Miracle-dz/UniFAR-FTUs huggingface dataset:
Age-Invariant Face Embedding using the Wasserstein Distance

In this work, we study face verification in datasets where images of the same individuals exhibit significant age differences. This poses a major challenge for current face recognition and verification techniques. To address this issue, we propose a novel approach that utilizes multitask learning and a Wasserstein distance discriminator to disentangle age and identity embeddings of facial images. Our approach employs multitask learning with a Wasserstein distance discriminator that minimizes the mutual information between the age and identity embeddings by minimizing the Jensen-Shannon divergence. This improves the encoding of age and identity information in face images and enhances the performance of face verification in age-variant datasets. We evaluate the effectiveness of our approach using multiple age-variant face datasets and demonstrate its superiority over state-of-the-art methods in terms of face verification accuracy.
Personalized Abstractive Summarization by Tri-agent Generation Pipeline

Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we propose a tri-agent generation pipeline comprising a generator, an instructor, and an editor to enhance output personalization. The generator produces an initial output, the instructor automatically generates editing instructions based on user preferences, and the editor refines the output to align with those preferences. The inference-only large language model (ChatGPT) serves as both the generator and editor, with a smaller model acting as the instructor to guide output generation. We train the instructor using editor-steered reinforcement learning, leveraging feedback from a large-scale editor model to optimize instruction generation. Experimental results on two abstractive summarization datasets demonstrate the effectiveness of our approach in generating outputs that better meet user expectations.
Translation invariant diagonal frame decomposition for the Radon transform

In this article, we address the challenge of solving the ill-posed reconstruction problem in computed tomography using a translation invariant diagonal frame decomposition (TI-DFD). First, we review the concept of a TI-DFD for general linear operators and the corresponding filter-based regularization. We then introduce the TI-DFD for the Radon transform on ${L^2}\left( {{\mathbb{R}^2}} \right)$ and provide an exemplary construction using the TI wavelet transform. Presented numerical results clearly demonstrate the benefits of our approach over non-translation invariant counterparts.
Hybrid quantum learning with data reuploading on a small-scale superconducting quantum simulator

Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning. Here, we study experimentally a hybrid classifier model accelerated by a quantum simulator - a linear array of four superconducting transmon artificial atoms - trained to solve multilabel classification and image recognition problems. We train a quantum circuit on simple binary and multi-label tasks, achieving classification accuracy around 95%, and a hybrid model with data re-uploading with accuracy around 90% when recognizing handwritten decimal digits. Finally, we analyze the inference time in experimental conditions and compare the performance of the studied quantum model with known classical solutions.
Universal sublinear resistivity in vanadium kagome materials hosting charge density waves

The recent discovery of a charge density (CDW) state in ScV$_6$Sn$_6$ at $T_{\textrm{CDW}}$ = 91 K offers new opportunities to understand the origins of electronic instabilities in topological kagome systems. By comparing to the isostructural non-CDW compound LuV$_6$Sn$_6$, we unravel interesting electrical transport properties in ScV$_6$Sn$_6$, above and below the charge ordering temperature. We observed that by applying a magnetic field along the $a$ axis, the temperature behavior of the longitudinal resistivity in ScV$_6$Sn$_6$ changes from metal-like to insulator-like above the CDW transition. We show that in the charge ordered state ScV$_6$Sn$_6$ follows the Fermi liquid behavior while above that, it transforms into a non-Fermi liquid phase in which the resistivity varies sublinearly over a broad temperature range. The sublinear resistivity, which scales by $T^{3/5}$ is a common feature among other vanadium-containing kagome compounds exhibiting CDW states such as KV$_3$Sb$_5$, RbV$_3$Sb$_5$, and CsV$_3$Sb$_5$. By contrast, the non-Fermi liquid behavior does not occur in LuV$_6$Sn$_6$. We explain the $T^{3/5}$ universal scaling behavior from the Coulomb scattering between Dirac electrons and Van Hove singularities; common features in the electronic structure of kagome materials. Finally, we show anomalous Hall-like behavior in ScV$_6$Sn$_6$ below $T_{\textrm{CDW}}$, which is absent in the Lu compound. Comparing the transport properties of ScV$_6$Sn$_6$ and LuV$_6$Sn$_6$ is valuable to highlight the impacts of the unusual CDW in the Sc compound.
Testing for no effect in regression problems: a permutation approach

