arXiv subjects

Xi Yan

Publications and source records attributed to Xi Yan.

23 records · Page 2Linked to original sources

arXiv2023

Biomedical Entity Linking with Triple-aware Pre-Training

Linking biomedical entities is an essential aspect in biomedical natural language processing tasks, such as text mining and question answering. However, a difficulty of linking the biomedical entities using current large language models (LLM) trained on a general corpus is that biomedical entities are scarcely distributed in texts and therefore have been rarely seen during training by the LLM. At the same time, those LLMs are not aware of high level semantic connection between different biomedical entities, which are useful in identifying similar concepts in different textual contexts. To cope with aforementioned problems, some recent works focused on injecting knowledge graph information into LLMs. However, former methods either ignore the relational knowledge of the entities or lead to catastrophic forgetting. Therefore, we propose a novel framework to pre-train the powerful generative LLM by a corpus synthesized from a KG. In the evaluations we are unable to confirm the benefit of including synonym, description or relational information.

cs.CL↗

arXiv2022

Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis

Data-driven systems need to be evaluated to establish trust in the scientific approach and its applicability. In particular, this is true for Knowledge Graph (KG) Question Answering (QA), where complex data structures are made accessible via natural-language interfaces. Evaluating the capabilities of these systems has been a driver for the community for more than ten years while establishing different KGQA benchmark datasets. However, comparing different approaches is cumbersome. The lack of existing and curated leaderboards leads to a missing global view over the research field and could inject mistrust into the results. In particular, the latest and most-used datasets in the KGQA community, LC-QuAD and QALD, miss providing central and up-to-date points of trust. In this paper, we survey and analyze a wide range of evaluation results with significant coverage of 100 publications and 98 systems from the last decade. We provide a new central and open leaderboard for any KGQA benchmark dataset as a focal point for the community - https://kgqa.github.io/leaderboard. Our analysis highlights existing problems during the evaluation of KGQA systems. Thus, we will point to possible improvements for future evaluations.

cs.CL↗

arXiv2022

Nanoscale control of the metal-insulator transition at LAO/KTO (110) and LAO/KTO (111) interfaces

Recent reports of superconductivity at KTaO3 (KTO) (110) and (111) interfaces have sparked intense interest due to the relatively high critical temperature as well as other properties that distinguish this system from the more extensively studied SrTiO3 (STO)-based heterostructures. Here we report nanoscale control of the metal-to-insulator transition at the LaAlO3/KTO (110) and (111) interfaces. Devices are created using two distinct methods previously developed for STO-based heterostructures: (1) conductive atomic-force microscopy lithography and (2) ultra-low-voltage electron-beam lithography. At low temperatures, KTO-based devices show superconductivity that is tunable by an applied back gate. A nanowire device shows single-electron-transistor (SET) behavior. These reconfigurable methods of creating nanoscale devices in KTO-based heterostructures offer new avenues for investigating mechanisms of superconductivity as well as development of quantum devices that incorporate strong spin-orbit interactions, superconducting behavior, and nanoscale dimensions.

cond-mat.mes-hall↗

arXiv2020

Discovery of two-dimensional anisotropic superconductivity at KTaO$_3$ (111) interfaces

The unique electronic structure found at interfaces between materials can allow unconventional quantum states to emerge. Here we observe superconductivity in electron gases formed at interfaces between (111) oriented KTaO$_3$ and insulating overlayers of either EuO or LaAlO$_3$. The superconducting transition temperature, approaching 2.2 K, is about one order of magnitude higher than that of the LaAlO$_3$/SrTiO$_3$ system. Strikingly, similar electron gases at (001) KTaO$_3$ interfaces remain normal down to 25 mK. The critical field and current-voltage measurements indicate that the superconductivity is two dimensional. Higher mobility EuO/KTaO$_3$ (111) samples show a large in-plane anisotropy in transport properties at low temperatures prior to onset of superconductivity, suggesting the emergence of a stripe like phase where the superconductivity is nearly homogeneous in one direction, but strongly modulated in the other.

cond-mat.supr-con↗

arXiv2020

Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data

Transfer learning has proven to be a successful technique to train deep learning models in the domains where little training data is available. The dominant approach is to pretrain a model on a large generic dataset such as ImageNet and finetune its weights on the target domain. However, in the new era of an ever-increasing number of massive datasets, selecting the relevant data for pretraining is a critical issue. We introduce Neural Data Server (NDS), a large-scale search engine for finding the most useful transfer learning data to the target domain. NDS consists of a dataserver which indexes several large popular image datasets, and aims to recommend data to a client, an end-user with a target application with its own small labeled dataset. The dataserver represents large datasets with a much more compact mixture-of-experts model, and employs it to perform data search in a series of dataserver-client transactions at a low computational cost. We show the effectiveness of NDS in various transfer learning scenarios, demonstrating state-of-the-art performance on several target datasets and tasks such as image classification, object detection and instance segmentation. Neural Data Server is available as a web-service at http://aidemos.cs.toronto.edu/nds/.

cs.CV↗