This paper uses Gaussian processes to cluster BC coastal rainfall patterns.
problem How to cluster BC coastal rainfall patterns effectively.
method Developed an approach for clustering multiple Gaussian processes observed on a comparable interval.
result Interesting insights into BC rainfall patterns, not simply clustering El Niño and La Niña years.
SVM predicts regional rainfall with varying accuracy, best in central US.
problem Regional rainfall prediction for social and economic impact planning.
method Support Vector Machine (SVM) applied to sequences of daily rainfall maps.
result SVM predictions for central region outperform untrained classifier.
Modeling rainfall with a flexible Hawkes process.
problem Capturing the complex clustering of rain cells.
method Combining heterogeneous data and Hawkes process formalism.
result Aggregated rainfall follows a rough fractional process.
Dataset for rainfall modeling in central Europe from 1981-2011.
problem Improving rainfall streamflow modeling beyond simple catchments.
method Spatially resolved meteorological and ancillary data compilation.
result Dataset for neural network-driven hydrological modeling.
LSTM model predicts rainfall runoff with high temporal resolution.
problem Accurate and efficient rainfall runoff simulations for flood risk management.
method Data-driven rainfall runoff model using Long-short-Term-Memory (LSTM) networks.
result LSTM model achieves high-resolution discharge predictions with improved performance.
Proposes FIPO-BC for efficient online calibration of complex models.
problem Efficiently calibrating computationally expensive models with large datasets.
method Fixed inducing points online Bayesian calibration (FIPO-BC) algorithm.
result FIPO-BC is at least ten times faster than standard methods and enables online updates.
Bayesian approach improves rain field reconstruction using CMLs and DMs.
problem Challenges in accurately reconstructing ground-level rainfall from CML path-integrated measurements.
method Bayesian inverse problem with Diffusion Models as priors.
result Improved performance in rainfall estimation compared to existing methods.
Study uses machine learning to predict rain in Australia.
problem Challenging task of predicting rainfall with uncertain outcomes.
method Machine learning techniques, including modeling inputs, methods, and pre-processing.
result Comparison of various machine learning techniques' reliability in predicting rainfall.
Study a risk model with tree-structured Poisson-Markov random field for rainfall events.
problem Dependence between rainfall frequencies in insurance portfolios.
method Tree-structured Markov random field with Poisson marginals.
result Asymptotic results for portfolio risk and risk allocation.
Study improves flood loss risk models using historical data and rainfall data.
problem Predicting financial losses from flooding events.
method Used neural networks, decision trees, and kernel-based regressors on NFIP dataset, incorporating rainfall data.
result Extreme Gradient Boosting provided the best results, and bias correction improved model performance.
Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature…
Let $Q^N_l\subset \bC\bP^{N+1}$ denote the standard real, nondegenerate hyperquadric of signature l and $M\subset \bC^{n+1}$ a real, Levi nondegenerate hypersurface of the same signature l. We shall assume that there is a holomorphic mapping $H_0\colon U\to \bC\bP^{N_0+1}$, where U is some neighborhood of M in …
Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all t…
Improved RL policies from offline data with relaxed BC constraints.
problem Overestimation bias in offline RL due to lack of interaction with environment.
method Introducing a policy constraint via behavioural cloning (BC) and adjusting the balance between RL and BC.
result Refined policies outperform baseline and match/exceed complex alternatives.
New algorithm estimates semi-continuous data density using entropy maximization.
problem Estimating density functions for semi-continuous data.
method Maximum entropy principle, requiring only constraint function samples.
result Estimate has significantly less bias compared to existing methods.
This paper develops a novel graph neural network to efficiently identify high betweenness centrality nodes.
problem Efficiently identifying high betweenness centrality nodes in large networks.
method A novel encoder-decoder framework using pairwise ranking loss.
result The model accurately identifies highly-ranked nodes without noticeable sacrifice in accuracy.
NeuralHydrology uses LSTMs to forecast rainfall-runoff, revealing interpretable patterns.
problem Difficulty in interpreting LSTMs in environmental sciences.
method Application of LSTMs for rainfall-runoff forecasting in hydrology, analyzing patterns internally.
result Trained LSTMs reveal patterns consistent with hydrological system understanding.
