我们通过在调整方案中找到有效的相似性测量，提出了一种朝向弦景观的真空退化问题的新方法。使用一百万个Calabi-yau歧管作为具体例子，少量机器学习和暹罗神经网络的范式代表它们作为R（3）的点，其中两个歧管之间的相似度得分是它们之间的欧几里德距离r（3）代表。使用这些方法，我们可以通过仅在几百个数据点上进行培训，将搜索空间压缩以获得极度罕见的歧管，以百分比在原始数据的一个百分比内。我们还展示了如何应用这些方法来表征真空代表的“典型性”。

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.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

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。

深度学习技术的最新进展引发了地面车辆的自主权的根本性进步。定期用于监视，监视和其他常规任务的海洋沿海自动级别的表面车辆（ASV）可以从这种自治中受益。长期的深海运输活动是额外的机会。这两个用例的地形非常不同 - 第一个是沿海水域 - 具有许多障碍，结构和人类的存在，而后者大多没有这样的障碍。环境条件的变化都是两种地形的共同点。绘制此类地形的强大标记数据集对于提高可以推动自主权的情境意识至关重要。但是，只有此类海事数据集有限，这些数据集主要由光学图像组成。虽然，长浪红外（LWIR）是对极端光条件下有助于的光谱的强烈补充，但目前尚不存在带有LWIR图像的标记的公共数据集。在本文中，我们通过在不同条件下呈现在沿海海上环境中捕获的2,900多个LWIR分段图像的标签数据集来填补这一空白。这些图像使用实例分割标记，并分为七个类别 - 天空，水，障碍物，生活障碍，桥梁，自我和背景。我们还评估了三个深度学习体系结构（UNET，PSPNET，DEEPLABV3）的数据集，并对其功效提供了详细的分析。尽管数据集专注于沿海地形，但可以同样有助于深海用例。这种地形的流量将较小，在混乱环境中训练的分类器将能够有效地处理稀疏场景。我们与研究界分享此数据集，希望它刺激新的场景理解海上环境中的能力。

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。

本文的重点是概念证明，机器学习（ML）管道，该管道从低功率边缘设备上获取的压力传感器数据中提取心率。 ML管道包括一个UPS采样器神经网络，信号质量分类器以及优化的1D横向扭转神经网络，以高效且准确的心率估计。这些型号的设计使管道小于40 kb。此外，开发了由UPS采样器和分类器组成的杂种管道，然后开发了峰值检测算法。管道部署在ESP32边缘设备上，并针对信号处理进行基准测试，以确定能量使用和推理时间。结果表明，与传统算法相比，提出的ML和杂种管道将能量和时间减少82％和28％。 ML管道的主要权衡是准确性，平均绝对误差（MAE）为3.28，而混合动力车和信号处理管道为2.39和1.17。因此，ML模型显示出在能源和计算约束设备中部署的希望。此外，ML管道的较低采样率和计算要求可以使自定义硬件解决方案降低可穿戴设备的成本和能源需求。