Partial MaxSAT Approach to Nonmonotonic Reasoning with System W
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceThe only recently introduced System W is a nonmonotonic inductive inference operator exhibiting some notable proper- ties like extending rational closure and satisfying syntax split- ting postulates for inference from conditional belief bases. A semantic
Christoph Beierle +2 more
doaj +1 more source
Entropy‐Driven Physical Amplification in Multivalent Biosensing
Advanced Science, EarlyView.Detecting scarce molecules usually demands enzymatic amplification such as PCR. A statistical‐mechanical theory now reveals a purely physical alternative: distributing a fixed binding strength over more linker arms exponentially lowers the concentration at which multivalent sensors switch on.
Yuhan Peng, Xiuyang Xia, Ran Ni
wiley +1 more source
Re‐Thinking Electronic Circuit Design Through Emerging Reconfigurable Field Effect Transistors
Advanced Science, EarlyView.Graphical illustration of reconfigurable field effect transistor (RFET) operation modes, including n‐type, p‐type, and ambipolar transport, as well as their areas of application discussed in this review: digital circuit design, analog circuit design, post‐Shannon computing, and sensing. ABSTRACT Reconfigurable field‐effect transistors, short RFETs, are
Jens Trommer +19 more
wiley +1 more source
Nonmonotonic Reasoning, Expectations Orderings, and Conceptual Spaces
, 2021 In Gärdenfors and Makinson (Artif Intell 65(2):197–245, 1994) and Gärdenfors (Knowledge representation and reasoning under uncertainty, Springer-Verlag, 1992) it was shown that it is possible to model nonmonotonic inference using a classical consequence ...
Gärdenfors, Peter, +6 more
core +2 more sources
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Diverging Approaches to Skeptical Inference in Non-monotonic Reasoning
Principia: An International Journal of EpistemologyOur paper addresses the problem of a two-fold approach to skeptical inferences in the context non-monotonic logics. We tackle the problem through the analysis of ambiguous theories, such as the Nixon Diamond, as instantiated in non-monotonic inheritance ...
Jorge Andrés Morales Delgado
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The Interoperability Challenge in DFT Workflows Across Implementations
Advanced Intelligent Discovery, EarlyView.Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen +13 more
wiley +1 more source
Crystallization of Water Mediated by Carbon
Geophysical Monograph Series, Page 77-86., 2020 This book is Open Access. A digital copy can be downloaded for free from Wiley Online Library.
Explores the behavior of carbon in minerals, melts, and fluids under extreme conditions
Carbon trapped in diamonds and carbonate-bearing rocks in subduction zones are examples of the continuing exchange of substantial carbon ...
Tianshu Li, Yuanfei Bi, Boxiao Cao
wiley +1 more source
Nonmonotonic Skeptical Consequence Relation in Constrained Default Logic
Studia Universitatis Babes-Bolyai: Series Informatica, 2010 This paper presents a study of the nonmonotonic consequence relation which models the skeptical reasoning formalised by constrained default logic. The nonmonotonic skeptical consequence relation is defined using the sequent calculus axiomatic system.
Mihaiela Lupea
doaj
Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network
Advanced Intelligent Discovery, EarlyView.We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang +16 more
wiley +1 more source