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Complement C7 is a novel risk gene for Alzheimer's disease in Han Chinese

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National Science Review 2019年第2期6卷 257-274页

作者： Deng-Feng Zhang Yu Fan Min Xu Guihong Wang Dong Wang Jin Li Li-Li Kong Hejiang Zhou Rongcan Luo Rui Bi Yong Wu Guo-Dong Li Ming Li Xiong-Jian Luo Hong-Yan Jiang Liwen Tan Chunjiu Zhong Yiru Fang Chen Zhang Nengyin Sheng Tianzi Jiang Yong-Gang Yao Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province Kunming Institute of ZoologyChinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming College of Life Science University of Chinese Academy of Sciences Center for Neurodegenerative Diseases Department of Neurology Beijing Tiantan Hospital Capital Medical University Brainnetome Center Institute of AutomationChinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Department of Psychiatry the First Affiliated Hospital of Kunming Medical University Mental Health Institute of the Second Xiangya Hospital Central South University Department of Neurology Zhongshan Hospital Fudan University Division of Mood Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences KIZ–CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases Kunming Institute of Zoology Chinese Academy of Sciences

Alzheimer's disease is the most common neurodegenerative disease,and has a high level of genetic heritability and population *** this study,we performed the whole-exome sequencing of Han Chinese patients with familial and/or early-onset Alzheimer's disease,followed by independent validation,imaging analysis and function *** identified an exome-wide significant rare missense variant rs3792646（p.K420Q）in the C7 gene in the discovery stage(P=1.09×10-6,odds ratio=7.853)and confirmed the association in different cohorts and a combined sample(1615 cases and 2832 controls,Pcombined=2.99×10-7,odds ratio=1.930).The risk allele was associated with decreased hippocampal volume and poorer working memory performance in early adulthood,thus resulting in an earlier age of disease *** of the mutant p.K420Q disturbed cell viability,immune activation and β-amyloid *** analyses showed that the mutant p.K420Q impairs the inhibitory effect of wild type C7 on the excitatory synaptic transmission in pyramidal *** findings suggested that C7 is a novel risk gene for Alzheimer’s disease in Han Chinese.

关键词： Alzheimer’s disease whole-exome sequencing C7 neuroimaging complement system

A resource-light method for cross-lingual semantic textual similarity

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arXiv 2018年

作者： Glavaš, Goran Franco-Salvador, Marc Ponzetto, Simone P. Rosso, Paolo Data and Web Science Group School of Business Informatics and Matemathics University of Mannheim B6 26 MannheimDE-68159 Germany Symanto Research Pretzfelder Strasse 15 NurembergDE-90425 Germany Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n ValenciaES-46022 Spain

Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual embeddings and word alignments. Requiring only a limited-size set of word translation pairs between the languages, the proposed approach is applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings. Experimental results on three different datasets for measuring semantic textual similarity show that our simple resource-light approach reaches performance close to that of supervised and resource-intensive methods, displaying stability across different language pairs. Furthermore, we evaluate the proposed method on two extrinsic tasks, namely extraction of parallel sentences from comparable corpora and cross-lingual plagiarism detection, and show that it yields performance comparable to those of complex resource-intensive state-of-the-art models for the respective tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

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International Journal of Communication Systems 2023年第9期36卷 e5481-e5481页

作者： Mohammadian, Vahid Navimipour, Nima Jafari Hosseinzadeh, Mehdi Darwesh, Aso Department of Computer Engineering Qeshm Branch Islamic Azad University Qeshm Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pattern Recognition and Machine Learning Lab Gachon University Seongnam Korea Republic of Information Technology Departments University of Human Development Sulaimaniyah Iraq

Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. © 2023 John Wiley & Sons Ltd.

关键词： Ant colony optimization

ELLE: Efficient Lifelong Pre-training for Emerging Data

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arXiv 2022年

作者： Qin, Yujia Zhang, Jiajie Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China Institute for Artificial Intelligence Tsinghua University Beijing China Pattern Recognition Center WeChat AI Tencent Inc China International Innovation Center of Tsinghua University Shanghai China Beijing Academy of Artificial Intelligence China Tsinghua University China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China

