Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions

Thanks to the rapidly developing technology, unmanned aerial vehicles (UAVs) are able to complete a number of tasks in cooperation with each other without need for human intervention. In recent years, UAVs, which are widely utilized in military missions, have begun to be deployed in civilian applications and mostly for commercial purposes. With their growing numbers and range of applications, UAVs are becoming more and more popular; on the other hand, they are also the target of various threats which can exploit various vulnerabilities of UAV systems in order to cause destructive effects. It is therefore critical that security is ensured for UAVs and the networks that provide communication between UAVs. This survey seeks to provide a comprehensive perspective on security within the domain of UAVs and FANETs. Our approach incorporates attack surface analysis and aligns it with the identification of potential threats. Additionally, we discuss countermeasures proposed in the existing literature in two categories: preventive and detection strategies. Our primary focus centers on the security challenges inherent to FANETs, acknowledging their susceptibility to insider threats due to their unique characteristics. Consequently, our study involves the simulation and analysis of four distinct routing attacks on FANETs. Hence, this study transcends a standard review by integrating an attack analysis based on extensive simulations. Finally, we rigorously examine open issues, and propose research directions to guide future endeavors in this field.
Magnetic ground states and excitations in Zn-doped averieite -- a family of oxide-based $S=1/2$ kagome antiferromagnets

Spin-1/2 kagome materials have recently attracted a resurgence of interest as they are considered an ideal host of the quantum spin liquid (QSL) state, which can underpin functionality such as superconductivity. Here we report the first synthesis and characterization of a new oxide-based distorted $S=1/2$ kagome antiferromagnet (KAFM) in the Zn$_x$Cu$_{5-x}$(VO$_4$)$_{2}$O$_2$CsCl (termed Zn$\mathbf {_x}$) series, namely Zn$_2$-averievite, Zn$\mathbf {_2}$ ($x=2$). Using magnetometry, synchrotron diffraction and neutron scattering we demonstrate an evolution of ground states with $x$ in Zn$\mathbf {_x}$; from long-range magnetic order in averievite ($x=0$), via a spin-glass-like ground state in Zn$\mathbf {_1}$, to a quantum spin liquid (QSL) in Zn$\mathbf {_2}$ for which inelastic neutron scattering reveals a gapless continuum of excitations. Similar to archetypal $S = 1/2$ KAFMs herbertsmithite and SrCr$_{8.19}$Ga$_{3.81}$O$_{19}$ (SCGO), the dynamic magnetic susceptibility of Zn$\mathbf {_2}$ shows scaling behavior consistent with proximity to a quantum critical point. The results demonstrate that the new Zn$\mathbf {_2}$ material is an excellent test bed for achieving the elusive goal of charge carrier doping in QSL states of $S = 1/2$ KAFMs, in-line with previous theoretical studies.
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods

Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible metrics and examine two natural extensions of policy optimization with Wasserstein and Sinkhorn trust regions, namely Wasserstein policy optimization (WPO) and Sinkhorn policy optimization (SPO). Instead of restricting the policy to a parametric distribution class, we directly optimize the policy distribution and derive their closed-form policy updates based on the Lagrangian duality. Theoretically, we show that WPO guarantees a monotonic performance improvement, and SPO provably converges to WPO as the entropic regularizer diminishes. Moreover, we prove that with a decaying Lagrangian multiplier to the trust region constraint, both methods converge to global optimality. Experiments across tabular domains, robotic locomotion, and continuous control tasks further demonstrate the performance improvement of both approaches, more robustness of WPO to sample insufficiency, and faster convergence of SPO, over state-of-art policy gradient methods.
Low-Rank Prune-And-Factorize for Language Model Compression

The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has been utilized to reduce the number of parameters in PLM. However, it fails to retain satisfactory performance under moderate to high compression rate. In this paper, we identify the \textit{full-rankness} of fine-tuned PLM as the fundamental bottleneck for the failure of matrix factorization and explore the use of network pruning to extract low-rank sparsity pattern desirable to matrix factorization. We find such low-rank sparsity pattern exclusively exists in models generated by first-order pruning, which motivates us to unite the two approaches and achieve more effective model compression. We further propose two techniques: sparsity-aware SVD and mixed-rank fine-tuning, which improve the initialization and training of the compression procedure, respectively. Experiments on GLUE and question-answering tasks show that the proposed method has superior compression-performance trade-off compared to existing approaches.
Learnable Differencing Center for Nighttime Depth Perception

Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images. Existing image-guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challenges, we propose a simple yet effective framework called LDCNet. Our key idea is to use Recurrent Inter-Convolution Differencing (RICD) and Illumination-Affinitive Intra-Convolution Differencing (IAICD) to enhance the nighttime color images and reduce the negative effects of the varying illumination, respectively. RICD explicitly estimates global illumination by differencing two convolutions with different kernels, treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective. IAICD softly alleviates local relative light intensity by differencing a single convolution, where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in RICD. On both nighttime depth completion and depth estimation tasks, extensive experiments demonstrate the effectiveness of our LDCNet, reaching the state of the art.
On Learning Latent Models with Multi-Instance Weak Supervision

We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function $\sigma$ of labels associated with multiple input instances. We formulate this problem as \emph{multi-instance Partial Label Learning (multi-instance PLL)}, which is an extension to the standard PLL problem. Our problem is met in different fields, including latent structural learning and neuro-symbolic integration. Despite the existence of many learning techniques, limited theoretical analysis has been dedicated to this problem. In this paper, we provide the first theoretical study of multi-instance PLL with possibly an unknown transition $\sigma$. Our main contributions are as follows. Firstly, we propose a necessary and sufficient condition for the learnability of the problem. This condition non-trivially generalizes and relaxes the existing small ambiguity degree in the PLL literature, since we allow the transition to be deterministic. Secondly, we derive Rademacher-style error bounds based on a top-$k$ surrogate loss that is widely used in the neuro-symbolic literature. Furthermore, we conclude with empirical experiments for learning under unknown transitions. The empirical results align with our theoretical findings; however, they also expose the issue of scalability in the weak supervision literature.
Selective inference using randomized group lasso estimators for general models

Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data, for example, and allows for categorical or grouped covariates as well as continuous covariates. A randomized group-regularized optimization problem is studied. The added randomization allows us to construct a post-selection likelihood which we show to be adequate for selective inference when conditioning on the event of the selection of the grouped covariates. This likelihood also provides a selective point estimator, accounting for the selection by the group lasso. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume. The selective inference method for grouped lasso is illustrated on data from the national health and nutrition examination survey while simulations showcase its behaviour and favorable comparison with other methods.
The Swift Deep Galactic Plane Survey (DGPS) Phase I Catalog

The Swift Deep Galactic Plane Survey (DGPS) is a Swift Key Project consisting of 380 tiled pointings covering ∼40 deg2 of the Galactic plane between longitude 10 < ∣l∣ < 30 deg and latitude ∣b∣ < 0.5 deg. Each pointing has a 5 ks exposure, yielding a total of 1.9 Ms spread across the entire survey footprint. Phase I observations were carried out between 2017 March and 2021 May. The survey is complete to depth L X > 1034 erg s−1 to the edge of the Galaxy. The main survey goal is to produce a rich sample of new X-ray sources and transients, while also covering a broad discovery space. Here, we introduce the survey strategy and present a catalog of sources detected during Phase I observations. In total, we identify 928 X-ray sources, of which 348 are unique to our X-ray catalog. We report on the characteristics of sources in our catalog and highlight sources newly classified and published by the DGPS team.
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

The widely used stochastic gradient methods for minimizing nonconvex composite objective functions require the Lipschitz smoothness of the differentiable part. But the requirement does not hold true for problem classes including quadratic inverse problems and training neural networks. To address this issue, we investigate a family of stochastic Bregman proximal gradient (SBPG) methods, which only require smooth adaptivity of the differentiable part. SBPG replaces the upper quadratic approximation used in SGD with the Bregman proximity measure, resulting in a better approximation model that captures the non-Lipschitz gradients of the nonconvex objective. We formulate the vanilla SBPG and establish its convergence properties under nonconvex setting without finite-sum structure. Experimental results on quadratic inverse problems testify the robustness of SBPG. Moreover, we propose a momentum-based version of SBPG (MSBPG) and prove it has improved convergence properties. We apply MSBPG to the training of deep neural networks with a polynomial kernel function, which ensures the smooth adaptivity of the loss function. Experimental results on representative benchmarks demonstrate the effectiveness and robustness of MSBPG in training neural networks. Since the additional computation cost of MSBPG compared with SGD is negligible in large-scale optimization, MSBPG can potentially be employed as an universal open-source optimizer in the future.
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance of GCNs on ML tasks. Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks. These subgraph-based sampling approaches rely on heuristics -- such as graph partitioning via edge cuts -- to identify clusters that are then treated as minibatches during GCN training. In this work, we hypothesize that rather than relying on such heuristics, one can learn a reinforcement learning (RL) policy to compute efficient clusters that lead to effective GCN performance. To that end, we propose PolicyClusterGCN, an online RL framework that can identify good clusters for GCN training. We develop a novel Markov Decision Process (MDP) formulation that allows the policy network to predict ``importance"weights on the edges which are then utilized by a clustering algorithm (Graclus) to compute the clusters. We train the policy network using a standard policy gradient algorithm where the rewards are computed from the classification accuracies while training GCN using clusters given by the policy. Experiments on six real-world datasets and several synthetic datasets show that PolicyClusterGCN outperforms existing state-of-the-art models on node classification task.
Krylov complexity of modular Hamiltonian evolution

