Results 41 to 50 of about 21,847 (188)
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Lower bounds on the size of semidefinite programming relaxations
, 2014 We introduce a method for proving lower bounds on the efficacy of semidefinite programming (SDP) relaxations for combinatorial problems. In particular, we show that the cut, TSP, and stable set polytopes on $n$-vertex graphs are not the linear image of ...
Briët Jop +4 more
core +1 more source
Crater Observing Bioinspired Rolling Articulator (COBRA)
Advanced Intelligent Systems, EarlyView.Crater Observing Bio‐inspired Rolling Articulator (COBRA) is a modular, snake‐inspired robot that addresses the mobility challenges of extraterrestrial exploration sites such as Shackleton Crater. Incorporating snake‐like gaits and tumbling locomotion, COBRA navigates both uneven surfaces and steep crater walls.
Adarsh Salagame +4 more
wiley +1 more source
Nonnegative Tensor Factorization, Completely Positive Tensors and an Hierarchical Elimination Algorithm [PDF]
, 2013 Nonnegative tensor factorization has applications in statistics, computer vision, exploratory multiway data analysis and blind source separation. A symmetric nonnegative tensor, which has a symmetric nonnegative factorization, is called a completely ...
Qi, Liqun, Xu, Changqing, Xu, Yi
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Recombining Knowledge for Climate Innovation: Evidence From US Energy Incumbents
Business Strategy and the Environment, EarlyView.ABSTRACT As the climate crisis intensifies, energy incumbents must strategically transform their fossil‐fueled legacies to remain competitive and sustainable. Yet, little is known about how internal knowledge architectures and external industry positions jointly shape their capacity for climate innovation.
Kyung‐Baek Min +2 more
wiley +1 more source
IEEE AccessTo address the clustering of high-dimensional data, this paper proposes a novel semi-supervised clustering method named Constrained Symmetric Non-negative Matrix Factorization guided by Pairwise Constraint Propagation (PCSNMF).
Weiqian Zhang
doaj +1 more source
On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations
, 2016 The problem of finding overlapping communities in networks has gained much attention recently. Optimization-based approaches use non-negative matrix factorization (NMF) or variants, but the global optimum cannot be provably attained in general. Model-based approaches, such as the popular mixed-membership stochastic blockmodel or MMSB (Airoldi et al ...
Mao, Xueyu +2 more
openaire +2 more sources
, 2014 Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modeled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor ...
Chklovskii, Dmitri B. +2 more
core +1 more source
Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
Total positivity in loop groups I: whirls and curls [PDF]
, 2008 This is the first of a series of papers where we develop a theory of total positivity for loop groups. In this paper, we completely describe the totally nonnegative part of the polynomial loop group GL_n(\R[t,t^{-1}]), and for the formal loop group GL_n(\
Lam, Thomas, Pylyavskyy, Pavlo
core