Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser-driven generation of collimated quasi-monoenergetic proton beam using double-layer target with interface modulations

Usage of double-layer targets consisting of heavy and light material with modulated interface between them provides a way for laser-driven generation of collimated ion beams. With extensive 2D3V PIC simulations we show that this configuration may result in a development of a relativistic instability with Rayleigh-Taylor and Richtmyer-Meshkov like features. Initially small perturbations are amplified during the laser-target interaction leading to the formation of low-density plasma regions and high-density bunches between them, which are accelerated by the laser radiation pressure as whole compact structures. That results in collimated quasi-monoenergetic proton beam with high average energy. The properties of this proton beam such as its low emittance (one order of magnitude lower compared to that of conventional accelerators) and divergence are discussed. Results are compared with similar acceleration schemes such as double-layer target without corrugation and single-layer target.
Resource Allocation for Secure IRS-Assisted Multiuser MISO Systems

In this paper, we study resource allocation design for secure communication in intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) communication systems. To enhance physical layer security, artificial noise (AN) is transmitted from the base station (BS) to deliberately impair the channel of an eavesdropper. In particular, we jointly optimize the phase shift matrix at the IRS and the beamforming vectors and AN covariance matrix at the BS for maximization of the system sum secrecy rate. To handle the resulting non-convex optimization problem, we develop an efficient suboptimal algorithm based on alternating optimization, successive convex approximation, semidefinite relaxation, and manifold optimization. Our simulation results reveal that the proposed scheme substantially improves the system sum secrecy rate compared to two baseline schemes.
Deep Learning for Fine-Grained Image Analysis: A Survey

Computer vision (CV) is the process of using machines to understand and analyze imagery, which is an integral branch of artificial intelligence. Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, \eg, species of birds or models of cars. The small inter-class variations and the large intra-class variations caused by the fine-grained nature makes it a challenging problem. During the booming of deep learning, recent years have witnessed remarkable progress of FGIA using deep learning techniques. In this paper, we aim to give a survey on recent advances of deep learning based FGIA techniques in a systematic way. Specifically, we organize the existing studies of FGIA techniques into three major categories: fine-grained image recognition, fine-grained image retrieval and fine-grained image generation. In addition, we also cover some other important issues of FGIA, such as publicly available benchmark datasets and its related domain specific applications. Finally, we conclude this survey by highlighting several directions and open problems which need be further explored by the community in the future.
Elements Generating a Proper Normal Subgroup of the Cremona Group

Consider an algebraically closed field ${\textbf{k}}$, and let $\textsf{Cr}_2({\textbf{k}})$ be the Cremona group of all birational transformations of the projective plane over ${\textbf{k}}$. We characterize infinite order elements $g\in \textsf{Cr}_2({\textbf{k}})$ having a power $g^n$, $n\neq 0$, generating a proper normal subgroup of $\textsf{Cr}_2({\textbf{k}})$.
Sparse Hierarchical Preconditioners Using Piecewise Smooth Approximations of Eigenvectors

When solving linear systems arising from PDE discretizations, iterative methods (such as Conjugate Gradient, GMRES, or MINRES) are often the only practical choice. To converge in a small number of iterations, however, they have to be coupled with an efficient preconditioner. The efficiency of the preconditioner depends largely on its accuracy on the eigenvectors corresponding to small eigenvalues, and unfortunately, black-box methods typically cannot guarantee sufficient accuracy on these eigenvectors. Thus, constructing the preconditioner becomes a very problem-dependent task. We describe a hierarchical approximate factorization approach which addresses this issue by focusing on improving the accuracy on smooth eigenvectors (such eigenvectors typically correspond to the small eigenvalues). The improved accuracy is achieved by preserving the action of the factorized matrix on piecewise polynomial functions of the PDE domain. Based on the factorization, we propose a family of sparse preconditioners with $\mathcal{O}\left(n\right)$ or $\mathcal{O}\left( n \log{n} \right)$ construction complexities. Our methods exhibit the optimal $\mathcal{O}\left(n \right)$ solution times in benchmarks run on large elliptic problems of different types, arising for example in flow or mechanical simulations. In the case of the linear elasticity equation the preconditioners are exact on the near-kernel rigid body modes.
The impact of baryonic physics and massive neutrinos on weak lensing peak statistics

