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.
Neyman-Pearson testing improves goodness of fit in detecting new physics.
Tensor networks improve anomaly detection at LHC for new physics.
Challenge uses unsupervised learning to detect new physics signals at LHC.
New Physics Learning Machine compares generative models for scientific research.
Study compares unsupervised and weakly-supervised methods for anomaly detection at the LHC.
Machine-learned anomaly detection in new-physics searches needs calibration and look-elsewhere correction
Anomaly Awareness detects anomalies in particle physics and computer vision.
Method detects new physics signals without prior knowledge.
Enhanced detection of sneutrinos at the LHC using machine learning.
We prove that the leaves of an inverse mean curvature flow provide a foliation of a future end of a cosmological spacetime under the necessary and sufficent assumptions that satisfies a future mean curvature barrier condition and a strong volume decay condition. Moreover, the flow parameter can be used to d…
Let X be a Calabi-Yau 3-fold, T=D^b(coh(X)) the derived category of coherent sheaves on X, and Stab(T) the complex manifold of Bridgeland stability conditions Z on T. It is conjectured that one can define rational numbers J^a(Z) for Z in Stab(T) and a in the numerical Grothendieck group K(T) generalizing Donaldson-Thom…
CoDA adapts dynamics models to new physical systems by conditioning on context.
Machine learning boosts physics research, especially at high energy experiments.
Abstract studies 3-manifolds and vertex algebras, expanding known connections.
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous actio…
The braneworld theory appear with the purpose of solving the problem of the hierarchy of the fundamental interactions. The perspectives of the theory emerge as a new physics, for example, deviation of the law of Newton's gravity. One of the principles of the theory is to suppose that the braneworld is local submanifold…
For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled …
In a recent paper in this journal [J. Stat. Mech. (2009) P02037] we proposed a new, physically motivated, distribution function for modeling individual incomes having its roots in the framework of the k-generalized statistical mechanics. The performance of the k-generalized distribution was checked against real data on…
Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been devel…
Improves machine learning models by incorporating physical laws into feature maps.
SSINNs learn Hamiltonian systems from data with interpretable, low-memory models.
CoLoRA models predict PDE solutions quickly and accurately with minimal data.
In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of t…
New method finds precise late-time behavior of wave equations.
In the double field theory, gauge symmetries are realized as generalized diffeomorphisms in the doubled spacetime. By consistency of the theory, dependence of tensor fields on the doubled coordinates is strongly constrained. This causes finite transformation law highly complicated, both technically and conceptually. In…
In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available. Specifically, the two types of multifi…
We reformulate wealth taxation using Fokker-Planck equations to ensure tax neutrality.
Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an extensive search is performed in the design space by trying out a large number of design combinations before reaching to the target characteris…
A new method validates generative models in high-dimensional data.
Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not triv…
Paper uses ResUNet-CMB to reconstruct cosmic polarization rotation from CMB data.
Diffusion maps help learn complex quantum phase transitions from data.
Two methods use simulation to improve anomaly detection in particle physics.
A new method trains physics-constrained neural networks more efficiently.
In this article we consider nonholonomic deformations of disk solutions in general relativity to generic off-diagonal metrics defining knew classes of exact solutions in 4D and 5D gravity. These solutions possess Lie algebroid symmetries and local anisotropy and define certain generalizations of manifolds with Killing …
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator. This augmented data can be used to train neural networks that precisely estimate …
Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed…
CSML learns causal structures for few-shot learning.
We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them…
The article describes a topological theory of quasiperiodic functions on the plane. The development of this theory was started (in different terminology) by the Moscow topology group in early 1980s. It was motivated by the needs of solid state physics, as a partial (nongeneric) case of Hamiltonian foliations of Fermi s…
AutoSciDACT detects scientific anomalies in noisy data.
EagleEye detects localized density anomalies in multivariate data.
We propose in this paper a new approach to the Kaluza-Klein idea of a five dimensional space-time unifying gravitation and electromagnetism, and extension to higher-dimensional space-time. By considering a natural geometric definition of a matter fluid and abandoning the usual requirement of a Ricci-flat five dimension…
Reduces complexity of financial contagion dynamics on networks.
Meta learning enables cross-domain Hamiltonian dynamics.
Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Au…
We present a new physics informed neural network (PINN) algorithm for solving brittle fracture problems. While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the proposed approach takes a different path by minimizing the variational energy o…
Optimizes signal detection in particle physics by decorrelating classifiers.