关节2D心脏分割和3D体积重建是建立统计心脏解剖模型的基础，并了解运动模式的功能机制。但是，由于CINE MR和高主体间方差的平面分辨率低，精确分割心脏图像并重建3D体积是具有挑战性的。在这项研究中，我们提出了一个基于潜在空间的端到端框架DeepRecon，该框架会产生多个临床上基本的结果，包括准确的图像分割，合成高分辨率3D图像和3D重建体积。我们的方法确定了Cine图像的最佳潜在表示，其中包含心脏结构的准确语义信息。特别是，我们的模型共同生成具有准确的语义信息的合成图像，并使用最佳潜在表示对心脏结构进行分割。我们进一步探索了3D形状重建和4D运动模式通过不同的潜在空间操纵策略进行适应的下游应用。同时生成的高分辨率图像具有评估心脏形状和运动的高可解释价值。实验性结果证明了我们的有效性在多个方面的方法，包括2D分割，3D重建，下游4D运动模式适应性。

Bipedal robots have received much attention because of the variety of motion maneuvers that they can produce, and the many applications they have in various areas including rehabilitation. One of these motion maneuvers is walking. In this study, we presented a framework for the trajectory optimization of a 5-link (planar) Biped Robot using hybrid optimization. The walking is modeled with two phases of single-stance (support) phase and the collision phase. The dynamic equations of the robot in each phase are extracted by the Lagrange method. It is assumed that the robot heel strike to the ground is full plastic. The gait is optimized with a method called hybrid optimization. The objective function of this problem is considered to be the integral of torque-squared along the trajectory, and also various constraints such as zero dynamics are satisfied without any approximation. Furthermore, in a new framework, there is presented a constraint called impact invariance, which ensures the periodicity of the time-varying trajectories. On the other hand, other constraints provide better and more human-like movement.

The importance of humanoid robots in today's world is undeniable, one of the most important features of humanoid robots is the ability to maneuver in environments such as stairs that other robots can not easily cross. A suitable algorithm to generate the path for the bipedal robot to climb is very important. In this paper, an optimization-based method to generate an optimal stairway for under-actuated bipedal robots without an ankle actuator is presented. The generated paths are based on zero and non-zero dynamics of the problem, and according to the satisfaction of the zero dynamics constraint in the problem, tracking the path is possible, in other words, the problem can be dynamically feasible. The optimization method used in the problem is a gradient-based method that has a suitable number of function evaluations for computational processing. This method can also be utilized to go down the stairs.

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data.

Knowledge Distillation (KD) has been extensively used for natural language understanding (NLU) tasks to improve a small model's (a student) generalization by transferring the knowledge from a larger model (a teacher). Although KD methods achieve state-of-the-art performance in numerous settings, they suffer from several problems limiting their performance. It is shown in the literature that the capacity gap between the teacher and the student networks can make KD ineffective. Additionally, existing KD techniques do not mitigate the noise in the teacher's output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. We propose a new KD method that addresses these problems and facilitates the training compared to previous techniques. Inspired by continuation optimization, we design a training procedure that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. Our method (Continuation-KD) achieves state-of-the-art performance across various compact architectures on NLU (GLUE benchmark) and computer vision tasks (CIFAR-10 and CIFAR-100).

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disregard the sequential connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.

Graph Learning (GL) is at the core of inference and analysis of connections in data mining and machine learning (ML). By observing a dataset of graph signals, and considering specific assumptions, Graph Signal Processing (GSP) tools can provide practical constraints in the GL approach. One applicable constraint can infer a graph with desired frequency signatures, i.e., spectral templates. However, a severe computational burden is a challenging barrier, especially for inference from high-dimensional graph signals. To address this issue and in the case of the underlying graph having graph product structure, we propose learning product (high dimensional) graphs from product spectral templates with significantly reduced complexity rather than learning them directly from high-dimensional graph signals, which, to the best of our knowledge, has not been addressed in the related areas. In contrast to the rare current approaches, our approach can learn all types of product graphs (with more than two graphs) without knowing the type of graph products and has fewer parameters. Experimental results on both the synthetic and real-world data, i.e., brain signal analysis and multi-view object images, illustrate explainable and meaningful factor graphs supported by expert-related research, as well as outperforming the rare current restricted approaches.

