Study confirms conjectures for topologically slice knots' concordance group.
problem Primary decomposition conjectures for knot concordance groups.
method Use of amenable L2-signatures, Ozsváth-Szabó d-invariants, and Némethi's Heegaard Floer homology results. result Smooth concordance group of topologically slice knots has large subgroup with true primary decomposition.
New findings on knot concordance show limitations to primary decompositions.
problem Understanding the structure of knot concordance groups.
method Analyzing homomorphisms and polynomial factorizations of Alexander polynomials.
result Primary decompositions of topologically slice knots cannot exist.
We characterize primary operations in differential cohomology via stacks, and illustrate by differentially refining Steenrod squares and Steenrod powers explicitly. This requires a delicate interplay between integral, rational, and mod p cohomology, as well as cohomology with U(1) coefficients and differential forms. A…
Study shows knots with coprime polynomials can't be concordant.
problem Primary decomposition of knot concordance.
method Using von Neumann rho-invariants and solvable filtration.
result Nonnegative integer n-solvable knots with coprime polynomials are not concordant.
For each sequence of polynomials, P=(p_1(t),p_2(t),...), we define a characteristic series of groups, called the derived series localized at P. Given a knot K in S^3, such a sequence of polynomials arises naturally as the orders of certain submodules of the sequence of higher-order Alexander modules of K. These group s…
We show that for each Seifert form of an algebraically slice knot with nontrivial Alexander polynomial, there exists an infinite family of knots having the Seifert form such that the knots are linearly independent in the knot concordance group and not concordant to any knot with coprime Alexander polynomial. Key ingred…
Revisits orbital minimization for neural operator decomposition.
problem Training neural networks to approximate eigenfunctions of operators.
method Adapts orbital minimization method (OMM) for neural networks.
result Justifies broader applicability of OMM in modern learning pipelines.
Bayesian approach approximates probability functions of Gaussian mixtures.
problem Approximating probability functions of non-spherical Gaussian mixtures.
method Bayesian decomposition, spherical radial decomposition, random sampling.
result Established differentiability and integral representation of gradient for probability functions.
Proposes a new method for analyzing multimodal neuroimaging data.
problem Combining interpretability and flexibility in multimodal data analysis.
method Orthogonalized kernel debiased machine learning approach.
result Established consistency and asymptotic normality of the estimated primary parameter.
The paper shows infinite rank in T_1/T_2 and new insights into Alexander polynomials and concordance.
problem Understanding the structure of topologically slice knots and their concordance.
method Analyzing surgery manifolds of satellite links and using Ozsváth-Szabó d-invariants.
result There exist infinitely many knots with specific Alexander polynomials that are not concordant to any knot with coprime Alexander polynomial.
New method extracts joint and individual signals from multi-view data.
problem Extract joint and individual signals from multi-view data.
method Double-matched matrix decomposition with optimization and iterative algorithm.
result Superior signal estimation performance compared to single-matching methods.
DCA algorithm applied to SVR with RBF kernel for nonconvex optimization.
problem Nonconvex optimization of SVR with Gaussian RBF kernel.
method DC algorithm with analytical DC decomposition of SVR objective.
result Convergence properties of DCA on RBF-SVR can be assessed through Cαρ. RID framework quantifies and regularizes task-relevant knowledge in distillation.
problem Distilling irrelevant information can hinder student model performance.
method Partial Information Decomposition to quantify and regularize task-relevant knowledge.
result RID framework leads to more resilient distillation under nuisance teachers.
Deep learning models can have low bias and variance, contrary to classical theory.
problem Understanding the performance of deep learning models at high complexity.
method Developed a fine-grained bias-variance decomposition for random feature kernel regression, analyzing the effects of sampling, initialization, and labels.
result The variance terms exhibit non-monotonic behavior and can diverge at the interpolation boundary, even in the absence of label noise.
