Miracle-dz/UniFAR-FTUs huggingface dataset:
DESIGN AND VALIDATION OF A QUESTIONNAIRE TO MEASURE HANTAVIRUS PREVENTIVE PRACTICES IN AN ENDEMIC COMMUNITY

Objective. To design and analyze the evidence of content validity, internal structure, and reliability of a questionnaire of preventive practices for hantavirus in an endemic community in the Panamanian context. Material and methods. Quantitative study of instrumental design. This research was conducted in four phases: Literature review, content validity through expert judgment with the individual aggregate method and the calculation of the V Aiken, pilot test and psychometric validation, through exploratory factor analysis (EFA) and reliability analysis of instrument scores with ordinal alpha. Results. Content validity was evidenced and V Aiken values higher than 0.70 were reported in the lower limit of 95% CI. In the internal structure we identified that the 8 items underlie a single factor that explains 60.70% of the total variance of the test and with factor loadings greater than 0.40; during the reliability analysis, we obtained an ordinal alpha value of 0.84, which is considered good. Conclusions. The Hantavirus preventive practical questionnaire is a brief instrument that shows acceptable psychometric properties to measure the activities or behaviors that people carry out to prevent hantavirus. Motivation for the study: To measure the practices or behaviors of people regarding hantavirus prevention is funda-mental to propose educational programs and strategies that seek to maintain and/or strengthen adequate practices and improve or restructure inadequate practices. It is important to use an instrument that truly measures what is needed and that complies with a rigorous design. Main findings: Instrument with eight questions and one di-mension that measures hantavirus preventive practices. Implications for public health : The instrument is valid and reliable to measure hantavirus preventive practices and future studies could integrate observation lists to complement the measurement.
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model

This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results, these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work, we introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity. To achieve this, we first develop a video diffusion model to encode temporal information. Second, to maintain the appearance coherence across frames, we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations, we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably, our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available.
RetouchUAA: Unconstrained Adversarial Attack via Image Retouching

Deep Neural Networks (DNNs) are susceptible to adversarial examples. Conventional attacks generate controlled noise-like perturbations that fail to reflect real-world scenarios and hard to interpretable. In contrast, recent unconstrained attacks mimic natural image transformations occurring in the real world for perceptible but inconspicuous attacks, yet compromise realism due to neglect of image post-processing and uncontrolled attack direction. In this paper, we propose RetouchUAA, an unconstrained attack that exploits a real-life perturbation: image retouching styles, highlighting its potential threat to DNNs. Compared to existing attacks, RetouchUAA offers several notable advantages. Firstly, RetouchUAA excels in generating interpretable and realistic perturbations through two key designs: the image retouching attack framework and the retouching style guidance module. The former custom-designed human-interpretability retouching framework for adversarial attack by linearizing images while modelling the local processing and retouching decision-making in human retouching behaviour, provides an explicit and reasonable pipeline for understanding the robustness of DNNs against retouching. The latter guides the adversarial image towards standard retouching styles, thereby ensuring its realism. Secondly, attributed to the design of the retouching decision regularization and the persistent attack strategy, RetouchUAA also exhibits outstanding attack capability and defense robustness, posing a heavy threat to DNNs. Experiments on ImageNet and Place365 reveal that RetouchUAA achieves nearly 100\% white-box attack success against three DNNs, while achieving a better trade-off between image naturalness, transferability and defense robustness than baseline attacks.
Enhancing exotic quantum fluctuations in a strongly entangled cavity BEC system

We show that the strong coupling of a quantum light field and correlated quantum matter induces exotic quantum fluctuations in the matter sector. We determine their spectral characteristics and reveal the impact of the atomic s-wave scattering. In particular, we derive the dissipative Landau and Beliaev processes from the microscopic Hamiltonian using imaginary time path integrals. By this, their strongly sub-Ohmic nature is revealed analytically. A competition between damping and antidamping channels is uncovered. Their intricate influence on physical observables is quantified analytically and the Stokes shift of the critical point is determined. This illustrates the tunability of the quantum matter fluctuations by exploiting strong light-matter coupling.
Simultaneous Analysis of Continuously Embedded Reissner-Mindlin Shells in 3D Bulk Domains

