Results 31 to 40 of about 16,917 (207)
Concatenated LDPC-Polar Codes Decoding Through Belief Propagation
Owing to their capacity-achieving performance and low encoding and decoding complexity, polar codes have drawn much research interests recently. Successive cancellation decoding (SCD) and belief propagation decoding (BPD) are two common approaches for ...
Abbas, Syed Mohsin +3 more
core +2 more sources
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source
We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich +2 more
wiley +1 more source
De Novo Multi‐Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning
Current AI‐driven peptide discovery often overlooks complex structural data. This study presents M3‐CAD, a generative pipeline that leverages 3D voxel coloring and a massive database of over 12 000 peptides to capture nuanced physicochemical contexts.
Xiaojuan Li +23 more
wiley +1 more source
Self-concatenated coding and multi-functional MIMO aided H.264 video telephony
— Robust video transmission using iteratively detected Self-Concatenated Coding (SCC), multi-dimensional Sphere Packing (SP) modulation and Layered Steered Space-Time Coding (LSSTC) is proposed for H.264 coded video transmission over correlated Rayleigh ...
Butt, Muhammad Fasih +4 more
core +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Design and Evaluation of Adaptive (Serial/Parallel) Concatenated Convolutional Codes
In this paper, parallel Concatenated Convolutional Codes (PCCCs) is modeled as a special case of Serial Concatenated Convolutional Code (SCCCs). Consequently, resulting in Adaptive (parallel/serial) concatenated convolutional code in which the same ...
Khamis A. Zidan, Raghad Z. Yousif
doaj
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv +11 more
wiley +1 more source
On the Error Statistics of Turbo Decoding for Hybrid Concatenated Codes Design
In this paper we propose a model for the generation of error patterns at the output of a turbo decoder. One of the advantages of this model is that it can be used to generate the error sequence with little effort.
Fulvio Babich, Francesca Vatta
doaj
Sensing and Filtering Environmental Fluctuations: The Case of Biomolecular Condensates in Plants
The diversity of plant condensates reflects constraints of sessile organisms to coordinate postembryonic development with environmental adaptation. This review examines how plants employ condensates to integrate temperature, light, redox, and nutrient signals.
Panagiotis N. Moschou, Dorothee Staiger
wiley +1 more source

