New BMI decoder robust to future neural variability.
problem Current BMIs become ineffective with changing neural conditions.
method Trained multiplicative recurrent neural network to handle diverse neural conditions and synthetic perturbations.
result Successfully learned and became more robust to various neural-to-kinematic mappings.
New BMI interface stabilizes user experience over long periods.
problem Inconsistent neural signal data leads to interface recalibration.
method Adversarial Domain Adaptation Network to match residual distributions.
result Adversarial Domain Adaptation Network outperforms other methods.
Machine learning improves neural decoding performance.
problem Traditional neural decoding methods are inefficient.
method Apply modern machine learning algorithms (neural networks, gradient boosting) for neural decoding.
result Modern methods significantly outperform traditional approaches.
EEG-TCNet improves MI-BMIs with high accuracy and low resource usage.
problem Improving motor-imagery brain-machine interfaces with high accuracy and low resource usage.
method Proposes EEG-TCNet, a novel TCN for embedded MI-BMIs.
result EEG-TCNet achieves 83.84% classification accuracy on MOABB, outperforming SoA by 0.25.
Deep neural networks decode natural visual scenes from neural spikes.
problem Decoding visual scenes from neural spikes for brain-machine interfaces.
method Developed a novel spike-image decoder (SID) using deep neural networks.
result SID reconstructs natural visual scenes from neural spikes with high accuracy.
Self-training improves gradual domain adaptation with unlabeled data.
problem Improving machine learning models' adaptability to gradually shifting data distributions.
method Proved upper bounds on self-training error, highlighted the importance of regularization and label sharpening, and demonstrated algorithmic insights.
result Self-training works well for gradual shifts, especially with small Wasserstein-infinity distance.
Paper reviews neuromorphic engineering features and compares analog vs digital systems.
problem Lack of consensus and unclear features in neuromorphic engineering.
method Review of recent work, comparison of machine learning accelerator chips.
result Analog processing and reduced bit precision architectures offer best efficiencies.
Study shows diffused interface flows to single diffused balls over time.
problem Volume-preserving mean curvature flow in Euclidean space.
method Diffused interface version, exponential convergence proof.
result Exponential convergence to single diffused balls.
Extends diffuse interface methods to graphs and hypergraphs with non-smooth potentials.
problem Semi-supervised learning on graphs and hypergraphs.
method Generalizes diffuse interface methods using non-smooth potential functions and hypergraph Laplacians.
result The diffuse interface method can be applied to both graph and hypergraph data.
Paper introduces probabilistic module interface for complex models and inference.
problem Handling complex probabilistic models with latent variables and custom inference methods.
method Develops a platform-agnostic interface for encapsulating models and inference programs, allowing sound approximate inference algorithms for networks of modules.
result Sound approximate inference algorithms can be constructed for networks of probabilistic modules.
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
Develops a 2D Allen-Cahn theory for interfaces with boundary.
problem Analyzing interfaces with boundary in the line-bundle framework.
method Constructs a model solution on the punctured plane and develops its stability and invertibility theory.
result Constructs Allen-Cahn sections concentrating on prescribed line segments.
Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
We consider the regularity of an interface between two incompressible and inviscid fluids flows in the presence of surface tension. We obtain local in time estimates on the interface in H23k+1 and the velocity fields in H23k. These estimates are obtained using geometric considerations which show th…
Study boundary behavior of limit interfaces in Riemannian manifolds without convexity assumptions.
problem Boundary behavior of limit interfaces in Riemannian manifolds.
method Proves limit-interface is a free boundary varifold, integer rectifiable up to boundary.
result No convexity assumption required; valid even when limit-interface clusters near boundary.
Deep learning predicts curvature of 2D interfaces in level-set method.
problem Estimating curvature in level-set method for complex interfaces.
method Deep learning using feed-forward neural networks trained on synthetic data.
result Deep learning models approximate curvature with comparable precision to traditional methods.
sktime provides a unified interface for time series machine learning.
problem Handling diverse time series learning tasks.
method Unified API for scikit-learn compatible time series tasks.
result Unified approach simplifies time series machine learning.