The minimum description length (MDL) principle in supervised learning is studied. One of the most important theories for the MDL principle is Barron and Cover's theory (BC theory), which gives a mathematical justification of the MDL principle. The original BC theory, however, can be applied to supervised learning only …
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input z to a sample x that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function…
We consider local CR-immersions of a strictly pseudoconvex real hypersurface $M\subset\bC^{n+1}$, near a point p∈M, into the unit sphere $\mathbb S\subset\bC^{n+d+1}$ with d>0. Our main result is that if there is such an immersion f:(M,p)→S and d<n/2, then f is {\em rigid} in the sense t…
The BC(n) Sutherland Hamiltonian with coupling constants parametrized by three arbitrary integers is derived by reductions of the Laplace operator of the group U(N). The reductions are obtained by applying the Laplace operator on spaces of certain vector valued functions equivariant under suitable symmetric subgroups o…
BinaryConnect is generalized and proven to converge.
problem Understanding and improving neural network quantization.
method Refined analysis of BC, introduction of ProxConnect.
result ProxConnect achieves competitive performance.
Study on harmonic forms on almost Hermitian 4-manifolds, calculating dimensions and invariants.
problem Understanding harmonic forms on almost Hermitian 4-manifolds.
method Analyzing Bott-Chern and ∂ˉ harmonic forms, calculating dimensions and invariants. result Dimensions of harmonic forms on almost Hermitian 4-manifolds are determined.
Improves BC policies by generating new plausible trajectories.
problem Sub-optimal data quality in BC leads to poor policy performance.
method Trajectory Stitching (TS) generates new plausible transitions.
result TS significantly improves behavioural policies over original data.
New method learns robot skills from data, matching or outperforming existing methods.
problem Learning robot skills from fixed datasets.
method Offline Reinforcement Learning via Supervised Learning using implicit models.
result Implicit models can match or outperform explicit models in acquiring robotic skills.
Interactive IL beats BC by state-wise annotation cost.
problem Behavior Cloning struggles with annotation cost in sequential decision making.
method Proved Stagger and Warm Stagger algorithms to outperform BC.
result Interactive and hybrid IL methods outperform BC with state-wise annotation.
New symplectic embedding obstructions found for polydisks into half-integer ellipsoids.
problem Obstructing symplectic embeddings of polydisks into half-integer ellipsoids.
method Combinatorial criterion developed by Hutchings to obstruct symplectic embeddings.
result Optimal inclusion conditions for symplectic embeddings of polydisks into half-integer ellipsoids.
Paper tackles offline RL from mixed datasets with adaptive KL regularizer.
problem Challenges in optimizing RL and BC signals with varying action coverage and multiple action modes.
method Adaptively weighted reverse KL divergence regularizer based on TD3 algorithm.
result Empirically outperforms existing offline RL algorithms in MuJoCo locomotion tasks.
Study calculates global sections on special geometric spaces.
problem Calculating global sections on compact Ricci-flat Kähler manifolds.
method Expressed as an invariant subspace of a βγ-bc system under Lie algebra action.
result Space of global sections is an invariant subspace.
Discrete-time hidden Markov models are a broadly useful class of latent-variable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common in practice to introduce temporal non-homogeneity into such models by making the transition probabilities dependent on ti…
BC-ACI corrects time series forecast bias, improving prediction intervals.
problem Persistent bias in time series forecasts leads to overly conservative prediction intervals.
method Augments ACI with an EWM estimate of forecast bias to correct nonconformity scores and re-center intervals.
result Reduces Winkler interval scores by 13-17% under distribution shifts, improving calibration.
COMBO network improves optical flow estimation by combining deep learning with brightness constancy.
problem Optical flow estimation using deep learning requires complex training schemes.
method COMBO network explicitly exploits brightness constancy and combines it with a data-driven approach.
result COMBO network outperforms state-of-the-art methods on various benchmarks.
BC-LLM uses LLMs to find concepts without predefined sets, improving interpretability and performance.
problem Finding a balance between interpretability and accuracy in concept extraction models.
method Bayesian approach with LLMs as both concept extractor and prior.
result BC-LLM outperforms interpretable and black-box models across various datasets.
