Miracle-dz/UniFAR-FTUs huggingface dataset:
Anaesthesia delivery systems in low and lower-middle-income Asian countries: A scoping review of capacity and effectiveness

ABSTRACT Background Literature on anaesthesia systems in low and lower middle-income countries is limited, focused on the Africa region, and provides minimal data on anaesthesia or associated disciplines within intensive care, pain management and emergency medicine. We thus conducted a review of primary and secondary research literature on low and lower middle-income countries in the Asia region from 2000-2021, to clarify existing knowledge, important gaps, and possible subsequent steps. Methods We applied Arksey and OMalleys scoping literature review method to search, screen, extract, and synthesise data under three themes: (i) availability and type of anaesthesia workforce; (ii) anaesthesia system infrastructure, equipment, and supplies; and (iii) effectiveness of anaesthesia provision. Results We included 25 eligible sources of 603 identified. Only ten (40%) were published in the last 5 years and Asian lower-income countries were primarily represented in 15 multi-country sources. Fifteen (60%) sources used quantitative methods and provided limited information on data collection, e.g. sampling criteria or geographic areas included. No sources included countrywide data, despite anaesthesia delivery and resources differing significantly sub-nationally (e.g., central versus rural/remote, or insecure areas). Data on anaesthesiology delivery were limited, with findings including insufficiencies in workforce, supplies, training and skills-building of anaesthesia personnel, along with the lack of consistent strategies for overcoming maldistribution of resources and improving anaesthesia delivery systems in the region. Conclusions This review, a first attempt to synthesise existing data on anaesthesia delivery systems in low and lower-middle-income Asian countries, shows the anaesthesia literature is still limited. Findings highlight the urgent need for additional research and collaboration nationally and regionally to strengthen anaesthesia delivery and surgical facilities in resource-constrained settings.
CLASSY. VI. The Density, Structure, and Size of Absorption-line Outflows in Starburst Galaxies

Galaxy formation and evolution are regulated by the feedback from galactic winds. Absorption lines provide the most widely available probe of winds. However, since most data only provide information integrated along the line of sight, they do not directly constrain the radial structure of the outflows. In this paper, we present a method to directly measure the gas electron density in outflows (n e ), which in turn yields estimates of outflow cloud properties (e.g., density, volume filling factor, and sizes/masses). We also estimate the distance (r n ) from the starburst at which the observed densities are found. We focus on 22 local star-forming galaxies primarily from the COS Legacy Archive Spectroscopic SurveY (CLASSY). In half of them, we detect absorption lines from fine-structure excited transitions of Si ii (i.e., Si ii*). We determine n e from relative column densities of Si ii and Si ii*, given Si ii* originates from collisional excitation by free electrons. We find that the derived n e correlates well with the galaxy’s star formation rate per unit area. From photoionization models or assuming the outflow is in pressure equilibrium with the wind fluid, we get r n ∼ 1–2r * or ∼5r *, respectively, where r * is the starburst radius. Based on comparisons to theoretical models of multiphase outflows, nearly all of the outflows have cloud sizes large enough for the clouds to survive their interaction with the hot wind fluid. Most of these measurements are the first ever for galactic winds detected in absorption lines and, thus, will provide important constraints for future models of galactic winds.
Morphology of the Northern Banded Newt, Ommatotriton ophryticus (Berthold, 1846) (Caudata: Salamandridae) in Uludağ (Bursa, Turkey)

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Evidence of First Stars-enriched Gas in High-redshift Absorbers

The first stars were born from chemically pristine gas. They were likely massive, and thus they rapidly exploded as supernovae, enriching the surrounding gas with the first heavy elements. In the Local Group, the chemical signatures of the first stellar population were identified among low-mass, long-lived, very metal-poor ([Fe/H] < −2) stars, characterized by high abundances of carbon over iron ([C/Fe] > +0.7): the so-called carbon-enhanced metal-poor stars. Conversely, a similar carbon excess caused by first-star pollution was not found in dense neutral gas traced by absorption systems at different cosmic time. Here we present the detection of 14 very metal-poor, optically thick absorbers at redshift z ∼ 3–4. Among these, 3 are carbon-enhanced and reveal an overabundance with respect to Fe of all the analyzed chemical elements (O, Mg, Al, and Si). Their relative abundances show a distribution with respect to [Fe/H] that is in very good agreement with those observed in nearby very metal-poor stars. All the tests we performed support the idea that these C-rich absorbers preserve the chemical yields of the first stars. Our new findings suggest that the first-star signatures can survive in optically thick but relatively diffuse absorbers, which are not sufficiently dense to sustain star formation and hence are not dominated by the chemical products of normal stars.
Soil Structure interaction of Laterally Loaded Piles

