Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Images Generated by Diffusers

This paper explores the task of detecting images generated by text-to-image diffusion models. To evaluate this, we consider images generated from captions in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable Diffusion and GLIDE. Our experiments show that it is possible to detect the generated images using simple Multi-Layer Perceptrons (MLPs), starting from features extracted by CLIP, or traditional Convolutional Neural Networks (CNNs). We also observe that models trained on images generated by Stable Diffusion can detect images generated by GLIDE relatively well, however, the reverse is not true. Lastly, we find that incorporating the associated textual information with the images rarely leads to significant improvement in detection results but that the type of subject depicted in the image can have a significant impact on performance. This work provides insights into the feasibility of detecting generated images, and has implications for security and privacy concerns in real-world applications. The code to reproduce our results is available at: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers
Atomic scale localization of Kohn–Sham wavefunction at SiO2/4H–SiC interface under electric field, deviating from envelope function by effective mass approximation

To clarify the cause of the low channel conductivity at the SiO2/4H–SiC interface, the wavefunction at the SiC conduction band minimum was calculated using density functional theory under an applied electric field. We found that the wavefunction for a 4H–SiC (0001) slab tends to be localized at the cubic site closest to the interface. Importantly, because the conduction electrons are distributed closer to the interface (<5 Å) than expected from the effective mass approximation (EMA), they are more frequently scattered by interface defects. This is expected to be the reason why the channel conductivity for the (0001) face is particularly low compared with that for other faces, such as (112¯0). The breakdown of the EMA for the (0001) interface is related to the long structural periodicity along the [0001] direction in 4H–SiC crystals.
A finite element toolbox for the Bogoliubov-de Gennes stability analysis of Bose-Einstein condensates

We present a finite element toolbox for the computation of Bogoliubov-de Gennes modes used to assess the linear stability of stationary solutions of the Gross-Pitaevskii (GP) equation. Applications concern one (single GP equation) or two-component (a system of coupled GP equations) Bose-Einstein condensates in one, two and three dimensions of space. An implementation using the free software FreeFem++ is distributed with this paper. For the computation of the GP stationary (complex or real) solutions we use a Newton algorithm coupled with a continuation method exploring the parameter space (the chemical potential or the interaction constant). Bogoliubov-de Gennes equations are then solved using dedicated libraries for the associated eigenvalue problem. Mesh adaptivity is proved to considerably reduce the computational time for cases implying complex vortex states. Programs are validated through comparisons with known theoretical results for simple cases and numerical results reported in the literature.
A Formal Equivalence of Deformation Quantization and Geometric Quantization (of Higher Groupoids) and Non-Perturbative Sigma Models

Based on work done by Bonechi, Cattaneo, Felder and Zabzine on Poisson sigma models, we formally show that Kontsevich's star product can be obtained from the twisted convolution algebra of the geometric quantization of a Lie 2-groupoid, one which integrates the Poisson structure. We show that there is an analogue of the Poisson sigma model which is valued in Lie 1-groupoids and which can often be defined non-perturbatively; it can be obtained by symplectic reduction using the quantization of the Lie 2-groupoid. We call these groupoid-valued sigma models and we argue that, when they exist, they can be used to compute correlation functions of gauge invariant observables. This leads to a (possibly non-associative) product on the underlying space of functions on the Poisson manifold, and in several examples we show that we recover strict deformation quantizations, in the sense of Rieffel. Even in the cases when our construction leads to a non-associative product we still obtain a $C^*$-algebra and a"quantization"map. In particular, we construct noncommutative $C^*$-algebras equipped with an $SU(2)$-action, together with an equivariant"quantization"map from $C^{\infty}(S^2)\,. $ No polarizations are used in the construction of these algebras.
CHAOTIC PHENOMENA IN THE INTERACTION OF VORTEX RINGS

The problem of interaction of several coaxial vortex rings in an inviscid fluid is investigated numerically. It is assumed that the core shape of the vortex rings remain circular. At the initial time the rings are located at the same distance ρ0 from the center of the system. This distance is a control parameter of the problem. The cases of interaction of three, four, and five vortex rings are studied. It is shown, that in spite of the nonintegrability of the problem, there are certain domains of values ρ0 where the motion of the vortex rings is quasiperiodical. The transition from one such domain to another occurs always through a domain of chaotic interaction. The results of the interaction of the vortex rings are compared with the interaction of their plane analogies–vortex pairs.
A third-order triangular multilayered plate finite element with continuous interlaminar stresses.

