Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors With Supervised Machine Learning: A Benchmark

Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Lack of precise temperature estimations leads to lesser device utilization and higher material cost. In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles, specifically, ordinary least squares, support vector regression, $k$-nearest neighbors, randomized trees, and neural networks. Having test bench data available, it is shown that ML approaches relying merely on collected data meet the estimation performance of classical thermal models built on thermodynamic theory. Through benchmarking, this work reveals the potential of simpler ML models in terms of regression accuracy, model size, and their data demand in comparison to parameter-heavy deep neural networks, which were investigated in the literature before. Especially linear regression and simple feed-forward neural networks with optimized hyperparameters mark strong predictive quality at low to moderate model sizes.
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs). However, attempts to combine existing techniques to create a practical model are still uncommon. In this study, we carry out extensive experiments to validate that carefully assembling these techniques and applying them to basic CNN models (e.g. ResNet and MobileNet) can improve the accuracy and robustness of the models while minimizing the loss of throughput. Our proposed assembled ResNet-50 shows improvements in top-1 accuracy from 76.3\% to 82.78\%, mCE from 76.0\% to 48.9\% and mFR from 57.7\% to 32.3\% on ILSVRC2012 validation set. With these improvements, inference throughput only decreases from 536 to 312. To verify the performance improvement in transfer learning, fine grained classification and image retrieval tasks were tested on several public datasets and showed that the improvement to backbone network performance boosted transfer learning performance significantly. Our approach achieved 1st place in the iFood Competition Fine-Grained Visual Recognition at CVPR 2019, and the source code and trained models are available at this https URL
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling

Multifidelity approximate Bayesian computation (MF-ABC) is a likelihood-free technique for parameter inference that exploits model approximations to significantly increase the speed of ABC algorithms (Prescott and Baker, 2020). Previous work has considered MF-ABC only in the context of rejection sampling, which does not explore parameter space particularly efficiently. In this work, we integrate the multifidelity approach with the ABC sequential Monte Carlo (ABC-SMC) algorithm into a new MF-ABC-SMC algorithm. We show that the improvements generated by each of ABC-SMC and MF-ABC to the efficiency of generating Monte Carlo samples and estimates from the ABC posterior are amplified when the two techniques are used together.
The radial acceleration relation in galaxy clusters

Recently, the discovery of the radial acceleration relation (RAR) in galaxies has been regarded as an indirect support of alternative theories of gravity such as modified Newtonian dynamics (MOND) and modified gravity. This relation indicates a tight correlation between dynamical mass and baryonic mass in galaxies with different sizes and morphology. However, if the RAR relation is scale independent and could be explained by alternative theories of gravity, this relation should be universal and true for galaxy clusters as well. In this article, by using the X-ray data of a sample of galaxy clusters, we investigate if there exists any tight correlation between dynamical mass and baryonic mass in galaxy clusters, assuming hot gas mass distribution almost representing baryonic distribution and that the galaxy clusters are virialized. We show that the resulting RAR of 52 non-cool-core galaxy clusters scatters in a large parameter space, possibly due to our simplifying assumptions and unclear matter content in galaxy clusters. This might indicate that the RAR is unlikely to be universal and scale independent.
SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On

Image-based virtual try-on for fashion has gained considerable attention recently. The task requires trying on a clothing item on a target model image. An efficient framework for this is composed of two stages: (1) warping (transforming) the try-on cloth to align with the pose and shape of the target model, and (2) a texture transfer module to seamlessly integrate the warped try-on cloth onto the target model image. Existing methods suffer from artifacts and distortions in their try-on output. In this work, we present SieveNet, a framework for robust image-based virtual try-on. Firstly, we introduce a multi-stage coarse-to-fine warping network to better model fine grained intricacies (while transforming the try-on cloth) and train it with a novel perceptual geometric matching loss. Next, we introduce a try-on cloth conditioned segmentation mask prior to improve the texture transfer network. Finally, we also introduce a duelling triplet loss strategy for training the texture translation network which further improves the quality of generated try-on result. We present extensive qualitative and quantitative evaluations of each component of the proposed pipeline and show significant performance improvements against the current state-of-the-art method.
The dual origin of the Galactic thick disc and halo from the gas-rich Gaia–Enceladus Sausage merger

