Miracle-dz/UniFAR-FTUs huggingface dataset:
The anisotropic oscillator on the 2D sphere and the hyperbolic plane

An integrable generalization on the 2D sphere S2 and the hyperbolic plane H2 of the Euclidean anisotropic oscillator Hamiltonian with ‘centrifugal’ terms given by is presented. The resulting generalized Hamiltonian depends explicitly on the constant Gaussian curvature κ of the underlying space, in such a way that all the results here presented hold simultaneously for S2 (κ > 0), H2 (κ < 0) and E2 (κ = 0). Moreover, is explicitly shown to be integrable for any values of the parameters δ, Ω, λ1 and λ2. Therefore, can also be interpreted as an anisotropic generalization of the curved Higgs oscillator, that is recovered as the isotropic limit Ω → 0 of . Furthermore, numerical integration of some of the trajectories for are worked out and the dynamical features arising from the introduction of a curved background are highlighted. The superintegrability issue for is discussed by focusing on the value Ω = 3δ, which is one of the cases for which the Euclidean Hamiltonian is known to be superintegrable (the 1 : 2 oscillator). We show numerically that for Ω = 3δ the curved Hamiltonian presents nonperiodic bounded trajectories, which seems to indicate that provides a non-superintegrable generalization of even for values of Ω that lead to commensurate frequencies in the Euclidean case. We compare this result with a previously known superintegrable curved analogue of the 1 : 2 Euclidean oscillator, which is described in detail, showing that the Ω = 3δ specialization of does not coincide with . Hence we conjecture that would be an integrable (but not superintegrable) curved generalization of the anisotropic oscillator that exists for any value of Ω and has constants of the motion that are quadratic in the momenta. Thus each commensurate Euclidean oscillator could admit another specific superintegrable curved Hamiltonian which would be different from and endowed with higher order integrals. Finally, the geometrical interpretation of the curved ‘centrifugal’ terms appearing in is also discussed in detail.
A Graph Sampling-Based Model for Influence Maximization in Large-Scale Social Networks

Social networks have attracted a great deal of attention and have, in fact, changed the way we produce, consume, and diffuse information. This change gave rise to the notion of social influence and today we talk about influential nodes. The process of detecting influential nodes in social networks aims to find entities that propagate information to a large portion of the network users. This process is often known as the influence maximization (IM) problem. Due to the explosive growth of social networks’ data, their structure is more complex and we talk about “big graph data. ” Moreover, modern networks are dynamic and their topology or/and information is likely to change over time. Detecting influential nodes in such networks is a challenging task. Several methods have been developed in this context. However, they concentrate on static networks and there is little work on large-scale social networks. We propose in this article a new model for IM called MapReduce-based dynamic selection of influential nodes (MR-DSINs) that has the ability to cope with the huge size of real social networks. In fact, our approach is based on a graph sampling step in order to reduce the network’s size. Given that reduced version, MR-DSIN is able to select dynamically influential nodes. Our proposal has the advantage of considering the dynamics of information that can be modeled by users’ social actions (e.g., “share,” “comment,” “retweet”). Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that MR-DSIN is efficient for identifying influential nodes, compared with three known proposals. We prove that our model is able to detect in the reduced graph an influence as important as in the original one.
Large data global regularity for the $2+1$-dimensional equivariant Faddeev model

This article addresses the large data global regularity for the equivariant case of the $2+1$-dimensional Faddeev model and shows that it holds true for initial data in $H^s\times H^{s-1}(\mathbb{R}^2)$ with $s>3$.
A Novel Multi-Training Method for Time-Series Urban Green Cover Recognition From Multitemporal Remote Sensing Images

