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This https://arxiv.org/abs/2401.05580 has been replaced.
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Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis
Diffuse correlation spectroscopy (DCS) is an emerging noninvasive technique that measures the tissue blood flow, by using near-infrared coherent point-source illumination to detect spectral changes. While machine learning has demonstrated significant potential for measuring blood flow index (BFi), an open question concerning the success of this approach pertains to its robustness in scenarios involving deviations between datasets with varying Signal-to-Noise Ratios (SNRs) originating from diver…

Imputing missing not-at-random longitudinal marker values in time-to-event analysis: fully conditional specification multiple imputation in joint modeling
Havi Murad, Nirit Agay, Rachel Dankner
https://arxiv.org/abs/2403.19192

Imputing missing not-at-random longitudinal marker values in time-to-event analysis: fully conditional specification multiple imputation in joint modeling
We propose a procedure for imputing missing values of time-dependent covariates in a survival model using fully conditional specification. Specifically, we focus on imputing missing values of a longitudinal marker in joint modeling of the marker and time-to-event data, but the procedure can be easily applied to a time-varying covariate survival model as well. First, missing marker values are imputed via fully conditional specification multiple imputation, and then joint modeling is applied for …

3D Printing in Microfluidics: Experimental Optimization of Droplet Size and Generation Time through Flow Focusing, Phase, and Geometry Variation
Adam Britel, Giulia Tomagra, Pietro Apr\`a, Veronica Varzi, Sofia Sturari, Nour-hanne Amine, Paolo Olivero, Federico Picollo
https://arxiv.org/abs/2402.17876

3D Printing in Microfluidics: Experimental Optimization of Droplet Size and Generation Time through Flow Focusing, Phase, and Geometry Variation
Droplet-based microfluidics systems have become widely used in recent years thanks to their advantages, varying from the possibility of handling small fluid volumes to directly synthesizing and encapsulating various living forms for biological-related applications. The effectiveness of such systems mainly depends on the ability to control some of these system's parameters, such as produced droplet size and formation time, which represents a challenging task. This work reports an experimental st…

Measurement of Proton-Induced Reactions on Lanthanum from 55--200 MeV by Stacked-Foil Activation
Jonathan T. Morrell, Ellen M. O'Brien, Michael Skulski, Andrew S. Voyles, Dmitri G. Medvedev, Veronika Mocko, Lee A. Bernstein, C. Etienne Vermeulen
https://arxiv.org/abs/2402.17893

Measurement of Proton-Induced Reactions on Lanthanum from 55--200 MeV by Stacked-Foil Activation
Cerium-134 is an isotope desired for applications as a chemical analogue to the promising therapeutic radionuclide $^{225}$Ac, for use in bio-distribution assays as an in vivo generator of the short-lived positron-emitting isotope $^{134}$La. In the 50-100 MeV energy range relevant to the production of $^{134}$Ce by means of high-energy proton bombardment of lanthanum, existing cross section data are discrepant and have gaps at important energies. To address these deficiencies, a series of 17 $…

Imputing missing not-at-random longitudinal marker values in time-to-event analysis: fully conditional specification multiple imputation in joint modeling
Havi Murad, Nirit Agay, Rachel Dankner
https://arxiv.org/abs/2403.19192

Imputing missing not-at-random longitudinal marker values in time-to-event analysis: fully conditional specification multiple imputation in joint modeling
We propose a procedure for imputing missing values of time-dependent covariates in a survival model using fully conditional specification. Specifically, we focus on imputing missing values of a longitudinal marker in joint modeling of the marker and time-to-event data, but the procedure can be easily applied to a time-varying covariate survival model as well. First, missing marker values are imputed via fully conditional specification multiple imputation, and then joint modeling is applied for …

Distributionally robust monopoly pricing: Switching from low to high prices in volatile markets
Tim S. G. van Eck, Pieter Kleer, Johan S. H. van Leeuwaarden
https://arxiv.org/abs/2403.19486

Distributionally robust monopoly pricing: Switching from low to high prices in volatile markets
Traditional monopoly pricing assumes sellers have full information about consumer valuations. We consider monopoly pricing under limited information, where a seller only knows the mean, variance and support of the valuation distribution. The objective is to maximize expected revenue by selecting the optimal fixed price. We adopt a distributionally robust framework, where the seller considers all valuation distributions that comply with the limited information. We formulate a maximin problem whi…

A Study of Three Influencer Archetypes for the Control of Opinion Spread in Time-Varying Social Networks
Michael DeBuse, Sean Warnick
https://arxiv.org/abs/2403.18163

A Study of Three Influencer Archetypes for the Control of Opinion Spread in Time-Varying Social Networks
In this work we consider the impact of information spread in time-varying social networks, where agents request to follow other agents with aligned opinions while dropping ties to neighbors whose posts are too dissimilar to their own views. Opinion control and rhetorical influence has a very long history, employing various methods including education, persuasion, propaganda, marketing, and manipulation through mis-, dis-, and mal-information. The automation of opinion controllers, however, has …

A Modelling Investigation for Solar Flare X-ray Stereoscopy with Solar Orbiter/STIX and Earth Orbiting Missions
Natasha L. S. Jeffrey, Säm Krucker, Morgan Stores, Eduard P. Kontar, Pascal Saint-Hilaire, Andrea F. Battaglia, Laura Hayes, Hannah Collier, Astrid Veronig, Yang Su, Srikar Paavan Tadepalli, Fanxiaoyu Xia
https://arXiv…

A Modelling Investigation for Solar Flare X-ray Stereoscopy with Solar Orbiter/STIX and Earth Orbiting Missions
The Spectrometer/Telescope for Imaging X-rays (STIX) on board Solar Orbiter (SolO) provides a unique opportunity to systematically perform stereoscopic X-ray observations of solar flares with current and upcoming X-ray missions at Earth. These observations will produce the first reliable measurements of hard X-ray (HXR) directivity in decades, providing a new diagnostic of the flare-accelerated electron angular distribution and helping to constrain the processes that accelerate electrons in fla…

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
https://arxiv.org/abs/2403.18375

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and a…

Thinking inside the bounds: Improved error distributions for indifference point data analysis and simulation via beta regression using common discounting functions
Mingang Kim, Mikhail N. Koffarnus, Christopher T Franck
https://arxiv.org/abs/2404.18000

Thinking inside the bounds: Improved error distributions for indifference point data analysis and simulation via beta regression using common discounting functions
Standard nonlinear regression is commonly used when modeling indifference points due to its ability to closely follow observed data, resulting in a good model fit. However, standard nonlinear regression currently lacks a reasonable distribution-based framework for indifference points, which limits its ability to adequately describe the inherent variability in the data. Software commonly assumes data follow a normal distribution with constant variance. However, typical indifference points do not…