We analyse a set of cosmological magneto-hydrodynamic simulations of the formation of Milky Way-mass galaxies identified to have a prominent radially anisotropic stellar halo component similar to the so-called “Gaia Sausage” found in the Gaia data. We examine the effects of the progenitor of the Sausage (the Gaia-Enceladus-Sausage, GES) on the formation of major galactic components analogous to the Galactic thick disc and inner stellar halo. We find that the GES merger is likely to have been gas-rich and contribute 10-50% of gas to a merger-induced centrally concentrated starburst that results in the rapid formation of a compact, rotationally supported thick disc that occupies the typical chemical thick disc region of chemical abundance space. We find evidence that gas-rich mergers heated the proto-disc of the Galaxy, scattering stars onto less-circular orbits such that their rotation velocity and metallicity positively correlate, thus contributing an additional component that connects the Galactic thick disc to the inner stellar halo. We demonstrate that the level of kinematic heating of the proto-galaxy correlates with the kinematic state of the population before the merger, the progenitor mass and orbital eccentricity of the merger. Furthermore, we show that the mass and time of the merger can be accurately inferred from local stars on counter-rotating orbits.
The high-redshift SFR–M* relation is sensitive to the employed star formation rate and stellar mass indicators: towards addressing the tension between observations and simulations

There is a severe tension between the observed star formation rate (SFR)–stellar mass (M⋆) relations reported by different authors at z = 1–4. In addition, the observations have not been successfully reproduced by state-of-the-art cosmological simulations that tend to predict a factor of 2–4 smaller SFRs at a fixed M⋆. We examine the evolution of the SFR–M⋆ relation of z = 1–4 galaxies using the skirt simulated spectral energy distributions of galaxies sampled from the Evolution and Assembly of GaLaxies and their Environments simulations. We derive SFRs and stellar masses by mimicking different observational techniques. We find that the tension between observed and simulated SFR–M⋆ relations is largely alleviated if similar methods are used to infer the galaxy properties. We find that relations relying on infrared wavelengths (e.g. 24 ${\rm \, \mu m}$, MIPS – 24, 70, and 160 ${\rm \, \mu m}$ or SPIRE – 250, 350, and 500 ${\rm \, \mu m}$) have SFRs that exceed the intrinsic relation by 0.5 dex. Relations that rely on the spectral energy distribution fitting technique underpredict the SFRs at a fixed stellar mass by −0.5 dex at z ∼ 4 but overpredict the measurements by 0.3 dex at z ∼ 1. Relations relying on dust-corrected rest-frame ultraviolet luminosities, are flatter since they overpredict/underpredict SFRs for low/high star-forming objects and yield deviations from the intrinsic relation from 0.10 to −0.13 dex at z ∼ 4. We suggest that the severe tension between different observational studies can be broadly explained by the fact that different groups employ different techniques to infer their SFRs.
Halo Counts-in-cells for Cosmological Models with Different Dark Energy

We examine the counts-in-cells (CiC) probability distribution functions (PDFs) that describe dark matter halos in the Dark Energy Universe Simulations (DEUS). We describe the measurements between redshifts z = 0 to z = 4 on both linear and nonlinear scales. The best fits of the gravitational quasi-equilibrium distribution (GQED), the negative binomial distribution (NBD), the Poisson-Lognormal distribution (PLN), and the Poisson-Lognormal distribution with a bias parameter (PLNB) are compared to simulations. The fits agree reasonably consistently over a range of redshifts and scales. To distinguish quintessence (RPCDM) and phantom (wCDM) dark energy from Λ dark energy, we present a new method that compares the model parameters of the CiC PDFs. We find that the mean and variance of the halo CiC on 2–25h−1 Mpc scales between redshifts 0.65 < z < 4 show significant percentage differences for different dark energy cosmologies. On 15–25 h−1 Mpc scales, the g parameter in NBD, ω parameter in PLN, and b and Cb parameters in PLNB show larger percentage differences for different dark energy cosmologies than on smaller scales. On 2–6 h−1 Mpc scales, the kurtosis and the b parameter in the GQED show larger percentage differences for different dark energy cosmologies than on larger scales. For cosmologies explored in the DEUS, the percentage differences between these statistics for the RPCDM and wCDM dark energy cosmologies relative to ΛCDM generally increases with redshift from a few percent to significantly larger percentages at z = 4. Applying our method to simulations and galaxy surveys can provide a useful way to distinguish among dark energy models and cosmologies in general.
Quantitative Estimates for Homogenization of Nonlinear Elliptic Operators in Perforated Domains

This paper was devoted to study the quantitative homogenization problems for nonlinear elliptic operators in perforated domains. We obtained a sharp error estimate $O(\varepsilon)$ when the problem was anchored in the reference domain $\varepsilon\omega$. If concerning a bounded perforated domain, one will see a bad influence from the boundary layers, which leads to the loss of the convergence rate by $O(\varepsilon^{1/2})$. Equipped with the error estimates, we developed both interior and boundary Lipschitz estimates at large-scales. As an application, we received the so-called quenched Calderon-Zygumund estimates by Shen's real arguments. To overcome some difficulties, we improved the extension theory from (\cite[Theorem 4.3]{OSY}) to $L^p$-versions with $\frac{2d}{d+1}-\epsilon<p<\frac{2d}{d-1}+\epsilon$ and $0<\epsilon\ll1$. Appealing to this, we established Poincare-Sobolev inequalities of local type on perforated domains. Some of results in the present literature are new even for related linear elliptic models.
Electrodynamics of Highly Spin-Polarized Tunnel Josephson Junctions

