Results 141 to 150 of about 7,255,480 (346)

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Bayesian network classifiers in Weka

, 2004
Various Bayesian network classifier learning algorithms are implemented in Weka [10].This note provides some user documentation and implementation details.
Remco R. Bouckaert, Bouckaert, Remco R.
core  

Propagation Computation for Mixed Bayesian Networks Using Minimal Strong Triangulation

Mathematics
In recent years, mixed Bayesian networks have received increasing attention across various fields for probabilistic reasoning. Though many studies have been devoted to propagation computation on strong junction trees for mixed Bayesian networks, few have
Yao Liu, Shuai Wang, Can Zhou, Xiaofei Wang   +3 more
doaj   +1 more source

Modular network construction using eQTL data: an analysis of computational costs and benefits

Frontiers in Genetics, 2014
Background: In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest.
Yen-Yi eHo, Leslie Michael Cope, Giovanni eParmigiani   +2 more
doaj   +1 more source

Bayesian Approach to Network Modularity

Physical Review Letters, 2008
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem ...
Hofman, Jake M., Wiggins, Chris H.
openaire   +4 more sources

Muscle Control of an Extra Robotic Digit

Advanced Robotics Research, EarlyView.
This study compares muscle‐ and movement‐based control for operating a supernumerary robotic thumb. While movement control performs better in the proposed tasks, muscle‐based (EMG) control promotes broader motor learning. The results highlight the promise and challenges of using biosignals for human augmentation, offering new insights into intuitive ...
Julien Russ, Lucy Dowdall, Francesco Cenciarelli, Kitty Goodridge, Mabel Ziman, Hristo Dimitrov, Danielle Clode, Tamar R. Makin   +7 more
wiley   +1 more source

Transformers are Bayesian Networks

CoRR
Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways. First, we prove that every sigmoid transformer with any weights implements weighted loopy belief propagation on its implicit factor graph.
openaire   +2 more sources

Consensus Formation and Change are Enhanced by Neutrality

Advanced Science, EarlyView.
Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag, Janina A. Hoffmann, Tim Rogers, Christian A. Yates   +3 more
wiley   +1 more source

American universities in the Middle East: A student’s perspective

Cogent Education, 2018
Many American universities located in the Middle East try to offer the stamp of higher quality in education that the United States provides and delivers. These institutions are doing an incredible job of providing opportunity for youths of that region to
Linda Smail, Ginger Silvera
doaj   +1 more source

Inference in Bayesian Networks.

AI Mag., 1999
A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly
openaire   +2 more sources