Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Mixing Models and Finite Volume Conditional Moment Closure Applied to Autoignition of Hydrogen Jets

In the present work, the processes of steady combustion and autoignition of hydrogen are investigated using the Conditional Moment Closure (CMC) model with a Reynolds Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) code. A study of the effects on the flowfield of changing turbulence model constants, specifically the turbulent Schmidt number, Sct, and Cǫ1 of the k − ǫ model, are investigated. The effects of two different mixing models are explored: the AMC model, which is commonly used in CMC implementations, and a model based on the assumption of inhomogeneous turbulence. The background equations required for implementation of the CMC model are presented, and all relevant closures are discussed. The numerical implementation of the CMC model, in addition to other techniques aimed at reducing computational expense of the CMC calculations, are provided. The CMC equation is discretised using finite volume (FV) method. The CFD and CMC calculations are fully coupled, allowing for simulations of steady flames or flame development after the occurrence of autoignition. Through testing of a steady jet flame, it is observed that the flowfield calculations follow typical k − ǫ model trends, with an overprediction of spreading and an underprediction of penetration. The CMC calculations are observed to perform well, providing good agreement with experimental measurements. Autoignition simulations are conducted for 3 different cases of turbulence constants and 7 different coflow temperatures to determine the final effect on the steady flowfield. In comparison to the standard constants, reduction of Sct results in a reduction of the centreline mixing intensity within the flowfield and a corresponding reduction of ignition length, while reducing Cǫ1 results in an increase of centreline mixing intensity and an increase in the ignition length. All scenarios tested result in an underprediction of ignition length in comparison to experimental results; however, good agreement with the experimental trends is achieved. At low coflow temperatures, the effects of mixing intensity within the flowfield are seen to have the largest influence on ignition length, while at high coflow temperatures, the chemical source term in the CMC equation increases in magnitude, resulting in very little difference between predictions for different sets of turbulence constants. The inhomogeneous mixing model is compared using the standard turbulence constants. A reduction of ignition lengths in comparison to the AMC model is observed. In steady state simulation of the autoigniting flow, the inhomogeneous model is observed to predict both lifted flames and fully anchored flames, depending on coflow temperature.
AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.
Investigating Clumpy Galaxies in the Sloan Digital Sky Survey Stripe 82 Using the Galaxy Zoo

Giant, star-forming clumps are a common feature prevalent among high-redshift star-forming galaxies and play a critical role in shaping their chaotic morphologies and yet, their nature and role in galaxy evolution remains to be fully understood. A majority of the effort to study clumps has been focused at high redshifts, and local clump studies have often suffered from small sample sizes. In this work, we present an analysis of clump properties in the local universe, and for the first time, performed with a statistically significant sample. With the help of the citizen science-powered Galaxy Zoo: Hubble project, we select a sample of 92 z < 0.06 clumpy galaxies in Sloan Digital Sky Survey Stripe 82 galaxies. Within this sample, we identify 543 clumps using a contrast-based image analysis algorithm and perform photometry as well as estimate their stellar population properties. The overall properties of our z < 0.06 clump sample are comparable to the high-redshift clumps. However, contrary to the high-redshift studies, we find no evidence of a gradient in clump ages or masses as a function of their galactocentric distances. Our results challenge the inward migration scenario for clump evolution for the local universe, potentially suggesting a larger contribution of ex situ clumps and/or longer clump migration timescales.
Optimization of Rice Bran Oil Encapsulation Using Jackfruit Seed Starch – Whey Protein Isolate Blend as Wall Material and Its characterization

Abstract Response surface methodology (RSM) was used to optimize rice bran oil encapsulation using jackfruit seed starch – whey protein isolate blend as wall material by spray drying technique. Oil concentration (20, 25 and 30 %), wall material (Jackfruit seed starch & whey protein isolate) starch-protein ratio (1:1, 3:1 and 5:1) and spray drying inlet air temperature (140, 150 and 160 °C) were considered as process variables for optimization. A three-factor, three-level Box-Behnken design of RSM was used to conduct the experiments with the aim of maximizing encapsulation efficiency and minimizing peroxide value in the encapsulated powder. A polynomial regression model was fitted using design expert software, and the optimum conditions obtained were 20 % oil concentration, 3:1 starch-protein ratio and 140 °C spray drying inlet air temperature. The encapsulated rice bran oil powers obtained at optimized conditions offered 85.90 % of encapsulation efficiency and 1.18 meq/kg oil of peroxide value. The characterization study revealed that powder particles size (diameter) varied from 3.40 to 300.51 µm and most of the particles were smooth spheres with little appendages.
LGA-RCNN: Loss-Guided Attention for Object Detection