Interactive interface for specifying complex tasks through demonstrations.
problem Challenges in specifying high-dimensional reward functions for complex tasks.
method Interactive interface with a set of increasingly complex policies trained on demonstrations.
result Successfully learned a complex task (Lunar Lander) with interactive demonstrations.
Proposes new feature transformation methods for brain interface models.
problem Sub-optimality of feature ranking and selection in brain interface models.
method Introduces maximum mutual information linear and nonlinear transformations.
result Significantly better performance in binary and multi-class decoding analyses.
Study on the regularity of p-Gauss curvature flow near flat interfaces.
problem Regularity of p-Gauss curvature flow near flat interfaces. method Analysis of convex hypersurface near the interface.
result Regularity of the convex hypersurface near the interface.
Analytic patch trees reveal new geometric structures and dimension fields.
problem Understanding the geometric and analytical properties of surface patch trees.
method Developed analytic surface patch trees and introduced interface curves to transmit state.
result Surface patch trees have natural foliations with one-dimensional curve trees and dimension fields.
OrbTouch uses deep learning to interpret touch gestures on stretchable surfaces.
problem Efficiently extracting information from soft, deformable interfaces.
method Convolutional neural networks applied to stretchable capacitor signals.
result Successfully mapped human touch gestures to categorical labels and touch location.
Agents learn user preferences with less explicit feedback via spatial interface valuing.
problem Learning user preferences with high cognitive load feedback.
method Spatial Interface Valuing for reduced explicit feedback.
result Agents learn faster with spatial interface valuing compared to explicit feedback.
Common models for two-phase lipid bilayer membranes are based on an energy that consists of an elastic term for each lipid phase and a line energy at interfaces. Although such an energy controls only the length of interfaces, the membrane surface is usually assumed to be at least C1 across phase boundaries. We consi…
Generative adversarial networks improve brain-computer interface performance with limited data.
problem Limited training samples in brain-computer interfaces.
method Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for data augmentation.
result Generated artificial EEG data improves classification accuracy in brain-computer interface tasks.
Improved node classification in signed social networks using diffuse interface methods.
problem Classifying nodes in signed social networks (positive and negative interactions).
method Diffuse interface methods based on Ginzburg-Landau functional and extended graph Laplacian.
result Performance improvement in real signed social networks, outperforming state of the art.
Study evaluates features and classifiers for brain-computer interface tasks.
problem Improving accuracy in brain-computer interface communication.
method Examined six classical features and twelve classifiers across nine datasets.
result Energy in α and η bands, and Bayesian classifier with Gaussian assumption, outperform other methods.
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
problem Reducing the cost of user interface experimentation in online retail.
method Modeling user interface experimentation as an opportunistic bandit problem, reducing the cost of exploration.
result Significant regret reduction and improved contextual information for testing.
Study motion of discrete interfaces on triangular lattice using Almgren, Taylor, and Wang's approach.
problem Motion of discrete interfaces on triangular lattice driven by ferromagnetic interactions.
method Coupling Almgren, Taylor, and Wang's minimizing movements approach with Braides, Gelli, and Novaga's discrete-to-continuum analysis.
result Limit motion of origin-symmetric convex hexagons compared to crystalline curvature evolution.
Study on evolving interfaces with complex curvature and density effects.
problem Understanding the dynamics of evolving heterogeneous elastic interfaces.
method Modeling an evolving curve with a density function, analyzing the associated gradient flow evolution.
result Analysis of the preservation and asymptotic behavior of geometric properties in the evolving system.
Study on liquid-vapor interfaces in stable equilibrium without assuming prior regularity.
problem Understanding the conditions for a liquid-vapor interface to be in stable equilibrium without assuming prior regularity.
method Proposes a weakest set of mathematical assumptions using varifold regularity theory and identifies a suitable stability condition.
result The liquid-vapor interface is a smoothly embedded analytic surface in stable equilibrium.
Study modular concept learning with different oracle interfaces.
problem Learning a concept that is a cross product of component concepts.
method Analyze different types of oracle interfaces and queries.
result Modular concept learning is easier with positive examples and membership queries.
New algorithm allows IGL to work with action-inclusive feedback.
problem IGL's failure in scenarios with action-inclusive feedback.
method Developed an algorithm and provided theoretical guarantees.
result Demonstrated effectiveness on large-scale experiments.
