随着诸如播客的长形话语内容的数量，许多平台希望从完整内容中提取短，有意义的和逻辑上相干段。这些段可以通过用户消耗以在潜水前进行采样内容，以及平台使用以推广和推荐内容。然而，很少发布的工作主要集中在话语内容的分割中，其中自动语音识别（ASR）服务生成的成绩单中的错误（噪声）构成了许多挑战。在这里，我们构建了400多个播客剧集的完整转录的新型数据集，其中我们标记了每个集中的介绍的位置。这些介绍包含有关剧集主题，主机和客人的信息，提供了剧集内容的有价值的摘要，因为它由作者创建。我们进一步使用Word替换增强了我们的数据集以增加可用培训数据的数量。我们根据预先训练的BERT和不同的增强策略训练三种变压器模型，与静态嵌入模型相比，实现了明显更好的性能，表明可以从嘈杂，松散组织的演讲中捕获广义的大规模结构信息数据。通过对模型的内部架构的分析进一步证明了这一点。我们的方法和数据集可用于促进未来的工作基于结构的出言内容的分割。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

The lack of standardization is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations due to differences in hardware and acquisition parameters. In recent years, MR harmonization using image synthesis with disentanglement has been proposed to compensate for the undesired contrast variations. Despite the success of existing methods, we argue that three major improvements can be made. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both Tw-weighted and T2-weighted images must be available), which limits their applicability. Third, existing methods generally are sensitive to imaging artifacts. In this paper, we present a novel approach, Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), to address these three issues. We first propose an anatomy fusion module that enables HACA3 to respect the anatomical differences between MR contrasts. HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts. Experiments show that HACA3 achieves state-of-the-art performance under multiple image quality metrics. We also demonstrate the applicability of HACA3 on downstream tasks with diverse MR datasets acquired from 21 sites with different field strengths, scanner platforms, and acquisition protocols.

Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking. We model this class of problems in a visual active search (VAS) framework, which takes as input an image of a broad area, and aims to identify as many examples of a target object as possible. It does this through a limited sequence of queries, each of which verifies whether an example is present in a given region. We propose a reinforcement learning approach for VAS that leverages a collection of fully annotated search tasks as training data to learn a search policy, and combines features of the input image with a natural representation of active search state. Additionally, we propose domain adaptation techniques to improve the policy at decision time when training data is not fully reflective of the test-time distribution of VAS tasks. Through extensive experiments on several satellite imagery datasets, we show that the proposed approach significantly outperforms several strong baselines. Code and data will be made public.

Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image samples. In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i.e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the identified datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets for different applications. We hope this paper can provide an overview for researchers who are looking for datasets for very short-term solar forecasting and related areas.

Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language. Such observations typically include instructions that, in a reinforcement learning (RL) setting, can directly or indirectly guide a player towards completing reward-worthy tasks. In this work, we study the ability of RL agents to follow such instructions. We conduct experiments that show that the performance of state-of-the-art text-based game agents is largely unaffected by the presence or absence of such instructions, and that these agents are typically unable to execute tasks to completion. To further study and address the task of instruction following, we equip RL agents with an internal structured representation of natural language instructions in the form of Linear Temporal Logic (LTL), a formal language that is increasingly used for temporally extended reward specification in RL. Our framework both supports and highlights the benefit of understanding the temporal semantics of instructions and in measuring progress towards achievement of such a temporally extended behaviour. Experiments with 500+ games in TextWorld demonstrate the superior performance of our approach.

注释滥用语言很昂贵，在逻辑上复杂，并造成了心理伤害的风险。但是，大多数机器学习研究都优先提高有效性（即F1或精度得分），而不是数据效率（即，最小化注释的数据量）。在本文中，我们在两个数据集上使用模拟实验，以不同比例的滥用，以证明基于变形金刚的主动学习是一种有前途的方法，可以实质上提高效率，同时仍然保持高效，尤其是当虐待内容是数据集中较小比例的情况下。这种方法需要大量的标记数据，以达到与完整数据集培训相等的性能。

背景：机器学习（ML）系统依靠数据来做出预测，与传统软件系统（例如数据处理管道，服务管道和模型培训）相比，该系统具有许多添加的组件。现有关于软件维护的研究研究了针对不同类型的问题（例如绩效和安全问题）的问题报告需求和解决过程。但是，ML系统具有特定的故障类别，报告ML问题需要特定于域的信息。由于ML和传统软件工程系统之间的特征不同，我们不知道报告需求在多大程度上不同，并且这些差异在多大程度上影响了问题解决过程。目的：我们的目标是调查ML和非ML问题之间分辨率时间的分布以及某些ML问题的分配时间是否存在差异。我们进一步研究了ML问题和非ML问题的修复大小。方法：我们在GitHub的最新活动应用ML项目中提取问题报告，提取请求和代码文件，并使用自动方法过滤ML和非ML问题。我们使用已知的深度学习错误分类法手动标记这些问题。我们测量了受控样本上ML和非ML问题的解决方案的分辨率时间和大小，并比较每个类别的分布。

我们考虑在重复的未知游戏中进行规避风险的学习，在这种游戏中，代理商的目标是最大程度地减少其个人产生高成本的风险。具体而言，代理商使用处于风险的条件值（CVAR）作为风险措施，并以每集选定动作的成本值的形式依靠强盗反馈来估算其CVAR值并更新其动作。使用匪徒反馈来估计CVAR的一个主要挑战是，代理只能访问其自身的成本值，但是，这取决于所有代理的行为。为了应对这一挑战，我们提出了一种新的规避风险的学习算法，并利用有关成本价值的完整历史信息。我们表明，该算法实现了子线性的遗憾，并匹配了文献中最著名的算法。我们为欧洲大师游戏提供了数值实验，该游戏表明我们的方法表现优于现有方法。

许多微体系式优化为深度神经网络解锁了巨大的处理能力，从而促进了AI革命。随着这种优化的精疲力尽，现代AI的增长现在是通过培训系统的性能，尤其是其数据流动的。我们没有专注于单个加速器，而是研究了全系统规模的大规模培训的数据移动特征。基于我们的工作量分析，我们设计了HammingMesh，这是一种新颖的网络拓扑，以低成本提供高的带宽，并具有很高的工作计划灵活性。具体而言，HammingMesh可以支持具有两个并行性的两个维度的深度学习培训工作的完整带宽和隔离。此外，它还为通用流量的高全球带宽提供支持。因此，HammingMesh将为未来的大规模深度学习系统供电，并具有极端的带宽要求。