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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.

168,742 papers · 148 categories

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1223 · Oct 201819922001200920172026
47 results for s-embeddings

Discrete maximal surfaces identified from s-embeddings.

problem Understanding the conformal invariance of the Ising model.
method Introduced a special class of isothermic s-embeddings that correspond to discrete S-maximal surfaces.
result Each S-maximal surface comes with a 1-parameter family of associated surfaces that are isometric.

This paper answers a question about discrete embeddings to maximal surfaces.

problem The question of whether discrete embeddings lift to maximal surfaces.
method Introduced a correspondence between s-embeddings and congruences of touching Lorentz spheres, identified isothermic s-embeddings that lift to S-isothermic surfaces.
result Isothermic s-embeddings lift to S-isothermic surfaces, which are key for obtaining discrete maximal surfaces.

Proved contractibility of geodesic triangulation space on hyperbolic surfaces.

problem Open problem on contractibility of geodesic triangulations on hyperbolic surfaces.
method Generalized Tutte's embedding theorem for negative curvature surfaces.
result Contractibility of geodesic triangulation space proved.

We consider a priori estimates of Weyl's embedding problem of (S2,g)(\mathbb{S}^2, g) in general 33-dimensional Riemannian manifold (N3,gˉ)(N^3, \bar g). We establish interior C2C^2 estimate under natural geometric assumption. Together with a recent work by Li and Wang, we obtain an isometric embedding of (S2,g)(\mathbb{S}^2,g) in…

2016-08-26abs ↗pdf ↗

While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called \emph{Word Mover's Distance…

2018-10-30abs ↗pdf ↗

We give a lower bound to the dimension of a contractible manifold on which a given group can act properly discontinuously. In particular, we show that the nn-fold product of nonabelian free groups cannot act properly discontinuously on R2n1\R^{2n-1}.

2000-10-13abs ↗pdf ↗

We explore the practicability of Nash's Embedding Theorem in vision and imaging sciences. In particular, we investigate the relevance of a result of Burago and Zalgaller regarding the existence of isometric embeddings of polyhedral surfaces in R3\mathbb{R}^3 and we show that their proof does not extended directly to hi…

2010-04-29abs ↗pdf ↗

Seiberg-Witten (Floer) theory, Ozsvath-Szabo's Heegaard Floer theory, Hutchings's embedded contact homology, in different stages of development, define (or are expected to define) packages of invariants for 3- and 4-manifolds (including manifolds with boundary and manifolds with certain types of corners). We describe w…

2017-09-08abs ↗pdf ↗

We find conditions under which a non-orientable closed surface S embedded into an orientable closed 4-manifold X can be represented by a connected sum of an embedded closed surface in X and an unknotted projective plane in a 4-sphere. This allows us to extend the Gabai 4-dimensional light bulb theorem and the Auckly-Ki…

2018-08-26abs ↗pdf ↗

We study the relationship between exotic R^4's and Stein surfaces as it applies to smoothing theory on more general open 4-manifolds. In particular, we construct the first known examples of large exotic R^4's that embed in Stein surfaces. This relies on an extension of Casson's Embedding Theorem for locating Casson han…

2014-10-23abs ↗pdf ↗

We establish a correspondence between the dimer model on a bipartite graph and a circle pattern with the combinatorics of that graph, which holds for graphs that are either planar or embedded on the torus. The set of positive face weights on the graph gives a set of global coordinates on the space of circle patterns wi…

2018-10-12abs ↗pdf ↗

Let (X,T1,0X)(X,T^{1,0}X) be a (2n+1+d)(2n+1+d)-dimensional compact CR manifold with codimension d+1d+1, d1d\geq1, and let GG be a dd-dimensional compact Lie group with CR action on XX and TT be a globally defined vector field on XX such that $\mathbb C TX=T^{1,0}X\oplus T^{0,1}X\oplus\mathbb C T\oplus\mathbb C\underline{\mathf…

2018-10-23abs ↗pdf ↗

For every gN0g\in\mathbb{N}_0 and ε>0ε>0, we construct a smooth genus gg surface embedded into the unit ball with area 8π and Willmore energy smaller than 8π+ε8π+ ε. From this we deduce that a minimising sequence for Willmore's energy in the class of genus gg surfaces embedded in the unit ball with area 8π converges …

2016-08-09abs ↗pdf ↗

In this paper, we present the idea that the formalism of string theory is connected with the dimension 4 in a new way, not covered by phenomenological or model-building approaches. The main connection is given by structures induced by small exotic smooth R^4's having intrinsic meaning for physics in dimension 4. We ext…

2011-02-16abs ↗pdf ↗

For leveled spatial graphs, we find a surface embedding that allows cellular embedding.

problem Finding a surface embedding for general spatial graphs is not always possible.
method Define leveled property, decompose graph into subgraphs, and construct surface.
result For leveled spatial graphs with a small number of levels, a surface can always be found.