A mechanical model and numerical method for the simultaneous analysis of Reissner-Mindlin shells with geometries implied by a continuous set of level sets (isosurfaces) over some three-dimensional bulk domain is presented. A three-dimensional mesh in the bulk domain is used in a tailored FEM formulation where the elements are by no means conforming to the level sets representing the shape of the individual shells. However, the shell geometries are bounded by the intersection curves of the level sets with the boundary of the bulk domain so that the boundaries are meshed conformingly. This results in a method which was coined Bulk Trace FEM before. The simultaneously considered, continuously embedded shells may be useful in the structural design process or for the continuous reinforcement of bulk domains. Numerical results confirm higher-order convergence rates.
Comparing intrusive and non-intrusive polynomial chaos for a class of exponential time differencing schemes

We consider the numerical approximation of different ordinary differential equations (ODEs) and partial differential equations (PDEs) with periodic boundary conditions involving a one-dimensional random parameter, comparing the intrusive and non-intrusive polynomial chaos expansion (PCE) method. We demonstrate how to modify two schemes for intrusive PCE (iPCE) which are highly efficient in solving nonlinear reaction-diffusion equations: A second-order exponential time differencing scheme (ETD-RDP-IF) as well as a spectral exponential time differencing fourth-order Runge-Kutta scheme (ETDRK4). In numerical experiments, we show that these schemes show superior accuracy to simpler schemes such as the EE scheme for a range of model equations and we investigate whether they are competitive with non-intrusive PCE (niPCE) methods. We observe that the iPCE schemes are competitive with niPCE for some model equations, but that iPCE breaks down for complex pattern formation models such as the Gray-Scott system.
Cooperative Abnormal Node Detection with Adversary Resistance: A Probabilistic Approach

This paper presents a novel probabilistic detection scheme called Cooperative Statistical Detection (CSD) for abnormal node detection while defending against adversarial attacks in cluster-tree networks. The CSD performs a two-phase process: 1) designing a likelihood ratio test (LRT) for a non-root node at its children from the perspective of packet loss; 2) making an overall decision at the root node based on the aggregated detection data of the nodes over tree branches. In most adversarial scenarios, malicious children knowing the detection policy can generate falsified data to protect the abnormal parent from being detected or frame its normal parent as an anomalous node. To resolve this issue, a modified Z-score-based falsification-resistant mechanism is presented in the CSD to remove untrustworthy information. Through theoretical analysis, we show that the LRT-based method achieves perfect detection, i.e., both the false alarm and missed detection probabilities decay exponentially to zero. Furthermore, the optimal removal threshold of the modified Z-score method is derived for falsifications with uncertain strategies and guarantees perfect detection of the CSD. As our simulation results show, the CSD approach is robust to falsifications and can rapidly reach $99\%$ detection accuracy, even in existing adversarial scenarios, which outperforms state-of-the-art technology.
A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections

Unimodality, pivotal in statistical analysis, offers insights into dataset structures and drives sophisticated analytical procedures. While unimodality's confirmation is straightforward for one-dimensional data using methods like Silverman's approach and Hartigans' dip statistic, its generalization to higher dimensions remains challenging. By extrapolating one-dimensional unimodality principles to multi-dimensional spaces through linear random projections and leveraging point-to-point distancing, our method, rooted in $\alpha$-unimodality assumptions, presents a novel multivariate unimodality test named mud-pod. Both theoretical and empirical studies confirm the efficacy of our method in unimodality assessment of multidimensional datasets as well as in estimating the number of clusters.
REDD+ in indigenous territories in Latin America

The demarcation and titling of indigenous territories in Latin America has constituted the most significant shift in the land ownership structure of the region since the agrarian reforms across the continent in the mid-20th century. Today, 375 indigenous groups hold 25.3% of the Amazon. Combined with protected areas – many of which are also home to indigenous people – these areas comprise a total of 41.2% of the total area (Benavides 2009; see Table 1). Indigenous people formally own or manage a total of 160 million hectares in Latin America. That most of these areas have very low population densities represents an additional challenge for REDD+. Table 1. Indigenous territories in the Amazon
Mirkovi\'c-Vilonen Polytopes from Combinatorics

Mirkovi\'c-Vilonen (MV) polytopes are a class of generalized permutahedra originating from geometric representation theory. In this paper we study MV polytopes coming from matroid polytopes, flag matroid polytopes, Bruhat interval polytopes, and Schubitopes. We give classifications and combinatorial conditions for when these polytopes are MV polytopes. We also describe how the crystal structure on MV polytopes manifests combinatorially in these situations. As a special case, we show that the Newton polytopes of Schubert polynomials and key polynomials are MV polytopes.
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent

This paper proposes a novel approach to adaptive step sizes in stochastic gradient descent (SGD) by utilizing quantities that we have identified as numerically traceable -- the Lipschitz constant for gradients and a concept of the local variance in search directions. Our findings yield a nearly hyperparameter-free algorithm for stochastic optimization, which has provable convergence properties when applied to quadratic problems and exhibits truly problem adaptive behavior on classical image classification tasks. Our framework enables the potential inclusion of a preconditioner, thereby enabling the implementation of adaptive step sizes for stochastic second-order optimization methods.
Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement

In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion. This task presents significant challenges due to three factors: the lack of high-quality datasets, fisheye camera distortion, and human body self-occlusion. To address these challenges, we propose a novel approach that leverages FisheyeViT to extract fisheye image features, which are subsequently converted into pixel-aligned 3D heatmap representations for 3D human body pose prediction. For hand tracking, we incorporate dedicated hand detection and hand pose estimation networks for regressing 3D hand poses. Finally, we develop a diffusion-based whole-body motion prior model to refine the estimated whole-body motion while accounting for joint uncertainties. To train these networks, we collect a large synthetic dataset, EgoWholeBody, comprising 840,000 high-quality egocentric images captured across a diverse range of whole-body motion sequences. Quantitative and qualitative evaluations demonstrate the effectiveness of our method in producing high-quality whole-body motion estimates from a single egocentric camera.
Revealing novel aspects of light-matter coupling in terahertz two-dimensional coherent spectroscopy: the case of the amplitude mode in superconductors

Recently developed terahertz (THz) two-dimensional coherent spectroscopy (2DCS) is a powerful technique to obtain materials information in a fashion qualitatively different from other spectroscopies. Here, we utilized THz 2DCS to investigate the THz nonlinear response of conventional superconductor NbN. Using broad-band THz pulses as light sources, we observed a third-order nonlinear signal whose spectral components are peaked at twice the superconducting gap energy $2\Delta$. With narrow-band THz pulses, a THz nonlinear signal was identified at the driving frequency $\Omega$ and exhibited a resonant enhancement at temperature when $\Omega = 2\Delta$. General theoretical considerations show that such a resonance can only arise from a disorder-activated paramagnetic coupling between the light and the electronic current. This proves that the nonlinear THz response can access processes distinct from the diamagnetic Raman-like density fluctuations, which are believed to dominate the nonlinear response at optical frequencies in metals. Our numerical simulations reveal that even for a small amount of disorder, the $\Omega=2\Delta$ resonance is dominated by the superconducting amplitude mode over the entire investigated disorder range. This is in contrast to other resonances, whose amplitude-mode contribution depends on disorder. Our findings demonstrate the unique ability of THz 2DCS to explore collective excitations inaccessible in other spectroscopies.
Symplectic higher Auslander correspondence for type A

We prove that the Fukaya-Seidel categories of a certain family of singularities on $\mathbb{C}^d$ are equivalent to the perfect derived categories of higher Auslander algebras of Dynkin type A. We relate these to the Fukaya-Seidel categories of Brieskorn-Pham singularities and to the partially wrapped Fukaya categories considered by Dyckerhoff-Jasso-Lekili. We provide a symplectic interpretation to higher Auslander correspondence of type A in terms of Fukaya-Seidel categories of Lefschetz fibrations.
Manipulating the Stacking in Two-Dimensional Hexagonal Boron Nitride Bilayers: Implications for Defect-Based Single Photon Emitters

Van der Waals heterostructures are promising for adding new functionalities to two-dimensional materials. In this study, we focus on single photon emitters hosted in one layer and adjacent to other insulating two-dimensional material. Specifically, we show how the emission energy is modified by such a heterostructure. We developed a general approach to elucidate the mechanisms affecting the emission energy and studied the particular case of carbon substitutions in the hexagonal boron nitride bilayer.
Power law creep and delayed failure of gels and fibrous materials under stress

Motivated by recent experiments studying the creep and breakup of a protein gel under stress, we introduce a simple mesoscopic model for the irreversible failure of gels and fibrous materials, and demonstrate it to capture much of the phenomenology seen experimentally. This includes a primary creep regime in which the shear rate decreases as a power law over several decades of time, a secondary crossover regime in which the shear rate attains a minimum, and a tertiary regime in which the shear rate increases dramatically up to a finite time singularity, signifying irreversible material failure. The model also captures a linear Monkman–Grant scaling of the failure time with the earlier time at which the shear rate attained its minimum, and a Basquin-like power law scaling of the failure time with imposed stress, as seen experimentally. The model furthermore predicts a slow accumulation of low levels of material damage during primary creep, followed by the growth of fractures leading to sudden material failure, as seen experimentally.
Above-threshold ionization by polarization-crafted pulses

