Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence Analysis of Parallel Multi-block ADMM via Discrete-time Recurrent Neural Networks

We consider two basic problems in parallel multi-block Alternating Direction Method of Multipliers (ADMM): constructing convergence conditions, and controlling the variables among a family of solving the subproblems. To address these problems, we propose a new NeuralADMM framework, which consists of two fundamental steps. First, ADMM is converted into a discrete-time recurrent neural network (DT-RNN) since ADMM recursively updates the variables or solve the subproblems. Second, we study the stability of DT-RNN for the convergence of ADMM by employing quadratic Lyapunov functions. We also propose some convergence conditions and design variable feedbacks for ensuring the convergence of ADMM. Semidefinite programming is easily employed to check these conditions. Numerical experiments further establish the effectiveness of our proposed framework.
Open quantum cosmological system

We derive the evolution equation for the density matrix of a UV- and IR- limited band of comoving momentum modes of the canonically normalized scalar degree of freedom in two examples of nearly de Sitter universes. Including the effects of a cubic interaction term from the gravitational action and tracing out a set of longer wavelength modes, we find that the evolution of the system is non-Hamiltonian and non-Markovian. We find linear dissipation terms for a few modes with wavelength near the boundary between system and bath and nonlinear dissipation terms for all modes. The non-Hamiltonian terms persist to late times when the scalar field dynamics is such that the curvature perturbation continues to evolve on super-Hubble scales.
Modeling the Earth-Moon Distance for Different Theories of Gravitation

Lunar Laser Ranging (LLR) measures the transit time of a laser pulse from an observatory on Earth to corner cube retroreflectors on the Moon, installed by Apollo astronauts and Russian rovers. A transit time measurement with 7 ps precision gives the Earth-Moon distance with millimeter precision. Successive measurements indicate the evolution of this distance and can constrain theories of gravitation. These theories include General Relativity (GR), which remains incompatible with Quantum Mechanics. This project presents simulations of the Earth-Moon-Sun system in Python that focus solely on the physics of gravitation and provide a means of comparing the evolution of the Earth-Moon distance for different gravitational frameworks. The comparisons thus far have focused on violations of the Strong Equivalence Principle (SEP) using a quasi-Newtonian formulation of gravitation. The comparisons between the Newtonian and quasi-Newtonian frameworks take two approaches. The first involves subtracting simulated Newtonian Earth-Moon distances from simulated SEP violation Earth-Moon distances and analyzing the results using a Fourier transform, which identifies periodic signals. The Fourier spectrum revealed an SEP violation signal as well as three other signals related to the Sun’s perturbation of the Earth-Moon system and the eccentricity of the lunar orbit. However, this method is not ideal because the rate of precession of the lunar orbit depends on the degree of SEP violation, or lack thereof. The more optimal, second approach consists of fitting SEP violation simulated data with a Newtonian model. The post-fit residuals should be dominated by the SEP violation signal. The results were varied: the SEP violation signal dominated some of the post-fit residuals, while in some fits with different initial conditions, the other periodic signals persisted. The reasons for this inconsistency remain unclear. Because of correlations between parameters, the model was able to absorb some of the SEP violation signal by adjusting parameters like the initial velocities and positions of the objects. However, all fits failed to fully mask the SEP violation, indicating that LLR is sensitive to an SEP violation. Future work may focus on optimizing the fit and increasing its accuracy, and on undertaking similar comparisons for GR and alternative theories.
Structural, elastic and thermodynamic properties of A15-type compounds V3X (X = Ir, Pt and Au) from first-principles calculations

The structural, elastic and thermodynamic properties of the A15 structure V3Ir, V3Pt and V3Au were studied using first-principles calculations based on the density functional theory (DFT) within generalized gradient approximation (GGA) and local density approximation (LDA) methods. The results have shown that both GGA and LDA methods can process the structural optimization in good agreement with the available experimental parameters in the compounds. Furthermore, the elastic properties and Debye temperatures estimated by LDA method are typically larger than the GGA methods. However, the GGA methods can make better prediction with the experimental values of Debye temperature in V3Ir, V3Pt and V3Au, signifying the precision of the calculating work. Based on the E–V data derived from the GGA method, the variations of the Debye temperature, coefficient of thermal expansion and heat capacity under pressure ranging from 0 GPa to 50 GPa and at temperature ranging from 0 K to 1500 K were obtained and analyzed for all compounds using the quasi-harmonic Debye model.
Deep ensemble inverse model for image-based estimation of solar cell parameters

