Results 181 to 190 of about 13,782,934 (217)

Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics‐Based Optimization with Limited Activity Data

Advanced Science, EarlyView.
Using a minimal set of experimentally labeled antimicrobial and non‐antimicrobial peptides, we reconstruct the effective lipid composition of a pathogenic membrane ‐demonstrated at the example of bacterial membranes‐ through a physics‐based evolutionary molecular dynamics (Evo‐MD) approach. Optimization based on peptide insertion reveals broad‐spectrum
Maximilian Krebs, Herre Jelger Risselada
wiley   +1 more source

Dipolar Coupling Errors in Nuclear Spin Qubits

Advanced Science, EarlyView.
Multi‐nuclear spin registers of phosphorus‐atom qubits in silicon have demonstrated 99.99% single and >$>$99.9% two qubit gate fidelities. Despite nanometer separation, read‐out fidelities between individual nuclear spins within the register are not identical.
I. D. Thorvaldson, P. Macha, J. Reiner, S. H. Misha, Y. Chung, L. Kranz, D. Keith, S. Monir, D. Poulos, S. Sutherland, Y.‐L. Hsueh, B. Thorgrimsson, R. Rahman, J. G. Keizer, S. K. Gorman, M. Y. Simmons   +15 more
wiley   +1 more source

Controlling Sparseness in Non-Negative Tensor Factorization

Lecture Notes in Computer Science, 2006
Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001). Until now, sparsity of the tensor factorization has been empirically observed in many cases, but there was no systematic way to control it.
Christoph Schnörr, Matthias Heiler
exaly   +4 more sources

Multichannel audio upmixing based on non-negative tensor factorization representation

2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2011
This paper proposes a new spatial audio coding (SAC) method that is based on parametrization of multichannel audio by sound objects using non-negative tensor factorization (NTF). The spatial parameters are estimated using perceptually motivated NTF model and are used for upmixing a downmixed and encoded mixture signal.
Joonas Nikunen, Tuomas Virtanen, Miikka Vilermo   +2 more
openaire   +3 more sources

VARIATIONAL BAYESIAN PARTIALLY OBSERVED NON-NEGATIVE TENSOR FACTORIZATION

2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018
Non-negative matrix and tensor factorization (NMF/NTF) have become important tools for extracting part based representations in data. It is however unclear when an NMF or NTF approach is most suited for data and how reliably the models predict when trained on partially observed data.
Jesper Løve Hinrich, Søren Føns Vind Nielsen, Kristoffer Hougaard Madsen, Morten Mørup   +3 more
openaire   +3 more sources

A non-negative tensor factorization approach to feature extraction for image analysis

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016
In this paper, a decomposition method is proposed for Separable Non-negative Tensor Factorization (SNTF), which yields a structure similar to the PARATUCK2 model for the decomposition of non-negative tensors. Among many different possibilities for performing tensor factorization, we develop a specific procedure for SNTF with an aim to decompose multi ...
Andersen Man Shun Ang, Yeung Sam Hung, Zhiguo Zhang 0001   +2 more
openaire   +3 more sources

Tensor Independent Component Analysis and Tensor Non-Negative Factorization

, 2009
In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches.
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang   +3 more
openaire   +2 more sources

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Neighborhood Preserving Non-Negative Tensor Factorization for Image Representation

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Non-negative Matrix Factorization (NMF) has become a powerful tool for image representation due to its enhanced semantic interpretability under non-negativity. Unfortunately, two types of neighborhood information essential to representation are lost in NMF. For individual image, the local structure information is missing in the vectorization, which can
Yu-Jin Zhang, Yu-Xiong Wang
exaly   +2 more sources

Non-negative Tri-factor tensor decomposition with applications

Knowledge and Information Systems, 2012
Non-negative matrix factorization (NMF) mainly focuses on the hidden pattern discovery behind a series of vectors for two-way data. Here, we propose a tensor decomposition model Tri-ONTD to analyze three-way data. The model aims to discover the common characteristics of a series of matrices and at the same time identify the peculiarity of each matrix ...
Zhongyuan Zhang, Tao Li 0001, Chris H. Q. Ding   +2 more
openaire   +2 more sources

FacetCube: a general framework for non-negative tensor factorization

Knowledge and Information Systems, 2012
Non-negative tensor factorization (NTF) has been successfully used to extract significant characteristics from polyadic data, such as data in social networks. Because these polyadic data have multiple dimensions (e.g., the author, content, and timestamp of a blog post), NTF fits in naturally and extracts data characteristics jointly from different data
Yun Chi, Shenghuo Zhu
openaire   +1 more source