强烈的引力透镜已成为一种有前途的方法，用于探测亚半乳尺度上的暗物质模型。最近的工作提出了Subhalo有效密度斜率比常用的Subhalo质量功能更可靠。 subhalo有效密度斜率是一个独立于对基础密度曲线的假设的测量值，可以通过传统的采样方法来推断单个Subhalos。为了超越单个Subhalo测量，我们利用机器学习的最新进展，并引入神经似然比估计器来推断Subhalos人群的有效密度斜率。我们证明我们的方法能够利用多个Subhalos（内部和跨多个图像）的统计能力来区分不同Subhalo种群的特征。神经似然比估计量对传统抽样的估计值所需的计算效率可以实现对暗物质遗传的统计研究，并且特别有用，因为我们希望从即将进行的调查中涌入强镜头系统。

矮星系是小的，以暗物质为主导的星系，其中一些嵌入了银河系中。他们缺乏重型物质（例如，恒星和气体）使它们成为探测暗物质特性的完美测试床 - 了解这些系统中的空间暗物质分布可用于限制影响形成和进化的微物理暗物质相互作用我们宇宙中的结构。我们介绍了一种新方法，该方法利用基于模拟的推理和基于图的机器学习，以推断出恒星的可观察到的恒星重力与这些系统结合的可观察到的矮星系的暗物质密度曲线。我们的方法旨在解决基于动态牛仔裤建模的既定方法的一些局限性。我们表明，这种新颖的方法可以对暗物质概况施加更强的约束，因此，有可能权衡与暗物质晕圈小规模结构（例如核心核心差异）相关的一些持续的难题。

从暗物质的性质到我们宇宙的扩张速率，通过强烈的重力透镜扭曲的遥远星系的观察有可能回答天体物理学中一些主要的开放问题。建模星系 - 半轴强透镜观测值提出了许多挑战，因为背景源和前景镜头星系的确切配置尚不清楚。及时的呼吁，由许多即将进行的预测高分辨率镜头图像的调查提示，要求可以有效地对其充分的复杂性进行对镜头进行建模的方法。在这项工作中，我们引入了一种使用连续神经场来非参数重建源银河系的复杂形态的方法，同时在前景镜头星系构型上同时推断分布。我们通过对靶向高分辨率镜头图像的模拟数据进行实验来证明我们的方法的功效，类似于近乎未来的天体物理调查中预期的图像。

ASTROMYRY - 天体物体的职位和运动的精确测量 - 已成为一个有希望的大道，用于在我们的银河系中表征暗物质人口。通过利用基于仿真的推断和神经网络架构的最近进步，我们介绍了一种新的方法来搜索天球暗物质引起的天体辐射数据集中的重力透镜签名。我们基于神经似然比估计的方法显示出与基于两点相关统计的现有方法相比，与测量噪声相比，对冷暗物质人群的敏感性显着提高了敏感性。我们通过将其稳健而言，展示了我们的方法的真实可行性，并且在天体测量中预期的非普通建模以及未拼模型的噪声功能。这使得机器学习作为一种强大的工具，用于使用artromicric数据表征暗物质。

The rapid development of remote sensing technologies have gained significant attention due to their ability to accurately localize, classify, and segment objects from aerial images. These technologies are commonly used in unmanned aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to capture data over large areas. This data is useful for various applications, such as monitoring and inspecting cities, towns, and terrains. In this paper, we presented a method for classifying and segmenting city road traffic dashed lines from aerial images using deep learning models such as U-Net and SegNet. The annotated data is used to train these models, which are then used to classify and segment the aerial image into two classes: dashed lines and non-dashed lines. However, the deep learning model may not be able to identify all dashed lines due to poor painting or occlusion by trees or shadows. To address this issue, we proposed a method to add missed lines to the segmentation output. We also extracted the x and y coordinates of each dashed line from the segmentation output, which can be used by city planners to construct a CAD file for digital visualization of the roads.

As information extraction (IE) systems have grown more capable at whole-document extraction, the classic task of \emph{template filling} has seen renewed interest as a benchmark for evaluating them. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of \emph{event individuation} -- the problem of distinguishing distinct events -- about which even human experts disagree. We show through annotation studies and error analysis that this raises concerns about the usefulness of template filling evaluation metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions.

Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.

This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The computation of our bound is time-efficient and memory-efficient and only requires finite samples. The proposed bound shows its value in one-class classification and domain shift analysis. Specifically, in one-class classification, we build a novel one-class classifier by converting the bound into a confidence score function. Unlike most one-class classifiers, the training process is not needed for our classifier. Additionally, the experimental results show that our classifier \textcolor{\colorname}{can be accurate with} only a small number of in-class samples and outperforms many state-of-the-art methods on various datasets in different one-class classification scenarios. In domain shift analysis, we propose a theorem based on our bound. The theorem is useful in detecting the existence of domain shift and inferring data information. The detection and inference processes are both computation-efficient and memory-efficient. Our work shows significant promise toward broadening the applications of overlap-based metrics.

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations.

Masked Language Modeling (MLM) has proven to be an essential component of Vision-Language (VL) pretraining. To implement MLM, the researcher must make two design choices: the masking strategy, which determines which tokens to mask, and the masking rate, which determines how many tokens to mask. Previous work has focused primarily on the masking strategy while setting the masking rate at a default of 15\%. In this paper, we show that increasing this masking rate improves downstream performance while simultaneously reducing performance gap among different masking strategies, rendering the uniform masking strategy competitive to other more complex ones. Surprisingly, we also discover that increasing the masking rate leads to gains in Image-Text Matching (ITM) tasks, suggesting that the role of MLM goes beyond language modeling in VL pretraining.