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Found 9 papers, 5 papers with code
Trojan Horse Hunt in Time Series Forecasting for Space Operations
Participants are provided with 1) a large public dataset of real-life multivariate satellite telemetry (without triggers), 2) a reference model trained on the clean data, 3) a set of poisoned neural hierarchical interpolation (N-HiTS) models for time series forecasting trained on the dataset with injected triggers, and 4) Jupyter notebook with the training pipeline and baseline algorithm (the latter will be published in the last month of the competition).
MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
Unlike existing approaches that either depend on model structure or autoencoder-based sampling, MASCOTS directly generates meaningful and diverse counterfactual observations in a model-agnostic manner, operating on both univariate and multivariate data.
The OPS-SAT benchmark for detecting anomalies in satellite telemetry
Detecting anomalous events in satellite telemetry is a critical task in space operations.
European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts.
DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction
The prediction of intrinsic disorder regions has significant implications for understanding protein functions and dynamics.
Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors
In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging.
Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma.
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM).
Improved robust weighted averaging for event-related potentials in EEG
The areas of improvement include significantly lower averaging error (45% lower RMSE and 37% lower maximum difference than for original implementation) and increased robustness to local minima, strong outliers and corrupted epochs common to real-life EEG signals, especially from low-cost devices.
