在不同的成像方式上建立自称的语义对应是一项基础但强大的计算机视觉任务。当前的多模式注册技术最大化手工制作的域间相似性功能，在建模非线性强度关系和变形方面受到限制，并且可能需要重新工程或在新任务，数据集和域配对上进行大量重新设计或表现不佳。这项工作提出了反合，这是多模式变形注册的一种无监督的对比表示学习方法。通过将学习的多尺度局部贴片特征投射到共同学习的域间嵌入空间上，Cortareg获得了对非刚性多模式对齐有用的表示形式。在实验上，与新生儿T1-T2脑MRI登记任务上的一系列基线和消融相比，通过在一系列基准中进行平滑且可逆的变形，实现了准确，稳健的结果，并在广泛的变形正则化强度范围内验证了所有方法。

多对比度MRI（MC-MRI）捕获了多种互补成像方式，以帮助放射决策。鉴于需要降低多次收购的时间成本，当前的深度加速MRI重建网络集中于利用多个对比度之间的冗余。但是，现有的作品在很大程度上受到了配对数据和/或过度昂贵的完全采样的MRI序列的监督。此外，重建网络通常依赖于卷积体系结构，这些卷积体系结构在建模远程相互作用的能力上受到限制，并可能导致良好的解剖学细节的次优恢复。对于这些目的，我们提出了一个双域自我监督的变压器（DSFORMER），用于加速MC-MRI重建。 DSFormer开发了一个深层条件级联变压器（DCCT），该变压器由几个级联的Swin Transformer重建网络（SWINRN）组成，该网络（SWINRN）在两种深层调理策略下训练，以实现MC-MRI信息共享。我们进一步提出了DCCT的双域（图像和K空间）自我监督的学习策略，以减轻获取完全采样的培训数据的成本。 DSFormer会生成高保真重建，从而超过电流完全监督的基线。此外，我们发现，通过全面监督或我们提出的双域自学训练，DSFORMER可以实现几乎相同的性能。

由于长距离，照明变化，有限的用户合作和移动科目，虹膜分割和定位在不受约束环境中具有挑战性。为了解决这个问题，我们介绍了一个U-Net，具有预先培训的MobileNetv2深神经网络方法。我们使用MobileNetv2的预先训练的权重，用于想象成数据集，并在虹膜识别和本地化域上进行微调。此外，我们推出了一个名为Kartalol的新数据集，以更好地评估虹膜识别方案中的检测器。为了提供域适应，我们可以在Casia-Iris-Asia，Casia-Iris-M1和Casia-Iris-Africa和Casia-Iris-Africa和我们的数据集中微调MobileNetv2模型。我们还通过执行左右翻转，旋转，缩放和亮度来增强数据。我们通过迭代所提供的数据集中的图像来选择二进制掩码的二值化阈值。沿着Kartalol DataSet，Casia-Iris-Asia，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1培训。实验结果强调了我们的方法在基于移动的基准上超越了最先进的方法。代码和评估结果在https://github.com/jalilnkh/kartalol-nir -isl2021031301上公开可用。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Speech-driven 3D facial animation has been widely explored, with applications in gaming, character animation, virtual reality, and telepresence systems. State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor, thus, resulting in unrealistic and inaccurate lip movements. To address this, we present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video and produces novel facial expressions matching the identity-specific speaking style and facial idiosyncrasies of the target actor. Specifically, we train a style-agnostic transformer on a large facial expression dataset which we use as a prior for audio-driven facial expressions. Based on this prior, we optimize for identity-specific speaking style based on a short reference video. To train the prior, we introduce a novel loss function based on detected bilabial consonants to ensure plausible lip closures and consequently improve the realism of the generated expressions. Through detailed experiments and a user study, we show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.

This paper deals with the problem of statistical and system heterogeneity in a cross-silo Federated Learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed ``G-Fedfilt''. The proposed aggregator enables a structured flow of information based on the graph's topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous trade-off between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional Federated Averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time-scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models' gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to $3.99\% $ better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local datasets, while it is capable of yielding up to $2.41\%$ higher accuracy than FedAvg in the case of testing the generalization of the models.

Solute transport in porous media is relevant to a wide range of applications in hydrogeology, geothermal energy, underground CO2 storage, and a variety of chemical engineering systems. Due to the complexity of solute transport in heterogeneous porous media, traditional solvers require high resolution meshing and are therefore expensive computationally. This study explores the application of a mesh-free method based on deep learning to accelerate the simulation of solute transport. We employ Physics-informed Neural Networks (PiNN) to solve solute transport problems in homogeneous and heterogeneous porous media governed by the advection-dispersion equation. Unlike traditional neural networks that learn from large training datasets, PiNNs only leverage the strong form mathematical models to simultaneously solve for multiple dependent or independent field variables (e.g., pressure and solute concentration fields). In this study, we construct PiNN using a periodic activation function to better represent the complex physical signals (i.e., pressure) and their derivatives (i.e., velocity). Several case studies are designed with the intention of investigating the proposed PiNN's capability to handle different degrees of complexity. A manual hyperparameter tuning method is used to find the best PiNN architecture for each test case. Point-wise error and mean square error (MSE) measures are employed to assess the performance of PiNNs' predictions against the ground truth solutions obtained analytically or numerically using the finite element method. Our findings show that the predictions of PiNN are in good agreement with the ground truth solutions while reducing computational complexity and cost by, at least, three orders of magnitude.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital role in the image properties such as image quality and file size. In recent years, several efforts based on population-based metaheuristic (PBMH) algorithms have been performed to find the proper QT(s) for a specific image, although they do not take into consideration the user's opinion. Take an android developer as an example, who prefers a small-size image, while the optimisation process results in a high-quality image, leading to a huge file size. Another pitfall of the current works is a lack of comprehensive coverage, meaning that the QT(s) can not provide all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user's opinion in the compression process, the file size of the output image can be controlled by a user in advance. Second, to tackle the lack of comprehensive coverage, we suggest a novel representation. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both changes in representation and objective function are independent of the search strategies and can be used with any type of population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the GNN framework along with the state measurements can improve the performance of the detection mechanism. The problem is formulated as a classification problem through a GNN with message passing mechanism to identify abnormal measurements. The residual block used in the aggregation process of message passing and the gated recurrent unit can lead to improved computational time and performance. The performance of the proposed model has been evaluated through extensive simulations of power system states and attack scenarios showing promising performance. The sensitivity of the model to intensity and location of the attacks and model's detection delay versus detection accuracy have also been evaluated.