Often the question arises whether $Y$ can be predicted based on $X$ using a certain model. Especially for highly flexible models such as neural networks one may ask whether a seemingly good prediction is actually better than fitting pure noise or whether it has to be attributed to the flexibility of the model. This paper proposes a rigorous permutation test to assess whether the prediction is better than the prediction of pure noise. The test avoids any sample splitting and is based instead on generating new pairings of $(X_i, Y_j)$. It introduces a new formulation of the null hypothesis and rigorous justification for the test, which distinguishes it from previous literature. The theoretical findings are applied both to simulated data and to sensor data of tennis serves in an experimental context. The simulation study underscores how the available information affects the test. It shows that the less informative the predictors, the lower the probability of rejecting the null hypothesis of fitting pure noise and emphasizes that detecting weaker dependence between variables requires a sufficient sample size.
Designing a realistic peer-like embodied conversational agent for supporting children\textquotesingle s storytelling

Advances in artificial intelligence have facilitated the use of large language models (LLMs) and AI-generated synthetic media in education, which may inspire HCI researchers to develop technologies, in particular, embodied conversational agents (ECAs) to simulate the kind of scaffolding children might receive from a human partner. In this paper, we will propose a design prototype of a peer-like ECA named STARie that integrates multiple AI models - GPT-3, Speech Synthesis (Real-time Voice Cloning), VOCA (Voice Operated Character Animation), and FLAME (Faces Learned with an Articulated Model and Expressions) that aims to support narrative production in collaborative storytelling, specifically for children aged 4-8. However, designing a child-centered ECA raises concerns about age appropriateness, children privacy, gender choices of ECAs, and the uncanny valley effect. Thus, this paper will also discuss considerations and ethical concerns that must be taken into account when designing such an ECA. This proposal offers insights into the potential use of AI-generated synthetic media in child-centered AI design and how peer-like AI embodiment may support children\textquotesingle s storytelling.
Identification of structural parameters using consistent mass transfer matrix

Abstract The stiffness parameters of structural members are identified by a novel inverse scheme based on consistent mass transfer matrix (TM), and the results are experimentally verified. The parameters are identified from the measurement of the state vector at a starting point, and displacements at few other locations. A new consistent mass-based TM is derived from the dynamic stiffness matrix of a beam element. The state vector at a location is the sum of the internal and external contributions of displacements, forces and moments at that point. When these are multiplied with the TM we obtain the state vector at the adjacent node. The identification algorithm proposed in this inverse problem involves predicting the displacements at certain locations using the TM, and comparing them with the measured displacements at those locations. The mean square deviations between the measured and predicted responses at all locations are minimized using an optimization algorithm, and the optimization variables are the unknown stiffness parameters in the TM. A non-classical heuristic particle swarm optimization algorithm (PSO) is used, since it is especially suited for global search. This TM method is particularly suited for local identification in large structures. Numerical simulations are carried out on two examples such as a cantilever, and a substructure of a nine-member frame structure. The method is also experimentally validated on a cantilever and substructure of a fixed beam. The speed and accuracy of the method are compared with existing structural identification methods, and advantages on the above are observed.
Two-fluid numerical simulations of turbulence inside Kelvin-Helmholtz vortices: Intermittency and reconnecting current sheets

The turbulence developing inside Kelvin-Helmholtz vortices has been studied using a two-fluid numerical simulation. From an initial large-scale velocity shear, the nonlinear evolution of the instability leads to the formation of a region inside the initial vortex characterized by small-scale fluctuations and structures. The magnetic energy spectrum is compatible with a Kolmogorov-like power-law decay, followed by a steeper power-law below proton scales, in agreement with other studies. The magnetic field increments show non-Gaussian distributions with increasing tails going towards smaller scales, consistent with presence of intermittency. The strong magnetic fieldfluctuations populating the tails of the distributions have been identified as current sheets by using the Partial Variance of the Increments (PVI) method. The strongest current sheets (largest PVI) appear around proton scales and below. By selecting several of such current sheets, it has been found that most of them are consistent with ongoing magnetic reconnection. The detailed study of one reconnecting current sheet as crossed by a virtual spacecraft is also presented. Inflow and outflow regions have been identified and the reconnection rate has been estimated. The observation of reconnection rates higher than typical fast rate suggests that reconnection in turbulentplasma can be faster than laminar reconnection. This study indicates that intermittency and reconnecting current sheets are important ingredients of turbulence within Kelvin-Helmholtz vortices and that reconnection can play an important role for energy dissipation therein. ∼0.1
Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study

