an average of ≈65% of the total detected radio power. One RQQ, J0935+4819, shows striking symmetric, double-lobed morphology and appears to be the first identified example of a radio-quiet QSO with FR II type morphology on ∼arcsec scale (projected size of ≳6 kpc). In addition to revealing RQQ subgalactic morphology, we employ counterparts from legacy (FIRST at 1.4 GHz) and recent (VLA Sky Survey at 3 GHz) VLA surveys to investigate radio spectral indices and potential variability over decades-long timescales for a subset of the RQQs and for the cores of radio-intermediate and radio-loud sources in the parent sample of 178 QSOs. These results support the growing notion that the RQQ population is not a monolithic phenomenon but instead consists of a mixture of mainly starburst-powered and jet-powered galaxies.
Shift-curvature, SGD, and generalization

A longstanding debate surrounds the related hypotheses that low-curvature minima generalize better, and that stochastic gradient descent (SGD) discourages curvature. We offer a more complete and nuanced view in support of both hypotheses. First, we show that curvature harms test performance through two new mechanisms, the shift-curvature and bias-curvature, in addition to a known parameter-covariance mechanism. The shift refers to the difference between train and test local minima, and the bias and covariance are those of the parameter distribution. These three curvature-mediated contributions to test performance are reparametrization-invariant even though curvature itself is not. Although the shift is unknown at training time, the shift-curvature as well as the other mechanisms can still be mitigated by minimizing overall curvature. Second, we derive a new, explicit SGD steady-state distribution showing that SGD optimizes an effective potential related to but different from train loss, and that SGD noise mediates a trade-off between low-loss versus low-curvature regions of this effective potential. Third, combining our test performance analysis with the SGD steady state shows that for small SGD noise, the shift-curvature is the dominant of the three mechanisms. Our experiments demonstrate the significant impact of shift-curvature on test loss, and further explore the relationship between SGD noise and curvature.
The Impact of Environmental Policy on Clean Innovation

This paper studies the effect of climate change mitigating policies on innovation in clean energy technologies. Results suggest that the tightening of environmental policies since the early 1990s have made a statistically and economically significant contribution to the increase in clean innovation. These effects generally materialized quickly, within 2 to 3 years of the policy change, and were driven by individually significant marginal effects of both market-based policies – such as feed-in tariffs and trading schemes – as well as non-market policies, such as R&D subsidies or emission limits. Looking at electricity innovation in particular, the paper finds that the estimated effect on total innovation is positive on net, meaning that increased innovation in clean and grey technologies is not offset by a decrease in innovation in dirty technologies. From a policy point of view, the paper’s results call for strong policy efforts to decisively shift innovation towards clean technologies. IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Random tree growth with general weight function

We extend the results of B. Bollobas, O. Riordan, J. Spencer, G. Tusnady, and Mori. We consider a model of random tree growth, where at each time unit a new node is added and attached to an already existing node chosen at random. The probability with which a node with degree $k$ is chosen is proportional to $w(k)$, where $w$ is a fixed weight function. We prove that if $w$ fulfills some asymptotic requirements then the degree sequence converges in probability, we give the limit. In particular if $w$ is asymptotically linear then the degree sequence decays with power law. Our method of proof is analytic rather than combinatorial, having the advantage of robustness: only asymptotic properties of the weight function $w$ are used, while in the cited papers the explicit law $w(k)=ak+b$ is assumed.
Identification of COVID-19 severity and associated genetic biomarkers based on scRNA-seq data

Bio-marker identification for COVID-19 remains a vital research area to improve current and future pandemic responses. Innovative artificial intelligence and machine learning-based systems may leverage the large quantity and complexity of single cell sequencing data to quickly identify disease with high sensitivity. In this study, we developed a novel approach to classify patient COVID-19 infection severity using single-cell sequencing data derived from patient BronchoAlveolar Lavage Fluid (BALF) samples. We also identified key genetic biomarkers associated with COVID-19 infection severity. Feature importance scores from high performing COVID-19 classifiers were used to identify a set of novel genetic biomarkers that are predictive of COVID-19 infection severity. Treatment development and pandemic reaction may be greatly improved using our novel big-data approach. Our implementation is available on https://github.com/aekanshgoel/COVID-19_scRNAseq.
The impact of gene sequence alignment and gene tree estimation error on summary-based species network estimation

