Miracle-dz/UniFAR-FTUs huggingface dataset:
Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning

Monitoring and managing Earth’s forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of ﬁve diﬀerent forest structure variables: 95 th height percentile, mean height, density, Gini coeﬃcient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the ﬁrst to propose a Bayesian deep learning approach so as to predict forest structure variables with well-calibrated uncertainty estimates. These increase the trustworthiness of the model and its suitability for downstream tasks that require reliable conﬁdence estimates, such as informed decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the ﬁve forest structure variables.
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey

This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial autoencoders, and their variants. We start with explaining adversarial learning and the vanilla GAN. Then, we explain the conditional GAN and DCGAN. The mode collapse problem is introduced and various methods, including minibatch GAN, unrolled GAN, BourGAN, mixture GAN, D2GAN, and Wasserstein GAN, are introduced for resolving this problem. Then, maximum likelihood estimation in GAN are explained along with f-GAN, adversarial variational Bayes, and Bayesian GAN. Then, we cover feature matching in GAN, InfoGAN, GRAN, LSGAN, energy-based GAN, CatGAN, MMD GAN, LapGAN, progressive GAN, triple GAN, LAG, GMAN, AdaGAN, CoGAN, inverse GAN, BiGAN, ALI, SAGAN, Few-shot GAN, SinGAN, and interpolation and evaluation of GAN. Then, we introduce some applications of GAN such as image-to-image translation (including PatchGAN, CycleGAN, DeepFaceDrawing, simulated GAN, interactive GAN), text-to-image translation (including StackGAN), and mixing image characteristics (including FineGAN and MixNMatch). Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.
DP-SEP: Differentially private stochastic expectation propagation

We are interested in privatizing an approximate posterior inference algorithm called Expectation Propagation (EP). EP approximates the posterior by iteratively reﬁning approximations to the local likelihoods, and is known to provide better posterior uncertainties than those by variational inference. However, using EP for large-scale datasets imposes a challenge in terms of memory requirements as it needs to maintain each of the local approximates in memory. To overcome this problem, stochastic expectation propagation (SEP) was proposed, which only considers a unique local factor that captures the average effect of each likelihood term to the posterior and reﬁnes it in a way analogous to EP. Therefore in this work, we focus on developing a differentially private stochastic expectation propagation (DP-SEP) algorithm, which outputs differentially private natural parameters of the exponential-family posteriors in each step of SEP.
Gradient Expansions for the Large-Coupling Strength Limit of the Møller–Plesset Adiabatic Connection

The adiabatic connection that has, as weak-interaction expansion, the Møller–Plesset perturbation series has been recently shown to have a large coupling-strength expansion, in terms of functionals of the Hartree–Fock density with a clear physical meaning. In this work, we accurately evaluate these density functionals and we extract second-order gradient coefficients from the data for neutral atoms, following ideas similar to the ones used in the literature for exchange, with some modifications. These new gradient expansions will be the key ingredient for performing interpolations that have already been shown to reduce dramatically MP2 errors for large noncovalent complexes. As a byproduct, our investigation of neutral atoms with large number of electrons N indicates that the second-order gradient expansion for exchange grows as N log(N) rather than as N, as often reported in the literature.
SLA2P: Self-supervised Anomaly Detection with Adversarial Perturbation

Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed as SLA$^2$P, for unsupervised anomaly detection. After extracting representative embeddings from raw data, we apply random projections to the features and regard features transformed by different projections as belonging to distinct pseudo classes. We then train a classifier network on these transformed features to perform self-supervised learning. Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these perturbed features. Our motivation is that because of the relatively small number and the decentralized modes of anomalies, 1) the pseudo label classifier's training concentrates more on learning the semantic information of normal data rather than anomalous data; 2) the transformed features of the normal data are more robust to the perturbations than those of the anomalies. Consequently, the perturbed transformed features of anomalies fail to be classified well and accordingly have lower anomaly scores than those of the normal samples. Extensive experiments on image, text and inherently tabular benchmark datasets back up our findings and indicate that SLA$^2$P achieves state-of-the-art results on unsupervised anomaly detection tasks consistently.
Comparative investigations on UHF and acoustic PD detection sensitivity in transformers