Background The outbreak of the COVID-19 pandemic had a major effect on the consumption of health care services. Changes in the use of routine diagnostic exams, increased incidences of postacute COVID-19 syndrome (PCS), and other pandemic-related factors may have influenced detected clinical conditions. Objective This study aimed to analyze the impact of COVID-19 on the use of outpatient medical imaging services and clinical findings therein, specifically focusing on the time period after the launch of the Israeli COVID-19 vaccination campaign. In addition, the study tested whether the observed gains in abnormal findings may be linked to PCS or COVID-19 vaccination. Methods Our data set included 572,480 ambulatory medical imaging patients in a national health organization from January 1, 2019, to August 31, 2021. We compared different measures of medical imaging utilization and clinical findings therein before and after the surge of the pandemic to identify significant changes. We also inspected the changes in the rate of abnormal findings during the pandemic after adjusting for changes in medical imaging utilization. Finally, for imaging classes that showed increased rates of abnormal findings, we measured the causal associations between SARS-CoV-2 infection, COVID-19–related hospitalization (indicative of COVID-19 complications), and COVID-19 vaccination and future risk for abnormal findings. To adjust for a multitude of confounding factors, we used causal inference methodologies. Results After the initial drop in the utilization of routine medical imaging due to the first COVID-19 wave, the number of these exams has increased but with lower proportions of older patients, patients with comorbidities, women, and vaccine-hesitant patients. Furthermore, we observed significant gains in the rate of abnormal findings, specifically in musculoskeletal magnetic resonance (MR-MSK) and brain computed tomography (CT-brain) exams. These results also persisted after adjusting for the changes in medical imaging utilization. Demonstrated causal associations included the following: SARS-CoV-2 infection increasing the risk for an abnormal finding in a CT-brain exam (odds ratio [OR] 1.4, 95% CI 1.1-1.7) and COVID-19–related hospitalization increasing the risk for abnormal findings in an MR-MSK exam (OR 3.1, 95% CI 1.9-5.3). Conclusions COVID-19 impacted the use of ambulatory imaging exams, with greater avoidance among patients at higher risk for COVID-19 complications: older patients, patients with comorbidities, and nonvaccinated patients. Causal analysis results imply that PCS may have contributed to the observed gains in abnormal findings in MR-MSK and CT-brain exams.
Load-balancing mechanism for real-time traffic in wireless ad hoc networks

Recently the requirement for wireless ad hoc networks to support multimedia services like videoconference and VOD has become more and more urgent, and load balancing is recognized as an effective mechanism to satisfy the real-time requirements of these services. But many current ad hoc routing protocols are single-path and best-effort routing, which aren't fit for load balancing. In this paper a node-disjoint multi-path routing protocol, namely Enhanced Multi-path Routing (EMR), is first presented to fulfill the short end-to-end delay and low package loss rate requirements of real-time traffic. Then the policy of how to balance load over multiple paths is discussed based on EMR in the paper. According to some key factors that can affect the performance of load balancing, a load-balancing weight (LBW) equation is formed to describe our load-balancing algorithm quantitatively. The simulation results indicate that the proposed load-balancing mechanism decreases the end-to-end delay and the packet loss rate of real-time traffic.
In Vivo Tumor Spatial Classification using PCA and K-Means with NIR-Hyperspectral Data

This paper presents new method of spatial classification and wavelength bands reduction of near-inferred (NIR) hyperspectral imaging data for medical application. Hyperspectral imaging data have more than several hundred wavelength bands. However hyperspectral data have sometimes redundant information to detect region of interest. The aim of this research is to archive became possible that a region of interest is distinguished by observing the particular wavelength bands without any markers in order to develop a special application such as a surgery supporting system. NIR light with wavelengths of 800-2000 nm, called as the ‘biological window,’ has received particular attention given that water and biological tissues have minimal optical loss caused by scattering and absorption at these wavelengths. NIR light can penetrate/see through deep tissues. NIR endoscope have a great potential as the surgery supporting system, however wavelength bands needs to reduce according to the limitation of NIR endoscope hardware performance. To consider only several wavelength bands are sometimes much effective case than to consider all wavelength bands. In this paper, we proposed the method of spatial classification and reduction a number of wavelength bands simultaneously by combined PCA and k-means, and assessed the cancer-caring nude mouse. The experimental results demonstrate that the proposed method can select valuable wavelength bands to distinguish the region of interest with comparable accuracy of the conventional method.
Anxiety, coping style and hopelessness during COVID‐19 pandemic: An Iranian population‐based study

The COVID‐19 pandemic has caused new conditions such as nationwide quarantine, a dramatic decrease in‐person interaction and an increase in death anxiety for governments and people. The pandemic of an unpredictable disease with no definite treatment can pose physical and psychological risks to individuals. The present study aimed to investigate the state of anxiety, coping styles, and hopelessness of people in the lockdown period and reopening in Iran.
Absolute stability with a generalized sector condition

