最近，在气象学中使用机器学习大大增加了。尽管许多机器学习方法并不是什么新鲜事物，但有关机器学习的大学课程在很大程度上是气象学专业的学生，​​不需要成为气象学家。缺乏正式的教学导致人们认为机器学习方法是“黑匣子”，因此最终用户不愿在每天的工作流程中应用机器学习方法。为了减少机器学习方法的不透明性，并降低了对气象学中机器学习的犹豫，本文对一些最常见的机器学习方法进行了调查。一个熟悉的气象示例用于将机器学习方法背景化，同时还使用普通语言讨论机器学习主题。证明了以下机器学习方法：线性回归；逻辑回归；决策树；随机森林；梯度增强了决策树；天真的贝叶斯；并支持向量机。除了讨论不同的方法外，本文还包含有关通用机器学习过程的讨论以及最佳实践，以使读者能够将机器学习应用于自己的数据集。此外，所有代码（以Jupyter笔记本电脑和Google Colaboratory Notebooks的形式）用于在论文中进行示例，以促进气象学中的机器学习使用。

鉴于人工智能（AI）和机器学习（ML）方法越来越多，环境科学各方面的方法，我们必须讨论关于AI的道德和负责任使用。事实上，大大可以从其他领域学习，通常是最好的意图，但经常导致意外的社会后果，如刑事司法系统中的硬编码种族偏见或通过金融体系增加经济不平等。常见的误解是，当使用AI时，环境科学对这种非预期的后果免疫，因为大多数数据来自观察，并且AI算法基于数学公式，这些公式通常被视为物镜。在本文中，我们争论可能就是这样。使用具体示例，我们展示了许多方式，其中使用AI可以引入环境科学的类似后果。本文将刺激讨论和研究努力。作为一个社区，我们应该通过引入AI来避免重复在其他域中的任何可预见的错误。事实上，通过适当的预防措施，AI可以成为帮助{\它减少}气候和环境不公正的伟大工具。我们主要关注天气和气候示例，但结论普遍存在环境科学中。

Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional representations needed for generalization. While some approaches have been adopted to refine representations via auxiliary self-supervised losses while simultaneously learning decision policies, learning compositional representations from hand-designed and context-independent self-supervised losses (multi-view) still adapts relatively slowly to the real world, which contains many non-IID subspaces requiring rapid distribution shift in both time and spatial attention patterns at varying levels of abstraction. In contrast, supervised language model cascades have shown the flexibility to adapt to many diverse manifolds, and hints of self-learning needed for autonomous task transfer. However, to date, transfer methods for language models like few-shot learning and fine-tuning still require human supervision and transfer learning using self-learning methods has been underexplored. We propose a self-supervised loss policy called contrastive distillation which manifests latent variables with high mutual information with both source and target tasks from weights to tokens. We show how this outperforms common methods of transfer learning and suggests a useful design axis of trading off compute for generalizability for online transfer. Contrastive distillation is improved through sampling from memory and suggests a simple algorithm for more efficiently sampling negative examples for contrastive losses than random sampling.

Realistic synthetic image data rendered from 3D models can be used to augment image sets and train image classification semantic segmentation models. In this work, we explore how high quality physically-based rendering and domain randomization can efficiently create a large synthetic dataset based on production 3D CAD models of a real vehicle. We use this dataset to quantify the effectiveness of synthetic augmentation using U-net and Double-U-net models. We found that, for this domain, synthetic images were an effective technique for augmenting limited sets of real training data. We observed that models trained on purely synthetic images had a very low mean prediction IoU on real validation images. We also observed that adding even very small amounts of real images to a synthetic dataset greatly improved accuracy, and that models trained on datasets augmented with synthetic images were more accurate than those trained on real images alone. Finally, we found that in use cases that benefit from incremental training or model specialization, pretraining a base model on synthetic images provided a sizeable reduction in the training cost of transfer learning, allowing up to 90\% of the model training to be front-loaded.

