Neonatal Systemic Juvenile Xanthogranuloma with an Ominous Presentation and Successful Treatment

This case report originated from a case of neonatal multisystemic juvenile xanthogranuloma (JXG). The patient presented with blue muffin rush, cervical mass, bone destruction, lung nodule, hepatosplenomegaly, and coagulopathy and was successfully treated with Langerhans cell histiocytosis (LCH) based chemotherapy treatment. Similar cases in literature were reviewed and it seems that JXG, a relatively benign entity, when presented in its systemic form with liver involvement, could have an aggressive course and portend quite poor prognosis. Challenges and special consideration of the diagnosis, treatment, and future case observation are discussed

Applying Bayesian networks in nuclear power plant safety analysis

AbstractOver the last decade, Nuclear energy has become one of important energy. Nuclear power systems become more complex and traditional safety methods are hard to be applied. This paper presents a novel approach for nuclear power plant safety analysis which called Bayesian Networks(BN). The BN model is constructed based on the combination of Failure Mode, Effect Analysis (FMEA) and Fault Trees Analysis(FTA). The probability of the model’s root nodes is estimated by Bayesian estimation method and Monte Carlo simulation. Bidirectional inference and sensitivity analysis of the model is also researched. At last, we use a case study to show the method’s advantages compared with traditional methods in nuclear power plant safety analysis

Breakdown of Conventional Winding Number Calculation in One-Dimensional Lattices with Interactions Beyond Nearest Neighbors

Topological indices, such as winding numbers, have been conventionally used to predict the number of topologically protected edge states (TPES) in topological insulators. In this Letter, we experimentally observe its breakdown in Su-Schrieffer-Heeger (SSH) lattices with beyond-nearest-neighbor interactions. We hereby resort to the Berry connection for accurate TPES prediction. Moreover, we decouple the complex phonon modes by examining the torsional ones, which have received much less attention than their transverse and longitudinal counterparts in existing metamaterial studies

A Grey Interval Relational Degree-Based Dynamic Multiattribute Decision Making Method and Its Application in Investment Decision Making

The purpose of this paper is to propose a three-dimensional grey interval relational degree model for dynamic Multiattribute decision making. In the model, the observed values are interval grey numbers. Elements are selected in the system as the points in an m-dimensional linear space. Then observation data of each element to different time and objects are as the coordinates of point. An optimization model is employed to obtain each scheme’s affiliate degree for the positive and negative ideal schemes. And a three-dimensional grey interval relational degree model based on time, index, and scheme is constructed in the paper. The result shows that the three-dimensional grey relational degree simplifies the traditional dynamic multiattribute decision making method and can better resolve the dynamic multiattribute decision making problem of interval numbers. The example illustrates that the method presented in the paper can be used to deal with problems of uncertainty such as dynamic multiattribute decision making

Learning from Few Demonstrations with Frame-Weighted Motion Generation

Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without the need for time-consuming robot programming and inefficient solution exploration. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized LfD (TP-LfD) encodes relevant contextual information into reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms typically require multiple demonstrations across various environmental conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel algorithm to learn skills from few demonstrations. By leveraging the reference frame weights that capture the frame importance or relevance during task executions, our method demonstrates excellent skill acquisition performance, which is validated in real robotic environments.Comment: Accepted by ISER. For the experiment video, see https://youtu.be/JpGjk4eKC3

Further improvement of fluidized bed models by incorporating zone method with Aspen Plus interface

While providing a fast and accurate tool of simulating fluidized beds, the major limitation of classical zero-dimensional ideal reactor models used in process simulators, such as models built into commercial software (e.g. Aspen Plus®), has been the difficulties of involving thermal reciprocity between each reactor model and incorporating heat absorption by the water wall and super-heaters which is usually specified as model inputs rather than predicted by the models themselves. This aspect is of particular importance to the geometry design and evaluation of operating conditions and flexibility of fluidized beds. This paper proposes a novel modelling approach to resolve this limitation by incorporating an external model that marries the advantages of zone method and Aspen Plus in a robust manner. The improved model has a relatively modest computing demand and hence may be incorporated feasibly into dynamic simulations of a whole power plant

All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)

This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. All in one: Multi-task prompting for graph neural networks. KDD 23) The paper introduces a novel approach to bridging the gap between pre-trained graph models and the diverse tasks they're applied to, inspired by the success of prompt learning in NLP. Recognizing the challenge of aligning pre-trained models with varied graph tasks (node level, edge level, and graph level), which can lead to negative transfer and poor performance, we propose a multi-task prompting method for graphs. This method involves unifying graph and language prompt formats, enabling NLP's prompting strategies to be adapted for graph tasks. By analyzing the task space of graph applications, we reformulate problems to fit graph-level tasks and apply meta-learning to improve prompt initialization for multiple tasks. Experiments show our method's effectiveness in enhancing model performance across different graph tasks. Beyond the original work, in this extended abstract, we further discuss the graph prompt from a bigger picture and provide some of the latest work toward this area.Comment: submitted to IJCAI 2024 Sister Conferences Track. The original paper can be seen at arXiv:2307.0150