We study the impact of baryonic processes and massive neutrinos on weak lensing peak statistics that can be used to constrain cosmological parameters. We use the BAHAMAS suite of cosmological simulations, which self-consistently include baryonic processes and the effect of massive neutrino free-streaming on the evolution of structure formation. We construct synthetic weak lensing catalogues by ray tracing through light-cones, and use the aperture mass statistic for the analysis. The peaks detected on the maps reflect the cumulative signal from massive bound objects and general large-scale structure. We present the first study of weak lensing peaks in simulations that include both baryonic physics and massive neutrinos (summed neutrino mass Mν = 0.06, 0.12, 0.24, and 0.48 eV assuming normal hierarchy), so that the uncertainty due to physics beyond the gravity of dark matter can be factored into constraints on cosmological models. Assuming a fiducial model of baryonic physics, we also investigate the correlation between peaks and massive haloes, over a range of summed neutrino mass values. As higher neutrino mass tends to suppress the formation of massive structures in the Universe, the halo mass function and lensing peak counts are therefore modified as a function of Mν. Over most of the S/N range, the impact of fiducial baryonic physics is greater (less) than neutrinos for 0.06 and 0.12 (0.24 and 0.48) eV models. Both baryonic physics and massive neutrinos should be accounted for when deriving cosmological parameters from weak lensing observations.
Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks

It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, for example, in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this article, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep-learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep-learning-assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning

Tensor-network techniques have enjoyed outstanding success in physics, and have recently attracted attention in machine learning, both as a tool for the formulation of new learning algorithms and for enhancing the mathematical understanding of existing methods. Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions. These factorizations include non-negative tensor-trains/MPS, which are in correspondence with hidden Markov models, and Born machines, which are naturally related to local quantum circuits. When used to model probability distributions, they exhibit tractable likelihoods and admit efficient learning algorithms. Interestingly, we prove that there exist probability distributions for which there are unbounded separations between the resource requirements of some of these tensor-network factorizations. Particularly surprising is the fact that using complex instead of real tensors can lead to an arbitrarily large reduction in the number of parameters of the network. Additionally, we introduce locally purified states (LPS), a new factorization inspired by techniques for the simulation of quantum systems, with provably better expressive power than all other representations considered. The ramifications of this result are explored through numerical experiments. Our findings imply that LPS should be considered over hidden Markov models, and furthermore provide guidelines for the design of local quantum circuits for probabilistic modeling.
Signal Reconstruction using Blind Super-resolution with Arbitrary Sampling

In this paper the problem of blind super-resolution of sparse signals using arbitrary sampling scheme and atomic lift is discussed. After comprehensive description on blind superresolution problem, it is shown that using Prolate Spheroidal Wave Functions (PSWFs), it is possible to derive a new SemiDefinite Program (SDP) for the blind super-resolution problem. Unlike the previous results, the newly proposed SDP can localize spikes without magnitude recovery. Several numerical simulations were conducted to compare the performance of the proposed method with the recent related research.
Improving Object Detection from Scratch via Gated Feature Reuse

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i.e., without pre-trained models). We call our module GFR (Gated Feature Reuse), which exhibits two main advantages. First, we introduce a novel gate-controlled prediction strategy enabled by Squeeze-and-Excitation to adaptively enhance or attenuate supervision at different scales based on the input object size. As a result, our model is more effective in detecting diverse sizes of objects. Second, we propose a feature-pyramids structure to squeeze rich spatial and semantic features into a single prediction layer, which strengthens feature representation and reduces the number of parameters to learn. We apply the proposed structure on DSOD and SSD detection frameworks, and evaluate the performance on PASCAL VOC 2007, 2012 and COCO datasets. With fewer model parameters, GFR-DSOD outperforms the baseline DSOD by 1.4%, 1.1%, 1.7% and 0.6%, respectively. GFR-SSD also outperforms the original SSD and SSD with dense prediction by 3.6% and 2.8% on VOC 2007 dataset. Code is available at: this https URL .
Cosmological number counts in Einstein and Jordan frames

