我们提出了一个新颖的范式，该范式是通过单眼视频输入来构建可动画的3D人类代表，以便可以以任何看不见的姿势和观点呈现。我们的方法基于由基于网格的参数3D人类模型操纵的动态神经辐射场（NERF），该模型用作几何代理。以前的方法通常依靠多视频视频或准确的3D几何信息作为其他输入；此外，大多数方法在概括地看不见的姿势时会降解质量。我们确定概括的关键是查询动态NERF的良好输入嵌入：良好的输入嵌入应定义完整量化空间中的注入映射，并在姿势变化下表面网格变形引导。基于此观察结果，我们建议将输入查询嵌入其与局部表面区域的关系，并在网格顶点上跨越一组地球的最近邻居跨越。通过包括位置和相对距离信息，我们的嵌入式定义了距离保存的变形映射，并可以很好地概括为看不见的姿势。为了减少对其他输入的依赖性，我们首先使用现成的工具初始化人均3D网格，然后提出一条管道以共同优化NERF并完善初始网格。广泛的实验表明，我们的方法可以在看不见的姿势和观点下合成合理的人类渲染结果。

In the context of an efficient network traffic engineering process where the network continuously measures a new traffic matrix and updates the set of paths in the network, an automated process is required to quickly and efficiently identify when and what set of paths should be used. Unfortunately, the burden of finding the optimal solution for the network updating process in each given time interval is high since the computation complexity of optimization approaches using linear programming increases significantly as the size of the network increases. In this paper, we use deep reinforcement learning to derive a data-driven algorithm that does the path selection in the network considering the overhead of route computation and path updates. Our proposed scheme leverages information about past network behavior to identify a set of robust paths to be used for multiple future time intervals to avoid the overhead of updating the forwarding behavior of routers frequently. We compare the results of our approach to other traffic engineering solutions through extensive simulations across real network topologies. Our results demonstrate that our scheme fares well by a factor of 40% with respect to reducing link utilization compared to traditional TE schemes such as ECMP. Our scheme provides a slightly higher link utilization (around 25%) compared to schemes that only minimize link utilization and do not care about path updating overhead.

Estimation of the T2 distribution from multi-echo T2-Weighted MRI (T2W) data can provide insight into the microscopic content of tissue using macroscopic imaging. This information can be used as a biomarker for several pathologies, such as tumor characterization, osteoarthritis, and neurodegenerative diseases. Recently, deep neural network (DNN) based methods were proposed for T2 distribution estimation from MRI data. However, these methods are highly sensitive to distribution shifts such as variations in the echo-times (TE) used during acquisition. Therefore, DNN-based methods cannot be utilized in large-scale multi-institutional trials with heterogeneous acquisition protocols. We present P2T2, a new physically-primed DNN approach for T2 distribution estimation that is robust to different acquisition parameters while maintaining state-of-the-art estimation accuracy. Our P2T2 model encodes the forward model of the signal decay by taking as input the TE acquisition array, in addition to the MRI signal, and provides an estimate of the corresponding T2 distribution as its output. Our P2T2 model has improved the robustness against distribution shifts in the acquisition process by more than 50% compared to the previously proposed DNN model. When tested without any distribution shifts, our model achieved about the same accuracy. Finally, when applied to real human MRI data, our P2T2 model produced the most detailed Myelin-Water fraction maps compared to both the MIML model and classical approaches. Our proposed physically-primed approach improved the generalization capacity of DNN models for T2 distribution estimation and their robustness against distribution shifts compared to previous approaches without compromising the accuracy.

Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been shown as the state-of-the-art (SOTA) approach to dealing with those selection problems. TE treats input texts as premises (P), options as hypotheses (H), then handles the selection problem by modeling (P, H) pairwise. Two limitations: first, the pairwise modeling is unaware of other options, which is less intuitive since humans often determine the best options by comparing competing candidates; second, the inference process of pairwise TE is time-consuming, especially when the option space is large. To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling. Context-TE is able to learn more reliable decision for the H since it considers various context. Second, we speed up Context-TE by coming up with Parallel-TE, which learns the decisions of multiple options simultaneously. Parallel-TE significantly improves the inference speed while keeping comparable performance with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity typing, intent detection and multi-choice QA) that are typical selection problems with different sizes of options. Experiments show our models set new SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k times in inference. Our code is publicly available at https://github.com/jiangshdd/LearningToSelect.

