Study phase transitions in RBMs with generic priors.
problem Understanding phase transitions in RBMs with various priors.
method Complete analysis of phase diagram, focusing on retrieval phase and paramagnetic phase boundary.
result Retrieval robustness for a wide range of priors and optimal training set size for generalization.
Machine learning identifies phase transitions in condensed matter physics.
problem Classifying phase transitions in condensed matter physics.
method Unsupervised and supervised machine learning techniques applied to the Ising model.
result Machine learning can detect multiple phases and regions within the paramagnetic phase.
New algorithm recovers stock market sectors using SPC and f-SPC.
problem Recovering hierarchical structure of stock market interactions.
method Super-Paramagnetic Clustering (SPC) and Fast Super-Paramagnetic Clustering (f-SPC) algorithms.
result f-SPC solutions converge to maximum entropy phase and perform better for high-dimensional data.
Lasso proves consistent model selection for high-dimensional Ising models.
problem Model selection consistency of Lasso for high-dimensional Ising models.
method Theoretical analysis of Lasso with and without post-thresholding for Ising models.
result Lasso without post-thresholding is model selection consistent in the whole paramagnetic phase with n=Ω(d3logp). A model studies deep neural networks with binary synapses under connection removal.
problem Understanding the mechanism of deep learning from a theoretical perspective.
method Random active path model with diluted binary synapses under removal perturbation.
result A critical value of perturbation separates spin glass and paramagnetic phases, with the latter having poor generalization performance.
Machine learning classifies phases of spin models using improved correlation configurations.
problem Classifying phases of spin models using machine learning.
method Improved correlation configuration estimator applied to machine learning.
result Classifies Berezinskii-Kosterlitz-Thouless transition in quantum XY model.
The study examines the retrieval capabilities of RBMs and generalized Hopfield networks under various prior distributions.
problem Characterizing the state of RBMs and Hopfield networks under different prior distributions.
method Equivalence between RBMs and generalized Hopfield networks, analysis of phase transitions, and study of retrieval capabilities.
result The retrieval phase is robust and exists at low load for every pattern distribution.
Inspired by the bankruptcy of Lehman Brothers and its consequences on the global financial system, we develop a simple model in which the Lehman default event is quantified as having an almost immediate effect in worsening the credit worthiness of all financial institutions in the economic network. In our stylized desc…
The paper analyzes ℓ1-LinR for Ising model selection using statistical mechanics.
problem Model selection consistency of ℓ1-LinR for Ising models. method Replica method from statistical mechanics, ℓ1-regularized linear regression (ℓ1-LinR). result Model selection consistency with sample complexity $M=\mathcal{O}\left(\log N
ight)$.
Study on detecting a single spike in high-dimensional data matrices.
problem Detecting a single unknown spike in high-dimensional rectangular data matrices.
method Analysis of likelihood ratio between spiked and null models, using Gaussian fluctuations and Talagrand's interpretation of cavity method.
result Asymptotic Gaussian fluctuations of the likelihood ratio below the BBP threshold, with open maximal parameter region.
Study finds almost contact structures in thermal QCD-like theories at intermediate coupling.
problem Understanding (Almost) Contact Structures in thermal QCD-like theories.
method Explicitly obtained (Almost) Contact Structures and SU(3) structures.
result Subspaces of C3S and AC3S are not mutually 'N-path connected' in the Infra-Red.
Solves Dirichlet problem for Lagrangian phase equation with critical and supercritical phase.
problem Solving Dirichlet problem for Lagrangian phase equation with critical and supercritical phase.
method Uses interior C2 estimate. result Result is sharp, showing existence of singular solutions in subcritical phase.
Convolutional neural networks learn phase-dependent frequency representations.
problem Capturing phase dependence in frequency representations for better signal analysis.
method Convolutional neural networks learn filters with different phases, which rectify to phase-dependent descriptors.
result Phase harmonics correlations can compressively represent signals with sparse wavelet coefficients.
Study on stable partitions in convex domains with three phases, finding disconnected phase stability.
problem Stability of partitions in convex domains with multiple phases.
method Careful derivation of the second variation of area, proving existence of stable partitions involving disconnected phases.
result Existence of stable partitions involving a disconnected phase in three phase problem.
