A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, but only stochastic approximations of the objective function and its associated derivatives can be computed via inexact probabilistic zeroth- and first-order oracles. Under reasonable assumptions, a high-probability bound on the iteration complexity of the algorithm to approximate first-order stationarity is derived. Numerical results on standard nonlinear optimization test problems illustrate the advantages and limitations of our proposed method.

Template generation is a crucial step of groupwise image registration which deforms a group of subjects into a common space. Existing traditional and deep learning-based methods can generate high-quality template images. However, they suffer from substantial time costs or limited application scenarios like fixed group size. In this paper, we propose an efficient groupwise template generative framework based on variational autoencoder models utilizing the arithmetic property of latent representation of input images. We acquire the latent vectors of each input and use the average vector to construct the template through the decoder. Therefore, the method can be applied to groups of any scale. Secondly, we explore a siamese training scheme that feeds two images to the shared-weight twin networks and compares the distances between inputs and the generated template to prompt the template to be close to the implicit center. We conduct experiments on 3D brain MRI scans of groups of different sizes. Results show that our framework can achieve comparable and even better performance to baselines, with runtime decreased to seconds.

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.

培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集，并具有清晰的出处。自然语言提示最近通过将现有的，有监督的数据集转换为多种新颖的预处理任务，突出了元数据策划的好处，从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功，但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足，因此仍然具有挑战性。为了应对这一挑战，我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库，目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划，并与当前的平台兼容，以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调，数据审核，贡献指南的过程，并概述了两个说明性用例：生物医学提示和大规模，多任务学习的零射门评估。 BigBio是一项持续的社区努力，可在https://github.com/bigscience-workshop/biomedical上获得。

通常需要对术前和术后大脑图像进行注册，以评估脑神经胶质瘤治疗的有效性。尽管最近基于深度学习的可变形注册方法在健康的大脑图像方面取得了显着的成功，但由于参考图像中缺乏对应关系，它们中的大多数人将无法与病理相处。在本文中，我们提出了一种基于深度学习的可变形登记方法，该方法共同估计缺乏对应关系和双向变形场的区域。前向后的一致性约束用于帮助从两个图像中缺乏对应关系的体素的切除和复发区域的定位。来自Brats-Reg挑战的3D临床数据的结果表明，与传统和深度学习的注册方法相比，我们的方法可以改善图像对齐方式，无论是否具有成本函数掩盖策略。源代码可在https://github.com/cwmok/dirac上获得。

Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced with the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey we analyze main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled as input-level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity.

迄今为止，迄今为止，众所周知，对广泛的互补临床相关任务进行了全面比较了医学图像登记方法。这限制了采用研究进展，以防止竞争方法的公平基准。在过去五年内已经探讨了许多新的学习方法，但优化，建筑或度量战略的问题非常适合仍然是开放的。 Learn2reg涵盖了广泛的解剖学：脑，腹部和胸部，方式：超声波，CT，MRI，群体：患者内部和患者内部和监督水平。我们为3D注册的培训和验证建立了较低的入境障碍，这帮助我们从20多个独特的团队中汇编了65多个单独的方法提交的结果。我们的互补度量集，包括稳健性，准确性，合理性和速度，使得能够独特地位了解当前的医学图像登记现状。进一步分析监督问题的转移性，偏见和重要性，主要是基于深度学习的方法的优越性，并将新的研究方向开放到利用GPU加速的常规优化的混合方法。

学习排名 - 制作特定于查询的项目的排名列表以及一组监督项目 - 是一个普遍兴趣的问题。我们认为的设置是没有分析描述构成良好排名的设置。取而代之的是，我们有一个包含（目标项目，有趣的项目集）对的表示和监督信息的集合。我们在仿真中进行了分析证明，在实际数据示例中，当监督与“这几个相似的项目相似”时，通过使用整数线性程序组合表示来进行排名是有效的。尽管这项提名任务是相当普遍的，但对于特异性，我们从图表中的顶点提名的角度介绍了我们的方法论。本文描述的方法是模型不可知论。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.