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。

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.

Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using Machine Learning (ML) for this problem generally requires manually annotated ground-truth segmentations, demanding extensive time and resources from radiologists. This work presents a novel weakly supervised approach that utilizes binary image-level labels, which are much simpler to acquire, to effectively segment anomalies in medical Magnetic Resonance (MR) images without ground truth annotations. We train a binary classifier using these labels and use it to derive seeds indicating regions likely and unlikely to contain tumors. These seeds are used to train a generative adversarial network (GAN) that converts cancerous images to healthy variants, which are then used in conjunction with the seeds to train a ML model that generates effective segmentations. This method produces segmentations that achieve Dice coefficients of 0.7903, 0.7868, and 0.7712 on the MICCAI Brain Tumor Segmentation (BraTS) 2020 dataset for the training, validation, and test cohorts respectively. We also propose a weakly supervised means of filtering the segmentations, removing a small subset of poorer segmentations to acquire a large subset of high quality segmentations. The proposed filtering further improves the Dice coefficients to up to 0.8374, 0.8232, and 0.8136 for training, validation, and test, respectively.

Deep learning techniques have greatly benefited computer-aided diagnostic systems. However, unlike other fields, in medical imaging, acquiring large fine-grained annotated datasets such as 3D tumour segmentation is challenging due to the high cost of manual annotation and privacy regulations. This has given interest to weakly-supervise methods to utilize the weakly labelled data for tumour segmentation. In this work, we propose a weakly supervised approach to obtain regions of interest using binary class labels. Furthermore, we propose a novel objective function to train the generator model based on a pretrained binary classification model. Finally, we apply our method to the brain tumour segmentation problem in MRI.

训练机器学习（ML）模型以分割肿瘤和医学图像中的其他异常是一个越来越受欢迎的研究领域，但通常需要手动注释的地面真实分段，这需要大量的时间和资源来创建。这项工作提出了一个使用二进制分类标签的ML模型的管道，可以轻松获取，以分割ROI，而无需进行地面真实注释。我们使用了来自多模式脑肿瘤分割挑战（BRAT）的2D磁共振成像（MRI）脑扫描2020数据集，标签表明存在高级神经胶质瘤（HGG）肿瘤来训练管道。我们的管道还引入了基于深度学习的超级像素生成的新颖变体，该变体能够以聚类的超像素为指导，并同时训练超像素聚类模型。在我们的测试集中，我们的管道的分割达到了61.7％的骰子系数，当使用流行的局部局部可解释的模型 - 敏捷解释（LIME）方法时，获得的42.8％骰子系数是一个实质性的改善。

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

基于尺寸的生物颗粒/细胞分离对于外泌体和DNA分离等应用的多种生物医学处理步骤至关重要。这种微流体设备的设计和改进是最佳回答生产均质最终结果的需求的挑战。确定性的横向位移（DLD）利用了类似的原则，该原理在多年来引起了广泛的关注。但是，缺乏对粒子轨迹及其诱导模式的预测性理解，使设计DLD设备成为迭代过程。因此，本文研究了一个快速的多功能设计自动化平台来解决此问题。为此，采用了卷积和人工神经网络来学习各种DLD配置的速度场和临界直径。后来，将这些网络与多目标进化算法结合使用，以构建自动化工具。在确保神经网络的准确性之后，对开发的工具进行了12个关键条件测试。达到施加的条件，自动化组件可靠地执行，误差小于4％。此外，该工具可以推广到其他基于现场的问题，并且由于神经网络是该方法不可或缺的一部分，因此它可以为类似物理学进行转移学习。本研究中生成和使用的所有代码与预先训练的神经网络模型都可以在https://github.com/hoseynaamiri/dldnn上获得。