Miracle-dz/UniFAR-FTUs huggingface dataset:
You Only Need Adversarial Supervision for Semantic Image Synthesis

Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the same time limiting the progress of GAN models for semantic image synthesis. In this work, we propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We re-design the discriminator as a semantic segmentation network, directly using the given semantic label maps as the ground truth for training. By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous. Moreover, we enable high-quality multi-modal image synthesis through global and local sampling of a 3D noise tensor injected into the generator, which allows complete or partial image change. We show that images synthesized by our model are more diverse and follow the color and texture distributions of real images more closely. We achieve an average improvement of $6$ FID and $5$ mIoU points over the state of the art across different datasets using only adversarial supervision.
A new model of regular black hole in (2+1) dimensions

We provide a new regular black hole solution in dimensions with the presence of matter fields in the energy momentum tensor. The inclusion of our proposed energy density plus a negative cosmological constant allows that the solution can have both flat as de Sitter or Anti-de Sitter core. This latter is a proper characteristic of our solution, because other models of regular black holes have only a single type of core. Since the first law of thermodynamics for regular black holes is modified by the presence of the matter fields, we provide a new version of the first law, where a local definition of the variation of energy is defined, and where the entropy and temperature are consistent with the ones previously known in the literature. At the hypothetical limit when the horizon radius the usual first law is recovered. The effectiveness of the formalism used to compute the mass of our regular black holes in dimensions suggests the potential applicability of this method to calculate the mass of other models of regular black holes in dimensions.
Effect of Oxygen Partial Pressure on Liquidus for the CaO-SiO2-FeOx System at 1573 K

Phase diagrams for the CaO-SiO 2 -FeO x system at various oxygen partial pressures are necessary for the design of raw material for ironmaking, the analysis of smelting reaction and ore sintering process. In this study, liquidus lines for the CaO-SiO 2 -FeO x systems at various oxygen partial pressures were observed at 1 573 K by using chemical equilibration technique. The liquid phase area changed with changing oxygen partial pressure from 10 - 3 to 10 2 Pa (10 - 8 to 10 - 3 atm). The effect of Fe 3 + /Fe 2 + ratio on the melting mechanism is discussed.
Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data.

Graphical models have been used extensively for modeling brain connectivity networks. However, unmeasured confounders and correlations among measurements are often overlooked during model fitting, which may lead to spurious scientific discoveries. Motivated by functional magnetic resonance imaging (fMRI) studies, we propose a novel method for constructing brain connectivity networks with correlated replicates and latent effects. In a typical fMRI study, each participant is scanned and fMRI measurements are collected across a period of time. In many cases, subjects may have different states of mind that cannot be measured during the brain scan: for instance, some subjects may be awake during the first half of the brain scan, and may fall asleep during the second half of the brain scan. To model the correlation among replicates and latent effects induced by the different states of mind, we assume that the correlated replicates within each independent subject follow a one-lag vector autoregressive model, and that the latent effects induced by the unmeasured confounders are piecewise constant. The proposed method results in a convex optimization problem which we solve using a block coordinate descent algorithm. Theoretical guarantees are established for parameter estimation. We demonstrate via extensive numerical studies that our method is able to estimate latent variable graphical models with correlated replicates more accurately than existing methods.
Magnetic Field Energy Harvesting in Railway

Magnetic field energy harvesting (MFEH) is a method by which a system can harness an ambient, alternating magnetic field in order to scavenge energy. Presented in this article is a novel application of the concept aimed at the magnetic fields surrounding the rail current in electrified railway. Due to its noninvasive nature, the approach has the potential to be widely deployed as a part of low-cost trackside condition monitoring systems in order to increase lifetime and reduce maintenance requirements. In this article, the viability of MFEH in railway is substantiated experimentally—two different configurations are assessed both in a controlled laboratory environment as well as in situ along Norwegian railway. When placed near an emulated section of railway carrying 200 A in the laboratory, the power output of the system is up to 40.5 mW at 50 Hz and 4.15 mW at 16 $\frac{2}{3}\; \mathrm{Hz}$. In the field, the prototype system harvests 109 mJ from a single freight train passing by, rendering an estimated daily energy output of 1.14 J in a moderately trafficked location. It is argued that the approach could indeed eliminate the need for battery replacements and potentially increase the lifetime of an energy-efficient, battery-powered condition monitoring system indefinitely.
Toward an Affective Touch Robot: Subjective and Physiological Evaluation of Gentle Stroke Motion Using a Human-Imitation Hand

