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Average-Case Analysis Using Kolmogorov Complexity [PDF]
This expository paper demonstrates how to use Kolmogorov complexity to do the average-case analysis via four examples, and exhibits a surprising property of the celebrated associated universal distribution. The four examples are: average case analysis of Heapsort [17, 15], average nni-distance between two binary rooted leave-labeled trees [20], compact
Vitanyi, P.M.B., Li, M.
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Implications of Transient Negative Capacitance Effect in Ferroelectric Polarization Dynamics
Transient voltage artifacts observed during ferroelectric switching are shown to originate from measurement circuitry rather than intrinsic negative capacitance. By correlating switching current, time scale, and series resistance, this work establishes practical design rules for reliable pulse‐switching experiments and circuit integration of ...
Marin Alexe
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ABSTRACT Amid rising food and fertilizer prices, understanding farmers' policy preferences is critical for effective crisis response. We use best‐worst scaling experiment to assess Kenyan mobile‐owning crop farmers' preferences for government support under high and normal price scenarios.
Mywish K. Maredia +4 more
wiley +1 more source
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi +2 more
wiley +1 more source
Effective Complexity of Stationary Process Realizations
The concept of effective complexity of an object as the minimal description length of its regularities has been initiated by Gell-Mann and Lloyd. The regularities are modeled by means of ensembles, which is the probability distributions on finite binary ...
Arleta Szkoła, Nihat Ay, Markus Müller
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Kolmogorov complexity in perspective
37 ...
Ferbus-Zanda, Marie, Grigorieff, Serge
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Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
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This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan +6 more
wiley +1 more source
Mucin Glycoprotein Nanoparticles Enable a Selective Antisense Therapy for Oncogenic MicroRNAs
Mucin glycoproteins are turned into nanoparticles by employing synthetic DNA strands, which have a dual function: they stabilize the nanoparticles and act as binding sites for intracellular miRNA‐21. Thus, upon internalization into tumor cells, these mucin nanoparticles can deplete miRNA‐21 from the cytosol, which induces apoptosis in vitro and in vivo.
Ceren Kimna +9 more
wiley +1 more source

