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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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23466992 · Jun 202019922001200920172026
48 results for temporal Hamming

New STH distance finds patterns in event timeseries without resampling.

problem Lack of efficient analysis methods for event and state timeseries.
method Define STE-ts, propose STH, leveraging both time and state duration.
result Improved precision and computation time compared to resampled metrics.

In this note we prove that for each positive integer mm there exists a bi-Lipschitz embedding ZmHam(S2)Z^m\to Ham(S^2), where Ham(S2)Ham(S^2) is equipped with the entropy metric. In particular, the same result holds when the entropy metric is substituted with the autonomous metric.

2019-09-12abs ↗pdf ↗

We prove that π1(Ham(M))π_1(\text{Ham}(M)) contains an infinite cyclic subgroup, where Ham(M)\text{Ham}(M) is the Hamiltonian group of the one point blow up of CP3{\Bbb C}P^3. We give a sufficient condition for the group π1(Ham(M))π_1(\text{Ham}(M)) to contain an infinite cyclic subgroup, when MM is a general toric manifold.

2005-06-09abs ↗pdf ↗

We verify here some variants of topological and dynamical flavor of the injectivity radius conjecture in Hofer geometry, Lalonde-Savelyev \cite{citeLalondeSavelyevOntheinjectivityradiusinHofergeometry} in the case of Ham(S2)Ham (S^2) and Ham(Σ,ω)Ham(Σ, ω), for ΣΣ a closed positive genus surface. In particular we show that any lo…

2015-01-12abs ↗pdf ↗

Let SS be a compact oriented surface. We construct homogeneous quasimorphisms on Diff(S,area)Diff(S, area), on Diff0(S,area)Diff_0(S, area) and on Ham(S)Ham(S) generalizing the constructions of Gambaudo-Ghys and Polterovich. We prove that there are infinitely many linearly independent homogeneous quasimorphisms on Diff(S,area)Diff(S, area), on $Diff_0(…

2017-07-19abs ↗pdf ↗

We present a lower bound for a fragmentation norm and construct a bi-Lipschitz embedding I ⁣:RnHam(M)I\colon \mathbb{R}^n\to\mathrm{Ham}(M) with respect to the fragmentation norm on the group Ham(M)\mathrm{Ham}(M) of Hamiltonian diffeomorphisms of a symplectic manifold (M,ω)(M,ω). As an application, we provide an answer to Brandenbursk…

2019-01-07abs ↗pdf ↗

Classifies homeomorphism groups of countable Stone spaces up to coarse equivalence.

problem Classifying non-locally compact topological groups using geometric group theory.
method Classification based on coarsely bounded sets and quasi-isometry.
result Groups in the second class are quasi-isometric to the Hamming cube.

In this work we construct Calabi quasi-morphisms on the universal cover of the group Ham(M) of Hamiltonian diffeomorphisms for some non-monotone symplectic manifolds. This complements a result by Entov and Polterovich which applies in the monotone case. Moreover, in contrast to their work, we show that these quasi-morp…

2005-08-04abs ↗pdf ↗

ExDAG solves DAG learning problems with low structural Hamming distance.

problem Learning DAGs with low structural Hamming distance under identifiability assumptions.
method Mixed-integer quadratic programming (MIQP) with branch-and-bound-and-cut algorithm and lazy constraints.
result ExDAG guarantees global convergence and provides a real-time quality assessment.

We introduce here a natural functional associated to any bQH(M,ω)b \in QH_* (M, ω): \emph{spectral length functional}, on the space of "generalized paths" in Ham(M,ω) \text {Ham}(M, ω), closely related to both the Hofer length functional and spectral invariants and establish some of its properties. This functional is smooth on its…

2010-07-19abs ↗pdf ↗

We prove that every RAAG (a Right-Angled Artin Group) embeds in the group of Hamiltonian symplectomorphisms of the 2-sphere.

2011-04-03abs ↗pdf ↗

In this paper, we present NESTA, a specialized Neural engine that significantly accelerates the computation of convolution layers in a deep convolutional neural network, while reducing the computational energy. NESTA reformats Convolutions into 3×33 \times 3 batches and uses a hierarchy of Hamming Weight Compressors to …

2019-10-01abs ↗pdf ↗

New metric space for ReLU codes connects to network safety and robustness.

problem Lack of metrics capturing network safety and robustness beyond accuracy.
method Introduces a metric space of ReLU activation codes with a truncated Hamming distance.
result Establishes an isometry between ReLU codes and polyhedral bodies related to safety and robustness.

Let VV be a maximal globally hyperbolic flat n+1n+1--dimensional space--time with compact Cauchy surface of hyperbolic type. We prove that VV is globally foliated by constant mean curvature hypersurfaces MτM_τ, with mean curvature ττ taking all values in (,0)(-\infty, 0). For n3n \geq 3, define the rescaled volume of $…

2001-10-22abs ↗pdf ↗

This paper analyzes the conflict between Hamming loss and subset accuracy in multi-label classification.

problem The conflict between Hamming loss and subset accuracy in multi-label classification.
method The paper analyzes the learning guarantees of algorithms optimizing Hamming loss and subset accuracy, providing theoretical bounds and experimental support.
result Optimizing Hamming loss with its surrogate loss can lead to good performance on subset accuracy in small label spaces, contrary to theoretical expectations.

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first o…

2017-05-23abs ↗pdf ↗

Study shows effective resistance distance yields more accurate network barycenter than Hamming distance.

problem Identifying the best metric for computing the Fréchet mean network.
method Compared the effectiveness of Hamming distance and effective resistance distance in capturing network topology.
result Effective resistance distance produces a more accurate Fréchet mean network.

