Hippocampal sparing in whole-brain radiotherapy for brain metastases: controversy, technology and the future

Whole-brain radiotherapy (WBRT) plays an irreplaceable role in the treatment of brain metastases (BMs), but cognitive decline after WBRT seriously affects patients’ quality of life. The development of cognitive dysfunction is closely related to hippocampal injury, but standardized criteria for predicting hippocampal injury and dose limits for hippocampal protection have not yet been developed. This review systematically reviews the clinical efficacy of hippocampal avoidance - WBRT (HA-WBRT), the controversy over dose limits, common methods and characteristics of hippocampal imaging and segmentation, differences in hippocampal protection by common radiotherapy (RT) techniques, and the application of artificial intelligence (AI) and radiomic techniques for hippocampal protection. In the future, the application of new techniques and methods can improve the consistency of hippocampal dose limit determination and the prediction of the occurrence of cognitive dysfunction in WBRT patients, avoiding the occurrence of cognitive dysfunction in patients and thus benefiting more patients with BMs

Are All Losses Created Equal: A Neural Collapse Perspective

While cross entropy (CE) is the most commonly used loss to train deep neural networks for classification tasks, many alternative losses have been developed to obtain better empirical performance. Among them, which one is the best to use is still a mystery, because there seem to be multiple factors affecting the answer, such as properties of the dataset, the choice of network architecture, and so on. This paper studies the choice of loss function by examining the last-layer features of deep networks, drawing inspiration from a recent line work showing that the global optimal solution of CE and mean-square-error (MSE) losses exhibits a Neural Collapse phenomenon. That is, for sufficiently large networks trained until convergence, (i) all features of the same class collapse to the corresponding class mean and (ii) the means associated with different classes are in a configuration where their pairwise distances are all equal and maximized. We extend such results and show through global solution and landscape analyses that a broad family of loss functions including commonly used label smoothing (LS) and focal loss (FL) exhibits Neural Collapse. Hence, all relevant losses(i.e., CE, LS, FL, MSE) produce equivalent features on training data. Based on the unconstrained feature model assumption, we provide either the global landscape analysis for LS loss or the local landscape analysis for FL loss and show that the (only!) global minimizers are neural collapse solutions, while all other critical points are strict saddles whose Hessian exhibit negative curvature directions either in the global scope for LS loss or in the local scope for FL loss near the optimal solution. The experiments further show that Neural Collapse features obtained from all relevant losses lead to largely identical performance on test data as well, provided that the network is sufficiently large and trained until convergence.Comment: 32 page, 10 figures, NeurIPS 202

Dynamic response analysis of intake tower in hydroelectric power station with high surrounding rock

This paper presents results of numerical analysis for the seismic response of hydropower station intake tower in step-like ground based on artificial boundary theory. A L topography finite element model was established to verify the correctness of the proposed method of viscous elasticity boundary by consider inconsistent reflective surface. The method was applied to an intake tower, and the acceleration of bedrock was obtained by seismic inversion method, then the equivalent load of each node was calculated. Five different models were established as follow: massless foundation, consistent input viscous elasticity boundary, inconsistent input viscous elasticity boundary and whether set contact, then displacement and stress were compared, the results show that the proposed method with contact was minimal. The base plate of intake tower and the foundation were in close adhesion state in the whole process of earthquake, both sides and rear side of intake tower without through disengagement phenomena from rock, it can conclude that the intake tower in the overall stability state

The stability evaluation of clay tunnels via the non-linear deterioration of physical and mechanical properties of surrounding rocks

Simple, fast, and reliable methods for the stability evaluation of tunnels can facilitate the construction and development of tunneling projects. The problems related to tunnel stability at this stage can be well analyzed via theoretical analysis method, model test method, or numerical analysis method. On the other hand, those methods are hard to be effectively analyzed these projects with higher importance, shorter decision and design period, and more urgent construction period. This paper proposed research works on the stability evaluation of clay tunnels. Firstly, a state function with the variables of stress and strain state is presented to predict the stress and strain states of surrounding rocks caused by tunnel excavation, which characterize the physical-mechanical state of surrounding rocks (also called stability state). Secondly, the non-linear deterioration of the physical and mechanical properties of surrounding rocks will be simulated, and the expressions and calculation methods of the tunnel stability reserve factor will be yielded. Finally, the results of the proposed method were compared with the strength reduction method and the limit equilibrium method with a clay tunnel example. The comparison between the three feature points of the arch crown, sidewall, and arch bottom showed that the stability reserve factor of the clay tunnel was smaller than those of the strength reduction method and the limit equilibrium method. The values of limit displacement obtained by the proposed method were closer to the field monitoring data than that of the strength reduction method. Therefore, this study could be better applied to the stability evaluation of clay tunnels

CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers. We propose CodeApex, a bilingual benchmark dataset focusing on the programming comprehension and code generation abilities of LLMs. CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks. Additionally, CodeApex utilizes algorithmic questions and corresponding test cases to assess the code quality generated by LLMs. We evaluate 14 state-of-the-art LLMs, including both general-purpose and specialized models. GPT exhibits the best programming capabilities, achieving approximate accuracies of 50% and 56% on the two tasks, respectively. There is still significant room for improvement in programming tasks. We hope that CodeApex can serve as a reference for evaluating the coding capabilities of LLMs, further promoting their development and growth. Datasets are released at https://github.com/APEXLAB/CodeApex.git. CodeApex submission website is https://apex.sjtu.edu.cn/codeapex/.Comment: 21 page