Test log-likelihood is commonly used to compare different models of the same data and different approximate inference algorithms for fitting the same probabilistic model. We present simple examples demonstrating how comparisons based on test log-likelihood can contradict comparisons according to other objectives. Specifically, our examples show that (i) conclusions about forecast accuracy based on test log-likelihood comparisons may not agree with conclusions based on other distributional quantities like means; and (ii) that approximate Bayesian inference algorithms that attain higher test log-likelihoods need not also yield more accurate posterior approximations.

通过一系列联邦举措和命令，美国政府一直在努力确保美国在AI中的领导。这些广泛的战略文件影响了美国空军美国部（DAF）等组织。DAF-MIT AI加速器是DAF和MIT之间的一项计划，以弥合AI研究人员与DAF任务要求之间的差距。DAF-MIT AI加速器支持的几个项目正在开发公共挑战问题，这些问题解决了许多联邦AI研究的重点。这些挑战是通过公开可用的大型AI-Ready数据集，激励开源解决方案，并为可以激发进一步研究的双重使用技术创建需求信号，来针对优先事项。在本文中，我们描述了正在开发的这些公共挑战以及它们的应用如何促进科学进步。

支架结构的构建支持所需的基序，赋予蛋白质功能，显示出对疫苗和酶设计的希望。但是，解决这个主题交易问题的一般解决方案仍然开放。当前的脚手架设计的机器学习技术要么仅限于不切实际的小脚手架（长达20个长度），要么难以生产多种不同的脚手架。我们建议通过E（3） - 等级图神经网络学习各种蛋白质主链结构的分布。我们开发SMCDIFF以有效地从给定主题的条件下从该分布中采样脚手架；我们的算法是从理论上确保从扩散模型中的有条件样品，以大规模计算限制。我们通过与Alphafold2预测的结构保持一致的方式来评估我们设计的骨干。我们表明我们的方法可以（1）最多80个残基的样品支架，以及（2）实现固定基序的结构多样的支架。

概率机器学习越来越越来越多地向医学，经济，政治和超越的关键决策促进。我们需要证据支持所产生的决定是充分创建的。为了帮助发展对这些决定的信任，我们开发了一个分类划分的分类划分，在分析中的信任可以分解：（1）在现实世界目标的翻译中对特定培训数据的目标，（2）在训练数据上翻译培训数据到一个具体的数学问题，（3）在使用算法来解决所述的数学问题，（4）在使用特定代码实现的选择算法。我们详细介绍了每一步的信任如何失败，并用两种案例研究说明我们的分类法：分析小额信贷和经济学家预测2020年2020年总统选举的疗效分析。最后，我们描述了各种各样的方法，可用于增加我们分类的每一步的信任。我们的分类学突出了关于信任的现有研究工作倾向于集中注意力的步骤，以及建立信任的步骤尤其具有挑战性。

Many scientific problems require identifying a small set of covariates that are associated with a target response and estimating their effects. Often, these effects are nonlinear and include interactions, so linear and additive methods can lead to poor estimation and variable selection. Unfortunately, methods that simultaneously express sparsity, nonlinearity, and interactions are computationally intractable -- with runtime at least quadratic in the number of covariates, and often worse. In the present work, we solve this computational bottleneck. We show that suitable interaction models have a kernel representation, namely there exists a "kernel trick" to perform variable selection and estimation in $O$(# covariates) time. Our resulting fit corresponds to a sparse orthogonal decomposition of the regression function in a Hilbert space (i.e., a functional ANOVA decomposition), where interaction effects represent all variation that cannot be explained by lower-order effects. On a variety of synthetic and real data sets, our approach outperforms existing methods used for large, high-dimensional data sets while remaining competitive (or being orders of magnitude faster) in runtime.

The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.

This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. Using this dataset, we test the analogical reasoning capabilities of several widely-used pretrained language models (LMs). We find that state-of-the-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding.

Understanding how the statistical and geometric properties of neural activations relate to network performance is a key problem in theoretical neuroscience and deep learning. In this letter, we calculate how correlations between object representations affect the capacity, a measure of linear separability. We show that for spherical object manifolds, introducing correlations between centroids effectively pushes the spheres closer together, while introducing correlations between the spheres' axes effectively shrinks their radii, revealing a duality between neural correlations and geometry. We then show that our results can be used to accurately estimate the capacity with real neural data.

We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models. Different from existing works, we show that code obfuscation, a standard code transformation operation, provides novel means to generate complementary `views' of a code that enable us to achieve both robust and accurate code models. To the best of our knowledge, this is the first systematic study to explore and exploit the robustness and accuracy benefits of (multi-view) code obfuscations in code models. Specifically, we first adopt adversarial codes as robustness-promoting views in CL at the self-supervised pre-training phase. This yields improved robustness and transferability for downstream tasks. Next, at the supervised fine-tuning stage, we show that adversarial training with a proper temporally-staggered schedule of adversarial code generation can further improve robustness and accuracy of the pre-trained code model. Built on the above two modules, we develop CLAWSAT, a novel self-supervised learning (SSL) framework for code by integrating $\underline{\textrm{CL}}$ with $\underline{\textrm{a}}$dversarial vie$\underline{\textrm{w}}$s (CLAW) with $\underline{\textrm{s}}$taggered $\underline{\textrm{a}}$dversarial $\underline{\textrm{t}}$raining (SAT). On evaluating three downstream tasks across Python and Java, we show that CLAWSAT consistently yields the best robustness and accuracy ($\textit{e.g.}$ 11$\%$ in robustness and 6$\%$ in accuracy on the code summarization task in Python). We additionally demonstrate the effectiveness of adversarial learning in CLAW by analyzing the characteristics of the loss landscape and interpretability of the pre-trained models.

目标。借助（子）毫米观测值的大量分子发射数据和詹姆斯·韦伯（James Webb）空间望远镜红外光谱，访问原磁盘的化学成分的快进模型至关重要。方法。我们使用了热化学建模代码来生成各种多样的原行星磁盘模型。我们训练了一个最初的邻居（KNN）回归剂，以立即预测其他磁盘模型的化学反应。结果。我们表明，由于所采用的原行业磁盘模型中局部物理条件之间的相关性，可以仅使用一小部分物理条件来准确地重现化学反应。我们讨论此方法的不确定性和局限性。结论。所提出的方法可用于对线排放数据的贝叶斯拟合，以从观测值中检索磁盘属性。我们提出了在其他磁盘化学模型集上再现相同方法的管道。