Study uses ML to forecast ionospheric scintillation severity.
problem Predicting amplitude scintillation severity using real-time data.
method Developed and tested six ML models, XGBoost being the most effective.
result XGBoost model achieved 77% prediction accuracy for scintillation severity.
Real-time ionospheric images created from limited data using parallel Kalman filters.
problem Monitoring ionospheric irregularities using limited spatio-temporal observations.
method Bayesian framework with parallel Kalman filters and connectivity information.
result Real-time ionospheric images with high spatio-temporal resolution can be produced.
A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weather introduce irregularities in the Earth's ionosphere, disrupting the propagation of the radio signals that GNSS relies upon. Such disruptio…
S4 model improves long sequence modeling efficiency.
problem Handling long-range dependencies efficiently in convolutional models.
method S4 model uses global convolution with decaying kernel weights.
result SGConv model achieves similar performance to S4 with improved efficiency.
S4 learns new self-supervision automatically, improving accuracy with less human effort.
problem Lack of direct supervision in machine learning.
method Combines deep learning and probabilistic logic to automatically generate and verify new self-supervision.
result S4 can automatically propose accurate self-supervision, matching supervised methods with less human effort.
New method improves deep learning model robustness and accuracy for long sequences.
problem Challenges in learning long-range sequence tasks using state-space models.
method Proposes a perturb-then-diagonalize (PTD) methodology to address ill-posed diagonalization problems in SSMs.
result Demonstrates improved robustness and accuracy of S5-PTD model on Long-Range Arena benchmark.
We calculate the group of dualization operations for triple vector bundles, showing that it has order 96 and not 72 as given in Mackenzie's original treatment. The group is a nonsplit extension of S4 by the Klein group. Dualization operations are interpreted as functors on appropriate categories and are said to be equa…
The paper introduces a new linking form for 3-manifolds in S4.
problem Embedding rational homology 3-spheres in S4. method Triple torsion linking form, higher order intersection form on 4-manifolds.
result An embedding obstruction for rational homology spheres in S4. New spheres can split a 4D link in ways not possible in 3D.
problem Exploring how splitting spheres behave in 4D space.
method Constructing specific 2-component surface-links in S4. result Found non-isotopic splitting spheres in S4∖Lm,n. The study explores how 3-manifolds embed locally flatly in S4.
problem Embedding homology 3-spheres in S4 locally flatly. method Survey of known results, focusing on Seifert fibred 3-manifolds and using 4-dimension surgery.
result Characterization of embeddings by properties of complementary regions.
The paper provides a theoretical justification for using stable SSM blocks in deep sequential models.
problem Developing generalization bounds for deep sequential models with varying sequence lengths.
method Using Rademacher contraction and stability constraints, the paper derives a PAC bound that is independent of sequence length.
result The derived PAC bound decreases as the stability of SSM blocks increases, providing theoretical justification for their use.
Computes extendable mapping classes for knotted surfaces in S4.
problem Computing extendable mapping classes for knotted surfaces in S4. method Using ordinary untwisted rim surgery and meridian-longitude rigidity conditions on knot groups.
result Exact computation of extendable mapping classes for specific knotted surfaces.
We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed…
KalMamba improves RL efficiency with probabilistic SSMs.
problem Efficiency in learning and inference for probabilistic SSMs in RL.
method Combines Mamba's scalability with Kalman filtering for efficient probabilistic SSMs.
result KalMamba outperforms state-of-the-art SSMs in RL, especially on longer sequences.
The gradient flow of the Yang-Mills action acts pointwise on closed loops of gauge fields. We construct a topologically nontrivial loop of SU(2) gauge fields on S4 that is locally stable under the flow. The stable loop is written explicitly as a path between two gauge fields equivalent under a topologically nontrivial …
As part of daily monitoring of human activities, wearable sensors and devices are becoming increasingly popular sources of data. With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently. In this paper,…
bioLeak addresses data leakage in biomedical machine learning studies.
problem Data leakage causes optimistic bias in machine learning models for biomedical studies.
method bioLeak provides leakage-aware resampling workflows and model audits in R.
result The package supports various machine learning tasks and can detect leakage mechanisms.
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models. The goal of lifted inference is to carry out probabilistic inference without needing to reason about each individual separately, by instead treating exchangeabl…
Improved Mamba model for long-range sequence tasks.
problem Mamba's poor performance on long-range sequential tasks.
method Proposed B2S6, combining block-wise selective dynamics and channel-specific bias.
result Empirically, B2S6 outperforms S4 and S4D on LRA tasks.
LS4 models time-series with latent states, outperforming previous methods.
problem Learning sharp transitions in time-series data.
method State space ODE with convolutional representation to bypass hidden states.
result LS4 significantly outperforms previous models in various metrics.
HOPE improves SSMs for long-memory tasks with robust initialization and training.
problem Improving state-space models for long-memory tasks with robust initialization and training.
method Developed a new parameterization scheme called HOPE using Hankel operators and Markov parameters.
result HOPE improves SSMs' performance on Long-Range Arena tasks and demonstrates non-decaying memory.