While modern Text-to-Speech (TTS) systems can produce speech rated highly in terms of subjective evaluation, the distance between real and synthetic speech distributions remains understudied, where we use the term \textit{distribution} to mean the sample space of all possible real speech recordings from a given set of speakers; or of the synthetic samples that could be generated for the same set of speakers. We evaluate the distance of real and synthetic speech distributions along the dimensions of the acoustic environment, speaker characteristics and prosody using a range of speech processing measures and the respective Wasserstein distances of their distributions. We reduce these distribution distances along said dimensions by providing utterance-level information derived from the measures to the model and show they can be generated at inference time. The improvements to the dimensions translate to overall distribution distance reduction approximated using Automatic Speech Recognition (ASR) by evaluating the fitness of the synthetic data as training data.

Self-supervised speech models have grown fast during the past few years and have proven feasible for use in various downstream tasks. Some recent work has started to look at the characteristics of these models, yet many concerns have not been fully addressed. In this work, we conduct a study on emotional corpora to explore a popular self-supervised model -- wav2vec 2.0. Via a set of quantitative analysis, we mainly demonstrate that: 1) wav2vec 2.0 appears to discard paralinguistic information that is less useful for word recognition purposes; 2) for emotion recognition, representations from the middle layer alone perform as well as those derived from layer averaging, while the final layer results in the worst performance in some cases; 3) current self-supervised models may not be the optimal solution for downstream tasks that make use of non-lexical features. Our work provides novel findings that will aid future research in this area and theoretical basis for the use of existing models.

图像组成有助于研究图像结构，并有助于发现跨艺术形式和样式描绘的基础场景的语义。近年来，随着艺术品的数字化，可能会将成千上万个特定场景或叙述的图像联系在一起。但是，将这些数据与一致的客观性联系起来可能是一项高度挑战和耗时的任务。在这项工作中，我们提出了一种称为图像组成画布（ICC ++）的新方法，以比较和检索具有相似组成元素的图像。 ICC ++是对ICC的改进，专门针对由Max Imdahl的工作激发的低水平和高级功能（组成元素）。为此，我们与传统和最先进的方法（SOTA）方法进行了严格的定量和定性比较，表明我们所提出的方法优于所有这些方法。结合深度功能，我们的方法优于最佳的基于深度学习的方法，为数字人文学科的可解释机器学习打开了研究方向。我们将发布代码和数据后的数据。

在这项工作中，我们统一了一个框架中的标点符号预测的几个现有的解码策略，并引入了一种新的策略，该策略在不同窗口中使用每个单词的多个预测。我们表明，通过在培训模型之后优化这些策略，可以实现显着的改进，只能导致推理时间的潜在增加，没有要求再培训。我们进一步使用我们的解码策略框架，以便在实时设置中的标记和分类方法的第一次比较。我们的研究结果表明，当较少或没有右侧上下文时，标点符号预测的分类方法可能是有益的。

我们介绍了一种用于跨语言训练ASR系统的方法，使用目标语言绝对没有转录的训练数据，并且没有相关语言的语音知识。我们的方法使用了一种解密算法的新应用，该算法仅在目标语言中仅操作不配对的语音和文本数据。我们将此破译应用于由通用电话识别器产生的电话序列，由语言语音语料库培训，我们遵循平稳半监督培训，以获得新语言的声学模型。据我们所知，这是零资源交叉语言ASR的第一种实用方法，不依赖于任何手工制作的语音信息。我们对来自Globalphone语料库的读语音进行了实验，并表明可以在目标语言中仅在20分钟的数据上学习解密模型。当用于生成半监督培训的伪标签时，我们获得了比在同一数据上培训的等同完全监督模型的25％至仅5％的绝对差。

疟疾是一种威胁生命的疾病，影响了数百万。基于显微镜的薄膜评估是（i）确定疟疾物种和（ii）定量高寄生虫感染的标准方法。通过机器学习（ML）对疟疾显微镜的完全自动化是一项具有挑战性的任务，因为预先准备的滑动在质量和表现方面差异很大，并且伪像通常超过相对较少的寄生虫。在这项工作中，我们描述了一个用于薄膜疟疾分析的完整，完全自动化的框架，该框架应用了ML方法，包括卷积神经网（CNN），该方法在大型且多样化的田间预先准备的薄膜数据集中进行了训练。定量和物种鉴定结果几乎足够准确地满足了耐药性监测和临床用例的混凝土需求。我们将方法和性能指标集中在现场用例要求上。我们讨论了将ML方法应用于疟疾显微镜的关键问题和重要指标。

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are reasonably well understood, the third component, whose role is to reduce the number of communication rounds needed to train the model, resisted all attempts at a satisfactory theoretical explanation. Malinovsky et al. (2022) identified four distinct generations of LT methods based on the quality of the provided theoretical communication complexity guarantees. Despite a lot of progress in this area, none of the existing works were able to show that it is theoretically better to employ multiple local gradient-type steps (i.e., to engage in LT) than to rely on a single local gradient-type step only in the important heterogeneous data regime. In a recent breakthrough embodied in their ProxSkip method and its theoretical analysis, Mishchenko et al. (2022) showed that LT indeed leads to provable communication acceleration for arbitrarily heterogeneous data, thus jump-starting the $5^{\rm th}$ generation of LT methods. However, while these latest generation LT methods are compatible with DS, none of them support CS. We resolve this open problem in the affirmative. In order to do so, we had to base our algorithmic development on new algorithmic and theoretical foundations.