Object detection is widely studied in computer vision filed. In recent years, certain representative deep learning based detection methods along with solid benchmarks are proposed, which boosts the development of related researchs. However, existing detection methods still suffer from undesirable performance under challenges such as camouflage, blur, inter-class similarity, intra-class variance and complex environment. To address this issue, we propose LGA-RCNN which utilizes a loss-guided attention (LGA) module to highlight representative region of objects. Then, those highlighted local information are fused with global information for precise classification and localization.
Bench tests for microscopic theory of Raman scattering in powders of disordered nonpolar crystals: Nanodiamonds and beyond

Andrey G. Yashenkin, Oleg I. Utesov1,2,3,∗ and Sergei V. Koniakhin4,5† Petersburg Nuclear Physics Institute NRC “Kurchatov Institute”, Gatchina 188300, Russia Department of Physics, St. Petersburg State University, St.Petersburg 199034, Russia St. Petersburg School of Physics, Mathematics, and Computer Science, HSE University, 190008 St. Petersburg, Russia Institute Pascal, PHOTON-N2, University Clermont Auvergne, CNRS, 4 Avenue Blaise Pascal, Aubière Cedex 63178, France and St. Petersburg Academic University Nanotechnology Research and Education Centre of the Russian Academy of Sciences, St.Petersburg 194021, Russia (Dated: May 13, 2021)
The quorum-sensing systems of Vibrio campbellii DS40M4 and BB120 are genetically and functionally distinct

Vibrio campbellii BB120 (previously classified as Vibrio harveyi) is a fundamental model strain for studying quorum sensing in vibrios. A phylogenetic evaluation of sequenced Vibrio strains in Genbank revealed that BB120 is closely related to the environmental isolate V. campbellii DS40M4. We exploited DS40M4’s competence for exogenous DNA uptake to rapidly generate >30 isogenic strains with deletions of genes encoding BB120 quorum-sensing system homologs. Our results show that the quorum-sensing circuit of DS40M4 is distinct from BB120 in three ways: 1) DS40M4 does not produce an acyl homoserine lactone autoinducer but encodes an active orphan LuxN receptor, 2) the quorum regulatory small RNAs (Qrrs) are not solely regulated by autoinducer signaling through the response regulator LuxO, and 3) the DS40M4 quorum-sensing regulon is much smaller than BB120 (~100 genes vs ~400 genes, respectively). Using comparative genomics to expand our understanding of quorum-sensing circuit diversity, we observe that conservation of LuxM/LuxN proteins differs widely both between and within Vibrio species. These strains are also phenotypically distinct: DS40M4 exhibits stronger interbacterial cell killing, whereas BB120 forms more robust biofilms and is bioluminescent. These results underscore the need to examine wild isolates for a broader view of bacterial diversity in the marine ecosystem. Originality-Significance Statement Wild bacterial isolates yield important information about traits that vary within species. Here, we compare environmental isolate Vibrio campbellii DS40M4 to its close relative, the model strain BB120 that has been a fundamental strain for studying quorum sensing for >30 years. We examine several phenotypes that define this species, including quorum sensing, bioluminescence, and biofilm formation. Importantly, DS40M4 is naturally transformable with exogenous DNA, which allows for the rapid generation of mutants in a laboratory setting. By exploiting natural transformation, we genetically dissected the functions of BB120 quorum-sensing system homologs in the DS40M4 strain, including two-component signaling systems, transcriptional regulators, and small RNAs.
Interstellar gas heating by primordial black holes

Interstellar gas heating is a powerful cosmology-independent observable for exploring the parameter space of primordial black holes (PBHs) formed in the early Universe that could constitute part of the dark matter (DM). We provide a detailed analysis of the various aspects for this observable, such as PBH emission mechanisms. Using observational data from the Leo T dwarf galaxy, we constrain the PBH abundance over a broad mass-range, M PBH ∼ 𝒪(1)M ⊙ − 107 M ⊙, relevant for the recently detected gravitational wave signals from intermediate-mass BHs. We also consider PBH gas heating of systems with bulk relative velocity with respect to the DM, such as Galactic clouds.
SIDE: I Infer the State I Want to Learn

