本文提出了在基于技术指标的股票交易的背景下的非主导分类遗传算法-II（NSGA-II），通过寻找销售买卖策略，使目标，即锐利比例和销售策略的最佳组合最大缩放分别最大化并最小化。选择NSGA-II，因为它是一种非常流行和强大的双目标进化算法。培训和测试使用了一种基于滚动的方法（两年培训和测试的一年），因此在没有主要经济波动的情况下，这种方法的结果在稳定的时期中似乎更好。此外，本研究的另一个重要贡献是通过整个建模方法纳入交易成本和领域专业知识。

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. Detecting these attacks is challenging due to the small magnitude of perturbation and the discrete nature of graph data. In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation. Specifically, we propose a novel graph generation approach combined with link prediction to detect suspicious adversarial edges. To effectively train the graph generative model, we sample several sub-graphs from the given graph data. We show that since the number of adversarial edges is usually low in practice, with low probability the sampled sub-graphs will contain adversarial edges based on the union bound. In addition, considering the strong attacks which perturb a large number of edges, we propose a set of novel features to perform outlier detection as the preprocessing for our detection. Extensive experimental results on three real-world graph datasets including a private transaction rule dataset from a major company and two types of synthetic graphs with controlled properties show that EDoG can achieve above 0.8 AUC against four state-of-the-art unseen attack strategies without requiring any knowledge about the attack type; and around 0.85 with knowledge of the attack type. EDoG significantly outperforms traditional malicious edge detection baselines. We also show that an adaptive attack with full knowledge of our detection pipeline is difficult to bypass it.

The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data. In this paper, we report the deployment of technology-driven data collection methods for creating a corpus of more than 60,000 translations from Hindi to Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. During this process, we help expand information access in Gondi across 2 different dimensions (a) The creation of linguistic resources that can be used by the community, such as a dictionary, children's stories, Gondi translations from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform; (b) Enabling its use in the digital domain by developing a Hindi-Gondi machine translation model, which is compressed by nearly 4 times to enable it's edge deployment on low-resource edge devices and in areas of little to no internet connectivity. We also present preliminary evaluations of utilizing the developed machine translation model to provide assistance to volunteers who are involved in collecting more data for the target language. Through these interventions, we not only created a refined and evaluated corpus of 26,240 Hindi-Gondi translations that was used for building the translation model but also engaged nearly 850 community members who can help take Gondi onto the internet.

This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge distillation (\textit{CoNMix}) as a solution to this problem. The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing. The source-free approach leverages target pseudo labels, which can be noisy, to improve the target adaptation. We introduce consistency between label preserving augmentations and utilize pseudo label refinement methods to reduce noisy pseudo labels. Further, we propose novel MixUp Knowledge Distillation (MKD) for better generalization on multiple target domains using various source-free STDA models. We also show that the Vision Transformer (VT) backbone gives better feature representation with improved domain transferability and class discriminability. Our proposed framework achieves the state-of-the-art (SOTA) results in various paradigms of source-free STDA and MTDA settings on popular domain adaptation datasets like Office-Home, Office-Caltech, and DomainNet. Project Page: https://sites.google.com/view/conmix-vcl

神经网络在与噪声扰动的图像分类中的精度较小。 CNN卷积神经网络以其在良性图像的分类中无与伦比的精度而闻名。但是我们的研究表明，它们极易受到噪声的攻击，而馈送前向神经网络，FNN与噪声扰动的对应性较小，几乎不受干扰地保持其准确性。观察到FNN可以更好地分类噪声密集的单通道图像，而这些图像只是人类视觉的巨大噪音。在我们的研究中，我们使用了以下架构的手写数字数据集，MNIST：具有1和2个隐藏层和CNN的FNN，带有3、4、6和8卷积，并分析了其准确性。 FNN脱颖而出表明，无论噪声强度如何，它们的分类精度超过85％。在我们通过此数据对CNN的分析中，CNN的分类准确性减速8卷积是其余CNN的一半。准确性趋势的相关分析和数学建模是这些结论的路线图。