This publication deals with UHF and acoustic partial discharge (PD) detection sensitivity assessment of paper/oil insulation configurations which are typical of power transformers. Even though the basis of this topic has been developed some time ago, the knowledge of the accuracy and limits of the sensitivity is of increasing importance for large power transformer test engineers who are confronted not only of reliable measurement of PD but also locating the source of discharges. Discharges representative of that occurring in a transformer were recorded by simulating them in a 220 kV test tank. The models simulated the basic insulation configuration of a transformer with multiple pressboard barriers and oil gaps. The influence of the effect of barriers on the sensitivity of UHF and acoustic PD detection were studied. Classical phase-resolved PD patterns were recorded alongside for analyzing the results of experiments. Experimental outcomes on these models were further verified on a 220 kV winding in a 400 kV test tank. Studies reveal that acoustic. sensitivity is seriously impaired by the presence of barriers, i.e. sensitivity of discharges shielded by insulating barriers is low as compared to those discharges in open oil volumes. For comparable distances, this sensitivity limitation is absent for UHF PD detection. Furthermore, merits and limits of individual methods are discussed that may lead to future research.
SchemaDB: Structures in Relational Datasets

In this paper we introduce the SchemaDB data-set; a collection of relational database schemata in both sql and graph formats. Databases are not commonly shared publicly for reasons of privacy and security, so schemata are not available for study. Consequently, an understanding of database structures in the wild is lacking, and most examples found publicly belong to common development frameworks or are derived from textbooks or engine benchmark designs. SchemaDB contains 2,500 samples of relational schemata found in public repositories which we have standardised to MySQL syntax. We provide our gathering and transformation methodology, summary statistics, and structural analysis, and discuss potential downstream research tasks in several domains.
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning

In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough data and prior model about the dangerous region? Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger. This fact causes that the agent cannot learn a zero-violation policy even after convergence. Otherwise, it would not receive any penalty and lose the knowledge about danger. In this paper, we propose the safe set actor-critic (SSAC) algorithm, which confines the policy update using safety-oriented energy functions, or the safety indexes. The safety index is designed to increase rapidly for potentially dangerous actions, which allows us to locate the safe set on the action space, or the control safe set. Therefore, we can identify the dangerous actions prior to taking them, and further obtain a zero constraint-violation policy after convergence.We claim that we can learn the energy function in a model-free manner similar to learning a value function. By using the energy function transition as the constraint objective, we formulate a constrained RL problem. We prove that our Lagrangian-based solutions make sure that the learned policy will converge to the constrained optimum under some assumptions. The proposed algorithm is evaluated on both the complex simulation environments and a hardware-in-loop (HIL) experiment with a real controller from the autonomous vehicle. Experimental results suggest that the converged policy in all environments achieves zero constraint violation and comparable performance with model-based baselines.
Non-local thermodynamic equilibrium spectral analysis of five hot, hydrogen-deficient pre-white dwarfs

Hot, compact, hydrogen-deficient pre-white dwarfs (pre-WDs) with effective temperatures of Teff > 70 000 K and a surface gravity of 5.0 < logg < 7.0 are rather rare objects despite recent and ongoing surveys. It is believed that they are the outcome of either single star evolution (late helium-shell flash or late helium-core flash) or binary star evolution (double WD merger). Their study is interesting because the surface elemental abundances reflect the physics of thermonuclear flashes and merger events. Spectroscopically they are divided in three different classes, namely PG1159, O(He), or He-sdO. We present a spectroscopic analysis of five such stars that turned out to have atmospheric parameters in the range Teff = 70 000–80 000 K and logg = 5.2–6.3. The three investigated He-sdOs have a relatively high hydrogen mass fraction (10%) that is unexplained by both single (He core flash) and binary evolution (He-WD merger) scenarios. The O(He) star JL 9 is probably a binary helium-WD merger, but its hydrogen content (6%) is also at odds with merger models. We found that RL 104 is the ‘coolest’ (Teff = 80 000 K) member of the PG1159 class in a pre-WD stage. Its optical spectrum is remarkable because it exhibits C IV lines involving Rydberg states with principal quantum numbers up to n = 22. Its rather low mass (0.48−0.02+0.03 M⊙) is difficult to reconcile with the common evolutionary scenario for PG1159 stars due to it being the outcome of a (very) late He-shell flash. The same mass-problem faces a merger model of a close He-sdO plus CO WD binary that predicts PG1159-like abundances. Perhaps RL 104 originates from a very late He-shell flash in a CO/He WD formed by a merger of two low-mass He-WDs.
Modal approach to modeling spin wave scattering

