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.
Study on signed graphs with random signs, focusing on community detection.
SELO model predicts link signs better than SDGNN using subgraph encoding and linear optimization.
We argue that the standard graph Laplacian is preferable for spectral partitioning of signed graphs compared to the signed Laplacian. Simple examples demonstrate that partitioning based on signs of components of the leading eigenvectors of the signed Laplacian may be meaningless, in contrast to partitioning based on th…
Novel GNN for signed and directed networks using magnetic signed Laplacian.
Sign equivariant networks improve model expressiveness for spectral geometric learning.
Defines signed quasiregular curves and proves growth theorem.
Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysi…
In this short note, we compare the combinatorial sign assignment of Manolescu, Ozsvath, Szabo and Thurston for grid homology of knots and links in 3-sphere with the sign assignment coming from a coherent system of orientations on Whitney disks. Although these constructions produce different signs, a small modification …
PyTorch Geometric Signed Directed fills the gap for GNNs on signed and directed graphs.
Proposes a privacy-preserving sign selection method for distributed systems.
Estimates graph curvature and diameter using Laplacian eigenvalues.
The paper discovers patterns in Maass forms' coefficients related to Fricke signs.
Novel CNN array for sign language recognition using wearable IMUs.
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the si…
In this paper we use theory of embedded graphs on oriented and compact -surfaces to construct minimal realizations of signed Gauss paragraphs. We prove that the genus of the ambient surface of these minimal realizations can be seen as a function of the maximum number of Carter's circles. For the case of signed Gaus…
CSNE embeds signed networks by separating structural and fine-grained information.
Signed seminorms linked to real tropical spaces and matroids.
The paper finds sign-changing solutions for a specific type of elliptic equation.
Unified sign-based compression for federated learning with faster convergence.
Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and negative edges. We pro…
Signed-permutation coordinate transport improves model alignment across checkpoints.
New method estimates tensors from noisy data with missing entries.
Signed heights of knotoids are defined and studied.
Develops method for learning signed graphs from smooth signals.
A new method learns node embeddings for signed directed networks by capturing both first-order and high-order topologies.
It is well-known that the Jones polynomial of an alternating knot is closely related to the Tutte polynomial of a special graph obtained from a regular projection of the knot. Relying on the results of Bollobás and Riordan, we introduce a generalization of Kauffman's Tutte polynomial of signed graphs for which describi…
We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network …
Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of …
Study on blow-up behavior of sign-changing solutions for Yamabe equation.
We analyse the problem of assigning sign choices to O-planes in orientifolds of type II string theory. We show that there exists a sequence of invariant -gerbes with , which give rise to sign choices and are related by coboundary maps. We prove that the sign choice homomorphisms stabilise with the dimension…
New nodal domain theorems for symmetric matrices via signed graphs.
For a spanning tree T of a connected graph G and for a labelling φ: E(T) \rightarrow {+, -}, φis called an alternating sign on a spanning tree T of a graph G if for any cotree edge e \in E(G)-E(T), the unique path in T joining both end vertices of e has alternating signs. In the present note, we prove that any graph ha…
Unified model for signed networks separates balance and anomaly effects.
Regularized spectral methods improve clustering in signed graphs, especially for sparse data.
Study on compact Kähler surfaces for sign-changing curvatures.
We introduce a property of mutation loops, called the sign stability, with a focus on an asymptotic behavior of the iteration of the tropical -transformation. A sign-stable mutation loop has a numerical invariant which we call the cluster stretch factor, in analogy with that of a pseudo-Anosov mapping clas…
Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-ofHearing (DHH) users. The objective of this study is to develop and evaluate a s…
Derives new orthogonal coordinates for evolving surfaces and curves.
Alternating-sign Hopf plumbing along a tree yields fibered alternating links whose homological monodromy is, up to a sign, conjugate to some alternating-sign Coxeter transformation. Exploiting this tie, we obtain results about the location of zeros of the Alexander polynomial of the fibered link complement implying a s…
For a signed cyclic graph G, we can construct a unique virtual link L by taking the medial construction and convert 4-valent vertices of the medial graph to crossings according to the signs. If a virtual link can occur in this way then we say that the virtual link is graphical. In the article we shall prove that a virt…
We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i.e. when the number of features is larger than the number of samples per feature. The stability of correlation signs, two-by-two relationships, is found to depend on thr…
The paper introduces DP algorithms using random projections and sign random projections for improved privacy in machine learning.
New method tackles adversarial sign-corrupted isotonic regression, estimating monotonic signals under heavy dependence.
Geometric framework for signed multivariate tail-dependence compatibility at various thresholds.
We define several homology theories for central hyperplane arrangements, categorifying well-known polynomial invariants including the characteristic polynomial, Poincare polynomial, and Tutte polynomial. We consider basic algebraic properties of such chain complexes, including long-exact sequences associated to deletio…
In this paper, we explore and detail our experiments in a high-dimensionality, multi-class image classification problem often found in the automatic recognition of Sign Languages. Here, our efforts are directed towards comparing the characteristics, advantages and drawbacks of creating and training Support Vector Machi…
Novel GNN method for semi-supervised clustering of signed networks.
Develops a new algorithm to calibrate signed datasets to specified marginals.