Even though we know that physical observations are frame independent, the frame dependence of cosmological perturbations is relatively subtle and has led to confusion in the past. In this paper we show that while the (unobservable) matter power spectrum is frame dependent, the observable number counts are not. We also determine how the frame dependence of the power spectrum depends on scale.
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between humans and their physical interactions with the scene. While the existing literature has explored some of these cues, they mainly ignored the multimodal nature of each human's future trajectory. In this paper, we present Social-BiGAT, a graph-based generative adversarial network that generates realistic, multimodal trajectory predictions by better modelling the social interactions of pedestrians in a scene. Our method is based on a graph attention network (GAT) that learns reliable feature representations that encode the social interactions between humans in the scene, and a recurrent encoder-decoder architecture that is trained adversarially to predict, based on the features, the humans' paths. We explicitly account for the multimodal nature of the prediction problem by forming a reversible transformation between each scene and its latent noise vector, as in Bicycle-GAN. We show that our framework achieves state-of-the-art performance comparing it to several baselines on existing trajectory forecasting benchmarks.
A Search for Multiplanet Systems with TESS Using a Bayesian N-body Retrieval and Machine Learning

Transiting exoplanets in multiplanet systems exhibit non-Keplerian orbits as a result of the gravitational influence from companions, which can cause the times and durations of transits to vary. The amplitude and periodicity of the transit time variations are characteristic of the perturbing planet’s mass and orbit. The objects of interest from the Transiting Exoplanet Survey Satellite (TESS) are analyzed in a uniform way to search for transit timing variations (TTVs) with sectors 1–3 of data. Due to the volume of targets in the TESS candidate list, artificial intelligence is used to expedite the search for planets by vetting nontransit signals prior to characterizing the light-curve time series. The residuals of fitting a linear orbit ephemeris are used to search for TTVs. The significance of a perturbing planet is assessed by comparing the Bayesian evidence between a linear and nonlinear ephemeris, which is based on an N-body simulation. Nested sampling is used to derive posterior distributions for the N-body ephemeris and in order to expedite convergence, custom priors are designed using machine learning. A dual-input, multi-output convolutional neural network is designed to predict the parameters of a perturbing body given the known parameters and measured perturbation (O − C). There is evidence for three new multiplanet candidates (WASP-18, WASP-126, TOI 193) with nontransiting companions using the two-minute cadence observations from TESS. This approach can be used to identify stars in need of longer radial velocity and photometric follow-up than those already performed.
Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions

We propose a collapsed Gibbs sampler for Bayesian vector autoregressions with predictors, or exogenous variables, and study the proposed algorithm's convergence properties. The Markov chain generated by our algorithm converges to its stationary distribution at least as fast as those of competing (non-collapsed) Gibbs samplers and is shown to be geometrically ergodic regardless of whether the number of observations in the underlying vector autoregression is small or large in comparison to the order and dimension of it. We also give conditions for when the geometric ergodicity is asymptotically stable as the number of observations tends to infinity. Specifically, the geometric convergence rate is shown to be bounded away from unity asymptotically, either almost surely or with probability tending to one, depending on what is assumed about the data generating process. Our results are among the first of their kind for practically relevant Markov chain Monte Carlo algorithms.
Fundamental parameters and evolutionary status of the magnetic chemically peculiar stars HD 188041 (V1291 Aquilae), HD 111133 (EP Virginis), and HD 204411: spectroscopy versus interferometry

The determination of fundamental parameters of stars is one of the main tasks of astrophysics. For magnetic chemically peculiar stars, this problem is complicated by the anomalous chemical composition of their atmospheres, which requires special analysis methods. We present the results of the effective temperature, surface gravity, abundance, and radius determinations for three CP stars HD 188041, HD 111133, and HD 204411. Our analysis is based on a self-consistent model fitting of high-resolution spectra and spectrophotometric observations over a wide wavelength range, taking into account the anomalous chemical composition of atmospheres and the inhomogeneous vertical distribution for three chemical elements: Ca, Cr, and Fe. For two stars, HD 188041 and HD 204411, we also performed interferometric observations that provided us with the direct estimates of stellar radii. Comparison of the radii determined from the analysis of spectroscopic/spectrophotometric observations with direct measurements of the radii by interferometry methods for seven CP stars shows that the radii agree within the limits of measurement errors, which proves indirect spectroscopic analysis capable of proving reliable determinations of the fundamental parameters of fainter Ap stars that are not possible to study with modern interferometric facilities.
3C 294 revisited: Deep Large Binocular Telescope AO NIR images and optical spectroscopy