The Weyl Law is proven for phase transitions, with applications to minimal hypersurfaces and separating interfaces.
problem Proving the Weyl Law for phase transition spectrum and understanding the density of limit interfaces.
method Techniques of Liokumovich-Marques-Neves and recent work of Chodosh-Mantoulidis.
result Density of separating limit interfaces and existence of infinitely many minimal hypersurfaces for generic metrics.
SG-DNI outperforms standard neural interfaces in speed and accuracy.
problem Inflexible structure of neural networks limits parallelization.
method Introduces synthetic gradients with decoupled neural interfaces (SG-DNI).
result SG-DNI is over 3-fold faster with comparable accuracy.
Prediction Factory automates predictive model development and evaluation.
problem Rapidly developing and sharing predictive models with domain experts.
method Data science automation system with three interfaces: baseline, full, and optional automation.
result Full automation interface generated reports funded 57.5% of the time, compared to 42.5% for baseline.
New AI assistant for power grid operators simplifies complex decision-making.
problem Complexity and uncertainty in power grid operations.
method Unified human-machine interface and AI integration.
result Development of a new assistant framework for power grid operators.
Study creates web interface to elicit user-preferred metrics.
problem Eliciting classification metrics that align with user preferences.
method Developed a web-based interface and conducted a user study.
result Users preferred metrics that align with their task and context.
ARIMLE optimizes classifier fusion for brain-computer interface.
problem Improving ensemble classifier aggregation performance.
method ARIMLE uses agreement rate to estimate classifier accuracy, then refines a maximum likelihood estimator.
result ARIMLE outperforms majority voting and other methods in brain-computer interface applications.
2d dualities linked to 4-simplex triangulation.
problem Exploring dualities between 2d and 3d theories via 4-manifold geometry.
method Using Pachner moves and supersymmetric half-indices.
result Identified IR dualities and abelian dualities.
The paper studies graphs minimizing Dirichlet energy with analytic boundaries, confirming a conjecture about singularities.
problem Understanding the singularities of area-minimizing currents with real analytic boundaries.
method Analyzing multi-valued graphs with real analytic interfaces that minimize Dirichlet energy.
result Dirichlet energy-minimizers with analytic boundary singularities are discrete in 2 dimensions, confirming a conjecture by B. White.
Study stability and bifurcation of liquid interfaces in cylindrical supports.
problem Stability and bifurcation of liquid interfaces in cylindrical support surfaces.
method Analysis of eigenvalues of the Jacobi operator, Plateau-Rayleigh instability, bifurcation theory.
result Conditions for the emergence of new morphologies and bifurcations from circular cylinders.
Generalizes Fermat's principle for wave propagation in cone structures.
problem Wave propagation in complex media with discontinuities and anisotropy.
method Generalizes Fermat's principle to smooth interfaces separating two cone structures representing wave propagation in various media.
result Conditions for critical points of arrival time functional, generalizing Snell's law and reflection.
Data-driven approach learns effective equations for phase field interfaces.
problem Learning accurate equations for phase field interface dynamics.
method Data-driven identification of partial differential equations from phase field data.
result Data-driven equations outperform analytical approximations in certain regimes.
Four solutions found for a complex equation on spheres.
problem Finding solutions to the Allen-Cahn equation on spheres.
method Combining recent results from literature.
result At least four solutions with spherical interfaces and index at most two.
PeL separates sensory interface optimization from decision learning.
problem Optimizing sensory interfaces without task-specific information.
method Formal separation of perception and decision learning, using metrics for stability, informativeness, and geometry.
result Updates preserving invariants are orthogonal to decision gradients.
Proves long-term smoothness of curved surfaces evolving under specific curvature rules.
problem Long-term regularity of curved surfaces evolving under p-Gauss curvature flow. method Transformed the curvature flow into a Monge-Ampère equation and studied its asymptotic cone.
result Proved regularity of the interface in all dimensions for $p> rac1n$.
New method extracts hidden phases in binary mixtures using tubular tilings.
problem Hidden phases in binary mixtures are difficult to observe.
method Introduce tubular tilings for discretizing binary mixtures on smooth manifolds.
result Recover topological information about hidden phases from observable phases and interfaces.