We study knots of order 2 in the grope filtration $\{\G_h\}$ and the solvable filtration $\{\F_h\}$ of the knot concordance group. We show that, for any integer n≥4, there are knots generating a Z2∞ subgroup of $\G_n/\G_{n.5}$. Considering the solvable filtration, our knots generate a Z2∞ subgro…
Study controls bifurcations in Eulerian flows with multiple Hopf singularities.
problem Bifurcation analysis and control of nonlinear Eulerian flows with non-resonant n-tuple Hopf singularities.
method Analysis of CW complex bifurcations of flow-invariant Clifford hypertori, using leaf-bifurcation varieties.
result Tertiary toral CW complex bifurcates from and persists outside a secondary toral CW complex.
Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…
Privacy subsidy found in market trading with noisy direction signals.
problem Analyzing welfare and bid-ask spread in a market with privacy mechanisms.
method Closed-form derivation of bid-ask spread and welfare under flip-noise direction observation.
result Privacy subsidy of μηΔ from liquidity pool to traders, robust across models. New method splits unknown covariance Gaussians into independent parts.
problem Splitting multivariate Gaussian data with unknown covariance.
method Developed a general algorithm for decomposing unknown covariance Gaussians.
result Demonstrated decomposition for single multivariate Gaussian with unknown covariance.
Develops HCQRF for estimating heterogeneous treatment effects with censored data.
problem Estimating heterogeneous treatment effects on censored responses with high-dimensional variables.
method Hybrid Censored Quantile Regression Forest (HCQRF) combining random forests and censored quantile regression.
result Demonstrates the effectiveness and stability of HCQRF through simulation studies and real-world application.
New method constructs Lefschetz fibrations with different regular fibers.
problem Constructing Lefschetz fibrations with varied regular fibers.
method Combinatorial extension of a simple construction method.
result Existence of PALFs with genus 1 regular fibers.
Constructs cohomology decompositions for symmetric stacks.
problem Cohomology of symmetric stacks.
method Constructs decompositions of cohomology, Borel--Moore homology, and vanishing cycle cohomology.
result Defines BPS cohomology and proves its equivalence to intersection cohomology for smooth stacks.
Study blockchain's impact on primary financial market challenges.
problem Challenges of blockchain in securities issuance and trading.
method Hybrid method combining interviews and surveys.
result Complex due diligence, mismatch, and difficult monitoring are significant challenges.
A new geometric model for V1 hypercolumns combines symplectic and spherical models.
problem Understanding the structure of V1 hypercolumns in the visual cortex.
method A differential geometric model based on conformal geometry.
result Combines features of symplectic and spherical models of hypercolumns.
Aux-NAS uses auxiliary labels to improve primary task performance without extra inference cost.
problem Improving primary task performance using auxiliary labels without increasing inference cost.
method Architecture-based approach with a flexible asymmetric structure for primary and auxiliary tasks, using Neural Architecture Search (NAS) to evolve networks with only primary-to-auxiliary connections.
result Achieves improved performance on multiple tasks without increasing inference cost.
Classifies Real primary Hopf surfaces and their associated groups.
problem Classifying Real primary Hopf surfaces and their associated groups.
method Complete classification up to Real biholomorphisms and equivariant diffeomorphisms.
result Detailed description of groups associated with Real primary Hopf surfaces.
The Riemannian geometry is one of the main theoretical pieces in Modern Mathematics and Physics. The study of Riemann Geometry in the relevant literature is performed by using a well defined analytical path. Usually it starts from the concept of metric as the primary concept and by using the connections as an intermedi…
Proposes a Structural Matrix Autoregressive model for joint analysis of asset returns, realized volatility, and trading volume.
problem Joint analysis of asset returns, realized volatility, and trading volume
method Structural Matrix Autoregressive model
result Volatility is primary driver of trading activity, with informational shocks incorporated through price variability.
The first purpose of this paper is to point out a curious result announced by Macaulay on the Hilbert function of a differential module in his famous book The Algebraic Theory of Modular Systems published in 1916. Indeed, on page 78/79 of this book, Macaulay is saying the following: " A polynomial ideal $\mathfrak{a} \…
CP-factorization for high-dimensional tensor time series and double projection iterations
problem Identifying and estimating factor loadings in CP decomposition for high-dimensional tensor time series
method One-pass estimation procedure using standard eigen-analysis for matrix constructed based on serial dependence
result Asymptotic properties established under general settings, adapt to sparsity, accommodates weak factors
This paper classifies holonomy groups of K-contact sub-pseudo-Riemannian manifolds.
problem The problem of subspace degeneracy in indefinite signature metrics.
method Adapted for metrics of indefinite signature, bypassing subspace degeneracy.
result Horizontal holonomy group either coincides with the adapted holonomy group or acts as its normal subgroup of codimension one.
