无线传感器网络是当前时代最有前途的技术之一，因为它们的规模较小，成本较低和易于部署。随着无线传感器数量的增加，生成丢失数据的概率也会上升。如果用于决策，这种不完整的数据可能会导致灾难性后果。有很多关于这个问题的文献。但是，大多数方法显示出大量数据丢失时性能降解。受图形信号处理的新兴领域的启发，本文对无线传感器网络中的Sobolev重建算法进行了一项新研究。对几个公开数据集进行的实验比较表明，该算法超过多个最新技术的最大利润率为54％。我们进一步表明，即使在大规模数据丢失情况下，该算法也会始终检索丢失的数据。

移动对象检测是计算机愿景中的讨论的核心主题，适用于自动驾驶汽车，视频监控，安全和执法中的广泛应用。神经形态视觉传感器（NVS）是生物启发传感器，用于模仿人眼的工作。与传统的基于帧的相机不同，这些传感器捕获异步'事件'流，其在前者上呈现多个优点，如高动态范围，低延迟，低功耗和减少的运动模糊。然而，这些优点成本高，因为事件相机数据通常包含更多噪声并具有低分辨率。此外，由于基于事件的相机只能捕获场景的亮度的相对变化，事件数据不包含来自普通摄像机的视频数据中的通常可视信息（如纹理和颜色）。因此，在基于事件的相机中移动对象检测成为一个极具挑战性的任务。在本文中，我们介绍了一种无监督的曲线谱聚类技术，用于在基于事件的数据中移动对象检测（GSceVentMod）。我们还展示了如何自动确定最佳的移动物体数量。公开数据集上的实验比较表明，所提出的GSceventMod算法优于最大限度的最新技术，最大余量为30％。

移动物体检测在计算机视觉中很重要。基于事件的相机是通过模仿人眼的工作来工作的生物启发相机。这些摄像机与传统的基于帧的相机具有多种优点，例如在高运动期间的降低的延迟，HDR，降低运动模糊等，尽管存在这些优点，但基于事件的相机是噪声敏感的并且具有低分辨率。此外，由于基于事件的传感器缺乏纹理和颜色，因此难以困难地难以移动对象检测的任务。在本文中，我们调查K-Means聚类技术在基于事件数据中检测移动对象的应用。

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

Long-term OCR services aim to provide high-quality output to their users at competitive costs. It is essential to upgrade the models because of the complex data loaded by the users. The service providers encourage the users who provide data where the OCR model fails by rewarding them based on data complexity, readability, and available budget. Hitherto, the OCR works include preparing the models on standard datasets without considering the end-users. We propose a strategy of consistently upgrading an existing Handwritten Hindi OCR model three times on the dataset of 15 users. We fix the budget of 4 users for each iteration. For the first iteration, the model directly trains on the dataset from the first four users. For the rest iteration, all remaining users write a page each, which service providers later analyze to select the 4 (new) best users based on the quality of predictions on the human-readable words. Selected users write 23 more pages for upgrading the model. We upgrade the model with Curriculum Learning (CL) on the data available in the current iteration and compare the subset from previous iterations. The upgraded model is tested on a held-out set of one page each from all 23 users. We provide insights into our investigations on the effect of CL, user selection, and especially the data from unseen writing styles. Our work can be used for long-term OCR services in crowd-sourcing scenarios for the service providers and end users.

Indian e-commerce industry has evolved over the last decade and is expected to grow over the next few years. The focus has now shifted to turnaround time (TAT) due to the emergence of many third-party logistics providers and higher customer expectations. The key consideration for delivery providers is to balance their overall operating costs while meeting the promised TAT to their customers. E-commerce delivery partners operate through a network of facilities whose strategic locations help to run the operations efficiently. In this work, we identify the locations of hubs throughout the country and their corresponding mapping with the distribution centers. The objective is to minimize the total network costs with TAT adherence. We use Genetic Algorithm and leverage business constraints to reduce the solution search space and hence the solution time. The results indicate an improvement of 9.73% in TAT compliance compared with the current scenario.

Handwritten Text Recognition (HTR) is more interesting and challenging than printed text due to uneven variations in the handwriting style of the writers, content, and time. HTR becomes more challenging for the Indic languages because of (i) multiple characters combined to form conjuncts which increase the number of characters of respective languages, and (ii) near to 100 unique basic Unicode characters in each Indic script. Recently, many recognition methods based on the encoder-decoder framework have been proposed to handle such problems. They still face many challenges, such as image blur and incomplete characters due to varying writing styles and ink density. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we enhance the performance of Indic handwritten text recognizers using global semantic information. We use a semantic module in an encoder-decoder framework for extracting global semantic information to recognize the Indic handwritten texts. The semantic information is used in both the encoder for supervision and the decoder for initialization. The semantic information is predicted from the word embedding of a pre-trained language model. Extensive experiments demonstrate that the proposed framework achieves state-of-the-art results on handwritten texts of ten Indic languages.

Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking. We model this class of problems in a visual active search (VAS) framework, which takes as input an image of a broad area, and aims to identify as many examples of a target object as possible. It does this through a limited sequence of queries, each of which verifies whether an example is present in a given region. We propose a reinforcement learning approach for VAS that leverages a collection of fully annotated search tasks as training data to learn a search policy, and combines features of the input image with a natural representation of active search state. Additionally, we propose domain adaptation techniques to improve the policy at decision time when training data is not fully reflective of the test-time distribution of VAS tasks. Through extensive experiments on several satellite imagery datasets, we show that the proposed approach significantly outperforms several strong baselines. Code and data will be made public.

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

最近已证明，平均场控制（MFC）是可扩展的工具，可近似解决大规模的多代理增强学习（MARL）问题。但是，这些研究通常仅限于无约束的累积奖励最大化框架。在本文中，我们表明，即使在存在约束的情况下，也可以使用MFC方法近似MARL问题。具体来说，我们证明，一个$ n $ agent的约束MARL问题，以及每个尺寸的尺寸$ | \ Mathcal {x} | $和$ | \ Mathcal {u} | $的状态和操作空间，可以通过与错误相关的约束MFC问题近似，$ e \ triangleq \ Mathcal {o} \ left（[\ sqrt {| \ Mathcal {| \ Mathcal {x} |} |}+\ sqrt {| ]/\ sqrt {n} \ right）$。在奖励，成本和状态过渡功能独立于人口的行动分布的特殊情况下，我们证明该错误可以将错误提高到$ e = \ nathcal {o}（\ sqrt {| | \ Mathcal {x x x } |}/\ sqrt {n}）$。另外，我们提供了一种基于自然策略梯度的算法，并证明它可以在$ \ Mathcal {o}（e）$的错误中解决受约束的MARL问题，并具有$ \ MATHCAL {O}的样本复杂性（E^{ - e^{ - 6}）$。