In this paper, we look to address the problem of estimating the dynamic direction of arrival (DOA) of a narrowband signal impinging on a sensor array from the far field. The initial estimate is made using a Bayesian compressive sensing (BCS) framework and then tracked using a Bayesian compressed sensing Kalman filter (…
The paper extends Bott-Chern Laplacian definition and explores its properties on almost Hermitian manifolds.
problem Exploring the properties of Bott-Chern Laplacian on almost Hermitian manifolds.
method Extending the definition of Bott-Chern Laplacian, proving ellipticity, and analyzing kernels on different types of manifolds.
result The dimensions of Bott-Chern and Dolbeault harmonic forms differ on almost complex 4-manifolds with specific metrics.
New model uses less site-specific data for accurate hydrologic predictions.
problem Accurate rainfall-runoff modeling in data-poor regions.
method Data-driven learned embedding to replace location-specific attributes.
result Achieves state-of-the-art results with significantly less information.
ORIL learns a reward function from unlabeled data to improve robot learning.
problem Leveraging unlabeled data for robot learning.
method ORIL learns a reward function from demonstrator and unlabeled trajectories, annotates data, and trains an agent via offline reinforcement learning.
result ORIL consistently outperforms BC agents on various robotic tasks.
In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio. We then input the mixed image to the model and train the model to output the mixing ratio. BC …
In this paper, we consider real hypersurfaces M in C3 (or more generally, 5-dimensional CR manifolds of hypersurface type) at uniformly Levi degenerate points, i.e. Levi degenerate points such that the rank of the Levi form is constant in a neighborhood. We also require the hypersurface to satisfy a certain s…
Registration, which aims to find an optimal 1-1 correspondence between shapes, is an important process in different research areas. Conformal mappings have been widely used to obtain a diffeomorphism between shapes that minimizes angular distortion. Conformal registrations are beneficial since it preserves the local ge…
Researchers recover Riemannian manifolds and lower order terms from travel time data.
problem Recovering Riemannian manifolds and lower order terms from travel time data.
method Adaptation of the Boundary Control method to recover lower order terms.
result Complete Riemannian manifolds and lower order terms can be uniquely recovered from a local source to solution map.
A topological invariant of a polynomial map p:X→B from a complex surface containing a curve C⊂X to a one-dimensional base is given by a rational second homology class in the compactification of the moduli space of genus g curves with n labeled points $\modmgn$. Here the generic fibre of p has genus …
RainfallBench benchmarks GNSS-based precipitation nowcasting models, addressing complex meteorological challenges.
problem Evaluation of precipitation nowcasting models in meteorology is insufficient due to focus on periodic variables.
method RainfallBench dataset and specialized evaluation protocols for multi-scale, multi-resolution, and extreme rainfall events.
result Bi-Focus Precipitation Forecaster (BFPF) enhances rainfall time series forecasting by incorporating domain-specific priors.
Decomposes harmonic forms on almost Kähler manifolds, revealing non-trivial structure.
problem Primitive decomposition of harmonic forms on compact almost Kähler manifolds.
method Primitive decomposition of ∂ˉ,∂, Bott-Chern and Aeppli-harmonic (k,k)-forms. result Primitive components of harmonic forms are constants multiples of ωk. This paper describes how to recover the topology of a closed manifold M from a good Morse function f on M. The essential method was suggested by Cohen, Jones and Segal. They constructed a topological category Cf and claimed that the classifying space BCf is homeomorphic to M. We prove it from a differ…
HydroNets use river structure to improve hydrologic predictions.
problem Scalable and accurate hydrologic models are needed for climate change impacts.
method HydroNets are deep neural networks that incorporate river network structure.
result HydroNets improve predictions with fewer data, especially at longer horizons.
New method uses SDEs for accurate non-uniformly sampled time series analysis.
problem Characterizing non-uniformly sampled time series with high accuracy.
method Stochastic Differential Equations (SDEs) for modeling, incremental estimation, and model truncation.
result Increased accuracy in characterizing non-uniformly sampled time series.
Study predicts stream turbidity using surrogate data and meta-model.
problem Costly turbidity sensor deployment limits monitoring networks.
method Dynamic regression (ARIMA), LSTM, GAM models; surrogate covariates (rainfall, water level, temperature, solar exposure); meta-model combining strengths of individual models.
result ARIMA and GAM models with all covariates outperform single models; meta-model yields highest accuracy.