Miracle-dz/UniFAR-FTUs huggingface dataset:
Unstructured LES of Reacting Multiphase Flows in Realistic Gas Turbine Combustors

As part of the Accelerated Strategic Computing Initiative (ASCI) program, an accurate and robust simulation tool is being developed to perform high-fidelity LES studies of multiphase, multiscale turbulent reacting flows in aircraft gas turbine combustor configurations using hybrid unstructured grids. In the combustor, pressurized gas from the upstream compressor is reacted with atomized liquid fuel to produce the combustion products that drive the downstream turbine. The Large Eddy Simulation (LES) approach is used to simulate the combustor because of its demonstrated superiority over RANS in predicting turbulent mixing, which is central to combustion. This paper summarizes the accomplishments of the combustor group over the past year, concentrating mainly on the two major milestones achieved this year: 1) Large scale simulation: A major rewrite and redesign of the flagship unstructured LES code has allowed the group to perform large eddy simulations of the complete combustor geometry (all 18 injectors) with over 100 million control volumes; 2) Multi-physics simulation in complex geometry: The first multi-physics simulations including fuel spray breakup, coalescence, evaporation, and combustion are now being performed in a single periodic sector (1/18th) of an actual Pratt & Whitney combustor geometry.
The circuits of healthcare: Understanding healthcare seeking behaviour—A qualitative study with tuberculosis patients in Lisbon, Portugal

Background Understanding health delivery service from a patient´s perspective, including factors influencing healthcare seeking behaviour, is crucial when treating diseases, particularly infectious ones, like tuberculosis. This study aims to trace and contextualise the trajectories patients pursued towards diagnosis and treatment, while discussing key factors associated with treatment delays. Tuberculosis patients’ pathways may serve as indicator of the difficulties the more vulnerable sections of society experience in obtaining adequate care. Methods We conducted 27 semi-structured interviews with tuberculosis patients attending a treatment centre in a suburban area of Lisbon. We invited nationals and migrant patients in active treatment to participate by sharing their illness experiences since the onset of symptoms until the present. The Health Belief Model was used as a reference framework to consolidate the qualitative findings. Results By inductive analysis of all interviews, we categorised participants’ healthcare seeking behaviour into 4 main types, related to the time participants took to actively search for healthcare (patient delay) and time the health system spent to diagnose and initiate treatment (health system delay). Each type of healthcare seeking behaviour identified (inhibited, timely, prolonged, and absent) expressed a mindset influencing the way participants sought healthcare. The emergency room was the main entry point where diagnostic care cascade was initiated. Primary Health Care was underused by participants. Conclusions The findings support that healthcare seeking behaviour is not homogeneous and influences diagnostic delays. If diagnostic delays are to be reduced, the identification of behavioural patterns should be considered when designing measures to improve health services’ delivery. Healthcare professionals should be sensitised and perform continuous capacity development training to deal with patients´ needs. Inhibited and prolonged healthcare seeking behaviour contributes significantly to diagnostic delays. These behaviours should be detected and reverted. Timely responses, from patients and the healthcare system, should be promoted.
The spread of agriculture in Iberia through Approximate Bayesian Computation and Neolithic projectile tools

In the present article we use geometric microliths (a specific type of arrowhead) and Approximate Bayesian Computation (ABC) in order to evaluate possible origin points and expansion routes for the Neolithic in the Iberian Peninsula. In order to do so, we divide the Iberian Peninsula in four areas (Ebro river, Catalan shores, Xúquer river and Guadalquivir river) and we sample the geometric microliths existing in the sites with the oldest radiocarbon dates for each zone. On this data, we perform a partial Mantel test with three matrices: geographic distance matrix, cultural distance matrix and chronological distance matrix. After this is done, we simulate a series of partial Mantel tests where we alter the chronological matrix by using an expansion model with randomised origin points, and using the distribution of the observed partial Mantel test’s results as a summary statistic within an Approximate Bayesian Computation-Sequential Monte-Carlo (ABC-SMC) algorithm framework. Our results point clearly to a Neolithic expansion route following the Northern Mediterranean, whilst the Southern Mediterranean route could also find support and should be further discussed. The most probable origin points focus on the Xúquer river area.
Research on logistics carbon emissions under the coupling and coordination scenario of logistics industry and financial industry

