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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Wasi, Sefatul Alam, Saadia Binte Rahman, Rashedur Amin, M Ashraful Kobashi, Syoji Independent University Center for Computational & Data Sciences Department of Computer Science and Engineering Dhaka Bangladesh1299 Bangladesh University of Hyogo Graduate School of Engineering 2167 Shosha Hyogo Himeji Japan

ISBN: (纸本)9781665464161

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models

Methodological Insights in Detecting Subtle Semantic Shifts ...

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2023 Findings of the Association for computational Linguistics: EMNLP 2023

作者： Hoeken, Sanne Alaçam, Özge Fokkens, Antske Sommerauer, Pia Computational Linguistics Dept. of Linguistics Bielefeld University Germany Center for Information and Language Processing LMU Munich Germany Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9798891760615

In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction which relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show that methods using static models and our masked token prediction method can detect differences in connotation of politically loaded terms, whereas methods that rely on measuring the distance between contextualized representations are not providing clear signals, even in synthetic scenarios of extreme shifts. © 2023 Association for computational Linguistics.

关键词： Semantics

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Dimensionality and dynamics for next-generation artificial neural networks

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Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

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Exploring the impact of stratification on the dynamics of bioconvective thixotropic fluid conveying tiny particles and Cattaneo-Christov model:Thermal storage system application

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Propulsion and Power Research 2024年第3期13卷 416-432页

作者： T.Oreyeni M.D.Shamshuddin A.M.Obalalu A.Saeed Nehad Ali Shah Department of Physical Sciences Mathematics Programme UnitPrecious Cornerstone UniversityIbadanNigeria Department of Computer Science and Artificial Intelligence(Mathematics) SR UniversityWarangalTelangana State 506371India Department of Mathematics Augustine University Ilara EpeLagosNigeria Center of Excellence in Theoretical and Computational Science(TaCS-CoE) Faculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)126 Pracha Uthit Rd.BangModThung KhruBangkok 10140Thailand Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh 11623Saudi Arabia

Stratified thermal storage promotes energy sustainability by storing excess energy during times of low demand for later use making it possible to integrate renewable energy sources like solar and *** communication discusses the significance of triple stratifica-tion using the Cattaneo-Christov model in the bioconvectiveflow of thixotropicfluid coexisting with nanoparticles and gyrotactic *** Cattaneo-Christov heat and massflux is incorporated into thefluid model allowing more accurate prediction of heat and mass phenomena in thefluid *** governing partial differential equations that describefluidflow are parametrized to yield an ordinary differential equation system by adopting suitable *** series solutions are obtained by applying the homotopy analysis method(HAM).The effects of relevant parameters on the various profiles are revealed and accurately *** is observed that amplified thermal stratification lowers the temperature of ***,Brownian motion is used to illustrate the random movement of small particles suspended in liquids,and it is envisioned that the concentration distribution is significantly influenced by the Brownian motion of nanoparticles.

关键词： Cattaneo-Christov model Stratification Bioconvection Nanofluid

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Practical Phase Retrieval Using Double Deep Image Priors 21

Practical Phase Retrieval Using Double Deep Image Priors

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IS and T International Symposium on Electronic Imaging: 21st computational Imaging, COIMG 2023

作者： Zhuang, Zhong Yang, David Hofmann, Felix Barmherzig, David A. Sun, Ju Electrical & Computer Engineering University of Minnesota Minneapolis United States Department of Engineering Science University of Oxford Oxford United Kingdom Center for Computational Mathematics Flatiron Institute New York City United States Computer Science & Engineering University of Minnesota Minneapolis United States

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality. Our paper is also available at: https: // arxiv. org/ abs/ 2211. 00799 . © 2023, Society for Imaging science and Technology.

