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.
Finite stature proven for cube complexes with cyclonormal edges.
Paper detects anomalous edges in social networks using edge exchangeability.
We study parallel surfaces and dual surfaces of cuspidal edges. We give concrete forms of principal curvature and principal direction for cuspidal edges. Moreover, we define ridge points for cuspidal edges by using those. We clarify relations between singularities of parallel and dual surfaces and differential geometri…
OL4EL optimizes edge learning on resource-constrained servers.
New GPs model edge functions on complex networks, capturing divergence and curl.
In L^3, cuspidal edges can have bounded mean curvature under specific conditions.
Along cuspidal edge singularities on a given surface in Euclidean 3-space, which can be parametrized by a regular space curve, a unit normal vector field is well-defined as a smooth vector field of the surface. A cuspidal edge singular point is called generic if the osculating plane of the cuspidal edge (as a regul…
Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual…
Edge augmentation connects disconnected graphs by elevating eigenvalues.
We prove several results about chordal graphs and weighted chordal graphs by focusing on exposed edges. These are edges that are properly contained in a single maximal complete subgraph. This leads to a characterization of chordal graphs via deletions of a sequence of exposed edges from a complete graph. Most interesti…
Under what conditions is an edge present in a social network at time t likely to decay or persist by some future time t + Delta(t)? Previous research addressing this issue suggests that the network range of the people involved in the edge, the extent to which the edge is embedded in a surrounding structure, and the age…
In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundi…
Study of cuspidal edges on focal surfaces of regular surfaces.
Method certifies edge predictions with cloud-level reliability.
Defense against user shilling attacks in collaborative filtering using edge reweighting.
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
CoMGNN models heterogeneous graphs with evolving nodes and edges.
Study relates Gaussian curvature signs to cuspidal edge types and geometric invariants.
This paper proposes a method to learn graph representations by partitioning edges into communities.
New research finds six bipartite intrinsically knotted graphs with 23 edges.
Graph neural networks improve with edge similarity constraints in RNA structure analysis.
Johnson, Kidwell, and Michael showed that intrinsically knotted graphs have at least 21 edges. Also it is known that K7 and the thirteen graphs obtained from K7 by rY moves are intrinsically knotted graphs with 21 edges. We prove that these 14 graphs are the only intrinsically knotted graphs with 21 edges.
This paper optimizes AI inference on edge devices with reduced communication and computation costs.
On-device federated learning updates edge models by exchanging trained results.
The study proves poor ideal three-edge triangulations are minimal for certain 3-manifolds.
Study on planar graph braid groups' second homology.
Previous work in network analysis has focused on modeling the mixed-memberships of node roles in the graph, but not the roles of edges. We introduce the edge role discovery problem and present a generalizable framework for learning and extracting edge roles from arbitrary graphs automatically. Furthermore, while existi…
The paper provides conditions for realizing graphs and polytopes with specified edge lengths.
Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, especially multi-dimensional edge features. In this paper, we build…
A known failing of many popular random graph models is that the Aldous-Hoover Theorem guarantees these graphs are dense with probability one; that is, the number of edges grows quadratically with the number of nodes. This behavior is considered unrealistic in observed graphs. We define a notion of edge exchangeability …
Two complete graphs are connected by adding some edges. The obtained graph is called the gluing graph. The more we add edges, the larger the Ricci curvature on it becomes. We calculate the Ricci curvature of each edge on the gluing graph and obtain the least number of edges that result in the gluing graph having positi…
SAM improves generalization by operating near the edge of stability.
Paper proves edge-connectivity equals minimum degree for graphs with non-negative curvature.
New method constructs tilings of the plane using directed edges and alignments.
Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While datacenter networks can be protected using conventional cybersecurity measures, edge neural networks bring a host of new security challenges. Unlike classic IoT applications, edge ne…
The tilings of the 2-dimensional sphere by congruent triangles have been extensively studied, and the edge-to-edge tilings have been completely classified. However, not much is known about the tilings by other congruent polygons. In this paper, we classify the simplest case, which is the edge-to-edge tilings of the 2-d…
Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business, etc. In many applications, we are faced with the reality of errorfully observed graphs. That is, the existence of an edge between two vert…
The paper studies Kähler-Einstein metrics with singularities and their limits.
Study on functional inequalities on simple edge spaces.
Study detects edge correlation between unlabeled random graphs.
In this paper we construct some invariants of spatial graphs by disk-summing the constituent knots and show the delta edge-homotopy invariance of them. As an application, we show that there exist infinitely many slice spatial embeddings of a planar graph up to delta edge-homotopy, and there exist infinitely many bounda…
Study Kähler-Einstein edge metrics on Hirzebruch surfaces, verifying a conjecture and finding a rigid singularity.
Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inferen…
A maximally linkless graph is a graph that can be embedded in without any links, but cannot be embedded in such a way if any other edge is added to the graph. Recently, a family of maximally linkless graphs was found with edges. We improve upon this by demonstrating a new family of maximally lin…
This study examines how removing edges from complete graphs affects Ollivier Ricci curvature.
Researchers develop a method to interpret GNNs by identifying unnecessary edges in NLP models.
This note demonstrates how both the concept of distance and the concept of holonomy can be constructed from a suitable network with directed edges (and no lengths). The number of different edge types depends on the signature of the metric and the dimension of the holonomy group. If the holonomy group is of dimension on…
Temporal Functional Circuits explain KAN forecasts with interpretable edge functions.