In order to measure the impact of the degree of coupling and coordination between logistics and financial industries on the carbon emissions of the logistics industry, a comprehensive evaluation index system for the logistics-financial industry from 2007 to 2017 was constructed, which was calculated using the multi-objective linear weighting method; and a combination of logistics and financial industry was constructed. The panel smooth conversion model of coordination degree and carbon emission of logistics industry. The empirical results show that the coupling coordination degree of logistics and financial industry in China’s 30 provinces is mostly in primary and medium coordination, and only a few provinces are on the verge of imbalance; the coupling coordination degree in the eastern region is overall moderate coordination, and the overall coordination degree in the central and western regions It is the primary coordination; with the growth of GDP per capita, the degree of coupling and coordination between the logistics and financial industries has promoted the increase of carbon emissions in various regions, but the promotion effect is an inverted U-shaped trend that first increases and then decreases.
An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks

Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA.
Master Teapots and Entropy Algorithms for the Mandelbrot Set

We construct an analogue of W. Thurston’s “Master teapot" for each principal vein in the Mandelbrot set, and generalize geometric properties known for the corresponding object for real maps. In particular, we show that eigenvalues outside the unit circle move continuously, while we show “persistence" for roots inside the unit circle. As an application, this shows that the outside part of the corresponding “Thurston set" is path connected. In order to do this, we define a version of kneading theory for principal veins, and we prove the equivalence of several algorithms that compute the core entropy. Date: December 29, 2021. FIGURE 1. The Master Teapot Υ1/5 for the 1/5-vein. 1 ar X iv :2 11 2. 14 59 0v 1 [ m at h. D S] 2 9 D ec 2 02 1 2 KATHRYN LINDSEY, GIULIO TIOZZO, CHENXI WU
A SiGe HBT BiCMOS technology for mixed signal RF applications

We present results of IBM's Silicon Germanium HBT 0.35 /spl mu/m L/sub eff/ BiCMOS process with 3 level metal on 200 mm wafers. CMOS devices, as well as resistors, capacitors, inductors and other key passive elements are integrated into a high performance SiGe HBT NPN technology without sacrificing key bipolar characteristics (f/sub t/, f/sub max/). These results demonstrate the potential of designing analog/mixed signal applications with "system-on-a-chip" functionality.
Simulating the performance of rainwater collection and reuse systems using behavioural models

The collection of rainwater from roofs and its storage and subsequent use make a significant saving in the use of potable water. The capacity of the rainwater store is important because it affects both system and initial costs. The investigation concentrates upon the use of behavioural models to simulate the performance of rainwater collectors. The input data, in time series form, is used to simulate the mass flows through the model, and will be based upon time intervals of a minute, an hour, a day or a month. A preliminary mapping exercise is described that evaluates the accuracy of behavioural models for the sizing of rainwater collection systems using different time intervals and different reservoir operating rules applied to a range of reservoir capacities and collection areas. The preliminary analysis indicated that the yield after spillage (YAS) reservoir operating algorithm based on a hourly time interval could be used as a standard against which other models could be compared and calibrated. The detailed analysis enabled constraints to be proposed for the application of hourly, daily and monthly models expressed in terms of storage fraction, S/AR, where S = storage capacity (m3 ), A = roof area (m2) and R = annual rainfall (m). Hourly models can be used for sizing small stores with a storage fraction below or equal to 0.01. Daily models can be applied to systems with storage fractions within the range 0.01 to 0.125. Monthly models are only recommended for use with storage fractions in excess of 0.125.
Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: (1) how to define a quantitative land-use configuration? (2) how to automate configuration planning? (3) how to evaluate the quality of a generated configuration? In this article, we systematically address the three challenges. Specifically, (1) We define a land-use configuration as a longitude-latitude-channel tensor. (2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data crawled from websites to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. (3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.
Hidden Conformal Symmetry in Higher Derivative Dynamics for the Kerr Black Hole

The Kerr/CFT correspondence provides a holographic description of spinning black holes that exist in our universe and the notion of hidden conformal symmetry allows for a formulation of this correspondence that is away from extremality. In this study, we examined how hidden conformal symmetry is manifest when we consider dynamics beyond the Klein–Gordon equation through studying the analytic structure of the higher derivative equations of the motion of a massless probe scalar field on a Kerr background, using the monodromy method. Since such higher derivative dynamics appear in known examples of holographic AdS/logCFT correspondences, we investigated whether or not a Kerr/logCFT correspondence could be possible.
Dense-to-Sparse Gate for Mixture-of-Experts

Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts that each only contains part of the full model, MoE keeps the model size unchanged and significantly reduces per-token computation, which effectively scales neural networks. However, we found that the current approach of jointly training experts and the sparse gate introduces a negative impact on model accuracy, diminishing the efficiency of expensive large-scale model training. In this work, we proposed Dense-To-Sparse gate (DTS-Gate) for MoE training. Specifically, instead of using a permanent sparse gate, DTS-Gate begins as a dense gate that routes tokens to all experts, then gradually and adaptively becomes sparser while routes to fewer experts. MoE with DTS-Gate naturally decouples the training of experts and the sparse gate by training all experts at first and then learning the sparse gate. Experiments show that compared with the state-of-the-art Switch-Gate in GPT-MoE(1.5B) model with OpenWebText dataset(40GB), DTSGate can obtain 2.0x speed-up to reach the same validation perplexity, as well as higher FLOPs-efficiency of a 1.42x speed-up.
SunnyNet: A neural network approach to 3D non-LTE radiative transfer

Context. Computing spectra from 3D simulations of stellar atmospheres when allowing for departures from local thermodynamic equilibrium (non-LTE) is computationally very intensive. Aims. We develop a machine learning based method to speed up 3D non-LTE radiative transfer calculations in optically thick stellar atmospheres. Methods. Making use of a variety of 3D simulations of the solar atmosphere, we trained a convolutional neural network, SunnyNet, to learn the translation from LTE to non-LTE atomic populations. Non-LTE populations computed with an existing 3D code were considered as the true values. The network was then used to predict non-LTE populations for other 3D simulations, and synthetic spectra were computed from its predicted non-LTE populations. We used a six-level model atom of hydrogen and H$\alpha$ spectra as test cases. Results. SunnyNet gives reasonable predictions for non-LTE populations with a dramatic speedup of about 10$^5$ times when running on a single GPU and compared to existing codes. When using different snapshots of the same simulation for training and testing, SunnyNet's predictions are within 20-40% of the true values for most points, which results in average differences of a few percent in H$\alpha$ spectra. Predicted H$\alpha$ intensity maps agree very well with existing codes. Most importantly, they show the telltale signs of 3D radiative transfer in the morphology of chromospheric fibrils. The results are not as reliable when the training and testing are done with different families of simulations. SunnyNet is open source and publicly available.
Unified theory for surface layers in atmospheric convective and stably stratified turbulence

I. Rogachevskii, N. Kleeorin, and S. Zilitinkevich Department of Mechanical Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410530, Israel Nordita, Stockholm University and KTH Royal Institute of Technology, 10691 Stockholm, Sweden Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, 00014 Helsinki, Finland Finnish Meteorological Institute, 00101 Helsinki, Finland (Dated: February 18, 2022)
Raw Produce Quality Detection with Shifted Window Self-Attention

Global food insecurity is expected to worsen in the coming decades with the accelerated rate of climate change and the rapidly increasing population. In this vein, it is important to remove inefficiencies at every level of food production. The recent advances in deep learning can help reduce such inefficiencies, yet their application has not yet become mainstream throughout the industry, inducing economic costs at a massive scale. To this point, modern techniques such as CNNs (Convolutional Neural Networks) have been applied to RPQD (Raw Produce Quality Detection) tasks. On the other hand, Transformer's successful debut in the vision among other modalities led us to expect a better performance with these Transformer-based models in RPQD. In this work, we exclusively investigate the recent state-of-the-art Swin (Shifted Windows) Transformer which computes self-attention in both intra- and inter-window fashion. We compare Swin Transformer against CNN models on four RPQD image datasets, each containing different kinds of raw produce: fruits and vegetables, fish, pork, and beef. We observe that Swin Transformer not only achieves better or competitive performance but also is data- and compute-efficient, making it ideal for actual deployment in real-world setting. To the best of our knowledge, this is the first large-scale empirical study on RPQD task, which we hope will gain more attention in future works.
Approximate symmetries, pseudo-Goldstones, and the second law of thermodynamics

