As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and performance characterization studies of GNNs is increasing. So far, we have seen many studies that investigate and present the performance and computational efficiency of GNNs. However, the work done so far has been carried out using a few high-level GNN frameworks. Although these frameworks provide ease of use, they contain too many dependencies to other existing libraries. The layers of implementation details and the dependencies complicate the performance analysis of GNN models that are built on top of these frameworks, especially while using architectural simulators. Furthermore, different approaches on GNN computation are generally overlooked in prior characterization studies, and merely one of the common computational models is evaluated. Based on these shortcomings and needs that we observed, we developed a benchmark suite that is framework independent, supporting versatile computational models, easily configurable and can be used with architectural simulators without additional effort. Our benchmark suite, which we call gSuite, makes use of only hardware vendor's libraries and therefore it is independent of any other frameworks. gSuite enables performing detailed performance characterization studies on GNN Inference using both contemporary GPU profilers and architectural GPU simulators. To illustrate the benefits of our new benchmark suite, we perform a detailed characterization study with a set of well-known GNN models with various datasets; running gSuite both on a real GPU card and a timing-detailed GPU simulator. We also implicate the effect of computational models on performance. We use several evaluation metrics to rigorously measure the performance of GNN computation.

Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. However, real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it. Methods: 1D Operational-GANs are used with generative neuron model optimized for blind restoration of any corrupted audio signal. Results: The proposed approach has been evaluated extensively over the benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with a random blend of artifacts each with a random severity to mimic real-world audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved, respectively, which are substantial when compared with the baseline methods. Significance: This is a pioneer study in blind audio restoration with the unique capability of direct (time-domain) restoration of real-world audio whilst achieving an unprecedented level of performance for a wide SDR range and artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally effective real-world audio restoration with significantly improved performance. The source codes and the generated real-world audio datasets are shared publicly with the research community in a dedicated GitHub repository1.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.

Gathering properly labelled, adequately rich, and case-specific data for successfully training a data-driven or hybrid model for structural health monitoring (SHM) applications is a challenging task. We posit that a Transfer Learning (TL) method that utilizes available data in any relevant source domain and directly applies to the target domain through domain adaptation can provide substantial remedies to address this issue. Accordingly, we present a novel TL method that differentiates between the source's no-damage and damage cases and utilizes a domain adaptation (DA) technique. The DA module transfers the accumulated knowledge in contrasting no-damage and damage cases in the source domain to the target domain, given only the target's no-damage case. High-dimensional features allow employing signal processing domain knowledge to devise a generalizable DA approach. The Generative Adversarial Network (GAN) architecture is adopted for learning since its optimization process accommodates high-dimensional inputs in a zero-shot setting. At the same time, its training objective conforms seamlessly with the case of no-damage and damage data in SHM since its discriminator network differentiates between real (no damage) and fake (possibly unseen damage) data. An extensive set of experimental results demonstrates the method's success in transferring knowledge on differences between no-damage and damage cases across three strongly heterogeneous independent target structures. The area under the Receiver Operating Characteristics curves (Area Under the Curve - AUC) is used to evaluate the differentiation between no-damage and damage cases in the target domain, reaching values as high as 0.95. With no-damage and damage cases discerned from each other, zero-shot structural damage detection is carried out. The mean F1 scores for all damages in the three independent datasets are 0.978, 0.992, and 0.975.

恢复质量差的图像与一组混合伪影对于可靠的诊断起着至关重要的作用。现有的研究集中在特定的恢复问题上，例如图像过度，去核和暴露校正，通常对伪影类型和严重性有很强的假设。作为盲X射线恢复的先驱研究，我们提出了一个通用图像恢复和分类的联合模型：恢复分类为分类的生成对抗网络（R2C-GAN）。这种共同优化的模型使恢复后保持任何疾病完整。因此，由于X射线图像质量的提高，这自然会导致更高的诊断性能。为了实现这一关键目标，我们将恢复任务定义为图像到图像的翻译问题，从差异，模糊或暴露不足/暴露不足的图像到高质量的图像域。提出的R2C-GAN模型能够使用未配对的训练样本在两个域之间学习前进和逆变换。同时，联合分类在恢复过程中保留了疾病标签。此外，R2C-GAN配备了操作层/神经元，可降低网络深度，并进一步增强恢复和分类性能。拟议的联合模型对2019年冠状病毒病（COVID-19）分类的卡塔-COV19数据集进行了广泛的评估。拟议的恢复方法达到了90％以上的F1得分，这显着高于任何深层模型的性能。此外，在定性分析中，R2C-GAN的恢复性能得到了一群医生的批准。我们在https://github.com/meteahishali/r2c-gan上共享软件实施。