Affective touch offers positive psychological and physiological benefits such as the mitigation of stress and pain. If a robot could realize human-like affective touch, it would open up new application areas, including supporting care work. In this research, we focused on the gentle stroking motion of a robot to evoke the same emotions that human touch would evoke: in other words, an affective touch robot. We propose a robot that is able to gently stroke the back of a human using our designed human-imitation hand. To evaluate the emotional effects of this affective touch, we compared the results of a combination of two agents (the human-imitation hand and the human hand), at two stroke speeds (3 and 30 cm/s). The results of the subjective and physiological evaluations highlighted the following three findings: 1) the subjects evaluated strokes similarly with regard to the stroke speed of the human and human-imitation hand, in both the subjective and physiological evaluations; 2) the subjects felt greater pleasure and arousal at the faster stroke rate (30 cm/s rather than 3 cm/s); and 3) poorer fitting of the human-imitation hand due to the bending of the back had a negative emotional effect on the subjects.
Deformed algebra and the effective dynamics of the interior of black holes

We consider the classical Hamiltonian of the interior of the Schwarzschild black hole in Ashtekar–Barbero connection formalism. Then, inspired by generalized uncertainty principle models, we deform the classical canonical algebra and derive the effective dynamics of the model under this modification. We show that such a deformation leads to the resolution of the singularity of the black hole and a minimum nonzero radius for the infalling two-spheres, provided that the deformation parameters are chosen to be negative.
Star–Gas Misalignment in Galaxies. II. Origins Found from the Horizon-AGN Simulation

There have been many studies aiming to reveal the origins of the star–gas misalignment found in galaxies, but there still is a lack of understanding of the contribution from each formation channel candidate. We aim to answer the question by investigating the misaligned galaxies in the Horizon-AGN simulation. There are 27,903 galaxies of stellar mass M * > 1010 M ⊙ in our sample, of which 5984 are in a group in the halo mass of M 200 > 1012 M ⊙. We have identified four main formation channels of misalignment and quantified their levels of contribution: mergers (35%), interaction with nearby galaxies (23%), interaction with dense environments or their central galaxies (21%), and secular evolution, including smooth accretion from neighboring filaments (21%). We found in the simulation that the gas, rather than stars, is typically more vulnerable to dynamical disturbances; hence, misalignment formation is mainly due to the change in the rotational axis of the gas rather than stars, regardless of the origin. We have also inspected the lifetime (duration) of the misalignment. The decay timescale of the misalignment shows a strong anticorrelation with the kinematic morphology (V/σ) and the cold gas fraction of the galaxy. The misalignment has a longer lifetime in denser regions, which is linked with the environmental impact on the host galaxy. There is a substantial difference in the length of the misalignment lifetime depending on the origin, and it can be explained by the magnitude of the initial position angle offset and the physical properties of the galaxies.
The first order expansion of a ground state energy of the ϕ4 model with cutoffs

In this paper, we investigate the $\phi^4$ model with cutoffs. By introducing a spatial cutoff and a momentum cutoff, the total Hamiltonian is a self-adjoint operator on a boson Fock space. Under regularity conditions of the momentum cutoff, we obtain the first order expansion of a non-degenerate ground state energy of the total Hamiltonian.
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting

<p>Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V. , B&#233;r&#233;ziat, D. , Brajard, J., Charantonis, A. , & Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015</p>
RESPONSE OF WHEAT (TRITICUM AESTIVUM) TO NITROGEN AND ZINC APPLICATION

A field experiment was conducted during winter season of 2003 on cultivar U.P. 2338 with three levels of nitrogen viz. 0, 75 and 100 kg/ha and 3 levels of zinc viz. 0, 5 and 10 kg/ha in randomized block design (RBD) with three replications.application of 100 kg N/ha and 5 kg Zn/ha to wheat variety UP 2338 results in higher yield of crop under western Uttar Pradesh conditions.
Fundamental studies of combustion of droplet and vapour mixtures

There are few experimental data of a fundamental nature that clearly demonstrate the similarities and differences in burning rates between single phase and two phase combustion, either in laminar or turbulent conditions. Such data are essential towards a better understanding of the spray combustion phenomena as well as a whole system. In the present study, experimental investigations of combustion of droplet and vapour air mixtures under quiescent and turbulence conditions have been conducted in a fan stirred combustion vessel. Aerosols were generated by expansion of gaseous pre-mixture to produce a homogeneously distributed suspension of fuel droplets. Spherically expanding flames following central ignition were employed to quantify the flame structure and propagation rate. The effect of droplets on flame propagation was investigated by comparing the burning rate of gaseous mixtures at initial pressure and temperature close to those of aerosol mixtures. In quiescent conditions, aerosols of two different fuels, isooctane and ethanol, were investigated at near atmospheric conditions. The effect of fuel droplets, up to 31 J.1m diameter, on laminar flame propagation was examined at a wide range of equivalence ratios. In the early stages of flame development, inertia of fuel droplets leads to local enrichment in equivalence ratio which increases the initial burning rate of lean aerosols but decreases that of rich ones. For the later stages of flame propagation, the presence of liquid droplets causes earlier onset of instabilities and cellularity than for gaseous flames, particularly at rich conditions. This leads to an enhanced burning rate and is probably due to heat loss from the flame and local disturbances due to droplet evaporation and subsequent diffusion processes. In turbulent studies, the effect of isooctane droplets up to 14 J.1m in diameter on flame propagation was examined at various values of root mean square turbulence velocities between zero and 4.0 mls. It is suggested that during early flame development, the turbulence was found to induce droplet motion before flame initiation which dominated over those resulting from the flame, negating the effect of droplet inertia. In the later stages, the presence of droplets in a low turbulent flame resulted in a significant burning rate enhancement. However, this enhancement became progressively less important as turbulent wrinkling became dominant. Between low and high turbulence, there was a transition regime between instability dominated and turbulence dominated regimes. As a consequence, the burning rate enhancement due to droplets under this transition range was rather complex.
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network

Computed tomography is widely used to examine internal structures in a non-destructive manner. To obtain high-quality reconstructions, one typically has to acquire a densely sampled trajectory to avoid angular undersampling. However, many scenarios require a sparse-view measurement leading to streak-artifacts if unaccounted for. Current methods do not make full use of the domain-specific information, and hence fail to provide reliable reconstructions for highly undersampled data. We present a novel framework for sparse-view tomography by decoupling the reconstruction into two steps: First, we overcome its ill-posedness using a super-resolution network, SIN, trained on the sparse projections. The intermediate result allows for a closed-form tomographic reconstruction with preserved details and highly reduced streak-artifacts. Second, a refinement network, PRN, trained on the reconstructions reduces any remaining artifacts. We further propose a light-weight variant of the perceptual-loss that enhances domain-specific information, boosting restoration accuracy. Our experiments demonstrate an improvement over current solutions by 4 dB.
Wavepacket modelling of broadband shock-associated noise in supersonic jets