The rapid development of the global economy has led to an increased energy demand throughout the world. More than 70% of the world’s energy consumption is from the use of fossil fuels. In the wake of rising global temperatures and increased carbon emissions, policymakers across the globe think about alternate and renewable ways of energy generation, among which wind energy is a promising energy source. Offshore wind turbines are new technologies and are challenging when it comes to their installation in harsh ocean environment as they are subjected to environmental loads caused by wind, wave and currents. These loads are then transferred to foundation. Monopiles are the most common foundations used in offshore wind turbines today and therefore its analysis is important. Monopiles are single large diameter tubular steel piles of diameter 3–7 m driven into the seabed with typical penetration depth of 25–30 m, and are subjected to lateral dynamic loads. Soil structure interaction of monopile is an important aspect to be considered in its design and for the present study, PLAXIS 3D geotechnical software is used for the soil-pile interaction and the corresponding displacement behaviour of the pile under study. The dynamic analysis due to the wave forces on the monopile is carried out using wave data off Gulf of Mannar, near the Tamil Nadu coast, and the embedment length of the pile is varied to study the effect of diameter of pile and embedment length on the displacement characteristics of the pile.
Estimation of Population Total in the Presence of Missing Values Using a Modified Murthy's Estimator and the Weight Adjustment Technique

Use of Murthy’s method in estimation of population parameters, such as population totals, population means, and population variances has been limited to surveys where survey data values are complete. This study applies weight adjustment technique to estimate a population total under simple random sampling without replacement. The asymptotic properties show that the estimated population total is sufficient for the true population total. The proposed estimator is obtained by symmetrizing Murthy’s estimator.
GEODESIC FLOWS ON CLOSED RIEMANNIAN MANIFOLDS WITHOUT FOCAL POINTS

In this paper it is proved that a geodesic flow on a two-dimensional compact manifold of genus greater than 1 with Riemannian metric without focal points is isomorphic with a Bernoulli flow. This result generalizes to the multidimensional case. The proof is based on establishing some metric properties of flows with nonzero Ljapunov exponents (the K-property, etc.), and also the construction of horospheres and leaves on a very wide class of Riemannian manifolds, together with a study of some of their geometric properties.Bibliography: 24 titles.
Influence of porosity on collisions between dust aggregates

Context. Collisions between dust particles may lead to agglomerate growth or fragmentation, depending on the porosity of the dust and the collision velocity. Aims. We study the effect of agglomerate porosity and collision velocity on aggregate fragmentation and agglomeration. Methods. Granular-mechanics simulations are used to study the outcome of head-on dust aggregate collisions. The aggregates are composed of silica grains of 0.76 μ m radius and have filling factors of between 0.08 and 0.21. The simulations incorporate repulsive and viscoelastic, dissipative normal forces, and intergrain adhesion. The tangential forces are composed of gliding, rolling, and torsional friction. To study the effect of aggregate porosity, we prepared spherical aggregates with identical radius but differing particle numbers. Results. The threshold velocity for agglomerate fragmentation decreases with the porosity of the aggregates. Porous aggregates tend to fragment more easily, and the fragments are irregularly shaped. In the agglomeration regime, the merged aggregate is more compact than the initial collision partners. The collision velocity at which compaction is highest is independent of the initial porosity.
A Constrained BA Algorithm for Rate-Distortion and Distortion-Rate Functions

The Blahut-Arimoto (BA) algorithm has played a fundamental role in the numerical computation of rate-distortion (RD) functions. This algorithm possesses a desirable monotonic convergence property by alternatively minimizing its Lagrangian with a fixed multiplier. In this paper, we propose a novel modification of the BA algorithm, wherein the multiplier is updated through a one-dimensional root-finding step using a monotonic univariate function, efficiently implemented by Newton's method in each iteration. Consequently, the modified algorithm directly computes the RD function for a given target distortion, without exploring the entire RD curve as in the original BA algorithm. Moreover, this modification presents a versatile framework, applicable to a wide range of problems, including the computation of distortion-rate (DR) functions. Theoretical analysis shows that the outputs of the modified algorithms still converge to the solutions of the RD and DR functions with rate $O(1/n)$, where $n$ is the number of iterations. Additionally, these algorithms provide $\varepsilon$-approximation solutions with $O\left(\frac{MN\log N}{\varepsilon}(1+\log |\log \varepsilon|)\right)$ arithmetic operations, where $M,N$ are the sizes of source and reproduced alphabets respectively. Numerical experiments demonstrate that the modified algorithms exhibit significant acceleration compared with the original BA algorithms and showcase commendable performance across classical source distributions such as discretized Gaussian, Laplacian and uniform sources.
Efficient Personalized Federated Learning via Sparse Model-Adaptation

Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribution, recent studies explore the personalized FL that learns and deploys distinct local models with the help of auxiliary global models. However, the clients can be heterogeneous in terms of not only local data distribution, but also their computation and communication resources. The capacity and efficiency of personalized models are restricted by the lowest-resource clients, leading to sub-optimal performance and limited practicality of personalized FL. To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models. With a lightweight trainable gating layer, pFedGate enables clients to reach their full potential in model capacity by generating different sparse models accounting for both the heterogeneous data distributions and resource constraints. Meanwhile, the computation and communication efficiency are both improved thanks to the adaptability between the model sparsity and clients' resources. Further, we theoretically show that the proposed pFedGate has superior complexity with guaranteed convergence and generalization error. Extensive experiments show that pFedGate achieves superior global accuracy, individual accuracy and efficiency simultaneously over state-of-the-art methods. We also demonstrate that pFedGate performs better than competitors in the novel clients participation and partial clients participation scenarios, and can learn meaningful sparse local models adapted to different data distributions.
Learning Language-Specific Layers for Multilingual Machine Translation

Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g., avoiding losing gender and formality information when translating through English). On the downside, adding more languages reduces model capacity per language, which is usually countered by increasing the overall model size, making training harder and inference slower.In this work, we introduce Language-Specific Transformer Layers (LSLs), which allow us to increase model capacity, while keeping the amount of computation and the number of parameters used in the forward pass constant. The key idea is to have some layers of the encoder be source or target language-specific, while keeping the remaining layers shared. We study the best way to place these layers using a neural architecture search inspired approach, and achieve an improvement of 1.3 chrF (1.5 spBLEU) points over not using LSLs on a separate decoder architecture, and 1.9 chrF (2.2 spBLEU) on a shared decoder one.
The edge-on protoplanetary disk HH 48 NE. II. Modeling ices and silicates

The abundance and distribution of ice in protoplanetary disks (PPD) is critical to understand the linkage between the composition of circumstellar matter and the composition of exoplanets. Edge-on PPDs are a useful tool to constrain such ice composition and its location in the disk, as ice spectral signatures can be observed in absorption against the continuum emission arising from the warmer central disk regions. The aim of this work is to model ice absorption features in PPDs and determine how well the abundance of the main ice species across the disk can be determined within the uncertainty of the physical parameter space. The edge-on PPD around HH 48 NE, a target of the JWST ERS program IceAge, is used as a reference system. We use RADMC-3D to raytrace the mid-infrared continuum. Using a constant parameterized ice abundance, ice opacities are added to the dust opacity in regions wherever the disk is cold enough for the main carbon, oxygen and nitrogen carriers to freeze out. The global abundance of the main ice carriers in HH 48 NE can be determined within a factor of 3, when taking the uncertainty of the physical parameters into account. Ice features in PPDs can be saturated at an optical depth<1, due to local saturation. Spatially observed ice optical depths cannot be directly related to column densities due to radiative transfer effects. Vertical snowlines will not be a clear transition due to the radially increasing height of the snowsurface, but their location may be constrained from observations using radiative transfer modeling. Radial snowlines are not really accesible. Not only the ice abundance, but also inclination, settling, grain size distribution and disk mass have strong impact on the observed ice absorption features in disks. Relative changes in ice abundance can be inferred from observations only if the source structure is well constrained
When Do Neural Nets Outperform Boosted Trees on Tabular Data?

