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 examines how business units can benefit from group cohesion under regulatory constraints.
The paper studies deformations of cohesive modules on complex manifolds.
The paper develops residue currents for cohesive modules and proves a generalized Poincaré-Lelong formula.
Unified classification of equivariant principal bundles using higher homotopy theory.
Policy-gradient method controls multiple non-cohesive targets.
Cyclification of orbifolds explained in cohesive higher topos theory.
Motivated by recent financial crises significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said about influence of financial news on financial markets. We propose a novel measure of collective behaviour in financial news on the Web, New…
We formulate differential cohomology and Chern-Weil theory -- the theory of connections on fiber bundles and of gauge fields -- abstractly in the context of a certain class of higher toposes that we call "cohesive". Cocycles in this differential cohomology classify higher principal bundles equipped with cohesive struct…
The paper generalizes current constructions to cohesive modules and characteristic forms.
Discriminative clustering uses mutual information to cluster data.
Using the concept of a cohesive module defined by Block, we use the theory of superconnections in the sense of Quillen to construct natural superconnections on Hermitian cohesive modules. By the Chern-Weil construction, we obtain characteristic classes with values in Bott-Chern cohomology which refines the usual deRham…
A new method improves graph node embeddings by considering both nearby and distant node similarities.
Higher gauge theory via differential nonabelian cohomology
Enhances kernel regression with network data for better predictions.
Intuitive clustering algorithm balances cluster size and cohesion.
Cohesion uses deep Koopman operators to generate long-range forecasts of chaotic dynamics.
New bounds improve linkage methods for clustering, distinguishing complete-link from single-link.
Introduces a new geometric framework for non-perturbative BV-theory.
We report a data mining pipeline and subsequent analysis to understand the core periphery power structure created in three national newspapers in Bangladesh, as depicted by statements made by people appearing in news. Statements made by one actor about another actor can be considered a form of public conversation. Name…
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when connections involving both types of genes are observed and their relationships to the…
Online PaLD extends PaLD for semi-supervised online applications.
This is a survey of motivations, constructions and applications of higher prequantum geometry. In section 1 we highlight the open problem of prequantizing local field theory in a local and gauge invariant way, and we survey how a solution to this problem exists in higher differential geometry. In section 2 we survey ex…
New models automate support group formation in online health communities.
This work investigates fundamental questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResNet, Inception, DenseNet and MobileNet indicate that weights near the center of a filter are larger than weights on the outside. Current regu…
Modernizes Thurston's proof of entropy theorem for traintrack maps.
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…
New index improves anomaly detection in correlated time series data.
This paper argues that the fundamental principle of contemporary financial economics is balanced reciprocity, not the principle of utility maximisation that is important in economics more generally. The argument is developed by analysing the mathematical Fundamental Theory of Asset Pricing with reference to the emergen…
Examines multiagent systems for complex learning tasks.
Extends partitioned local depth concept with probabilistic considerations.
The paper explains the importance of diffeological groupoids in modern geometry and physics.
SNJ recovers latent tree models from similarity matrices.
This paper presents a simple agent-based model of an economic system, populated by agents playing different games according to their different view about social cohesion and tax payment. After a first set of simulations, correctly replicating results of existing literature, a wider analysis is presented in order to stu…
Synthetic theory defines orbifolds as microlinear types with finite identifications.
Graph embeddings from commute networks identify socioeconomic disparities in urban areas.
The paper explores how to fairly share longevity risk among participants of tontine schemes.
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…
The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations.…
survex explains machine learning survival models, improving model transparency.
Networks capture pairwise interactions between entities and are frequently used in applications such as social networks, food networks, and protein interaction networks, to name a few. Communities, cohesive groups of nodes, often form in these applications, and identifying them gives insight into the overall organizati…
Survival analysis models predict economic convergence across Americas.
RoME optimizes mobile health interventions by modeling user and time-specific effects.
Tutorials on preference learning with Gaussian Processes.
Inferencing with network data necessitates the mapping of its nodes into a vector space, where the relationships are preserved. However, with multi-layered networks, where multiple types of relationships exist for the same set of nodes, it is crucial to exploit the information shared between layers, in addition to the …
In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that cre…
Research from a variety of fields including psychology and linguistics have found correlations and patterns in personal attributes and behavior, but efforts to understand the broader heterogeneity in human behavior have not yet integrated these approaches and perspectives with a cohesive methodology. Here we extract pa…
Cluster analysis is used to explore structure in unlabeled data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computationally obtained clusters. A large number of different internal indices have been developed for validation in the offline setting. However, thi…
RobPy offers robust statistical methods in Python.