The continuous development of superconducting electronics is encouraging several studies on hybrid Josephson junctions (JJs) based on superconductor/ferromagnet/superconductor (SFS) heterostructures, as either spintronic devices or switchable elements in quantum and classical circuits. Recent experimental evidence of macroscopic quantum tunneling and of an incomplete 0-pi transition in tunnel-ferromagnetic spin-filter JJs could enhance the capabilities of SFS JJs also as active elements. Here, we provide a self-consistent electrodynamic characterization of NbN/GdN/NbN spin-filter JJs as a function of the barrier thickness, disentangling the high-frequency dissipation effects due to the environment from the intrinsic low-frequency dissipation processes. The fitting of the IV characteristics at 4.2K and at 300mK by using the Tunnel Junction Microscopic model allows us to determine the subgap resistance Rsg, the quality factor Q and the junction capacitance C. These results provide the scaling behavior of the electrodynamic parameters as a function of the barrier thickness, which represents a fundamental step for the feasibility of tunnel ferromagnetic JJs as active elements in classical and quantum circuits, and are of general interest for tunnel junctions other than conventional SIS JJs.
DNNs as Layers of Cooperating Classifiers

A robust theoretical framework that can describe and predict the generalization ability of DNNs in general circumstances remains elusive. Classical attempts have produced complexity metrics that rely heavily on global measures of compactness and capacity with little investigation into the effects of sub-component collaboration. We demonstrate intriguing regularities in the activation patterns of the hidden nodes within fully-connected feedforward networks. By tracing the origin of these patterns, we show how such networks can be viewed as the combination of two information processing systems: one continuous and one discrete. We describe how these two systems arise naturally from the gradient-based optimization process, and demonstrate the classification ability of the two systems, individually and in collaboration. This perspective on DNN classification offers a novel way to think about generalization, in which different subsets of the training data are used to train distinct classifiers; those classifiers are then combined to perform the classification task, and their consistency is crucial for accurate classification.
A system of local/nonlocal p-Laplacians: The eigenvalue problem and its asymptotic limit as p → ∞

In this work, given $p\in (1,\infty)$, we prove the existence and simplicity of the first eigenvalue $\lambda_p$ and its corresponding eigenvector $(u_p,v_p)$, for the following local/nonlocal PDE system \begin{equation}\label{Eq0} \left\{ \begin{array}{rclcl} -\Delta_p u + (-\Delta)^r_p u & = & \frac{2\alpha}{\alpha+\beta}\lambda |u|^{\alpha-2}|v|^{\beta}u & \mbox{in} & \Omega \\ -\Delta_p v + (-\Delta)^s_p v& = & \frac{2\beta}{\alpha+\beta}\lambda |u|^{\alpha}|v|^{\beta-2}v & \mbox{in} & \Omega u& =& 0&\text{ on } & \mathbb{R}^N \setminus \Omega v& =& 0&\text{ on } & \mathbb{R}^N \setminus \Omega, \end{array} \right. \end{equation} where $\Omega$$\subset$ $\mathbb{R}^N$ is a bounded open domain, $0<r, s<1$ and $\alpha(p)+\beta(p) = p$. Moreover, we address the asymptotic limit as $p \to \infty$, proving the explicit geometric characterization of the corresponding first $\infty-$eigenvalue, namely $\lambda_{\infty}$, and the uniformly convergence of the pair $(u_p,v_p)$ to the $\infty-$eigenvector $(u_{\infty},v_{\infty})$. Finally, the triple $(u_{\infty},v_{\infty},\lambda_{\infty})$ verifies, in the viscosity sense, a limiting PDE system.
Robust Two Echelon Vehicle and Drone Routing for Post Disaster Humanitarian Operations

Following a disaster, humanitarian logistics providers have two major responsibilities: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and communications networks, often rendered unusable by the disaster. In this work, we propose a two-echelon vehicle routing problem framework for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones). Our setup employs ground vehicles carrying drones that serve to both identify demands for medical and other aid (e.g., epi-pens, medical supplies, dry food, water) and satisfy them. We present a deterministic and a stochastic approach for handling the emergency aid delivery problem faced by such a humanitarian logistics provider. In the deterministic approach, we consider two types of drones: hotspot drones for capturing demand and delivery drones for satisfying demand. We present a framework to identify optimal routes for both types of drones following a column-generation scheme. In the stochastic case, we avoid the extra cost of using hotspot drones to capture demands deterministically and develop a two-stage robust optimization framework to handle the resulting demand uncertainty. To solve the resulting two-stage robust model for the two-echelon vehicle and drone routing problem, we combine an outer level column-and constraint-generation (CCG) method with an inner level column generation (CG) scheme. We present a case study based on real-life data from Hurricane Maria in Puerto Rico and showcase the applicability of our framework. The data generated for this research is available online at this http URL
The significant effects of stellar mass estimation on galaxy pair fractions.

