Search for: All records

Summary Estimation of crossed random effects models commonly incurs computational costs that grow faster than linearly in the sample size $ N $, often as fast as $$ \Omega(N^{3/2}) $$, making them unsuitable for large datasets. For non-Gaussian responses, integrating out the random effects to obtain a marginal likelihood poses significant challenges, especially for high-dimensional integrals for which the Laplace approximation may not be accurate. In this article we develop a composite likelihood approach to probit models that replaces the crossed random effects model with some hierarchical models that require only one-dimensional integrals. We show how to consistently estimate the crossed effects model parameters from the hierarchical model fits. We find that the computation scales linearly in the sample size. The method is illustrated by applying it to approximately five million observations from Stitch Fix, where the crossed effects formulation would require an integral of dimension larger than $$ 700\,000 $$. 

We report on a new class of Ising machines (IMs) that rely on coupled parametric frequency dividers (PFDs) as macroscopic artificial spins. Unlike the IM counterparts based on subharmonic-injection locking (SHIL), PFD IMs donot require strong injected continuous-wave signals or applied dc voltages. Therefore, they show a significantly lower power consumption per spin compared to SHIL-based IMs, making it feasible to accurately solve large-scale combinatorial optimization problems that are hard or even impossible to solve by using the current von Neumann computing architectures. Furthermore, using high quality factor resonators in the PFD design makes PFD IMs able to exhibit a nanowatt-level power per spin. Also, it remarkably allows a speedup of the phase synchronization among the PFDs, resulting in shorter time to solution and lower energy to solution despite the resonators’ longer relaxation time. As a proof of concept, a 4-node PFD IM has been demonstrated. This IM correctly solves a set of Max-Cut problems while consuming just 600 nanowatts per spin. This power consumption is 2 orders of magnitude lower than the power per spin of state-of-the-art SHIL-based IMs operating at the same frequency. 

In this report, the relative efficiency of cellulose nanocrystals (CNCs) and nanofibers (CNFs) to capture circulating tumor cells (CTCs) from the blood sample of head and neck cancer (HNC) patients was evaluated. Detection and enumeration of CTCs are critical for monitoring cancer progression. Both types of nanostructured cellulose were chemically modified with Epithelial Cell Adhesion Molecule (EpCAM) antibody and iron oxide nanoparticles. The EpCAM antibody facilitated the engagement of CTCs, promoting entrapment within the cellulose cage structure. Iron oxide nanoparticles, on the other hand, rendered the cages activatable via the use of a magnet for the capture and separation of entrapped CTCs. The efficiency of the network structures is shown in head and neck cancer (HNC) patients' blood samples. It was observed that the degree of chemical functionalization of hydroxyl groups located within the CNCs or CNFs with anti-EpCAM determined the efficiency of the system's interaction with CTCs. Further, our result indicated that inflexible scaffolds of nanocrystals interacted more efficiently with CTCs than that of the fibrous CNF scaffolds. Network structures derived from CNCs demonstrated comparable CTC capturing efficiency to commercial standard, OncoDiscover®. The output of the work will provide the chemical design principles of cellulosic materials intended for constructing affordable platforms for monitoring cancer progression in 'real time'.