Tabular data is one of the most commonly used types of data in machine learning. Despite recent advances in neural nets (NNs) for tabular data, there is still an active discussion on whether or not NNs generally outperform gradient-boosted decision trees (GBDTs) on tabular data, with several recent works arguing either that GBDTs consistently outperform NNs on tabular data, or vice versa. In this work, we take a step back and question the importance of this debate. To this end, we conduct the largest tabular data analysis to date, comparing 19 algorithms across 176 datasets, and we find that the 'NN vs. GBDT' debate is overemphasized: for a surprisingly high number of datasets, either the performance difference between GBDTs and NNs is negligible, or light hyperparameter tuning on a GBDT is more important than choosing between NNs and GBDTs. A remarkable exception is the recently-proposed prior-data fitted network, TabPFN: although it is effectively limited to training sets of size 3000, we find that it outperforms all other algorithms on average, even when randomly sampling 3000 training datapoints. Next, we analyze dozens of metafeatures to determine what properties of a dataset make NNs or GBDTs better-suited to perform well. For example, we find that GBDTs are much better than NNs at handling skewed or heavy-tailed feature distributions and other forms of dataset irregularities. Our insights act as a guide for practitioners to determine which techniques may work best on their dataset. Finally, with the goal of accelerating tabular data research, we release the TabZilla Benchmark Suite: a collection of the 36 'hardest' of the datasets we study. Our benchmark suite, codebase, and all raw results are available at https://github.com/naszilla/tabzilla.
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward

We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback. The delay and compositeness of rewards mean that rewards generated as a result of taking an action at a given state are fragmented into different components, and they are sequentially realized at delayed time instances. The partial anonymity attribute implies that a learner, for each state, only observes the aggregate of past reward components generated as a result of different actions taken at that state, but realized at the observation instance. We propose an algorithm named $\mathrm{DUCRL2}$ to obtain a near-optimal policy for this setting and show that it achieves a regret bound of $\tilde{\mathcal{O}}\left(DS\sqrt{AT} + d (SA)^3\right)$ where $S$ and $A$ are the sizes of the state and action spaces, respectively, $D$ is the diameter of the MDP, $d$ is a parameter upper bounded by the maximum reward delay, and $T$ denotes the time horizon. This demonstrates the optimality of the bound in the order of $T$, and an additive impact of the delay.
Lemonade from Lemons: Information Design and Adverse Selection

A seller posts a price for a single object. The seller's and buyer's values may be interdependent. We characterize the set of payoff vectors across all information structures. Simple feasibility and individual-rationality constraints identify the payoff set. The buyer can obtain the entire surplus; often, other mechanisms cannot enlarge the payoff set. We also study payoffs when the buyer is more informed than the seller, and when the buyer is fully informed. All three payoff sets coincide (only) in notable special cases -- in particular, when there is complete breakdown in a ``lemons market'' with an uninformed seller and fully-informed buyer.
Multi-image x-ray interferometer module: I. Design concept and proof-of-concept experiments with fine-pitch slits

Abstract. We propose an x-ray imaging system, multi-image x-ray interferometer module (MIXIM), with which a very high angular resolution can be achieved even with a small system size. MIXIM is composed of equally spaced multiple slits and an x-ray detector, and its angular resolution is inversely proportional to the distance between them. Here, we report our evaluation experiments of MIXIM with a newly adopted CMOS sensor with a high spatial resolution of 2.5 μm. Our previous experiments with a prototype MIXIM were limited to one-dimensional imaging, and more importantly, the achieved angular resolution was only ∼1 ″ , severely constrained due to the spatial resolution of the adopted sensor with a pixel size of 4.25 μm. By contrast, one-dimensional images obtained in this experiment had a higher angular resolution of 0.5″ when a configured system size was only ∼1 m, which demonstrates that MIXIM can simultaneously realize a high angular resolution and compact size. We also successfully obtained a two-dimensional profile of an x-ray beam for the first time for MIXIM by introducing a periodic pinhole mask. The highest angular resolution achieved in our experiments is smaller than 0.1″ with a mask-sensor distance of 866.5 cm, which shows the high scalability of MIXIM.
Complexity and asymptotics of structure constants

Kostka, Littlewood-Richardson, Kronecker, and plethysm coefficients are fundamental quantities in algebraic combinatorics, yet many natural questions about them stay unanswered for more than 80 years. Kronecker and plethysm coefficients lack ``nice formulas'', a notion that can be formalized using computational complexity theory. Beyond formulas and combinatorial interpretations, we can attempt to understand their asymptotic behavior in various regimes, and inequalities they could satisfy. Understanding these quantities has applications beyond combinatorics. On the one hand, the asymptotics of structure constants is closely related to understanding the [limit] behavior of vertex and tiling models in statistical mechanics. More recently, these structure constants have been involved in establishing computational complexity lower bounds and separation of complexity classes like VP vs VNP, the algebraic analogs of P vs NP in arithmetic complexity theory. Here we discuss the outstanding problems related to asymptotics, positivity, and complexity of structure constants focusing mostly on the Kronecker coefficients of the symmetric group and, less so, on the plethysm coefficients. This expository paper is based on the talk presented at the Open Problems in Algebraic Combinatorics coneference in May 2022.