We investigate the complexity of states and operators evolved with the modular Hamiltonian by using the Krylov basis. In the first part, we formulate the problem for states and analyse different examples, including quantum mechanics, two-dimensional conformal field theories and random modular Hamiltonians, focusing on relations with the entanglement spectrum. We find that the modular Lanczos spectrum provides a different approach to quantum entanglement, opening new avenues in many-body systems and holography. In the second part, we focus on the modular evolution of operators and states excited by local operators in two-dimensional conformal field theories. We find that, at late modular time, the spread complexity is universally governed by the modular Lyapunov exponent $\lambda^{mod}_L=2\pi$ and is proportional to the local temperature of the modular Hamiltonian. Our analysis provides explicit examples where entanglement entropy is indeed not enough, however the entanglement spectrum is, and encodes the same information as complexity.
Discrete Glowworm Swarm Optimization Algorithm for TSP Problem

A discrete glowworm swarm optimization(DGSO) algorithm is designed to tackle the travelling salesman problem.A new encoding schema and decoding schema are given with the characteristics of the TSP problem,and a new distance formula and encoding update formula for the new algorithm are given.In order to enhance the capability of the algorithm local searching,and to speed up the algorithm convergence speed,the 2-opt local search scheme is integrated into the new algorithm for solving TSP problem.The proposed algorithm was evaluated on 10 TSP test problems.The numerical experiments show that the proposed algorithm can find the global optimal solution with less computation and evolving time.In case of large scale TSP algorithm can achieve optimal solution of the theory and the error of the optimal solution is also less than 1%.
On a Class of Gibbs Sampling over Networks

We consider the sampling problem from a composite distribution whose potential (negative log density) is $\sum_{i=1}^n f_i(x_i)+\sum_{j=1}^m g_j(y_j)+\sum_{i=1}^n\sum_{j=1}^m\frac{\sigma_{ij}}{2\eta} \Vert x_i-y_j \Vert^2_2$ where each of $x_i$ and $y_j$ is in $\mathbb{R}^d$, $f_1, f_2, \ldots, f_n, g_1, g_2, \ldots, g_m$ are strongly convex functions, and $\{\sigma_{ij}\}$ encodes a network structure. % motivated by the task of drawing samples over a network in a distributed manner. Building on the Gibbs sampling method, we develop an efficient sampling framework for this problem when the network is a bipartite graph. More importantly, we establish a non-asymptotic linear convergence rate for it. This work extends earlier works that involve only a graph with two nodes \cite{lee2021structured}. To the best of our knowledge, our result represents the first non-asymptotic analysis of a Gibbs sampler for structured log-concave distributions over networks. Our framework can be potentially used to sample from the distribution $ \propto \exp(-\sum_{i=1}^n f_i(x)-\sum_{j=1}^m g_j(x))$ in a distributed manner.
The Origin of Star Formation in Early-type Galaxies Inferred from Spatially Resolved Spectroscopy

We investigate the origin of star formation activity in early-type galaxies with current star formation using spatially resolved spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory in the Sloan Digital Sky Survey (SDSS). We first identify star-forming early-type galaxies from the SDSS sample, which are morphologically early-type but show current star formation activity in their optical spectra. We then construct comparison samples with different combinations of star formation activity and morphology, which include star-forming late-type galaxies, quiescent early-type galaxies, and quiescent late-type galaxies. Our analysis of the optical spectra reveals that the star-forming early-type galaxies have two distinctive episodes of star formation, which is similar to late-type galaxies but different from quiescent early-type galaxies with a single star formation episode. Star-forming early-type galaxies have properties in common with star-forming late-type galaxies, which include stellar population, gas and dust content, mass, and environment. However, the physical properties of star-forming early-type galaxies derived from spatially resolved spectroscopy differ from those of star-forming late-type galaxies in the sense that the gas in star-forming early-type galaxies is more concentrated than their stars, and is often kinematically misaligned with stars. The age gradient of star-forming early-type galaxies also differs from those of star-forming late-type galaxies. Our findings suggest that the current star formation in star-forming early-type galaxies has an external origin including galaxy mergers or accretion gas from the cosmic web.
A Simple and Effective Baseline for Attentional Generative Adversarial Networks

Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve the performance of the generator and the quality of image generation, and Stack-GAN++, which gradually improves the details and quality of the image by stacking multiple generators and discriminators. However, this series of improvements to GAN all have redundancy to a certain extent, which affects the generation performance and complexity to a certain extent. We use the popular simple and effective idea (1) to remove redundancy structure and improve the backbone network of AttnGAN. (2) to integrate and reconstruct multiple losses of DAMSM. Our improvements have significantly improved the model size and training efficiency while ensuring that the model's performance is unchanged and finally proposed our SEAttnGAN. Code is avalilable at https://github.com/jmyissb/SEAttnGAN.
Exploring kaon induced reactions for unraveling the nature of the scalar meson 
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In this study, we comprehensively investigate the production of isovector scalar meson $a_{0}(1817)$ using the effective Lagrangian approach. Specifically, we employ the Reggeized $t$ channel Born term to calculate the total and differential cross sections for the reaction $K^{-}p \rightarrow a_{0}(1817)\Lambda$. Our analysis reveals that the optimal energy range for detecting the $a_{0}(1817)$ meson lies between $W=3.4$ GeV and $W=3.6$ GeV, where the predicted total cross section reaches a minimum value of 112 nb. Notably, the $t$ channel, as predicted by the Regge model, significantly enhances the differential cross sections, particularly at extreme forward angles. Furthermore, we investigate the Dalitz processes of $2\rightarrow 3$ and discuss the feasibility of detecting the $a_{0}(1817)$ meson in experiments such as J-PARC.
The limitations of the patch test

A simple approximation by nonconforming finite elements is presented that passes the patch test of Irons and Strang but does not yield approximate solutions converging to the solution of the given boundary value problem. It is constructed from continuous piecewise linear functions perturbed by step functions. Further, strange convergence properties of such approximations are explained in all details because they may be typical for the behaviour of nonconforming finite elements violating the basic precondition for convergence.
A control theoretic approach to manned-vehicle systems analysis

Modern control and estimation theory is used to establish a framework for the analysis of manned-vehicle systems. By assuming that the human behaves "optimally" in some sense, subject to his inherent psychophysical limitations, a quantitative model is developed for the response characteristics of the human operator. The resultant model can be used to predict task performance and human control characteristics. The model is described in detail and is used to predict quantities measured experimentally in both simple and reasonably complex manual control tasks. Remarkable agreement between measured and predicted quantities is obtained, demonstrating the value and potential of the optimization approach to manned-vehicle systems analysis.
Modulation of forced isotropic turbulence by an anchored droplet with near-Kolmogorov diameter and varying volatility

Abstract Utilizing a fan-stirred chamber and two-dimensional particle image velocimetry, we analyse the modification of homogeneous and isotropic turbulence ($50 \leq Re_\lambda \leq 140$, with supplementary data out to $Re_\lambda = 310$, where $Re_\lambda$ is the longitudinal Taylor Reynolds number) induced by both a non-volatile (water) and a volatile (ethanol) isolated and anchored droplet in the range $(0.3 \leq d/\eta \leq 5.1)$, where $d/\eta$ is the ratio of droplet diameter to the Kolmogorov length scale. The dissipation rate, $\varepsilon$, is calculated via the corrected spatial gradient method, and the resultant fields of both turbulent kinetic energy, $k$, and $\varepsilon$ are presented as spatial heat maps and as shell averages, ${\overline {k_{\Delta r}}}$ and ${\overline {\varepsilon _{\Delta r}}}$, vs the radial coordinate normalized by the droplet radius, $r/R$. The dissipation rate near the water droplet surface may exceed the corresponding unladen flow value by a factor of twenty or more. The normalized radius of recovery, $r^*$, which designates the radial location where ${\overline {k_{\Delta r}}}$ or ${\overline {\varepsilon _{\Delta r}}}$ has returned to within 10 % of the unladen value, is reasonably expressed as $r^* \propto (d/\lambda )^{-C_2}$ in either case, where $\lambda$ is the longitudinal Taylor microscale and $C_2$ is a positive empirical fitting parameter. Recovery of ${\overline {k_{\Delta r}}}$ and ${\overline {\varepsilon _{\Delta r}}}$ may take up to 14 normalized radii when $d/\lambda$ is small. Trend line extrapolation suggests that the attenuation region becomes negligible as $d/\lambda \to 1$. Ethanol, which evaporates up to five times faster than water, induces a much smaller dissipation spike near the surface. The mass ejection phenomenon appears to reduce the strong near-surface damping of the radial root-mean-square component. However, the radius of recovery trend for fields surrounding a volatile ethanol droplet falls directly in line with the non-volatile water droplet data for both $k$ and $\varepsilon$, indicating that droplet vaporization has little effect on the far-field return to isotropy.