Medical professionals frequently work in a data constrained setting to provide insights across a unique demographic. A few medical observations, for instance, informs the diagnosis and treatment of a patient. This suggests a unique setting for meta-learning, a method to learn models quickly on new tasks, to provide insights unattainable by other methods. We investigate the use of meta-learning and robustness techniques on a broad corpus of benchmark text and medical data. To do this, we developed new data pipelines, combined language models with meta-learning approaches, and extended existing meta-learning algorithms to minimize worst case loss. We find that meta-learning on text is a suitable framework for text-based data, providing better data efficiency and comparable performance to few-shot language models and can be successfully applied to medical note data. Furthermore, meta-learning models coupled with DRO can improve worst case loss across disease codes.

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}).

Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: ~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.

Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning. However, from another perspective, under a known environment model, methods such as sampling-based planning can robustly find collision-free paths in environments without learning. In this work, we propose Control Transformer which models return-conditioned sequences from low-level policies guided by a sampling-based Probabilistic Roadmap (PRM) planner. Once trained, we demonstrate that our framework can solve long-horizon navigation tasks using only local information. We evaluate our approach on partially-observed maze navigation with MuJoCo robots, including Ant, Point, and Humanoid, and show that Control Transformer can successfully navigate large mazes and generalize to new, unknown environments. Additionally, we apply our method to a differential drive robot (Turtlebot3) and show zero-shot sim2real transfer under noisy observations.

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.

基于各种非负矩阵分解（NMF）方法为成本函数添加了新术语，以使模型适应特定任务，例如聚类或保留减少空间中的某些结构属性（例如，局部不变性）。附加的术语主要由高参数加权，以控制整体公式的平衡，以指导优化过程实现目标。结果是一种参数化的NMF方法。但是，NMF方法采用了无监督的方法来估计分解矩阵。因此，不能保证使用新的特征执行预测（例如分类）的能力。这项工作的目的是设计一个进化框架，以学习参数化NMF的超参数，并以监督的方式估算分解矩阵，以更适合分类问题。此外，我们声称，将基于NMF的算法分别应用于不同的类对，而不是将其应用于整个数据集，从而提高了矩阵分解过程的有效性。这导致训练具有不同平衡参数值的多个参数化的NMF算法。采用了交叉验证组合学习框架，并使用遗传算法来识别最佳参数值集。我们对真实和合成数据集进行的实验证明了所提出的方法的有效性。

要识别蒙面的脸，可能的解决方案之一可能是首先恢复面部的遮挡部分，然后应用面部识别方法。受到最新图像介绍方法的启发，我们提出了一个端到端杂交遮罩的面部识别系统，即HIMFR，由三个重要部分组成：遮罩的面部探测器，脸部涂上涂料和脸部识别。蒙面的面部检测器模块应用了预验证的视觉变压器（VIT \ _B32），以检测面部是否被掩盖覆盖。该模块使用基于生成对抗网络（GAN）的微调图像插入模型来恢复面部。最后，基于VIT的混合面部识别模块具有有效的NETB3骨架，可以识别面部。我们已经在四个不同的公开数据集上实施并评估了我们提出的方法：Celeba，ssdmnv2，mafa，{bubfig83}与我们本地收集的小数据集，即面对5。全面的实验结果表明，提出的HIMFR方法具有竞争性能的功效。代码可从https://github.com/mdhosen/himfr获得