显着对象检测（SOD）最近引起了人们的关注，但对高分辨率（HR）图像的研究较少。不幸的是，与低分辨率（LR）图像和注释相比，HR图像及其像素级注释肯定是更耗费劳动力和耗时的。因此，我们建议没有任何HR数据集的HR预测，建议基于图像金字塔的SOD框架，逆显着性金字塔重建网络（INSPYRENET）。我们设计了Inspyrenet，以产生严格的图像金字塔结构，使其能够将多个结果与基于金字塔的图像混合在一起。为了进行HR预测，我们设计了一种金字塔混合方法，该方法从同一图像中从一对LR和HR量表中合成了两个不同的图像金字塔，以克服有效的接受场（ERF）差异。我们对公共LR和HR SOD基准的广泛评估表明，Inspyrenet超过了各种SOD指标和边界准确性的最新方法（SOTA）方法。

我们考虑在排名一的尖刺模型中检测信号的存在的问题。对于一般的非高斯噪声，假设信号是从rademacher先验中汲取的，我们证明，当信号噪声比率低于信号噪声时，尖峰模型的对数可能性比（LR）收敛到高斯一定的阈值。阈值是最佳的，因为在其上方，可以通过转换的主组件分析（PCA）进行可靠的检测。从对数LR的限制高斯的平均值和方差，我们计算了I型误差之和的限制以及似然比测试的类型II误差。对于噪声不对称，但信号是对称的，我们还证明了一个排名一的尖峰IID模型的结果相似。

This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems.

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on two real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.

子图是图中丰富的子结构，并且可以在现实世界任务中部分观察到它们的节点和边缘。在部分观察结果下，现有的节点或子图级消息传播会产生次优表示。在本文中，我们制定了一项新的学习表征的新任务。为了解决此问题，我们建议将部分子图信息（PSI）框架（PSI）框架概括为我们的框架中现有的Infomax模型，包括DGI，Intograph，MVGRL和GraphCl。这些模型最大程度地提高了部分子图的摘要与从节点到完整子图的各种子结构之间的共同信息。此外，我们建议使用$ K $ -HOP PSI的新型两阶段模型，它重建了完整子图的表示，并提高了其从不同局部全球结构中的表现力。在为此问题设计的培训和评估协议下，我们在三个现实世界数据集上进行实验，并证明PSI模型的表现优于基准。

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.

最近在自我监督学习中的最先进的框架最近表明，与传统的CNN型号相比，基于变压器的模型可以导致性能提升。繁荣以最大化图像的两个视图的相互信息，现有的作品对最终陈述具有对比损失。在我们的工作中，我们通过通过对比损失允许中间表示从最终层学习来进一步利用这一点，这可以最大化原始目标的上限和两层之间的相互信息。我们的方法，自蒸馏自我监督学习（SDSSL），胜过竞争基础（SIMCLR，BYOL和MOCO V3）使用各种任务和数据集。在线性评估和K-NN协议中，SDSSL不仅导致最终层的性能优异，而且在大多数下层中也是如此。此外，正负对准用于解释如何更有效地形成表示。代码将可用。

跨域的建议显示了解决数据范围和冷启动问题方面有希望的结果。尽管取得了这样的进展，但现有的方法集中在知识转移的可提供的域可共享信息（重叠的用户或相同的上下文）上，并且在没有此类要求的情况下，它们无法很好地概括。为了解决这些问题，我们建议利用大多数电子商务系统一般的审核文本。我们的模型（命名为SER）使用三个文本分析模块，由单个域歧视器指导，用于分离表示表示。在这里，我们提出了一种新颖的优化策略，可以提高域分解的质量，并使源域的有害信息衰弱。此外，我们将编码网络从单个域扩展到多个域，事实证明，这对于基于审核的推荐系统具有强大的功能。广泛的实验和消融研究表明，与最先进的单域和跨域推荐方法相比，我们的方法是有效，健壮和可扩展的。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.