Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Existing work has combined these in simple "retrieve-then-read" pipelines in which the RM retrieves passages that are inserted into the LM prompt. To begin to fully realize the potential of frozen LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware demonstrations, search for relevant passages, and generate grounded predictions, systematically breaking down problems into small transformations that the LM and RM can handle more reliably. We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings, establishing in early evaluations new state-of-the-art in-context learning results and delivering 37-200%, 8-40%, and 80-290% relative gains against vanilla LMs, a standard retrieve-then-read pipeline, and a contemporaneous self-ask pipeline, respectively.

Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some dimensions of this progress are illusory: the majority of the popular IR benchmarks today focus exclusively on downstream task accuracy and thus conceal the costs incurred by systems that trade away efficiency for quality. Latency, hardware cost, and other efficiency considerations are paramount to the deployment of IR systems in user-facing settings. We propose that IR benchmarks structure their evaluation methodology to include not only metrics of accuracy, but also efficiency considerations such as a query latency and the corresponding cost budget for a reproducible hardware setting. For the popular IR benchmarks MS MARCO and XOR-TyDi, we show how the best choice of IR system varies according to how these efficiency considerations are chosen and weighed. We hope that future benchmarks will adopt these guidelines toward more holistic IR evaluation.

神经信息检索（IR）具有极大的搜索和其他知识密集型语言任务。虽然许多神经IR方法将查询和文档编码为单载表示，但后期交互模型在每个令牌的粒度下产生多向量表示，并将相关性建模分解为可伸缩的令牌级计算。这种分解已被证明可以使迟到的交互更有效，但它以幅度的数量级膨胀这些模型的空间占地面积。在这项工作中，我们介绍了Colbertv2，这是一种猎犬，其与去噪的监督策略相结合的侵略性的残余压缩机制，同时提高晚期互动的质量和空间足迹。我们在各种基准中评估COLBertv2，在培训域内和外部建立最先进的质量，同时减少了晚期互动模型的空间足迹5-8 $ \ times $。

深度神经网络（DNN）模型和数据集的快速增长大小引起了各种分布策略，如数据，张量模型，管道并行和其混合组合。这些策略中的每一个都提供自己的权衡，并在不同的模型和硬件拓扑上展示最佳性能。选择给定设置的最佳策略集是具有挑战性的，因为搜索空间组合增长，并且在群集上调试和测试昂贵。在这项工作中，我们提出了DISTIR，对于分布式DNN计算，这是针对有效分析而定制的分布式DNN计算的表达中间表示，例如模拟。这使得能够自动识别顶级执行策略，而无需在物理硬件上执行。与事先工作不同，Distir自然可以表达许多分发策略，包括管道并行性，具有任意时间表。我们对MLP培训和GPT-2推理模型的评估演示了DISTIR及其模拟器启用快速网格在跨越1000多种配置的复杂分配空间上搜索，以某些制度的数量级递减优化时间。

AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。

There is a vast amount of data generated every second due to the rapidly growing technology in the current world. This area of research attempts to determine the feelings or opinions of people on social media posts. The dataset we used was a multi-source dataset from the comment section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were employed to perform sentiment analysis on the obtained dataset. In this paper, we provide a comparative analysis using techniques of lexicon-based, machine learning and deep learning approaches. The Machine Learning algorithm used in this work is Naive Bayes, the Lexicon-based approach used in this work is TextBlob, and the deep-learning algorithm used in this work is LSTM.

