Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT benefits from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we propose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. Finally, TaMOs achieves highly competitive results on single-object GOT datasets, setting a new state-of-the-art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.

转移学习可以在源任务上重新使用知识来帮助学习目标任务。一种简单的转移学习形式在当前的最先进的计算机视觉模型中是常见的，即预先训练ILSVRC数据集上的图像分类模型，然后在任何目标任务上进行微调。然而，先前对转移学习的系统研究已经有限，并且预计工作的情况并不完全明白。在本文中，我们对跨越不同的图像域进行了广泛的转移学习实验探索（消费者照片，自主驾驶，空中图像，水下，室内场景，合成，特写镜头）和任务类型（语义分割，物体检测，深度估计，关键点检测）。重要的是，这些都是与现代计算机视觉应用相关的复杂的结构化的输出任务类型。总共执行超过2000年的转移学习实验，包括许多来源和目标来自不同的图像域，任务类型或两者。我们系统地分析了这些实验，了解图像域，任务类型和数据集大小对传输学习性能的影响。我们的研究导致了几个见解和具体建议：（1）对于大多数任务，存在一个显着优于ILSVRC'12预培训的来源; （2）图像领域是实现阳性转移的最重要因素; （3）源数据集应该\ \ emph {include}目标数据集的图像域以获得最佳结果; （4）与此同时，当源任务的图像域比目标的图像域时，我们只观察小的负面影响; （5）跨任务类型的转移可能是有益的，但其成功严重依赖于源和目标任务类型。

Determining and predicting reservoir formation properties for newly drilled wells represents a significant challenge. One of the variations of these properties evaluation is well-interval similarity. Many methodologies for similarity learning exist: from rule-based approaches to deep neural networks. Recently, articles adopted, e.g. recurrent neural networks to build a similarity model as we deal with sequential data. Such an approach suffers from short-term memory, as it pays more attention to the end of a sequence. Neural network with Transformer architecture instead cast their attention over all sequences to make a decision. To make them more efficient in terms of computational time, we introduce a limited attention mechanism similar to Informer and Performer architectures. We conduct experiments on open datasets with more than 20 wells making our experiments reliable and suitable for industrial usage. The best results were obtained with our adaptation of the Informer variant of Transformer with ROC AUC 0.982. It outperforms classical approaches with ROC AUC 0.824, Recurrent neural networks with ROC AUC 0.934 and straightforward usage of Transformers with ROC AUC 0.961.

This article presents a dataset of 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.

Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community. Previous research has shown that jointly using clinical notes with electronic health record (EHR) data improved predictive performance for patient monitoring in the intensive care unit (ICU). In this work, we explore the underlying reasons for these improvements. While relying on a basic attention-based model to allow for interpretability, we first confirm that performance significantly improves over state-of-the-art EHR data models when combining EHR data and clinical notes. We then provide an analysis showing improvements arise almost exclusively from a subset of notes containing broader context on patient state rather than clinician notes. We believe such findings highlight deep learning models for EHR data to be more limited by partially-descriptive data than by modeling choice, motivating a more data-centric approach in the field.

Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal Judgement Prediction (LJP) task. We investigate the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights (in English) and the Federal Supreme Court of Switzerland (in German, French and Italian). Our results show that zero-shot LPE is better compared to the baselines, but it still falls short compared to current state of the art supervised approaches. Nevertheless, the results are important, since there was 1) no explicit domain-specific data used - so we show that the transfer to the legal domain is possible for general-purpose LLMs, and 2) the LLMs where directly applied without any further training or fine-tuning - which in turn saves immensely in terms of additional computational costs.

Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.

IMPORTANCE: An interpretable machine learning model can provide faithful explanations of each prediction and yet maintain higher performance than its black box counterpart. OBJECTIVE: To design an interpretable machine learning model which accurately predicts EEG protopatterns while providing an explanation of its predictions with assistance of a specialized GUI. To map the cEEG latent features to a 2D space in order to visualize the ictal-interictal-injury continuum and gain insight into its high-dimensional structure. DESIGN, SETTING, AND PARTICIPANTS: 50,697 50-second cEEG samples from 2,711 ICU patients collected between July 2006 and March 2020 at Massachusetts General Hospital. Samples were labeled as one of 6 EEG activities by domain experts, with 124 different experts providing annotations. MAIN OUTCOMES AND MEASURES: Our neural network is interpretable because it uses case-based reasoning: it compares a new EEG reading to a set of learned prototypical EEG samples from the training dataset. Interpretability was measured with task-specific neighborhood agreement statistics. Discriminatory performance was evaluated with AUROC and AUPRC. RESULTS: The model achieves AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, 0.80 for classes Seizure, LPD, GPD, LRDA, GRDA, Other respectively. This performance is statistically significantly higher than that of the corresponding uninterpretable (black box) model with p<0.0001. Videos of the ictal-interictal-injury continuum are provided. CONCLUSION AND RELEVANCE: Our interpretable model and GUI can act as a reference for practitioners who work with cEEG patterns. We can now better understand the relationships between different types of cEEG patterns. In the future, this system may allow for targeted intervention and training in clinical settings. It could also be used for re-confirming or providing additional information for diagnostics.

自动副标题是将视听产品的语音自动转化为短文本的任务，换句话说，字幕及其相应的时间戳。生成的字幕需要符合多个空间和时间要求（长度，阅读速度），同时与语音同步并以促进理解的方式进行分割。鉴于其相当大的复杂性，迄今为止，通过分别处理转录，翻译，分割为字幕并预测时间戳的元素来解决自动字幕。在本文中，我们提出了第一个直接自动字幕模型，该模型在单个解决方案中从源语音中生成目标语言字幕及其时间戳。与经过内外数据和外域数据训练的最先进的级联模型的比较表明，我们的系统提供了高质量的字幕，同时在整合性方面也具有竞争力，并具有维护单个模型的所有优势。

字幕（替代）的语音翻译是通过将符合特定显示指南的字幕划分插入字幕中断，将语音数据自动转化为良好的字幕。与语音翻译（ST）类似，模型训练需要并行数据，其中包括音频输入与其文本翻译配对。然而，在替代方面，还必须用字幕断裂来注释文本。到目前为止，这一要求代表了系统开发的瓶颈，如公开可用的替代公司所证实。为了填补这一空白，我们提出了一种在不干预的情况下将现有的ST Corpora转换为替代资源的方法。我们构建了一个分段模型，该模型通过以多模式的方式利用音频和文本来自动将文本片段分为适当的字幕，从而在零拍摄条件下实现了高分子的质量。对手动和自动分割培训的替代系统的比较实验导致相似的性能，显示了我们方法的有效性。