Urban green space plays a crucial role in the construction of ecological city and livable environment. While multitemporal remote sensing images provide strong support for urban green cover monitoring, they often suffer from data shifting, where the data distribution varies from phase to phase. Designing a general multitemporal framework to extract urban green cover is challenging, mainly due to possible time-consuming data labeling and inconsistent prediction. To address that, we propose multi-training, a novel method for land cover classification on multitemporal remote sensing images. Multi-Training is a two-stage method to independently train classifier on each phase in the training stage and then to combine the information from all the classifiers in the communication stage. As a semi-supervised learning method, multi-training adopts a new rule to obtain the confidence of unlabeled samples’ prediction, which reduces the dependence on labeled data and increases the result's consistency between phases. The experimental results show that multi-training outperforms self-training, co-training, tri-training, and super-training on both accuracy and consistency on multitemporal remote sensing image datasets. Furthermore, we have analyzed the necessary parameters in our method and conclude that the number and the combination of phases will dominate the prediction results.
Solving the linear fractional derivatives ordinary differential equations with constant matrix coefficients

The Cauchy problem for fractional derivatives linear systems of ordinary differential equations with constant coefficients is considered, where at first the analytic expressions are given through the matrix exponent of its corresponding solution. On the basis of the obtained results the conditions are given, providing the asymptotic stability of the initial system. The results are illustrated on the numerical example, where it is shown that when the order of the fractional derivative tends to unity, so the solution tends to the corresponding exponential function.
Bombieri-type theorem for convolution of arithmetic functions on Number field

Let $K$ be an imaginary quadratic number field of class number one and $\mathcal{O}_K$ be its ring of integers. We show that, if the arithmetic functions $f, g:\mathcal{O}_K\rightarrow \mathbb{C}$ both have level of distribution $\vartheta$ for some $0<\vartheta\leq 1/2$ then the Dirichlet convolution $f*g$ also have level of distribution $\vartheta$.
Women’s Higher Education, Assortative Mating, and Empowerment: Long-Term Evidence from College Enrollment Expansion in South Korea

Abstract This study examines the impact of higher education on women’s bargaining power in the household in South Korea. Utilising the nationwide college expansion programs in South Korea in 1979 and 1981 as a natural experiment, this study uses 6181 married women born between 1943 and 1976 and adopts the birth cohort as an instrumental variable to identify women that benefitted from the programs. The results show that more years of schooling increased women’s property ownership and decision-making power at home, which is further explained by three channels. The first channel, related to women’s economic resources, indicates that education increased women’s likelihood of being employed, having a white-collar job, absolute income, and age at marriage. However, it did not increase women’s relative income compared to their husbands’. Second, consistent with the assortative mating theory, higher education encouraged women to marry a partner who has a prestigious job and is closer to their education level and age. Third, the channel of gender role attitudes revealed that more years of schooling led women to realize the need for financial independence from their spouses. These findings show how access to higher education for women improved gender equality in South Korean society.
Variation of Seed Traits and Initial Quality among Selected Cowpea, Mungbean, and Soybean Accessions

Seed traits are essential quantitative variables to assess seed quality and are also indicators of crop success. The World Vegetable Center Genebank in Arusha, Tanzania, has an important legume collection; however, there is limited information on seed traits per genotype, i.e., 1000-seed weight, the number of seeds per gram, germination percentage (GP), and mean germination time (MGT). In this study, 8 cowpea, 54 mungbean, and 15 soybean accessions were evaluated for the abovementioned parameters. A significant difference was observed among the genotypes of the selected legumes with respect to all the measured parameters. An intraspecific variation was also observed for the measured seed traits. The 1000-seed weight decreased with an increase in the number of seeds per gram. Unlike cowpea, the GP of mungbean and soybean accessions was significantly and negatively influenced by the number of seeds per 1 g. Hence, the smaller-sized seeds (with many seeds in 1 g) exhibited lower GP. It is concluded that the seed traits and initial quality among the selected legumes are strongly influenced by their genetic makeup. These results are useful to inform seed management, exchange, utilization, and have a high potential in breeding programs, and require further research attention.
A Systematic Review of Tropical Disease Prevalence among Migrants