Current pre-trained language models (PLM) are typically trained with static data, ignoring that in real-world scenarios, streaming data of various sources may continuously grow. This requires PLMs to integrate the information from all the sources in a lifelong manner. Although this goal could be achieved by exhaustive pre-training on all the existing data, such a process is known to be computationally expensive. To this end, we propose ELLE, aiming at efficient lifelong pretraining for emerging data. Specifically, ELLE consists of (1) function preserved model expansion, which flexibly expands an existing PLM's width and depth to improve the efficiency of knowledge acquisition;and (2) pretrained domain prompts, which disentangle the versatile knowledge learned during pretraining and stimulate the proper knowledge for downstream tasks. We experiment ELLE with streaming data from 5 domains on BERT and GPT. The results show the superiority of ELLE over various lifelong learning baselines in both pre-training efficiency and downstream performances. The codes are publicly available at https://***/thunlp/ELLE. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

Packed Levitated Marker for Entity and Relation Extraction

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arXiv 2021年

作者： Ye, Deming Lin, Yankai Li, Peng Sun, Maosong Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China International Innovation Center of Tsinghua University Shanghai China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China Institute Guo Qiang Tsinghua University China Pattern Recognition Center WeChat AI China Tsinghua University China

Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder. In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information. Furthermore, for those more complicated span pair classification tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects to model the interrelation between the same-subject span pairs. The experimental results show that, with the enhanced marker feature, our model advances baselines on six NER benchmarks, and obtains a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models on ACE04 and ACE05. Our code and models are publicly available at https://***/thunlp/PL-Marker. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

MoEfication: Transformer Feed-forward Layers are Mixtures of Experts

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arXiv 2021年

作者： Zhang, Zhengyan Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China Pattern Recognition Center WeChat AI Tencent Inc China International Innovation Center Tsinghua University Shanghai China Beijing Academy of Artificial Intelligence China Tsinghua University China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China

Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. However, the computational patterns of FFNs are still unclear. In this work, we study the computational patterns of FFNs and observe that most inputs only activate a tiny ratio of neurons of FFNs. This phenomenon is similar to the sparsity of the human brain, which drives research on functional partitions of the human brain. To verify whether functional partitions also emerge in FFNs, we propose to convert a model into its MoE version with the same parameters, namely MoEfication. Specifically, MoEfication consists of two phases: (1) splitting the parameters of FFNs into multiple functional partitions as experts, and (2) building expert routers to decide which experts will be used for each input. Experimental results show that MoEfication can conditionally use 10% to 30% of FFN parameters while maintaining over 95% original performance for different models on various downstream tasks. Besides, MoEfication brings two advantages: (1) it significantly reduces the FLOPS of inference, i.e., 2x speedup with 25% of FFN parameters, and (2) it provides a fine-grained perspective to study the inner mechanism of FFNs. The source code of this paper can be obtained from https://***/thunlp/MoEfication. Copyright © 2021, The Authors. All rights reserved.

关键词： Brain

A Simple but Effective Pluggable Entity Lookup Table for Pre-trained language Models

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arXiv 2022年

作者： Ye, Deming Lin, Yankai Li, Peng Sun, Maosong Liu, Zhiyuan Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China International Innovation Center of Tsinghua University Shanghai China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China Institute Guo Qiang Tsinghua University China Pattern Recognition Center WeChat AI China Tsinghua University China

Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose to build a simple but effective Pluggable Entity Lookup Table (PELT) on demand by aggregating the entity's output representations of multiple occurrences in the corpora. PELT can be compatibly plugged as inputs to infuse supplemental entity knowledge into PLMs. Compared to previous knowledge-enhanced PLMs, PELT only requires 0.2%∼5% pre-computation with capability of acquiring knowledge from out-of-domain corpora for domain adaptation scenario. The experiments on knowledge-related tasks demonstrate that our method, PELT, can flexibly and effectively transfer entity knowledge from related corpora into PLMs with different architectures. Our code and models are publicly available at https://***/thunlp/PELT. Copyright © 2022, The Authors. All rights reserved.

关键词： Table lookup

Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images

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arXiv 2021年

作者： Sáenz-Gamboa, Jhon Jairo Domenech, Julio Alonso-Manjarrés, Antonio Gómez, Jon A. de la Iglesia-Vayá, Maria Av. de Catalunya 21 València46020 Spain Orthopedic Surgery Department Hospital Arnau de Vilanova Carrer de San Clemente s/n València46015 Spain Radiology Department Hospital Arnau de Vilanova Carrer de San Clemente s/n València46015 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camí de Vera s/n València46022 Spain Regional Ministry of Universal Health and Public Health in Valencia Carrer de Misser Mascó 31 València46010 Spain

One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a class label to each pixel of an image. Classes were defined by radiologists and correspond to different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to define the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different *** Codes 92B20, 92C50, 68T07, 68T45, 68U10, 92B10 © 2021, CC BY.

关键词： Convolution