Efficient numerical methods are required for the design of optimised devices. In magnonics, the primary computational tool is micromagnetic simulations, which solve the Landau-Lifshitz equation discretised in time and space. However, their computational cost is high, and the complexity of their output hinders insight into the physics of the simulated system, especially in the case of multimode propagating wave-based devices. We propose a finite-element modal method allowing an efficient solution of the scattering problem for dipole-exchange spin waves propagating perpendicularly to the magnetisation direction. The method gives direct access to the scattering matrix of the whole system and its components. We extend the formula for the power carried by a magnetostatic mode in the Damon-Eshbach configuration to the case with exchange, allowing the scattering coefficients to be normalised to represent the fraction of the input power transferred to each output channel. We apply the method to the analysis of spin-wave scattering on a basic functional block of magnonic circuits, consisting of a resonator dynamically coupled to a thin film. The results and the method are validated by comparison with micromagnetic simulations.
Low-rank variational Bayes correction to the Laplace method

Approximate inference methods like the Laplace method, Laplace approximations and variational methods, amongst others, are popular methods when exact inference is not feasible due to the complexity of the model or the abundance of data. In this paper we propose a hybrid approximate method called Low-Rank Variational Bayes correction (VBC), that uses the Laplace method and subsequently a Variational Bayes correction in a lower dimension, to the joint posterior mean. The cost is essentially that of the Laplace method which ensures scalability of the method, in both model complexity and data size. Models with fixed and unknown hyperparameters are considered, for simulated and real examples, for small and large datasets.
A New Multilevel Inverter with Reduced Number of Switches

In recent day’s Multilevel inverter (MLI) technologies become a incredibly main choice in the area of high power medium voltage energy control. Though multilevel inverter has a number of advantages it has drawbacks in the vein of higher levels because of using more number of semiconductor switches. This may leads to vast size and price of the inverter is very high. So in order to overcome this problem the new multilevel inverter is proposed with reduced number of switches. The proposed method is well suited for a high power application and it built with three Dc sources and six Switches. Multi carrier pwm technique is used for sine wave generation. The results are validated through the harmonic spectrum of the FFT window by using Matlab/simulink. The result of the proposed MLI is compared with the conventional MLI and other seven level existing topologies. DOI: http://dx.doi. org/10.11591/ijpeds.v5i1. 6089 Full Text: PDF
15.3: Specificities of a Psycho‐Physical Test Room Dedicated for Medical Display Applications

This paper presents specificities for implementing a psychophysical test room dedicated for medical display applications. The main use is to perform subjective measures in a normalized environment where results from other psycho‐physical studies can be compared. Standards‐Norms exist and are based on CRT measures, they must be adapted for studies on medical LCD.
Implicit Data-Driven Regularization in Deep Neural Networks under SGD

Much research effort has been devoted to explaining the success of deep learning. Random Matrix Theory (RMT) provides an emerging way to this end: spectral analysis of large random matrices involved in a trained deep neural network (DNN) such as weight matrices or Hessian matrices with respect to the stochastic gradient descent algorithm. In this paper, we conduct extensive experiments on weight matrices in different modules, e.g., layers, networks and data sets, to analyze the evolution of their spectra. We find that these spectra can be classified into three main types: Marčenko-Pastur spectrum (MP), Marčenko-Pastur spectrum with few bleeding outliers (MPB), and Heavy tailed spectrum (HT). Moreover, these discovered spectra are directly connected to the degree of regularization in the DNN. We argue that the degree of regularization depends on the quality of data fed to the DNN, namely Data-Driven Regularization. These findings are validated in several NNs, using Gaussian synthetic data and real data sets (MNIST and CIFAR10). Finally, we propose a spectral criterion and construct an early stopping procedure when the NN is found highly regularized without test data by using the connection between the spectra types and the degrees of regularization. Such early stopped DNNs avoid unnecessary extra training while preserving a much comparable generalization ability.
Quantum multiplication through equivariant Schubert calculus