New algorithms handle phase retrieval with rank d measurements, revealing phase transitions.
problem Phase retrieval with rank d measurements.
method Random duality theory (RDT) and descending phase retrieval algorithms (dPR).
result Minimal sample complexity ratio for dPR's success exhibits phase transitions.
New estimators improve efficiency in two-phase designs with coarsened data.
problem Efficient estimation in two-phase designs with incomplete data.
method Developed new estimators within the TMLE framework.
result New estimators are asymptotically equivalent and more efficient.
Paper develops estimates for Lagrangian phase changes in 2D.
problem Interior estimates for Lagrangian phase changes in 2D.
method Modified doubling technique to handle degenerate Jacobi inequalities.
result Interior Hessian and gradient estimates established for critical phase.
Machine learning predicts phase behavior in active matter suspensions.
problem Predicting phase behavior in active matter systems using machine learning.
method Used deep learning techniques, including fully connected networks and graph neural networks, to predict motility-induced phase separation (MIPS) in ABP suspensions.
result Strong agreement between machine learning predictions and MIPS binodal from simulations, suggesting machine learning as an effective method for phase behavior determination.
In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respect…
Machine learning approximates phase transitions using Fisher information.
problem Understanding phase transitions from data using machine learning.
method Information geometry and Fisher information.
result Machine learning indicators approximate the square root of Fisher information.
UPR hybrid model improves phase retrieval performance.
problem Recovering signals from phase-less measurements.
method Model-based data-driven deep architecture (UPR).
result UPR shows potential in improving phase retrieval.
If a given behavior of a multi-agent system restricts the phase variable to a invariant manifold, then we define a phase transition as change of physical characteristics such as speed, coordination, and structure. We define such a phase transition as splitting an underlying manifold into two sub-manifolds with distinct…
DeepPhase uses deep learning to recognize surgical phases in cataract surgery videos.
problem Automating surgical workflow analysis for better standardization and post-surgical assessment.
method Deep learning for instrument detection and phase classification in cataract surgery videos.
result DeepPhase models achieve 99% accuracy in instrument detection and 78% in phase recognition.
The big phase space, the geometric setting for the study of quantum cohomology with gravitational descendents, is a complex manifold and consists of an infinite number of copies of the small phase space. The aim of this paper is to define a Hermitian geometry on the big phase space. Using the approach of Dijkgraaf and …
Diffusion maps help learn complex quantum phase transitions from data.
problem Learning quantum phase transitions from experimental data is challenging.
method Diffusion maps for nonlinear dimensionality reduction and spectral clustering.
result Diffusion maps can learn complex phase transitions unsupervised.
The paper models market crashes as phase transitions, finding dynamic transitions offer better predictions.
problem Understanding and predicting extreme financial events like market crashes.
method Employing phase transition theory, focusing on endogenous crashes, and comparing DPT, CPT, and SPT.
result Dynamic phase transitions provide more accurate predictions of market crashes compared to critical and stochastic models.
Deep learning predicts phase segregation in binary mixtures.
problem Predicting phase segregation in binary mixtures.
method Conditional generative convolutional neural networks.
result Deep learning model accurately predicts phase segregation up to 98%.
Paper uses smart meter data to accurately estimate multi-phase topology and identify bus phases in unbalanced distribution grids.
problem Accurate topology knowledge is needed for monitoring and controlling uncertainties in unbalanced distribution grids.
method Converts multi-phase unbalanced systems into symmetrical components and uses information theory, power flow equations, and conditional independence relationships to estimate topology and identify bus phases.
result The algorithm accurately estimates multi-phase topology and identifies bus phases in unbalanced distribution grids, even with strong load unbalancing and DERs.
Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems…
Artificial neural networks map quantum phases of disordered topological superconductors.
problem Classifying quantum phases of disordered topological superconductors.
method Supervised artificial neural network trained on ensemble averages of quasiparticle distributions.
result Artificial neural networks can classify quantum phases with high confidence, identifying unknown phases.
Study Hessian geometry of Gibbons-Hawking metrics and their phase changes.
problem Understanding phase changes in Gibbons-Hawking metrics.
method Analysis via moment maps of Hessian geometry.
result Characterization of phase changes in Gibbons-Hawking metrics.
