Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.

大数据和深度学习的结合是一项破坏世界的技术，如果正确使用，可以极大地影响任何目标。随着深度学习技术中大量医疗保健数据集和进步的可用性，系统现在可以很好地预测任何健康问题的未来趋势。从文献调查中，我们发现SVM用于预测心力衰竭的情况，而无需关联客观因素。利用电子健康记录（EHR）中重要历史信息的强度，我们利用长期记忆（LSTM）建立了一个智能和预测的模型，并根据该健康记录预测心力衰竭的未来趋势。因此，这项工作的基本承诺是使用基于患者的电子药用信息的LSTM来预测心脏的失败。我们已经分析了一个数据集，该数据集包含在Faisalabad心脏病学研究所和Faisalabad（巴基斯坦旁遮普邦）的盟军医院收集的299例心力衰竭患者的病历。这些患者由105名女性和194名男性组成，年龄在40岁和95岁之间。该数据集包含13个功能，这些功能报告了负责心力衰竭的临床，身体和生活方式信息。我们发现我们的分析趋势越来越多，这将有助于促进心中预测领域的知识。

全球一百多个国家的主食是大米（Oryza sativa）。大米的种植对于全球经济增长至关重要。但是，农业产业面临的主要问题是水稻疾病。农作物的质量和数量下降了，这是主要原因。由于任何国家的农民对水稻疾病都没有太多了解，因此他们无法正确诊断稻叶疾病。这就是为什么他们不能适当照顾米叶的原因。结果，生产正在减少。从文献调查中，Yolov5表现出更好的结果与其他深度学习方法相比。由于对象检测技术的不断发展，Yolo家族算法具有非常高的精度和更好的速度，已在各种场景识别任务中使用，以构建稻叶疾病监测系统。我们已经注释了1500个收集的数据集，并提出了基于Yolov5深学习的水稻疾病分类和检测方法。然后，我们训练并评估了Yolov5模型。模拟结果显示了本文提出的增强Yolov5网络的对象检测结果的改进。所需的识别精度，召回，MAP值和F1得分的水平分别为90 \％，67 \％，76 \％和81 \％\％被视为性能指标。

具有基于块体系结构的运动建模已被广泛用于视频编码中，其中框架分为固定尺寸的块，这些块是独立补偿的。这通常会导致编码效率低下，因为固定尺寸的块几乎与对象边界不符。尽管已经引入了层次结构分区来解决这一问题，但运动矢量的增加限制了收益。最近，与立方体分配的图像的近似分割已经普及。可变大小的矩形片段（立方体）不仅容易适应基于块的图像/视频编码技术，而且还可以很好地与对象边界保持一致。这是因为立方分区基于同质性约束，从而最大程度地减少了平方误差的总和（SSE）。在本文中，我们研究了针对可扩展视频编码中使用的固定尺寸块的运动模型的潜力。具体而言，我们使用图片组（GOP）中的锚框的立方分区信息构建了运动补偿帧。然后，预测的当前帧已用作基础层，同时使用可扩展的HEVC编码器编码当前帧作为增强层。实验结果确认4K视频序列上节省了6.71％-10.90％的比特率。

随着沉浸式视频序列的快速增长，实现无缝和高质量的压缩3D含量更为关键。 MPEG最近开发了一种基于视频的点云压缩（V-PCC），用于动态点云编码。但是，使用V-PCC进行重建的点云会遭受不同的工件的影响，包括在应用现有视频编码技术之前在预处理过程中丢失数据，例如高效视频编码（HEVC）。贴片世代和2D投影中3D的自封点是使用V-PCC丢失数据的主要原因。本文提出了一种新方法，将重叠切片作为贴片生成的替代方法，以减少生成的贴片数量和丢失的数据量。在提出的方法中，整个点云已根据自锁定点的数量将整个点云分为横截面，以便在斑块生成过程和投影中可以最大程度地减少数据丢失。为此，考虑了可变数量的层，部分重叠以保留自锁定点。所提出的方法的额外优势是减少位置的需求并使用切片底座编码几何数据。实验结果表明，与标准的V-PCC方法相比，提出的方法比标准V-PCC方法更灵活，改善了率延伸性能，并且与标准V-PCC方法相比，数据丢失显着降低。

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain?

Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization.

An expansion of aberrant brain cells is referred to as a brain tumor. The brain's architecture is extremely intricate, with several regions controlling various nervous system processes. Any portion of the brain or skull can develop a brain tumor, including the brain's protective coating, the base of the skull, the brainstem, the sinuses, the nasal cavity, and many other places. Over the past ten years, numerous developments in the field of computer-aided brain tumor diagnosis have been made. Recently, instance segmentation has attracted a lot of interest in numerous computer vision applications. It seeks to assign various IDs to various scene objects, even if they are members of the same class. Typically, a two-stage pipeline is used to perform instance segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo takes dataset as picture format and corresponding text file. You Only Look Once (YOLO) is a viral and widely used algorithm. YOLO is famous for its object recognition properties. You Only Look Once (YOLO) is a popular algorithm that has gone viral. YOLO is well known for its ability to identify objects. YOLO V2, V3, V4, and V5 are some of the YOLO latest versions that experts have published in recent years. Early brain tumor detection is one of the most important jobs that neurologists and radiologists have. However, it can be difficult and error-prone to manually identify and segment brain tumors from Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the condition, an automated brain tumor detection system is necessary. The model of the research paper has three classes. They are respectively Meningioma, Pituitary, Glioma. The results show that, our model achieves competitive accuracy, in terms of runtime usage of M2 10 core GPU.

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.

Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a differentiable rendering pipeline. We emphasize two key contributions: (1) instead of solving the conventional 2D to 3D correspondence problem and computing reprojection errors, images (rendered using the 3D model) are compared only in the 2D feature space via sparse 2D feature correspondences. (2) Instead of an analytical image formation model, we compute an approximate local gradient of the rendering process through online learning. The learning data consists of image features extracted from multi-viewpoint renders at small perturbations in the pose neighborhood. The gradients are propagated through the rendering pipeline for the 6-DoF pose estimation using nonlinear least squares. This gradient-based optimization regresses directly upon the pose parameters by aligning the 3D model to reproduce a reference image shape. Using representative experiments, we demonstrate the application of our approach to pose estimation in proximity operations.