Risultati della ricerca - decoding (algorithmics OR (algorithm OR (algorithm OR algorithm)))

Video‐Rate Spectral Imaging Based on Diffractive‐Refractive Hybrid Optics di Hao Xu, Haiquan Hu, Nan Xu, Bingkun Chen, Peng Luo, Tingting Jiang, Zhihai Xu, Qi Li, Shiqi Chen, Yueting Chen

Sommario: “... on encoding–decoding have emerged, among which phase‐encoding spectral imaging systems have attracted...”
Testo

Dynamic Information Encryption and Coding using Liquid Crystal Soft Helical Microstructures with Spiropyran Photochromic Switch di Guangyin Qu, Xiaojuan Zhang, Siqi Li, Yan Kuai, Feng Xu, Liang Lu, Yu Liu, Zhigang Cao, Benli Yu, Anderson S. L. Gomes, Zhijia Hu

Sommario: “... information encoder is developed to mimic the processes of information encoding and decoding, bypassing...”
Testo

Programmable Nanostructured Liquid Crystalline Film with Tailored Optical Degrees‐of‐Freedom toward Information Encryption di Xuan Liu, Boyuan Li, Honglong Hu, Xia Duan, Junhui Wang, Peizhi Sun, Zhi‐Gang Zheng

Sommario: “... still rely on computationally intensive inverse‐design algorithms and bulky spatial light modulation...”
Testo

Bioinspired Ultratough and Rapidly Responsive Hydrogels Empower Silent Communication di Shuaibo Zhang, Donglei Fu, Junjie Deng, Ruyi Wei, Xinghai Liu

Sommario: “... decoding against motion‐induced noise, a finite state machine (FSM)‐based algorithm is employed...”
Testo

Priming: Hybrid State Space Models From Pre-trained Transformers di Chattopadhyay, Aditya, Nunez, Elvis, Kaul, Prannay, Bowman, Benjamin, Becker, Evan, Zancato, Luca, Thomas, David, Xia, Wei, Soatto, Stefano

Sommario: “...-Value caches and faster decoding than Transformers, along with a richer architectural design space...”
Testo

MMLG ‐Point: Unsupervised Pretraining Approach for Cattle Point Cloud Segmentation and Measurement di Zhi Weng, Yuzhe Bian, Zhiqiang Zheng, Wenwen Hao

Sommario: “... measurement, which introduces a Multilevel Geometric Perception Encoder and a Transformer‐based decoder...”
Testo

A New Comprehensive Framework for Multi-Exposure Stereo Coding Utilizing Low Rank Tucker-ALS and 3D-HEVC Techniques di Sharma, Mansi, Grover, Jyotsana

Sommario: “... personalization simultaneously. Efficient algorithms to compress HDR stereo data is critical. Direct capturing...”
Testo

Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training di Zoumpourlis, Georgios, Patras, Ioannis

Sommario: “...In this work, we study the problem of cross-subject motor imagery (MI) decoding from...”
Testo

LadleNet: A Two-Stage UNet for Infrared Image to Visible Image Translation Guided by Semantic Segmentation di Zou, Tonghui, Chen, Lei

Sommario: “... on these principles, we propose an improved algorithm based on U-net called LadleNet. This network utilizes a two...”
Testo

Enigma: Application-Layer Privacy for Quantum Optimization on Untrusted Computers di Ayanzadeh, Ramin, Mousavi, Ahmad, Basareh, Amirhossein, Alavisamani, Narges, Taram, Kazem

Sommario: “... at the application layer to enable practical deployment while remaining agnostic to algorithms, computing models...”
Testo

UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity di Zuo, Jialong, Zhou, Hanyu, Nie, Ying, Zhang, Feng, Guo, Tianyu, Sang, Nong, Wang, Yunhe, Gao, Changxin

Sommario: “...{algorithm} especially designed for text-based person retrieval with ultra fine-grained texts. It achieves...”
Testo

Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework di Tian, Yuan, Zhou, Wenqi, Viscione, Michele, Dong, Hao, Kammer, David, Fink, Olga

Sommario: “... leverages reinforcement learning without relying on a transformer-based decoder. This formulation allows...”
Testo

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI di Garg, Sahil, Schneider, Anderson, Raj, Anant, Rasul, Kashif, Nevmyvaka, Yuriy, Gopal, Sneihil, Dhurandhar, Amit, Cecchi, Guillermo, Rish, Irina

Sommario: “... and then decoding or denoising them to match the true data distribution. In contrast, our method is grounded...”
Testo

Exploring the Dynamics of Data Transmission in 5G Networks: A Conceptual Analysis di Smirnov, Nikita, Tomforde, Sven

Sommario: “... acquisition and encoding to final decoding, all aspects of their transmission and reception via the WebRTC...”
Testo

Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space di Koopas, Rasoul Najafi, Rezaei, Shahed, Rauter, Natalie, Ostwald, Richard, Lammering, Rolf

Sommario: “... conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed...”
Testo

Lite-SAM Is Actually What You Need for Segment Everything di Fu, Jianhai, Yu, Yuanjie, Li, Ningchuan, Zhang, Yi, Chen, Qichao, Xiong, Jianping, Yin, Jun, Xiang, Zhiyu

Sommario: “... traditional prompt encoder, and a mask decoder. All these components are integrated within the SAM framework...”
Testo

A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility di Poddar, Prithvi, Paul, Steve, Chowdhury, Souma

Sommario: “..., and transport cost profiles, and a Multi-head attention (MHA) based decoder. Expert demonstrations are used...”
Testo

Coordinated Half-Duplex/Full-Duplex Cooperative Rate-Splitting Multiple Access in Multi-Cell Networks di Elhattab, Mohamed, Khisa, Shreya, Assi, Chadi, Ghrayeb, Ali, Qaraqe, Marwa, Kaddoum, Georges

Sommario: “... in either half-duplex (HD) or full-duplex (FD) decode-and-forward (DF) relaying mode. In this setup, we aim...”
Testo

Improving Achievability of Cache-Aided Private Variable-Length Coding with Zero Leakage di Zamani, Amirreza, Skoglund, Mikael

Sommario: “...;\mathcal{C})=0$; ii. User $i$ can decode its demand, $Y_{d_i}$, by using the shared key $W$, $\cal C...”
Testo

Aligned Datasets Improve Detection of Latent Diffusion-Generated Images di Rajan, Anirudh Sundara, Ojha, Utkarsh, Schloesser, Jedidiah, Lee, Yong Jae

Sommario: “... that in addition to these algorithmic choices, we also require a well aligned dataset of real/fake images to train...”
Testo