With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand motion recognition is widely used to enable these interactions. Hand configuration classification and MCP joint angle detection is important for a comprehensive reconstruction of hand motion. sEMG and other technologies have been used for the detection of hand motions. Forearm ultrasound images provide a musculoskeletal visualization that can be used to understand hand motion. Recent work has shown that these ultrasound images can be classified using machine learning to estimate discrete hand configurations. Estimating both hand configuration and MCP joint angles based on forearm ultrasound has not been addressed in the literature. In this paper, we propose a CNN based deep learning pipeline for predicting the MCP joint angles. The results for the hand configuration classification were compared by using different machine learning algorithms. SVC with different kernels, MLP, and the proposed CNN have been used to classify the ultrasound images into 11 hand configurations based on activities of daily living. Forearm ultrasound images were acquired from 6 subjects instructed to move their hands according to predefined hand configurations. Motion capture data was acquired to get the finger angles corresponding to the hand movements at different speeds. Average classification accuracy of 82.7% for the proposed CNN and over 80% for SVC for different kernels was observed on a subset of the dataset. An average RMSE of 7.35 degrees was obtained between the predicted and the true MCP joint angles. A low latency (6.25 - 9.1 Hz) pipeline has been proposed for estimating both MCP joint angles and hand configuration aimed at real-time control of human-machine interfaces.

本文提议使用修改的完全连接层转移初始化，以进行1900诊断。卷积神经网络（CNN）在图像分类中取得了显着的结果。但是，由于图像识别应用程序的复杂性，培训高性能模型是一个非常复杂且耗时的过程。另一方面，转移学习是一种相对较新的学习方法，已在许多领域使用，以减少计算来实现良好的性能。在这项研究中，Pytorch预训练的模型（VGG19 \ _bn和WideresNet -101）首次在MNIST数据集中应用于初始化，并具有修改的完全连接的层。先前在Imagenet中对使用的Pytorch预培训模型进行了培训。提出的模型在Kaggle笔记本电脑中得到了开发和验证，并且在网络培训过程中没有花费巨大的计算时间，达到了99.77％的出色精度。我们还将相同的方法应用于SIIM-FISABIO-RSNA COVID-19检测数据集，并达到80.01％的精度。相比之下，以前的方法在训练过程中需要大量的压缩时间才能达到高性能模型。代码可在以下链接上找到：github.com/dipuk0506/spinalnet

夫妻通常在一起管理慢性疾病，管理层对患者及其浪漫伴侣造成了情感上的伤害。因此，认识到日常生活中每个伴侣的情绪可以提供对他们在慢性疾病管理中的情感健康的见解。当前，评估每个伴侣的情绪的过程是手动，时间密集和昂贵的。尽管夫妻之间存在着关于情感识别的作品，但这些作品都没有使用夫妻在日常生活中的互动中收集的数据。在这项工作中，我们收集了85小时（1,021个5分钟样本）现实世界多模式智能手表传感器数据（语音，心率，加速度计和陀螺仪）和自我报告的情绪数据（n = 612）（13个伙伴）（13）夫妻）在日常生活中管理2型糖尿病。我们提取了生理，运动，声学和语言特征，以及训练有素的机器学习模型（支持向量机和随机森林），以识别每个伴侣的自我报告的情绪（价和唤醒）。我们最佳模型的结果比偶然的结果更好，唤醒和价值分别为63.8％和78.1％。这项工作有助于建立自动情绪识别系统，最终使伙伴能够监视他们在日常生活中的情绪，并能够提供干预措施以改善其情感幸福感。

在过去的几年中，自动驾驶的感知系统在其表现方面取得了重大进步。但是，这些系统在极端天气条件下努力表现出稳健性，因为在这些条件下，传感器和相机等传感器套件中的主要传感器都会下降。为了解决此问题，摄像机雷达融合系统为所有可靠的高质量感知提供了独特的机会。相机提供丰富的语义信息，而雷达可以通过遮挡和在所有天气条件下工作。在这项工作中，我们表明，当摄像机输入降解时，最新的融合方法的性能很差，这实际上导致失去了他们设定的全天可靠性。与这些方法相反，我们提出了一种新方法RadSegnet，该方法使用了独立信息提取的新设计理念，并在所有情况下都可以在所有情况下真正实现可靠性，包括遮挡和不利天气。我们在基准ASTYX数据集上开发并验证了我们的系统，并在辐射数据集上进一步验证了这些结果。与最先进的方法相比，Radsegnet在ASTYX上提高了27％，辐射增长了41.46％，平均精度得分，并且在不利天气条件下的性能明显更好