LEADER 04497nam 22006253 450 001 9910968350503321 005 20240102235744.0 010 $a9780813179445 010 $a0813179440 010 $a9780813179452 010 $a0813179459 010 $a9780813179469 010 $a0813179467 035 $a(MiAaPQ)EBC30373951 035 $a(Au-PeEL)EBL30373951 035 $a(MiAaPQ)EBC6177132 035 $a(CKB)27168691700041 035 $a(OCoLC)1151407756 035 $a(Perlego)1457667 035 $a(EXLCZ)9927168691700041 100 $a20230626d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRevolutionary Pairs $eMarx and Engels, Lenin and Trotsky, Gandhi and Nehru, Mao and Zhou, Castro and Guevara 205 $a1st ed. 210 1$aLexington :$cUniversity Press of Kentucky,$d2020. 210 4$d©2021. 215 $a1 online resource (279 pages) 311 08$aPrint version: Ceplair, Larry Revolutionary Pairs Lexington : University Press of Kentucky,c2020 327 $aIntro -- Half title -- Title -- Copyright -- Dedication -- Contents -- Introduction -- 1. Karl Marx (1818-1883) and Friedrich Engels (1820-1895) -- 2. Vladimir Ilyich Lenin (1870-1924) and Lev Davidovich Trotsky (1879-1940) -- 3. Mohandas K. Gandhi (1869-1948) and Jawaharlal Nehru (1889-1964) -- 4. Mao Zedong (1893-1976) and Zhou Enlai (1898-1976) -- 5. Fidel Castro (1926-2016) and Ernesto "Che" Guevara (1928-1967) -- Conclusion -- Acknowledgments -- Notes -- Bibliography -- Index. 330 $a"In the history of revolution, there are few figures more widely known than Karl Marx, who began the "working-class" revolution with his poignant criticism of capitalist economic systems. With the help of his close friend and colleague, writer Friedrich Engels, Marx's free-thinking spirit was inspired, and his writings were expanded. This friendship began one of the most significant social revolutions in modern history. Four of the most influential revolutions were led by pairs: V. I. Lenin and L. D. Trotsky (Russia); Mohandas K. Gandhi and Jawaharlal Nehru (India); Mao Zedong and Zhou Enlai (China); and Fidel Castro and Ernesto "Che" Guevara (Cuba). Marx and Engels, while the godfathers of three of those revolutions, participated in only one revolution, but did not, in their respective lifetimes, witness the success they had worked so hard to inspire. The members of each pair were completely dissimilar, save for their devotion to the cause. In Revolutionary Pairs, author Larry Ceplair tells the stories of five revolutionary struggles through the lens of these famous figures, examining their political relationships and personal histories to explain what led to the phenomenon of their radical companionships. While previous works on revolutionaries attempt to perform a psychoanalytic study of the pairs or individuals' behaviors, Ceplair takes a more practical approach, choosing instead to focus on the natural order of events and elements of personal history that the pairs shared. Some of these pairings were politically convenient, such as Lenin's contentious partnership with Trotsky during the Bolshevik revolution. Many were born of other factors, such as the mentorship between Gandhi and Nehru, or were simply a combination of respect and fear, which was the case for Zhou Enlai and Mao Zedong during the communist revolution in China. Ceplair's comparative exploration of these relationships sheds light on the complex nature of modern revolutionary history."--$cProvided by publisher. 606 $aRevolutionaries$y20th century$vBiography 606 $aRevolutionaries$zGermany$xHistory$y19th century 606 $aRevolutionaries$zSoviet Union$xHistory$y20th century 606 $aRevolutionaries$zIndia$xHistory$y20th century 606 $aRevolutionaries$zChina$xHistory$y20th century 606 $aRevolutionaries$zCuba$xHistory$y20th century 615 0$aRevolutionaries 615 0$aRevolutionaries$xHistory 615 0$aRevolutionaries$xHistory 615 0$aRevolutionaries$xHistory 615 0$aRevolutionaries$xHistory 615 0$aRevolutionaries$xHistory 676 $a322.420922 700 $aCeplair$b Larry$0618265 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910968350503321 996 $aRevolutionary