InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference

Single-particle tracking (SPT) grants unprecedented insight into cellular function at the molecular scale [1]. Throughout the cell, the movement of single-molecules is generally heterogeneous and complex. Hence, there is an imperative to understand the multi-scale nature of single-molecule dynamics in biological systems. We have previously shown that with high-density SPT, spatial maps of the parameters that dictate molecule motion can be generated to intricately describe cellular environments [2,3,4]. To date, however, there exist no publically available tools that reconcile trajectory data to generate the aforementioned maps. We address this void in the SPT community with InferenceMAP: an interactive software package that uses a powerful Bayesian method to map the dynamic cellular space experienced by individual biomolecules.Comment: 56 page

Boyer-Moore strategy to efficient approximate string matching

International audienceWe propose a simple but e cient algorithm for searching all occurrences of a pattern or a class of patterns (length m) in a text (length n) with at most k mismatches. This algorithm relies on the Shift-Add algorithm of Baeza-Yates and Gonnet [6], which involves representing by a bit number the current state of the search and uses the ability of programming languages to handle bit words. State representation should not, therefore, exceeds the word size w, that is, m(⌈log2(k+1)⌉+1 )≤w. This algorithm consists in a preprocessing step and a searching step. It is linear and performs 3n operations during the searching step. Notions of shift and character skip found in the Boyer-Moore (BM) [9] approach, are introduced in this algorithm. Provided that the considered alphabet is large enough (compared to the Pattern length), the average number of operations performed by our algorithm during the searching step becomes n(2+(k+4)/(m-k))

Performing transient vibroacoustic analysis on a continuously variable transmission using a Vold-Kalman filter

Abstract: A continuously variable transmission (CVT) is a type of automatic transmission system that uses belts and pulleys to provide an infinite number of gear ratios. The system consists of two pulleys: a primary drive pulley and a secondary drive pulley, each with a fixed and a moving sheave. In snowmobiles, he primary pulley is connected to the crankshaft of a two-stroke engine, while the secondary pulley is connected to the track element. The use of a two-stroke combustion engine can cause torque fluctuations to propagate through the crankshaft to the CVT, resulting in excessive vibration and noise levels. To evaluate the vibroacoustic emissions of CVTs, alternative methods have been developed due to the limitations of road noise testing. One such method is to use an adaptive controller on a dynamometer to perform a repeatable acceleration phase, allowing for efficient and sophisticated acoustic analysis. Vold-Kalman order tracking (VKF-OT) with a phase-locked loop (PLL) is used to analyze the non-stationary periodic components of the noise generated by the CVT. This study conducted dynamometer experiments to identify the noise generated by the CVT during the acceleration phase. However, the analysis of the acoustic data can be difficult due to the lack of repeatability of the speed ramps during dynamometer tests. To solve this problem, post-processing methods have been proposed to synchronize the acoustic measurements taken at different speed ramps. The objective is to develop an adaptive approach to identify the prominent noise order, allowing a comparison of vibroacoustic performance between different CVT designs. Experimental results demonstrated the effectiveness of this experimental tool to analyze the attenuation of certain low order intensities during the acceleration phase.Résumé de la communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

It’s All Rocket Science: On the Equivalence of Development Timelines for Aerospace and Nuclear Technologies

Early in the lifecycle of a system development, systems engineers must execute trade studies to allocate resources between different research and development efforts that are developing technologies to be deployed into the system, and they must prepare risk management plans for the selected technologies. We have been developing a statistical model for schedule and cost uncertainty based on a small number of inputs that are quite objective and are already integrated with technology readiness assessment. An algorithm that transforms Technical Maturity (TM) scores from Department of Energy projects into a Technology Readiness Level (TRL) score was created, allowing us to add data from a US Department of Energy to an existing set of data from NASA. We statistically tested whether the two samples (i.e. the DoE and NASA datasets) were randomly drawn from the same population and concluded that the transition times for developing aerospace and nuclear technologies are very similar

When Random Tensors meet Random Matrices

Relying on random matrix theory (RMT), this paper studies asymmetric order-$d$ spiked tensor models with Gaussian noise. Using the variational definition of the singular vectors and values of (Lim, 2005), we show that the analysis of the considered model boils down to the analysis of an equivalent spiked symmetric \textit{block-wise} random matrix, that is constructed from \textit{contractions} of the studied tensor with the singular vectors associated to its best rank-1 approximation. Our approach allows the exact characterization of the almost sure asymptotic singular value and alignments of the corresponding singular vectors with the true spike components, when $\frac{n_i}{\sum_{j=1}^d n_j}\to c_i\in (0, 1)$ with $n_i$'s the tensor dimensions. In contrast to other works that rely mostly on tools from statistical physics to study random tensors, our results rely solely on classical RMT tools such as Stein's lemma. Finally, classical RMT results concerning spiked random matrices are recovered as a particular case

Real time scatterometry: a new metrology for in situ microelectronics process control

In situ and real time control of the different process steps in semiconductor device manufacturing becomes a critical challenge, especially for the lithography and plasma etching processes. Real time scatterometry is among the few solutions able to meet the requirement for in line monitoring. In this paper we demonstrate that real time scatterometry can be used as a real time monitoring technique during the resist trimming process. For validation purposes the real time scatterometry measurements are compared with 3D Atomic Force Microscopy measurements made in the same process conditions. The agreement between both is excellent

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 \textit{in-silico} type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people