Study reveals striking uniformity in triply graded link homology for specific braids.
problem Investigating the structure of reduced triply graded link homology in specific degrees.
method Diagrammatic approach to Hochschild cohomology of Soergel bimodules.
result Homology often zero, especially in negative braid case, with striking uniformity.
Geometric triangulations of surfaces with uniform vertex degree are shown.
problem Characterizing triangulations of surfaces with uniform vertex degree.
method Combination of combinatorial topology and induction to prove uniqueness of triangulations.
result Any degree-regular triangulation of a surface is geometric.
Paper shows how to identify and reconstruct degree-d PTFs robustly from their Fourier coefficients.
problem Identifying and reconstructing degree-d polynomial threshold functions (PTFs) from their Fourier coefficients.
method Proves a robust version of the theorem that degree-d Chow parameters uniquely characterize degree-d PTFs, and uses this to develop efficient algorithms.
result Boolean degree-d PTFs are robustly identifiable from their degree-d Chow parameters.
New bounds for learning polynomial surrogates with L∞ guarantees.
problem Learning polynomial surrogates for bounded binary functions with L∞ error guarantees. method Characterized minimax sample complexity for two classes of polynomials under subgaussian noise.
result Sample complexity rates differ from noiseless case, scaling as nd+1 for degree d polynomials and ns2 for sparse polynomials. Alternative proof of uniform boundary condition using geometric Følner arguments.
problem Uniform boundary condition in normed chain complexes.
method Geometric Følner arguments on the chain level.
result Integral refinements of the uniform boundary condition.
New study shows limits of low-degree algorithms in finding large independent sets in sparse hypergraphs.
problem Finding large independent sets in sparse random hypergraphs.
method Low-degree polynomial algorithms are analyzed to determine their limits.
result Low-degree algorithms can find independent sets of density up to \(\left(\frac{\log d}{(r-1)d}
ight)^{1/(r-1)}\), but no larger.
The paper studies Blaschke products, proving uniformization and non-degeneracy of pressure metrics.
problem Analytic aspects of Blaschke products and their moduli space.
method Definition of complex structure and proof of uniformization theorem.
result Pressure semi-norms are non-degenerate outside the super-attracting locus.
Study on Kähler manifolds connects curvature decay with growth of holomorphic functions.
problem Analyzing properties of Kähler manifolds with nonnegative bisectional curvature.
method Established precise relations among minimal degree, volume growth, and scalar curvature decay.
result Unified understanding of Kähler-Ricci flow through polynomial growth holomorphic functions.
The paper proves conditions for existence of constant scalar curvature Kähler metrics with cone singularities.
problem Existence of constant scalar curvature Kähler metrics with cone singularities.
method Log K-polystability and G-uniform log K-stability are established. result Uniform log K-stability is achieved for normal varieties. The sinh-Gordon equation is solved on finite, symmetric graphs.
problem Solving the sinh-Gordon equation with nonzero prescribed functions on finite graphs.
method Uniform a priori estimate to define topological degree, case-by-case calculation of degree, classical sinh-Gordon equation analysis.
result The classical sinh-Gordon equation with nonzero prescribed function is always solvable on finite, symmetric graphs.
Paper characterizes optimal graph clustering limits under a new model.
problem Graph clustering under varying edge density signals.
method Introduced Popularity-Adjusted Block Model (PABM) to address SBM and DCBM limitations.
result Cluster recovery possible even when edge density signals vanish, highlighting local connectivity differences.
New algorithm achieves strong consistency in binary non-uniform hypergraph classification.
problem Node classification on binary non-uniform hypergraphs with varying edge probabilities.
method Proposes a refinement algorithm using power iteration on weighted adjacency matrices.
result Proves optimality of the refinement algorithm, achieving strong consistency and IT lower bound.
The entropy-degree theorem applies to Alexandrov spaces with curvature constraints.
problem Geometric obstructions and volume bounds in singular spaces.
method Developed new degree theorem for Alexandrov spaces using integral currents.
result Entropy-volume minimization prevents metric singularities in Gromov-Hausdorff limits.
The study provides a criterion for solving complex Hessian-type equations on projective manifolds.
problem Solving complex Hessian-type equations on projective manifolds.
method Proving Nakai-Moishezon-type criteria for these equations.
result Uniform criteria for solving these equations, including complex Hessian and Hessian quotient equations.
