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

Research on Competitive Strategy of Electric Company—Taking China Railway High-speed Railway Electric Equipment Co.

China has introduced a series of supporting measures for the problem of high-speed railway equipment jam, which increases the pressure of market competition to a certain extent. In order to strengthen the market competition, China Railway Electric Equipment Company must develop a scientific competitive strategy to enhance the long-term development ability of the enterprise.In this paper, China Railway Electric Equipment Company is selected as the research object, the competitive environment of the industry is analyzed based on the Porter five-force model, and then SWOT analysis is used to describe the advantages, disadvantages, opportunities and threats of the company, the SWOT matrix is constructed, and the differentiated competitive strategy of China Railway Electric Equipment Company is selected according to the above analysis results. Finally, China Railway Electric Equipment Company chooses differentiated competitive strategy, strengthen research and development and technical innovation, realize the differentiation of technical products; actively expand sales channels, distinguish direct sales channels and overseas sales channels; actively introduce quality service marketing, introduce and improve customer satisfaction survey system, and actively introduce quality service marketing

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

Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling

The objective of this study is to improve automated feedback tools designed for English Language Learners (ELLs) through the utilization of data science techniques encompassing machine learning, natural language processing, and educational data analytics. Automated essay scoring (AES) research has made strides in evaluating written essays, but it often overlooks the specific needs of English Language Learners (ELLs) in language development. This study explores the application of BERT-related techniques to enhance the assessment of ELLs' writing proficiency within AES. To address the specific needs of ELLs, we propose the use of DeBERTa, a state-of-the-art neural language model, for improving automated feedback tools. DeBERTa, pretrained on large text corpora using self-supervised learning, learns universal language representations adaptable to various natural language understanding tasks. The model incorporates several innovative techniques, including adversarial training through Adversarial Weights Perturbation (AWP) and Metric-specific AttentionPooling (6 kinds of AP) for each label in the competition. The primary focus of this research is to investigate the impact of hyperparameters, particularly the adversarial learning rate, on the performance of the model. By fine-tuning the hyperparameter tuning process, including the influence of 6AP and AWP, the resulting models can provide more accurate evaluations of language proficiency and support tailored learning tasks for ELLs. This work has the potential to significantly benefit ELLs by improving their English language proficiency and facilitating their educational journey.Comment: This article was accepted by 2023 International Conference on Information Network and Computer Communications(INCC