Absolute stability is a classical problem in nonlinear systems and control. In this paper, we study the absolute stability problem with a generalized sector condition. We introduce the notions of generalized sector and absolute contractive invariance for estimating the domain of attraction of the origin. Necessary and sufficient conditions are identified under which an ellipsoid is absolutely contractively invariant. In the case that the sector is bounded by piecewise linear concave and/or convex functions, these conditions can be exactly stated as linear matrix inequalities. Moreover, if we have a set of absolutely contractively invariant (ACI) ellipsoids, then their convex hull is also ACI and inside the domain of attraction. We also present optimization technique to maximize the absolutely contractively invariant ellipsoids for the purpose of estimating the domain of attraction. The effectiveness of the proposed method is illustrated with examples.
Asthma control and care among six public health clinic attenders in Malaysia: A cross‐sectional study

Asthma is common in Malaysia but neglected. Achieving optimal asthma control and care is a challenge in the primary care setting. In this study, we aimed to identify the risk factors for poor asthma control and pattern of care among adults and children (5–17 years old) with asthma attending six public health clinics in Klang District, Malaysia.
On a convolution operation obtained by adding level sets : classical and new results

On etudie la convolution x ∈ X → Inf {f(x−v) V g(v) : v ∈ X} de deux fonctions f et g. Cette operation se rencontre dans diverses branches des mathematiques, mais son etude systematique n'a pas ete entreprise. On rappelle ici la plupart des proprietes connues de cette operation et nous etablissons plusieurs nouveaux resultats
Multi-resolution stereo vision with application to the automated measurement of logs

A serial multi-resolution stereo matching algorithm is presented that is based on the Marr-Poggio matcher (Marr and Poggio, 1979). It is shown that the Marr-Poggio feature disambiguation and in-range/out-of-range mechanisms are unreliable for non-constant disparity functions. It is proposed that a disparity function estimate reconstructed from the disparity samples at the lower resolution levels be used to disambiguate possible matches at the high resolutions. Also presented is a disparity scanning algorithm with a similar control structure, which is based on an algorithm recently proposed by Grimson (1985). It is seen that the proposed algorithms will function reliably only if the disparity measurements are accurate and if the reconstruction process is accurate. The various sources of errors in the matching are analyzed in detail. The reconstruction process is analyzed in detail. Current methods for performing the reconstruction are reviewed. A new method for reconstructing functions from arbitrarily distributed samples based on applying coordinate transformations to the sampled function is presented. The error due to the reconstruction process is analyzed, and a general formula for the error as a function of the function spectra, sample distribution and reconstruction filter impulse response is derived. Experimental studies are presented which show how the matching algorithms perform with surfaces of varying bandwidths, and with additive image noise. It is proposed that matching of scale space feature maps can eliminate many of the problems that the Marr-Poggio type of matchers have. A method for matching scale space maps which operates in the domain of linear disparity functions is presented. This algorithm is used to experimentally verify the effect of spatial filtering on the disparity measurements for non-constant disparity functions. It is shown that measurements can be made on the binocular scale space maps that give an independent estimate of the disparity gradient. This leads to the concept of binocular diffrequency. It is shown that the diffrequency measurements are not affected by the spatial filtering effect for linear disparities. Experiments are described which show that the disparity gradient can be obtained by diffrequency measurement. An industrial application for stereo vision is described. (Abstract shortened with permission of author.)
Growth of self-integrated atomic quantum wires and junctions of a Mott semiconductor

Continued advances in quantum technologies rely on producing nanometer-scale wires. Although several state-of-the-art nanolithographic technologies and bottom-up synthesis processes have been used to engineer these wires, critical challenges remain in growing uniform atomic-scale crystalline wires and constructing their network structures. Here, we discover a simple method to fabricate atomic-scale wires with various arrangements, including stripes, X-junctions, Y-junctions, and nanorings. Single-crystalline atomic-scale wires of a Mott insulator, whose bandgap is comparable to those of wide-gap semiconductors, are spontaneously grown on graphite substrates by pulsed-laser deposition. These wires are one unit cell thick and have an exact width of two and four unit cells (1.4 and 2.8 nm) and lengths up to a few micrometers. We show that the nonequilibrium reaction-diffusion processes may play an essential role in atomic pattern formation. Our findings offer a previously unknown perspective on the nonequilibrium self-organization phenomena on an atomic scale, paving a unique way for the quantum architecture of nano-network.
An efficient way to perform the assembly of finite element matrices in vector languages

We describe different optimization techniques to perform the assembly of finite element matrices in vector languages (e.g. Matlab, Octave, Python), from the standard approach to recent vectorized ones, without any low level language used. We finally obtain a simple and efficient vectorized algorithm. A comparison is given with a compiled language such as C. Unlike what was expected and generally accepted, examples show that the performances of Matlab, Octave and Python are not as far as the ones of C. The principle of this assembly algorithm is general, we present it for a large class of partial differential operators in the scalar or vector cases in 2d and 3d, in the P1 finite elements case, with an application to linear elasticity. We present numerical results which illustrate the computational costs of the different approaches.