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.
Framework improves marine mammal monitoring in noisy underwater environments.
Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year. Understanding the number and location of potential mosquito vectors is of paramount importance to aid the reduction of malaria transmission cases. In recent years, deep learning has become widely used for bioacous…
In this paper, we first give the regular point reduction and the two types of Hamilton-Jacobi equation for a regular controlled Hamiltonian (RCH) system with symmetry and momentum map on the generalization of a semidirect product Lie group. Next, as an application of the theoretical results, we consider the underwater …
Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the permanent exposure to the marine environment, and constant monitoring of component conditions is requi…
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…
Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and…
Current auto loans converge to super-prime credit despite remaining underwater.
We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse app…
How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to reject outliers and produce trustworthy position estimates? The stringent acoustic communication channel and the accuracy needs of our geophysi…
Deployment and operation of autonomous underwater vehicles is expensive and time-consuming. High-quality realistic sonar data simulation could be of benefit to multiple applications, including training of human operators for post-mission analysis, as well as tuning and validation of autonomous target recognition (ATR) …
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
Study uses AUVs and RL to map river plumes over multiple days.
Proposes a learned Bayesian Cramér-Rao bound for unknown measurement models.
This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learn…
Paper develops ML-based PLA verifiers that operate like the likelihood test.
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to estimate the frequency of each component in a multisinusoidal signal from a finite number of noisy samples. A recent machine-learning approach u…
We study the convolutional phase retrieval problem, of recovering an unknown signal from measurements consisting of the magnitude of its cyclic convolution with a given kernel . This model is motivated by applications such as channel estimation, optics, and u…
The paper tackles data-driven optimal control of unknown nonlinear systems using RKHS.
Paper presents a new port-Hamiltonian model for vehicle manipulators.
A scenario in which regulators take the drastic step of requiring coverage of all venture bank investment loans using interbank borrowed funds is considered. In this scenario, a minimal amount of default insurance is used, such that Tier 1 and 2 capital requirements are still met. To do this, the default insurance perc…
The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.
The paper develops a new framework for managing asymmetric volatility.
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
Study predicts stream turbidity using surrogate data and meta-model.