Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of the Quantum Coherence in a Hierarchical Environment System by Weak Measurement and Weak Measurement Reversal

The quantum coherence of a two‐qubit system in which each qubit is coupled to its own hierarchical environment is studied. The effect of the hierarchical environment is explored in order to improve the coherence effectively. It is discovered that the dynamics of coherence can be manipulated by the number N of the second‐layer cavities ( mn$m_{n}$ ), the coupling strengths Ω0, Ω, κ, respectively for the qubit‐cavity (m0), nearest‐neighbor, and m0‐ mn$m_{n}$ cavities. It depends also on the decay rate Γ of second‐layer cavities. Furthermore, an effective scheme for enhancing coherence is proposed by using weak measurement and weak measurement reversal. The explicit conditions of the optimal measurement strengths to improve the coherence comparatively are derived. It is seen that the final stable value of the coherence is independent of the number N of second‐layer cavities but is related to the weak measurement strength m when the coupling strength between two nearest‐neighbor cavities is taken into account.
Population Oscillations in Excited Argon Atoms Initiated by Attosecond Pulses

This project aims to study the process of electron excitation by a light pulse, a process at the core of many important contemporary and possible future devices. Data from an experiment where the electron dynamics is probed with attosecond laser pulses are analyzed in order to gain insight on the rapid population oscillations within the excited atomic system and simulations are constructed with the goal of mimicking structures found in the experimental data. The results imply that the effect of a third state, which is reached by interaction with an incident pulse, on the population oscillations might explain the experimentally observed structures. Some control might be possible to exempt on the composition of the superposition state of the electron, by varying the time when the pulse is incident on the atom. (Less)
Electromechanical modeling of anisotropic cardiac tissues

We model cardiac muscle contractions in the framework of finite elasticity with large distortions and couple a mechanical model with reaction–diffusion equations representing electrophysiological activity. Both models are implemented using anisotropic constitutive relations: we use stress–strain relations for fiber-reinforced materials, and anisotropic diffusion tensors for both the membrane potential and calcium ions. The effects of these choices on the electromechanical behavior are presented and discussed.
Analytic hierarchy model of the selection of an optimal organization variant

In this paper is given a model of selecting an optimal organization variant alternative by using the analytic hierarchy process. The problem is described, its hierarchical structure is recognized, and the theoretical foundation of the method is presented. The proposed model is illustrated by a numerical example which is considered from a practical viewpoint. Further research directions in this field are outlined.
Crystallographic and biophysical analysis of the fusion core from
 SARS‐CoV‐2
 spike protein

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a newly emerging infectious pathogen causing coronavirus disease 2019 (COVID-19). The virus primarily infects cells via its spike glycoprotein, which is cleaved into S1 and S2 subunits to aid in cell attachment and membrane fusion, respectively. Heptad repeat 1 (HR1) and heptad repeat 2 (HR2) of the S2 subunit are essential for membrane fusion, culminating in an expected six-helix bundle termed fusion core. To better understand the structural and biophysical features of the SARS-CoV-2 fusion core, we designed, constructed, and bacterially produced a recombinant single-chain HR1-L6-HR2 protein and conducted a series of biochemical and biophysical experiments. Our findings demonstrate that the HR1-L6-HR2 protein spontaneously assembles into a highly stable trimeric complex, further confirmed by x-ray crystallographic analysis. The crystal structure of the fusion core reveals a trimeric coiled-coil structure of HR1 antiparallelly surrounded by three HR2 to form a six-helical bundle. Additionally, four residues of HR1 that contribute to binding with HR2 through the formation of hydrogen bonds and salt bridges were observed. These results indicate that the SARS-CoV-2 fusion core exhibits similar characteristics to other class I viral glycoproteins, suggesting potential for drug repurposing as an alternative strategy to combat COVID-19. © 2023 Chemical Society Located in Taipei and Wiley-VCH GmbH.
Regression for randomly sampled spatial series: the trigonometric case

The model Y(t) = s(t | θ) + ε(t) is studied in the case that observations are made at scattered points τ j in a subset of Rp and θ is a finite-dimensional parameter. The particular cases of 0 = (α, β) and (α, β, ω) are considered in detail. Consistency and asymptotic normality results are developed assuming that the spatial series ε(·) and the point process {τ j } are independent, stationary and mixing. The estimates considered are equivalent to least squares asymptotically and are not generally asymptotically efficient. Contributions of the paper include: study of the Rp case, management of irregularly placed observations, allowance for abnormal domains of observation and the discovery that aliasing complications do not arise when the point process {τ j } is mixing. There is a brief discussion of the construction and properties of maximum likelihood estimates for the spatial-temporal case.
Making educational videos more engaging and enjoyable for all ages: an exploratory study on the influence of embedded questions