人工智能（AI）是21世纪最有前途的技术之一，对社会和经济产生了明显影响。通过这项工作，我们简要概述了全球趋势，行业应用以及我们在工业和学术界的国际经验和工作中的精选用例。目的是提出全球和地区的积极实践，并就将B＆H定位在全球AI场景中定位的现实目标和机会提供明智的意见。

te reo m \ = aori（称为m \ = aori），新西兰的土著语言在语言技术中的资源不足。 m \ = aori扬声器是双语的，其中m \ = aori用英语进行了代码开关。不幸的是，M \ = AORI语言技术，语言检测和M \ = Aori-English对之间的代码转换检测的资源最少。英语和M \ = AORI都使用罗马衍生的拼字法制作基于规则的系统来检测语言和代码转换限制性。大多数M \ = AORI语言检测是由语言专家手动完成的。这项研究构建了66,016,807个单词的Aori英语双语数据库，并带有单词级语言注释。新西兰议会汉萨德辩论报告用于构建数据库。语言标签是使用特定语言规则和专家手册注释分配的。 M \ = AORI和英语的单词具有相同的拼写，但含义不同。这些词不能根据单词级的语言规则将其归类为M \ = AORI或英语。因此，需要手动注释。还报道了报告数据库的各个方面的分析，例如元数据，逐年分析，经常出现的单词，句子长度和n-grams。这里开发的数据库是新西兰Aotearoa的未来语言和语音技术开发的宝贵工具。遵循标签数据库的方法也可以遵循其他低资源的语言对。

与单个决策树相比，Tree Ensemble（TE）模型（例如，增强的树木和随机森林）通常提供更高的预测性能。但是，由于人类难以理解其决策逻辑，因此TE模型通常缺乏透明度和可解释性。本文提出了一种新颖的方法，可以将经过训练的二进制分类任务的TE转换为规则列表（RL），该规则列表（RL）等同于TE，对于人类来说是可理解的。该RL捕获了TE决策的所有必要条件。基准数据集上的实验表明，与最先进的方法相比，（i）TE2RULES生成的RL的预测相对于原始TE具有很高的保真度，（ii）TE2RULES的RL具有高的解释性，由高可解释性衡量。决策规则的数量和长度，（iii）TE2RULES算法的运行时间可以大大减少，以稍低的保真度，（iv）RL是最新的替代品的快速替代 - 基于ART规则的实例级结果解释技术。

作为“进化计算研究中的新领域”，进化转移优化（ETO）将克服传统的零重复利用相关经验和知识的范式，这些范式在进化计算研究中解决了过去的问题。在通过ETO的计划申请中，可以为智能调度和绿色日程安排形成一个非常吸引人且高度竞争的框架“会议”，尤其是对于来自中国的“碳中立性”的誓言。据我们所知，当多目标优化问题“满足”离散案例中的单目标优化问题（而不是多任务优化）时，我们在此处安排的论文是一类ETO框架的第一项工作。更具体地说，可以通过新的核心转移机制和学习技巧来使用用于置换流程调度问题（PFSP）的新核心转移机制和学习技术，可以使用用于工业应用传达的关键知识，例如具有遗传算法的位置构建块。关于良好研究基准的广泛研究验证了我们提出的ETO-PFSP框架的企业有效性和巨大的普遍性。我们的调查（1）丰富了ETO框架，（2）有助于遗传算法和模因算法的基本基础的经典和基本理论，（3）（3）朝着通过范例和范式进行学习的范式进行进化调整的范式转移，中国“工业情报”的“基于知识和建筑块的计划”（KAB2S）。

在过去的几年中，从工业检查到与健康相关的应用，基于纹理的分类问题已证明其在许多领域的重要性。近年来已经开发了新技术和基于CNN的架构，以解决基于纹理的分类问题。这些方法的局限性在于，它们都没有声称最适合所有类型的纹理。每种技术都比特定纹理类型具有优势。为了解决这个问题，我们提出了一个框架，该框架结合了现有技术以提取纹理功能并显示出比目前的技术更好的结果。所提出的框架在大多数纹理类型上都很好地工作，在此框架中，还可以添加新技术以获得比现有的技术更好的结果。我们还使用建议的框架结合了三种现有技术，在FMD和KTH数据集上介绍了SOTA结果。

我们提出了一个用于视频中时间精确的动作发现的模型，该模型使用一组密集的检测锚，预测了每个锚的检测置信度和相应的细粒时间位移。我们尝试两个行李箱体系结构，两者都能够合并大的时间上下文，同时保留精确本地化所需的较小规模的功能：U-NET的一维版本和变压器编码器（TE）。我们还建议通过应用清晰度最小化（SAM）和混合数据扩展来提出这种培训模型的最佳实践。我们在Soccernet-V2上实现了新的最新技术，这是同类的最大足球视频数据集，其时间定位明显改善。此外，我们的消融表明：预测时间位移的重要性；U-Net和TE Trunks之间的权衡；以及与SAM和MIDUP培训的好处。