Thanks in part to rapid advances in next-generation sequencing technologies, recent phylogenomic studies have demonstrated the pivotal role that non-tree-like evolution plays in many parts of the Tree of Life - the evolutionary history of all life on Earth. As such, the Tree of Life is not necessarily a tree at all, but is better described by more general graph structures such as a phylogenetic network. Another key ingredient in these advances consists of the computational methods needed for reconstructing phylogenetic networks from large-scale genomic sequence data. But virtually all of these methods either require multiple sequence alignments (MSAs) as input or utilize gene trees or other inputs that are computed using MSAs. All of the input MSAs and gene trees must be estimated on empirical data. The methods themselves do not directly account for upstream estimation error, and, apart from prior studies of phylogenetic tree reconstruction and anecdotal evidence, little is understood about the impact of estimated MSA and gene tree error on downstream species network reconstruction. We therefore undertake a performance study to quantify the impact of MSA error and gene tree error on state-of-the-art phylogenetic network inference methods. Our study utilizes synthetic benchmarking data as well as genomic sequence data from mosquito and yeast. We find that upstream MSA and gene tree estimation error can have first-order effects on the accuracy of downstream network reconstruction and, to a lesser extent, its computational runtime. The effects become more pronounced on more challenging datasets with greater evolutionary divergence and more sampled taxa. Our findings highlight an important need for computational methods development: namely, scalable methods are needed to account for estimated MSA and gene tree error when reconstructing phylogenetic networks using unaligned biomolecular sequence data.
Optimising neonatal antiretroviral therapy using raltegravir: a qualitative analysis of healthcare workers’ and caregivers’ perspectives

Background In 2020, Zimbabwe adopted the WHO’s recommendation to use raltegravir (RAL) granule-based regimens for treatment of neonates identified with HIV at the time of birth testing. This study explores the acceptability of RAL granules by caregivers and healthcare workers (HCWs). Methods Interviews were conducted with 15 caregivers and 12 HCWs from 8 health facilities in Zimbabwe participating in the introductory pilot of RAL granules treatment for newborns. Eligible caregivers included those who had administered RAL to their infant and attended either 8th or 28th day of life appointments. Caregivers of neonates recently initiated on RAL were selected through convenience sampling. Eligible HCWs who provided RAL preparation, administration instructions and support to caregivers of neonates on RAL for at least 3 months were recruited from the same facilities as the caregivers. Interview transcripts were coded and thematically analysed. Results Caregivers reported that their babies looked healthier after RAL initiation, with improved skin appearance and weight gain. Some caregivers wanted their child to remain on RAL beyond 28 days instead of switching regimens, as recommended by national guidelines. HCWs observed that RAL granules improved health outcomes compared with other regimens. HCWs reported challenges with caregivers understanding dosing instructions, measuring with a syringe, swirling and not shaking the medicine, discarding unused medication and following the changes in the dosing schedule and amount when RAL was initiated a few days after birth. HCWs stated that adequate counselling and repeat demonstrations were crucial to ensure that caregivers clearly understood RAL dosing and administration instructions. HCWs requested more standardised training targeting nurses with guidance on handling missed doses and clarification on mixing RAL granules with water and not breastmilk. Conclusion While feedback from caregivers and HCWs on RAL implementation was positive, barriers were also noted. Adequate training and sufficient instruction and support for caregivers would help to ensure that RAL granules are prepared, dosed and administered correctly.
Indo‐Pacific Sector Dominates Southern Ocean Carbon Outgassing

The Southern Ocean modulates the climate system by exchanging heat and carbon dioxide (CO2) between the atmosphere and deep ocean. While this region plays an outsized role in the global oceanic anthropogenic carbon uptake, CO2 is also released to the atmosphere across large swaths of the Antarctic Circumpolar Current (ACC). Southern Ocean outgassing has long been attributed to remineralized carbon from upwelled deep water, but the precise mechanisms by which this water reaches the surface are not well constrained from observations. Using data from a novel array of autonomous biogeochemical profiling floats, we examine Southern Ocean air‐sea CO2 fluxes and the pathways that transfer carbon from the ocean interior into the mixed layer where air‐sea exchange occurs. These float‐based flux estimates of unprecedented spatial resolution indicate that carbon outgassing occurs predominantly in the Indo‐Pacific sector of the ACC due to variations in the mean surface ocean partial pressure of CO2 (pCO2). We show that this zonal asymmetry in surface pCO2, and consequently air‐sea carbon fluxes, stems primarily from regional variability in the mixed‐layer entrainment of upwelled carbon‐rich deep water. These results suggest that a sustained circumpolar observing system is crucial to monitor future changes in oceanic carbon release and uptake.
Glasgow University Radiocarbon Measurements VI

The following list presents results obtained during 1970-1971 and is a continuation of research of which data have been pub. previously (Baxter et al., 1969; Ergin et al., 1970; Baxter and Walton, 1970; Harkness and Walton, 1972; Ergin et al., 1972). The major research efforts at Glasgow are aimed at evaluation of (a) natural annual C14 levels and (b) burdens and residence times of artificial C14 in the environment and human tissues. The results of these studies are presented as δC14 and Δ values based on age-corrected activities, although this correction is very small. The errors quoted are counting uncertainties only, expressed at the 1σ level. Pretreatment procedures are outlined in the text and analytical methods are essentially unchanged. Gas proportional counting of both CO2 and CH4 is employed in 2.6L and 0.5L detectors, respectively. Mass spectrometric analyses are performed to a precision of 0.1‰ (± 2σ) on a V.G. Micromass 602B stable isotope mass spectrometer.