我们提出了一种新的学习算法，使用传统的人工神经网络（ANN）作为代理训练尖刺神经网络（SNN）。我们分别与具有相同网络架构和共享突触权重的集成和火（IF）和Relu神经元进行两次SNN和ANN网络。两个网络的前进通过完全独立。通过假设具有速率编码的神经元作为Relu的近似值，我们将SNN中的SNN的误差进行了回复，以更新共享权重，只需用SNN的ANN最终输出替换ANN最终输出。我们将建议的代理学习应用于深度卷积的SNNS，并在Fahion-Mnist和CiFar10的两个基准数据集上进行评估，分别为94.56％和93.11％的分类准确性。所提出的网络可以优于培训的其他深鼻涕，训练，替代学习，代理梯度学习，或从深处转换。转换的SNNS需要长时间的仿真时间来达到合理的准确性，而我们的代理学习导致高效的SNN，模拟时间较短。

最近的研究表明，卷积神经网络（CNNS）不是图像分类的唯一可行的解决方案。此外，CNN中使用的重量共享和反向验证不对应于预测灵长类动物视觉系统中存在的机制。为了提出更加生物合理的解决方案，我们设计了使用峰值定时依赖性塑性（STDP）和其奖励调制变体（R-STDP）学习规则训练的本地连接的尖峰神经网络（SNN）。使用尖刺神经元和局部连接以及强化学习（RL）将我们带到了所提出的架构中的命名法生物网络。我们的网络由速率编码的输入层组成，后跟局部连接的隐藏层和解码输出层。采用尖峰群体的投票方案进行解码。我们使用Mnist DataSet获取图像分类准确性，并评估我们有益于于不同目标响应的奖励系统的稳健性。

我们最近提出了S4NN算法，基本上是对多层尖峰神经网络的反向化的适应，该网上网络使用简单的非泄漏整合和火神经元和一种形式称为第一峰值编码的时间编码。通过这种编码方案，每次刺激最多一次都是神经元火灾，但射击令携带信息。这里，我们引入BS4NN，S4NN的修改，其中突触权重被约束为二进制（+1或-1），以便减少存储器（理想情况下，每个突触的一个比特）和计算占地面积。这是使用两组权重完成：首先，通过梯度下降更新的实际重量，并在BackProjagation的后退通行证中使用，其次是在前向传递中使用的迹象。类似的策略已被用于培训（非尖峰）二值化神经网络。主要区别在于BS4NN在时域中操作：尖峰依次繁殖，并且不同的神经元可以在不同时间达到它们的阈值，这增加了计算能力。我们验证了两个流行的基准，Mnist和Fashion-Mnist上的BS4NN，并获得了这种网络的合理精度（分别为97.0％和87.3％），具有可忽略的准确率，具有可忽略的重量率（0.4％和0.7％，分别）。我们还展示了BS4NN优于具有相同架构的简单BNN，在这两个数据集上（分别为0.2％和0.9％），可能是因为它利用时间尺寸。建议的BS4NN的源代码在HTTPS://github.com/srkh/bs4nn上公开可用。

Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

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.

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights. The code is publicly available at https://github.com/mindflow-institue/DAEFormer.

A track-before-detect (TBD) particle filter-based method for detection and tracking of low observable objects based on a sequence of image frames in the presence of noise and clutter is studied. At each time instance after receiving a frame of image, first, some preprocessing approaches are applied to the image. Then, it is sent to the detection and tracking algorithm which is based on a particle filter. Performance of the approach is evaluated for detection and tracking of an object in different scenarios including noise and clutter.

In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks.