动力系统的演变通常由非线性偏微分方程（PDE）控制，在模拟框架中，其解决方案需要大量的计算资源。在这项工作中，我们提出了一种新颖的方法，该方法将超网络求解器与傅立叶神经操作员体系结构相结合。我们的方法分别处理时间和空间。结果，它通过采用部分差分运算符的一般组成特性，成功地在连续时间步骤中成功传播了初始条件。在先前的工作之后，在特定时间点提供监督。我们在各个时间演化PDE上测试我们的方法，包括一个，两个和三个空间维度中的非线性流体流。结果表明，新方法在监督点的时间点提高了学习准确性，并能够插入和解决任何中间时间的解决方案。

Maximum Inner Product Search (MIPS) is a popular problem in the machine learning literature due to its applicability in a wide array of applications, such as recommender systems. In high-dimensional settings, however, MIPS queries can become computationally expensive as most existing solutions do not scale well with data dimensionality. In this work, we present a state-of-the-art algorithm for the MIPS problem in high dimensions, dubbed BanditMIPS. BanditMIPS is a randomized algorithm that borrows techniques from multi-armed bandits to reduce the MIPS problem to a best-arm identification problem. BanditMIPS reduces the complexity of state-of-the-art algorithms from $O(\sqrt{d})$ to $O(\text{log}d)$, where $d$ is the dimension of the problem data vectors. On high-dimensional real-world datasets, BanditMIPS runs approximately 12 times faster than existing approaches and returns the same solution. BanditMIPS requires no preprocessing of the data and includes a hyperparameter that practitioners may use to trade off accuracy and runtime. We also propose a variant of our algorithm, named BanditMIPS-$\alpha$, which employs non-uniform sampling across the data dimensions to provide further speedups.

Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget. All of our experimental results are reproducible via a one-line script at https://github.com/ThrunGroup/FastForest.

DER is the primary metric to evaluate diarization performance while facing a dilemma: the errors in short utterances or segments tend to be overwhelmed by longer ones. Short segments, e.g., `yes' or `no,' still have semantic information. Besides, DER overlooks errors in less-talked speakers. Although JER balances speaker errors, it still suffers from the same dilemma. Considering all those aspects, duration error, segment error, and speaker-weighted error constituting a complete diarization evaluation, we propose a Balanced Error Rate (BER) to evaluate speaker diarization. First, we propose a segment-level error rate (SER) via connected sub-graphs and adaptive IoU threshold to get accurate segment matching. Second, to evaluate diarization in a unified way, we adopt a speaker-specific harmonic mean between duration and segment, followed by a speaker-weighted average. Third, we analyze our metric via the modularized system, EEND, and the multi-modal method on real datasets. SER and BER are publicly available at https://github.com/X-LANCE/BER.

任何稀疏编码方法的最终目标是从几个嘈杂的线性测量值（一个未知的稀疏向量）中准确恢复。不幸的是，这个估计问题通常是NP-HARD，因此始终采用近似方法（例如Lasso或正交匹配的追踪）来接近它，从而使准确性以较小的计算复杂性进行了交易。在本文中，我们为稀疏编码开发了一种量子启发的算法，前提是，与经典近似方法相比，量子计算机和ISING机器的出现可能会导致更准确的估计。为此，我们将最一般的稀疏编码问题作为二次不受约束的二进制优化（QUBO）任务提出，可以使用量子技术有效地最小化。为了在旋转数量（空间复杂性）方面也有效地得出QUBO模型，我们将分析分为三种不同的情况。这些由表达基础稀疏向量所需的位数来定义：二进制，2位和一般的定点表示。我们使用有关Lightsolver量子启发的数字平台的模拟数据进行数值实验，以验证我们的QUBO公式的正确性，并证明其优于基线方法的优势。

