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.
Study uses AUVs and RL to map river plumes over multiple days.
Paper presents a GAN model for realistic river image synthesis.
Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effect…
The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.
New method converts video of dye plumes into PDEs for better understanding.
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
HydroNets use river structure to improve hydrologic predictions.
Online algorithms for identifying river pollution sources.
Deep learning improves probabilistic river discharge forecasting for hydroelectric power.
Looped transformers outperform standard transformers in complex reasoning tasks due to a specific loss landscape geometry.
We consider remodeling the planar search patterns, in the presence of the river-type perturbation represented by the weak vector field, basing on the time-optimal paths as Finslerian solutions to the Zermelo navigation problem via Randers metric.
Modeling daily river flow distribution with seasonal and long-term trends.
WSD schedule improves model training efficiency by adapting learning rates dynamically.
MethaneMapper detects methane emissions with high accuracy and reduced model size.
The study analyzes river water quality using statistical and machine learning methods.
We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a "mixture of s…
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifierusing an expectation maximization algorithm. Wederive the generalization bounds of the proposedapproach. Through an extensive simulation study, we show that the proposed method …
Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new simple and flexible methodology…
Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parame…
New framework reveals thermodynamic principles for LLM training.
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river discharge measurements, which can be quite scarce and unreliable, particularly in regions where floods cause the most damage every year. Accordi…
Method reduces model bias in water temperature prediction using physics-guided GNNs.
CausalRivers benchmarks causal discovery methods on real-world river discharge data.
Engine predicts real-time air quality with high resolution.
Dataset for rainfall modeling in central Europe from 1981-2011.
xVAE models extreme turbulence events in turbulent flows.
The flat plumbing basket presentation of a link is introduced by Furihata, Hirasawa and Kobayashi. In this paper, we show that the pass-equivalence and an equivalence introduced by using the flat plumbing basket presentation are the same relation. Furthermore, we obtain an evaluation of the minimal number of bands used…
Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.
OML-AD detects anomalies in non-stationary time series data.
The distribution of the returns for a stock are not well described by a normal probability density function (pdf). Student's t-distributions, which have fat tails, are known to fit the distributions of the returns. We present pricing of European call or put options using a log Student's t-distribution, which we call a …
Reduced-order model improves LES for atmospheric pollutant dispersion.
This paper uses VAE to generate extreme events from multivariate data.
We generalize Conway's approach to integral binary quadratic forms on Q to study integral binary hermitian forms on quadratic imaginary extensions of Q. In Conway's case, an indefinite form that doesn't represent 0 determines a line ("river") in the spine T associated with SL(2,Z) in the hyperbolic plane. In our genera…
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
New method SF-AdamW trains large models without decay phases or memory overhead.
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learning. We focus on matrix regression tasks where the rank of the weight matrix is constrained to reduce sample complexity. We introduce the comm…
Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of some interest, but current classifications are simplistic and ad hoc.…
Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution …
PIML model improves hydrological predictions by blending physics and ML.
Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore arti…
Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning tec…
Quantization-aware training can recover accuracy lost by post-training quantization.
We give a graphical theory of integral indefinite binary Hamiltonian forms analogous to the one by Conway for binary quadratic forms and the one of Bestvina-Savin for binary Hermitian forms. Given a maximal order in a definite quaternion algebra over , we define the waterworld of , analog…
New method estimates root-directed tree from extreme data.
Deep learning speeds up real-time emission monitoring.
In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water p…
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of th…
Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in…