我们认为，作为离散位置向量值体积功能的采样点云的属性。为了压缩所提供的位置属性，我们压缩体积函数的参数。我们通过平铺空间成块，并通过基于坐标的，或隐式的，神经网络的偏移较每个块中的函数的体积函数建模。输入到网络包括空间坐标和每个块的潜矢量。我们代表使用区域自适应分级的系数潜矢量变换在MPEG基于几何形状的点云的编解码器G-PCC使用（RAHT）。的系数，这是高度可压缩的，是速率 - 失真通过在自动解码器配置的速率 - 失真拉格朗日损失由反向传播最优化。结果由2-4分贝优于RAHT。这是第一工作由局部坐标为基础的神经网络为代表的压缩体积的功能。因此，我们希望它是适用超越的点云，例如高分辨率的神经辐射场的压缩。

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models -- algorithms that have been trained on data rather than explicitly programmed -- are an important target for this because of the ever-increasing pace with which AI is transforming scientific and engineering domains. In this paper, we propose a practical definition of FAIR principles for AI models and create a FAIR AI project template that promotes adherence to these principles. We demonstrate how to implement these principles using a concrete example from experimental high energy physics: a graph neural network for identifying Higgs bosons decaying to bottom quarks. We study the robustness of these FAIR AI models and their portability across hardware architectures and software frameworks, and report new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. Enabled by publishing FAIR AI models, these studies pave the way toward reliable and automated AI-driven scientific discovery.

In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the model. We implement transformer models for two distinct InfoSec data formats - specifically URLs and PE files - in a novel end-to-end approach, and explore a variety of architectural designs, training regimes, and experimental settings to determine the ingredients necessary for performant detection models. We show that in contrast to conventional transformers trained on more standard NLP-related tasks, our URL transformer model requires a different training approach to reach high performance levels. Specifically, we show that 1) pre-training on a massive corpus of unlabeled URL data for an auto-regressive task does not readily transfer to binary classification of malicious or benign URLs, but 2) that using an auxiliary auto-regressive loss improves performance when training from scratch. We introduce a method for mixed objective optimization, which dynamically balances contributions from both loss terms so that neither one of them dominates. We show that this method yields quantitative evaluation metrics comparable to that of several top-performing benchmark classifiers. Unlike URLs, binary executables contain longer and more distributed sequences of information-rich bytes. To accommodate such lengthy byte sequences, we introduce additional context length into the transformer by providing its self-attention layers with an adaptive span similar to Sukhbaatar et al. We demonstrate that this approach performs comparably to well-established malware detection models on benchmark PE file datasets, but also point out the need for further exploration into model improvements in scalability and compute efficiency.

Planet formation is a multi-scale process in which the coagulation of $\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1.5\times 10^8 \,\mathrm{km}$). Studies are therefore dependent on subgrid models to emulate the micro physics of dust coagulation on top of a large scale hydrodynamic simulation. Numerical simulations which include the relevant physical effects are complex and computationally expensive. Here, we present a fast and accurate learned effective model for dust coagulation, trained on data from high resolution numerical coagulation simulations. Our model captures details of the dust coagulation process that were so far not tractable with other dust coagulation prescriptions with similar computational efficiency.

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

对比模式挖掘（CPM）是数据挖掘的重要且流行的子场。传统的顺序模式无法描述不同类别数据之间的对比度信息，而涉及对比概念的对比模式可以描述不同对比条件下数据集之间的显着差异。根据该领域发表的论文数量，我们发现研究人员对CPM的兴趣仍然活跃。由于CPM有许多研究问题和研究方法。该领域的新研究人员很难在短时间内了解该领域的一般状况。因此，本文的目的是为对比模式挖掘的研究方向提供最新的全面概述。首先，我们对CPM提出了深入的理解，包括评估歧视能力的基本概念，类型，采矿策略和指标。然后，我们根据CPM方法根据其特征分类为基于边界的算法，基于树的算法，基于进化模糊的系统算法，基于决策树的算法和其他算法。此外，我们列出了这些方法的经典算法，并讨论它们的优势和缺点。提出了CPM中的高级主题。最后，我们通过讨论该领域的挑战和机遇来结束调查。

