Method identifies galaxies with recent star formation variations.
problem Identify galaxies with recent star formation variations.
method Approximate Bayesian Computation (ABC) with machine learning.
result Flexible star formation histories are needed for accurate modeling.
New study finds environment significantly suppresses star formation in galaxies, contrary to previous beliefs.
problem Understanding the role of environment in galaxy formation and evolution.
method Applied causal inference framework to IllustrisTNG simulations.
result Environment suppresses star formation by a factor of ~100, contrary to previous beliefs.
Researchers create initial data for multiple collapsing boson stars.
problem Forming multiple trapped surfaces in spacetime.
method Constructed Cauchy initial data for EMKG system.
result Multiple trapped surfaces form in finite time.
Generative models help explore astrophysical phenomena like galaxy evolution.
problem Exploring hypotheses in astrophysics and other areas using data-driven methods.
method Using a neural network to learn a latent space representation of data and generate artificial data to test hypotheses.
result Demonstrated the ability to independently manipulate physical attributes in artificial data.
Novel spectral graph technique highlights global and local structure in SDSS galaxy data.
problem Characterize natural variations in galaxy spectra data.
method Locally-biased semi-supervised eigenvectors applied to Sloan Digital Sky Survey (SDSS) data.
result Method reveals fine local structure and strong correlations with star formation rate.
ICA identifies nine independent components for galaxy classification.
problem Subjective galaxy classification limits understanding of galaxy formation and evolution.
method Independent Component Analysis (ICA) followed by K-means clustering.
result Galaxies can be grouped into ten distinct and homogeneous categories.
STAR-GCN improves recommender systems by learning node representations.
problem Cold start problem in recommender systems.
method Stacked and reconstructed Graph Convolutional Networks (GCN) with intermediate supervision and node embedding reconstruction.
result Significant improvements in predicting ratings, especially in the cold start scenario.
New proof of trapped surface formation using signature for decay rates.
problem Trapped surface formation in gravitational collapse.
method Systematic approach with signature for decay rates, rescaling argument.
result Reproof and extension of a scale-critical theorem.
Gradient descent with preconditioning finds global optima in overparameterized nonconvex factorization.
problem Finding global optima in nonconvex Burer-Monteiro factorization.
method Preconditioned gradient descent for overparameterized nonconvex function minimization.
result Gradient descent with preconditioning achieves linear convergence in the overparameterized case.
Gaia will obtain astrometry and spectrophotometry for essentially all sources in the sky down to a broad band magnitude limit of G=20, an expected yield of 10^9 stars. Its main scientific objective is to reveal the formation and evolution of our Galaxy through chemo-dynamical analysis. In addition to inferring position…
Sharp Lipschitz bounds for flow-matching and diffusion models with optimal sampling rates.
problem Establishing optimal Lipschitz regularity for flow-matching and diffusion models.
method Sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores.
result Achieves optimal sampling rate of d / N \sqrt{d}/N d / N for Euler-type samplers in dimension d d d . Optimal investment and consumption model with habit formation constraint.
problem Formulating an optimal investment and consumption model with habit formation constraint.
method Formulated an infinite-horizon optimal investment and consumption problem with habit formation model, derived explicit policies, and analyzed the system of differential equations.
result Optimal investment and consumption policies derived explicitly, showing different consumption and investment strategies based on habit formation level.
Predicts Yelp star reviews using deep learning and network structure.
problem Predicting Yelp star reviews based on network structure and features.
method Compared multiple models including deep learning on network and item features.
result Deep learning models combining node-level and network features outperform others.
New algorithm for model selection in contextual bandits reduces regret.
problem Adapting to the complexity of the optimal policy in contextual bandits.
method Designing an algorithm that balances exploration and exploitation, achieving optimal regret bounds.
result Achieves i l d e O ( T 2 / 3 d m ⋆ 1 / 3 ) ilde{O}(T^{2/3}d^{1/3}_{m^\star}) i l d e O ( T 2/3 d m ⋆ 1/3 ) regret with no prior knowledge of the optimal dimension d m ⋆ d_{m^\star} d m ⋆ . The study shows infinitely many Reeb orbits on star-shaped hypersurfaces with growth rate like prime numbers.
problem Growth rate of Reeb orbits on star-shaped hypersurfaces.
method Analyzing fiberwise star-shaped hypersurfaces in cotangent bundles with topological conditions.
result The number of Reeb orbits with period at most T grows at least like T/log(T).
The study identifies features making cross-impact relevant in explaining price variance of US assets.
problem Understanding the relevance of cross-impact in explaining price variance of US assets.
method Using tick-by-tick data spanning 5 years for 500 US assets, the study investigates the features making cross-impact relevant.
result Price formation is endogenous within highly liquid assets, influencing less liquid correlated products with a constrained impact velocity.
