本文提出了一种延时3D细胞分析的方法。具体而言，我们考虑了准确定位和定量分析亚细胞特征的问题，以及从延时3D共聚焦细胞图像堆栈跟踪单个细胞的问题。细胞的异质性和多维图像的体积提出了对细胞形态发生和发育的完全自动化分析的主要挑战。本文是由路面细胞生长过程和构建定量形态发生模型的动机。我们提出了一种基于深度特征的分割方法，以准确检测和标记每个细胞区域。基于邻接图的方法用于提取分段细胞的亚细胞特征。最后，提出了使用多个单元格特征的基于强大的图形跟踪算法在不同的时间实例中关联单元格。提供了广泛的实验结果，并证明了所提出的方法的鲁棒性。该代码可在GitHub上获得，该方法可通过Bisque Portal作为服务可用。

Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on simplified assumptions leading to ineffective cardinality estimates or build large models to understand the data distributions, leading to long planning times and a lack of generalizability across queries. In this paper, we propose a new framework FactorJoin for estimating join queries. FactorJoin combines the idea behind the classical join-histogram method to efficiently handle joins with the learning-based methods to accurately capture attribute correlation. Specifically, FactorJoin scans every table in a DB and builds single-table conditional distributions during an offline preparation phase. When a join query comes, FactorJoin translates it into a factor graph model over the learned distributions to effectively and efficiently estimate its cardinality. Unlike existing learning-based methods, FactorJoin does not need to de-normalize joins upfront or require executed query workloads to train the model. Since it only relies on single-table statistics, FactorJoin has small space overhead and is extremely easy to train and maintain. In our evaluation, FactorJoin can produce more effective estimates than the previous state-of-the-art learning-based methods, with 40x less estimation latency, 100x smaller model size, and 100x faster training speed at comparable or better accuracy. In addition, FactorJoin can estimate 10,000 sub-plan queries within one second to optimize the query plan, which is very close to the traditional cardinality estimators in commercial DBMS.

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

We introduce M-VADER: a diffusion model (DM) for image generation where the output can be specified using arbitrary combinations of images and text. We show how M-VADER enables the generation of images specified using combinations of image and text, and combinations of multiple images. Previously, a number of successful DM image generation algorithms have been introduced that make it possible to specify the output image using a text prompt. Inspired by the success of those models, and led by the notion that language was already developed to describe the elements of visual contexts that humans find most important, we introduce an embedding model closely related to a vision-language model. Specifically, we introduce the embedding model S-MAGMA: a 13 billion parameter multimodal decoder combining components from an autoregressive vision-language model MAGMA and biases finetuned for semantic search.

Few-shot learning is a rapidly evolving area of research in machine learning where the goal is to classify unlabeled data with only one or "a few" labeled exemplary samples. Neural networks are typically trained to minimize a distance metric between labeled exemplary samples and a query set. Early few-shot approaches use an episodic training process to sub-sample the training data into few-shot batches. This training process matches the sub-sampling done on evaluation. Recently, conventional supervised training coupled with a cosine distance has achieved superior performance for few-shot. Despite the diversity of few-shot approaches over the past decade, most methods still rely on the cosine or Euclidean distance layer between the latent features of the trained network. In this work, we investigate the distributions of trained few-shot features and demonstrate that they can be roughly approximated as exponential distributions. Under this assumption of an exponential distribution, we propose a new maximum log-likelihood metric for few-shot architectures. We demonstrate that the proposed metric achieves superior performance accuracy w.r.t. conventional similarity metrics (e.g., cosine, Euclidean, etc.), and achieve state-of-the-art inductive few-shot performance. Further, additional gains can be achieved by carefully combining multiple metrics and neither of our methods require post-processing feature transformations, which are common to many algorithms. Finally, we demonstrate a novel iterative algorithm designed around our maximum log-likelihood approach that achieves state-of-the-art transductive few-shot performance when the evaluation data is imbalanced. We have made our code publicly available at https://github.com/samuelhess/MLL_FSL/.

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.

神经科学方面的巨大努力正在努力绘制许多新物种的连接群，包括果蝇果蝇的接近完成。重要的是要问这些模型是否可以使人工智能受益。在这项工作中，我们提出了两个基本问题：（1）生物连接组可以在机器学习中提供的何处以及何时提供使用，（2）哪些设计原理对于提取连接组的良好表示是必要的。为此，我们将秀丽隐杆线虫线虫的运动电路转化为以不同水平的生物物理现实主义水平的人工神经网络，并评估了这些网络在运动和非运动行为任务上训练这些网络的结果。我们证明，生物物理现实主义不必维持使用生物回路的优势。我们还确定，即使没有保留确切的接线图，建筑统计数据也提供了有价值的先验。最后，我们表明，虽然秀丽隐杆线虫运动电路对运动问题提供了强大的感应偏见，但其结构可能会阻碍与运动无关的任务（例如视觉分类问题）。

需要在机器学习模型中对最小参数设置的需求，以避免耗时的优化过程。$ k $ - 最终的邻居是在许多问题中使用的最有效，最直接的模型之一。尽管具有众所周知的性能，但它仍需要特定数据分布的$ K $值，从而需要昂贵的计算工作。本文提出了一个$ k $ - 最终的邻居分类器，该分类器绕过定义$ k $的值的需求。考虑到训练集的数据分布，该模型计算$ k $值。我们将提出的模型与标准$ K $ - 最近的邻居分类器和文献中的两个无参数版本进行了比较。11个公共数据集的实验证实了所提出方法的鲁棒性，因为所获得的结果相似甚至更好。

对网络科学的受约束聚类的关键字搜索返回仅3,000个文档。我们对这些人进行了自动分析，并汇编了我们自己的183篇论文书目，我们根据其主题和实验研究（如果有的话）进行了更详细的分析。本文使用引文计数和出版年份通过帕累托分析介绍了该地区及其子主题的一般趋势。我们列出可用的软件并分析参考收集的实验部分。我们发现缺乏大型比较实验。在我们审查的主题中，应用程序研究最近最丰富，以及深入学习，积极学习和合奏学习。

对制造工艺的机器化的需求很大，因此单调劳动。一些需要特定技能的制造任务（焊接，绘画等）缺乏工人。机器人已在这些任务中使用，但是它们的灵活性受到限制，因为它们仍然很难通过非专家编程/重新编程，从而使它们无法访问大多数公司。机器人离线编程（OLP）是可靠的。但是，直接来自CAD/CAM的生成路径不包括代表人类技能的相关参数，例如机器人最终效应器的方向和速度。本文提出了一个直观的机器人编程系统，以捕捉人类制造技能并将其转变为机器人程序。使用连接到工作工具的磁跟踪系统记录人类熟练工人的演示。收集的数据包括工作路径的方向和速度。位置数据是从CAD/CAM中提取的，因为磁跟踪器捕获时的误差很明显。路径姿势在笛卡尔空间中转换，并在模拟环境中进行验证。生成机器人程序并将其转移到真正的机器人。关于玻璃粘合剂应用过程的实验证明了拟议框架捕获人类技能并将其转移到机器人方面的使用和有效性的直觉。