arXiv subjects

Daniel Lizotte

Publications and source records attributed to Daniel Lizotte.

5 recordsLinked to original sources

arXiv2026

Pulse-Burst Excitation Reveals Time-Dose Reciprocity Breakdown in Mixed-Halide Perovskites

Time-dose reciprocity, commonly associated with the Bunsen-Roscoe law, states that the response of a photosensitive system depends only on the total exposure dose, regardless of how that energy is delivered over time. Light-sensitive processes in mixed-halide perovskites, such as photoinduced halide segregation, often exhibit threshold-like behavior that may violate this principle and enable material-state control by photon timing. We test this using pulse-burst excitation, which introduces an additional temporal control dimension beyond conventional parameters such as pulse fluence, repetition rate, and average power. By redistributing the same photon dose over microsecond-to-millisecond timescales, we create distinct nonequilibrium excitation conditions and show that mixed-halide perovskites can evolve into different metastable states, revealing a breakdown of time-dose reciprocity in the combined processes of halide segregation and remixing. This additional temporal degree of freedom not only enables control of the material state but also provides a new experimental framework for disentangling the competing processes underlying photoinduced halide redistribution. Our findings establish photon timing as a control parameter for perovskite photochemistry and open additional opportunities for optical memory and neuromorphic photonic applications.

cond-mat.mtrl-sci↗

arXiv2023

Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks

Contrastive representation learning has emerged as a promising technique for continual learning as it can learn representations that are robust to catastrophic forgetting and generalize well to unseen future tasks. Previous work in continual learning has addressed forgetting by using previous task data and trained models. Inspired by event models created and updated in the brain, we propose a new mechanism that takes place during task boundaries, i.e., when one task finishes and another starts. By observing the redundancy-inducing ability of contrastive loss on the output of a neural network, our method leverages the first few samples of the new task to identify and retain parameters contributing most to the transfer ability of the neural network, freeing up the remaining parts of the network to learn new features. We evaluate the proposed methods on benchmark computer vision datasets including CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the task-incremental, class-incremental, and domain-incremental continual learning scenarios.

cs.CV↗

arXiv2023

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection

Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detecting these attacks. Although numerous machine learning-based IDS have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained with the NSL-KDD dataset for four attack categories as well as normal network flow. Ultimately, our findings demonstrate that training the DRL on specific synthetic datasets can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.

cs.CR↗

arXiv2017

FRAMR-EMR: Framework for Prognostic Predictive Model Development Using Electronic Medical Record Data with a Case Study in Osteoarthritis Risk

Background-Prognostic predictive models are used in the delivery of primary care to estimate a patients risk of future disease development. Electronic medical record, EMR, data can be used for the construction of these models. Objectives- To provide a framework for those seeking to develop prognostic predictive models using EMR data, and to illustrate these steps using osteoarthritis risk estimation as an example. FRAMR-EMR-The FRAmework for Modelling Risk from EMR data, FRAMR-EMR, was created, which outlines step-by-step guidance for the construction of a prognostic predictive model using EMR data. Throughout these steps, several potential pitfalls specific to using EMR data for predictive purposes are described and methods for addressing them are suggested. Case Study-We used the DELPHI, DELiver Primary Healthcare Information, database to develop our prognostic predictive model for estimation of osteoarthritis risk. We constructed a retrospective cohort of 28447 eligible primary care patients. Patients were included if they had an encounter with their primary care practitioner between 1 January 2008 and 31 December 2009. Patients were excluded if they had a diagnosis of osteoarthritis prior to baseline. Construction of a prognostic predictive model following FRAMR-EMR yielded a predictive model capable of estimating 5-year risk of osteoarthritis diagnosis. Logistic regression was used to predict osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Internal validation of the models performance demonstrated good discrimination and moderate calibration. Conclusions-This study provides guidance to those interested in developing prognostic predictive models based on EMR data. The production of high quality prognostic predictive models allows for practitioner communication of accurately estimated risks of developing future disease among primary care patients.

stat.AP↗

arXiv2014

Theory and Application of Shapelets to the Analysis of Surface Self-assembly Imaging

A method for quantitative analysis of local pattern strength and defects in surface self-assembly imaging is presented and applied to images of stripe and hexagonal ordered domains. The presented method uses "shapelet" functions which were originally developed for quantitative analysis of images of galaxies ($\propto 10^{20}\mathrm{m}$). In this work, they are used instead to quantify the presence of translational order in surface self-assembled films ($\propto 10^{-9}\mathrm{m}$) through reformulation into "steerable" filters. The resulting method is both computationally efficient (with respect to the number of filter evaluations), robust to variation in pattern feature shape, and, unlike previous approaches, is applicable to a wide variety of pattern types. An application of the method is presented which uses a nearest-neighbour analysis to distinguish between uniform (defect-free) and non-uniform (strained, defect-containing) regions within imaged self-assembled domains, both with striped and hexagonal patterns.

cs.CV↗