TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…In addition, we construct a generative model that learns only a unidirectional modality conversion mapping, thereby capturing the associations between their visual contents. …”
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Pengembangan Auto-AI Model Generatif Analisis Kompleksitas Waktu Algoritma Untuk Data Multi-Sensor IoT Pada Node-RED Menggunakan Extreme Learning Machine by Imam Cholissodin, Dahnial Syauqy, Dwi Ady Firmanda, Ibrahim Aji, Edy Rahman, Syazwandy Harahap, Fernando Septino

“…The steps in the study are utilized to create a generative model based on the Extreme Learning Machine (ELM) algorithm according to the recording of computational time values on several tests to automate the determination of time complexity equation model of the algorithm in general including the search of best cases, worst cases, and average cases for non-recursive, and base cases and recurrent cases for recursive, as well as algorithms that contain both. …”
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Progressing Beyond the Standard Model by B. A. Robson

“…This paper examines in some detail the basic assumptions upon which the Standard Model is built and compares these with the assumptions of an alternative model, the Generation Model. The Generation Model provides agreement with the Standard Model for those phenomena which the Standard Model is able to describe, but it is shown that the assumptions inherent in the Generation Model allow progress beyond the Standard Model.…”
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Data acquisition system for OLED defect detection and augmentation of system data through diffusion model by Byungjoon Kim, Yongduek Seo

“…The proposed system acquires a hypothetical base dataset and employs an image generation model for data augmentation. While image generation models have been instrumental in overcoming data scarcity, time and cost constraints in various fields, they still pose limitations in generating images with regular patterns and detecting defects within such data. …”
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Application of Distributed Hydrological Model Based on NRIHM in Humid Watershed by XU Qin, LIU Lulin, LONG Jie, JIN Chen, CAI Jing, LIN Xiaoqing, ZHANG Kun

“…Firstly, based on the construction idea of the NRIHM runoff generation model, a one-layer runoff generation model and a two-layer runoff generation model were constructed. …”
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A Cross-Modal Tactile Reproduction Utilizing Tactile and Visual Information Generated by Conditional Generative Adversarial Networks by Koki Hatori, Takashi Morikura, Akira Funahashi, Kenjiro Takemura

“…We performed a cross-modal tactile reproduction experiment using the previously developed tactile information generation model to input signals to a tactile display, alongside the images generated by the visual information generation model. …”
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Generation-based linguistic steganography with controllable security by Jiameng MEI, Yanzhen REN, Lina WANG

“…Generation-based linguistic steganography hides secret information through controllable modification and mapping of words in the candidate pool.It usually consists of three parts: text generation model, candidate pool probability distribution truncation and steganographic embedding algorithm.Due to the huge difference in the probability distribution of the text generation model outputs at different times, existing algorithms usually use top-k or top-p methods to truncate the probability distribution of words in the candidate pool to reduce the low-probability generated words and improve the security of the generated text.When the probability distribution of the candidate pool output by the text generation model is over-concentrated or over-flat, the original top-k or top-p truncation method will be not enough to cope with the change of the probability distribution, and it is easy to generate low-probability words or ignore high-probability words.This will lead to abnormal security metrics of the generated text.To address these problems, a generation-based linguistic steganography with controllable security was proposed.When selecting generated words with controllability in the candidate pool according to secret information, the proposed algorithm was based on the parameter constraints of perplexity and KL divergence.The truncation of the candidate pool probability distribution made all words satisfy the parameter constraints, which improved the security of the generated text.Experiment results showed that the perplexity and KL divergence of the steganographic text generated by the proposed algorithm are controllable.Under the same KL divergence, the perplexity of the text generated by the proposed algorithm is reduced by up to 20%～30% compared with the existing algorithm.This algorithm could control the perplexity and KL divergence at the same time, and make the generated text satisfy both perplexity and KL divergence when the indicators are reasonable.When using the three text steganalysis algorithms to detect the generated steganographic text, the detection accuracy is about 50%, showing excellent statistical security.…”
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Generative Image Steganography via Encoding Pose Keypoints by Yi Cao, Wentao Ge, Chengsheng Yuan, Quan Wang