社区检测是网络科学中的经典问题，在各个领域都有广泛的应用。最常用的方法是设计算法，旨在最大程度地跨越网络分配到社区中的不同方式，以最大化效用函数，模块化。尽管它们的名称和设计理念，但当前的模块化最大化算法通常无法最大化模块化或保证与最佳解决方案的任何接近。我们提出了Bayan算法，该算法与现有方法不同，该算法返回网络分区，以确保最佳或靠近最佳解决方案。 Bayan算法的核心是一种分支和切割方案，该方案解决了模块化最大化问题的稀疏整数编程公式，以最佳或在一个因素内近似它。我们使用合成和真实网络分析了Bayan对22种现有算法的性能。通过广泛的实验，我们不仅在最大化模块化方面展示了Bayan的独特能力，而且更重要的是在准确检索地面真实群落方面。 Bayan的比较性能水平在数据（图）生成过程中噪声量的变化上保持稳定。 Bayan作为确切的模块化最大化算法的性能也揭示了在社区准确检索中最大模块化分区的理论能力限制。总体而言，我们的分析指出，通过精确（近似）最大化的网络中的模块化（近似$ \ sim10^3 $边缘（和较大的网络）），BAYAN是对社区进行方法基础检测的合适选择。图形优化和整数编程的前瞻性进步可以进一步推动这些限制。

光学图像和视频中的小对象检测（SOD）是一个具有挑战性的问题，即使是最先进的通用对象检测方法也无法准确定位和识别此类对象。通常，由于较大的摄像头距离，小物体出现在现实世界中。由于小物体仅占据输入图像中的一个小区域（例如，少于10％），因此从这样的小区域中提取的信息并不总是足够丰富，足以支持决策。在深度学习和计算机愿景的界面上工作的研究人员正在开发多学科策略，以增强基于SOD深度学习的方法的性能。在本文中，我们对2017年至2022年之间发表的160篇研究论文进行了全面评论，以调查这一不断增长的主题。本文总结了现有文献，并提供了一种分类法，以说明当前研究的广泛了解。我们研究了如何在海上环境中提高小物体检测的性能，在海上环境中，提高性能至关重要。通过建立通用和海上SOD研究之间的联系，已经确定了未来的方向。此外，讨论了用于通用和海上应用程序的SOD的流行数据集，并提供了一些数据集的最新方法的众所周知的评估指标。

实例优化系统的新兴类别通过专门研究特定的数据和查询工作负载来显示出高性能的潜力。特别是，机器学习（ML）技术已成功地应用于构建各种实例优化的组件（例如，学习的索引）。本文研究以利用ML技术来增强给定数据和查询工作负载的空间索引，尤其是R-Tree的性能。当R-Tree索引节点覆盖的区域在空间中重叠，在搜索空间中的特定点时，可能会探索从根到叶子的多个路径。在最坏的情况下，可以搜索整个R-Tree。在本文中，我们定义并使用重叠比来量化范围查询所需的外叶节点访问的程度。目的是提高传统的R-Tree对高度重叠范围查询的查询性能，因为它们往往会产生长时间的跑步时间。我们介绍了一个新的AI-Tree，将R-Tree的搜索操作转换为多标签分类任务，以排除外部叶子节点访问。然后，我们将传统的R-Tree扩大到Ai-Tree，形成混合“ AI+R” -tree。 “ AI+R” -tree可以使用学习模型自动区分高和低封闭的查询。因此，“ AI+R” -Tree使用AI-Tree处理高重叠的查询，并使用R-Tree处理低重叠的查询。实际数据集上的实验表明，“ AI+R” -Tree可以在传统的R-Tree上提高查询性能高达500％。