Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment option, but their large-scale identification and synthesis require reliable prediction methods, which is still a problem. In this paper, we present an intuitive classification strategy that differs from the traditional \emph{black box} method and is based on the well-known statistical theory of \emph{sparse-representation classification} (SRC). Specifically, we create over-complete dictionary matrices by embedding the \emph{composition of the K-spaced amino acid pairs} (CKSAAP). Unlike the traditional SRC frameworks, we use an efficient \emph{matching pursuit} solver instead of the computationally expensive \emph{basis pursuit} solver in this strategy. Furthermore, the \emph{kernel principal component analysis} (KPCA) is employed to cope with non-linearity and dimension reduction of the feature space whereas the \emph{synthetic minority oversampling technique} (SMOTE) is used to balance the dictionary. The proposed method is evaluated on two benchmark datasets for well-known statistical parameters and is found to outperform the existing methods. The results show the highest sensitivity with the most balanced accuracy, which might be beneficial in understanding structural and chemical aspects and developing new ACPs. The Google-Colab implementation of the proposed method is available at the author's GitHub page (\href{https://github.com/ehtisham-Fazal/ACP-Kernel-SRC}{https://github.com/ehtisham-fazal/ACP-Kernel-SRC}).

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELOare in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches R2 values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks.

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.

当地球经历全球变暖时，自然灾害，如洪水，龙卷风或野火，越来越普遍普遍。很难预测事件的何时何时会发生，所以及时的应急响应对于拯救受破坏事件危害的人的生命至关重要。幸运的是，技术可以在这些情况下发挥作用。社交媒体帖子可以用作低延迟数据源来了解灾难的进展和后果，但解析此数据无需自动化方法。在前的工作主要集中在基于文本的过滤，但基于图像和基于视频的过滤仍然很大程度上是未开发的。在这项工作中，我们介绍了一个大规模的多标签数据集，其中包含977,088个图像，43个事件和49个地方。我们提供数据集建设，统计和潜在偏差的详细信息;介绍和训练事件检测模型;在Flickr和Twitter上为数百万图像进行图像过滤实验。我们还提出了一些关于事件分析的申请，以鼓励和使未来的人道主义援助中的计算机愿景工作。代码，数据和模型可在http://incidentsdataset.csail.mit.edu上获得。

在临床工作流程中成功部署AI的计算机辅助诊断（CAD）系统的一个主要障碍是它们缺乏透明决策。虽然常用可解释的AI方法提供了一些对不透明算法的洞察力，但除了高度训练的专家外，这种解释通常是复杂的，而不是易于理解的。关于皮肤病图像的皮肤病病变恶性的决定的解释需要特别清晰，因为潜在的医疗问题定义本身是模棱两可的。这项工作提出了exaid（可解释的ai用于皮肤科），是生物医学图像分析的新框架，提供了由易于理解的文本解释组成的多模态概念的解释，该概念由可视地图证明预测的视觉映射。 Exap依赖于概念激活向量，将人类概念映射到潜在空间中的任意深度学习模型学习的人，以及概念本地化地图，以突出输入空间中的概念。然后，这种相关概念的识别将用于构建由概念 - 明智地点信息补充的细粒度文本解释，以提供全面和相干的多模态解释。所有信息都在诊断界面中全面呈现，用于临床常规。教育模式为数据和模型探索提供数据集级别解释统计和工具，以帮助医学研究和教育。通过严谨的exaid定量和定性评估，即使在错误的预测情况下，我们展示了CAD辅助情景的多模态解释的效用。我们认为突然将为皮肤科医生提供一种有效的筛查工具，他们都理解和信任。此外，它将是其他生物医学成像领域的类似应用的基础。

从社交媒体共享的静止图像中检测战斗是限制暴力场景分布所需的重要任务，以防止它们的负面影响。出于这个原因，在本研究中，我们解决了从网络和社交媒体收集的静止图像的战斗检测问题。我们探索一个人可以从单个静态图像中检测到战斗的程度。我们还提出了一个新的数据集，名为社交媒体的战斗图像（SMFI），包括现实世界的战斗行为图像。拟议数据集的广泛实验结果表明，可以从静止图像中成功识别战斗行动。也就是说，即使在不利用时间信息，也可以通过仅利用外观来检测高精度的斗争。我们还执行跨数据集实验以评估收集数据集的表示能力。这些实验表明，与其他计算机视觉问题一样，存在用于战斗识别问题的数据集偏差。虽然在同一战斗数据集上训练和测试时，该方法可实现接近100％的精度，但是交叉数据集精度显着降低，即，当更多代表性数据集用于培训时，约为70％。 SMFI数据集被发现是使用的五个战斗数据集中的两个最代表性的数据集之一。

使用人工智能（AI）赋予无线网络中数据量的前所未有的数据量激增，为提供无处不在的数据驱动智能服务而开辟了新的视野。通过集中收集数据集和培训模型来实现传统的云彩中心学习（ML）基础的服务。然而，这种传统的训练技术包括两个挑战：（i）由于数据通信增加而导致的高通信和能源成本，（ii）通过允许不受信任的各方利用这些信息来威胁数据隐私。最近，鉴于这些限制，一种新兴的新兴技术，包括联合学习（FL），以使ML带到无线网络的边缘。通过以分布式方式培训全局模型，可以通过FL Server策划的全局模型来提取数据孤岛的好处。 FL利用分散的数据集和参与客户的计算资源，在不影响数据隐私的情况下开发广义ML模型。在本文中，我们介绍了对FL的基本面和能够实现技术的全面调查。此外，提出了一个广泛的研究，详细说明了无线网络中的流体的各种应用，并突出了他们的挑战和局限性。进一步探索了FL的疗效，其新兴的前瞻性超出了第五代（B5G）和第六代（6G）通信系统。本调查的目的是在关键的无线技术中概述了流动的技术，这些技术将作为建立对该主题的坚定了解的基础。最后，我们向未来的研究方向提供前进的道路。

最近在灾害信息学的研究证明了人工智能的实用而重要的用例，以拯救人类生命和基于社交媒体内容（文本和图像）的自然灾害期间的痛苦。虽然使用文本的显着进度，但利用图像的研究仍然相对较低。要提前基于图像的方法，我们提出了Medic（可用于：https://crisisnlp.qcri.org/medic/index.html），这是人道主义响应的最大社交媒体图像分类数据集，由71,198个图像组成在多任务学习设置中的四个不同任务。这是它的第一个数据集：社交媒体图像，灾难响应和多任务学习研究。该数据集的一个重要属性是它的高潜力，可以为多任务学习进行贡献，该研究最近从机器学习界获得了很多兴趣，并在内存，推理速度，性能和泛化能力方面显示出显着的结果。因此，所提出的数据集是用于推进基于图像的灾害管理和多任务机器学习研究的重要资源。