Results 81 to 90 of about 140,423 (144)

CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming [PDF]

arXiv, 2009
This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability ...
arxiv  

Advances in 3D and 4D Printing of Soft Robotics and Their Applications

Advanced Intelligent Systems, EarlyView.
This article summarizes the development of 3D‐printed soft robotics in the recent decade. The article discusses the printing capabilities of different additive manufacturing technologies in terms of soft polymers, multimaterial printability, soft robotic printing, and 4D printing.
Hao Liu, Changchun Wu, Senyuan Lin, James Lam, Ning Xi, Yonghua Chen   +5 more
wiley   +1 more source

Artificial Intelligence‐Enhanced, Closed‐Loop Wearable Systems Toward Next‐Generation Diabetes Management

Advanced Intelligent Systems, EarlyView.
Recent advancements in wearable healthcare have brought accessible continuous glucose monitoring systems (CGMs) for diabetes management. To address the limitations of CGMs, closed‐loop systems utilizing monitored glucose levels for insulin dosing are being developed.
Wei Huang, Ivo Pang, Jing Bai, Binbin Cui, Xiaojuan Qi, Shiming Zhang   +5 more
wiley   +1 more source

Probabilistic Concurrent Reasoning in Outcome Logic: Independence, Conditioning, and Invariants [PDF]

arXiv
Although randomization has long been used in concurrent programs, formal methods for reasoning about this mixture of effects have lagged behind. In particular, no existing program logics can express specifications about the distributions of outcomes resulting from programs that are both probabilistic and concurrent.
arxiv  

Neural Probabilistic Logic Programming in DeepProbLog [PDF]

arXiv, 2019
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language.
arxiv  

Deep Learning Based Large‐Area Contact Sensing for Safe Human–Robot Interaction Using Conformal Kirigami Structure‐Enabled Robotic E‐Skin

Advanced Intelligent Systems, EarlyView.
This work presents a collision detection technique for robots, using piezoelectric sensors and deep learning to identify collision locations and intensities. A large area conformal kirigami design enables cost‐effective sensor deployment on complex surfaces.
Rui Jiao, Zhengjun Wang, Ruoqin Wang, Qian Xu, Jiacheng Jiang, Boyang Zhang, Simin Yang, Yang Li, Yik Kin Cheung, Fan Shi, Hongyu Yu   +10 more
wiley   +1 more source

Modular Reasoning about Error Bounds for Concurrent Probabilistic Programs [PDF]

arXiv
We present Coneris, the first higher-order concurrent separation logic for reasoning about error probability bounds of higher-order concurrent probabilistic programs with higher-order state. To support modular reasoning about concurrent (non-probabilistic) program modules, state-of-the-art program logics internalize the classic notion of ...
arxiv  

Exploring the expanse between theoretical questions and experimental approaches in the modern study of evolvability

Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, Volume 340, Issue 1, Page 8-17, January 2023., 2023
Experimental tests of theories about evolvability have largely been limited to examining the mechanisms by which genetic variation is produced; namely, mutation, and recombination. However, evolvability theory has a much broader domain, and opportunities abound to test ideas about developmental variability, environmental heterogeneity, and the ...
Jeremy A. Draghi, C. Brandon Ogbunugafor
wiley   +1 more source

Loglinear models for first-order probabilistic reasoning [PDF]

arXiv, 2013
Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves.
arxiv  

Model Checking with Probabilistic Tabled Logic Programming [PDF]

arXiv, 2012
We present a formulation of the problem of probabilistic model checking as one of query evaluation over probabilistic logic programs. To the best of our knowledge, our formulation is the first of its kind, and it covers a rich class of probabilistic models and probabilistic temporal logics.
arxiv