Optical Memory and Neural Networks最新文献_第3页

Solution of an Inverse Problem of Optical Spectroscopy Using Kolmogorov-Arnold Networks 利用Kolmogorov-Arnold网络求解光谱学反演问题

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700747

G. Kupriyanov, I. Isaev, K. Laptinskiy, T. Dolenko, S. Dolenko

引用次数: 0

Mastering Long-Context Multi-Task Reasoning with Transformers and Recurrent Memory 掌握长上下文多任务推理与变形和循环记忆

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700735

A. Bulatov, Y. Kuratov, M. Burtsev

引用次数: 0

Sea-SHINE: Semantic-Aware 3D Neural Mapping Using Implicit Representations Sea-SHINE：使用隐式表示的语义感知三维神经映射

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700711

V. Bezuglyj, D. A. Yudin

{"title":"Sea-SHINE: Semantic-Aware 3D Neural Mapping Using Implicit Representations","authors":"V. Bezuglyj, D. A. Yudin","doi":"10.3103/S1060992X24700711","DOIUrl":"10.3103/S1060992X24700711","url":null,"abstract":"Semantic-aware mapping is crucial for advancing robotic navigation and interaction within complex environments. Traditional 3D mapping techniques primarily capture geometric details, missing the semantic richness necessary for autonomous systems to understand their surroundings comprehensively. This paper presents Sea-SHINE, a novel approach that integrates semantic information within a neural implicit mapping framework for large-scale environments. Our method enhances the utility and navigational relevance of 3D maps by embedding semantic awareness into the mapping process, allowing robots to recognize, understand, and reconstruct environments effectively. The proposed system leverages dual decoders and a semantic awareness module, which utilizes Feature-wise Linear Modulation (FiLM) to condition mapping on semantic labels. Extensive experiments on datasets such as SemanticKITTI, KITTI-360, and ITLP-Campus demonstrate significant improvements in map precision and recall, particularly in recognizing crucial objects like road signs. Our implementation bridges the gap between geometric accuracy and semantic understanding, fostering a deeper interaction between robots and their operational environments. The code is publicly available at https://github.com/VitalyyBezuglyj/Sea-SHINE.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S445 - S456"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Streptococci Recognition in Microscope Images Using Taxonomy-based Visual Features 基于分类的视觉特征在显微镜图像中的链球菌识别

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700693

A. Samarin, A. Savelev, A. Toropov, A. Nazarenko, A. Motyko, E. Kotenko, A. Dozorceva, A. Dzestelova, E. Mikhailova, V. Malykh

引用次数: 0

Classifier of Motor EEG Images for Real Time BCI 用于实时脑机接口的运动脑电信号分类器

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700772

L. A. Stankevich, S. A. Kolesov

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On the Role of the Transcendental in the Life of Artificial Beings 论先验在人工生命中的作用

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700760

V. B. Kotov, Z. B. Sokhova

{"title":"On the Role of the Transcendental in the Life of Artificial Beings","authors":"V. B. Kotov, Z. B. Sokhova","doi":"10.3103/S1060992X24700760","DOIUrl":"10.3103/S1060992X24700760","url":null,"abstract":"The paper considers the effect of transcendental factors on behavior of artificial beings, agents and robots. The boundary between the rational and transcendental relies on the type of an individual or more specifically, his sensory and intellectual abilities. For agents and simple robots all, except for tangible elements of the environment and manipulations with them, is transcendental. Such transcendental factors as environmental changes and algorithm modifications determined by the programmer, supervisor, or operator have significant effects on agents and communities of agents. Hardware malfunctions (transcendental events from an agent’s point of view) can be crucial for agents. Agents can take advantages from transcendental effects if the programmer realizes a feedback. Generation of a mental copy of an agent for making new agents allows continuity and social development. For intellectual robots the boundaries of the transcendental move away because of their ability to accommodate to new environment. However, in most cases the role of the transcendental even increases with the improvement of robots because there are consciousness and growth of communication possibilities. The consciousness changes as a result of learning of transcendental information, making robots change the behavior. Robot’s communication abilities enable transcendental (along with rational) information to be received from the data base in any amount. For people living together with intelligent robots, this sort of communication can become a tool for introducing human culture in the community of robots. This in turn would result in humanization of robots and establishment of good relations between robots and human beings.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S490 - S496"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Improved Robust Adversarial Model against Evasion Attacks on Intrusion Detection Systems 针对入侵检测系统规避攻击的改进鲁棒对抗模型

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700681

R. N. Anaedevha, A. G. Trofimov

引用次数: 0

Adaptive Curriculum Learning: Optimizing Reinforcement Learning through Dynamic Task Sequencing 适应性课程学习：通过动态任务排序优化强化学习

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X2470070X

M. Nesterova, A. Skrynnik, A. Panov

{"title":"Adaptive Curriculum Learning: Optimizing Reinforcement Learning through Dynamic Task Sequencing","authors":"M. Nesterova, A. Skrynnik, A. Panov","doi":"10.3103/S1060992X2470070X","DOIUrl":"10.3103/S1060992X2470070X","url":null,"abstract":"Curriculum learning in reinforcement learning utilizes a strategy that sequences simpler tasks in order to optimize the learning process for more complex problems. Typically, existing methods are categorized into two distinct approaches: one that develops a teacher (a curriculum strategy) policy concurrently with a student (a learning agent) policy, and another that utilizes selective sampling based on the student policy’s experiences across a task distribution. The main issue with the first approach is the substantial computational demand, as it requires simultaneous training of both the low-level (student) and high-level (teacher) reinforcement learning policies. On the other hand, methods based on selective sampling presuppose that the agent is capable of maximizing reward accumulation across all tasks, which may lead to complications when the primary mission is to master a specific target task. This makes those models less effective in scenarios requiring focused learning. Our research addresses a particular scenario where a teacher needs to train a new student in a new short episode. This constraint compels the teacher to rapidly master the curriculum planning by identifying the most appropriate tasks. We evaluated our framework across several complex scenarios, including a partially observable grid-world navigation environment, and in procedurally generated open-world environment Crafter.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S435 - S444"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Sentiment Analysis and Text Segmentation for Chinese Language 中文可解释情感分析与文本切分

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700759

Hou Zhenghao, A. Kolonin

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Deep Learning for Single Photo 3D Reconstruction of Cultural Heritage 基于深度学习的单张照片文物三维重建

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700723

V. Kniaz, V. Knyaz, T. Skrypitsyna, P. Moshkantsev, A. Bordodymov

引用次数: 0