我们重新访问重尾损坏的最小二乘线性回归，假设最多损坏了$ n $ n $ n $ sized的标签 - 功能样本，最多是$ \ epsilon n $ nutialary Outliers。我们希望估计给定标签 - 功能对$（y，x）$满足$ y = \ y = \ langle x，b^*\ rangle+xi $的标签 - 功能对$（y，x）$的样本给定$ p $ -dimensional参数$ b^*$ - 尾$（x，\ xi）$。我们只假设$ x $ is $ l^4-l^2 $超债券与常数$ l> 0 $，并具有协方差矩阵$ \ sigma $，最低eigenvalue $ 1/\ mu^2> 0 $和有限条件号$ \ \ \ \ \ \ \ \ kappa> 0 $。只要$ \ xi x $具有有限的协方差矩阵$ \ xi $，噪声$ \ xi $可以任意取决于$ x $，而非对称性。我们提出了一个基于功率方法的近乎最佳的计算估计器，假设对$（\ sigma，\ xi）$也不了解$ \ xi $的运算符规范。如果概率至少$ 1- \ delta $，我们提出的估计器达到了统计率$ \ mu^2 \ vert \ xi \ xi \ vert^{1/2}（\ frac {p} {n} {n}+\ frac {\ log（\ log（\ log（ 1/\ delta）}} {n}+\ epsilon）^{1/2} $ and beckdown-point $ \ epsilon \ epsilon \ sillesim \ frac {1} {l^4 \ kappa^2} $ \ ell_2 $ - norm，假设最小最小样本大小$ l^4 \ kappa^2（p \ log p + p + \ log（1/\ delta））\ sillsim n $，最多为log fix因数。据我们所知，这是同时满足所有提到的所有属性的第一个计算障碍算法。我们的估计器基于两阶段的乘量重量更新算法。第一阶段估计了（未知）预先条件的内部产品$ \ langle \ sigma（\ cdot），\ cdot \ rangle $。第二阶段估计下降方向$ \ sigma \ hat v $相对于（已知的）内部产品$ \ langle \ cdot，\ cdot \ rangle $，而无需了解或估计$ \ sigma $。

在时间图上的表示学习吸引了大量的研究注意力，因为它在各种各样的现实应用程序中的基本重要性。尽管许多研究成功地获得了时间依赖的表示，但它仍然面临重大挑战。一方面，大多数现有方法都以一定的曲率限制了嵌入空间。然而，实际上，潜在的几何形状随着时间的推移而变化的曲率超球，零曲率欧几里得和负曲率双曲空间发生了变化。另一方面，这些方法通常需要丰富的标签来学习时间表示，从而明显限制了它们在真实应用程序的未标记图中的广泛使用。为了弥合这一差距，我们首次尝试研究一般的Riemannian空间中自我监督的时间图表示学习的问题，从而支持随时间变化的曲率在超球，欧几里得和双曲线空间之间转移。在本文中，我们提出了一种新颖的自我监督的Riemannian图神经网络（SEXTRGNN）。具体而言，我们设计了具有理论上的时间编码的曲率变化的Riemannian GNN，并随着时间的推移制定功能性曲率，以模拟正，零和负曲率空间之间的演变转换。为了启用自我监督的学习，我们提出了一种新颖的重新处理自我对比的方法，探索Riemannian空间本身而无需增强，并提出了一种基于边缘的自我监督的曲率学习，并使用RICCI曲率进行。广泛的实验表明了SelfRGNN的优越性，此外，案例研究表明了现实中时间图的时变曲率。

现成的实用工具采矿（OSUM）是数据挖掘的新兴研究方向。它的目的是发现在销售时间内具有相对效用的物品集。与传统的公用事业开采相比，Osum可以在现实生活中找到更多实用和有意义的模式。但是，传统Osum有一个主要的缺点。对于普通用户而言，很难定义最低阈值细节，以挖掘适量的货架上的高实用物品集。一方面，如果设置阈值太高，则图案的数量将不够。另一方面，如果设定阈值太低，则会发现太多模式，并导致不必要的时间和记忆消耗。为了解决此问题，用户通常直接指定一个参数k，其中仅考虑顶级相对实用项目集。因此，在本文中，我们提出了一种通用算法TOIT，用于挖掘Top-K On-Shelf高耗时模式来解决此问题。 TOIT采用了一种新颖的策略来根据架子上的数据集提高细节。此外，还采用了两种名为Subtree实用程序的新型上限策略，并应用了本地实用程序来修剪搜索空间。通过采用上述策略，TOIT算法可以尽早缩小搜索空间，提高采矿效率并降低记忆消耗，从而比其他算法获得更好的性能。在具有不同样式的真实数据集上进行了一系列实验，以将效果与最新的Koshu算法进行比较。实验结果表明，TOIT在运行时间和内存消耗中都优于Koshu。