在当前的数字化时代，在线支付系统吸引了相当大的兴趣。提高支付系统的效率很重要，因为它对企业的收入有很大影响。网关是每次交易都被路由的付款系统的一个组成部分。在在线支付系统中，付款处理器通过各种配置与这些网关集成，例如定价，方法，风险检查等。这些配置称为终端。每个网关都可以有多个与之相关的终端。通过最佳终端路由付款交易至关重要，以提高付款交易的概率成功。机器学习（ML）和人工智能（AI）技术可用于基于先前的性能和各种支付相关属性准确地预测最佳终端。我们设计了一种由静态和动态模块组成的管道。静态模块使用静态规则和预测网关下降时间的逻辑回归模型进行终端初始过滤。随后，动态模块基于成功率，支付属性，时间滞后等来计算大量的新颖功能以准确地模拟终端行为。使用反馈循环实时使用自适应时间衰减速率算法更新这些功能，并传递给随机林分类器以预测每个终端的成功概率。该管道目前正在razorpay在Razorpay提供数百万次交易中实时生产，并在所有支付方法（信用卡，借记卡，UPI，净银行）的成功率上有4-6 \％。这使得我们的支付系统更加适应表现下降，这已经提高了用户体验，灌输了更多信任商家，并提升了业务的收入。

This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer. Our ECCM approach models the jammer-radar interaction as a Principal Agent Problem (PAP), a popular economics framework for interaction between two entities with an information imbalance. In our setup, the radar does not know the jammer's utility. Instead, the radar learns the jammer's utility adaptively over time using inverse reinforcement learning. The radar's adaptive ECCM objective is two-fold (1) maximize its utility by solving the PAP, and (2) estimate the jammer's utility by observing its response. Our adaptive ECCM scheme uses deep ideas from revealed preference in micro-economics and principal agent problem in contract theory. Our numerical results show that, over time, our adaptive ECCM both identifies and mitigates the jammer's utility.

In this paper, we propose and showcase, for the first time, monocular multi-view layout estimation for warehouse racks and shelves. Unlike typical layout estimation methods, MVRackLay estimates multi-layered layouts, wherein each layer corresponds to the layout of a shelf within a rack. Given a sequence of images of a warehouse scene, a dual-headed Convolutional-LSTM architecture outputs segmented racks, the front and the top view layout of each shelf within a rack. With minimal effort, such an output is transformed into a 3D rendering of all racks, shelves and objects on the shelves, giving an accurate 3D depiction of the entire warehouse scene in terms of racks, shelves and the number of objects on each shelf. MVRackLay generalizes to a diverse set of warehouse scenes with varying number of objects on each shelf, number of shelves and in the presence of other such racks in the background. Further, MVRackLay shows superior performance vis-a-vis its single view counterpart, RackLay, in layout accuracy, quantized in terms of the mean IoU and mAP metrics. We also showcase a multi-view stitching of the 3D layouts resulting in a representation of the warehouse scene with respect to a global reference frame akin to a rendering of the scene from a SLAM pipeline. To the best of our knowledge, this is the first such work to portray a 3D rendering of a warehouse scene in terms of its semantic components - Racks, Shelves and Objects - all from a single monocular camera.

A robot finds it really hard to learn creatively and adapt to new unseen challenges. This is mainly because of the minimal information it has access to or experience towards. Paulius et al. [1] presented a way to construct functional graphs that encapsulate. Sakib et al. [2] further expanded FOON objects for robotic cooking. This paper presents a comparative study of Breadth First Search (BFS), Greedy Breadth First search (GBFS) with two heuristic functions, and Iterative Depth First Search (IDFS) and provides a comparison of their performance.

神经肌肉疾病，例如脊柱肌肉萎缩（SMA）和Duchenne肌肉营养不良症（DMD），导致6,000名儿童中有1例的渐进性肌肉变性和运动功能丧失。传统的上肢运动功能评估不能定量测量患者的性能，这使得很难跟踪进度的增量变化。评估神经肌肉疾病儿童的运动功能特别具有挑战性，因为他们在实验过程中可能会紧张或兴奋，或者简直太年轻而无法遵循精确的说明。这些挑战转化为混杂因素，例如执行臂卷曲的不同部分较慢或更快（相位变异性），从而影响评估的运动质量。本文使用曲线注册和形状分析来暂时对齐轨迹，同时提取平均参考形状。距这种平均形状的距离用于评估运动质量。所提出的指标是混杂因素（例如相位变异性）的不变性，同时提出了几种临床相关的见解。首先，控制和患者人群的功能分数在统计上存在显着差异（p $ = $ 0.0213 $ \ le $ 0.05）。接下来，患者队列中的几名患者能够与健康队列进行运动，反之亦然。我们的指标是根据可穿戴设备计算的，与Brooke的分数有关（（P $ = $ 0.00063 $ \ le $ $ 0.05））以及基于功能测定法的电动机功能评估（（P $ = $ = $ 0.0006 $ \ le $ 0.05）） 。这些结果表明了日常生活中无处不在的运动质量评估的希望。

在本文中，我们开发了多元回归模型和神经网络模型，以预测湍流热对流中的雷诺数（RE）和泡沫编号。我们将他们的预测与早期模型的对流模型进行比较：Grossmann-Lohse〜[物理。rev. lett。\ textbf {86}，3316（2001）]，修订了Grossmann-LoHse〜[phys。Fluids \ TextBF {33}，015113（2021）]和Pandey-Verma [物理。Rev. E \ TextBF {94}，053106（2016）]模型。我们观察到，尽管对所有模型的预测相互接近，但在本工作中开发的机器学习模型提供了与实验性和数值结果的最佳匹配。

自我关注已成为最近网络架构的一个组成部分，例如，统治主要图像和视频基准的变压器。这是因为自我关注可以灵活地模拟远程信息。出于同样的原因，研究人员最近使尝试恢复多层Perceptron（MLP）并提出一些类似MLP的架构，显示出极大的潜力。然而，当前的MLP样架构不擅长捕获本地细节并缺乏对图像和/或视频中的核心细节的逐步了解。为了克服这个问题，我们提出了一种新颖的Morphmlp架构，该架构专注于在低级层处捕获本地细节，同时逐渐改变，以专注于高级层的长期建模。具体地，我们设计一个完全连接的层，称为Morphfc，两个可变过滤器，其沿着高度和宽度尺寸逐渐地发展其接收领域。更有趣的是，我们建议灵活地调整视频域中的Morphfc层。为了我们最好的知识，我们是第一个创建类似MLP骨干的用于学习视频表示的骨干。最后，我们对图像分类，语义分割和视频分类进行了广泛的实验。我们的Morphmlp，如此自我关注的自由骨干，可以与基于自我关注的型号一样强大。