“…This method employs an LSTM-based sequence generation model to embed secret information into the generation process of pose keypoint sequences. …”
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Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation by Junkil Park, Youhan Lee, Jihan Kim

“…Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. …”
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Application of generative adversarial networks for financial data by CUI Yihao, LIU Sen, YE Guangnan

“…The advantages of the GAN model compared with other generative models in the financial field were analyzed. …”
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A data-driven group retrosynthesis planning model inspired by neurosymbolic programming by Xuefeng Zhang, Haowei Lin, Muhan Zhang, Yuan Zhou, Jianzhu Ma

“…Abstract Deep generative models have garnered significant attention for their efficiency in drug discovery, yet the synthesis of proposed molecules remains a challenge. …”
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Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement by J. J. Gu, M. Meng, A. Cook, P. X. Liu

“…Two generations of robotic prosthetic eye models have been developed. The first generation model uses an external infrared sensor array mounted on the frame of a pair of eyeglasses to detect the natural eye movement and to feed the control system to drive the artificial eye to move with the natural eye. …”
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Improving Image Quality and Controllability in Speed + Angular Velocity to Image Generation Through Synthetic Data for Driving Simulator Generation by Yuto Imai, Tomoya Senda, Yusuke Kajiwara

“…To address these challenges, this study focuses on a driving simulation generation model, which produces driving scenarios from speed and steering angle information. …”
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A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection by Murat Sarıateş, Erdal Özbay

“…It was also compared to next-generation models such as vision transformer (ViT) and MaxViT-v2. …”
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ACTUAL PROBLEMS OF PERSONNEL POLICY AT THE ENTERPRISES OF PASSENGER MOTOR TRANSPORT by Y. Proshina

“…The focus is on driving employment potential as the dominant factor of influence on the personnel policy of the public transport company. The generated model of a real personnel policy passenger transport company to date and it identifies the main "bottlenecks". …”
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A robust adaptive meta-sample generation method for few-shot time series prediction by Chao Zhang, Defu Jiang, Kanghui Jiang, Jialin Yang, Yan Han, Ling Zhu, Libo Tao

“…Therefore, this paper focuses on few-shot time series prediction (FTSP) and plans to combine meta-learning and generative models to alleviate the problems caused by insufficient training data. …”
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Advances in generative adversarial network by Wanliang WANG, Zhuorong LI

“…Generative adversarial network (GAN) have swiftly become the focus of considerable research in generative models soon after its emergence,whose academic research and industry applications have yielded a stream of further progress along with the remarkable achievements of deep learning.A broad survey of the recent advances in generative adversarial network was provided.Firstly,the research background and motivation of GAN was introduced.Then the recent theoretical advances of GAN on modeling,architectures,training and evaluation metrics were reviewed.Its state-of-the-art applications and the extensively used open source tools for GAN were introduced.Finally,issues that require urgent solutions and works that deserve further investigation were discussed.…”
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The many dimensions of the digital model by Francesca Fatta

“…This gives rise to the entire asset of a project, where various disciplines, or spheres of knowledge, integrate to generate models that make it possible to simulate its construction and predict its impact. …”
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Environmental design multisensory experience. Integrated space for simulation activities by Stefania Palmieri, Mario Bisson, Alessandro Ianniello

“…The EDME Laboratory, established within the Polytechnic of Milano, is the first result of a path of multidisciplinary integration, which synthesizes multiscalar relationships, outlining the identity of an instrument of investigation, interpretation and representation of experiential scenarios. The generated model integrates with a physical space innovative ICT technology and materials of the latest generation, to carry out research involving simulations of complex activities and interactions, and predictions on the perceptual and digital control aspects of the environments where such activities are carried out. …”
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Functional Approach for Solving Reduced Order of Index-Four Hessenberg Differential-Algebraic Control System by Ghazwa F. Abd

“…Illustrations have been ranked from an 8 × 8 test system of index-four Hessenberg linear DAEs to a 4 × 4 DAE of rotating masses as well as an 8 × 8 differential-algebraic generator model, where it reformulated index-four linear Hessenberg DAEs. …”
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