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theory and applications of models of computation - 5th international conference, TAMC 2008, Proceedings

Theory and Applications of Models of Computation - 5th Inter...

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5th international conference on theory and applications of models of computation, TAMC 2008

ISBN: (纸本)3540792279

the proceedings contain 50 papers. the topics discussed include: on the complexity of measurement in classical physics;quantum walk based search algorithms;genome rearrangement algorithms for unsigned permutations with O(log n) singletons;on the complexity of the hidden subgroup problem;indistinguishability and first-order logic;derandomizing graph tests for homomorphism;definable filters in the structure of bounded turing reductions;distance constrained labelings of trees;a characterization of NCk by first order functional programs;the structure of detour degrees;Hamiltonicity of matching composition networks with conditional edge faults;local 7-coloring for planar subgraphs of unit disk graphs;generalized domination in degenerate graphs: a complete dichotomy of computational complexity;more on weak bisimilarity of normed basic parallel processes;extensions of embeddings in the computably enumerable degrees;and an improved parameterized algorithm for a generalized matching problem.

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theory and applications of models of computation - 10th international conference, TAMC 2013, Proceedings

Theory and Applications of Models of Computation - 10th Inte...

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10th international conference on theory and applications of models of computation, TAMC 2013

ISBN: (纸本)9783642382352

the proceedings contain 31 papers. the topics discussed include: throughput maximization for speed-scaling with agreeable deadlines;temperature aware online algorithms for minimizing flow time;modelling the power supply network - hardness and approximation;an algorithm for TSP in degree-3 graphs via circuit procedure and amortization on connectivity structure;minimax regret 1-sink location problems in dynamic path networks;a notion of a computational step for partial combinatory algebras;Turing machines can be efficiently simulated by the general purpose analog computer;on efficient constructions of short lists containing mostly Ramsey graphs;the algorithm for the two-sided scaffold filling problem;energy-efficient threshold circuits detecting global pattern in 1-dimentional arrays;resolving rooted triplet inconsistency by dissolving multigraphs;and on parameterized and kernelization algorithms for the hierarchical clustering problem.

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Perspective Analysis Toward Deep Learning models for Wireless Capsule Endoscopy Images for Gastrointestinal Tract 4th

Perspective Analysis Toward Deep Learning Models for Wireles...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Sharma, Rakesh Lamba, C.S. Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819751990

Analysis of medical image is new technique to solve the medical problems by examine the images that has been generated through some detailed clinical practice. For better clinical diagnosis, the goal is to extract information in a way that is both affective and efficient. Abdominal bleeding, ulcers, tumors, celiac disease, ulcerative colitis, and other gastrointestinal disorders are difficult to diagnose due to the uncertainty of accessing a volute condition in the human body. WCE is a non-invasive, painless, and patient-controlled gastrointestinal tract examination. Although using deep learning models to detect problems in WCE photos enhances detection accuracy, model training necessitates a large volume of labeled data. these deep models, on the other hand, are difficult to describe and do not incorporate expert input into the model decision-making process. Deep learning-based machine learning breakthroughs have improved the ability to categories, detect, and quantify patterns in medical images. Deep learning approaches are quickly being applied in medical imaging to increase performance in a variety of medical applications. Due to recent advancements in the field of biomedical engineering, medical image analysis has become one of the most important research and development areas. the use of machine learning algorithms for the processing of medical images is one of the reasons for this progress. Deep learning has been successfully applied as a machine learning technology, allowing a neural network to learn information automatically. this contrasts with methods that rely on traditional handcrafted elements. the selection and computation of these characteristics are a difficult task. Deep convolution networks, a type of deep learning approach, are widely employed in medical image processing. With these considerations in mind, the purpose of this research is to see if employing semi-supervised deep learning models over supervised deep learning methods for detecting a

关键词： Endoscopy

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Investigating Fine-Tuning of Large Language models for Text Summarisation 12th

Investigating Fine-Tuning of Large Language Models for Text ...

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12th international conference on Frontiers of Intelligent Computing: theory and applications, FICTA 2024

作者： Khaliq, Usama Patel, Preeti London Metropolitan University Holloway Road London166-220 United Kingdom

ISBN: (纸本)9789819601387

this research aim was to investigate whether fine-tuning medium-sized Large Language models on text summarisation across different fields, from news to scientific papers and governmental reports, would improve the quality of the summaries generated in the given field. the performance was evaluated quantitatively using ROUGE and BLEU scores and qualitatively through human analysis and judgement. However, fine-tuning led to minimal performance improvements compared to the untrained models. the generated summaries were generally accurate;surprisingly, one model introduces false information. In contrast, bigger models, like GPT-4, with its 1.7 trillion parameters, generated near-perfect summaries without having been trained on a specific dataset. the limited performance increase from the fine-tuned models was likely due to small datasets and medium-sized LLMs. Deploying and fine-tuning these models is computationally expensive, requiring careful planning and optimisation. Overall, this project provided insights into transformer fine-tuning, capabilities and limitations that can guide future work to adapt these powerful models for real-world applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BART Large Language models (LLMs) PEGASUS ProphetNet ROUGE BLEU Text summarisation

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Modified Baum-Welch Algorithm for Joint Blind Channel Estimation and Turbo Equalization 14

Modified Baum-Welch Algorithm for Joint Blind Channel Estima...