SGD achieves near optimal convergence rate in smooth interpolation regime.
problem Optimization of smooth convex objectives with zero noise at optimum.
method Stochastic Gradient Descent (SGD) with large stepsize analysis.
result Last iterate of SGD achieves expected excess risk of O(1/T + σ* / √T) with optimal stepsize.
A new clustering method handles uncertain covariates efficiently.
problem Clustering with uncertain covariates in datasets.
method Greedy and optimistic clustering algorithm using non-linear transformation and empirical uncertainty sets.
result Improved performance in finding sibling stars.
PrecGD restores linear convergence in over-parameterized nonconvex matrix factorization.
problem Slow convergence of local search algorithms in over-parameterized nonconvex matrix factorization.
method Preconditioned Gradient Descent (PrecGD) with an inexpensive ℓ 2 \ell_2 ℓ 2 regularization. result PrecGD restores linear convergence rate even in the over-parameterized case.
Bayesian PINN improves estimation of PDE solutions from noisy data.
problem Estimating solutions of PDEs from noisy measurements.
method Bayesian approach to Physics-informed neural networks (PINNs) for inverse problems.
result Convergence rate of Bayesian posterior mean error in PDE solutions.
The paper generalizes offset Rademacher complexities to convex and non-convex problems.
problem Improper learning and convexity in statistical learning.
method Generalization of offset Rademacher complexities to convex and non-convex problems.
result The offset complexity provides versatile analytic tools for both convex and non-convex learning.
Sparse tensor additive regression models tensor covariates for scalar responses.
problem Modeling scalar responses from tensor covariates with sparse and low-rank structures.
method Proposes a non-convex optimization problem and an efficient penalized alternating minimization algorithm.
result Establishes an error bound for the estimator and demonstrates the model's efficacy in simulations and online advertising.
Study on the formation of singularities in mean curvature flow.
problem Formation of singularities in mean curvature flow.
method Combining methods from blowup of nonlinear heat equations, mean curvature flow, and invented techniques.
result Find key parameters with favorable signs and sharp decay rates.
The formation of price in a financial market is modelled as a chain of Ising spin with three fundamental figures of trading. We investigate the time behaviour of the model, and we compare the results with the real EURO/USD change rate. By using the test of local Poisson hypothesis, we show that this minimal model leads…
We analyze a class of estimators based on convex relaxation for solving high-dimensional matrix decomposition problems. The observations are noisy realizations of a linear transformation X \mathfrak{X} X of the sum of an approximately) low rank matrix Θ ⋆ Θ^\star Θ ⋆ with a second matrix Γ ⋆ Γ^\star Γ ⋆ endowed with a complementary …
Study learns a projection and function in Gaussian models.
problem Learning a one-dimensional projection and a univariate function in high-dimensional Gaussian models.
method Gradient flow dynamics of alternating scheme, RKHS adaptation.
result Gradient flow dynamics converge with rate controlled by Gaussian regularity.
We solve an optimal consumption problem with habit formation constraints.
problem Maximizing utility with habit formation constraints.
method Formulated and solved a deterministic optimal consumption problem.
result Optimal consumption policies derived explicitly.
Study on consensus formation in manifolds with curvature constraints.
problem Long-time behavior of solutions to nonlocal PDEs on Riemannian manifolds.
method Analytical and numerical methods applied to self-collective models.
result Sufficient conditions for consensus formation and convergence rates quantified.
We use standard physics techniques to model trading and price formation in a market under the assumption that order arrival and cancellations are Poisson random processes. This model makes testable predictions for the most basic properties of a market, such as the diffusion rate of prices, which is the standard measure…
The study compares uniform-price and discriminatory auctions in terms of learning difficulty.
problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.
Fast, format-agnostic web content detection for security.
problem Detecting malicious web content efficiently and accurately.
method Deep learning on static HTML tokens, avoiding complex parsing.
result 97.5% detection rate at 0.1% false positive rate.
Shape manifold and elastic energy regularization help reconstruct complex obstacles from scattering data.
problem Reconstructing non-star-shaped obstacles from scattered waves.
method Shape manifold, Tikhonov regularization, Möbius energy penalization.
result The approach yields stable and accurate reconstructions of complex obstacles.
The study optimizes machine learning classifiers for variable stars using CRTS data.
problem Classifying variable stars from CRTS data efficiently and accurately.
method Used multi-class, binary, and hierarchical ML schemes; optimized via cross-validation; applied Information Theory for feature selection.
result Random Forest classifier performs best in CRTS dataset, achieving balanced-accuracy of ~99% for δ δ δ -Scuti and ACEP. Introduces Star-Shaped deviation measures for risk analysis.
problem Risk measurement and analysis in finance.
method Characterizes Star-Shaped deviation measures through acceptance sets and convex deviation measures.
result Exposes the relationship between Star-Shaped risk measures and deviation measures.