The paper introduces a method to decompose variance in twin networks for better treatment effect estimation.
problem Accurate treatment effect estimation requires reliable uncertainty measures to locate model failures.
method Layer-wise variance decomposition using Monte Carlo Dropout in twin networks.
result The encoder component dominates under distributional shift, providing a practical diagnostic for data collection.
Paper develops a new method for solving IBVPs on star-shaped domains.
problem Solving Inverse Boundary Value Problems (IBVP) for parallel transport equations.
method Covariant tomography, integrating geometric decomposition with specific interior extensions.
result Formal solvability criterion for higher-order IBVPs, validated through examples.
This paper uses a mean-field game to model stablecoin market dynamics and recovery.
problem Understanding who restores the peg during de-pegging events of stablecoins.
method Dynamic, agent-based mean-field game framework for fiat-collateralized stablecoins.
result The equilibrium formulation endogenously maps market frictions into a price path and order flows, allowing for stress testing and attribution of peg-reverting pressure.
The paper tackles online learning with two types of losses and shows it's impossible without certain assumptions.
problem Online learning with primary and secondary losses where the secondary loss is bounded by a linear threshold.
method Analyzes the feasibility of achieving low regret with respect to the primary loss while keeping the secondary loss within a linear threshold.
result Achieving the goal is impossible without bounded variance assumption on the secondary loss.
This paper is a computation of the homotopy type of K, the space of long knots in R^3, the same space of knots studied by Vassiliev via singularity theory. Each component of K corresponds to an isotopy class of long knot, and we `enumerate' the components via the companionship trees associated to the knot. The knots wi…
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model wh…
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class f…
New method identifies causal effects with categorical unobserved confounders.
problem Estimating causal effects in the presence of unobserved confounders.
method Mixture learning and tensor decomposition for consistent estimation.
result Causal effects are identifiable with categorical unobserved confounders under suitable conditions.
We study holomorphic locally homogeneous geometric structures modelled on line bundles over the projective line. We classify these structures on primary Hopf surfaces. We write out the developing map and holonomy morphism of each of these structures explicitly on each primary Hopf surface.
This paper gives a partial description of the homotopy type of K, the space of long knots in 3-dimensional Euclidean space. The primary result is the construction of a homotopy equivalence between K and the free little 2-cubes object over the space of prime knots. In proving the freeness result, a close correspondence …
In this study, we generalize double tangent bundles to double jet bundles. We present a secondary vector bundle structure on a 1-jet of a vector bundle. We show that 1-jet of a vector bundle carries two vector bundle structures, namely primary and secondary structures. We also show that the manifold charts induced by p…
Automorphisms of Kodaira surfaces are shown to be affine transformations.
problem Characterizing automorphisms of Kodaira surfaces.
method Analyzing lifts to the universal cover and conditions on affine transformations.
result Precise description of Kodaira surfaces' automorphism groups.
The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time. With the emergence of intelligent edge applications that use these models, the critical challenge is to obtain the same inference capability on a resource-constrained device while providi…
New resurgent analysis reveals dual q-series for Chern-Simons theory crossing natural boundaries.
problem Understanding crossing natural boundaries in Chern-Simons theory.
method Resurgent analysis and Mordell integrals to identify dual q-series. result Practical numerical algorithm generates dual q-series. In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better po…
Privacy-preserving crypto exchanges adjust prices based on Gaussian noise.
problem Ensuring fair pricing in privacy-preserving cryptocurrency exchanges.
method Derive Kyle equilibrium with Gaussian noise perturbation, rescaling price-impact and strategy factors.
result Identify a privacy subsidy as a transfer from LP pool to traders, invariant to noise.
Randomly chosen primary hidden units and derived secondary units reduce neural network complexity.
problem Large number of hidden units in neural networks.
method Introducing primary and secondary hidden units with random weights for primary units and derived weights for secondary units.
result Significant reduction in the number of hidden units without compromising accuracy.