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.
The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.
ML methods improve planetary science data analysis.
Machine learning improves planetary space physics by incorporating physical knowledge.
Explains planetary motion in a sub-Riemannian setting.
Residual neural networks improve collision prediction in planetary simulations.
Bayesian neural network predicts planetary instability.
Planetary exploration missions with Mars rovers are complicated, which generally require elaborated task planning by human experts, from the path to take to the images to capture. NASA has been using this process to acquire over 22 million images from the planet Mars. In order to improve the degree of automation and th…
We describe a new public-domain open-source simulator of an electronic financial exchange, and of the traders that interact with the exchange, which is a truly distributed and cloud-native system that been designed to run on widely available commercial cloud-computing services, and in which various components can be pl…
Modular knots follow Chebotarev law from surgeries on hyperbolic fibered links.
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type …
The green area of economy is the key of healthy living. It is necessary to convene economic and ecologic framework to establish a market attentive to drastic reduction of emissions damaging our climate and landscapes in rural areas, to the protection of biological diversity of the planet, to stop producing nuclear wast…
In this study we introduce a new technique for symbolic regression that guarantees global optimality. This is achieved by formulating a mixed integer non-linear program (MINLP) whose solution is a symbolic mathematical expression of minimum complexity that explains the observations. We demonstrate our approach by redis…
We introduce the logistic model of consumption growth, which captures a negative feedback loop preventing an unlimited growth of consumption due to finite biophysical resources of our planet. This simple dynamic model allows for derivation of the expression describing the declining long-term tail of a social discount c…
Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across dif…
We discuss the relationship between two analogues in a 3-manifold of the set of prime ideals in a number field. We prove that if is a sequence of knots obeying the Chebotarev law in the sense of Mazur and McMullen, then is a stably generic link in the sense of Mih…
New method disentangles sources of different timescales in planetary seismic data.
Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science (observations that don't fit a given theory can lead to new discoveries). Many image data analysis systems are turning to convolutional neur…
New method stabilizes tensegrity structures suitable for engineering.
NVA combines variational posteriors, annealing, and natural-gradient learning for multimodal optimization.
Researchers identify surfaces with special fluid flow fields.
A decentralized routing framework for lunar exploration robots.
The study finds the best elliptical trajectory for planets using a variation of the hodograph theorem.
Cellular regulatory dynamics is driven by large and intricate networks of interactions at the molecular scale, whose sheer size obfuscates understanding. In light of limited experimental data, many parameters of such dynamics are unknown, and thus models built on the detailed, mechanistic viewpoint overfit and are not …
Python tool creates machine-learning-ready solar dataset.
A machine learning method predicts rock permeability from 3D images.
This paper speeds up simulations of hypersonic reentry by combining traditional and neural methods.
Neural Physicist learns physical dynamics from images.
Bayesian deep learning predicts satellite collisions.
Particle- and agent-based systems are a ubiquitous modeling tool in many disciplines. We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors w…
Study compares machine learning methods for improving wind gust forecasts.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
This is a prejudiced survey on the Ahlfors (extremal) function and the weaker {\it circle maps} (Garabedian-Schiffer's translation of "Kreisabbildung"), i.e. those (branched) maps effecting the conformal representation upon the disc of a {\it compact bordered Riemann surface}. The theory in question has some well-known…