Novel M-theory approach classifies topological phases of matter.
problem Classifying and understanding topological phases of matter.
method Establishing a correspondence between (2+1)d topological field theories and non-hyperbolic 3-manifolds, identifying topological phases from internal wrapped 3-manifolds.
result Paves a new route toward the classification of topological phases of matter, including fermionic and non-unitary phases.
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…
Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact…
Study oscillatory integrals with degenerate singular points in multivariable phase functions.
problem Analyzing oscillatory integrals with degenerate singular points in phase functions.
method Using asymptotic expansions and results from one variable, the study examines multivariable phase functions.
result Asymptotic expansions of oscillatory integrals for multivariable phase functions with degenerate singular points.
The stability of money value is an important requisite for a functioning economy, yet it critically depends on the actions of participants in the market themselves. Here we model the value of money as a dynamical variable that results from trading between agents. The basic trading scenario can be recast into an Ising t…
The Allen-Cahn system on manifolds yields multiple phase distributions.
problem Finding the number of solutions to the Allen-Cahn system on manifolds.
method Volume-fixing variations approach to classify isoperimetric clusters.
result The number of solutions is bounded by topological invariants for parallelizable manifolds.
This paper shows how learning the phase-amplitude coupling improves bio-signal classification.
problem Discarding phase component in bio-signal feature extraction leads to poor generalization.
method Introducing a novel self-supervised learning task called Phase-Swap to detect phase-amplitude coupling.
result Neural networks trained on Phase-Swap task generalize better across subjects and recording sessions.
A new deep learning model improves phase retrieval performance.
problem Recovering signals from phaseless measurements.
method Hybrid model-based data-driven deep architecture (Unfolded Phase Retrieval, UPR).
result Significant improvement in phase retrieval performance.
The phase space of relativistic particle mechanics is defined as the 1st jet space of motions regarded as timelike 1-dimensional submanifolds of spacetime. A Lorentzian metric and an electromagnetic 2-form define naturally on the odd-dimensional phase space a generalized contact structure. In the paper infinitesimal sy…
New approach connects quantum phases to VQA trainability, enabling better scaling.
problem Scalability issues in VQAs, especially barren plateaus.
method Analog VQA ansätze composed of quenches of a disordered Ising chain, tuning disorder strength.
result Thermalized and MBL phases reach maximal expressivity at large M, but barren plateaus emerge at smaller M in the thermalized phase. Descending phase retrieval algorithms show a phase transition with increasing sample complexity.
problem Theoretical limits of descending phase retrieval algorithms.
method Utilizing Random duality theory (RDT), the study develops a generic program to characterize algorithm performance.
result As sample complexity increases, the parametric manifold transitions from multi to single funneling points, leading to a phase transition in algorithm success.
Study shows reverberant phase is not essential for weakly-supervised dereverberation.
problem Evaluating the role of reverberant phase in weakly-supervised dereverberation.
method Statistical Wave Field Theory and recent weak supervision framework.
result Wet phase carries limited useful information and is not essential for weakly supervised dereverberation.
Paper predicts GNSS phase scintillations with machine learning.
problem Predicting phase scintillations due to ionosphere disturbances.
method Proposes a novel machine learning architecture and loss function.
result Achieves state-of-the-art prediction of phase scintillations 1 hour in advance.
Oil economy modeled using phase plots and Benard convection analogy.
problem Understanding the dynamics of world oil production, price, and EROEI.
method Phase plot of oil economy data, analogy with Benard convection, interpretation and forecast methods.
result Proposed methods for interpreting and forecasting oil economy behavior.
Study Hessian geometry of multi-Taub-NUT metrics and their phase changes.
problem Understanding phase changes in multi-Taub-NUT metrics.
method Analysis via moment maps of Hessian geometry.
result Generalization of earlier work on toric Gibbons-Hawking metrics.
The paper studies phase transitions in geodesic flows on curved manifolds.
problem Understanding phase transitions in geodesic flows on geometrically finite manifolds.
method Defined a class of potentials and constructed geometrically finite manifolds to exhibit phase transitions.
result Geometric potential exhibits a phase transition on certain manifolds.
Transformer improves sequence generation with insertion and deletion phases.
problem Sequence generation challenges in machine translation.
method Insertion-Deletion Transformer with iterative insertion and deletion phases.
result Significant BLEU score improvement over insertion-only models.