End of the World Brane meets TTˉT\bar{T}

End of the world branes in AdS have been recently used to study problems deeply connected to quantum gravity, such as black hole evaporation and holographic cosmology. With non-critical tension and Neumann boundary condition, the end of the world brane often represents part of the degrees of freedom in AdS gravity and geometrically it is only part of the entire boundary. On the other hand, holographic $T\bar{T}$ deformation can also give a boundary as a cutoff surface for AdS gravity. In this paper we consider AdS gravity with both the end of the world boundary and the cutoff boundary. Using partial reduction we obtain a brane world gravity glued to a $T\bar{T}$ deformed bath. We compute both entanglement entropy and Page curve, and find agreement between the holographic results and island formula results.Comment: 1+26 pages, 11 figure

PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text semantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In this paper, we present PromptKG, a prompt learning framework for KG representation learning and application that equips the cutting-edge text-based methods, integrates a new prompt learning model and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). PromptKG is publicly open-sourced at https://github.com/zjunlp/PromptKG with long-term technical support.Comment: Work in progres

From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer

Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completion to sequence-to-sequence generation task with the pre-trained language model. We further introduce relation-guided demonstration and entity-aware hierarchical decoding for better representation learning and fast inference. Experimental results on three datasets show that our approach can obtain better or comparable performance than baselines and achieve faster inference speed compared with previous methods with pre-trained language models. We also release a new large-scale Chinese knowledge graph dataset AliopenKG500 for research purpose. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/GenKGC.Comment: Accepted by WWW 2022 Poste

Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

In neutral hydrogen (HI) galaxy survey, a significant challenge is to identify and extract the HI galaxy signal from observational data contaminated by radio frequency interference (RFI). For a drift-scan survey, or more generally a survey of a spatially continuous region, in the time-ordered spectral data, the HI galaxies and RFI all appear as regions which extend an area in the time-frequency waterfall plot, so the extraction of the HI galaxies and RFI from such data can be regarded as an image segmentation problem, and machine learning methods can be applied to solve such problems. In this study, we develop a method to effectively detect and extract signals of HI galaxies based on a Mask R-CNN network combined with the PointRend method. By simulating FAST-observed galaxy signals and potential RFI impacts, we created a realistic data set for the training and testing of our neural network. We compared five different architectures and selected the best-performing one. This architecture successfully performs instance segmentation of HI galaxy signals in the RFI-contaminated time-ordered data (TOD), achieving a precision of 98.64% and a recall of 93.59%.Comment: 17 pages, 9 figures, 1 tables. Accepted for publication in RA

Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.Comment: OpenBG. Work in Progres

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effects of Micropit Depths on Tribology Performance of Textured Port Plate Pair

In order to improve the friction and wear performance of textured port plate pair, effects of the micropit depth on the tribology performance is studied in the paper. The relation between the micropit depth and the port plate pair’s oil loading carrying capacity is analyzed in theory; with the friction coefficient, the wear volume and the surface roughness as the evaluation criteria, effects of the micropits’ depth on the tribology performance are investigated. The conclusions are shown as follows: oil loading capacity would come to its peak when the oil film thickness is equal to the micropit depth; the optimal micropit depth is unrelated to the area ratios and micropits’ diameters. With the same parameters, the effects of antifriction is optimal when the micropits’ depth is 10 μm, while antiwear and surface integrity are optimal when 15 μm. When the micropits’ depth is 5 μm, the antiwear, surface roughness, and antifriction are worse compared with those of the untextured port plate pair

A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection