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arXiv 2022年

作者： Meng, Han He, Xiaosong Chen, Zexing Zhou, Feng Beijing University of Post and Telecommunications School of Computer Science Beijing China OPPO Research Institute Beijing China University of Electronic Science and Technology of China UoG-UESTC Joint School Chengdu China University of Glasgow Glasgow United Kingdom

Some Natural Language Generation (NLG) tasks require for both faithfulness and diversity. The decoding strategy is intensively related to the quality of generated text. Strategies such as beam search, greedy search, etc. perform with low diversity and high repetition. On the other hand, guided decoding, the solution towards diversity, may cause unfaithful generation. To this end, this paper presents Information Filter upon Diversity-Improved Decoding (IFDID) to obtain the tradeoff between diversity and faithfulness. IFDID is a two-stage decoding strategy leveraging Enhance-Filter framework, for which achieves the tradeoff by increasing the probability of some typical tokens and subsequently filters them by their information amount. To verify the effectiveness of the proposed method, we compare our method with other baselines on related CommonGEN, RocStories and AdGen benchmarks, which cover Chinese and English datasets. Our numerical experimental results and human evaluation outcomes verify the effectiveness of the proposed approach, as our approach achieves 1.24 higher ROUGE score describing faithfulness as well as higher diversity represented by 62.5% higher upon Dist-2 than traditional approaches, demonstrating that IFDID is a novel SOTA decoding strategy for tradeoff between diversity and faithfulness1 Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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NTRK3 exhibits a pro-oncogenic function in upper tract urothelial carcinomas

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Kaohsiung Journal of Medical sciences 2024年第5期40卷 445-455页

作者： Lim, Lee-Moay Lee, Yi-Chen Lin, Ting-Wei Hong, Zi-Xuan Hsu, Wei-Chi Ke, Hung-Lung Hwang, Daw-Yang Chung, Wen-Yu Li, Wei-Ming Lin, Hui-Hui Kuo, Hung-Tien Huang, A-Mei Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Anatomy School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institute Tainan Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Urology Ministry of Health and Welfare Pingtung Hospital Pingtung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Doctoral Degree Program in Toxicology College of Pharmacy Kaohsiung Medical University Kaohsiung Taiwan Department of Biochemistry School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Research Center for Environmental Medicine Kaohsiung Medical University Kaohsiung Taiwan

Neurotrophic receptor tyrosine kinase 3 (NTRK3) has pleiotropic functions: it acts not only as an oncogene in breast and gastric cancers but also as a dependence receptor in tumor suppressor genes in colon cancer and neuroblastomas. However, the role of NTRK3 in upper tract urothelial carcinoma (UTUC) is not well documented. This study investigated the association between NTRK3 expression and outcomes in UTUC patients and validated the results in tests on UTUC cell lines. A total of 118 UTUC cancer tissue samples were examined to evaluate the expression of NTRK3. Survival curves were generated using Kaplan–Meier estimates, and Cox regression models were used for investigating survival outcomes. Higher NTRK3 expression was correlated with worse progression-free survival, cancer-specific survival, and overall survival. Moreover, the results of an Ingenuity Pathway Analysis suggested that NTRK3 may interact with the PI3K-AKT-mTOR signaling pathway to promote cancer. NTRK3 downregulation in BFTC909 cells through shRNA reduced cellular migration, invasion, and activity in the AKT-mTOR pathway. Furthermore, the overexpression of NTRK3 in UM-UC-14 cells promoted AKT-mTOR pathway activity, cellular migration, and cell invasion. From these observations, we concluded that NTRK3 may contribute to aggressive behaviors in UTUC by facilitating cell migration and invasion through its interaction with the AKT-mTOR pathway and the expression of NTRK3 is a potential predictor of clinical outcomes in cases of UTUC. © 2024 The Authors. The Kaohsiung Journal of Medical sciences published by John Wiley & Sons Australia, Ltd on behalf of Kaohsiung Medical University.

关键词： AKT-mTOR pathway neurotrophic receptor tyrosine kinase 3 (NTRK3) prognosis tropomyosin-related kinase (TrK) upper tract urothelial carcinoma (UTUC)

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Pore Structure and Permeability Characterization of Highrank Coal Reservoirs: A Case of the Bide-Santang Basin, Western Guizhou, South China

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Acta Geologica Sinica(English Edition) 2020年第2期94卷 243-252页

作者： GUO Chen QIN Yong MA Dongmin XIA Yucheng BAO Yuan CHEN Yue LU Lingling College of Geology and Environment Xi'an University of Science and TechnologyXi'an 710054China Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation Xi’an 710054China Post-doctoral Research Station of Geological Resources and Geological Engineering Xi'an University of Science and TechnologyXi'an 710054China Geological Research Institute for Coal Green Mining Xi'an Universityof Science and Technology Xi'an 710054China Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process Ministry of EducationChina University of Mining and TechnologyXuzhou 221116China

The methods of nuclear magnetic resonance(NMR)spectroscopy,mercury injection porosimetry(MIP),and gas-water relative permeability(GWRP)were used to reveal the pore structure and permeability characteristics of high-rank coal reservoirs in the Bide-Santang basin,western Guizhou,South China,to provide guidance for coalbed methane(CBM)exploration and exploitation and obtain direct insights for the development of CBM *** results indicate that the coal reservoirs in the study area are characterized by well-developed adsorption pores and poorly developed seepage *** bimodal NMR transverse relaxation time(T2)spectra and the mutation in the fractal characteristic of the MIP pore volume indicate poor connectivity between the adsorption pores and the seepage *** a result,the effective porosity is relatively low,with an average of 1.70%.The irreducible water saturation of the coal reservoir is relatively high,with an average of 66%,leading to a low gas relative permeability under irreducible water *** is the main reason for the low recovery of high-rank CBM reservoirs,and effective enhanced CBM recovery technology urgently is *** a nondestructive and less time-consuming technique,the NMR is a promising method to quantitatively characterize the pores and fractures of coals.

关键词： coalbed methane reservoir characterization pore structure relative permeability nuclear magnetic resonance

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Common Association Rules for Dispersed Information Systems

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Procedia computer science 2022年 207卷 4613-4620页

作者： Mikhail Moshkov Beata Zielosko Evans Teiko Tetteh Computer Electrical and Mathematical Sciences and Engineering Division and Computational Bioscience Research Center King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Institute of Computer Science University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Doctoral School University of Silesia in Katowice Bankowa 14 40-007 Katowice Poland

Association rules are popular form for knowledge discovery domain. They are used for finding interesting relationships and patterns hidden in large data sets and in the area of associative classification, where usually rules with one item in the right-hand side are considered. There are many different approaches and algorithms for mining association rules. One of the most popular group are methods which are based on mining frequent itemsets, usually applied for data in transaction format. Such data can be transformed to binary information system which corresponds to matrix data format. Technological development means that we are dealing with an increasing amount of data that can be heterogeneous, taking into account their format and location. In this paper, we assume that dispersed data is represented by a finite set S of information systems with equal sets of attributes. We discuss one of the possible ways to the study association rules common to all information systems from the set S : building a joint information system for which the set of true association rules that are realizable for a given row r and have given attribute f on the right-hand side coincides with the set of association rules that are true for all information systems from S , are realizable for the row r , and have the attribute f on the right-hand side. We show how to build a joint information system in a polynomial time. When we build such an information system, we can apply to it various association rule learning algorithms.

关键词： Dispersed data Set of information systems Association rules

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SIMS U-Pb Zircon Geochronological and Carbon Isotope Chemostratigraphic Constraints on the Ediacaran-Cambrian Boundary Succession in the Three Gorges Area, South China

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Journal of Earth science 2020年第1期31卷 69-78页

作者： Taiyu Huangc Daizhao Chen Yi Ding Xiqiang Zhou Gongjing Zhang Key Laboratory of Petroleum Resources Research Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Institution of Earth Science Chinese Academy of SciencesBeijing 100029China Post-Doctoral Research Station of Geological Resource and Geological Engineering Chengdu University of TechnologyChengdu 610059China

The Ediacaran–Cambrian succession in South China records dramatic biological,oceanic and geochemical changes,but it is not well constrained *** study reports a new SIMS U-Pb date of 543.4±3.5 Ma(MSWD=1.2)from a tuffaceous layer in the Zhoujiaao Section,and carbonate C-O isotopes in both Zhoujiaao and Sixi sections,Three Gorges *** tuffaceous layer is present in the Upper Dengying Formation(i.e.,the Baimatuo Member)which is characterized by a stableδ13Ccarb plateau and the beginning of a negativeδ13Ccarb shift near its upper *** accordance with the existing biostratigraphic and chemostratigraphic data,this new date corroborates that the upper boundary of the Dengying Formation in South China is approximately equivalent to the Ediacaran-Cambrian boundary(ca.541 Ma).This age also provides the minimum age of the last appearance of the Shibantan biota in the Three Gorges area,indicating that the terminal Ediacaran index fossils(e.g.,Cloudina,Sinotubulites)are not reliable stratigraphic markers for further subdivision of the uppermost Ediacaran.

关键词： U-Pb dating carbon isotope chemostratigraphy Dengying Formation Ediacaran-Cambrian boundary Three Gorges area Shibantan biota geochemistry

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Detection of Breast Cancer using Quasi-Cross Focused Transformer-Guided Quantum Network with Lotus Optimization

Detection of Breast Cancer using Quasi-Cross Focused Transfo...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Munirathnam, Rajesh Shukla, Vishal Devaraj, Deepashree Thacker, Chintan Nandekar, Ujwal Natrayan, L. TechnoGen Inc. Data & Analytics Division Hillsborough NJ United States Ramdeobaba College of Engineering and Management Department of Mechanical Engineering Maharashtra Nagpur India RV College of Engineering Electronics and Instrumentation Engineering Karnataka Bengaluru India Parul University Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Post Limda Gujarat India Symbiosis International (Deemed University) Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Pune Pune India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331512088

Patient outcomes improve if breast cancer is discovered early. Although many deep learning methods have been studied for breast cancer identification, the efficacy of many has been limited. It presents a new method encompassing the Quasi Cross Focused Transformer Guided Quantum Network with Lotus Optimization (QCFT-GQNet-LO) which is novel to resolve these issues. This technique uses Quasi-Cross Filtering (QCF) for image pre-processing, which improves image quality by reducing noise and artefacts. It was evaluated on histopathological images from the MIAS (Mammographic Image Analysis Society) dataset. A Focused Transformer with U-Net (FTUNet) is utilized to precisely segment malignant regions, guaranteeing precise identification of crucial places. The Self-Guided Quantum Network with Triple Attention (SGQNet-TA) controls classification and feature extraction, significantly improving the model's diagnostic abilities. The whole system is optimized using the Lotus Effect Optimization Algorithm (LEOA) to improve the efficiency and robustness. The QCFT-GQNetLO approach is shown to outperform other approaches, with experimentally demonstrated accuracy of 99.9%, and recall rate of 99.8% on the MIAS dataset. This significantly improves diagnostic precision of lesion type categorization and early breast malignancy diagnosis in histopathological images. © 2025 IEEE.

关键词： Breast cancer Focused Transformer Hhistopathological images Lotus Effect Optimization Quasi-Cross Filtering

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Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems

Neuromorphic-Driven Agentic AI for Autonomous Decision-Makin...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Manjunath Kamath K Samata Mehta.S Akshaya H. L Shilpashree N Girish Jadhav Abhijit Mitra Department of Cyber Security NMAM Institute of Technology NITTE (Deemed to be University) Karkala India Department Of CSE (IOT & Cyber Security including Block Chain Technology) Brindavan College of Engineering Bengaluru India Department of Computer Science Sarada Vilas College Mysuru India Department Of Electrical Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cognitive frameworks, Agentic AI goes beyond traditional rule-based and reactive models, enabling autonomous systems to make informed decisions, anticipate future states, and learn from experience. This paper explores the theoretical foundations and practical applications of Agentic AI, highlighting its potential to transform a variety of fields, including robotics, autonomous driving, finance, and healthcare. Through a detailed review of state-of-the-art research, we illustrate how cognitive architectures such as ACT-R and Soar, combined with advanced RL techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), contribute to the development of AI agents with human-like reasoning and decision-making capabilities. Experimental results demonstrate that Agentic AI significantly outperforms conventional AI approaches in terms of adaptability, learning efficiency, and decision accuracy. The findings suggest that Agentic AI offers a robust framework for creating intelligent systems capable of complex problem-solving, long-term planning, and proactive behavior, paving the way for the next generation of AI-driven applications.