Abstract We present a two-point model to investigate the underlying source mechanisms for broadband shock-associated noise (BBSAN) in shock-containing supersonic jets. In the model presented, the generation of BBSAN is assumed to arise from the nonlinear interaction between downstream-propagating coherent structures with the quasi-periodic shock cells in the jet plume. The turbulent perturbations are represented as axially extended wavepackets and the shock cells are modelled as a set of stationary waveguide modes. Unlike previous BBSAN models, the physical parameters describing the hydrodynamic components are not scaled using the acoustic field. Instead, the source characteristics of both the turbulent and shock components are extracted from the hydrodynamic region of large-eddy simulation and particle image velocimetry datasets. Apart from using extracted data, a reduced-order description of the wavepacket structure is obtained using parabolised stability equations. The validity of the model is tested by comparing far-field sound pressure level predictions to azimuthally decomposed experimental acoustic data from a cold Mach 1.5 underexpanded jet. At polar angles and frequencies where BBSAN dominates, encouraging comparisons of the radiated noise spectra for the first three azimuthal modes, in both frequency and amplitude (${\pm }2\ \textrm {dB}\,\textrm {St}^{-1}$ at peak frequency), reinforce the suitability of using reduced-order wavepacket sources for predicting BBSAN peaks. On the other hand, wavepacket jitter is found to have a critical role in recovering sound amplitude at interpeak frequencies. The paper presents a quantitative demonstration that the wavepacket–shock interaction, carefully reconstructed by extracting components from data or linearised models, contains the correct essential flow physics that accounts for most features of the far-field BBSAN spectra.
Inhomogeneous confining-deconfining phases in rotating plasmas

We discuss the effects of rotation on confining properties of gauge theories focusing on compact electrodynamics in two spatial dimensions as an analytically tractable model. We show that at finite temperature, the rotation leads to a deconfining transition starting from a certain distance from the rotation axis. A uniformly rotating confining system possesses, in addition to the usual confinement and deconfinement phases, a mixed inhomogeneous phase which hosts spatially separated confinement and deconfinement regions. The phase diagram thus has two different deconfining temperatures. The first deconfining temperature can be made arbitrarily low by sufficiently rapid rotation while the second deconfining temperature is largely unaffected by the rotation. Implications of our results for the phase diagram of QCD are presented. We point out that uniformly rotating quark-gluon plasma should therefore experience an inverse hadronization effect when the hadronization starts from the core of the rotating plasma rather than from its boundary.
First Observations of Solar Disk Gamma Rays over a Full Solar Cycle

The solar disk is among the brightest gamma-ray sources in the sky. It is also among the most mysterious. No existing model fully explains the luminosity, spectrum, time variability, and morphology of its emission. We perform the first analysis of solar-disk gamma rays over a full 11-year solar cycle, utilizing a powerful new method to differentiate solar signals from astrophysical backgrounds. We produce: (i) a robustly measured spectrum from 100 MeV to 100 GeV, reaching a precision of several percent in the 1-10 GeV range, (ii) new results on the anti-correlation between solar activity and gamma-ray emission, (iii) strong constraints on short-timescale variability, ranging from hours to years, and (iv) new detections of the equatorial and polar morphologies of high-energy gamma rays. Intriguingly, we find no significant energy dependence in the time variability of solar-disk emission, indicating that strong magnetic-field effects close to the solar surface, rather than modulation throughout the heliosphere, must primarily control the flux and morphology of solar-disk emission.
Predicting seasonal influenza using supermarket retail records

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.
Milling kinetics of chosen rock materials under dry conditions considering strength and statistical properties of bed

The article presents the results of investigations concerning the analysis of milling kinetics of chosen rock materials taking into consideration their strength properties. Investigations were carried out for a mill operating on a semi-industrial scale. Milling was conducted in a periodical mode using grinding media differing in diameters. Mineral raw materials differing in terms of crystallographic structure were subjected to grinding. The process of milling was carried out for quartzite, granite and greywacke. Input fraction of ground material was of size 5 – 8 mm. Grain strength from particular size fractions and its influence on the process kinetics were determined. Grinding rate of particular size fractions and basic parameters used in the theory of statistical moments were calculated.