BACKGROUND: Few studies have assessed the burden of tropical diseases among migrants into non-endemic countries. AIM: This study aimed to systematically review the existing data of the prevalence of tropical diseases globally, including neglected tropical diseases globally. MATERIALS AND METHODS: The authors conducted a systematic review reporting prevalence (including seroprevalence) of tropical diseases following the PRISMA guidelines and based on the database from PUBMED, WoS, and PROQUEST. All the identified records were screened according to the inclusion and exclusion criteria. The selected articles’ quality was appraised using the mixed methods appraisal tool to ensure its quality. RESULTS: Overall, 19 studies conducted in 13 countries published between the year 2017–2020 were included in the study. Based on the thematic analysis, two themes (type of organism) and 11 sub-themes (disease) were used. The prevalence of tropical diseases among migrants ranged from 0.2 to 31% for malaria; 3–20% for Chagas Disease; 3.2–3.5% for Giardiasis; 31.7–57.4% for Toxoplasmosis; 0.1–51%, for Schistosomiasis; 0.1–15.8%, for Strongyloidiasis; 0.3–3.8% for Trichuriasis; 0.2–0.9% for Ascariasis; 6.4–9.7% for Toxocariasis; 0.3% for Loiasis; and 0.5% for Filariasis. All migrants warrant thorough screening and testing, based on the country of origin of their last visit. Routine screening and follow-up may reduce the re-emergence of tropical disease in non-endemic countries. CONCLUSION: Multiple approaches in managing social and health issues among migrants are vital to secure healthy labor forces for the country’s economy and development. Public health sectors should implement strategic promotive, preventive, and curative programs targeted to this group.
Backstepping-Based Tracking Control of Underactuated Aquatic Robots

Increased interest in the use of autonomous aquatic robots in a variety of applications has led to the need for efficient and precise control of these robots. In particular, accurate trajectory control has become of importance in many of these applications. However, the highly nonlinear and underactuated dynamics of many aquatic robots present significant challenges in control. In this work, we propose a trajectory-tracking control approach for a general class of underactuated aquatic robotic systems undergoing planar motion. We demonstrate how a backstepping-based control scheme can be synthesized to ultimately bound three tracking errors (2-D position and orientation), despite the underactuated nature of the system. Via multi-time-scale analysis of singularly perturbed systems, we prove how the control scheme achieves boundedness and convergence of the tracking errors to a neighborhood of the origin. Finally, we implement the proposed scheme on a robotic fish and demonstrate its efficacy via experimental results. This article is complemented with a video: https://youtu.be/7007tUQd3KI.
Unrolled Convolutional Neural Network for Full-Wave Inverse Scattering

An unrolled deep learning scheme for solving full-wave nonlinear inverse scattering problems (ISPs) is proposed. Inspired by the so- called unrolled method, an iterative neural network structure combining the contrast source inversion (CSI) method and residual network (ResNet) is designed. By embedding the CSI iterations into the deep learning model, the domain knowledge is well incorporated into the learning process. Thorough numerical tests are carried out to evaluate the performance, stability, robustness, and reliability of the proposed approach. Comparisons with the widely used U-net structure and CSI exhibit the advantage of the proposed approach.
Permanency of the age-structured population model on several temporally variable patches

We consider a system of nonlinear partial differential equations that describes an age-structured population inhabiting several temporally varying patches. We prove existence and uniqueness of solution and analyze its large-time behavior in cases when the environment is constant and when it changes periodically. A pivotal assumption is that individuals can disperse and that each patch can be reached from every other patch, directly or through several intermediary patches. We introduce the net reproductive operator and characteristic equations for time-independent and periodical models and prove that permanency is defined by the net reproductive rate for the whole system. If the net reproductive rate is less or equal to one, extinction on all patches is imminent. Otherwise, permanency on all patches is guaranteed. The proof is based on a new approach to analysis of large-time stability.
Flexible Patch Antenna Array Operating at Microwaves Based on Thin Composite Material