关键词： Adaptation models Adaptive systems Decision making Finance Medical services Reinforcement learning Cognition Autonomous vehicles Robots Optimization

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Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

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Gondwana research 2024年

作者： Kumar Lilhore, Umesh Simaiya, Sarita Dalal, Surjeet Radulescu, Magdalena Balsalobre-Lorente, Daniel Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Amity University Gurugram Haryana India Institute of Doctoral and Post-Doctoral Studies University Lucian Blaga of Sibiu Sibiu Romania UNEC Research Methods Application Center Azerbaijan State University of Economics (UNEC) Baku 1001 Istiqlaliyyat Str. 6 Azerbaijan Department of Applied Economics I University Castilla-La Mancha Spain Department of Management Faculty of Economics and Management Czech University of Life Sciences Prague Prague 16500 Czech Republic Economic Research Center (WCERC) Western Caspian University Baku Azerbaijan

The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the ’TrashNet Waste’ database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models’ trash classification performance and provided specific viewpoints. The results explain each technique's pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method's excellent performance suggests its seamless integration into practical waste management solutions. © 2024 International Association for Gondwana research

关键词： Convolutional neural network (CNN) Deep learning Smart Waste Management Transfer Learning Waste classification

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Cryo-EM structure of the plant 26S proteasome

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Plant Communications 2022年第3期3卷 116-126页

作者： Susanne Kandolf Irina Grishkovskaya Katarina Belacic Derek L.Bolhuis Sascha Amann Brent Foster Richard Imre Karl Mechtler Alexander Schleiffer Hemant D.Tagare Ellen D.Zhong Anton Meinhart Nicholas G.Brown David Haselbach Research Institute of Molecular Pathology(IMP) Vienna BioCenter(VBC)Campus-Vienna-BioCenter 11030 ViennaAustria Department of Radiology and Biomedical Imaging Yale UniversityNew HavenCT 06510USA Department of Pharmacology and Lineberger Comprehensive Cancer Center University of North Carolina School of MedicineChapel HillNC 27599USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA Vienna BioCenter PhD Program Doctoral School of the University at Vienna and Medical University of ViennaVienna BioCenter(VBC)ViennaAustria Institute of Physical Chemistry University of FreiburgAlbertstraße 21Freiburg 79104Germany CIBSS Centre for Integrative Biological Signalling Studies University of FreiburgFreiburgGermany

Targeted proteolysis is a hallmark of *** is especially important in long-lived cells that can be found in higher eukaryotes,like *** task is mainly fulfilled by the ubiquitin–proteasome ***,proteolysis by the 26S proteasome is vital to development,immunity,and cell *** the yeast and animal proteasomes are well characterized,there is only limited information on the plant *** determined the first plant 26S proteasome structure from Spinacia oleracea by single-particle electron cryogenic microscopy at an overall resolution of 3.3 A°.We found an almost identical overall architecture of the spinach proteasome compared with the known structures from mammals and ***,we noticed a structural difference in the proteolytic active b1 ***,we uncovered an unseen compression state by characterizing the proteasome’s conformational *** suspect that this new conformation of the 20S core protease,in correlation with a partial opening of the unoccupied gate,may contribute to peptide release after *** data provide a structural basis for the plant proteasome,which is crucial for further studies.

关键词： 26S proteasome spinach UPS cryo-EM conformational landscape

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A Robust Subtree-Based Authentication Scheme for TMIS Using Chaotic Hash Function

A Robust Subtree-Based Authentication Scheme for TMIS Using ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Chandrashekhar Meshram Mohammad S. Obaidat Balqies Sadoun Agbotiname Lucky Imoize Vishesh P. Gaikwad Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post Graduate College College of Chhindwara University Betul M.P. India King Abdullah II School of Information Technology University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University SRM Nagar Kattankulathur TN India Amity University Noida UP India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

The widespread availability of broadband telecommunication and the relatively affordable cost of vital signs monitoring devices have enabled telemedicine to bring its benefits directly to patients' homes. In contemporary healthcare, Telecare Medicine Information System (TMIS) facilitates remote disease diagnosis, treatment, and healthcare delivery through the capabilities of wireless communication and information technology. To protect patient confidentiality, TMIS must develop robust authentication systems since it operates on the public Internet, which exposes its operations to security vulnerabilities. To ensure patient privacy, we present a novel Subtree-based Authentication technique for TMIS. An evaluation of the proposed scheme reveals that it is resistant to known potential attacks, and offers more security effectiveness and resource efficiency than existing schemes, making it an excellent solution for enhancing the security of TMIS implementations.

关键词： Privacy Chaotic communication Telemedicine Authentication Passwords Internet Security

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