Belgian Economy characteristics and its experiences

Belgium ranks middle in EU countries with highly specified economy, clear property system, efficient and healthy legal as well as financial institutions. It encourages thrifty and diligence which have been a representative sample of high efficiency economy since 12th century. Its success has provided beneficial experiences for China’s economy transformation. Key words: high efficiency economy, thrifty and diligence, high-tech and small companies Résumé: La Belgique se figure parmi les pays d’UE avec son économie bien spécifique, le système de propriété clair, les institutions légales et financières efficaces et saines. Elle encourage l’économie & diligence qui était un échantillon représentatif de l’économie de haute efficacité depuis le 12e siècle. Son succès a offert des expériences bénéfiques pour la transformation économique de la Chine. Mots-Clés: économie de haute efficacité, économie & diligence, haute technologie & petites entreprise

Statistical delay distribution analysis on high-speed railway trains

Published by SpringerOpen, Heidelber

THE REGULATION OF LEG STIFFNESS AND EMG ACTIVITIES ON PERSON WITH VISUAL IMPAIRED DURING STEP-DOWN WALKING

The purpose of present study was to evaluate leg muscular regulation and neuromuscular activation by investigating the stiffness and EMG amplitude of normal vision students and visually impaired students. 10 normal vision (age: 24.3±20 years; height: 171.5±4.6cm; mass: 65.9±8.0kg) and 10 visually impaired students (age: 23.2±2.4 years; height: 163.4±9.6cm; mass: 62.8±15.0kg) were served as subjects. AMTI force platform (1200 Hz), Peak Performance motion analysis system (60Hz) and Biovision EMG system were used synchronously to record the ground reaction force, the kinematic parameters and EMG signals of lower extremity during the subjects stepped down from height 20, 30 and 40cm. The results revealed that the regulation of neuromuscular system of the impaired is less efficient compared to the normal one because of lower muscle stiffness and EMG activity

Bulge formation from SSCs in a responding cuspy dark matter halo

We simulate the bulge formation in very late-type dwarf galaxies from circumnuclear super star clusters (SSCs) moving in a responding cuspy dark matter halo (DMH). The simulations show that (1) the response of DMH to sinking of SSCs is detectable only in the region interior to about 200 pc. The mean logarithmic slope of the responding DM density profile over that area displays two different phases: the very early descent followed by ascent till approaching to 1.2 at the age of 2 Gyrs. (2) the detectable feedbacks of the DMH response on the bulge formation turned out to be very small, in the sense that the formed bulges and their paired nuclear cusps in the fixed and the responding DMH are basically the same, both are consistent with $HST$ observations. (3) the yielded mass correlation of bulges to their nuclear (stellar) cusps and the time evolution of cusps' mass are accordance with recent findings on relevant relations. In combination with the consistent effective radii of nuclear cusps with observed quantities of nuclear clusters, we believe that the bulge formation scenario that we proposed could be a very promising mechanism to form nuclear clusters.Comment: 27 pages, 11 figures, accepted for publication in Ap

Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields

Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images. Despite its impressive performance, NeRF is plagued by its necessity for numerous calibrated views and its accuracy diminishes significantly in a few-shot setting. To address this challenge, we propose Self-NeRF, a self-evolved NeRF that iteratively refines the radiance fields with very few number of input views, without incorporating additional priors. Basically, we train our model under the supervision of reference and unseen views simultaneously in an iterative procedure. In each iteration, we label unseen views with the predicted colors or warped pixels generated by the model from the preceding iteration. However, these expanded pseudo-views are afflicted by imprecision in color and warping artifacts, which degrades the performance of NeRF. To alleviate this issue, we construct an uncertainty-aware NeRF with specialized embeddings. Some techniques such as cone entropy regularization are further utilized to leverage the pseudo-views in the most efficient manner. Through experiments under various settings, we verified that our Self-NeRF is robust to input with uncertainty and surpasses existing methods when trained on limited training data.Comment: 11 pages, 11 figure

Localized Sparse Incomplete Multi-view Clustering

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a l1 norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC.Comment: Published in IEEE Transactions on Multimedia (TMM). The code is available at Github https://github.com/justsmart/LSIMV

Electrostatic effect due to patch potentials between closely spaced surfaces

The spatial variation and temporal variation in surface potential are important error sources in various precision experiments and deserved to be considered carefully. In the former case, the theoretical analysis shows that this effect depends on the surface potentials through their spatial autocorrelation functions. By making some modification to the quasi-local correlation model, we obtain a rigorous formula for the patch force, where the magnitude is proportional to ${\frac{1}{{{a}^{2}}}{{(\frac{a}{w})}^{\beta (a/w)+2}}}$ with ${a}$ the distance between two parallel plates, ${w}$ the mean patch size, and ${\beta}$ the scaling coefficient from ${-2}$ to ${-4}$. A torsion balance experiment is then conducted, and obtain a 0.4 mm effective patch size and 20 mV potential variance. In the latter case, we apply an adatom diffusion model to describe this mechanism and predicts a ${f^{-3/4}}$ frequency dependence above 0.01 ${\rm mHz}$. This prediction meets well with a typical experimental results. Finally, we apply these models to analyze the patch effect for two typical experiments. Our analysis will help to investigate the properties of surface potentials