Due to the low signal-to-noise ratio and limited resolution of functional MRI data, and the high complexity of natural images, reconstructing a visual stimulus from human brain fMRI measurements is a challenging task. In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail. In particular, we train a surface-based convolutional network model that maps from brain response to semantic image features first (Cortex2Semantic). We then combine this model with a high-quality image generator (Instance-Conditioned GAN) to train another mapping from brain response to fine-grained image features using a variational approach (Cortex2Detail). Image reconstructions obtained by our proposed method achieve state-of-the-art semantic fidelity, while yielding good fine-grained similarity with the ground-truth stimulus. Our code is available at: https://github.com/zijin-gu/meshconv-decoding.git.

We develop a new framework for trajectory planning on predefined paths, for general N-link manipulators. Different from previous approaches generating open-loop minimum time controllers or pre-tuned motion profiles by time-scaling, we establish analytic algorithms that recover all initial conditions that can be driven to the desirable target set while adhering to environment constraints. More technologically relevant, we characterise families of corresponding safe state-feedback controllers with several desirable properties. A key enabler in our framework is the introduction of a state feedback template, that induces ordering properties between trajectories of the resulting closed-loop system. The proposed structure allows working on the nonlinear system directly in both the analysis and synthesis problems. Both offline computations and online implementation are scalable with respect to the number of links of the manipulator. The results can potentially be used in a series of challenging problems: Numerical experiments on a commercial robotic manipulator demonstrate that efficient online implementation is possible.

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. Neoadjuvant chemotherapy treatment has recently risen in usage as it may result in a patient having a pathologic complete response (pCR), and it can shrink inoperable breast cancer tumors prior to surgery so that the tumor becomes operable, but it is difficult to predict a patient's pathologic response to neoadjuvant chemotherapy. In this paper, we investigate the efficacy of leveraging learnt volumetric deep features from a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDI$^s$) for the purpose of pCR prediction. More specifically, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features using the post-treatment response. As the first study to explore the utility of CDI$^s$ within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities, and found that the proposed approach can provide enhanced pCR prediction performance and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, this approach to leverage volumetric deep radiomic features (which we name Cancer-Net BCa) can be further extended to other applications of CDI$^s$ in the cancer domain to further improve prediction performance.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

使用手动生成标签训练的卷积神经网络通常用于语义或实例分割。在精确的农业中，自动花探测方法使用监督模型和后处理技术，这些技术可能不会始终如一地表现为花朵的出现，并且数据采集条件有所不同。我们提出了一种自我监督的学习策略，以使用自动生成的伪标签来增强分割模型对不同花种物种的敏感性。我们采用数据增强和完善方法来提高模型预测的准确性。然后将增强的语义预测转换为全景伪标签，以迭代训练多任务模型。可以通过现有的后处理方法来完善自我监督的模型预测，以进一步提高其准确性。对多物种果树花数据集的评估表明，我们的方法的表现优于最先进的模型，而无需计算昂贵的后处理步骤，为花朵检测应用提供了新的基线。

公平被广泛认为是医疗保健道德的基础。在临床决策的背景下，它取决于智力的比较忠诚（基于证据或直观），指导每个患者的管理。尽管当代机器学习的个性化力量最近引起了人们的关注，但这种认知公平是在任何决策指导的背景下，无论是传统还是创新的。然而，目前没有一般的量化框架，更不用说保证了。在这里，我们根据模型的忠诚度来制定认知公平性，这些模型是对所学的多维表述评估的，这些身份的多维表示，旨在最大程度地提高人口的捕获多样性，从而引入了代表性道德模型校准的全面框架。我们证明了该框架在来自英国生物库的大规模多模式数据上的使用来得出人口的各种表示，量化模型绩效并提出了响应良好的补救。我们提供方法作为量化和确保医疗保健认知公平的原则解决方案，并在整个研究，临床和监管领域中进行了应用。