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An analysis to go with a blog post on psychonomic.org, inspired by the XKCD cartoon "Settled".
Data Types:
• Software/Code
CLCNLDAIS is a continual learning classification method with new labeled data. It is inspired by the intelligent mechanism that vaccine can enhance immunity. Its performance can be improved by learning the new labeled data during testing stage. The less type to train, the more advantages it has when there are some new labeled data coming from the unknown types. It only has one parameter and can do in parallel for high-dimensional data. I have not written the parallel code. The data should be prepared like the examples.
Data Types:
• Software/Code
This contains the Python version of the code and experimental data. The ‘FKDA_**’ file in ‘FKDA_**’ includes batch experiments, incremental experiments and novelty detection experiment. The ‘FKDA_**_Utils’ file includes all the functions.
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• Software/Code
We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods. We propose a novel approach to designing higher-order zero-phase low-pass, high-pass, and band-pass infinite impulse response filters as matrices, using spectral transformation of the state-space representation of digital filters. We also propose a proximal gradient-based technique to factorize a special class of zero-phase high-pass and band-pass digital filters so that the factorization product preserves the zero-phase property of the filter and also incorporates a sparse-derivative component of the input in the signal model. To demonstrate applications of our novel filter designs, we validate and propose new signal models to simultaneously denoise and identify patterns of interest. We begin by using our proposed filter design to test an existing signal model that simultaneously combines linear time invariant (LTI) filters and sparsity-based methods. We develop a new signal model called sparsity-assisted signal denoising (SASD) by combining our proposed filter designs with the existing signal model. Using simulated data, we demonstrate the robustness of the SASD signal model across different orders of filter and noise levels. Thereafter, we propose and derive a new signal model called sparsity-assisted pattern recognition (SAPR). In SAPR, we combine LTI band-pass filters and sparsity-based methods with orthogonal multiresolution representations, such as wavelets, to detect specific patterns in the input signal. Finally, we combine the signal denoising and pattern recognition tasks, and derive a new signal model called the sparsity-assisted signal denoising and pattern recognition (SASDPR). We illustrate the capabilities of the SAPR and SASDPR frameworks using sleep-electroencephalography data to detect K-complexes and sleep spindles, respectively.
Data Types:
• Software/Code
This code 'capsule' is meant to accompany the manuscript "Life cycle progression and sexual development of the apicomplexan parasite Cryptosporidium parvum." It is an fully self-contained environment that allows anyone to reproduce analyses and figures in this manuscript. The apicomplexan parasite Cryptosporidium is a leading global cause of severe diarrhoeal disease and an important contributor to early childhood mortality. Currently, there are no fully effective treatments or vaccines available. Parasite transmission occurs through ingestion of oocysts, through either direct contact or consumption of contaminated water or food. Oocysts are meiotic spores and the product of parasite sex. Cryptosporidium has a single-host life cycle in which both asexual and sexual processes occur in the intestine of infected hosts. Here, we genetically engineered strains of Cryptosporidium to make life cycle progression and parasite sex tractable. We derive reporter strains to follow parasite development in culture and in infected mice and define the genes that orchestrate sex and oocyst formation through mRNA sequencing of sorted cells. After 2 d, parasites in cell culture show pronounced sexualization, but productive fertilization does not occur and infection falters. By contrast, in infected mice, male gametes successfully fertilize female parasites, which leads to meiotic division and sporulation. To rigorously test for fertilization, we devised a two-component genetic-crossing assay using a reporter that is activated by Cre recombinase. Our findings suggest obligate developmental progression towards sex in Cryptosporidium, which has important implications for the treatment and prevention of the infection.
Data Types:
• Software/Code
R code for generating unique identifiers for use in survey research.
Data Types:
• Software/Code
This capsule contains the code to reproduce the figures and analysis framework presented in the paper titled "Bounding Computational Complexity under Cost Function Scaling in Predictive Control." We propose a method of computing the computational complexity bounds for first-order methods applied to solving the constrained LQR quadratic program (QP). Specifically, we have implemented the Fast Gradient Method and Dual Gradient Projection method in this capsule. These bounds are computed using system-theoretic techniques (such as the H-infinity norm) applied to Toeplitz operators, allowing for the horizon-independent bounds to be computed without forming the actual QP matrices. The code provided in this capsule does the following: 1) Compute the horizon-independent bounds 2) Compute the effect of cost function scaling on the bounds and simulate the algorithm under each scaling factor 3) Compute a new preconditioner using a closed-form expression and demonstrate its perfromance as the cost function scales versus the existing optimal SDP preconditioner.
Data Types:
• Software/Code
This work deals with mathematical modeling of tumour growth, we present an example of a model of a solid cancer tumour and this modeling uses cellular automata which is consider as a grid of size $N\times N$. We begin the simulation with four proliferating cancer stem cells which reside in the centre of the grid of the Cellular Automaton and they react with the environment via certain partial differential equations (PDEs). We show how these proliferating cells develop by consumption and production of nutrients in the medium (oxygen, glucose, hydrogen), and how it react with the constraint ECM by secreting MDE, and the decision of life cycle of each cells is taken by neural network which takes the last chemicls filed concentrations as a input data. The multiplication of the cells introduces new proliferating cells, quiescent, necrotic or in apopotosis. After the tumour has reached to a certain size the process of formation of new blood vessels (angiogenesis) re-supply the tumour with sufficient nutrients for growth, and this process enlarge the probability of occurrence of bad mutation. The primary objective of this part is to present the function of the cellular automaton and the evolution of the tumour and regarding the results. We applied the simulations using Python sofware which provides us with a number of software, libraries and functions that simulate solid heterogenous tumour.
Data Types:
• Software/Code
The field of Pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community.
Data Types:
• Software/Code
This code introduces data-driven matched field processing, a framework to build models of multimodal propagation environments from measured data and then uses these models to locate damage. The example code demonstrates data-driven matched field processing's localization performance with two nearby scatterers from experimental measurements on an aluminum plate. When compared with delay-based models that are commonly used in structural health monitoring, data-driven matched field processing successfully localizes both scatterers with significantly smaller localization errors and finer resolutions.
Data Types:
• Software/Code
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