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14th international ITG conference on Systems, Communications and Coding, SCC 2025

作者： Chen, Chin-Hung Karanov, Boris Nikoloska, Ivana Van Houtum, Wim Wu, Yan Alvarado, Alex Karlsruhe Institute of Technology Communications Engineering Lab Germany Eindhoven University of Technology Information and Communication Theory Lab Netherlands NXP Semiconductors Eindhoven Netherlands

ISBN: (纸本)9798331522896

Blind estimation of intersymbol interference channels based on the Baum-Welch (BW) algorithm, a specific implementation of the expectation-maximization (EM) algorithm for training hidden Markov models, is robust and does not require labeled data. However, it is known for its extensive computation cost, slow convergence, and frequently converges to a local maximum. In this paper, we modified the trellis structure of the BW algorithm by associating the channel parameters with two consecutive states. this modification enables us to reduce the number of required states by half while maintaining the same performance. Moreover, to improve the convergence rate and the estimation performance, we construct a joint turbo-BW-equalization system by exploiting the extrinsic information produced by the turbo decoder to refine the BW-based estimator at each EM iteration. Our experiments demonstrate that the joint system achieves convergence in 10 EM iterations, which is 8 iterations less than a separate system design for a signal-to-noise ratio (SNR) of 4 dB. Additionally, the joint system provides improved estimation accuracy with a mean square error (MSE) of 10-4 for an SNR of 6 dB. We also identify scenarios where a joint design is not preferable, especially when the channel is noisy (e.g., SNR=2 dB) and the decoder cannot provide reliable extrinsic information for a BW-based estimator. © 2025 IEEE.

关键词： Hidden Markov models

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Optimization of Resource Allocation and Distribution in Industrial Supply Chains and Logistics Networks Using Genetic Algorithm and Game theory 5th

Optimization of Resource Allocation and Distribution in Indu...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Giri, Chandadevi Harale, Nitin Univ Boras Allegatan 1 S-50332 Boras Sweden

ISBN: (纸本)9783031807749;9783031807756

Efficient resource allocation and distribution are essential for optimizing industrial supply chains and logistics networks. this paper presents a novel hybrid approach that combines genetic algorithm (GA) with game-theoretic analysis to tackle the complex, multi-objective challenges inherent in these systems. the proposed framework focuses on minimizing transportation costs and carbon emissions while optimizing routing from source locations through intermediate facilities to final demand points. the genetic algorithm effectively navigates the search space to identify near-optimal solutions, while the game-theoretic analysis models strategic interactions among stakeholders, promoting stable and equitable resource allocation. the proposed approach exhibits advantages as it enhances cost efficiency and reduces emissions. the results provide the practical implications for improving the efficiency and sustainability of logistics operations across various industries. this study contributes to the advancement of resilient and adaptive supply chains by offering a comprehensive optimization framework that leverages the strengths of both genetic algorithms and game theory.

关键词： Resource allocation Distribution Logistics network optimization Genetic algorithm Game theory

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Controlling the Mutation in Large Language models for the Efficient Evolution of Algorithms 28th

Controlling the Mutation in Large Language Models for the...

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28th European conference on applications of Evolutionary computation, Evoapplications 2025, held as part of EvoStar 2025

作者： Yin, Haoran Kononova, Anna V. Bäck, thomas van Stein, Niki LIACS Leiden University Leiden Netherlands

ISBN: (纸本)9783031900648

the integration of Large Language models (LLMs) with evolutionary computation has introduced a promising paradigm for automating the design of metaheuristic algorithms. However, existing frameworks, such as the Large Language Model Evolutionary Algorithm (LLaMEA), often lack precise control over mutation mechanisms, leading to inefficiencies in solution space exploration and potentially suboptimal convergence. this paper introduces a novel approach to mutation control within LLM-driven evolutionary frameworks, inspired by theory of genetic algorithms. Specifically, we propose dynamic mutation prompts that adaptively regulate mutation rates, leveraging a heavy-tailed power-law distribution to balance exploration and exploitation. Experiments using GPT-3.5-turbo and GPT-4o models demonstrate that the former fails to adhere to the specific mutation instructions. At the same time, the latter is able to control its mutation rate under the instruction of well-constructed mutation prompts. Further experiments show that the introduction of these dynamic rates can improve the convergence speed and adaptability of LLaMEA when using GPT-4o. this work sets a starting point for better controlled LLM-based mutations in code optimization tasks, paving the way for further advancements in automated metaheuristic design. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cellular automata

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Accelerating Training of Large Neural models by Gradient-Based Growth Learning 29th

Accelerating Training of Large Neural Models by Gradient-Bas...

