Results 221 to 230 of about 904,037 (316)

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Trends in government and donor funding for vertical and horizontal community health worker programmes in sub-Saharan Africa. [PDF]

BMJ Glob Health
Shukla S, Mupara L, Scotney S, Palazuelos D, Mogaka J, Westgate CC, O'Donovan J, Brieger B, La Grenade C, Uwaliraye P, Munguti D.   +10 more
europepmc   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Pathing the way from regulatory approval to market access for gene therapy products (GTPs): An integrative review in the US, EU5, Japan and China. [PDF]

Regen Ther
Shi J, Hu H, Ung COL.
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Adaptive Compressed Sensing Differential Privacy Federated Learning Based on Orbital Spatiotemporal Characteristics in Space-Air-Ground Networks. [PDF]

Sensors (Basel)
Li W, Li L, Zhu L.
europepmc   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

"What's the point, when we're already dead?" Implementation challenges of COVID-19 public policies for indigenous peoples in the Peruvian Amazon: A sequential multi-method qualitative study. [PDF]

PLoS One
Valdivia-Gago A, García PJ, Harper SL, Soria A, Zavaleta-Cortijo C.   +4 more
europepmc   +1 more source

QS4D: Quantization‐Aware Training for Efficient Hardware Deployment of Structured State‐Space Sequential Models

Advanced Intelligent Systems, EarlyView.
Quantization‐aware training creates resource‐efficient structured state space sequential S4(D) models for ultra‐long sequence processing in edge AI hardware. Including quantization during training leads to efficiency gains compared to pure post‐training quantization.
Sebastian Siegel, Ming‐Jay Yang, Younes Bouhadjar, Maxime Fabre, Emre Neftci, John Paul Strachan   +5 more
wiley   +1 more source

An impending crisis for the translational science pipeline: The dire impact of proposed NIH budget cuts on early-career researchers. [PDF]

J Clin Transl Sci
Karnik NS, Ellingrod VL, Meagher EA.
europepmc   +1 more source