ABSTRACT There is Individual variation in how people interact with videos presented in online distance education. Educational videos can be embedded with interactive content to increase engagement and make cognition more efficient. Accordingly, we predicted that embedding questions during videos (rather than after) would enhance the performance of question-answering and be preferred by students. We also hypothesised that the benefits of presenting questions during videos might increase with age. Using a counter-balanced within-subject design, each participant watched short videos with questions embedded either during the video or presented after the video, and we then surveyed their experiences. Although there were no differences in correct responses, participants answered questions posed during videos more efficiently than questions presented after. Females enjoyed questions during videos more than males. Younger individuals (e.g. 25–34) seemed to benefit more from questions during videos than slightly older students (35–44). Interestingly, with increasing age (from 25 to 74), there was a shift in preference towards answering questions after, rather than during, videos. Overall, embedding questions was an effective and well-liked method for enhancing the interactivity of module-related videos. The age of students should be considered when embedding questions.
The minimum spanning tree and duality in graphs

Several algorithms for the minimum spanning tree are known. The Blue-red algorithm is a generic algorithm in this field. A new proof for this algorithm is presented, based upon the duality of circuits and cuts in a graph. The Blue-red algorithm is genetic, because the other algorithms can be regarded as special instances. This is shown using the same duality.
Minimum-time feedforward technique for PID control

This study deals with the determination of the (reference) command input to be applied to a closed-loop control system in order to achieve a minimum-time output transition subject to constraints on both the control variable and the system output. In particular, the authors consider the important practical case of a process described by a first-order-plus-dead-time transfer function with a proportional-integral-derivative feedback controller. The optimal command input is determined by suitably approximating the state variables and the input signal by means of Chebyshev series and by subsequently solving a constrained optimisation problem. Simulation as well as experimental results demonstrate the effectiveness of the methodology.
Improving Synthetically Generated Image Detection in Cross-Concept Settings

New advancements for the detection of synthetic images are critical for fighting disinformation, as the capabilities of generative AI models continuously evolve and can lead to hyper-realistic synthetic imagery at unprecedented scale and speed. In this paper, we focus on the challenge of generalizing across different concept classes, e.g., when training a detector on human faces and testing on synthetic animal images – highlighting the ineffectiveness of existing approaches that randomly sample generated images to train their models. By contrast, we propose an approach based on the premise that the robustness of the detector can be enhanced by training it on realistic synthetic images that are selected based on their quality scores according to a probabilistic quality estimation model. We demonstrate the effectiveness of the proposed approach by conducting experiments with generated images from two seminal architectures, StyleGAN2 and Latent Diffusion, and using three different concepts for each, so as to measure the cross-concept generalization ability. Our results show that our quality-based sampling method leads to higher detection performance for nearly all concepts, improving the overall effectiveness of the synthetic image detectors.
Effective Brain Segmentation Method based on MR Physics Image Processing

Object recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on Susceptibility Weighted Imaging (SWI) within the magnetic resonance (MR) theory. When we do pre-processing, proposed method was composed of SWI process. And then we do the Gray-white matter segmentation by Active Contour Model(ACM) in post-processing. In this study, the experiment had been conducted using images including the brain region and by getting up contrast enhancement image of SWI for segmentation to extract region (white matter) segmentation even when the border line was not clear. As a result, an average area difference of 8.2%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.
Coevolution of Camouflage

Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, and prey must avoid being found. This work simulates an abstract model of that adversarial relationship. It looks at crypsis through evolving prey camouflage patterns (as color textures) in competition with evolving predator vision. During their"lifetime"predators learn to better locate camouflaged prey. The environment for this 2D simulation is provided by a set of photographs, typically of natural scenes. This model is based on two evolving populations, one of prey and another of predators. Mutual conflict between these populations can produce both effective prey camouflage and predators skilled at"breaking"camouflage. The result is an open source artificial life model to help study camouflage in nature, and the perceptual phenomenon of camouflage more generally.
An $O(n^2\log^4 n \log \log n)$ Time Matrix Multiplication Algorithm

We show, for the input vectors $(a_0, a_1, ., a_{n-1})$ and $(b_0, b_1, ., b_{n-1})$, where $a_i$'s and $b_j$'s are real numbers, after $O(n\log^4 n)$ time preprocessing for each of them, the vector multiplication $(a_0, a_1, ., a_{n-1})(b_0, b_1, ., b_{n-1})^T $ can be computed in $O(\log^4 n \log \log n)$ time. This enables the matrix multiplication for two $n\times n$ matrices to be computed in $O(n^2 \log^4 n\log \log n)$ time.
Faster estimation of the Knorr-Held Type IV space-time model (preprint)

