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Optical Memory and Neural Networks最新文献

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Adaptive Curriculum Learning: Optimizing Reinforcement Learning through Dynamic Task Sequencing
IF 1
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,&nbsp;A. Skrynnik,&nbsp;A. Panov","doi":"10.3103/S1060992X2470070X","DOIUrl":"10.3103/S1060992X2470070X","url":null,"abstract":"<p>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.</p>","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
IF 1
Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700759
Hou Zhenghao, A. Kolonin
{"title":"Interpretable Sentiment Analysis and Text Segmentation for Chinese Language","authors":"Hou Zhenghao,&nbsp;A. Kolonin","doi":"10.3103/S1060992X24700759","DOIUrl":"10.3103/S1060992X24700759","url":null,"abstract":"<p>In this paper, we explored the performance of interpretable sentiment analysis models of different combinations for the Chinese text in social media. We made experiment to study how performance varies with the change of combination of different segmentation strategies and dictionary of words or n-grams. We found that with some good combination of segmentation strategies and dictionary of words or n-grams, the result can be improved and overtake the performance of ordinary sentiment analysis model of Chinese language. This way we show the importance of selection of segmentation strategies and dictionary for the sentiment analysis of Chinese text.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S483 - S489"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109112","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
Deep Learning for Single Photo 3D Reconstruction of Cultural Heritage
IF 1
Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700723
V. Kniaz, V. Knyaz, T. Skrypitsyna, P. Moshkantsev, A. Bordodymov
{"title":"Deep Learning for Single Photo 3D Reconstruction of Cultural Heritage","authors":"V. Kniaz,&nbsp;V. Knyaz,&nbsp;T. Skrypitsyna,&nbsp;P. Moshkantsev,&nbsp;A. Bordodymov","doi":"10.3103/S1060992X24700723","DOIUrl":"10.3103/S1060992X24700723","url":null,"abstract":"<p>In this paper, we propose a new single-photo 3D reconstruction model <span>DiffuseVoxels</span> focused on 3D inpainting of destroyed parts of a building. We use frustum-voxel model 3D reconstruction pipeline as a starting point for our research. Our main contribution is an iterative estimation of destroyed parts from a Gaussian noise inspired by diffusion models. Our input is twofold. Firstly, we mask the destroyed region in the input 2D image with a Gaussian noise. Secondly, we remove the noise through many iterations to improve the 3D reconstruction. The resulting model is represented as a semantic frustum voxel model, where each voxel represents the class of the reconstructed scene. Unlike classical voxel models, where each unit represents a cube, frustum voxel models divides the scene space into trapezium shaped units. Such approach allows us to keep the direct contour correspondence between the input 2D image, input 3D feature maps, and the output 3D frustum voxel model.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S457 - S465"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109241","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
Numerical Modeling of the Electromagnetic Field Measurement Process by the Aluminum Aperture Cantilever 铝孔悬臂梁电磁场测量过程的数值模拟
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700516
E. S. Kozlova, S. S. Stafeev, V. V. Kotlyar, E. A. Kadomina
{"title":"Numerical Modeling of the Electromagnetic Field Measurement Process by the Aluminum Aperture Cantilever","authors":"E. S. Kozlova,&nbsp;S. S. Stafeev,&nbsp;V. V. Kotlyar,&nbsp;E. A. Kadomina","doi":"10.3103/S1060992X24700516","DOIUrl":"10.3103/S1060992X24700516","url":null,"abstract":"<p>In this research we estimate the polarisation influence of the incident radiation on the measurement by a pyramidal aperture cantilever. The numerical modelling of the detection process was made by applying the frequency depended finite-difference time-domain method. We numerically demonstrated that the angle of incidence and the plane of inclination can affect on the measurement process by the aperture aluminum cantilever while the aperture shape has not any influence on the measurement process for both proposed types of incident light polarization: linear and circular left. Simulation results show that as the tilt angle for rotation of incident light increases the total intensity inside the cantilever decreases by about 50 and 30% for the linearly and circularly polarized light. It prooves that aperture aluminum cantilever is weakly sensitive to the longitudinal component.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S226 - S236"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875168","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
Spin-Orbit Conversion in Vector Optical Vortices in the Paraxial Approximation 近轴近似下矢量光旋涡的自旋-轨道转换
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700620
S. S. Stafeev, V. V. Kotlyar
{"title":"Spin-Orbit Conversion in Vector Optical Vortices in the Paraxial Approximation","authors":"S. S. Stafeev,&nbsp;V. V. Kotlyar","doi":"10.3103/S1060992X24700620","DOIUrl":"10.3103/S1060992X24700620","url":null,"abstract":"<p>In this work, spin-orbit conversion in a vector optical vortex will be considered. The polarization in such a beam corresponds to the polarization of a cylindrical vector beam, that is, it is initially linear at each point. It is shown numerically and analytically using the Richards-Wolf formalism that zones with non-zero longitudinal spin angular momentum are formed in the focal spot, i.e. zones with elliptical polarization. It has been experimentally shown that for the case when the topological charge of the optical vortex coincides with the order of the beam, the observed spin-orbit conversion is large enough to be recorded in the paraxial approximation.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S305 - S312"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875255","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
Common Topological Charge of a Superposition of Several Identical Off-Axis Vortex Beams with an Arbitrary Circularly Symmetric Transverse Shape 具有任意圆对称横向形状的几个相同离轴涡旋光束叠加的公共拓扑电荷
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700577
A. A. Kovalev, V. V. Kotlyar, A. G. Nalimov
{"title":"Common Topological Charge of a Superposition of Several Identical Off-Axis Vortex Beams with an Arbitrary Circularly Symmetric Transverse Shape","authors":"A. A. Kovalev,&nbsp;V. V. Kotlyar,&nbsp;A. G. Nalimov","doi":"10.3103/S1060992X24700577","DOIUrl":"10.3103/S1060992X24700577","url":null,"abstract":"<p>We investigate the common topological charge of a superposition of parallel identical vortex beams with an arbitrary transverse shape, either Laguerre–Gaussian beams or Bessel–Gaussian beams or some other vortex beams with rotationally symmetric intensity distribution. It is known that if all the beams in the superposition have the same phase then the common topological charge of the whole superposition equals the topological charge of each constituent beam <i>n</i>. We show that if the beams are located on a circle and their phases increase linearly along this circle so that the phase delay between the neighbor beams on the circle is 2π<i>p</i>/<i>N</i> with <i>N</i> being the number of beams and <i>p</i> being an integer number, then the common topological charge of the superposition is equal to <i>n</i> + <i>p</i>.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S285 - S294"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875269","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
Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods 基于人工智能方法的SD-OCT 2rt激光治疗AMD适应症自动确定技术
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700565
A. Yu. Ionov, N. Yu. Ilyasova, N. S. Demin, E. A. Zamytskiy, E. Yu. Zubkova
{"title":"Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods","authors":"A. Yu. Ionov,&nbsp;N. Yu. Ilyasova,&nbsp;N. S. Demin,&nbsp;E. A. Zamytskiy,&nbsp;E. Yu. Zubkova","doi":"10.3103/S1060992X24700565","DOIUrl":"10.3103/S1060992X24700565","url":null,"abstract":"<p>The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and efficiency of AMD diagnosis, as well as to provide faster and more accurate treatment assignment to each patient. The U-Net architecture was chosen as the neural network architecture to extract the area of interest in the retinal OCT image. The VGG16 architecture was used as the neural network architecture for classification. These architectures are well established. As a result of training, the model showed a fairly high accuracy of 90% for segmentation and 98% for classification. Automatic localization and classification based on SD-OST images will allow the most accurate determination of indications for 2RT laser treatment. This will significantly reduce the burden on physicians and make diagnostics more accessible.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S277 - S284"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875169","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
Topological Properties of Reflection Zeros of Optical Differentiators Based on Layered Metal-Dielectric-Metal Structures 基于层状金属-介电-金属结构的光学微分器反射零的拓扑性质
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700607
A. I. Kashapov, E. A. Bezus, D. A. Bykov, A. A. Mingazov, L. L. Doskolovich
{"title":"Topological Properties of Reflection Zeros of Optical Differentiators Based on Layered Metal-Dielectric-Metal Structures","authors":"A. I. Kashapov,&nbsp;E. A. Bezus,&nbsp;D. A. Bykov,&nbsp;A. A. Mingazov,&nbsp;L. L. Doskolovich","doi":"10.3103/S1060992X24700607","DOIUrl":"10.3103/S1060992X24700607","url":null,"abstract":"<p>We investigate the topological properties of reflection zeros of three-layer structures consisting of a dielectric layer sandwiched between two metal layers, which can be used as optical differentiators. We show that the reflection zeros possess non-zero topological charges, which makes them topologically protected. With a small perturbation of the parameters of the structure (e.g., a change in one of the layer thicknesses), the reflection zero does not disappear, but shifts in the parameter space, i.e., appears at different wavelength and angle of incidence. We demonstrate that with a further parameter change, two zeros with opposite topological charges (+1 and –1) approach each other, merge, and then disappear. We believe that the obtained results give useful insight regarding the operation of layered metal-dielectric-metal structures possessing reflection zeros.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S313 - S319"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875226","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
Influence of Atmospheric Turbulence on the Topological Charge of the Superposition of Optical Vortices 大气湍流对光学涡旋叠加拓扑电荷的影响
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X2470053X
D. O. Shilov, E. S. Kozlova, E. A. Kadomina
{"title":"Influence of Atmospheric Turbulence on the Topological Charge of the Superposition of Optical Vortices","authors":"D. O. Shilov,&nbsp;E. S. Kozlova,&nbsp;E. A. Kadomina","doi":"10.3103/S1060992X2470053X","DOIUrl":"10.3103/S1060992X2470053X","url":null,"abstract":"<p>The paper considers beams in the form of geometric progression of optical vortices. Numerical modelling of the propagation of such optical fields in turbulent media is simulated using the Fresnel integral. The topological charges of the initial and resulting fields have been calculated. As expected, the analysis of the obtained results showed that superpositions with a smaller number of beams are more resistant to distortions by strongly turbulent media. However, in the case of a superposition in the form of a geometric progression with a parameter, the stability of beam propagation is affected not only by the medium parameters, but also by the parameters of the superposition.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S249 - S260"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875190","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
Diffractive Optical Elements for Multi-Channel Atmospheric Communication Systems in the Visible and Near-IR Ranges 可见光和近红外多通道大气通信系统的衍射光学元件
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700504
N. A. Ivliev
{"title":"Diffractive Optical Elements for Multi-Channel Atmospheric Communication Systems in the Visible and Near-IR Ranges","authors":"N. A. Ivliev","doi":"10.3103/S1060992X24700504","DOIUrl":"10.3103/S1060992X24700504","url":null,"abstract":"<p>Currently, wireless laser communication technologies demonstrate high throughput, but the implemented communication systems are not widely used. This feature is due to the low reliability of the communication channels being formed. Recent technological developments have shown successful results in the field of sealing and increasing the noise immunity of communication channels. Therefore, this paper presents a review of modern achievements in the field of multichannel atmospheric optical communication in the visible and near–infrared ranges. The advantages of using diffraction optical elements (DOE) in such systems, which form vortex beams of laser radiation with the required amplitude-phase structure for multiplexing tasks and increasing the noise immunity of information channels, are shown.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S217 - S225"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875187","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
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