As one of the solutions to the Dec-POMDP problem, the value decomposition method has achieved good results recently. However, most value decomposition methods require the global state during training, but this is not feasible in some scenarios where the global state cannot be obtained. Therefore, we propose a novel value decomposition framework, named State Inference for value DEcomposition (SIDE), which eliminates the need to know the true state by simultaneously seeking solutions to the two problems of optimal control and state inference. SIDE can be extended to any value decomposition method, as well as other types of multi-agent algorithms in the case of Dec-POMDP. Based on the performance results of different algorithms in Starcraft II micromanagement tasks, we verified that SIDE can construct the current state that contributes to the reinforcement learning process based on past local observations.
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning

Several recent works [40, 24] observed an interesting phenomenon in neural network pruning: A larger finetuning learning rate can improve the final performance significantly. Unfortunately, the reason behind it remains elusive up to date. This paper is meant to explain it through the lens of dynamical isometry [42]. Specifically, we examine neural network pruning from an unusual perspective: pruning as initialization for finetuning, and ask whether the inherited weights serve as a good initialization for the finetuning? The insights from dynamical isometry suggest a negative answer. Despite its critical role, this issue has not been well-recognized by the community so far. In this paper, we will show the understanding of this problem is very important -- on top of explaining the aforementioned mystery about the larger finetuning rate, it also unveils the mystery about the value of pruning [5, 30]. Besides a clearer theoretical understanding of pruning, resolving the problem can also bring us considerable performance benefits in practice.
SM gauge fields localized on non-Abelian vortices in 6 dimensions

: A brane-world SU (5) GUT model with global non-Abelian vortices is constructed in six-dimensional spacetime. We find a solution with a vortex associated to SU (3) separated from another vortex associated to SU (2) . This 3 − 2 split configuration achieves a geometric Higgs mechanism for SU (5) → SU (3) × SU (2) × U (1) symmetry breaking. A simple deformation potential induces a domain wall between non-Abelian vortices, leading to a linear confining potential. The confinement stabilizes the vortex separation moduli, and assures the vorticity of SU (3) group and of SU (2) group to be identical. This dictates the equality of the numbers of fermion zero modes in the fundamental representation of SU (3) (quarks) and of SU (2) (leptons), leading to quark-lepton generations. The standard model massless gauge fields are localized on the non-Abelian vortices thanks to a field-dependent gauge kinetic function. We perform fluctuation analysis with an appropriate gauge fixing and obtain a four-dimensional effective Lagrangian of unbroken and broken gauge fields at quadratic order. We find that SU (3) × SU (2) × U (1) gauge fields are localized on the vortices and exactly massless. Complications in analyzing the spectra of gauge fields with the nontrivial gauge kinetic function are neatly worked out by a vector-analysis like method. is based on a GUT-inspired G = SU (5) gauge theory with a field-dependent gauge kinetic function in six spacetime dimensions. It contains complex scalar fields in a singlet and an adjoint representation of SU (5) , which are combined into a 5 × 5 matrix-valued field T . The model admits topologically stable non-Abelian global vortices. We find that the massless gauge fields of H = SU (3) × SU (2) × U (1) are localized on the four-dimensional world volume of the non-Abelian global vortices with multiple winding numbers. 1 By a simple potential with the 5 × 5 matrix field T we can obtain vortex equations for each diagonal components T = diag( T 1 , T 2 , T 3 , T 4 , T 5 ) independent of each other. Then we obtain five different species of vortices I = 1 , · · · , 5 corresponding to vortices in the I -th diagonal component T I . We find two important phenomena due to the vortices:
A new characterization of discrete decomposable models

Decomposable graphical models, also known as perfect DAG models, play a fundamental role in standard approaches to probabilistic inference via graph representations in modern machine learning and statistics. However, such models are limited by the assumption that the data-generating distribution does not entail strictly context-specific conditional independence relations. The family of staged tree models generalizes DAG models so as to accommodate context-specific knowledge. We provide a new characterization of perfect discrete DAG models in terms of their staged tree representations. This characterization identifies the family of balanced staged trees as the natural generalization of discrete decomposable models to the context-specific setting.
Multiaccess Coded Caching with Private Demands