Uniformly finite homology is a coarse homology theory, defined via chains that satisfy a uniform boundedness condition. By construction, uniformly finite homology carries a canonical ℓ∞-semi-norm. We show that, for uniformly discrete spaces of bounded geometry, this semi-norm on uniformly finite homology in …
For harmonic maps of degree 2, a similar quantitative stability estimate does not hold uniformly.
problem Investigate the quantitative stability of harmonic maps of degree 2.
method Prove a local quantitative stability result for harmonic maps of degree 2, showing dependence on the given harmonic map.
result A uniformly quantitative stability estimate does not hold for degree 2 harmonic maps.
Uniformizes branched surfaces into Higgs bundles.
problem Uniformizing branched surfaces into cone metrics.
method Describes Higgs bundles corresponding to uniformization of conical metrics.
result Family of Higgs bundles parametrized by open subset of cohomology space.
We establish conditions for a continuous map of nonzero degree between a smooth closed manifold and a negatively curved manifold of dimension greater than four to be homotopic to a smooth cover, and in particular a diffeomorphism when the degree is one. The conditions hold when the volumes or entropy-volumes of the two…
We study a natural map from representations of a free group of rank g in GL(n,C), to holomorphic vector bundles of degree 0 over a compact Riemann surface X of genus g, associated with a Schottky uniformization of X. Maximally unstable flat bundles are shown to arise in this way. We give a necessary and sufficient cond…
Bias and flexibility trade off in learning algorithms.
problem Understanding the trade-off between bias and expressivity in learning algorithms.
method Measuring expressivity using entropy on algorithm outcome distributions, and deriving bounds on bias and expressivity.
result There is a necessary trade-off between bias and expressivity in learning algorithms.
Paper studies a generalized mean field equation on closed Riemann surfaces.
problem Existence of solutions to a generalized mean field equation on closed Riemann surfaces.
method Uniform bound derivation and Leray-Schauder degree theory, minimax method.
result Existence results for solutions when α<λ1(Σ). The paper studies random covers of torus knot complements and their statistical properties.
problem Understanding the statistical behavior of finite covers of torus knot complements.
method Asymptotic subgroup growth analysis and Benjamini-Schramm limit theorems.
result Determination of the linear growth rate of Betti numbers for random covers of torus knot complements.
We study recursive-cube-of-rings (RCR), a class of scalable graphs that can potentially provide rich inter-connection network topology for the emerging distributed and parallel computing infrastructure. Through rigorous proof and validating examples, we have corrected previous misunderstandings on the topological prope…
A new loss function HUG decouples and generalizes neural collapse.
problem Neural collapse limits in deep learning models.
method Hyperspherical uniformity gap (HUG) as a unified framework.
result HUG decouples and generalizes neural collapse, improving model flexibility and robustness.
Detects dense subhypergraphs in random hypergraphs using low-degree polynomials.
problem Detecting a planted dense subhypergraph in a random hypergraph model.
method Degree-n^o(1) polynomials of adjacency tensor entries.
result Thresholds for detection in different density regimes.
In this work we study the degree distribution, the maximum vertex and edge flow in non-uniform random Delaunay triangulations when geodesic routing is used. We also investigate the vertex and edge flow in Erdös-Renyi random graphs, geometric random graphs, expanders and random k-regular graphs. Moreover we show that …
Degree-Quant improves GNN efficiency by quantizing them without losing accuracy.
problem Efficiency of graph neural networks at inference time.
method Architecturally-agnostic method, Degree-Quant, for quantizing GNNs.
result Degree-Quant trained models perform as well as full-precision models and achieve up to 26% gains.
New characteristic classes for manifold bundles with specific forms.
problem Characteristic classes for manifold bundles with indefinite intersection forms.
method Using a technique of Millson--Raghunathan, cohomology for non-uniform arithmetic lattices is produced.
result New characteristic classes defined on a finite cover of BDiff(M), nontrivial for specific manifolds. Bayesian structure learning is the NP-hard problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian structure learning under graph theoretic restrictions on the super-structure. The super-structure …
Neural networks of depth two can't approximate certain functions well.
problem Approximating certain functions with depth two neural networks.
method Analyzing neural networks of depth two and three, showing limitations for depth two networks.
result Depth two neural networks require exponentially many neurons to approximate certain functions.
A new family of conformal test martingales based on Legendre polynomials for online exchangeability testing.
problem Detecting variance, skewness, and higher-order deviations from uniformity in online data.
method A family of conformal test martingales based on shifted Legendre polynomials.
result The Variational Legendre Jumper reduces exponential scaling to linear time with minimal loss in power.