Study cash-subadditive risk measures without quasi-convexity.
problem Cash subadditivity without quasi-convexity.
method Represent cash-subadditive risk measures as lower envelopes of quasi-convex measures and introduce quasi-star-shapedness.
result General cash-subadditive risk measures can be represented as lower envelopes of quasi-convex measures.
Lower bounds on gradient queries for minimizing convex quadratic functions.
problem Proving lower bounds on the number of gradient queries needed to minimize convex quadratic functions.
method Careful reduction from adaptively estimating a planted vector in a deformed Wigner model.
result Lower bounds on the number of gradient queries required, showing Ω ( κ ) Ω(\sqrtκ) Ω ( κ ) for condition number κ κ κ . This paper proposes a new weight representation scheme for efficient model compression and performance enhancement.
problem Challenges in achieving performance enhancement on devices due to irregular sparse matrix representations.
method Fine-grained and unstructured pruning method combined with structured weight encryption.
result Achieved high compression ratios and performance on various deep learning models.
We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …
Analyzes wage-price spiral and stagflation dynamics in economic models.
problem Understanding the formation of wage-price spirals and stagflation.
method Analytical solution to classical gravitation and price-wage spiral models.
result Elementary solution to rates of return differentiation in economic models.
APGD algorithm efficiently recovers over-parameterized matrices from noisy measurements.
problem Matrix sensing problem with over-parameterization and noisy measurements.
method Alternating preconditioned gradient descent (APGD) algorithm incorporating preconditioning terms.
result APGD converges to a near-optimal error at a linear rate.
Unified learning-rate scale for CNNs and ResNets, avoiding depth imbalance.
problem Challenges in choosing an appropriate learning rate for deep networks, especially as depth increases.
method Introduces Arithmetic-Mean μ μ μ P (AM- μ μ μ P), constraining network-wide average pre-activation second moment to a constant scale, combined with residual-aware He fan-in initialization. result Demonstrates a − 3 / 2 -3/2 − 3/2 scaling law for learning rates across depths, enabling zero-shot learning-rate transfer. Study calibrates high-dimensional binary classifiers using angle between estimator and true weights.
problem Calibrating high-dimensional binary classifiers with provable properties.
method Interpolates with a chance classifier to construct well-calibrated predictor based on angle between estimator and true weights.
result Angular calibration approach is provably well-calibrated in high dimensions, minimizing Bregman divergence.
Algorithm achieves optimal regret for unknown Lipschitz convex losses.
problem Online learning with unknown Lipschitz constant and target vector norm.
method Develops an online learning algorithm without knowledge of G G G or ∥ w ⋆ ∥ \|w_\star\| ∥ w ⋆ ∥ . result Matches optimal regret bound G ∥ w ⋆ ∥ T G\|w_\star\|\sqrt{T} G ∥ w ⋆ ∥ T up to logarithmic factors. New star-shaped acceptability indexes generalize existing methods.
problem Generalizing existing acceptability measures.
method Characterizing acceptability indexes through star-shaped risk measures and sets.
result Introducing concrete examples linked to various financial measures.
HYVINT generates hypergraphs with intensity-driven incidence formation and variational learning.
problem Challenges in generating hypergraphs with mechanistic interpretation and limited latent space.
method HYVINT uses intensity-driven incidence formation and a lower-bound variational estimator for latent representations.
result HYVINT achieves strong fidelity and novelty on synthetic and real-world hypergraphs.
Paper proves conjecture about star-shaped curves evolving under GAPF, but not always preserves star shape.
problem What conditions guarantee global existence of Gage's area-preserving flow for nonconvex initial curves?
method Using Dittberner's singularity analysis theory, constructed a ``flying wing'' curve to show limitations.
result Gage's area-preserving flow does not always preserve star-shapedness of evolving curves.
New tensor introduced for complex quadric hypersurfaces, no Hopf hypersurfaces found.
problem Characterizing real hypersurfaces in complex quadric using star-Ricci tensors.
method Introducing and analyzing star-Ricci tensors in real hypersurfaces of complex quadric.
result No Hopf hypersurfaces exist in Q m , m ≥ 3 Q^m, m\geq3 Q m , m ≥ 3 , with certain star-Ricci tensors. Classifies star products on Lie algebroid duals and extends to projectable quantizations.
problem Classifying and extending quantizations on Lie algebroid duals.
method Classification through second Lie algebroid cohomology, extension to projectable quantizations.
result Quantization commutes with reduction in the considered setting.