New bound on sliding circuit set size in braid groups.
problem Bounding the size of sliding circuit sets in braid groups.
method Constructing examples of braids with multiple subsurfaces to suggest a geometric property.
result Found a family of braids with a sliding circuit set of at most C⋅LN−2 elements, suggesting a geometric property. We show that reducible braids which are, in a Garside-theoretical sense, as simple as possible within their conjugacy class, are also as simple as possible in a geometric sense. More precisely, if a braid belongs to a certain subset of its conjugacy class which we call the stabilized set of sliding circuits, and if it …
In this paper we study the reduction curves of a braid, and how they can be used to decompose the braid into simpler ones in a precise way, which does not correspond exactly to the decomposition given by Thurston theory. Then we study how a cyclic sliding (which is a particular kind of conjugation) affects the normal f…
Weakly supervised model segments tumor areas from whole slide images.
problem Training segmentation models on noisy labeled data from whole slide images.
method Online patch sampling and robust KL divergence extension.
result Model successfully segments tumor areas with strong morphological consistency.
Novel time series forecasting method using sliding window signatures.
problem Challenges in forecasting nonlinear and delayed time series data.
method Ridge regression with signature features calculated on sliding windows.
result Signature features effectively encode temporal and nonlinear dependencies, leading to accurate forecasts.
A sliding surface stabilizes rigid body attitude control.
problem Stabilizing rigid body attitude control robustly.
method Proposed sliding surface as Lie subgroup, designed sliding-mode controller.
result Closed-loop system is robust against disturbances and unwinding.
The paper tests if LLMs' capabilities are executed by small subnetworks (circuits).
problem Understanding how LLMs execute their capabilities.
method Formalized criteria for circuits, developed hypothesis tests, applied to six circuits.
result Synthetic circuits align with idealized properties, while Transformer circuits vary in their alignment.
Study evaluates capacity and trainability of parametrized quantum circuits.
problem Finding the best type of circuits for hybrid quantum-classical algorithms.
method Geometric structure of parameter space, effective quantum dimension, and circuit expressiveness.
result Identifies a transition in quantum geometry leading to decay of quantum natural gradient for deep circuits.
Sliding window algorithm for RL in non-stationary MDPs with varying rewards and transitions.
problem Reinforcement learning in Markov Decision Processes with changing state-transition probabilities and reward functions.
method Sliding window approach for handling non-stationarity.
result Performance guarantees and optimal window size for the algorithm, along with a sample complexity bound.
The statistical complexity of quantum circuits is studied using Rademacher complexity.
problem Measuring the richness of quantum hypothesis spaces.
method Applying Rademacher complexity to quantum circuits, investigating dependencies on resources, depth, width, and input/output registers.
result Bounds on the capacity of quantum neural networks constrained by circuit depth, width, and resource measures.
Kirby color defined in Khovanov homology for 4D handlebodies.
problem Invariance of 4D handlebodies under Kirby moves.
method Functoriality and cabling properties of Khovanov homology, handle slide isomorphism.
result Kirby-colored Khovanov homology is invariant under handle slide moves.
Study improves probabilistic circuits using transformations for better predictions.
problem Predictive limitations of probabilistic circuits in robotic scenarios.
method Integrates transformations into joint probability trees, extending their capabilities.
result Achieves higher likelihoods with fewer parameters on various data sets.
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
Study shows limitations and possibilities of learning quantum circuit output distributions.
problem Learnability of output distributions of local quantum circuits.
method Investigated within two oracle models: statistical query model and direct sample access model.
result Output distributions of super-logarithmic depth Clifford circuits are not efficiently learnable in the statistical query model.
Single T-gate makes distribution learning hard for deep circuits.
problem Learning probability distributions from quantum circuits.
method Characterization of learnability and simulatability of quantum circuit outputs.
result Injection of a single T-gate into depth n^Ω(1) circuits makes distribution learning hard.
Study predicts cryptocurrency trends using LSTM model.
problem Predicting cryptocurrency price trends.
method Combination of window-sliding and prediction range method with LSTM model.
result Established model for cryptocurrency price trend prediction.
The paper analyzes the sliding regret of stochastic bandit algorithms.
problem Measuring the one-shot behavior of no-regret algorithms in stochastic bandits.
method Introducing sliding regret to measure the worst pseudo-regret over a time-window.
result Randomized methods have optimal sliding regret, while index policies have the worst possible sliding regret.