Pairs$94345810 997 $aUNINA LEADER 06952nam 22006615 450 001 9910887875703321 005 20251225195120.0 010 $a3-031-72344-9 024 7 $a10.1007/978-3-031-72344-5 035 $a(MiAaPQ)EBC31680990 035 $a(Au-PeEL)EBL31680990 035 $a(CKB)35895627200041 035 $a(DE-He213)978-3-031-72344-5 035 $a(OCoLC)1457083027 035 $a(EXLCZ)9935895627200041 100 $a20240917d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Neural Networks and Machine Learning ? ICANN 2024 $e33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17?20, 2024, Proceedings, Part V /$fedited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (462 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15020 311 08$a3-031-72343-0 327 $a -- Graph Neural Networks. -- 3D Lattice Deformation Prediction with Hierarchical Graph Attention Networks. -- Beyond Homophily: Attributed Graph Anomaly Detection via Heterophily-aware Contrastive Learning Network. -- Boosting Attributed Graph Anomaly Detection via Negative Sample Awareness. -- CauchyGCN: Preserving Local Smoothness in Graph Convolutional Networks via a Cauchy-Based Message-Passing Scheme and Clustering Analysis. -- ComMGAE: Community Aware Masked Graph AutoEncoder. -- CTQW-GraphSAGE: Trainabel Continuous-Time Quantum Walk On Graph. -- Edged Weisfeiler-Lehman algorithm. -- Enhancing Fraud Detection via GNNs with Synthetic Fraud Node Generation and Integrated Structural Features. -- Graph-Guided Multi-View Text Classification: Advanced Solutions for Fast Inference. -- Invariant Graph Contrastive Learning for Mitigating Neighborhood Bias in Graph Neural Network based Recommender Systems. -- Key Substructure-Driven Backdoor Attacks on Graph Neural Networks. -- Missing Data Imputation via Neighbor Data Feature-enriched Neural Ordinary Differential Equations. -- Multi-graph Fusion and Virtual Node Enhanced Graph Neural Networks. -- STGNA: Spatial-Temporal Graph Convolutional Networks with Node Level Attention for Shortwave Communications Parameters Forecasting. -- Virtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Information Aggregation. -- Large Language Models. -- A Three-Phases-LORA Finetuned Hybrid LLM Integrated with Strong Prior Module in the Eduation Context. -- An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration. -- Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models. -- BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks. -- CSAFT: Continuous Semantic Augmentation Fine-Tuning for Legal Large Language Models. -- FashionGPT: A Large Vision-Language Model for Enhancing Fashion Understanding. -- Generative Chain-of-Thought for Zero-shot Cognitive Reasoning. -- Generic Joke Generation with Moral Constraints. -- Large Language Model Ranker with Graph Reasoning for Zero-Shot Recommendation. -- REM: A Ranking-based Automatic Evaluation Method for LLMs. -- Semantics-Preserved Distortion for Personal Privacy Protection in Information Management. -- Towards Minimal Edits in Automated Program Repair: A Hybrid Framework Integrating Graph Neural Networks and Large Language Models. -- Unveiling Vulnerabilities in Large Vision-Language Models: The SAVJ Jailbreak Approach. 330 $aThe ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17?20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15020 606 $aArtificial intelligence 606 $aComputers 606 $aApplication software 606 $aComputer networks 606 $aArtificial Intelligence 606 $aComputing Milieux 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aApplication software. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aComputing Milieux. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 676 $a006.3 700 $aWand$b Michael$01323454 701 $aMalinovská$b Kristína$01769610 701 $aSchmidhuber$b Ju?rgen$00 701 $aTetko$b Igor V$01769612 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910887875703321 996 $aArtificial Neural Networks and Machine Learning ? ICANN 2024$94241388 997 $aUNINA