受到吉布森（Gibson）在人类视野中提供的对象的概念的启发，我们提出了一个问题：代理商如何学会对只有单一瞥见的新物体或环境进行整个行动政策进行预测？为了解决这个问题，我们介绍了通用政策功能（UPF）的概念，这些概念是状态到行动映射，不仅可以推广到新目标，而且最重要的是对新颖，看不见的环境。具体而言，我们考虑了有效地学习计算能力和通信能力有限的代理商的政策的问题，这些策略是在边缘设备中经常遇到的约束。我们提出了Hyper-Universal策略近似器（HUPA），这是一种基于超网络的模型，可从单个图像中生成小型任务和环境条件策略网络，具有良好的概括属性。我们的结果表明，HUPA的表现明显优于基于嵌入的替代方案，用于生成大小约束的策略。尽管这项工作仅限于简单的基于地图的导航任务，但未来的工作包括将HUPA背后的原理应用于学习对象和环境的更多一般负担。

人类的视野涉及使用基于部分整体层次结构的结构化表示形式解析和表示对象和场景。计算机视觉和机器学习研究人员最近试图使用胶囊网络，参考框架和主动预测编码来模仿此功能，但是缺乏生成模型的配方。我们介绍递归神经程序（RNP），据我们所知，这是解决部分整体层次学习问题的第一个神经生成模型。 RNPS模型图像作为概率感觉运动程序的分层树，递归重复使用学习感觉运动原始图，以在不同的参考帧中建模图像，形成递归图像语法。我们将RNP表示为用于推理和采样的结构化变异自动编码器（SVAE），并展示了MNIST，Omniglot和Fashion-Mnist数据集的基于零件的解析，采样和单次传输学习，展示了模型的表现力。我们的结果表明，RNP提供了组合对象和场景的直观和可解释的方式，从而可以根据部分整体层次结构对对象的丰富组成性和直观的解释。

跟踪湍流羽流以定位其源是一个复杂的控制问题，因为它需要多感觉集成，并且必须强大地间歇性气味，更改风向和可变羽流统计。这项任务是通过飞行昆虫进行常规进行的，通常是长途跋涉，以追求食物或配偶。在许多实验研究中已经详细研究了这种显着行为的几个方面。在这里，我们采用硅化方法互补，采用培训，利用加强学习培训，开发对支持羽流跟踪的行为和神经计算的综合了解。具体而言，我们使用深增强学习（DRL）来训练经常性神经网络（RNN）代理以定位模拟湍流羽毛的来源。有趣的是，代理人的紧急行为类似于飞行昆虫，而RNNS学会代表任务相关变量，例如自上次气味遭遇以来的头部方向和时间。我们的分析表明了一种有趣的实验可测试的假设，用于跟踪风向改变的羽毛 - 该试剂遵循局部羽状形状而不是电流风向。虽然反射短记忆行为足以跟踪恒定风中的羽毛，但更长的记忆时间表对于跟踪切换方向的羽毛是必不可少的。在神经动力学的水平下，RNNS的人口活动是低维度的，并且组织成不同的动态结构，与行为模块一些对应。我们的Silico方法提供了湍流羽流跟踪策略的关键直觉，并激励未来的目标实验和理论发展。

智能手机已经使用基于生物识别的验证系统，以在高度敏感的应用中提供安全性。视听生物识别技术因其可用性而受欢迎，并且由于其多式化性质，欺骗性将具有挑战性。在这项工作中，我们介绍了一个在五个不同最近智能手机中捕获的视听智能手机数据集。考虑到不同的现实情景，这个新数据集包含在三个不同的会话中捕获的103个科目。在该数据集中获取三种不同的语言，以包括扬声器识别系统的语言依赖性问题。这些数据集的这些独特的特征将为实施新的艺术技术的单向或视听扬声器识别系统提供途径。我们还报告了DataSet上的基准标记的生物识别系统的性能。生物识别算法的鲁棒性朝向具有广泛实验的重播和合成信号等信号噪声，设备，语言和呈现攻击等多种依赖性。获得的结果提出了许多关于智能手机中最先进的生物识别方法的泛化特性的担忧。

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Monte-Carlo Tree Search (MCTS) is an adversarial search paradigm that first found prominence with its success in the domain of computer Go. Early theoretical work established the game-theoretic soundness and convergence bounds for Upper Confidence bounds applied to Trees (UCT), the most popular instantiation of MCTS; however, there remain notable gaps in our understanding of how UCT behaves in practice. In this work, we address one such gap by considering the question of whether UCT can exhibit lookahead pathology -- a paradoxical phenomenon first observed in Minimax search where greater search effort leads to worse decision-making. We introduce a novel family of synthetic games that offer rich modeling possibilities while remaining amenable to mathematical analysis. Our theoretical and experimental results suggest that UCT is indeed susceptible to pathological behavior in a range of games drawn from this family.

Artificial Intelligence (AI) and its data-centric branch of machine learning (ML) have greatly evolved over the last few decades. However, as AI is used increasingly in real world use cases, the importance of the interpretability of and accessibility to AI systems have become major research areas. The lack of interpretability of ML based systems is a major hindrance to widespread adoption of these powerful algorithms. This is due to many reasons including ethical and regulatory concerns, which have resulted in poorer adoption of ML in some areas. The recent past has seen a surge in research on interpretable ML. Generally, designing a ML system requires good domain understanding combined with expert knowledge. New techniques are emerging to improve ML accessibility through automated model design. This paper provides a review of the work done to improve interpretability and accessibility of machine learning in the context of global problems while also being relevant to developing countries. We review work under multiple levels of interpretability including scientific and mathematical interpretation, statistical interpretation and partial semantic interpretation. This review includes applications in three areas, namely food processing, agriculture and health.

Several studies have been reported in the literature about SN P system and its variants. Often, the results provide universality of various variants and the classes of languages that these variants generate and recognize. The state of SN P system is its configuration. We refer to our previous result on reachability of configuration as the {\it Fundamental state equation for SN P system.} This paper provides a preliminary investigation on the behavioral and structural properties of SN P system without delay that depend primarily to this fundamental state equation. Also, we introduce the idea of configuration graph $CG_{\Pi}$ of an SN P system $\Pi$ without delay to characterize behavioral properties of $\Pi$ with respect to $CG_{\Pi}.$ The matrix $M_{\Pi}$ of an SN P system $\Pi$ without delay is used to characterize structural properties of $\Pi.$

In the 2010, matrix representation of SN P system without delay was presented while in the case of SN P systems with delay, matrix representation was suggested in the 2017. These representations brought about series of simulation of SN P systems using computer software and hardware technology. In this work, we revisit these representation and provide some observations on the behavior of the computations of SN P systems. The concept of reachability of configuration is considered in both SN P systems with and without delays. A better computation of next configuration is proposed in the case of SN P system with delay.

To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.