Prescribing σk curvature equations are fully nonlinear generalizations of the prescribing Gaussian or scalar curvature equations. Given a positive function K to be prescribed on the 4-dimensional round sphere. We obtain asymptotic profile analysis for potentially blowing up solutions to the σ2 curvature equatio…
The paper studies the convergence of harmonic metrics on Higgs bundles.
problem Analyzing the asymptotic behavior of harmonic metrics on Higgs bundles.
method Investigates the convergence of harmonic metrics on stable Higgs bundles of degree 0.
result The sequence of harmonic metrics converges to a decoupled harmonic metric at an exponential rate.
New results on plane graphs linked to rational functions.
problem Existence problems for plane graphs with specific degree conditions.
method Recent results on the Hurwitz existence problem.
result Description of Belyi functions corresponding to such graphs.
Boltzmann-Gibbs distribution arises as the statistical equilibrium probability distribution of money among the agents of a closed economic system where random and undirected exchanges are allowed. When considering a model with uniform savings in the exchanges, the final distribution is close to the gamma family. In thi…
Spectral algorithm recovers community structure in sparse hypergraphs.
problem Community detection in sparse random hypergraphs with community structure and higher-order interactions.
method Spectral algorithm with three steps: hyperedge selection, spectral partition, and correction/merging.
result Weak consistency achieved for weak signal-to-noise ratio.
We present a method for proving upper bounds on the eigenvalues of the graph Laplacian. A main step involves choosing an appropriate "Riemannian" metric to uniformize the geometry of the graph. In many interesting cases, the existence of such a metric is shown by examining the combinatorics of special types of flows. T…
The study shows that random surfaces built from polygons converge to a Poisson-Dirichlet partition.
problem Understanding geometric properties of random surfaces constructed from polygons.
method Uniformly pairing polygon sides to form surfaces, analyzing degree sequences and geometric properties using probabilistic techniques.
result Several geometric properties of the graph are universal, converging to a Poisson-Dirichlet partition as no∞. Uniform proof of π1-injectivity for certain maps in low dimensions.
problem Characterizing π1-injective maps in low-dimensional manifolds. method Uniform group-theoretic proof based on residual finiteness and numerical invariants.
result Uniform determination of when maps induce π1-isomorphisms. Develops a new classical network ensemble framework.
problem Lack of information-theoretic frameworks for complex networks.
method Optimal trade-off between compressed representation and actual network ensemble.
result Power-law degree distribution is optimal for networks with only expected degrees as constraints.
Paper establishes tight lower bounds for minimizing certain smooth and convex functions.
problem Minimizing high-order Hölder smooth and uniformly convex functions.
method Analyzes two asymmetric cases of q>p+ν and q<p+ν using worst-case oracle complexities. result Establishes worst-case oracle complexities for reaching an ε-approximate solution.
A n-dimensional Lie group G equipped with a left invariant symplectic form $\om^+$ is called a symplectic Lie group. It is well-known that $\om^+$ induces a left invariant affine structure on G. Relatively to this affine structure we show that the left invariant Poisson tensor π+ corresponding to $\om^+$ is po…
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with n. In addition to the case of known error-variance, we define and study versions of the estimators when the error-varian…
We construct a Teichmueller curve uniformized by the Fuchsian triangle group (m,n,\infty) for every m<n. Our construction includes the Teichmueller curves constructed by Veech and Ward as special cases. The construction essentially relies on properties of hypergeometric differential operators. For small m, we find Bill…
Self-attention networks localize when eigenspectrum variance is small.
problem Self-attention mechanisms can lead to rank and entropy collapses, reducing model expressivity and trainability.
method Characterized attention localization using query-key eigenspectrum variance.
result Small eigenspectrum variance prevents both rank and entropy collapses, improving model performance.
We give conceptual proofs of some well known results concerning compact non-positively curved locally symmetric spaces. We discuss vanishing and non-vanishing of Pontrjagin numbers and Euler characteristics for these locally symmetric spaces. We also establish vanishing results for Stiefel-Whitney numbers of (finite co…
Optimal bounds on rational points on algebraic curves established.
problem Bounding the number of rational points on algebraic curves of degree d. method Combination of smooth parametrizations and Pólya's criterion.
result Optimal upper bound Cd2H2/d(logH)κ with constants C and κ. It is known that the order of a finite group of diffeomorphisms of a 3-dimensional handlebody of genus g > 1 is bounded by the linear polynomial 12(g-1), and that the order of a finite group of diffeomorphisms of a 4-dimensional handlebody (or equivalently, of its boundary 3-manifold), faithful on the fundamental group…