There exist discrepancies in measurements of the number and evolution of galaxy pairs. The pair fraction appears to be sensitive to both the criteria used to select pair fraction and the methods used to analyze survey data. This paper explores the connection between stellar mass estimation and the pair fraction of galaxies making use of STEEL, the Statistical sEmi-Emprical modeL. Previous results have found the pair fraction is sensitive to choices made when selecting what qualifies as a pair, for example luminosity or stellar mass selections. We find that different estimations of stellar mass such as photometric choice mass-to-light ratio or IMF that effect the stellar mass function also significantly affect the derived galaxy pair fraction. By making use of the galaxy halo connection we investigate these systematic affects on the pair fraction. We constrain the galaxy halo connection using the stellar-mass-halo-mass relationship for two observed stellar mass functions, and the Illustris TNG stellar mass function. Furthermore, we also create a suite of toy models where the stellar-mass-halo-mass relationship is manually changed. For each stellar-mass-halo-mass relation the pair fraction, and its evolution, are generated. We find that enhancements to the number density of high mass galaxies cause steepening of the stellar-mass-halo mass relation, resulting in a reduction of the pair fraction. We argue this is a considerable cause of bias that must be accounted for when comparing pair fractions.
Biopsy versus resection in the management of malignant gliomas: a systematic review and meta-analysis

Object. The aim of this study was to answer the question whether quality of life and progression-free and overall survival are increased in adults with supratentorial malignant glioma who are treated with cytoreductive resection as compared with those who only undergo biopsy. Methods. A literature search of the electronic databases MEDLINE, EMBASE, and CENTRAL was performed to identify relevant studies published before May 2008. Hand-searching of reference lists of the identified studies and relevant review articles was also performed. A study was considered eligible, regardless of study design (prospective or retrospective), if: 1) quality of life and/or progression-free and/or overall survival was compared among adult patients undergoing biopsy or resection, and 2) patient age and Karnofsky Performance Scale scores were not significantly different among the 2 groups compared. Results. One randomized controlled trial and 4 retrospective studies (involving a total of 1111 patients) were found eligible for this systematic review. A meta-analysis of the eligible studies demonstrated a significant increase in overall survival in the patients treated with resection instead of biopsy (hazard ratio 0.61, 95% CI 0.52–0.71, p < 0.0001, fixed-effect model). Although statistical pooling was not feasible, the available data suggest that quality of life was increased in patients treated with resection rather than biopsy, while there did not seem to be any significant difference in progression-free survival between the 2 groups. Conclusions. Based on the best available evidence, it appears that cytoreductive resection in adults with supratentorial malignant glioma is associated with improved overall survival as compared with biopsy. However, welldesigned prospective studies are needed for more solid conclusions to be drawn. (DOI: 10.3171/2009.7.JNS09758) key W ords • biopsy • glioma • meta-analysis • overall survival • resection
Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning

Semi-supervised learning aims to take advantage of a large amount of unlabeled data to improve the accuracy of a model that only has access to a small number of labeled examples. We propose curriculum labeling, an approach that exploits pseudo-labeling for propagating labels to unlabeled samples in an iterative and self-paced fashion. This approach is surprisingly simple and effective and surpasses or is comparable with the best methods proposed in the recent literature across all the standard benchmarks for image classification. Notably, we obtain 94.91% accuracy on CIFAR-10 using only 4,000 labeled samples, and 88.56% top-5 accuracy on Imagenet-ILSVRC using 128,000 labeled samples. In contrast to prior works, our approach shows improvements even in a more realistic scenario that leverages out-of-distribution unlabeled data samples.
GraphBGS: Background Subtraction via Recovery of Graph Signals

Background subtraction is a fundamental preprocessing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static and moving camera sequences. Several deep learning methods for background subtraction have been proposed in the literature with competitive performances. However, these models show performance degradation when tested on unseen videos; and they require huge amount of data to avoid overfitting. Recently, graph-based algorithms have been successful approaching unsupervised and semi-supervised learning problems. Furthermore, the theory of graph signal processing and semi-supervised learning have been combined leading to new insights in the field of machine learning. In this paper, concepts of recovery of graph signals are introduced in the problem of background subtraction. We propose a new algorithm called Graph BackGround Subtraction (GraphBGS), which is composed of: instance segmentation, background initialization, graph construction, graph sampling, and a semi-supervised algorithm inspired from the theory of recovery of graph signals. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both: static and moving camera videos. GraphBGS outperforms unsupervised and supervised methods in several challenging conditions on the publicly available Change Detection (CDNet2014), and UCSD background subtraction databases.