In this note, we rederive quantum Pieri’s formula and the rim hook algorithm in quantum Schubert calculus by studying multiplication in the equivariant cohomology ring of Grassmannians with respect to equivariant Schubert classes which are characteristic classes. We also extend this idea in studying equivariant quantum Schubert calculus, and obtain the equivariant quantum Giambelli’s and Pieri’s formulae in terms of characteristic classes, with the former formula shown to be free of quantum deformation.
Sketch-Guided Equality Saturation: Scaling Equality Saturation to Complex Optimizations in Languages with Bindings

Equality saturation is a technique for implementing rewrite-driven compiler optimizations by efficiently representing many equivalent programs in so-called e-graphs. To improve performance, the set of equivalent programs is grown by applying rewrites in a purely additive way until a fixed point is reached (saturation), or the search times out. In practice, two issues limit the application of equality saturation in programming language compilers. First, equality saturation is not efficient for the name bindings (variables) that appear in almost all programming languages. Second, equality saturation does not scale to complex optimizations with long rewrite sequences such as loop blocking. This paper addresses both issues, thereby enabling equality saturation to be applied to more realistic programs and compiler optimizations. First, we demonstrate how to drastically improve the efficiency of equality saturation for a functional language based on the typed lambda calculus. Second, we introduce sketch-guided equality saturation , a semi-automatic technique that allows programmers to provide sketches guiding rewriting when performing complex optimizations. Weevaluate sketch-guided equality saturation by performing a series of realistic optimizations of matrix multiplication expressed in the Rise functional language. The optimizations include loop blocking, vectorization, and parallelization. We demonstrate that naive equality saturation does not scale to these optimizations, even with hours of exploration time. Previous work on orchestrating rewrite sequences shows that these optimizations can be expressed as rewrites, at the cost of weeks of programmer effort. Our guided equality saturation combines the advantages of both techniques: minimal programmer guidance enables complex compiler optimizations to be applied in seconds.
Sidescan Sonar Image Super Resolution Based on Sparse Representation

With the rapid development of autonomous underwater vehicles, image produced from sidescan sonar mounted on these vehicles has attracted more and more attention. Many general super resolution approaches can not get satisfied results after applying to sidescan sonar image for its low levels of contrast. Compressive sensing theory suggested that high resolution image can be recovered from low resolution image with sparse representation. After training two dictionaries for high resolution and low resolution image patches, the sparse representation of given low resolution image can be applied to the dictionary of high resolution patch to get super resolution result image. Sparse representation based image super resolution approach applied to sidescan sonar image is discussed.
Wideband balun and vertical transition between microstrip and parallel‐strip transmission line

A wideband vertical transition between microstrip and parallel‐strip transmission lines is presented. It performs as an unbalanced‐to‐balanced transformer as well. Its back‐to‐back configuration is fabricated, and the transition characteristics are measured. Measurement results reveal low insertion loss over wide frequency band. The performance of the microstrip transition is compared with those of the vertical transitions involving a coplanar waveguide and a conductor‐backed coplanar waveguide, and comments on the comparison results are provided. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1530–1532, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22482
Machine Learning the Square-Lattice Ising Model

Recently, machine-learning methods have been shown to be successful in identifying and classifying different phases of the square-lattice Ising model. We study the performance and limits of classification and regression models. In particular, we investigate how accurately the correlation length, energy and magnetisation can be recovered from a given configuration. We find that a supervised learning study of a regression model yields good predictions for magnetisation and energy, and acceptable predictions for the correlation length.
Homogeneous Low-Resolution Face Recognition Method based Correlation Features

Face recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc. Security surveillance is a typical scenario of face recognition technology. Because the low-resolution feature of surveillance video and images makes it difficult for high-resolution face recognition algorithms to extract effective feature information, Algorithms applied to high-resolution face recognition are difficult to migrate directly to low-resolution situations. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low-resolution surveillance cameras. This paper study on the Correlation Features-based Face Recognition (CoFFaR) method which using for homogeneous low-resolution surveillance videos, the theory, experimental details, and experimental results are elaborated in detail. The experimental results validate the effectiveness of the correlation features method that improves the accuracy of homogeneous face recognition in surveillance security scenarios.