Based on a refined third-order shear deformation plate theory recently proposed by the author, a three-node, fully conforming, multilayered anisotropic plate element of arbitrary triangular shape is developed in this paper. The element incorporates 10 nodal d.o. f. , namely the two in-plane displacements, the two shear rotations, the transverse displacement and its first and second derivatives, thus giving a total of 30 d.o. f. The formulation includes extension, bending and transverse shear deformation states; moreover, it fulfils a priori the geometric and stress continuity conditions at the interfaces, and it requires only five generalized displacements to describe the kinematics of the plate deformation. The formulated plate element is assessed for its performance comparing its predictions either with exact solutions from the plate model or with other approximate two-dimensional solutions.
Hamilton cycles in pseudorandom graphs

Finding general conditions which ensure that a graph is Hamiltonian is a central topic in graph theory. An old and well known conjecture in the area states that any $d$-regular $n$-vertex graph $G$ whose second largest eigenvalue in absolute value $\lambda(G)$ is at most $d/C$, for some universal constant $C>0$, has a Hamilton cycle. We obtain two main results which make substantial progress towards this problem. Firstly, we settle this conjecture in full when the degree $d$ is at least a small power of $n$. Secondly, in the general case we show that $\lambda(G) \leq d/ C(\log n)^{1/3}$ implies the existence of a Hamilton cycle, improving the 20-year old bound of $d/ \log^{1-o(1)} n$ of Krivelevich and Sudakov. We use in a novel way a variety of methods, such as a robust P\‘osa rotation-extension technique, the Friedman-Pippenger tree embedding with rollbacks and the absorbing method, combined with additional tools and ideas. Our results have several interesting applications, giving best bounds on the number of generators which guarantee the Hamiltonicity of random Cayley graphs, which is an important partial case of the well known Hamiltonicity conjecture of Lov\‘asz. They can also be used to improve a result of Alon and Bourgain on additive patterns in multiplicative subgroups.
The epidemiological footprint of contact structures in models with two levels of mixing

Models with several levels of mixing (households, workplaces), as well as various corresponding formulations for R0, have been proposed in the literature. However, little attention has been paid to the impact of the distribution of the population size within social structures, effect that can help plan effective interventions. We focus on the influence on the model outcomes of teleworking strategies, consisting in reshaping the distribution of workplace sizes. We consider a stochastic SIR model with two levels of mixing, accounting for a uniformly mixing general population, each individual belonging also to a household and a workplace. The variance of the workplace size distribution appears to be a good proxy for the impact of this distribution on key outcomes of the epidemic, such as epidemic size and peak. In particular, our findings suggest that strategies where the proportion of individuals teleworking depends sublinearly on the size of the workplace outperform the strategy with linear dependence. Besides, one drawback of the model with multiple levels of mixing is its complexity, raising interest in a reduced model. We propose a homogeneously mixing SIR ODE-based model, whose infection rate is chosen as to observe the growth rate of the initial model. This reduced model yields a generally satisfying approximation of the epidemic. These results, robust to various changes in model structure, are very promising from the perspective of implementing effective strategies based on social distancing of specific contacts. Furthermore, they contribute to the effort of building relevant approximations of individual based models at intermediate scales.
Smoothed Analysis of Sequential Probability Assignment

We initiate the study of smoothed analysis for the sequential probability assignment problem with contexts. We study information-theoretically optimal minmax rates as well as a framework for algorithmic reduction involving the maximum likelihood estimator oracle. Our approach establishes a general-purpose reduction from minimax rates for sequential probability assignment for smoothed adversaries to minimax rates for transductive learning. This leads to optimal (logarithmic) fast rates for parametric classes and classes with finite VC dimension. On the algorithmic front, we develop an algorithm that efficiently taps into the MLE oracle, for general classes of functions. We show that under general conditions this algorithmic approach yields sublinear regret.
Where Are the Nearby Gas-rich Low Surface Brightness Galaxies?

