20 papers · ranked by Valyu relevance
Abolfazl Asudeh, H. V. Jagadish
Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by a function (mechanism) that takes a set of features as input and generates a score. The scoring functions are either machine-learned or…
Jeremie Coullon, Robert J. Webber
We introduce a new Markov chain Monte Carlo (MCMC) sampler for infinite-dimensional inverse problems. Our new sampler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensemble sampler for the first time to…
Zezhen Wang, Weihao Mai, Yuming Chai, Kexin Qi + 8 more
'Chen Shen' 'Shiwu Zhang' 'Guodong Tan' 'Yu Hu' 'Quan Wen' 'Peter Latham' 'Panayiota Poirazi'] Understanding neural activity organization is vital for deciphering brain function. By recording whole-brain calcium activity in larval zebrafish during hunting and spontaneous behaviors, we find that the shape of the neural…
Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri
How do people actively explore to learn about functional rules, that is, how continuous inputs map onto continuous outputs? We introduce a novel paradigm to investigate information search in continuous, multi-feature function learning scenarios. Participants either actively selected or passively observed information to…
Jinyin Zha, Zhen Zheng, Jie Zhong, Weihua Wang + 12 more
Rational discovery of function-specific protein modulators as well as activity-enhanced engineering proteins underscore the need to identify function-related metastable states (FMSs) of proteins. However, current experimental and computational methods struggle to generate these states directly from their native state…
Philip R. Baldwin, Dmitry Lyumkis
A complete understanding of how an orientation distribution contributes to a cryo-EM reconstruction remains lacking. It is necessary to begin critically assessing the set of views to gain an understanding of its effect on experimental reconstructions. Toward that end, we recently suggested that the type of orientation…
Chandrika Kamath
Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from…
Xueyang Yao, Natalie Baddour, Yilun Shang
The theory of the continuous two-dimensional (2D) Fourier Transform in polar coordinates has been recently developed but no discrete counterpart exists to date. In the first part of this two-paper series, we proposed and evaluated the theory of the 2D Discrete Fourier Transform (DFT) in polar coordinates. The theory of…
S. M. Abrarov, Brendan M. Quine
In this paper we show that a methodology based on a sampling with the Gaussian function of kind h e−(t/c) 2 / (c √ π), where c and h are some constants, leads to the Fourier transform that can be represented as a weighted sum of the complex error functions. Due to remarkable property of the complex error function, the…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Mike D. Rinderknecht, Olivier Lambercy, Roger Gassert
When estimating psychometric functions with sampling procedures, psychophysical assessments should be precise and accurate while being as efficient as possible to reduce assessment duration. The estimation performance of sampling procedures is commonly evaluated in computer simulations for single psychometric functions…
Amos A. Hari, Sefi Givli
This paper addresses a disconnect between the pivotal role of functional (path) integrals in modern theories, such as quantum mechanics and statistical thermodynamics, and the currently limited ability to perform the actual calculation. We present a new method for calculating functional integrals, based on a…
Paromita Dubey, Hans‐Georg Müller
Summary. Functional data analysis provides a popular toolbox of functional models for the analysis of samples of random functions that are real-valued. In recent years, samples of time-varying object data such as time-varying networks that are not in a vector space have been increasingly collected. These data can be…
Michael R. Lindstrom, Hyuntae Jung, Denis Larocque
We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained formally through…
Christian Damgaard
When estimating plant species abundance using plot-based methods in irregular polygons, there are some constraints to the spatial configuration of the sampling plots: i) the sample plots must be positioned within the polygon, ii) the sample plots may not overlap, iii) due to the spatial variation in abundance often…
Weifeng Liu, Ying Jiang, Yuesheng Xu, Karsten Keller
Sample entropy, an approximation of the Kolmogorov entropy, was proposed to characterize complexity of a time series, which is essentially defined as $(-log(B/A))$, where B denotes the number of matched template pairs with length m and A denotes the number of matched template pairs with $(m+1)$, for a predetermined…
Zhimian Hao, Chonghuan Zhang, Alexei Lapkin
We propose a workflow for reduction in the time required for data generation during generation of statistical digital twins. This methodology is particularly relevant for real-world engineering problems when data generation is expensive. A prerequisite for building surrogates is sufficient input/output data, whereas…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Timofey Shevgunov, Evgeny Efimov, Oksana Guschina, Oleg Varlamov
This article addresses the problem of estimating the spectral correlation function (SCF), which provides quantitative characterization in the frequency domain of wide-sense cyclostationary properties of random processes which are considered to be the theoretical models of observed time series or discrete-time signals.…
Daniel Kosiorowski, Jerzy P. Rydlewski, Małgorzata Snarska
Functional data analysis (FDA) (Ramsay et al. (2009); Ramsay and Silverman (2005)) is a part of modern multivariate statistics that analyses data providing information about curves, surfaces or anything else varying over a certain continuum. In economics and empirical finance we often have to deal with time series of…