我们提出了一种新型的深度学习方法，以分类19.Covid-19患者的肺CTS。具体而言，我们将扫描分为健康的肺组织，非肺部区域，以及两个不同但视觉上相似的病理性肺组织，即地面玻璃透明度和巩固。这是通过独特的端到端层次网络架构和整体学习来实现的，这有助于分割并为细分不确定性提供衡量标准。提出的框架为三个Covid-19数据集实现了竞争成果和出色的概括能力。我们的方法在COVID-19 CT图像细分的公共Kaggle竞赛中排名第二。此外，分割不确定性区域显示与两种不同放射科医生的手动注释之间的分歧相对应。最后，在比较患者的COVID-19严重程度评分（基于临床指标）和分割的肺病理时，显示了我们的私人数据集的初步有希望的对应结果。代码和数据可在我们的存储库中找到：https：//github.com/talbenha/covid-seg

近年来，变压器模型的引入引发了自然语言处理（NLP）的革命。伯特（Bert）是仅使用注意机制的第一批文本编码者之一，没有任何复发部分来实现许多NLP任务的最新结果。本文使用拓扑数据分析介绍了文本分类器。我们将BERT的注意图转换为注意图作为该分类器的唯一输入。该模型可以解决诸如将垃圾邮件与HAM消息区分开的任务，认识到语法正确的句子，或将电影评论评估为负面还是正面。它与BERT基线相当表现，并在某些任务上表现优于它。此外，我们提出了一种新方法，以减少拓扑分类器考虑的BERT注意力头的数量，这使我们能够修剪从144个下降到只有10个，而不会降低性能。我们的工作还表明，拓扑模型比原始的BERT模型表现出对对抗性攻击的鲁棒性，该模型在修剪过程中维持。据我们所知，这项工作是第一个在NLP背景下以对抗性攻击的基于拓扑的模型。

消费者设备中的语音代理的激增需要新的工具，用于评估这些系统超出其技术功能。本文提出了一种用于评估语音用户界面（VUIS）的工具包，其目的是测量用户体验中主观享受的关键因素。使用现有文献的元分析构建了Pueva Toolkit，结构化N = 20和半结构化N = 18个访谈和一个受试者实验室研究。由此产生的问卷包含35项，其中三类代表12种规模：（1）个性（2）用途和（3）令人愉快的态度。Pueva Toolkit将我们迁移到评估和比较科学之间的主观，快乐体验的能力以及对象内研究设计。

我们通过反馈信息研究了离线和在线上下文优化的问题，而不是观察损失，我们会在事后观察到最佳的动作，而是对目标功能充分了解的甲骨文。我们的目标是最大程度地减少遗憾，这被定义为我们的损失与全知的甲骨所产生的损失之间的区别。在离线设置中，决策者可以从过去段中获得信息，并且需要做出一个决策，而在在线环境中，决策者在每个时期内都会动态地基于一组新的可行动作和上下文功能，以动态进行决策。 。对于离线设置，我们表征了最佳的最小策略，确定可以实现的性能，这是数据引起的信息的基础几何形状的函数。在在线环境中，我们利用这种几何表征来优化累积遗憾。我们开发了一种算法，该算法在时间范围内产生了对数的第一个遗憾。

Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal models are not always perfect: for example, it is easy to underestimate a robot's inertia. Nevertheless, these models change and improve over time as humans gather more experience. Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change. In this work we take a step towards enabling robots to understand the influence they have, leverage it to better assist people, and help human models more quickly align with reality. Our key idea is to model the human's learning as a nonlinear dynamical system which evolves the human's internal model given new observations. We formulate a novel optimization problem to infer the human's learning dynamics from demonstrations that naturally exhibit human learning. We then formalize how robots can influence human learning by embedding the human's learning dynamics model into the robot planning problem. Although our formulations provide concrete problem statements, they are intractable to solve in full generality. We contribute an approximation that sacrifices the complexity of the human internal models we can represent, but enables robots to learn the nonlinear dynamics of these internal models. We evaluate our inference and planning methods in a suite of simulated environments and an in-person user study, where a 7DOF robotic arm teaches participants to be better teleoperators. While influencing human learning remains an open problem, our results demonstrate that this influence is possible and can be helpful in real human-robot interaction.