Results 141 to 150 of about 5,958,314 (335)

Selective Role of the Putamen in Serial Reversal Learning in the Marmoset

open access: yesCerebral Cortex, 2018
Fronto-striatal circuitry involving the orbitofrontal cortex has been identified as mediating successful reversal of stimulus-outcome contingencies. The region of the striatum that most contributes to reversal learning remains unclear, with studies in ...
S. Jackson   +6 more
semanticscholar   +1 more source

Atomic Size Misfit for Electrocatalytic Small Molecule Activation

open access: yesAdvanced Functional Materials, EarlyView.
This review explores the application and mechanisms of atomic size misfit in catalysis for small molecule activation, focusing on how structural defects and electronic properties can effectively lower the energy barriers of chemical bonds in molecules like H2O, CO2, and N2.
Ping Hong   +3 more
wiley   +1 more source

Neophobia is negatively related to reversal learning ability in females of a generalist bird of prey, the Chimango Caracara, Milvago chimango

open access: yesAnimal Cognition, 2017
In an ever-changing environment, the ability to adapt choices to new conditions is essential for daily living and ultimately, for survival. Behavioural flexibility allows animals to maximise survival and reproduction in novel settings by adjusting their ...
J. Guido   +3 more
semanticscholar   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

open access: yesAdvanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone   +11 more
wiley   +1 more source

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

open access: yesAdvanced Functional Materials, EarlyView.
By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto   +8 more
wiley   +1 more source

A Comparison of the Effects of Different Doses of GABAB Receptor Ligands on Spatial Learning and Memory and Memory Flexibility

open access: yes, 2015
The principal inhibitory neurotransmitter in the brain, gamma amino-butyric acid (GABA), mediates several types of learning and memory. Of the two main receptor subtypes for GABA, the in vivo role of GABAB receptor in learning and memory is less well ...
Heaney, Chelcie
core   +1 more source

Acute vagus nerve stimulation enhances reversal learning in rats

open access: yesNeurobiology of Learning and Memory, 2021
Lindsay Altidor   +13 more
semanticscholar   +1 more source

Bimetallic Nanoparticles as Cocatalysts for Photocatalytic Hydrogen Production

open access: yesAdvanced Functional Materials, EarlyView.
Recent developments have introduced bimetallic nanoparticles as effective cocatalysts for photocatalytic systems. This review explores the rapidly expanding research on bimetallic cocatalysts for photocatalytic production of hydrogen, emphasizing the creation of carrier‐selective contacts, localized surface plasmon resonance effects, methodologies for ...
Yufen Chen   +4 more
wiley   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

open access: yesAdvanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska   +7 more
wiley   +1 more source

Unsupervised Domain Adaptation by Backpropagation [PDF]

open access: yes, 2015
Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different ...
Ganin, Yaroslav, Lempitsky, Victor
core  

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