Coherent light has revolutionized scientific research, spanning biology, chemistry, and physics. To delve into ultrafast phenomena, the development of high-energy, high-tunable light sources is instrumental. Here, the photo-electric effect is a pivotal tool for dissecting electron correlations and system structures. Particularly, above-threshold ionization (ATI), characterized by simultaneous multi-photon absorption, has been widely explored, both theoretical and experimentally. ATI decouples laser field effects from the structural information carried by photo-electrons, particularly when utilizing ultra-short pulses. In this contribution we study ATI driven by polarization-crafted (PC) pulses, which offer precise control over the electron emission directions, through an accurate change of the polarization state. PC pulses enable the manipulation of electron trajectories, opening up new avenues for understanding and harnessing coherent light. Our work explores how structured light could allow a high degree of control of the emitted photo-electrons.
New definitions in the theory of Type 1 computable topological spaces

Motivated by the two remarks, that the study of computability based on the use of numberings -- Type 1 computability -- does not have to be restricted to countable sets equipped with onto numberings, and that computable topologies have been in part developed with the implicit hypothesis that the considered spaces should be computably separable, we propose new definitions for Type 1 computable topological spaces. We define computable topological spaces without making reference to a basis. Following Spreen, we show that the use of a formal inclusion relation should be systematized, and that it cannot be avoided if we want computable topological spaces to generalize computable metric spaces. We also compare different notions of effective bases. The first one, introduced by Nogina, is based on an effective version of the statement"a set $O$ is open if for any point in $O$, there is a basic set containing that point and contained in $O$''. The second one, associated to Lacombe, is based on an effective version of"a set $O$ is open if it can be written as a union of basic open sets''. We show that neither of these notions of basis is completely satisfactory: Nogina bases do not permit to define computable topologies unless we restrict our attention to countable sets, and the conditions associated to Lacombe bases are too restrictive, and they do not apply to metric spaces unless we add effective separability hypotheses. We define a new notion of basis, based on an effective version of the Nogina statement, but adding to it several classically empty conditions, expressed in terms of formal inclusion relations. Finally, we obtain a new version of the theorem of Moschovakis which states that the Lacombe and Nogina approaches coincide on countable recursive Polish spaces, but which applies to sets equipped with non-onto numberings, and with effective separability as a sole hypothesis.
Spectral Multiplexing of Rare‐Earth Emitters in a Co‐Doped Crystalline Membrane

The spectral addressing of many individual rare‐earth dopants in optical resonators offers great potential for realizing distributed quantum information processors. To this end, it is required to understand and control the spectral properties of the emitters in micron‐scale devices. Here, erbium emitters are investigated in a Fabry–Perot resonator that contains a 10 µm thin membrane of crystalline yttrium orthosilicate that is co‐doped with europium. The co‐doping allows for tailoring the inhomogeneous distribution of the emitter frequency. With this approach, more than 360 spectrally resolved emitters are observed with Purcell factors exceeding 35, each of which constitutes an individually addressable qubit within the micron‐scale resonator. In addition to this spectral multiplexing, the optical coherence is preserved up to 0.62(3) ms under dynamical decoupling. Without decoupling, the coherence still reaches the lifetime limit for the emitters with the strongest Purcell enhancement that leads up to a 110‐fold lifetime reduction, down to 0.104(9) ms. Future work may combine this with long‐lived nuclear spin memories, which makes the investigated co‐doped membranes a promising platform for quantum repeaters and distributed quantum computers.
The Transformative Influence of Large Language Models on Software Development

The increasing adoption and commercialization of generalized Large Language Models (LLMs) have profoundly impacted various aspects of our daily lives. Initially embraced by the computer science community, the versatility of LLMs has found its way into diverse domains. In particular, the software engineering realm has witnessed the most transformative changes. With LLMs increasingly serving as AI Pair Programming Assistants spurred the development of specialized models aimed at aiding software engineers. Although this new paradigm offers numerous advantages, it also presents critical challenges and open problems. To identify the potential and prevailing obstacles, we systematically reviewed contemporary scholarly publications, emphasizing the perspectives of software developers and usability concerns. Preliminary findings underscore pressing concerns about data privacy, bias, and misinformation. Additionally, we identified several usability challenges, including prompt engineering, increased cognitive demands, and mistrust. Finally, we introduce 12 open problems that we have identified through our survey, covering these various domains.