Glioblastoma is a highly aggressive and malignant brain tumor type that
requires early diagnosis and prompt intervention. Due to its heterogeneity in
appearance, developing automated detection approaches is challenging. To
address this challenge, Artificial Intelligence (AI)-driven approaches in
healthcare have generated interest in efficiently diagnosing and evaluating
brain tumors. The Brain Tumor Segmentation Challenge (BraTS) is a platform for
developing and assessing automated techniques for tumor analysis using
high-quality, clinically acquired MRI data. In our approach, we utilized a
multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to
perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET),
Tumor Core (TC), and Whole Tumor (WT). The multi-scale feature extraction
provides better contextual feature aggregation in high resolutions and the Swin
Transformer blocks improve the global feature extraction at deeper levels of
the model. The segmentation mask generation in the decoder branch is guided by
the attention-refined features gathered from the encoder branch to enhance the
important attributes. Moreover, hierarchical supervision is used to train the
model efficiently. Our model's performance on the validation set resulted in
92.19, 87.75, and 83.18 Dice Scores and 89.09, 84.67, and 82.15 Lesion-wise
Dice Scores in WT, TC, and ET, respectively. The code is publicly available at
this https URL

基于多尺度关注引导 U-Net 模型以及 Swin Transformer 的三维脑肿瘤分割方法的性能评估。

注意力增强的混合特征聚合网络用于三维脑肿瘤分割

Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor  Segmentation

Large Language Models (LLMs) have driven substantial progress in artificial
intelligence in recent years, exhibiting impressive capabilities across a wide
range of tasks, including mathematical problem-solving. Inspired by the success
of subgoal-based methods, we propose a novel framework called
\textbf{SE}quential sub\textbf{G}oal \textbf{O}ptimization (SEGO) to enhance
LLMs' ability to solve mathematical problems. By establishing a connection
between the subgoal breakdown process and the probability of solving problems,
SEGO aims to identify better subgoals with theoretical guarantees. Addressing
the challenge of identifying suitable subgoals in a large solution space, our
framework generates problem-specific subgoals and adjusts them according to
carefully designed criteria. Incorporating these optimized subgoals into the
policy model training leads to significant improvements in problem-solving
performance. We validate SEGO's efficacy through experiments on two benchmarks,
GSM8K and MATH, where our approach outperforms existing methods, highlighting
the potential of SEGO in AI-driven mathematical problem-solving.
Data and code associated with this paper will be available at
this https URL

提出一种名为 SEGO 的新框架，通过建立子目标分解过程与解决问题的概率之间的联系，优化大型语言模型在数学问题求解中的能力，实验证明 SEGO 的有效性并超越以往方法。

SEGO: 数学问题求解的顺序子目标优化

SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving

In contrast to the rapid digitalization of several industries, agriculture
suffers from low adoption of climate-smart farming tools. Even though AI-driven
digital agriculture can offer high-performing predictive functionalities, it
lacks tangible quantitative evidence on its benefits to the farmers. Field
experiments can derive such evidence, but are often costly and time consuming.
To this end, we propose an observational causal inference framework for the
empirical evaluation of the impact of digital tools on target farm performance
indicators. This way, we can increase farmers' trust by enhancing the
transparency of the digital agriculture market, and in turn accelerate the
adoption of technologies that aim to increase productivity and secure a
sustainable and resilient agriculture against a changing climate. As a case
study, we perform an empirical evaluation of a recommendation system for
optimal cotton sowing, which was used by a farmers' cooperative during the
growing season of 2021. We leverage agricultural knowledge to develop a causal
graph of the farm system, we use the back-door criterion to identify the impact
of recommendations on the yield and subsequently estimate it using several
methods on observational data. The results show that a field sown according to
our recommendations enjoyed a significant increase in yield (12% to 17%).

本文提出了一个观察因果推断的框架，用于对数字农业工具对目标农场绩效指标的影响进行实证评估，并就一个针对棉花播种的推荐系统进行了实证评估，结果表明其极大地提高了棉农产量。