部分微分方程（PDE）用于对科学和工程中的各种动力系统进行建模。深度学习的最新进展使我们能够以新的方式解决维度的诅咒，从而在更高的维度中解决它们。但是，深度学习方法受到训练时间和记忆的约束。为了解决这些缺点，我们实施了张量神经网络（TNN），这是一种量子启发的神经网络体系结构，利用张量网络的想法来改进深度学习方法。我们证明，与经典密集神经网络（DNN）相比，TNN提供了明显的参数节省，同时获得了与经典密集的神经网络相同的准确性。此外，我们还展示了如何以相同的精度来比DNN更快地训练TNN。我们通过将它们应用于求解抛物线PDE，特别是Black-Scholes-Barenblatt方程，该方程广泛用于金融定价理论，基于基准测试。还讨论了进一步的例子，例如汉密尔顿 - 雅各比 - 贝尔曼方程。

深度卷积神经网络（CNN）最近已达到最先进的手写文本识别（HTR）性能。但是，最近的研究表明，典型的CNN的学习性能是有限的，因为它们是具有简单（线性）神经元模型的同质网络。由于它们的异质网络结构结合了非线性神经元，最近提出了操作神经网络（ONNS）来解决这一缺点。自我结合是具有生成神经元模型的ONN的自组织变化，可以使用泰勒近似来生成任何非线性函数。在这项研究中，为了提高HTR的最新性能水平，提出了新型网络模型核心中的2D自组织（自我强调）。此外，本研究中使用了可变形的卷积，最近被证明可以更好地解决写作风格的变化。 IAM英语数据集和Hadara80p阿拉伯数据集中的结果表明，具有自我影响的操作层的拟议模型显着提高了字符错误率（CER）和单词错误率（WER）。与同行CNN相比，Hadara80p中的自我强调将CER和3.4％降低，在IAM数据集中，自我强调将CER降低1.2％和3.4％，为0.199％和1.244％。基准IAM上的结果表明，与自相互紧缩的操作层的拟议模型通过显着的边缘优于最近的深CNN模型，而使用具有可变形卷积的自我冲突表明了出色的结果。

本文提出了一个低成本且高度准确的ECG监测系统，用于针对可穿戴移动传感器的个性化早期心律不齐检测。对个性化心电图监测的早期监督方法需要异常和正常的心跳来训练专用分类器。但是，在真实的情况下，个性化算法嵌入了可穿戴设备中，这种训练数据不适合没有心脏障碍史的健康人。在这项研究中，（i）我们对通过稀疏字典学习获得的健康信号空间进行了无空间分析，并研究了如何简单的无效空间投影或基于最小二乘的规范性分类方法可以降低计算复杂性，而无需牺牲牺牲计算的复杂性。与基于稀疏表示的分类相比，检测准确性。 （ii）然后，我们引入了基于稀疏表示的域适应技术，以便将其他现有用户的异常和正常信号投射到新用户的信号空间上，使我们能够训练专用的分类器而无需​​新用户的任何异常心跳。因此，无需合成异常的心跳产生，可以实现零射学习。在基准MIT-BIH ECG数据集上执行的一组大量实验表明，当该基于域的基于域的训练数据生成器与简单的1-D CNN分类器一起使用时，该方法以明显的差距优于先前的工作。 （iii）然后，通过组合（i）和（ii），我们提出了一个整体分类器，以进一步提高性能。这种零射门心律失常检测的方法的平均准确性水平为98.2％，F1得分为92.8％。最后，使用上述创新提出了一个个性化的节能ECG监测计划。

我们提出了用于自我监督的视觉表示学习的变换不变性和协方差对比度（TICO）。与其他最新的自我监督学习方法类似，我们的方法基于同一图像的不同变形版本之间的嵌入之间的一致性，这推动了编码器产生变换不变表示。为了避免编码器生成恒定向量的微不足道解，我们通过惩罚低等级解决方案将嵌入的嵌入的协方差矩阵正常化。通过共同最大程度地减少变换不变性损失和协方差对比损失，我们得到了一个能够为下游任务产生有用表示的编码器。我们分析了我们的方法，并表明它可以被视为MOCO的变体，具有无限尺寸的隐式存储器库，无需额外的内存成本。这使我们的方法在使用小批量尺寸时的性能要比替代方法更好。 TICO也可以看作是Barlow双胞胎的修改。通过将对比度和冗余方法联系起来，TICO为我们提供了有关关节嵌入方法如何工作的新见解。