Study of a beam sliding freely along two curves.
problem Finding the shortest curve with free sliding endpoints.
method Geometric formulation on smooth manifolds with boundary.
result Rigorous geometric approach to free boundary problems.
Presentations for involutions on non-orientable surfaces up to genus 5.
problem Representing involutions on non-orientable surfaces.
method Dehn twist--crosscap slide presentations.
result Presentations for involutions on non-orientable surfaces of genera up to 5.
Unified tractability conditions for various compositional inference queries.
problem Analyzing tractability of probabilistic and causal inference queries.
method Algebraic perspective on circuits, focusing on semiring operators.
result Unified sufficient conditions for tractable composition of operators.
New algorithms achieve optimal regret in sliding window model with limited memory.
problem Experts problem in the sliding window model with limited information.
method 2 queries, polylog(nT) memory, exponential improvement on memory.
result Achieve optimal regret of sqrt(nW)polylog(nT) with 2 queries and polylog(nT) memory.
Deeper quantum circuits can improve performance on unseen data, contrary to traditional views.
problem Understanding scaling behavior of parameterized quantum circuits and their generalization.
method Gradient-based PQCs, add-one-in perturbation techniques, spectral properties of random matrices.
result Gradient-based PQCs can exhibit improved performance on unseen data as model size increases, displaying double descent behavior.
We present a new operation to be performed on elements in a Garside group, called cyclic sliding, which is introduced to replace the well known cycling and decycling operations. Cyclic sliding appears to be a more natural choice, simplifying the algorithms concerning conjugacy in Garside groups and having nicer theoret…
Quantum neural tangent kernels help understand variational quantum circuits in machine learning.
problem Designing and predicting performance of variational quantum circuits.
method Using quantum neural tangent kernels and dynamical equations for loss functions.
result Analytical solutions for training dynamics in variational quantum circuits.
Proposes a sliding window method for better portfolio trading.
problem Log-optimal portfolio problem with time-varying weights.
method Data-driven sliding window approach to solve log-optimal portfolio problem.
result Trading strategy outperforms classical log-optimal portfolio in cumulative returns.
Study detects if a circuit bounds a disc using curve intersections.
problem Determining if a circuit bounds an embedded disc.
method Analyzing the group generated by Dehn twists about curves in a circuit.
result Cycle relation between Dehn twists detects disc-boundability.
Quantum circuits can generate samples but lack likelihood; we devise a gradient-based learning algorithm.
problem Quantum circuits lack likelihood for generating samples, making training difficult.
method Developed a gradient-based learning algorithm to minimize the kernelized maximum mean discrepancy loss.
result Demonstrated the effectiveness of the algorithm on generative modeling tasks.
Metalearned neural circuit performs inference over open classes.
problem Nonparametric Bayesian models' practical barriers in real-world applications.
method Extract inductive bias from nonparametric Bayesian model and transfer to neural network.
result Metalearned neural circuit achieves comparable or better performance than particle filter-based methods.
Paper simplifies proof of slide-equivalence in crown diagrams.
problem Proving slide-equivalence of crown diagrams.
method Diagrammatic approach to generalized shift moves.
result Slide-equivalence of vanishing cycles in crown diagrams.
Evolutionary strategy optimizes quantum circuit design and parameters.
problem Optimizing quantum circuit design and parameters for NISQ devices.
method Simple evolutionary strategy to optimize both circuit architecture and parameters.
result Minor slowdown on actual quantum hardware compared to simulations, with insights into mutation operations.
Gradient descent optimization improved by circuit perspective.
problem Efficient training of large AI systems.
method Utilizing circuit perspective for mechanistic interpretability.
result Designing a curriculum for efficient learning.
CCs learn high-dimensional distributions from heterogeneous data.
problem Learning high-dimensional distributions from heterogeneous data.
method Introducing characteristic circuits (CCs) that learn from data and use spectral domain.
result CCs outperform state-of-the-art density estimators on common benchmark data sets.
New protocols implement logical gates on encoded qubits with minimal overhead.
problem Efficiently performing universal logical gates on encoded qubits with minimal overhead.
method Using topological codes associated to hyperbolic surfaces, we introduce protocols to implement Dehn twists through constant depth unitary circuits.
result Demonstrated the possibility of applying universal logical gate sets on encoded qubits through constant depth unitary circuits and with constant space overhead.
