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.
Visualizes DNNs using topographic maps for better understanding.
The uninformative ordering of artificial neurons in Deep Neural Networks complicates visualizing activations in deeper layers. This is one reason why the internal structure of such models is very unintuitive. In neuroscience, activity of real brains can be visualized by highlighting active regions. Inspired by those te…
This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topog…
The paper develops models to understand sensory coding and cortical topography.
This paper proposes a brain-inspired approach to quantum machine learning with the goal of circumventing many of the complications of other approaches. The fact that quantum processes are unitary presents both opportunities and challenges. A principal opportunity is that a large number of computations can be carried ou…
DBS uses swarm intelligence to cluster data without needing a global objective function.
Machine learning recreates the periodic table from element properties.
Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli. Rarely do researchers attempt to model and examine how individual participants vary from each other -- a question that should be addressable even in small samples given the right statistical tools. We propose …
We prove recognition theorems for codimension one manifold factors of dimension . In particular, we formalize topographical methods and introduce three ribbons properties: the crinkled ribbons property, the twisted crinkled ribbons property, and the fuzzy ribbons property. We show that i…
Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although massive maps are available in the digital era, how to effectively and accurately access the required map remains a challenge today. Previo…
We introduce the problem of reconstructing a sequence of multidimensional real vectors where some of the data are missing. This problem contains regression and mapping inversion as particular cases where the pattern of missing data is independent of the sequence index. The problem is hard because it involves possibly m…
In some applications and in order to address real world situations better, data may be more complex than simple vectors. In some examples, they can be known through their pairwise dissimilarities only. Several variants of the Self Organizing Map algorithm were introduced to generalize the original algorithm to this fra…
This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point…
Enhances ocean floor mapping with adaptive uncertainty estimates.
New non-isotopic Seifert surfaces found in 4-ball.
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite im…
New method aligns brain surfaces based on functional signatures.
The shortage of high-resolution urban digital elevation model (DEM) datasets has been a challenge for modelling urban flood and managing its risk. A solution is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current …
Computed linking number of modular knots and Lorenz links.
Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control. While alpha waves (8-12 Hz) are known to closely resemble short sinusoids, and thus are revealed by Fouri…
Mixtures of Gaussians, factor analyzers (probabilistic PCA) and hidden Markov models are staples of static and dynamic data modeling and image and video modeling in particular. We show how topographic transformations in the input, such as translation and shearing in images, can be accounted for in these models by inclu…
A large amount of data accommodated in knowledge graphs (KG) is actually metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like cities, chemical compounds or celestial objects. In this paper, we propose a novel approach that transfers orometric (topographic) measures to…
Study assesses drought and late-frost risks in Bavaria using vine copulas.
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and a…
We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of l_2- or l_infinity-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address here the ca…
Develops approximately equivariant neural processes for better data modeling.
Deep learning improves cancer report classification accuracy.
Spatially-aware model improves earthquake hazard assessment accuracy.
Paper uses Gaussian processes and neural nets to model sub-km wind accurately.
Novel path planning improves UAV detection of extreme anomalies.
New framework uses time series features for predicting streamflow in ungauged areas.
PeakWeather provides Swiss weather station data for machine learning.
Develops deep models to handle nonstationary spatial extremal dependence.
Deep learning classifies keratoconus patients with high accuracy.
Study examines equity in post-Snow Uri recovery, finds disparities.
Generic pseudo-Anosov mapping classes in mapping class groups.
This paper constructs real algebraic maps that are topologically special generic maps.
A Reeb space is defined as the space of all the connected components of inverse images of a smooth map, which is a fundamental tool in studying smooth manifolds using generic smooth maps whose codimensions are not positive such as Morse functions, their higher dimensional versions including fold maps and general stable…
The study finds abundant normal generators for mapping class groups.
Study on generalized ξ-parallel maps in Riemannian geometry.
The paper derives Liouville theorems for various generalized maps on Riemannian manifolds.
New class of maps restricts manifolds strongly in algebraic topology.
New descriptions of a subgroup in mapping class groups.
Introduces generalized moment maps for almost Hermitian settings.
Abstracts a theorem for non-smooth maps in infinite dimensions.
The paper studies maps from pseudo-Hermitian to Kähler manifolds, proving harmonic map properties.
A special generic map is a smooth map regarded as a natural generalization of Morse functions with just 2 singular points on homotopy spheres. Canonical projections of unit spheres are simplest examples of such maps and manifolds admitting special generic maps into the plane are completely determined by Saeki in 1993 a…
The study restricts manifolds with certain explicit SGL maps and constructs them.