Physical models can help improve solar cell efficiency during the design phase and for quality control after the fabrication process. We present a data-driven approach to inverse modeling that can predict the underlying parameters of a finite element method solar cell model based on an electroluminescence (EL) image of a solar cell with known cell geometry and laser scribed defects. For training the inverse model, 75 000 synthetic EL images were generated with randomized parameters of the physical cell model. We combine 17 deep convolutional neural networks based on a modified VGG19 architecture into a deep ensemble to add uncertainty estimates. Using the silicon solar cell model, we show that such a novel approach to data-driven statistical inverse modeling can help apply recent developments in deep learning to new engineering applications that require real-time parameterizations of physical models augmented by confidence intervals. The trained network was tested on four different physical solar cell samples, and the estimated parameters were used to create the corresponding model representations. Resimulations of the measurements yielded relative deviations of the calculated and the measured junction voltage values of 0.2% on average with a maximum of 10%, demonstrating the validity of the approach.
The preventive role of Snell’s law in mode conversion from Z- to whistler-mode waves in an inhomogeneous magnetoplasma with a low density

Electromagnetic waves with different modes, such as Z-, whistler-, LOand RXmodes are found in different regions of the Earth magnetosphere and the magnetosphere of other planets. Since whistler-mode waves influence the behavior of the magnetosphere, and they are used as experimental tools to investigate the upper atmosphere, they are important. On the other hand, the mode conversion process can be considered as one of the processes of generating electromagnetic waves that can occur under certain conditions. Usually, propagation waves in an inhomogeneous plasma are a necessary, but not a sufficient condition for a mode conversion process. Snell’s law has an important role in the mode conversion process. Although, this law lets a mode conversion occur from Zto LO-mode waves, in a case from Zmode to whistler mode waves, it plays a preventive role. The aim of this paper is to demonstrate the preventive role of Snell’s law in a mode conversion from Zto Whistler-mode waves in an inhomogeneous magnetoplasma with a low density. We used the dispersion relation in the magnetoplasma with a low density and for an oblique wave normal angle. By applying the Snell’s law, we showed that with the propagation of the Z-mode waves in an inhomogeneous plasma, there is not any matching point between Zand Whistler mode waves, and for any wave normal angle always an evanescent layer exists between the two modes. In this case, Snell’s law prevents the mode conversion from occurring. It also prevents the transfer of energy from one to another mode waves.
Nonlinear absorption properties of indium selenide and its application for demonstrating pulsed Er-doped fiber laser

Two-dimensional materials are now receiving continuously increasing attention due to their exceptional optical properties. Recently, it has been proved that indium selenide (InSe) is also an excellent photoelectric material, with wide applications in the field of photodetecters and so on. In our work, layered InSe was prepared through the liquid-phase exfoliation method, and the nonlinear saturable absorption properties of InSe were also experimentally investigated. In addition, InSe-polyvinyl alcohol film was successfully prepared and employed as an saturable absorber for demonstrating an Er-doped passive Q-switched mode-locked fiber laser for the first time. The largest Q-switched pulse energy was as high as 112.97 nJ and the narrowest mode-locked pulse duration was 2.96 ns at a repetition rate of 1.74 MHz. Our results fully indicate that InSe also has excellent nonlinear absorption properties in comparison with other commonly used 2D materials. Thus, the applications of InSe can be deeply investigated to promote the development of ultra-fast optics in the near future.
WiCRF2: Multi-Weighted LiDAR Odometry and Mapping With Motion Observability Features

Accurate localization is a key technology for automated mobile robot systems. Light detection and ranging (LiDAR) is widely used in simultaneous localization and mapping (SLAM) systems because of its stable and high-precision measurements. Feature extraction and motion constraint construction, as two core modules of feature-based SLAM, have attracted extensive research in recent years. However, existing methods mostly innovate for both separately, ignoring the interactions between features and motion constraints. To this end, this article constructs a highly accurate and robust LiDAR SLAM based on features with well motion observability. The method screens feature with well motion observability by estimating the unit contribution to motion constraints on six degrees of freedom (DoF). In addition, the reprojection constraints of each feature are weighted according to the cumulative contribution to motion constraints on each DoF. Compared with traditional methods, the close correlation between feature extraction and motion constraint construction reduces redundant features and constraints. Balanced motion constraints effectively improve the robustness and accuracy of the proposed method, especially in feature-poor environments. Further, feature vectors are introduced in the map, and the feature vectors of the current keyframe are verified using multiframe observations in the map to improve the consistency and accuracy of the map and reduce the impact of feature vector errors on localization accuracy. The proposed SLAM system is tested in environments with sparse and inhomogeneous distributed features and compared with existing methods. The experimental results show that our method has higher accuracy and robustness.
PRUS: Product Recommender System Based on User Specifications and Customers Reviews

The rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs. The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity. To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences. This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations. The next step is to link the polarity to the review’s sentence-level features. Products are prioritized following user needs by assigning relative importance to each of the polarities. The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated. Experimental evaluation of PRUS utilizes rank score ( $RS$ ) and normalized discounted cumulative gain ( $nDCG$ ) score. Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products.
Continuous-Time Flows for Deep Generative Models

Normalizing flows have been developed recently as a method for drawing samples from an arbitrary distribution. This method is attractive due to its intrinsic ability to approximate a target distribution arbitrarily well. In practice, however, normalizing flows only consist of a finite number of deterministic transformations, and thus there is no guarantees on the approximation accuracy. In this paper we study the problem of learning deep generative models with {\em continuous-time} flows (CTFs), a family of diffusion-based methods that are able to asymptotically approach a target distribution. We discretize the CTF to make training feasible, and develop theory on the approximation error. A framework is then adopted to distill knowledge from a CTF to an efficient inference network. We apply the technique to deep generative models, including a CTF-based variational autoencoder and an adversarial-network-like density estimator. Experiments on various tasks demonstrate the superiority of the proposed CTF framework compared to existing techniques.
Correlation Analyses Between Childhood Behavioral Disturbance and Maternal Alexithymia: An Observational Study

Purpose Caregivers’ responses have an impact on children’s emotional and behavioral development. The inability of caregivers to perceive their own emotions affects their responses to children. This study aims to examine the relationship between a mother’s difficulties in emotional expression and children’s emotional and behavioral disorders. Patient and Methods A total of 78 cases, aged between 3 and 15 years old, were included in this study. The mothers completed the Taiwan version of the “Toronto Alexithymia Scale” (TAS) and “Children’s Behavior Checklist” (CBCL) questionnaires, which served as research tools. TAS consists of three factors: difficulty in distinguishing emotions (Factor 1, F1), difficulty in expressing feelings (Factor 2, F2), and an externally oriented thinking style (Factor 3, F3). CBCL represents a child’s behavior profile. The Mann–Whitney test and correlation analyses were used for follow-up analysis. Results Our analysis revealed that F1, F2, F3, and total scores of TAS (TAS-Total) correlate positively with the somatic complaint subscale of CBCL (F1: p = 0.003; F2: p = 0.002; F3: p = 0.034, TAS-Total p=0.002). Additionally, TAS-F1 was positively correlated with the internalizing problems subscale of CBCL (p = 0.020) and the withdrawn (WD) subscale of CBCL (p = 0.044). We also found a significant association between TAS-F1 and WD of CBCL in boys alone (p = 0.022). However, we observed a negative association between TAS-F3 and the social problems subscale of CBCL in boys alone (p = 0.038). Conclusion Mothers with alexithymic features are more likely to have children who internalize emotions, leading to withdrawal and somatic expression. Boys tend to exhibit withdrawal traits associated with internalization. This study provides initial evidence of a partial correlation between maternal alexithymia and children’s emotional and behavioral disorders, emphasizing the importance of investigating caregivers’ emotional traits for a better understanding of children’s development in the future.
Design and performance of kinetic inductance detectors for cosmic microwave background polarimetry

Design and performance of kinetic inductance detectors for cosmic microwave background polarimetry Heather McCarrick This thesis presents the development of kinetic inductance detectors (KIDs) for cosmic microwave background (CMB) polarimetry. Increasingly precise measurements of the CMB have led to much of our understanding of the observable universe; future measurements of the CMB will require the development of new detectors as progressively fainter signals are targeted. In particular, a measurement of the primordial B-mode polarization signal, which would offer strong evidence of inflation, will require at least a 50 times increase in detector count. KIDs are an attractive detector option for next-generation CMB experiments due to their low-noise and high-multiplexing factor. In this thesis, I present KIDs optimized for ground-based CMB observations, which are sensitive to a 150 GHz spectral band where the CMB spectrum peaks. This research demonstrates the first systematic studies of lumpedelement KIDs (LEKIDs) optimized for CMB surveys and shows the readiness of the detectors for on-sky observations. First, I present the design and performance of horn-coupled LEKIDs, which are sensitive to a single polarization. I show that KIDs can meet the stringent noise and sensitivity requirements necessary for a competitive CMB detector. Second, I present a novel method for reducing crosstalk between LEKIDs, which is important for controlling instrument systematics. Third, I present the design and performance of dual-polarization LEKIDs, which are sensitive to orthogonal polarizations within a single spectral band and double the number of detectors per array, increasing the sensitivity. Finally, I present the initial analysis of millimeter-wave observations of a nearby galaxy cluster, Abell 2443, taken with the LEKID-based NIKA2 instrument on the IRAM 30 m telescope. This is part of ongoing research to make high-resolution measurements of the Sunyaev–Zel’dovich effect, seen as a distortion in the CMB spectrum.
Creative Brain Training Apps and Games Can Help Improve Memory, Cognitive Abilities, and Promote Good Mental Health for The Elderly