We propose a general hydrodynamic framework for systems with spontaneously broken approximate symmetries. The second law of thermodynamics naturally results in relaxation in the hydrodynamic equations, and enables us to derive a universal relation between damping and diffusion of pseudo- Goldstones. We discover entirely new physical effects sensitive to explicitly broken symmetries. We focus on systems with approximate U(1) and translation symmetries, with direct applications to pinned superfluids and charge density waves. We also comment on the implications for chiral perturbation theory.
Biochemical pathways of forward/backward steps of motor protein kinesin-1

Kinesin-1 is an ATP-driven, two-headed motor protein that transports intracellular cargoes along microtubule. Based on recent experimental observations, we formulate a mechanochemical model for it, in which forward/backward/futile cycle of kinesin-1 can be realized through multiple biochemical pathways. Our results show that both forward and futile cycles consist of two biochemical pathways, while backward cycle may be realized through six possible pathways. Backward motion of kinesin-1 is mainly through backward sliding along microtubule when it is in semi-detach state, and usually coupled with ATP hydrolysis. Under low external load, about 80% of ATP is wasted by kinesin-1, and this proportion of waste is almost independent of ATP concentration. At high ATP concentration or under high external load, both heads of kinesin are always in ATP or ADP·Pi binding state and bind to microtubule tightly. But at low ATP concentration and low load, kinesin mainly stays at the one head bound state. In contrast to mean run length, the mean run time of kinesin-1 along microtubule decreases with ATP concentration. Unless external load is near the stall force, the motion of kinesin-1 is almost deterministic, not as predicted by usual Browian ratchet models.
A Correctly Rounded Newton Step for the Reciprocal Square Root

The reciprocal square root is an important computation for which many sophisticated algorithms exist (see for example \cite{Moroz,863046,863031} and the references therein). A common theme is the use of Newton's method to refine the estimates. In this paper we develop a correctly rounded Newton step that can be used to improve the accuracy of a naive calculation (using methods similar to those developed in \cite{borges}) . The approach relies on the use of the fused multiply-add (FMA) which is widely available in hardware on a variety of modern computer architectures. We then introduce the notion of {\em weak rounding} and prove that our proposed algorithm meets this standard. We then show how to leverage the exact Newton step to get a Halley's method compensation which requires one additional FMA and one additional multiplication. This method appears to give correctly rounded results experimentally and we show that it can be combined with a square root free method for estimating the reciprocal square root to get a method that is both very fast (in computing environments with a slow square root) and, experimentally, highly accurate.
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems

Motivated by the problem of online canonical correlation analysis, we propose the \emph{Stochastic Scaled-Gradient Descent} (SSGD) algorithm for minimizing the expectation of a stochastic function over a generic Riemannian manifold. SSGD generalizes the idea of projected stochastic gradient descent and allows the use of scaled stochastic gradients instead of stochastic gradients. In the special case of a spherical constraint, which arises in generalized eigenvector problems, we establish a nonasymptotic finite-sample bound of $\sqrt{1/T}$, and show that this rate is minimax optimal, up to a polylogarithmic factor of relevant parameters. On the asymptotic side, a novel trajectory-averaging argument allows us to achieve local asymptotic normality with a rate that matches that of Ruppert-Polyak-Juditsky averaging. We bring these ideas together in an application to online canonical correlation analysis, deriving, for the first time in the literature, an optimal one-time-scale algorithm with an explicit rate of local asymptotic convergence to normality. Numerical studies of canonical correlation analysis are also provided for synthetic data.
On Computing with Some Convex Relaxations for the Maximum-Entropy Sampling Problem

Based on a factorization of an input covariance matrix, we define a mild generalization of an upper bound of Nikolov and of Li and Xie for the NP-hard constrained maximum-entropy sampling problem ( CMESP ). We demonstrate that this factorization bound is invariant under scaling and independent of the particular factorization chosen. We give a variable-fixing methodology that could be used in a branch-and-bound scheme based on the factorization bound for exact solution of CMESP , and we demonstrate that its ability to fix is independent of the factorization chosen. We report on successful experiments with a commercial nonlinear programming solver. We further demonstrate that the known “mixing” technique can be successfully used to combine the factorization bound with the factorization bound of the complementary CMESP and with the “linx bound” of Anstreicher. History: Andrea Lodi, area editor for Design & Analysis of Algorithms-Discrete. Funding: This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 305444/2019-0 and 434683/2018-3] and the Air Force Office of Scientific Research [Grant A9550-19-1-0175].