The Fisher-Tully (F-T) “10 Mpc Catalog of Late-Type Galaxies” is remarkably complete. Despite the considerable effort that has been spent searching for and cataloging low surface brightness (LSB) galaxies, almost no new H I-rich galaxies have been added to the volume to which the F-T observations were both sensitive [d < 10(MH I/108.2 M☉)5/12 Mpc] and intended to be complete (d < 10 Mpc). It has not yet been demonstrated by the new surveys that H I-rich LSB galaxies are not properly represented in this catalog of nearby galaxies. Although new optical surveys are discovering heretofore uncataloged galaxies at greater distances than the depth of the F-T catalog, they do not alter the completeness of the F-T catalog for H I-rich LSB objects. Thus, unless the galaxy population of the local volume is atypical, there is at present no evidence for a significant, new population of gas-rich galaxies that has escaped recognition until now.
Intriguing Property of GAN for Remote Sensing Image Generation

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, we discover an extraordinary phenomenon: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation (Fig.2). In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data (Fig.3). Based on this discovery, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on the NWPU-RESISC45 and PatternNet datasets show that our methods outperform the wellestablished models on RS image generation tasks.
Task Aware Dreamer for Task Generalization in Reinforcement Learning

A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. The ability to generalize across tasks is important as it determines an agent's adaptability to real-world scenarios where reward mechanisms might vary. In this work, we first show that training a general world model can utilize similar structures in these tasks and help train more generalizable agents. Extending world models into the task generalization setting, we introduce a novel method named Task Aware Dreamer (TAD), which integrates reward-informed features to identify consistent latent characteristics across tasks. Within TAD, we compute the variational lower bound of sample data log-likelihood, which introduces a new term designed to differentiate tasks using their states, as the optimization objective of our reward-informed world models. To demonstrate the advantages of the reward-informed policy in TAD, we introduce a new metric called Task Distribution Relevance (TDR) which quantitatively measures the relevance of different tasks. For tasks exhibiting a high TDR, i.e., the tasks differ significantly, we illustrate that Markovian policies struggle to distinguish them, thus it is necessary to utilize reward-informed policies in TAD. Extensive experiments in both image-based and state-based tasks show that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and display a strong generalization ability to unseen tasks.
Spontaneous off-stoichiometry as the knob to control the dielectric properties of gapped metals.

Using first-principles calculations and La3Te4 as an example of an n-type gapped metal, we demonstrate that gapped metals can develop spontaneous defect formation resulting in off-stoichiometric compounds. Importantly, these compounds have different free carrier concentrations and can be realized by optimizing the synthesis conditions. The ability to tune the free carrier concentration allows the tailoring of the intraband and interband transitions, thus controlling the optoelectronic properties of materials in general. Specifically, by realizing different off-stoichiometric La3-xTe4 compounds, it is possible to reach specific crossings of the real part of the dielectric function with the zero line, reduce the plasma frequency contribution to the absorption spectra, or, more generally, induce metal-to-insulator transition. This is particularly important in the context of optoelectronic, plasmonic, and epsilon-near-zero materials, as it enables materials design with a target functionality. While this work is limited to the specific gapped metal, we demonstrate that the fundamental physics is transferable to other gapped metals and can be generally used to design a wide class of new optoelectronic/plasmonic materials.
Selection criterion based on analytic hierarchy process for matching region in gravity aided INS

The gravity matching results are different in different regions of digital gravity map.The well matched regions in gravity map can provide a reliable basis for path planning.In this paper,the gravity field's local standard deviation,longitude roughness,latitude roughness,longitude slope and relevant coefficient were integrated with the analytic hierarchy process method based on the normalization and classification statistics.Then a new selection criterion of gravity matching was proposed to determine the good matching regions and bad matching regions for the gravity aided inertial navigation system.The simulation results with MAD and MSD algorithms show that the navigation errors in good matching regions are less than a gravity grid,which is much better than those in non-matching region.
Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach

Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning to user-generated digital traces on social media for depression detection. Such methods have distinct advantages in combating depression because they can facilitate innovative approaches to fight depression and alleviate its social and economic burden. However, most existing studies lack effective means to incorporate established medical domain knowledge in depression detection or suffer from feature extraction difficulties that impede greater performance. Following the design science research paradigm, we propose a Deep Knowledge-aware Depression Detection (DKDD) framework to accurately detect social media users at risk of depression and explain the critical factors that contribute to such detection. Extensive empirical studies with real-world data demonstrate that, by incorporating domain knowledge, our method outperforms existing state-of-the-art methods. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and NLP research in IS. Practically, by providing early detection and explaining the critical factors, DKDD can supplement clinical depression screening and enable large-scale evaluations of a population's mental health status.
An exactly solvable model of subdiffusion–reaction front propagation