This qualitative research study aims to explore the effectiveness of creative brain training apps and games in enhancing memory, cognitive abilities, and promoting good mental health among the elderly. The research is based on feedback and experiences provided by a second-year university student who has close relationships with elderly individuals. Data will be collected between June and July 2023. The informant's firsthand experiences and insights will be utilized to evaluate games and applications specifically designed to strengthen memory, thinking, analysis, brain exercises, eyesight, and other cognitive functions. By investigating the impact of these interventions, this study aims to contribute valuable information to enhance the mental well-being and cognitive capabilities of the elderly population.
How to achieve fair and efficient cooperative vehicle routing?

A cooperative is a business entity with the primary objective of providing benefits, services, and goods to its members, who both own and exercise democratic control over it. In the context of a cooperative, a fleet typically consists of vehicles owned by self-concerned individually rational owners who prioritize their own efficiency and the fairness of the system. This fairness refers to how their individual gain aligns with the gain of others. In this paper, we focus on the routing of such cooperative fleets. Considering only the fleet’s efficiency in terms of minimising its overall cost, the studied problem corresponds to the multiple Traveling Salesman Problem (mTSP). However, our interest lies in finding both efficient and fair solutions, so we propose two new variants of this problem that integrate and maximise the fleet’s egalitarian and elitist social welfare. Additionally, to enhance the balance between fleet efficiency and fairness, we propose the systematic elitist and systematic egalitarian social welfare optimisation algorithm. Through simulation results, we observe a wide diversity of routes depending on the approach considered. Therefore, a cooperative may choose a model that best balances its fleet’s efficiency and fairness based on its specific requirements.
Distribution pattern of households’ income inequality in Eastern Cape, South Africa

Income inequality is a pervasive problem in South Africa and particularly affects the Eastern Cape, where poverty levels remain high despite several efforts to address this societal issue. The datasets from 1499 households used for this study, were extracted from the 2021 South African General Household Survey. This study thus examined the distributional pattern of households’ income inequality in Eastern Cape, South Africa, through the use of exploratory data analysis and application of regression-based decomposition of inequality. The EDA results revealed that relatively older households are more concentrated than those young people, female-headed households were also predominant in the study area, while the majority of the households are Black South Africans. The magnitude of the monetary variables’ departure from the mean inferred clear evidence of a widened households’ income disparity or inequality in the study area. The decomposition analysis indicated the contributions of various socioeconomic factors to income inequality. The findings from the regression-based decomposition of inequality found household size, access to basic infrastructure services, possession of assets, internet communication facilities, and households’ population group, as the major drivers of the households’ income inequality in the province, while livelihood diversity has a relatively moderate proportionate contribution. On the other hand, factors such as age and gender of the household head, as well as households’ involvements in agriculture have a minor effect on the households’ income inequality. This paper finally concludes that the relative contributions of each factor contributing to inequality were deemed important for designing effective policy-relevant interventions which can be used to promote economic growth and to address this persistent challenge of income inequality in society.
Chitinase-3-like 1 regulates TH2 cells, TFH cells and IgE responses to helminth infection

Introduction Data from patient cohorts and mouse models of atopic dermatitis, food allergy and asthma strongly support a role for chitinase-3-like-1 protein (CHI3L1) in allergic disease. Methods To address whether Chi3l1 also contributes to TH2 responses following nematode infection, we infected Chi3l1 -/- mice with Heligmosomoides polygyrus (Hp) and analyzed T cell responses. Results As anticipated, we observed impaired TH2 responses in Hp-infected Chi3l1 -/- mice. However, we also found that T cell intrinsic expression of Chi3l1 was required for ICOS upregulation following activation of naïve CD4 T cells and was necessary for the development of the IL-4+ TFH subset, which supports germinal center B cell reactions and IgE responses. We also observed roles for Chi3l1 in TFH, germinal center B cell, and IgE responses to alum-adjuvanted vaccination. While Chi3l1 was critical for IgE humoral responses it was not required for vaccine or infection-induced IgG1 responses. Discussion These results suggest that Chi3l1 modulates IgE responses, which are known to be highly dependent on IL-4-producing TFH cells.