如今，越来越多的人被诊断出患有心血管疾病（CVD），这是全球死亡的主要原因。鉴定这些心脏问题的金标准是通过心电图（ECG）。标准的12铅ECG广泛用于临床实践和当前的大多数研究。但是，使用较少的铅可以使ECG更加普遍，因为它可以与便携式或可穿戴设备集成。本文介绍了两种新型技术，以提高当前深度学习系统的3铅ECG分类的性能，从而与使用标准12铅ECG训练的模型相提并论。具体而言，我们提出了一种以心跳回归数量的形式的多任务学习方案，以及将患者人口统计数据整合到系统中的有效机制。随着这两个进步，我们在两个大规模的ECG数据集（即Chapman和CPSC-2018）上以F1分数为0.9796和0.8140的分类性能，这些数据分别超过了当前最新的ECG分类方法，该方法超过了当前的ECG分类方法。甚至那些接受了12条铅数据的培训。为了鼓励进一步开发，我们的源代码可在https://github.com/lhkhiem28/lightx3ecg上公开获得。

COVID-19大流行已经暴露了全球医疗服务的脆弱性，增加了开发新颖的工具来提供快速且具有成本效益的筛查和诊断的需求。临床报告表明，Covid-19感染可能导致心脏损伤，心电图（ECG）可以作为Covid-19的诊断生物标志物。这项研究旨在利用ECG信号自动检测COVID-19。我们提出了一种从ECG纸记录中提取ECG信号的新方法，然后将其送入一维卷积神经网络（1D-CNN）中，以学习和诊断疾病。为了评估数字信号的质量，标记了基于纸张的ECG图像中的R峰。之后，将从每个图像计算的RR间隔与相应数字化信号的RR间隔进行比较。 COVID-19 ECG图像数据集上的实验表明，提出的数字化方法能够正确捕获原始信号，平均绝对误差为28.11 ms。我们提出的1D-CNN模型在数字化的心电图信号上进行了训练，允许准确识别患有COVID-19和其他受试者的个体，分类精度为98.42％，95.63％和98.50％，用于分类COVID-19 vs.正常，与正常人分类， COVID-19与异常心跳和Covid-19和其他类别分别与其他阶级。此外，提出的方法还为多分类任务实现了高级的性能。我们的发现表明，经过数字化的心电图信号训练的深度学习系统可以作为诊断Covid-19的潜在工具。

心血管疾病（CVD）是一组心脏和血管疾病，是对人类健康最严重的危险之一，此类患者的数量仍在增长。早期，准确的检测在成功治疗和干预中起着关键作用。心电图（ECG）是识别各种心血管异常的金标准。在临床实践和当前大多数研究中，主要使用标准的12铅ECG。但是，使用较少的铅可以使ECG更加普遍，因为可以通过便携式或可穿戴设备来方便地记录它。在这项研究中，我们开发了一种新颖的深度学习系统，以仅使用三个ECG铅来准确识别多个心血管异常。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

我们介绍了Lavis，这是一个开源深度学习库，用于语言视觉研究和应用。拉维斯（Lavis）的目标是作为一个一站式综合图书馆，它为研究人员和从业人员提供了可访问语言视觉领域的最新进步，并赋予未来的研究和发展。它具有统一的界面，可轻松访问最新的图像语言，视频语言模型和常见数据集。 Lavis支持对各种任务的培训，评估和基准测试，包括多模式分类，检索，字幕，视觉问题答案，对话和预训练。同时，该库还高度可扩展且可配置，从而促进了未来的开发和定制。在此技术报告中，我们描述了图书馆的设计原理，关键组成部分和功能，并在常见的语言视觉任务中提出基准测试结果。该库可在以下网址获得：https：//github.com/salesforce/lavis。

神经网络修剪可以有效地用于压缩自动语音识别（ASR）模型。但是，在多语言ASR中，执行语言不足的修剪可能会导致某些语言的严重性能降解，因为语言 - 敏捷的修剪口罩可能不符合所有语言，并丢弃了重要的语言特定参数。在这项工作中，我们提出了ASR路径，这是一种稀疏的多语言ASR模型，该模型激活了特定语言的子网络（“路径”），从而明确地学习了每种语言的参数。通过重叠的子网络，共享参数还可以通过联合多语言培训来实现较低资源语言的知识传输。我们提出了一种新型算法来学习ASR途径，并通过流式RNN-T模型评估了4种语言的建议方法。我们提出的ASR途径的表现都优于密集模型（平均-5.0％）和语言不足的修剪模型（平均-21.4％），并且与单语稀疏模型相比，低资源语言的性能更好。

无人驾驶汽车（UAV）在许多领域都受雇于摄影，紧急，娱乐，国防，农业，林业，采矿和建筑。在过去的十年中，无人机技术在许多施工项目阶段中找到了应用程序，从现场映射，进度监控，建筑物检查，损坏评估和材料交付等等。尽管已经对无人机在各种施工相关的过程中的优势进行了广泛的研究，但关于提高任务能力和效率的无人机协作的研究仍然很少。本文提出了一种基于塔格狩猎游戏和粒子群优化（PSO）的多个无人机的新合作路径计划算法。首先，定义了每个无人机的成本函数，并包含多个目标和约束。然后，开发了无人机游戏框架，以将多功能路径计划制定到寻找回报优势均衡的问题。接下来，提出了基于PSO的算法来获得无人机的最佳路径。由三个无人机检查的大型建筑工地的仿真结果表明，在检查任务期间，提出的算法在为无人机形成的可行和高效飞行路径生成可行，高效的飞行路径上的有效性。