This paper presents an innovative route to fabricate conformal patch antenna arrays based on a flexible mesh-style conductive fabric / E-glass fiber composite laminate with improved performance at microwaves. On the one hand, the ohmic loss in the antenna array is restricted by integrating a highly conductive fabric into an E-glass fiber composite laminate and by using it as feeding lines and radiating elements after its precise machining by laser technology. On the other hand, the dielectric loss is restricted by inserting a foam layer (with dielectric characteristics close to those of the air) between the laminate substrate and the ground plane which is made of the highly conductive mesh-style fabric. The bending effect of the antenna array on its radiation pattern is minimized by applying a phase offset through phase shifters at the feeding lines of the most curved patches. The high radiation performance in gain and efficiency at 5.8 GHz (11.9 dBi and 75% in a flat configuration, and 8.7 dBi and 75% in a 50 mm-radius bending configuration, respectively), the seamless integration process, and the flexible mechanical properties of such composite antenna arrays pave the way for their promising and efficient integration into various devices, objects, and vehicles made of composite laminate materials.
Two-Stage Underwater Object Detection Network Using Swin Transformer

Underwater object detection plays an essential role in ocean exploration, and the increasing amount of underwater object image data makes the study of advanced underwater object detection algorithms of great practical significance. However, there are problems with colour offset, low contrast, and target blur in underwater image data. An underwater object detection algorithm based on Faster R-CNN is proposed to solve these problems. First, the Swin Transformer is used as the backbone network of the algorithm. Second, by adding the path aggregation network, the deep feature map and the shallow feature map are superimposed and fused. Third, online hard example mining, makes the training process more efficient. Fourth, the ROI pooling is improved to ROI align, eliminating the two quantization errors of ROI pooling and improving the detection performance. Compared with other algorithms, the proposed algorithm’s based on improved Faster-RCNN on URPC2018 dataset is improved to 80.54%, and basically solve the problem of missed detection and false detection of objects of different sizes in a complex environment.
Langmuir‐Blodgett Assembly to order Nanoparticles and Colloidal Objects

Bottom-up assembly of nanoparticles and colloidal objects pose a formidable challenge when processing devices. Speed, compatibility with various materials, defect tolerance and cost effectiveness are among the desired properties of a suitable nanoscale assembly process. In this regard, the Langmuir‐Blodgett (LB) technique is a highly sought-after candidate which aids in arranging a large number of nanostructures on solid surfaces. This mini-review aims to provide a concise account on the LB technique and four distinct ways of how it allows to assemble systems made of nanoparticles and colloidal objects: namely, close-packed nanoparticle superlattices by compression, micrometer scale nanoparticle fingering patterns by dip coating, single nanoparticle lines by stick-slip deposition and one-step patterning of aligned nanowire arrays.
Improvement of eigenfunction estimates on manifolds of nonpositive curvature

Let $(M,g)$ be a compact, boundaryless manifold of dimension $n$ with the property that either (i) $n=2$ and $(M,g)$ has no conjugate points, or (ii) the sectional curvatures of $(M,g)$ are nonpositive. Let $\Delta$ be the positive Laplacian on $M$ determined by $g$. We study the $L^{2}\to{}L^{p}$ mapping properties of a spectral cluster of $\sqrt{\Delta}$ of width $1/\log\lambda$. Under the geometric assumptions above, \cite{berard77} B\'{e}rard obtained a logarithmic improvement for the remainder term of the eigenvalue counting function which directly leads to a $(\log\lambda)^{1/2}$ improvement for H\"ormander's estimate on the $L^{\infty}$ norms of eigenfunctions. In this paper we extend this improvement to the $L^p$ estimates for all $p>\frac{2(n+1)}{n-1}$.
AI-Driven Packet Forwarding With Programmable Data Plane: A Survey

