With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighborhood average for modeling and clustering the time series of countries. The clustering results provide valuable insight into public opinion regarding this conflict. Among other things, we can mention the similar thoughts of users from the United States, Canada, the United Kingdom, and most Western European countries versus the shared views of Eastern European, Scandinavian, Asian, and South American nations toward the conflict.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

A self-supervised adaptive low-light video enhancement (SALVE) method is proposed in this work. SALVE first conducts an effective Retinex-based low-light image enhancement on a few key frames of an input low-light video. Next, it learns mappings from the low- to enhanced-light frames via Ridge regression. Finally, it uses these mappings to enhance the remaining frames in the input video. SALVE is a hybrid method that combines components from a traditional Retinex-based image enhancement method and a learning-based method. The former component leads to a robust solution which is easily adaptive to new real-world environments. The latter component offers a fast, computationally inexpensive and temporally consistent solution. We conduct extensive experiments to show the superior performance of SALVE. Our user study shows that 87% of participants prefer SALVE over prior work.

基于深度学习的图生成方法具有显着的图形数据建模能力，从而使它们能够解决广泛的现实世界问题。使这些方法能够在生成过程中考虑不同的条件，甚至通过授权它们生成满足所需标准的新图形样本来提高其有效性。本文提出了一种条件深图生成方法，称为SCGG，该方法考虑了特定类型的结构条件。具体而言，我们提出的SCGG模型采用初始子图，并自动重新收获在给定条件子结构之上生成新节点及其相应的边缘。 SCGG的体系结构由图表表示网络和自动回归生成模型组成，该模型是端到端训练的。使用此模型，我们可以解决图形完成，这是恢复缺失的节点及其相关的部分观察图的猖and固有的困难问题。合成数据集和现实世界数据集的实验结果证明了我们方法的优势与最先进的基准相比。

对比度学习是视觉表示学习最成功的方法之一，可以通过在学习的表示上共同执行聚类来进一步提高其性能。但是，现有的联合聚类和对比度学习的方法在长尾数据分布上表现不佳，因为多数班级压倒了少数群体的损失，从而阻止了学习有意义的表示形式。由此激励，我们通过适应偏见的对比损失，以避免群集中的少数群体类别的不平衡数据集来开发一种新颖的联合聚类和对比度学习框架。我们表明，我们提出的修改后的对比损失和分歧聚类损失可改善多个数据集和学习任务的性能。源代码可从https://anonymon.4open.science/r/ssl-debiased-clustering获得

准确的几何表示对于开发有限元模型至关重要。尽管通常只有很少的数据在准确细分精美特征，例如缝隙和薄结构方面，虽然只有很少的数据就有良好的深度学习分割方法。随后，分段的几何形状需要劳动密集型手动修改，以达到可用于模拟目的的质量。我们提出了一种使用转移学习来重复使用分段差的数据集的策略，并结合了交互式学习步骤，其中数据对数据进行微调导致解剖上精确的分割适合模拟。我们使用改良的多平台UNET，该UNET使用下髋关节分段和专用损耗函数进行预训练，以学习间隙区域和后处理，以纠正由于旋转不变性而在对称类别上的微小不准确性。我们证明了这种可靠但概念上简单的方法，采用了临床验证的髋关节扫描扫描的临床验证结果。代码和结果3D模型可在以下网址提供：\ url {https://github.com/miccai2022-155/autoseg}

COVID-19的诊断对于预防和控制该疾病是必要的。深度学习方法已被认为是一种快速准确的方法。在本文中，通过三个众所周知的预训练网络的平行组合，我们试图将感染的冠状病毒样品与健康样本区分开。负模样损耗函数已用于模型训练。SARS-COV-2数据集中的CT扫描图像用于诊断。SARS-COV-2数据集包含2482张肺CT扫描图像，其中1252张图像属于COVID-19感染的样品。提出的模型接近97％的准确性。

在缩短的繁殖周期内生产高质量的农作物可确保全球粮食可利用性和安全性，但是由于存储限制，这种改进在全年繁殖过程中对种子工业的后勤和生产力挑战加剧了。在2021年分析中​​的先正达农作物挑战中，先正达提出了问题，以设计2020年全年繁殖过程中种植时间计划的优化模型，因此每周都有一致的收获数量。他们释放了一个数据集，其中包含2569种种子种群的种植窗，需要增长的学位单位进行收获，并在两个地点进行收获数量。为了应对这一挑战，我们开发了一个新框架，该框架由天气时间序列模型和一个优化模型组成，以安排种植时间。设计了一个深层复发的神经网络，以预测未来的天气，并且开发了时间序列模型的高斯过程模型，以模拟预测天气的不确定性。拟议的优化模型还安排了种子种群在最少的几周数，每周收获数量更加一致。与原始的种植时间相比，使用提出的优化模型可以在站点0时将所需的容量降低69％，在站点1下降到51％。

如今，随着深度学习算法的兴起，大数据上的场景图像表示方法（例如，Sun-397）在分类方面取得了重大的性能。但是，性能仍然受到限制，因为场景图像在本质上大多是复杂的，具有较高的阶层差异和类间相似性问题。为了解决此类问题，文献中提出了几种具有自己的优势和局限性的方法。必须对以前的作品进行详细研究，以了解其图像表示和分类方面的利弊。在本文中，我们回顾了广泛用于图像分类的现有场景图像表示方法。为此，我们首先使用本日期中文献中提出的开创性现有方法来设计分类法。接下来，我们将它们的性能进行定性比较（例如，产出，优点/缺点等）和定量（例如准确性）。最后，我们推测场景图像表示任务中的突出研究方向。总体而言，这项调查提供了有关传统计算机视觉（CV）方法，基于深度学习（DL）的方法和基于搜索引擎（SE）基于基于的基于的计算机视觉方法（CV）的最新场景图像表示方法的深入见解和应用。