Optimizes sliding window approach for tracking Gaussian densities.
problem Improving tracking performance of Gaussian density estimation.
method Theoretical analysis of sliding window Gaussian Kernel Density Estimators.
result Empirical evidence shows improved tracking performance with optimal weight sequence.
This work uses SVM to identify track component failures in AC Track Circuits.
problem Detecting and identifying specific track component failures in AC Track Circuits.
method Applied SVM classifier to STDS track circuit data.
result Successfully classified 15 different track component failures.
Adversarial quantum-classical model learns and infers data faster.
problem Training quantum circuits is harder than classical neural networks.
method Coupling quantum generator with classical discriminator for training.
result Quantum circuit can infer missing data with quadratic speed up.
Let Ng denote a closed nonorientable surface of genus g. For g≥2 the mapping class group M(Ng) is generated by Dehn twists and one crosscap slide (Y-homeomorphism) or by Dehn twists and a crosscap transposition. Margalit and Schleimer observed that Dehn twists have nontrivial roots. We gi…
Quantum reinforcement learning protocols implemented in superconducting circuits.
problem Improving quantum devices through learning processes.
method Implementation of quantum reinforcement learning protocols using superconducting circuits.
result Feasibility analysis of quantum reinforcement learning protocols in superconducting circuits.
Quantum circuits learn to classify non-orthogonal quantum states.
problem Classifying non-orthogonal quantum states is crucial in quantum information.
method Trained quantum circuits using Adam optimization to discover parameters of unknown POVMs.
result Shallow quantum circuits can learn to discriminate among various quantum states with comparable performance to optimal POVMs.
Paper proposes efficient AUC estimation in sliding windows.
problem Efficiently monitoring AUC in large sliding windows over data streams.
method Algorithm groups data points to estimate AUC with O((logk)/ε) time per update. result Achieves significant speed-up over exact computation with modest accuracy loss.
Small bubbles sliding on a boundary maintain half-spherical shape.
problem Preserving the shape of small bubbles sliding on a boundary.
method Area-preserving Willmore flow, asymptotic analysis, convergence proof.
result The flow keeps a half-spherical shape for all times.
The Frenet frame generalizes the Park transform for multi-phase circuits.
problem Generalizing the Park transform for multi-phase circuits.
method Using the Frenet frame and Cartan's moving frames.
result The Frenet frame provides a new approach to circuit analysis.
The study examines how quantum resources enhance the complexity of quantum circuits.
problem Quantum resource enhancement on circuit complexity.
method Utilizing quantum resource theories, the study analyzes statistical complexities of quantum circuits with limited quantum resources.
result Bounds for statistical complexities of quantum circuits are derived and applied to specific cases.
Neural circuit model re-purposed for robotic control tasks.
problem Learning simple robotic control tasks.
method Re-purposing a biological neural circuit model to control robotic tasks using a search-based optimization algorithm.
result Neuronal Circuit Policies (NCPs) perform on par and in some cases surpass contemporary deep learning models with fewer parameters and interpretable dynamics.
New TS algorithms improve performance in non-stationary multi-armed bandit problems.
problem Sequential decision-making with evolving action rewards.
method Sliding-window Thompson sampling approaches with different priors.
result Unified regret upper bound for arbitrary non-stationary MABs.
Deep Learning model diagnoses four lymphoma categories with high accuracy.
problem Automated detection of lymphoma categories using digital pathology images.
method Convolutional neural network algorithm trained on 128 cases of lymph node images.
result Excellent diagnostic accuracy (95% image-by-image, 10% set-by-set).
COLEP improves robustness of conformal prediction via probabilistic circuits.
problem Adversarial perturbations can undermine the coverage guarantees of conformal prediction.
method COLEP uses probabilistic circuits to learn and reason about different semantic concepts, providing certifiable coverage guarantees.
result COLEP achieves higher prediction coverage and accuracy than a single model, especially with non-trivial knowledge models.
Machine learning identifies key metabolic control circuits in bacterial pathways.
problem Identifying regulated metabolic pathways in bacteria.
method Machine learning approach analyzing multi-omics data.
result Identification of E. coli Glycolysis regulatory circuits.