The Influence of Family on Children’s Second Language Learning

This major paper talks about the influence of family on foreign language learning of children. Families in different countries, regions, and cultural background have different attitudes and views towards learning a foreign language and may hold unique opinions on the ways and methods of learning foreign languages. This paper introduces the current situation of children learning a foreign language in different countries, at different ages, and discusses whether children\u27s learning a foreign language is deeply influenced by their families. The paper also attempts the analysis from various perspectives, including the attitudes of the parents towards second language learning, the social-economic status of parents, and the educational background and cultural influence of parents. It explores the relationship between the Critical Period Hypothesis and foreign language learning and provides recommendations about how to learn foreign language effectively. Moreover, in this study, it was found out the family environment indeed affect the foreign language learning of children, and provide recommendation from three groups of people: children, parents and teachers

Shape Design and Optimization for 3D Printing

In recent years, the 3D printing technology has become increasingly popular, with wide-spread uses in rapid prototyping, design, art, education, medical applications, food and fashion industries. It enables distributed manufacturing, allowing users to easily produce customized 3D objects in office or at home. The investment in 3D printing technology continues to drive down the cost of 3D printers, making them more affordable to consumers. As 3D printing becomes more available, it also demands better computer algorithms to assist users in quickly and easily generating 3D content for printing. Creating 3D content often requires considerably more efforts and skills than creating 2D content. In this work, I will study several aspects of 3D shape design and optimization for 3D printing. I start by discussing my work in geometric puzzle design, which is a popular application of 3D printing in recreational math and art. Given user-provided input figures, the goal is to compute the minimum (or best) set of geometric shapes that can satisfy the given constraints (such as dissection constraints). The puzzle design also has to consider feasibility, such as avoiding interlocking pieces. I present two optimization-based algorithms to automatically generate customized 3D geometric puzzles, which can be directly printed for users to enjoy. They are also great tools for geometry education. Next, I discuss shape optimization for printing functional tools and parts. Although current 3D modeling software allows a novice user to easily design 3D shapes, the resulting shapes are not guaranteed to meet required physical strength. For example, a poorly designed stool may easily collapse when a person sits on the stool; a poorly designed wrench may easily break under force. I study new algorithms to help users strengthen functional shapes in order to meet specific physical properties. The algorithm uses an optimization-based framework — it performs geometric shape deformation and structural optimization iteratively to minimize mechanical stresses in the presence of forces assuming typical use scenarios. Physically-based simulation is performed at run-time to evaluate the functional properties of the shape (e.g., mechanical stresses based on finite element methods), and the optimizer makes use of this information to improve the shape. Experimental results show that my algorithm can successfully optimize various 3D shapes, such as chairs, tables, utility tools, to withstand higher forces, while preserving the original shape as much as possible. To improve the efficiency of physics simulation for general shapes, I also introduce a novel, SPH-based sampling algorithm, which can provide better tetrahedralization for use in the physics simulator. My new modeling algorithm can greatly reduce the design time, allowing users to quickly generate functional shapes that meet required physical standards

Boxelization: folding 3D objects into boxes

We present a method for transforming a 3D object into a cube or a box using a continuous folding sequence. Our method produces a single, connected object that can be physically fabricated and folded from one shape to the other. We segment the object into voxels and search for a voxel-tree that can fold from the input shape to the target shape. This involves three major steps: finding a good voxelization, finding the tree structure that can form the input and target shapes' configurations, and finding a non-intersecting folding sequence. We demonstrate our results on several input 3D objects and also physically fabricate some using a 3D printer

Scene Text Segmentation via Inverse Rendering

Abstract—Recognizing text in natural photographs that contain specular highlights and focal blur is a challenging problem. In this paper we describe a new text segmentation method based on inverse rendering, i.e. decomposing an input image into basic rendering elements. Our technique uses iterative optimization to solve the rendering parameters, including light source, material properties (e.g. diffuse/specular reflectance and shininess) as well as blur kernel size. We combine our segmentation method with a recognition component and show that by accounting for the rendering parameters, our approach achieves higher text recognition accuracy than previous work, particularly in the presence of color changes and image blur. In addition, the derived rendering parameters can be used to synthesize new text images that imitate the appearance of an existing image. I

Point Sampling with General Noise Spectrum

Point samples with different spectral noise properties (often defined using color names such as white, blue, green, and red) are important for many science and engineering disciplines including computer graphics. While existing techniques can easily produce white and blue noise samples, relatively little is known for generating other noise patterns. In particular, no single algorithm is available to generate different noise patterns according to user-defined spectra. In this paper, we describe an algorithm for generating point samples that match a user-defined Fourier spectrum function. Such a spectrum function can be either obtained from a known sampling method, or completely constructed by the user. Our key idea is to convert the Fourier spectrum function into a differential distribution function that describes the samples ’ local spatial statistics; we then use a gradient descent solver to iteratively compute a sample set that matches the target differential distribution function. Our algorithm can be easily modified to achieve adaptive sampling, and we provide a GPU-based implementation. Finally, we present a variety of different sample patterns obtained using our algorithm, and demonstrate suitable applications

Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling

With the rapid increase in residents in megacities, the passenger demand of metro systems is rising sharply and steadily, bringing immense pressure to train operations. To improve the service quality, this paper discusses systematically investigating a joint optimization of the robust passenger flow control strategy and train timetable on a congested metro line. A deterministic model for train timetabling and passenger flow control at each station is first developed to make a trade-off between operation efficiency and service fairness. Then, the uncertain passenger demand is further considered at each station, and three integer linear programming models are formulated to derive the robust passenger flow control strategies. The first two models are related to the technique of Light Robustness, in which the uncertainty is handled by inserting expected protection levels at stations or on trains. In addition, with a stochastic scenario set that characterizes the uncertain passenger information, the last model aims to find a solution that is feasible for all involved scenarios, and thus, reduces the impact of the uncertainty in metro systems. To improve the computational efficiency of large-scale instances, a customized decomposition-based algorithm is developed. Finally, some real-world case studies based on the operation data of the Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed approaches

Synthesis of 2,4,6,8,10-pentaaza[3.3.3]propellane substituted with different groups

Polyaza[3.3.3]propellanes have good symmetry and strong ring tension, which are suitable as the skeleton structure of functional materials such as high energy density materials. Based on the single benzyl derivative of 3,7,9.11-tetraoxo-2,4,6,8,10-pentaaza[3.3.3]propellane (compound 3 ), a series of 2,4,6,8,10- pentaaza[3.3.3]propellane derivatives, such as 4a-c and their reduced derivatives 5a-c, were synthesized successfully

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019 (COVID-19) epidemic prevention and control have raised new requirements that necessitate operation-strategy innovation in urban rail transit. To alleviate increasingly serious congestion and further reduce the risk of cross-infection, a novel two-stage distributionally robust optimization (DRO) model is explicitly constructed, in which the probability distribution of stochastic scenarios is only partially known in advance. In the proposed model, the mean-conditional value-at-risk (CVaR) criterion is employed to obtain a tradeoff between the expected number of waiting passengers and the risk of congestion on an urban rail transit line. The relationship between the proposed DRO model and the traditional two-stage stochastic programming (SP) model is also depicted. Furthermore, to overcome the obstacle of model solvability resulting from imprecise probability distributions, a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form. A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming (MILP) solver is developed to improve the computational efficiency of large-scale instances. Finally, a series of numerical examples with real-world operation data are executed to validate the proposed approaches

An efficient protocol for regenerating shoots from paper mulberry (Broussonetia papyrifera) leaf explants

Paper mulberry (Broussonetia papyrifera) is a tree species that has many economic, ecological, and social uses. This study developed an efficient protocol for regenerating shoots from leaf explants using Murashige and Skoog (MS) medium supplemented with different concentrations of plant growth regulators (PGRs), which play vital roles in shoot regeneration. The best result, 86.67% induction frequency and 4.35 shoots per explant, was obtained in the MS medium containing 2.0 mg/L N6-benzyladenine (BA) and 0.05 mg/L indole-3-butyric acid. The effects of explant age, orientation, and genotype were also investigated. Explants from young leaves had a greater regeneration frequency than those from old leaves, and the results were better when the distal end of the leaf explant contacted the medium versus the proximal end. Approximately 70.96% of the shoots rooted well in the MS medium containing 0.4 mg/L α-naphthalene acetic acid (NAA). Although some genotypes achieved poorer results, the regeneration protocol is still applicable for mass multiplication and genetic transformation