我们研究了一个顺序决策问题，其中学习者面临$ k $武装的随机匪徒任务的顺序。对手可能会设计任务，但是对手受到限制，以在$ m $ and的较小（但未知）子集中选择每个任务的最佳组。任务边界可能是已知的（强盗元学习设置）或未知（非平稳的强盗设置）。我们设计了一种基于Burnit subsodular最大化的减少的算法，并表明，在大量任务和少数最佳武器的制度中，它在两种情况下的遗憾都比$ \ tilde {o}的简单基线要小。 \ sqrt {knt}）$可以通过使用为非平稳匪徒问题设计的标准算法获得。对于固定任务长度$ \ tau $的强盗元学习问题，我们证明该算法的遗憾被限制为$ \ tilde {o}（nm \ sqrt {m \ tau}+n^{2/3} m \ tau）$。在每个任务中最佳武器的可识别性的其他假设下，我们显示了一个带有改进的$ \ tilde {o}（n \ sqrt {m \ tau}+n^{1/2} {1/2} \ sqrt的强盗元学习算法{m k \ tau}）$遗憾。

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.

Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, it is considered a scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer) or a potential fire spot in a given area. In order to obtain the shortest path to the target, a general framework is provided to model the problem of target detection when the target's location is probabilistically known. To this end, two algorithms are proposed: Path planning and target detection. The path planning algorithm is based on Bayesian inference and the target detection is accomplished by means of a residual neural network (ResNet) trained on the image dataset captured by the drone as well as existing pictures and datasets on the web. Through simulation and experiment, the proposed path planning algorithm is compared with two benchmark algorithms. It is shown that the proposed algorithm significantly decreases the average time of the mission.

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

本文着重于根据数据包输送比率（PDR）（即，在远程广阔的区域（Lorawan）中通过End Devices（EDS）发送）的数据包数量来改善资源分配算法。设置传输参数会显着影响PDR。我们采用强化学习（RL）提出了一种资源分配算法，该算法使ED可以以分布式方式配置其传输参数。我们将资源分配问题建模为多臂强盗（MAB），然后通过提出一种名为Mix-MAB的两相算法来解决它，该算法由探索和开发（EXP3）和连续消除（SE）组成，该算法由指数重量组成（SE）算法。我们通过仿真结果评估混合MAB性能，并将其与其他现有方法进行比较。数值结果表明，就收敛时间和PDR而言，所提出的解决方案的性能优于现有方案。

这项工作提出了一种基于连续的子空间学习（SSL）的生成建模方法。与文献中的大多数生成模型不同，我们的方法不利用神经网络来分析基本源分布和合成图像。所得的方法称为渐进属性引导可扩展的鲁棒图像生成（PAGER）模型，在数学透明度，渐进式内容生成，较低的训练时间，较少的训练样本以及对条件图像生成的扩展性方面具有优势。 Pager由三个模块组成：核心生成器，分辨率增强器和质量助推器。核心发电机了解低分辨率图像的分布并执行无条件的图像生成。分辨率增强子通过条件产生增加图像分辨率。最后，质量助推器为生成的图像增加了更细节。进行了有关MNIST，时尚摄影和Celeba数据集的广泛实验，以证明Pager的生成性能。

我们提出了一个强大的框架，以执行线性回归，而功能中缺少条目。通过考虑椭圆形数据分布，特别是多元正常模型，我们能够为缺失条目制定分布并提出一个强大的框架，这最大程度地减少了由于缺失数据的不确定性而造成的最严重的情况。我们表明，所提出的公式自然考虑了不同变量之间的依赖性，最终减少了凸面程序，可以为其提供自定义和可扩展的求解器。除了提供此类求解器的详细分析外，我们还渐近地分析了所提出的框架的行为，并进行了技术讨论以估算所需的输入参数。我们通过对合成，半合成和真实数据进行的实验进行补充，并展示提出的配方如何提高预测准确性和鲁棒性，并优于竞争技术。