Simply-supported Box Girder Construction Technique of 40 m Movable Formwork Method Cast-in-Situ Railway Passenger Dedicated Line

The movable formwork has the characteristics of light self weight, large bearing capacity, small rod type number, rapid assembling, and simplicity to construct. Therefore, when cast-in-situ concrete box girder is constructed, the moveable formwork has great meanings in cost reduction and project time limit shortening. By using this method, the difficulty that brackets cannot be distributed as the ground bearing capacity of bridges. Besides that, bridge sites is low is reasonably solved, cost of material and labor can be reduced, influence to underbridge is minimize, and the construction progress is accelerated. The simply-supported box girder construction technique of 40 m movable formwork method cast-in-situ railway passenger dedicated line is particularly analyzed

Tiny Corpus Applications with Transformation-Based Error-Driven Learning : Evaluations of Automatic Grammar Induction and Partial Parsing of SaiSiyat

This paper reports a preliminary result on automatic grammar induction based on the framework of Brill and Markus (1992) and binary-branching syntactic parsing of Esperanto and SaiSiyat (a Formosan language). Automatic grammar induction requires large corpus and is found implausible to process endangered minor languages. Syntactic parsing, on the contrary, needs merely tiny corpus and works along with corpora segmented by intonation-unit which results in high accuracy

Regularize implicit neural representation by itself

This paper proposes a regularizer called Implicit Neural Representation Regularizer (INRR) to improve the generalization ability of the Implicit Neural Representation (INR). The INR is a fully connected network that can represent signals with details not restricted by grid resolution. However, its generalization ability could be improved, especially with non-uniformly sampled data. The proposed INRR is based on learned Dirichlet Energy (DE) that measures similarities between rows/columns of the matrix. The smoothness of the Laplacian matrix is further integrated by parameterizing DE with a tiny INR. INRR improves the generalization of INR in signal representation by perfectly integrating the signal's self-similarity with the smoothness of the Laplacian matrix. Through well-designed numerical experiments, the paper also reveals a series of properties derived from INRR, including momentum methods like convergence trajectory and multi-scale similarity. Moreover, the proposed method could improve the performance of other signal representation methods.Comment: Highlight paper in CVPR 202

Stochastic Response Characteristic and Equivalent Damping of Weak Nonlinear Energy Dissipation System under Biaxial Earthquake Action

The random response characteristic of weak nonlinear structure under biaxial earthquake excitation is investigated. The structure has a SDOF (single degree of freedom) with supporting braces and viscoelastic dampers. First, it adopts integral constitutive relation and establishes a differential and integral equations of motion. Then, according to the principle of energy balance, the equation is linearized. Finally, based on the stochastic averaging method, the general analytical solution of the variance of the displacement and velocity response and the equivalent damping is deduced and derived. At the same time, the joint probability density function of the amplitude and phase and displacement and velocity of the energy dissipation structure are also given. The dynamic characteristics of a structure with viscoelastic dampers are determined as a solution to the variance of displacement response, so the equivalent damping is taken into consideration as a solution to replace the original nonlinear damping. It means it has established a unified analytical solution of stochastic response analysis and equivalent damping of a SDOF nonlinear dissipation structure with the brace under biaxial earthquake action in this paper

AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix Completion

The explicit low-rank regularization, e.g., nuclear norm regularization, has been widely used in imaging sciences. However, it has been found that implicit regularization outperforms explicit ones in various image processing tasks. Another issue is that the fixed explicit regularization limits the applicability to broad images since different images favor different features captured by different explicit regularizations. As such, this paper proposes a new adaptive and implicit low-rank regularization that captures the low-rank prior dynamically from the training data. The core of our new adaptive and implicit low-rank regularization is parameterizing the Laplacian matrix in the Dirichlet energy-based regularization, which we call the regularization \textit{AIR}. Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training. We validate AIR's effectiveness on various benchmark tasks, indicating that the AIR is particularly favorable for the scenarios when the missing entries are non-uniform. The code can be found at https://github.com/lizhemin15/AIR-Ne

Mechanism-based site-directed mutagenesis to shift the optimum pH of the phenylalanine ammonia-lyase from Rhodotorula glutinis JN-1

AbstractPhenylalanine ammonia-lyase (RgPAL) from Rhodotorula glutinis JN-1 stereoselectively catalyzes the conversion of the l-phenylalanine into trans-cinnamic acid and ammonia, and was used in chiral resolution of dl-phenylalanine to produce the d-phenylalanine under acidic condition. However, the optimum pH of RgPAL is 9 and the RgPAL exhibits low catalytic efficiency at acidic side. Therefore, a mutant RgPAL with a lower optimum pH is expected. Based on catalytic mechanism and structure analysis, we constructed a mutant RgPAL-Q137E by site-directed mutagenesis, and found that this mutant had an extended optimum pH 7–9 with activity of 1.8-fold higher than that of the wild type at pH 7. As revealed by Friedel–Crafts-type mechanism of RgPAL, the improvement of the RgPAL-Q137E might be due to the negative charge of Glu137 which could stabilize the intermediate transition states through electrostatic interaction. The RgPAL-Q137E mutant was used to resolve the racemic dl-phenylalanine, and the conversion rate and the eeD value of d-phenylalanine using RgPAL-Q137E at pH 7 were increased by 29% and 48%, and achieved 93% and 86%, respectively. This work provides an effective strategy to shift the optimum pH which is favorable to further applications of RgPAL

Taxation In Germany And Romania

Taxation is a symbol of national sovereignty and a central part of a country’s overall economic policy, helping finance public spending and redistribute wealth. Furthermore, for international business executives, taxation is an important consideration in investment decisions. This paper discusses the taxation in two European Union (EU) member countries, Germany and Romania. These two countries are selected because of their different stages of economic development and their unique characteristics in taxation

A regularized deep matrix factorized model of matrix completion for image restoration

It has been an important approach of using matrix completion to perform image restoration. Most previous works on matrix completion focus on the low-rank property by imposing explicit constraints on the recovered matrix, such as the constraint of the nuclear norm or limiting the dimension of the matrix factorization component. Recently, theoretical works suggest that deep linear neural network has an implicit bias towards low rank on matrix completion. However, low rank is not adequate to reflect the intrinsic characteristics of a natural image. Thus, algorithms with only the constraint of low rank are insufficient to perform image restoration well. In this work, we propose a Regularized Deep Matrix Factorized (RDMF) model for image restoration, which utilizes the implicit bias of the low rank of deep neural networks and the explicit bias of total variation. We demonstrate the effectiveness of our RDMF model with extensive experiments, in which our method surpasses the state of art models in common examples, especially for the restoration from very few observations. Our work sheds light on a more general framework for solving other inverse problems by combining the implicit bias of deep learning with explicit regularization

DEVELOPING AN ONLINE CORPUS OF FORMOSAN LANGUAGES

Information technologies have now matured to the point of enabling researchers to create a repository of language resources, especially for those languages facing the crisis of endangerment. The development of an online platform of corpora, made possible by recent advances in data storage, character-encoding and web technology, has profound consequences for the accessibility, quantity, quality and interoperability of linguistic field data. This is of particular significance for Formosan languages in Taiwan, many of which are on the verge of extinction. As a response to the recognition of this burgeoning problem, the key objectives of the establishment of the NTU Corpus of Formosan Languages aim to document and thus preserve valuable linguistic data, as well as relevant ethnological and cultural information. This paper will introduce some of the theoretical bases behind this initiative, as well as the procedures, transcription conventions, database normalization, in-house system and three special features in the creation of this corpus