Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability.

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

尽管当前的显着对象检测（SOD）作品已经取得了重大进展，但在预测的显着区域的完整性方面，它们受到限制。我们在微观和宏观水平上定义了完整性的概念。具体而言，在微观层面上，该模型应突出显示属于某个显着对象的所有部分。同时，在宏观层面上，模型需要在给定图像中发现所有显着对象。为了促进SOD的完整性学习，我们设计了一个新颖的完整性认知网络（ICON），该网络探讨了学习强大完整性特征的三个重要组成部分。 1）与现有模型不同，该模型更多地集中在功能可区分性上，我们引入了各种功能集合（DFA）组件，以汇总具有各种接受场（即内核形状和背景）的特征，并增加了功能多样性。这种多样性是挖掘积分显着物体的基础。 2）基于DFA功能，我们引入了一个完整性通道增强（ICE）组件，其目标是增强功能通道，以突出积分显着对象，同时抑制其他分心的对象。 3）提取增强功能后，采用零件整体验证（PWV）方法来确定零件和整个对象特征是否具有很强的一致性。这样的部分协议可以进一步改善每个显着对象的微观完整性。为了证明我们图标的有效性，对七个具有挑战性的基准进行了全面的实验。我们的图标在广泛的指标方面优于基线方法。值得注意的是，我们的图标在六个数据集上的平均假阴影（FNR）（FNR）方面，相对于以前的最佳模型的相对改善约为10％。代码和结果可在以下网址获得：https：//github.com/mczhuge/icon。

Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predicting pseudo labels for new domain datasets. Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions. Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-level visual domain prompt for target domains while having the source model parameters frozen. During testing, the changing target datasets can be adapted to the source model by reformulating the input data with the learned visual prompts. Specifically, we devise two types of prompts, i.e., domains-specific prompts and domains-agnostic prompts, to extract current domain knowledge and maintain the domain-shared knowledge in the continual adaptation. Furthermore, we design a homeostasis-based prompt adaptation strategy to suppress domain-sensitive parameters in domain-invariant prompts to learn domain-shared knowledge more effectively. This transition from the model-dependent paradigm to the model-free one enables us to bypass the catastrophic forgetting and error accumulation problems. Experiments show that our proposed method achieves significant performance gains over state-of-the-art methods on four widely-used benchmarks, including CIFAR-10C, CIFAR-100C, ImageNet-C, and VLCS datasets.

The activity of the grid cell population in the medial entorhinal cortex (MEC) of the mammalian brain forms a vector representation of the self-position of the animal. Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal. In doing so, the grid cell system effectively performs path integration. In this paper, we investigate the algebraic, geometric, and topological properties of grid cells using recurrent network models. Algebraically, we study the Lie group and Lie algebra of the recurrent transformation as a representation of self-motion. Geometrically, we study the conformal isometry of the Lie group representation where the local displacement of the activity vector in the neural space is proportional to the local displacement of the agent in the 2D physical space. Topologically, the compact abelian Lie group representation automatically leads to the torus topology commonly assumed and observed in neuroscience. We then focus on a simple non-linear recurrent model that underlies the continuous attractor neural networks of grid cells. Our numerical experiments show that conformal isometry leads to hexagon periodic patterns in the grid cell responses and our model is capable of accurate path integration. Code is available at \url{https://github.com/DehongXu/grid-cell-rnn}.

数学推理是人类智力的核心能力，在抽象思维和逻辑推理中对机器提出了独特的挑战。最近的大型预训练的语言模型（例如GPT-3）在以文本形式（例如数学单词问题（MWP））编写的数学推理任务上取得了显着的进步。但是，未知模型是否可以处理更复杂的问题，这些问题涉及数学推理，例如表格数据。为了填补空白，我们提出了表格数学单词问题（TABMWP），这是一个包含38,431个开放域级等级问题的新数据集，这些问题需要在文本和表格数据上进行数学推理。 TABMWP中的每个问题都与表格上下文对齐，该上下文作为图像，半结构化文本和结构化表。有两种类型的问题：自由文本和多选择，每个问题都用金解决方案注释以揭示多步推理过程。我们在TABMWP上评估了不同的预训练模型，包括在几次设置中的GPT-3模型。正如先前的研究所表明的那样，由于很少有GPT-3依赖于内在的示例的选择，因此其性能是不稳定的，并且可能会降解为几乎机会。处理TABMWP等复杂问题时，不稳定的问题更为严重。为了减轻这种情况，我们进一步提出了一种新颖的方法，即PresspG，该方法利用策略梯度学习从少量培训数据中选择中文示例，然后为测试示例构造相应的提示。实验结果表明，与随机选择相比，我们的方法在准确性度量上优于最佳基线，并显着降低了预测方差，这验证了其在选择性上下文示例中的有效性。

准确的蛋白质结合亲和力预测在药物设计和许多其他分子识别问题中至关重要。尽管基于机器学习技术的亲和力预测取得了许多进步，但由于蛋白质 - 配体结合取决于原子和分子的动力学，它们仍然受到限制。为此，我们策划了一个包含3,218个动态蛋白质配合物的MD数据集，并进一步开发了DynaFormer，这是一个基于图的深度学习框架。 DynaFormer可以通过考虑相互作用的各种几何特征来完全捕获动态结合规则。我们的方法显示出优于迄今报告的方法。此外，我们通过将模型与基于结构的对接整合在一起，对热休克蛋白90（HSP90）进行了虚拟筛选。我们对其他基线进行了基准测试，表明我们的方法可以鉴定具有最高实验效力的分子。我们预计大规模的MD数据集和机器学习模型将形成新的协同作用，为加速药物发现和优化提供新的途径。

潜在空间基于能量的模型（EBM），也称为基于能量的先验，引起了对生成建模的日益兴趣。由于其在潜在空间的配方和强大的建模能力方面的灵活性所推动，最近构建的作品已经进行了有趣的尝试，目的是针对文本建模的解释性。但是，潜在空间EBM还继承了数据空间中EBM的一些缺陷。实践中退化的MCMC抽样质量会导致培训中的发电质量和不稳定差，尤其是在具有复杂潜在结构的数据上。受到最近的努力的启发，该努力利用扩散恢复的可能性学习是解决抽样问题的一种方法，我们在变异学习框架中引入了扩散模型和潜在空间EBM之间的新型共生，这是潜在扩散能量基于能量的模型。我们与信息瓶颈共同开发基于几何聚类的正则化，以进一步提高学到的潜在空间的质量。对几个具有挑战性的任务进行的实验证明了我们模型在可解释的文本建模上的优越性能而不是强大的同行。

Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its importance, calibration optimization often suffers from a problem called "maximization bias". Maximization bias refers to the phenomenon that the maximum of predicted values overestimates the true maximum. The problem is introduced because the calibration is computed on the set selected by the prediction model itself. It persists even if unbiased predictions can be achieved on every datapoint and worsens when covariate shifts exist between the training and test sets. To mitigate this problem, we theorize the quantification of maximization bias and propose a variance-adjusting debiasing (VAD) meta-algorithm in this paper. The algorithm is efficient, robust, and practical as it is able to mitigate maximization bias problems under covariate shifts, neither incurring additional online serving costs nor compromising the ranking performance. We demonstrate the effectiveness of the proposed algorithm using a state-of-the-art recommendation neural network model on a large-scale real-world dataset.

To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.