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29th international conference on Database Systems for Advanced applications (DASFAA)

作者： Jiang, Haowei Yu, Jianxing Zheng, Libin Zhu, Huaijie Liu, Wei Yin, Jian Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai Peoples R China Sun Yat Sen Univ Key Lab Sustainable Tourism Smart Assessment Tech Minist Culture & Tourism China Zhuhai Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

ISBN: (纸本)9789819757787;9789819757794

With the development of large neural models, the scale of their parameters is increasing rapidly. the amount of their training computation increases almost exponentially, leading to high costs. To address the problem, this paper proposes a new accelerated method called Grad-Grow (Gradient-based Growth Learning) to improve training efficiency and obtain competitive performance. this method adopts the local-to-global learning strategy, which first optimizes a sub-model and then growingly trains larger sub-models step-by-step. Eventually, the whole model is learned. We design the growth strategy from the perspective of progressive optimization and use gradient signals to guide the growth direction. With this growth learning way, the small sub-model can be trained quickly and provides a better initialization to learn the larger sub-model in the next stage. that can boost convergence and reduce computational costs. Extensive experiments are conducted on two tasks' datasets with different network architectures. the results verify the effectiveness of our approach, with 30% training acceleration on average.

关键词： Training acceleration Growth learning Large neural models

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Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine 11

Advancing Real-Time Land Cover Classification for Biomass De...

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11th international conference on Geographical Information Systems theory, applications and Management, GISTAM 2025

作者： Abudu, Dan Bastin, Lucy Chong, Katie Röder, Mirjam Energy and Bioproducts Research Institute Aston University BirminghamB4 7ET United Kingdom School of Computer Science and Digital Technologies Aston University BirminghamB4 7ET United Kingdom

ISBN: (纸本)9789897587412

Addressing climate change requires timely and accurate biomass and carbon stocks information. Traditional biomass estimation techniques rely on infrequent ground surveys and manual processing, limiting their scalability. this study proposes a novel framework that advances land cover classification to estimate biomass and carbon stocks using machine learning algorithms in Google Earth Engine. By integrating remote sensing data, machine learning algorithms, and allometric models, the framework automates above-ground biomass (ABG) and below-ground biomass (BGB) calculations, facilitating large-scale carbon stock assessments. the methodology leverages Landsat imagery, alongside derived Normalized Difference Vegetation Indices, to classify seven land cover types and estimate biomass. Equations are applied to derive AGB, with BGB calculated as a fraction of AGB. Carbon stock is estimated using a standard conversion factor of 0.47. Real-time processing capabilities of GEE ensure continuous monitoring and updates, enhancing accuracy and scalability. Findings demonstrate the potential for real-time biomass mapping and the identification of carbon-dense regions. the proposed approach is vital for sustainable land practices, carbon accounting, and forest conservation initiatives, to provide policymakers with accurate, real-time data, that supports climate mitigation efforts and contribute to realizing the Sustainable Development Goals 13 and 15. © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Global warming

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Systemic thinking in IT Management of the Future: Where are the Benefits? 19thth

Systemic Thinking in IT Management of the Future: Where are ...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

作者： Brenner, Walter Schneider, Ralf Brenner, Barbara Institute of Information Management Institute of Information Systems and Digital Business St. Gallen Switzerland Allianz SE Munich Germany

ISBN: (纸本)9783031829598

the digitalization of the economy and society continues unabated. Generative artificial intelligence, especially ChatGPT, has triggered a new wave of digitalization since its availability in November 2022. In addition to new technologies such as generative AI, companies and their IT function are still busy with the transformation of applications and IT infrastructure into the cloud, the expansion of capabilities in data analytics, the agilization of application development, new regulatory requirements, the increase in IT security and the shortage of skilled workers. Furthermore, many companies are developing new digital business models that place new demands on the IT function. Social developments, such as sustainability and diversity, are also leading to new challenges for the IT function. the pace of change in companies is also accelerating. the complexity that IT management must deal with has grown in recent years and continues to grow at a rapid pace. Traditional IT management models and IT operating models, such as ITIL or COBIT, are not able to cope with these challenges. Against this backdrop, this contribution discusses whether and how systems thinking can be an integral part of future IT management models and IT operating models and can help deal with the increased complexity brought about by digitalization. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System theory

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