视频检索随着视觉模型的发展取得了巨大进展。但是，进一步改进这些模型需要其他标记的数据，这是一项巨大的手动努力。在本文中，我们提出了一个框架MKTVR，该框架利用了从多语言模型的知识转移来提高视频检索的性能。我们首先使用最先进的机器翻译模型来构建伪真实的多语言视频文本对。然后，我们使用这些数据来学习视频文本表示，其中英语和非英语文本查询在基于预审前的多语言模型的常见嵌入空间中表示。我们在四个英语视频检索数据集上评估了我们提出的方法，例如MSRVTT，MSVD，DIDEMO和CHARADES。实验结果表明，我们的方法在所有数据集上实现了最先进的结果，超过了先前的模型。最后，我们还在涵盖六种语言的多语言视频回程数据集上评估了我们的模型，并表明我们的模型在零拍设置中优于先前的多语言视频检索模型。

钢铁生产行业中最紧迫的挑战之一是识别表面缺陷。早期鉴定铸造缺陷可以帮助提高性能，包括简化生产过程。不过，深度学习模型帮助弥合了这一差距并自动化了大多数此类过程，但需要提出轻量级模型，可以随着更快的推理时间轻松部署这些模型。这项研究提出了一种轻巧的体系结构，该体系结构在准确性和推理时间方面与复杂的预训练的CNN体​​系结构（如Mobilenet，Inception和Resnet）相比，在精度和推理时间方面有效，包括视觉变压器。已经实验了方法，以最大程度地减少计算需求，例如深度分离卷积和全球平均池（GAP）层，包括提高建筑效率和增强的技术。我们的结果表明，具有深度可分离卷积的590K参数的自定义模型优于预审计的架构，例如重新连接和视觉变压器的准确性（81.87％）（81.87％），并舒适地超越了诸如重置，inception和Vision Transformers等体系结构。推理时间（12毫秒）。 Blurpool表现出了其他技术的表现，精度为83.98％。增强对模型性能有矛盾的影响。在推理时间上，深度和3x3卷积之间没有直接相关性，但是，它们通过使网络能够更深入并减少可训练参数的数量来提高模型效率，从而在提高模型效率方面发挥了直接作用。我们的工作阐明了一个事实，即可以构建具有高效体系结构和更快推理时间的自定义网络，而无需依靠预训练的架构。

与单个决策树相比，Tree Ensemble（TE）模型（例如，增强的树木和随机森林）通常提供更高的预测性能。但是，由于人类难以理解其决策逻辑，因此TE模型通常缺乏透明度和可解释性。本文提出了一种新颖的方法，可以将经过训练的二进制分类任务的TE转换为规则列表（RL），该规则列表（RL）等同于TE，对于人类来说是可理解的。该RL捕获了TE决策的所有必要条件。基准数据集上的实验表明，与最先进的方法相比，（i）TE2RULES生成的RL的预测相对于原始TE具有很高的保真度，（ii）TE2RULES的RL具有高的解释性，由高可解释性衡量。决策规则的数量和长度，（iii）TE2RULES算法的运行时间可以大大减少，以稍低的保真度，（iv）RL是最新的替代品的快速替代 - 基于ART规则的实例级结果解释技术。

近年来，具有两个较高架构的视觉语言（VL）模型主导了视觉表示的学习。当前的VL模型要么使用轻型Uni-Modal编码器，并在交叉模式编码器中同时提取，对齐和融合这两种模态，或者将最后一层的Uni-Modal-Modal特征直接馈入顶部的交叉模式编码器，而忽略了语义深度单模式编码器中不同级别的信息。两种方法都可能限制视觉表示学习和限制模型性能。在本文中，我们介绍了多个桥梁层，该层在Uni-Modal编码器的顶层和跨模式编码器的每一层之间建立了连接。这可以在不同语义级别的视觉和文本表示之间进行全面的自下而上相互作用，从而导致更有效的跨模式对齐和融合。我们提出的桥梁可以预先训练，仅需$ 4 $ m的图像，可以在各种下游视觉语言任务上实现最先进的性能。在VQAV2 Test-STD集合中，Bridge-Tower的准确性为$ 78.73 \％$，与以前的最先进的仪表型号相同的the Art仪表均优于先前的最先进的仪表\％$ $，并且几乎没有其他参数，并且几乎没有其他参数和其他参数计算成本。值得注意的是，当进一步扩展模型时，桥梁可以达到81.15美元\％$的准确性，超过了在较大的数据集中预先训练的模型。代码可在https://github.com/microsoft/bridgetower上找到。