Searching for Ξcc+\Xi_{cc}^+ in Relativistic Heavy Ion Collisions

We study the doubly charmed baryon $\Xi_{cc}^+$ in high energy nuclear collisions. We solve the three-body Schroedinger equation with relativistic correction and calculate the $\Xi_{cc}^+$ yield and transverse momentum distribution via coalescence mechanism. For $\Xi_{cc}^+$ production in central Pb+Pb collisions at LHC energy, the yield is extremely enhanced, and the production cross section per binary collision is one order of magnitude larger than that in p+p collisions. This indicates that, it is most probable to discover $\Xi_{cc}^+$ in heavy ion collisions and its discovery can be considered as a probe of the quark-luon plasma formation.Comment: 5 pages and 4 figure

Nantes vs Shenzhen: Comparison and reflection on the development characteristics of green cities in France and China

Under the background that all countries in the world are facing the environmental pollution brought by the industrial revolution, the concept of sustainable development provides a new direction for the economic development of all countries. For cities that are both the center of human activities and the source of environmental pollution, green reform has become a key measure for countries to practice the concept of sustainable development, so green cities came into being. Under the guidance of national policies and in combination with its own characteristics, Nantes of France has successfully completed the transformation of a green city in less than 20 years through the green art transformation of transportation and public space of Shenzhen. China also relies on high technology to continuously reform and innovate in industrial structure, greening, haze control and water control, so as to realize the transformation of green city. The successful green city construction experience of the two cities provides reference for the development of green cities all over the world

Génération de maillage à partir d'images 3D en utilisant l'adaptation de maillage anisotrope et une équation de réinitialisation

Imaging techniques have well improved in the last decades. They may accurately provide numerical descriptions from 2D or 3D images, opening perspectives towards inner information, not seen otherwise, with applications in different fields, like medicine studies, material science or urban environments. In this work, a technique to build a numerical description under the mesh format has been implemented and used in numerical simulations when coupled to finite element solvers. Firstly, mathematical morphology techniques have been introduced to handle image information, providing the specific features of interest for the simulation. The immersed image method was then proposed to interpolate the image information on a mesh. Then, an iterative anisotropic mesh adaptation operator was developed to construct the optimal mesh, based on the estimated error concerning the image interpolation. The mesh is thus directly constructed from the image information. We have also proposed a new methodology to build a regularized phase function, corresponding to the objects we wish to distinguish from the image, using a redistancing method. Two main advantages of having such function are: the gradient of the regularized function performs better for mesh adaptation; the regularized function may be directly used for the finite element solver. Stabilized finite element flow and advection solvers were coupled to the constructed anisotropic mesh and the redistancing function, allowing its application to multiphase flow numerical simulations. All these developments have been extended in a massively parallel context. An important objective of this work is the simplification of the image based computations, through a modified way to segment the image and by coupling all to an automatic way to construct the mesh used in the finite element simulations.Ces dernières années, les techniques d'imagerie ont fait l'objet de beaucoup d'améliorations. Elles permettent de fournir des images numériques 2D ou 3D précises de zones parfois invisibles à l’œil nu. Ces techniques s'appliquent dans de nombreux domaines comme l'industrie cinématographique, la photographie ou l'imagerie médicale... Dans cette thèse, l'imagerie sera utilisée pour effectuer des simulations numériques en la couplant avec un solveur éléments finis. Nous présenterons, en premier lieu, la morphologie mathématique et la méthode d'immersion d'image. Elles permettront l'extraction d'informations permettant la transformation d'une image dans un maillage exploitable. Puis, une méthode itérative d'adaptation de maillage basée sur un estimateur d'erreur sera utilisée afin de construire un maillage optimal. Ainsi, un maillage sera construit uniquement avec les données d'une image. Nous proposerons également une nouvelle méthodologie pour construire une fonction régulière a l'aide d'une méthode de réinitialisation de la distance signée. Deux avantages sont à noter : l'utilisation de la fonction régularisée permet une bonne adaptation de maillage. De plus, elle est directement utilisable par le solveur éléments finis. Les simulations numériques sont donc réalisées en couplant éléments finis stabilisés, adaptation de maillage anisotrope et réinitialisation. L'objectif de cette thèse est donc de simplifier le calcul numérique à partir d'image, d'améliorer la précision numérique, la construction d'un maillage automatique et de réaliser des calculs numériques parallèles efficaces. Les applications envisagées peuvent être dans le domaine médical, de la physique des matériaux ou du design industriel

BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization

In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Considering the dominance of hand and body in sign language expression, we organize them as pose triplet units and feed them into the Transformer backbone in a frame-wise manner. Pre-training is performed via reconstructing the masked triplet unit from the corrupted input sequence, which learns the hierarchical correlation context cues among internal and external triplet units. Notably, different from the highly semantic word token in BERT, the pose unit is a low-level signal originally located in continuous space, which prevents the direct adoption of the BERT cross-entropy objective. To this end, we bridge this semantic gap via coupling tokenization of the triplet unit. It adaptively extracts the discrete pseudo label from the pose triplet unit, which represents the semantic gesture/body state. After pre-training, we fine-tune the pre-trained encoder on the downstream SLR task, jointly with the newly added task-specific layer. Extensive experiments are conducted to validate the effectiveness of our proposed method, achieving new state-of-the-art performance on all four benchmarks with a notable gain.Comment: Accepted by AAAI 2023 (Oral