Hachem et al. formulated a multiaccess coded caching model which consists of a central server connected to K users via an error-free shared link, and K cache-nodes. Each cache-node is equipped with a local cache and each user can access L neighbouring cache-nodes in a cyclic wraparound fashion. In this paper, we take the privacy of the users’ demands into consideration, i.e., each user, while retrieving its own demanded file, cannot obtain any information on the demands of the other users. By storing some private keys at the cache-nodes, we develop a novel transformation approach to turn any non-private coded caching scheme (satisfying some constraints) into a private one.
Lattice simulation of $(2+1)D$ phonetic solitons and the Renormalization group

The outline of lattice simulations of (2 + 1)D soliton-propagations in the background of Weyl spinors is presented. Clifford algebra is applied on Luescher’s domain decomposition method. The Clifford algebra shows that there are loop parts and interpolating surface parts in the Wilson’s lattice action. We adopt the Migdal-Kadanoff prescription and the fixed point action in momentum space of Benfatto and Gallavotti, and shows a road map for simulating phonetic solitons in materials. Detections of topological anomalies (APS index) in nondestructive testing are discussed.
Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.
Principled Exploration via Optimistic Bootstrapping and Backward Induction

One principled approach for provably efficient exploration is incorporating the upper confidence bound (UCB) into the value function as a bonus. However, UCB is specified to deal with linear and tabular settings and is incompatible with Deep Reinforcement Learning (DRL). In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I). OB2I constructs a general-purpose UCB-bonus through non-parametric bootstrap in DRL. The UCB-bonus estimates the epistemic uncertainty of state-action pairs for optimistic exploration. We build theoretical connections between the proposed UCB-bonus and the LSVI-UCB in a linear setting. We propagate future uncertainty in a time-consistent manner through episodic backward update, which exploits the theoretical advantage and empirically improves the sample-efficiency. Our experiments in the MNIST maze and Atari suite suggest that OB2I outperforms several state-of-the-art exploration approaches.
Thermal instability in a ferrimagnetic resonator strongly coupled to a loop-gap microwave cavity

We study nonlinear response of a ferrimagnetic sphere resonator (FSR) strongly coupled to a microwave loop gap resonator (LGR). The measured response in the regime of weak nonlinearity allows the extraction of the FSR Kerr coefficient and its cubic damping rate. We find that there is a certain range of driving parameters in which the system exhibits instability. In that range, selfsustained modulation of the reflected power off the system is generated. The instability is attributed to absorption-induced heating of the FSR above its Curie temperature.
Macroscopic and Multi-Scale Models for Multi-Class Vehicular Dynamics with Uneven Space Occupancy: A Case Study

In this paper, we propose two models describing the dynamics of heavy and light vehicles on a road network, taking into account the interactions between the two classes. The models are tailored for two-lane highways where heavy vehicles cannot overtake. This means that heavy vehicles cannot saturate the whole road space, while light vehicles can. In these conditions, the creeping phenomenon can appear, i.e., one class of vehicles can proceed even if the other class has reached the maximal density. The first model we propose couples two first-order macroscopic LWR models, while the second model couples a second-order microscopic follow-the-leader model with a first-order macroscopic LWR model. Numerical results show that both models are able to catch some second-order (inertial) phenomena such as stop and go waves. Models are calibrated by means of real data measured by fixed sensors placed along the A4 Italian highway Trieste–Venice and its branches, provided by Autovie Venete S.p.A.
House Price Prediction using Satellite Imagery

In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferring learning from an Inception-v3 model pretrained on ImageNet, we could achieve an improvement of ~10% in R-squared score compared to two baseline models that only use non-image features of the house.
Spelling Correction with Denoising Transformer

We present a novel method of performing spelling correction on short input strings, such as search queries or individual words. At its core lies a procedure for generating artificial typos which closely follow the error patterns manifested by humans. This procedure is used to train the production spelling correction model based on a transformer architecture. This model is currently served in the HubSpot product search. We show that our approach to typo generation is superior to the widespread practice of adding noise, which ignores human patterns. We also demonstrate how our approach may be extended to resource-scarce settings and train spelling correction models for Arabic, Greek, Russian, and Setswana languages, without using any labeled data.