elleobrien

arxiv_id,category,field,title,abstract
2607.05393v1,astro-ph.GA,Astrophysics,Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification,"Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We aim to develop a Real-Bogus classification framework that can be trained without human-labeled data using injected transients and bogus-dominated survey data, remains robust under strong class contamination, and provides calibrated uncertainty quantification. We combine simulated transient injections with a contaminated survey class and train a dual-network model using asymmetric co-teaching for classes with different label-noise levels. We evaluate performance on a benchmark subset and analyze the learned representation with latent-space visualization tools. For uncertainty quantification (UQ), we comp

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abstract_id,abstract
ABS-001,"Background and Objectives : Migraine is a common neurological condition that can affect daily life and well-being. Lifestyle factors such as physical activity, sleep, diet, and stress are often discussed in relation to migraine, but their role is not always consistent. This study aimed to examine how selected lifestyle factors are related to migraine frequency and intensity in adults, with a focus on physical activity and dietary habits. Materials and Methods : A cross-sectional study was conducted among 300 adults recruited through an online migraine-focused community from 1 January to 28 February 2026. Participants completed a questionnaire addressing migraine history, frequency and duration of attacks, pain intensity, physical activity, sleep duration, perceived stress, and dietary habits. Associations were assessed using Spearman correlation and multiple linear regression analyses. Results : Most participants were female (88%), and 48% reported physician-diagnosed migraine. 

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from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import numpy as np

# Make synthetic data
X,y = make_classification(n_samples = 1000)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(

elleobrien

MNIST classification example using DVC to manage dataset and pipelines.

1. Install dvc in your computer if you haven't done it already

2. In your project repository, initialize Git and DVC.

git init
dvc init

elleobrien

# A function that prints the game board as it currently stands
def make_grid(board):
    print("\t       GAME        \t\t        KEY         \n")
    print("\t  {}  |   {}  |  {}   \t\t  1  |   2  |  3  ".format(board['1'], board['2'], board['3']))
    
    lineA = "\t_____|______|_____\t\t_____|______|_____"
    lineB = "\t     |      |     \t\t     |      |     "
    
    print(lineA)
    print(lineB)