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AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy

AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Special...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pan, Rui Nguyen, Tuan Dung Arora, Hardik Accomazzi, Alberto Ghosal, Tirthankar Ting, Yuan-Sen University of Illinois Urbana-Champaign Department of Computer Science Urbana United States University of Pennsylvania Department of Computer and Information Science PhiladelphiaPA19104 United States Indian Institute of Technology Patna India Harvard University Center for Astrophysics Cambridge United States Oak Ridge National Laboratory National Center for Computational Sciences Oak RidgeTN United States The Ohio State University Department of Astronomy Columbus United States

ISBN: (纸本)9798350355543

Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy-focused benchmarks has hindered objective evaluation of these specialized LLM models. Leveraging a recent initiative to curate high-quality astronomical MCQs, this study aims to quantitatively assess specialized LLMs in astronomy. We find that the previously released AstroLLaMA series, based on LLaMA-2-7B, underperforms compared to the base model. We demonstrate that this performance degradation can be partially mitigated by utilizing high-quality data for continual pretraining, such as summarized text from arXiv. Despite the observed catastrophic forgetting in smaller models, our results indicate that continual pretraining on the 70B model can yield improvements. However, the current supervised fine-tuning dataset still constrains the performance of instruct models. In conjunction with this study, we introduce a new set of models, AstroLLaMA-3-8B and AstroLLaMA-2-70B, building upon the previous AstroLLaMA series. © 2024 IEEE.

关键词： astronomy-tailored LLMs continual pretraining quantitative evaluation

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Studying the Well-Posedness of the Boundary Value Problem for a System of Riccati Type Equations Based on the Concept of Mean Field Games

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Moscow University computational Mathematics and Cybernetics 2025年第2期49卷 150-164页

作者： Fedorov, F. A. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Computer Science and Control Federal Research Center Russian Academy of Sciences Moscow Russia

When describing the group behavior of high-frequency traders, there arises a boundary value problem based on the concept of mean field games. The system consists of two coupled partial differential equations: the Hamilton–Jacobi–Bellman equation which describes the evolution of the average payoff function in backward time and the Kolmogorov–Fokker–Planck equation which describes the evolution of the distribution density of traders in forward time. The system is inherently ill-conditioned due to the turnpike effect. Under certain assumptions, it is possible to perform reduction to a system of Riccati equations; however, the question of well-posedness of the reduced problem remains open. This work investigates this question, namely, the conditions for the existence and uniqueness of the solution to the boundary value problem depending on the model parameters.

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Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19 25

Comparative Analysis of Laboratory and Radiation Methods of ...

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25th International Conference on Digital Signal Processing and its Applications, DSPA 2023

作者： Kents, Angelica Zotin, Aleksandr Simonov, Konstantin Center for Radiation Diagnostics Federal Siberian Research and Clinical Center of Fmba of Russia X-ray Department Krasnoyarsk Russia Reshetnev Siberian State University of Science and Technology Candidate of Technical Sciences Department of Informatics and Computer Techniques Krasnoyarsk Russia Institute of Computational Modeling Sb Ras Doctor of Technical Sciences Department of Computational Mechanics of Deformable Media Krasnoyarsk Russia

ISBN: (纸本)9798350337235

The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.

关键词： COVID-19

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Towards Automatic Dataset Discovery From Scientific Publications

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IEEE Access 2025年

作者： Kumar, Sandeep Ghosal, Tirthankar Ekbal, Asif Indian Institute of Technology Department of Computer Science and Engineering Patna India Oak Ridge National Laboratory National Center for Computational Sciences United States

Datasets are a crucial artifact in research, and there is always a high demand for good datasets. Due to rapid scientific progress and exponential growth in the scientific literature, there has been a proportionate increase in the number of datasets. However, many datasets and research studies are left unexplored and under-utilized as these are not discoverable easily, leading to duplicate efforts. Building good datasets is costly in terms of time, money, and human effort. Hence, automated tools to facilitate the search and discovery of datasets are crucial to the scientific community. In this work, we investigate a deep neural network-based architecture to automate the dataset discovery from scientific publications. In this paper, we perform two tasks namely dataset mention extraction and entity linking. Our method outperforms the earlier ones and achieves an F1 score of 56.24 in extracting dataset mentions from research papers on a popular corpus of social science publications. Our approach also outperforms the prior research and achieves a precision score of 88.63 in linking research papers to a dataset knowledge base for another popular corpus of social science publications. We hope that this system will further promote data sharing, offset the researchers' workload in identifying the right dataset and increase the reusability of datasets. © 2013 IEEE.

关键词： Discovering Datasets Extracting Dataset References Mining Publications Natural Language Processing

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