The existing packet forwarding technology cannot meet the increasing requirements of Internet development due to its rigid framework. Application of artificial intelligence (AI) for efficient packet forwarding is gaining more and more interest as a new direction. Recently, the explosive development of programmable data plane (PDP) has provided a potential impetus to packet forwarding driven by AI. Therefore, this paper presents a survey on the recent research in AI-driven packet forwarding with PDP. First, we describe two of the most representative frameworks of the packet forwarding, i.e., the traditional AI-driven forwarding framework and the new one assisted by the PDP. Then, we focus on capacity of the packet forwarding under the two frameworks in four measures: delay, throughput, security, and reliability. For each measure, we organize the content with the evolution from simple packet forwarding, to packet forwarding capacity enhancement with the assistance of AI, to the latest research on AI-driven packet forwarding supported by the PDP. Finally, we identify three directions in the development of AI-driven packet forwarding, and highlight the challenges and issues in future research.
Higher order expansions for the probabilistic local Cauchy theory of the cubic nonlinear Schrödinger equation on ℝ³

We consider the cubic nonlinear Schrödinger equation (NLS) on R 3 \mathbb {R}^3 with randomized initial data. In particular, we study an iterative approach based on a partial power series expansion in terms of the random initial data. By performing a fixed point argument around the second order expansion, we improve the regularity threshold for almost sure local well-posedness from our previous work. We further investigate a limitation of this iterative procedure. Finally, we introduce an alternative iterative approach, based on a modified expansion of arbitrary length, and prove almost sure local well-posedness of the cubic NLS in an almost optimal regularity range with respect to the original iterative approach based on a power series expansion.
Successive Convexification: A Superlinearly Convergent Algorithm for Non-convex Optimal Control Problems

This paper presents the SCvx algorithm, a successive convexification algorithm designed to solve non-convex optimal control problems with global convergence and superlinear convergence-rate guarantees. The proposed algorithm handles nonlinear dynamics and non-convex state and control constraints by linearizing them about the solution of the previous iterate, and solving the resulting convex subproblem to obtain a solution for the current iterate. Additionally, the algorithm incorporates several safe-guarding techniques into each convex subproblem, employing virtual controls and virtual buffer zones to avoid artificial infeasibility, and a trust region to avoid artificial unboundedness. The procedure is repeated in succession, thus turning a difficult non-convex optimal control problem into a sequence of numerically tractable convex subproblems. Using fast and reliable Interior Point Method (IPM) solvers, the convex subproblems can be computed quickly, making the SCvx algorithm well suited for real-time applications. Analysis is presented to show that the algorithm converges both globally and superlinearly, guaranteeing the local optimality of the original problem. The superlinear convergence is obtained by exploiting the structure of optimal control problems, showcasing the superior convergence rate that can be obtained by leveraging specific problem properties when compared to generic nonlinear programming methods. Numerical simulations are performed for an illustrative non-convex quad-rotor motion planning example problem, and corresponding results obtained using Sequential Quadratic Programming (SQP) solver are provided for comparison. Our results show that the convergence rate of the SCvx algorithm is indeed superlinear, and surpasses that of the SQP-based method by converging in less than half the number of iterations.
Pavement Cracks Segmentation Algorithm Based on Conditional Generative Adversarial Network

In long-term use, cracks will show up on the road, delivering monetary losses and security hazards. However, the road surface with a complex background has various disturbances, so it is challenging to segment the cracks accurately. Therefore, we propose a pavement cracks segmentation method based on a conditional generative adversarial network in this paper. U-net3+ with the attention module is used in the generator to generate segmented images for pavement cracks. The attention module highlights crack features and suppresses noise features from two dimensions of channel and space, then fuses the features generated by these two dimensions to obtain more complementary crack features. The original image is stitched with the manual annotation of cracks and the generated segmented image as the input of the discriminator. The PatchGAN method is used in the discriminator. Moreover, we propose a weighted hybrid loss function to improve the segmentation accuracy by exploiting the difference between the generated and annotated images. Through alternating gaming training of the generator and the discriminator, the segmentation image of cracks generated by the generator is very close to the actual segmentation image, thus achieving the effect of crack detection. Our experimental results using the Crack500 datasets show that the proposed method can eliminate various disturbances and achieve superior performance in pavement crack detection with complex backgrounds.