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.
Bayesian neural networks improve stellar age predictions with reduced uncertainty.
GPRNs accurately model stellar activity affecting RV measurements of exoplanets.
Proves common stellar subdivisions for all PL homeomorphic polyhedra.
Study on stellar models' topology and mass using minimal surfaces.
Method maps imperfect simulations to observed stellar spectra using unsupervised domain adaptation.
New method uses neural networks to infer dark matter subhalo abundance from stellar streams.
Study of 13,456 hot stellar systems reveals multi-layered grouping.
Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from t…
New method uses neural networks to interpolate stellar atmospheres with high precision.
Alexander's conjecture extended to infinite simplicial complexes.
A scalable Bayesian additive model for stellar flare detection using Gaussian process inference and hidden Markov models.
Debate over the existence of branches in the stellar activity-rotation diagrams continues. Application of modern time series analysis tools to study the mean cycle periods in chromospheric activity index is lacking. We develop such models, based on Gaussian processes, for one-dimensional time series and apply it to the…
Study spherical doubly warped spacetimes for stellar collapse and cosmology.
Shellable tilings on simplicial complexes help understand their structure.
Cycle-StarNet bridges theory and data by adapting synthetic spectra to observational data.
This study uses deep learning to infer stellar parameters from short TESS and K2 observations.
Meta-learning framework for few-shot one-class classification using order-equivariant networks.
Multivariate clustering in astrophysics is a recent development justified by the bigger and bigger surveys of the sky. The phylogenetic approach is probably the most unexpected technique that has appeared for the unsupervised classification of galaxies, stellar populations or globular clusters. On one side, this is a s…
We construct a one-dimensional deformation retract of the unordered k-point configuration space of a star S. This retract suggests an explicit set of free generators Beta_k for the corresponding braid group of the star B_k and shows that the natural map from B_k-1 to B_k sends Beta_k-1 to Beta_k injectively.
Here are versions of the proofs of two classic theorems of combinatorial topology. The first is the result that piecewise linearly homeomorphic simplicial complexes are related by stellar moves. This is used in the proof, modelled on that of Pachner, of the second theorem. This states that moves from only a finite coll…
Predicts the age of astronomical transients from real-time data.
The paper models star dynamics using Ricci flow and Perelman entropy, revealing chaotic behavior.
Considering a Hamiltonian Dynamical System describing the motion of charged particle in a Tokamak or a Stellarator, we build a change of coordinates to reduce its dimension. This change of coordinates is in fact an intricate succession of mappings that are built using Hyperbolic Partial Differential Equations, Differen…
Pricing Bermudan swaptions with few exercise dates using analytic methods.
Detects anomalies in astronomical time series data.
LatentNN corrects neural network attenuation bias in astronomical data.
Investors in Target Date Funds are automatically switched from high risk to low risk assets as their retirements approach. Such funds have become very popular, but our analysis brings into question the rationale for them. Based on both a model with parameters fitted to historical returns and on bootstrap resampling, we…
It is expected that matter composed of a perfect fluid cannot be at rest outside of a black hole if the spacetime is asymptotically flat and static (non-rotating). However, there has not been a rigorous proof for this expectation without assuming spheical symmetry. In this paper, we provide a proof of non-existence of …
A model-free framework extracts risk-neutral densities from short-dated options.
Designing a photometric system to best fulfil a set of scientific goals is a complex task, demanding a compromise between conflicting requirements and subject to various constraints. A specific example is the determination of stellar astrophysical parameters (APs) - effective temperature, metallicity etc. - across a wi…
The Dybvig-Ingersoll-Ross (DIR) theorem states that, in arbitrage-free term structure models, long-term yields and forward rates can never fall. We present a refined version of the DIR theorem, where we identify the reciprocal of the maturity date as the maximal order that long-term rates at earlier dates can dominate …
I introduce an algorithm for estimating parameters from multidimensional data based on forward modelling. In contrast to many machine learning approaches it avoids fitting an inverse model and the problems associated with this. The algorithm makes explicit use of the sensitivities of the data to the parameters, with th…
Catastrophe risk is a major threat faced by individuals, companies, and entire economies. Catastrophe (CAT) bonds have emerged as a method to offset this risk and a corresponding literature has developed that attempts to provide a market-consistent pricing methodology for these and other long-dated, insurance-type cont…
In this letter, we propose a method for period estimation in light curves from periodic variable stars using correntropy. Light curves are astronomical time series of stellar brightness over time, and are characterized as being noisy and unevenly sampled. We propose to use slotted time lags in order to estimate corrent…
Paper explores volatility swaps in rough volatility models.
A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings fr…
Meta-learning framework for credit risk assessment of SMEs, aligning financial statement dates with evaluation dates.
We detect lookahead bias in LLM forecasts using a novel statistical method.
We discovered secular trend bias in a drug effectiveness study for a recently approved drug. We compared treatment outcomes between patients who received the newly approved drug and patients exposed to the standard treatment. All patients diagnosed after the new drug's approval date were considered. We built a machine …
Probabilistic programming aids in automatically dating ice cores, reducing manual error and uncertainty.
Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient de…
We study the local volatility function in the Foreign Exchange market where both domestic and foreign interest rates are stochastic. This model is suitable to price long-dated FX derivatives. We derive the local volatility function and obtain several results that can be used for the calibration of this local volatility…
In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regressio…
[Abridged] Although galaxies are found to follow a tight relation between their star formation rate and stellar mass, they are expected to exhibit complex star formation histories (SFH), with short-term fluctuations. The goal of this pilot study is to present a method that will identify galaxies that are undergoing a s…
New study finds environment significantly suppresses star formation in galaxies, contrary to previous beliefs.
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed ban…
Geometric integrator preserves coadjoint orbits in dissipative systems.
Paper presents new expansions for option pricing with cash dividends.