We propose a classification of knots in S^1 x S^2 that admit a longitudinal surgery to a lens space. Any lens space obtainable by longitudinal surgery on some knots in S^1 x S^2 may be obtained from a Berge-Gabai knot in a Heegaard solid torus of S^1 x S^2, as observed by Rasmussen. We show that there are yet two other…

2013-02-27abs ↗pdf ↗

A new method combines regularization and generative rehearsal for continual learning.

problem Catastrophic forgetting in neural networks over past tasks.
method Uses a normalizing flow to conditionally store past task data and regularize network embeddings.
result Performs favorably compared to state-of-the-art approaches with constant memory overhead.

A new method combines generative models and regularization to prevent forgetting in continual learning.

problem Catastrophic forgetting in neural networks when learning new tasks.
method Uses a normalizing flow as a generative model to keep past data embeddings and regularize them.
result Performs favorably compared to existing methods with constant memory overhead.

Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in the MIR community and as an acute practical problem for authors and composers societies. Original algorithms proposed for this task have prove…

2019-07-03abs ↗pdf ↗

Geometrically proves Zabrodin-Wiegmann conjecture for integer QH states.

problem Proving a geometric version of Zabrodin-Wiegmann conjecture for integer Quantum Hall states.
method Using Riemann surfaces, canonical sections, and asymptotic expansions, the authors construct a canonical element in cohomology and relate its norm to the partition function.
result The constant term of the asymptotic expansion of the partition function matches a geometric version of Zabrodin-Wiegmann's prediction.

Unsupervised domain adaptation techniques have been successful for a wide range of problems where supervised labels are limited. The task is to classify an unlabeled `target' dataset by leveraging a labeled `source' dataset that comes from a slightly similar distribution. We propose metric-based adversarial discriminat…

2018-07-06abs ↗pdf ↗

We develop a novel Gaussian process method for manifold data.

problem Challenges in Gaussian processes on manifold-based predictors, especially in high dimensions.
method Intrinsic approach for constructing Gaussian processes on general manifolds, using the exponential map for heat kernel estimation.
result Remarkable efficiency gains and applicability to high-dimensional manifolds.

Cryptocurrency markets show higher spreads during extreme fear and greed phases.

problem Understanding and predicting liquidity withdrawal in cryptocurrency markets.
method Analysis of Crypto Fear & Greed Index and Bitcoin daily data.
result Extreme fear and greed regimes exhibit significantly higher spreads than neutral periods.

Time-delayed embeddings avoid self-intersections for high enough delay.

problem Analyzing self-intersections in time-delayed embeddings.
method Study of time-delayed coordinate maps for diffeomorphisms on compact manifolds.
result For high enough delay, time-delayed embeddings avoid self-intersections almost everywhere.

BERTopic improves financial text analysis with FinTextSim's contextual embeddings.

problem Analyzing financial text data for insights and predictions.
method Integrates BERTopic with FinTextSim for topic modeling and clustering.
result BERTopic performs better with FinTextSim's embeddings, improving topic clarity and reducing misclassification.

Graph neural networks improve SME credit risk assessment.

problem Improving credit risk assessment for small and medium enterprises (SMEs).
method Graph neural networks were used to model the relationships between financial indicators of enterprises, creating a graph structure and embedding representations for credit risk prediction.
result The proposed model accurately predicts enterprise credit levels, demonstrating robustness and effectiveness.

This paper improves continuous adversarial training for LLMs using in-context learning theory.

problem Efficiently defending large language models (LLMs) against jailbreak attacks.
method The paper presents a theoretical analysis of continuous adversarial training (CAT) for LLMs based on in-context learning (ICL) theory, proving a robust generalization bound and proposing an improved regularization term.
result The robust generalization bound explains why CAT can defend against jailbreak prompts and shows that LLM robustness is related to embedding matrix singular values.

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance…

2019-05-20abs ↗pdf ↗

This paper improves neural network compression by using robust low-rank approximations.

problem Neural network compression sensitivity to outliers.
method Introduces robust low-rank approximations using p\ell_p norms (for p[1,2]p\in [1,2]) and provides efficient algorithms.
result Achieves up to 28% compression with minimal accuracy loss compared to existing methods.

Improved neural model for social recommendation by integrating social and interest networks.

problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.

Detects exotic surfaces without smooth invariants, providing first example of knotted RP².

problem Detecting exotic surfaces without smooth invariants.
method Two versions of families (Seiberg-Witten) generalizations of Donaldson's diagonalization theorem.
result First example of exotically knotted RP² with diffeomorphic complements.