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

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods(goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods(energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture,machine-to-machine communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems; Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process.

深度神经网络（DNN）已经在许多领域实现了最先进的性能。然而，DNN需要高计算时间，并且人们始终期望在较低的计算中进行更好的性能。因此，我们研究人类躯体传感系统并设计神经网络（SPINANNET），以实现更高的计算准确性，计算较少。传统NNS中的隐藏层接收前一层中的输入，应用激活函数，然后将结果传送到下一个图层。在拟议的脊柱植物中，每层分为三个分裂：1）输入分割，2）中间分割，3）输出分割。每个层的输入拆分接收到输入的一部分。每个层的中间分割接收先前层的中间分离的输出和电流层的输入分割的输出。输入权重的数量明显低于传统的DNN。 SPINANNET还可以用作DNN的完全连接或分类层，并支持传统的学习和转移学习。我们在大多数DNN中观察到具有较低计算成本的显着误差。 VGG-5网络上的传统学习具有SPINALNET分类层，为QMNIST，Kuzushiji-Mnist，EMNIST（字母，数字和平衡）数据集提供了最先进的（SOTA）性能。传统学习与Imagenet预训练的初始重量和Spinalnet分类层提供了STL-10，水果360，Bird225和CALTECH-101数据集的SOTA性能。拟议的SPINANNET的脚本可按以下链接提供：https://github.com/dipuk0506/spinalnet

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive; such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there exists no single established protocol for evaluating offline RL methods.In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.

In manufacturing, the production is often done on out-of-the-shelf manufacturing lines, whose underlying scheduling heuristics are not known due to the intellectual property. We consider such a setting with a black-box job-shop system and an unknown scheduling heuristic that, for a given permutation of jobs, schedules the jobs for the black-box job-shop with the goal of minimizing the makespan. Here, the jobs need to enter the job-shop in the given order of the permutation, but may take different paths within the job shop, which depends on the black-box heuristic. The performance of the black-box heuristic depends on the order of the jobs, and the natural problem for the manufacturer is to find an optimum ordering of the jobs. Facing a real-world scenario as described above, we engineer the Monte-Carlo tree-search for finding a close-to-optimum ordering of jobs. To cope with a large solutions-space in planning scenarios, a hierarchical Monte-Carlo tree search (H-MCTS) is proposed based on abstraction of jobs. On synthetic and real-life problems, H-MCTS with integrated abstraction significantly outperforms pure heuristic-based techniques as well as other Monte-Carlo search variants. We furthermore show that, by modifying the evaluation metric in H-MCTS, it is possible to achieve other optimization objectives than what the scheduling heuristics are designed for -- e.g., minimizing the total completion time instead of the makespan. Our experimental observations have been also validated in real-life cases, and our H-MCTS approach has been implemented in a production plant's controller.

Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-adaptive uncertainty-aware explorations (UEs) based on a ratio uncertainty level. The proposed PPO-UE is designed to improve convergence speed and performance with an optimized ratio uncertainty level. Through extensive sensitivity analysis by varying the ratio uncertainty level, our proposed PPO-UE considerably outperforms the baseline PPO in Roboschool continuous control tasks.

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method.