In this paper we study the type IV Knorr Held space time models. Such models typically apply intrinsic Markov random fields and constraints are imposed for identifiability. INLA is an efficient inference tool for such models where constraints are dealt with through a conditioning by kriging approach. When the number of spatial and/or temporal time points become large, it becomes computationally expensive to fit such models, partly due to the number of constraints involved. We propose a new approach, HyMiK, dividing constraints into two separate sets where one part is treated through a mixed effect approach while the other one is approached by the standard conditioning by kriging method, resulting in a more efficient procedure for dealing with constraints. The new approach is easy to apply based on existing implementations of INLA. We run the model on simulated data, on a real data set containing dengue fever cases in Brazil and another real data set of confirmed positive test cases of Covid-19 in the counties of Norway. For all cases we get very similar results when comparing the new approach with the tradition one while at the same time obtaining a significant increase in computational speed, varying on a factor from 3 to 23, depending on the sizes of the data sets.
Uniform convergence of finite element method on Bakhvalov-type mesh for a 2-D singularly perturbed convection-diffusion problem with exponential layers

On Bakhvalov-type mesh, uniform convergence analysis of finite element method for a 2-D singularly perturbed convection-diffusion problem with exponential layers is still an open problem. Previous attempts have been unsuccessful. The primary challenges are the width of the mesh subdomain in the layer adjacent to the transition point, the restriction of the Dirichlet boundary condition, and the structure of exponential layers. To address these challenges, a novel analysis technique is introduced for the first time, which takes full advantage of the characteristics of interpolation and the connection between the smooth function and the layer function on the boundary. Utilizing this technique in conjunction with a new interpolation featuring a simple structure, uniform convergence of optimal order k+1 under an energy norm can be proven for finite element method of any order k. Numerical experiments confirm our theoretical results.
Controlled illumination for perception and manipulation of Lambertian objects

Controlling illumination can generate high quality information about object surface normals and depth discontinuities at a low computational cost. In this work we demonstrate a robot workspace-scaled controlled illumination approach that generates high quality information for table top scale objects for robotic manipulation. With our low angle of incidence directional illumination approach we can precisely capture surface normals and depth discontinuities of Lambertian objects. We demonstrate three use cases of our approach for robotic manipulation. We show that 1) by using the captured information we can perform general purpose grasping with a single point vacuum gripper, 2) we can visually measure the deformation of known objects, and 3) we can estimate pose of known objects and track unknown objects in the robot's workspace. Additional demonstrations of the results presented in the work can be viewed on the project webpage https://anonymousprojectsite.github. io/.
SimplyMime: A Control at Our Fingertips

The utilization of consumer electronics, such as televisions, set-top boxes, home theaters, and air conditioners, has become increasingly prevalent in modern society as technology continues to evolve. As new devices enter our homes each year, the accumulation of multiple infrared remote controls to operate them not only results in a waste of energy and resources, but also creates a cumbersome and cluttered environment for the user. This paper presents a novel system, named SimplyMime, which aims to eliminate the need for multiple remote controls for consumer electronics and provide the user with intuitive control without the need for additional devices. SimplyMime leverages a dynamic hand gesture recognition architecture, incorporating Artificial Intelligence and Human-Computer Interaction, to create a sophisticated system that enables users to interact with a vast majority of consumer electronics with ease. Additionally, SimplyMime has a security aspect where it can verify and authenticate the user utilising the palmprint, which ensures that only authorized users can control the devices. The performance of the proposed method for detecting and recognizing gestures in a stream of motion was thoroughly tested and validated using multiple benchmark datasets, resulting in commendable accuracy levels. One of the distinct advantages of the proposed method is its minimal computational power requirements, making it highly adaptable and reliable in a wide range of circumstances. The paper proposes incorporating this technology into all consumer electronic devices that currently require a secondary remote for operation, thus promoting a more efficient and sustainable living environment.
StyLess: Boosting the Transferability of Adversarial Examples

Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible perturbations to benign examples. The attack transferability enables adversarial examples to attack blackbox DNNs with unknown architectures or parameters, which poses threats to many realworld applications. We find that existing transferable attacks do not distinguish between style and content features during optimization, limiting their attack transferability. To improve attack transferability, we propose a novel attack method called style-less perturbation (StyLess). Specifically, instead of using a vanilla network as the surrogate model, we advocate using stylized networks, which encode different style features by perturbing an adaptive instance normalization. Our method can prevent adversarial examples from using non-robust style features and help generate transferable perturbations. Comprehensive experiments show that our method can significantly improve the transferability of adversarial examples. Furthermore, our approach is generic and can outperform state-of-the-art transferable attacks when combined with other attack techniques.11Our code is available at https://github.com/uhiu/StyLess
Regional photochemical model calculations for Europe concerning ozone levels in a changing climate

Regional photochemical model calculations were performed for three decadal time periods: 1991-2000, 2041-2050, and 2091-2100 under changing climate conditions for Europe. While the climatic conditions changed between the decades, all other input fields of the chemical model were held constant in order to separate climate effects from others. In particular, model results for summer ozone concentrations were investigated. These show increasing ozone values in response to higher temperatures and net radiation associated with increases in anthropogenic greenhouse gases. The increase of ozone is stronger in the second half of the 21st century than in the first half. In the course of the whole century, the average summer ozone maxima will rise by several ppbv over most parts of Europe with the highest change in the northwest of the continent. This increase suggests more frequent concentrations above legal thresholds in the future and is partly caused by higher emissions of biogenic hydrocarbons in the model calculation.
Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation

Translated texts bear several hallmarks distinct from texts originating in the language (“translationese”). Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language and can affect model performance. We frame the novel task of translationese reduction and hypothesize that Abstract Meaning Representation (AMR), a graph-based semantic representation which abstracts away from the surface form, can be used as an interlingua to reduce the amount of translationese in translated texts. By parsing English translations into an AMR and then generating text from that AMR, the result more closely resembles originally English text across three quantitative macro-level measures, without severely compromising fluency or adequacy. We compare our AMR-based approach against three other techniques based on machine translation or paraphrase generation. This work represents the first approach to reducing translationese in text and highlights the promise of AMR, given that our AMR-based approach outperforms more computationally intensive methods.
Simulation of Supersonic Jet Noise with Nonlinear CFD and Kirchhoff Surface Integral

Abstract An acoustic prediction capability for supersonic axisymmetric jets was developed on the basis of the OVERFLOW Navier-Stokes CFD (Computational Fluid Dynamics) code. Reynolds-averaged turbulent stresses in the flow field are modeled with the aid of Spalart-Allmaras one-equation turbulence model. Appropriate acoustic and outflow boundary conditions were implemented to compute time-dependent acoustic pressure in the nonlinear source-field. Based on the specification of acoustic pressure, its temporal and normal derivatives on the Kirchhoff surface, the near-field and the far-field sound pressure levels are computed via Kirchhoff surface integral, with the Kirchhoff surface chosen to enclose the nonlinear sound source region described by the CFD code. The method is validated by a comparison of the predictions of sound pressure levels with the available data for axisymmetric turbulent supersonic perfectly expanded jets.
Testing growth rate dependence in cosmological perturbation theory using scale-free models

We generalize previously derived analytic results for the one-loop power spectrum (PS) in scale-free models (with linear PS $P(k) \propto k^n$) to a broader class of such models in which part of the matterlike component driving the Einstein de Sitter expansion does not cluster. These models can be conveniently parametrized by $\alpha$, the constant logarithmic linear growth rate of fluctuations (with $\alpha=1$ in the usual case). For $-3<n<-1$, where the one-loop PS is both infrared and ultraviolet convergent and thus explicitly self-similar, it is characterized conveniently by a single numerical coefficient $c(n, \alpha)$. We compare the analytical predictions for $c(n=-2, \alpha)$ with results from a suite of $N$-body simulations with $\alpha \in [0.25, 1]$ performed with an appropriately modified version of the GADGET code. Although the simulations are of small ($256^3$) boxes, the constraint of self-similarity allows the identification of the converged PS at a level of accuracy sufficient to test the analytical predictions for the $\alpha$ dependence of the evolved PS. Good agreement for the predicted dependence on $\alpha$ of the PS is found. To treat the UV sensitivity of results which grows as one approaches $n =-1$, we derive exact results incorporating a regularization $k_c$ and obtain expressions for $c(n, \alpha, k_c/k)$. Assuming that this regularization is compatible with self-similarity allows us to infer a predicted functional form of the PS equivalent to that derived in effective field theory (EFT). The coefficient of the leading EFT correction at one loop has a strong dependence on $\alpha$, with a change in sign at $\alpha \approx 0.16$, providing a potentially stringent test of EFT.