The propagation of a reaction front is considered in the framework of the Seki–Lindenberg reaction–subdiffusion model, which is appropriate in the case of a diffusion-limited reaction. Typically, this kind of problem is solved using approximate approaches or numerical methods. We apply a model piecewise linear reaction function, which allows for exact analytical solutions. For a front between two stable homogeneous states, a unique value of the front velocity is found. In the case of fronts between a stable and an unstable state, a family of traveling wave solutions is revealed. The conditions for the existence of a special ‘pushed’ front are considered. The influence of advection on the front velocity is also analyzed.
Restoration based Generative Models

Denoising diffusion models (DDMs) have recently attracted increasing attention by showing impressive synthesis quality. DDMs are built on a diffusion process that pushes data to the noise distribution and the models learn to denoise. In this paper, we establish the interpretation of DDMs in terms of image restoration (IR). Integrating IR literature allows us to use an alternative objective and diverse forward processes, not confining to the diffusion process. By imposing prior knowledge on the loss function grounded on MAP-based estimation, we eliminate the need for the expensive sampling of DDMs. Also, we propose a multi-scale training, which improves the performance compared to the diffusion process, by taking advantage of the flexibility of the forward process. Experimental results demonstrate that our model improves the quality and efficiency of both training and inference. Furthermore, we show the applicability of our model to inverse problems. We believe that our framework paves the way for designing a new type of flexible general generative model.
On Stability of Solitons for a Rotating Charge at Rest in the Maxwell Field

We consider the system of Maxwell equations and Lorentz torque equation which describes a motion of charge in electromagnetic field. Under certain symmetry conditions on charge distribution and on initial fields the charge remains at rest but rotates. The system admits stationary soliton-type solutions. We study the Lyapunov and the orbital stability of solitons exploiting the energy conservation law the angular momentum argument for the system. The bibliography: 20 refs.
Orbital stability of two circumbinary planets around misaligned eccentric binaries

With n-body simulations we investigate the stability of tilted circumbinary planetary systems consisting of two nonzero mass planets. The planets are initially in circular orbits that are coplanar to each other, as would be expected if they form in a flat but tilted circumbinary gas disc and decouple from the disc within a time difference that is much less than the disc nodal precession period. We constrain the parameters of stable multiple planet circumbinary systems. Both planet-planet and planet-binary interactions can cause complex planet tilt oscillations which can destabilise the orbits of one or both planets. The system is considerably more unstable than the effects of these individual interactions would suggest, due to the interplay between these two interactions. The stability of the system is sensitive to the binary eccentricity, the orbital tilt and the semi-major axes of the two circumbinary planets. With an inner planet semi-major axis of 5 ab, where ab is semi-major axis of the binary, the system is generally stable if the outer planet is located at ≳ 8 ab, beyond the 2:1 mean motion resonance with the inner planet. For larger inner planet semi-major axis the system is less stable because the von-Zeipel–Kozai–Lidov mechanism plays a significant role, particularly for low binary-eccentricity cases. For the unstable cases, the most likely outcome is that one planet is ejected and the other remains bound on a highly eccentric orbit. Therefore we suggest that this instability is an efficient mechanism for producing free-floating planets.
Uncertainty-Aware DNN for Multi-Modal Camera Localization

Camera localization, i.e., camera pose regression, represents an important task in computer vision since it has many practical applications such as in the context of intelligent vehicles and their localization. Having reliable estimates of the regression uncertainties is also important, as it would allow us to catch dangerous localization failures. In the literature, uncertainty estimation in Deep Neural Networks (DNNs) is often performed through sampling methods, such as Monte Carlo Dropout (MCD) and Deep Ensemble (DE), at the expense of undesirable execution time or an increase in hardware resources. In this work, we considered an uncertainty estimation approach named Deep Evidential Regression (DER) that avoids any sampling technique, providing direct uncertainty estimates. Our goal is to provide a systematic approach to intercept localization failures of camera localization systems based on DNNs architectures, by analyzing the generated uncertainties. We propose to exploit CMRNet, a DNN approach for multi-modal image to LiDAR map registration, by modifying its internal configuration to allow for extensive experimental activity on the KITTI dataset. The experimental section highlights CMRNet's major flaws and proves that our proposal does not compromise the original localization performances but also provides, at the same time, the necessary introspection measures that would allow end-users to act accordingly.