An analogue of the Hofer metric ϱH\varrho_H on the Hamiltonian group Ham(M,Λ)Ham(M,Λ) of a Poisson manifold (M,Λ)(M,Λ) can be defined but there is the problem of its non-degeneracy. First we observe that ϱH\varrho_H is a genuine metric on Ham(M,Λ)Ham(M,Λ) when the union of all closed leaves (as subsets of MM) of the corresponding sy…

2015-07-16abs ↗pdf ↗

Asynchronous Gibbs sampling has been recently shown to be fast-mixing and an accurate method for estimating probabilities of events on a small number of variables of a graphical model satisfying Dobrushin's condition~\cite{DeSaOR16}. We investigate whether it can be used to accurately estimate expectations of functions…

2018-11-26abs ↗pdf ↗

Following \cite{citeSavelyevVirtualMorsetheoryonOmegaOmegaHam(Momega)(Momega).}, we develop here a connection between Morse theory for the (positive) Hofer length functional L:ΩHam(M,ω)RL: Ω\text {Ham}(M, ω) \to \mathbb{R}, with Gromov-Witten/Floer theory, for monotone symplectic manifolds (M,ω) (M, ω) . This gives some immediate restrictio…

2013-08-15abs ↗pdf ↗

We present a powerful new loss function and training scheme for learning binary hash codes with any differentiable model and similarity function. Our loss function improves over prior methods by using log likelihood loss on top of an accurate approximation for the probability that two inputs fall within a Hamming dista…

2018-10-01abs ↗pdf ↗

Let (F,u)\to P\to N be a symplectic fibration in math.SG/0503268 McDuff has defined a subgroup Ham^s(F,u) of the group of symplectic automorphisms of(F,u). She has shown that the cohomology class [u] of u can be extended to P if and only if the symplectic fibration has an Ham^s reduction. To show this result, she const…

2005-04-13abs ↗pdf ↗

PDHAMS improves sampling for discrete distributions with quadratic potential functions.

problem Sampling discrete distributions efficiently and accurately.
method Integrates a second-order approximation of the potential function and uses Gaussian integral trick.
result PDHAMS yields superior performance compared to other methods.

Differentially private data structures for estimating distances between strings.

problem Estimating distances between query strings and database strings while ensuring privacy.
method Proposes differentially private data structures for Hamming and edit distances using randomized response technique.
result Efficient data structures that provide accurate distance estimates with strong privacy guarantees.

The paper explores simple and relatively simple transformation groups and their universal coverings.

problem Understanding the structure of universal coverings of transformation groups.
method Study of relatively simple groups and generalization of Tsuboi's metric space.
result Tsuboi's metric space of Ham~(M,ω)\widetilde{\mathrm{Ham}}(M, ω) is not quasi-isometric to the half line.

Our work proves robustness of embedding schemes to discrete changes in text.

problem Discrete changes in text, like replacing a word, affect model robustness.
method Formal proofs and quantitative bounds for embedding schemes (concatenation, TF-IDF, Paragraph Vector).
result Embedding schemes are robust to discrete changes in text with Hölder or Lipschitz properties.

A new, efficient kk-modes algorithm improves clustering of categorical data.

problem Clustering categorical data using existing methods like kk-means is inefficient.
method Developed a novel kk-modes algorithm called OTQT, which improves on existing methods.
result OTQT finds more accurate clusters per iteration and is faster overall.

We use the criteria of Lalonde and McDuff to determine a new class of examples of length minimizing paths in the group Ham(M)Ham(M). For a compact symplectic manifold MM of dimension two or four, we show that a path in Ham(M)Ham(M), generated by an autonomous Hamiltonian and starting at the identity, which induces no non-cons…

1999-05-18abs ↗pdf ↗

Given a closed symplectic manifold (M,ω)(M,ω) we introduce a certain quantity associated to a tuple of conjugacy classes in the universal cover of the group Ham(M,ω){\hbox{\it Ham}} (M,ω) by means of the Hofer metric on Ham(M,ω){\hbox{\it Ham}} (M,ω). We use pseudo-holomorphic curves involved in the definition of the multiplicative s…

2000-09-11abs ↗pdf ↗

Develops gradient boosting for multi-label classification.

problem Lack of customizable learning algorithms for multi-label classification.
method Generalizes gradient boosting to multi-output problems and proposes an algorithm for learning multi-label classification rules.
result Ability to minimize both decomposable and non-decomposable loss functions.

Let γγ be a non-degenerate Ustilovsky geodesic in Ham(M,ω)Ham (M, ω) generated by HH. We give a simple proof of a generalization of the conjecture stated in \cite{virtmorse}, relating the Morse index of γ γ, as a critical point of the Hofer length functional, with the Conley Zehnder index of the extremizers of HH, consid…

2012-04-13abs ↗pdf ↗

Let Ham(M,ω)Ham (M,ω) denote the Frechet Lie group of Hamiltonian symplectomorphisms of a monotone symplectic manifold (M,ω)(M, ω) . Let NFuk(M,ω)NFuk (M, ω) be the AA _{\infty} -nerve of the Fukaya category Fuk(M,ω)Fuk (M, ω), and let (S,NFuk(M,ω))(|\mathbb{S}|, NFuk (M, ω)) denote the NFuk(M,ω)NFuk (M, ω) component of the ``space of \infty-categories''…

2013-07-15abs ↗pdf ↗