Context. High redshift radio galaxies are among the most massive galaxies at their redshift, are often found at the center of protoclusters of galaxies, and are expected to evolve into the present day massive central cluster galaxies. Thus they are a useful tool to explore structure formation in the young Universe. Aims. 3C 294 is a powerful FR II type radio galaxy at z = 1.786. Past studies have identified a clumpy structure, possibly indicative of a merging system, as well as tentative evidence that 3C 294 hosts a dual active galactic nucleus (AGN). Due to its proximity to a bright star, it has been subject to various adaptive optics imaging studies. Methods. In order to distinguish between the various scenarios for 3C 294, we performed deep, high-resolution adaptive optics near-infrared imaging and optical spectroscopy of 3C 294 with the Large Binocular Telescope. Results. We resolve the 3C 294 system into three distinct components separated by a few tenths of an arcsecond on our images. One is compact, the other two are extended, and all appear to be non-stellar. The nature of each component is unclear. The two extended components could be a galaxy with an internal absorption feature, a galaxy merger, or two galaxies at different redshifts. We can now uniquely associate the radio source of 3C 294 with one of the extended components. Based on our spectroscopy, we determined a redshift of z = 1.784 ± 0.001, which is similar to the one previously cited. In addition we found a previously unreported emission line at λ6749. 4 Å in our spectra. It is not clear that it originates from 3C 294. It could be the Ne [IV] doublet λ2424/2426 Å at z = 1.783, or belong to the compact component at a redshift of z ∼ 4.56. We thus cannot unambiguously determine whether 3C 294 hosts a dual AGN or a projected pair of AGNs.
Distributed Proximal Algorithms for Multiagent Optimization With Coupled Inequality Constraints

This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to <inline-formula><tex-math notation="LaTeX">$O(1/\sqrt{k})$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$k>0$</tex-math></inline-formula> is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm.
Instabilities and turbulence in low-β guide field reconnection exhausts with kinetic Riemann simulations

The role of turbulence in low-β, guide-field reconnection exhausts is explored in 2D reconnection and 2D and 3D Riemann simulations. The structure of the exhaust and associated turbulence is controlled by a pair of rotational discontinuities (RDs) at the exhaust boundary and a pair of slow shocks (SSs) that are generated by counterstreaming ions beams. In 2D, the exhaust develops large-amplitude striations at the ion Larmor radius scale that are produced by electron-beam-driven ion cyclotron waves. The electron beams driving the instability are injected into the exhaust from one of the RDs. However, in 3D Riemann simulations, the additional dimension (in the out-of-plane direction) results in strong Buneman and electron-electron streaming instabilities at the RD which suppress the electron beam formation and therefore the striations in the exhaust. The strength of the streaming instabilities at the RD is controlled by the ratio of the electron thermal speed to Alfven speed, with the lower thermal speed being more unstable. In the 3D simulations, an ion-ion streaming instability acts to partially thermalize the counterstreaming ion beams at the SSs. This instability is controlled by the ratio of the sound speed to Alfven speed and is expected to be stable in the low β solar corona. The results suggest that in a guide field reconnection exhaust with 1 ≫ β > m e / m i, the kinetic-scale turbulence that develops will be too weak to play a significant role in energy conversion and particle acceleration. Therefore, the energy conversion will be mostly controlled by laminar physics or multi-x-line reconnection.The role of turbulence in low-β, guide-field reconnection exhausts is explored in 2D reconnection and 2D and 3D Riemann simulations. The structure of the exhaust and associated turbulence is controlled by a pair of rotational discontinuities (RDs) at the exhaust boundary and a pair of slow shocks (SSs) that are generated by counterstreaming ions beams. In 2D, the exhaust develops large-amplitude striations at the ion Larmor radius scale that are produced by electron-beam-driven ion cyclotron waves. The electron beams driving the instability are injected into the exhaust from one of the RDs. However, in 3D Riemann simulations, the additional dimension (in the out-of-plane direction) results in strong Buneman and electron-electron streaming instabilities at the RD which suppress the electron beam formation and therefore the striations in the exhaust. The strength of the streaming instabilities at the RD is controlled by the ratio of the electron thermal speed to Alfven speed, with the lower thermal speed be.