Results 71 to 80 of about 1,111,935 (267)
External Quantum Self-Attention Model
Self-attention mechanisms have revolutionized machine learning domains. Quantum self-attention models have emerged as promising extensions, yet they suffer from quadratic computational complexity like their classical counterparts.
Fu Chen +3 more
doaj +1 more source
ABSTRACT Introduction Peritoneal dialysis (PD) is an established home‐based kidney replacement therapy (KRT), but its uptake remains low in Japan. We evaluated whether individualized education in a dedicated outpatient clinic was associated with the initiation of PD.
Yasuko Ito +7 more
wiley +1 more source
Organoids in pediatric cancer research
Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley +1 more source
A Structured Self-attentive Sentence Embedding
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model.
Zhouhan Lin +6 more
openaire +4 more sources
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source
Differential Gated Self-Attention
Transformers excel across a large variety of tasks but remain susceptible to corrupted inputs, since standard self-attention treats all query-key interactions uniformly. Inspired by lateral inhibition in biological neural circuits and building on the recent use by the Differential Transformer's use of two parallel softmax subtraction for noise ...
Elpiniki Maria Lygizou +2 more
openaire +3 more sources
Modelling stem cell differentiation related processes—A practical overview for biologists
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Exploring Self-Attention for Visual Odometry
Visual odometry networks commonly use pretrained optical flow networks in order to derive the ego-motion between consecutive frames. The features extracted by these networks represent the motion of all the pixels between frames. However, due to the existence of dynamic objects and texture-less surfaces in the scene, the motion information for every ...
Hamed Damirchi +2 more
openaire +3 more sources
Reconstructing enzyme evolution by protein engineering
Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
Double Attention: An Optimization Method for the Self-Attention Mechanism Based on Human Attention
Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and ...
Zeyu Zhang +4 more
doaj +1 more source

