Framework fuses satellite and OSM data for local climate zone classification.
problem Heterogeneity of multimodal satellite and OSM data.
method Separate processing of satellite and OSM data, followed by fusion at the model level.
result Framework outperforms baseline by 6-2% in classification accuracy.
In this paper an approach based on expectation maximization (EM) clustering to find the climate regions and a support vector machine to build a predictive model for each of these regions is proposed. To minimize the biases in the estimations a ten cross fold validation is adopted both for obtaining clusters and buildin…
New clustering method improves climate data analysis in Lesser Antilles.
problem Inducing undesirable effects in clustering algorithms using Euclidean distance.
method Replacing Euclidean distance with Expert Deviation (ED) based on symmetrized Kullback-Leibler divergence.
result KMS-ED produces more interpretable clusters with better discrimination of daily situations.
Unified framework detects shifts in climate boundaries using GP regression and MAD test.
problem Challenges in quantifying and testing for temporal shifts in spatial boundaries from noisy data.
method Combines heteroskedastic GP regression with scaled MAD GET.
result No significant decade-scale changes in arid and semi-arid interfaces, but localized shifts during extreme droughts identified.
Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.
problem Tackles permafrost decline and infrastructure risk assessment in Arctic territories.
method Hybrid physics-machine learning framework integrating 2.9 million observations.
result Projects mean permafrost fraction decline of -20.3 pp under RCP8.5 forcing, with high-risk zones identified.
Study uses DNN to accurately estimate daily ET o in various climates.
problem Precise estimation of reference evapotranspiration (ET o ) for irrigation and water management.
method Investigated artificial neural network (ANN) and deep neural network (DNN) models using six meteorological inputs.
result DNN models, especially P-DNN-SeLU, achieve high accuracy in daily ET o estimation.
Proposes a multilingual email segmentation benchmark and model.
problem Lack of multilingual email zoning corpora and models.
method Analysis of existing corpora, development of multilingual benchmark, introduction of OKAPI model.
result OKAPI model achieves state-of-the-art performance in English and generalizes well to unseen languages.
Brillouin zones were introduced by Brillouin in the thirties to describe quantum mechanical properties of crystals, that is, in a lattice in Rn. They play an important role in solid-state physics. It was shown by Bieberbach that Brillouin zones tile the underlying space and that each zone has the same area. We gene…
Study option pricing in sideways markets and target zones.
problem Option pricing in sideways markets and target zones.
method Closed-form option pricing formulas for sideways markets and target zones.
result Closed-form option pricing formulas for sideways markets and target zones.
Machine learning predicts CO2 emissions in power grids, reducing uncertainty.
problem Forecasting CO2 emission intensities in power grids.
method Developed a machine learning algorithm using LASSO, feature selection, and Softmax weighted average.
result Marginal emissions are independent of DK2 zone conditions, suggesting external generators.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
First-best climate policy is a uniform carbon tax which gradually rises over time. Civil servants have complicated climate policy to expand bureaucracies, politicians to create rents. Environmentalists have exaggerated climate change to gain influence, other activists have joined the climate bandwagon. Opponents to cli…
Study shows critical width for rigidity of equatorial zones on spheres.
problem Mean curvature rigidity of equatorial zones on spheres.
method Used tangency principle and trap-slice lemma for strong rigidity, and constructed nontrivial perturbations using Delaunay surfaces for non-rigidity.
result Critical width exists for rigidity, beyond which zones are non-rigid.
Paper introduces a new power-dominance axis in estimator design.
problem Estimator design trade-off between bias and variance.
method Introduces a third power regime, `power-dominant', with an unavoidable error penalty.
result Any estimator in the `power-dominant' regime is structurally sub-optimal.
For a given lattice, we establish an equivalence involving a closed zone of the corresponding Voronoi polytope, a lamina hyperplane of the corresponding Delaunay partition and a quadratic form of rank 1 being an extreme ray of the corresponding L-type domain.
We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using…
The Zeeman-Hamilton operators of free charged particles are identified with the Laplacians of certain Riemannian manifolds, called Zeeman manifolds. The quantum Hilbert space decomposes into subspaces (Zeeman zones) which are invariant under the actions both of the Zeeman operator and the natural Heisenberg group repre…
The Zone of Avoidance makes it difficult for astronomers to catalogue galaxies at low latitudes to our galactic plane due to high star densities and extinction. However, having a complete sky map of galaxies is important in a number of fields of research in astronomy. There are many unclassified sources of light in the…
Model predicts and optimizes trading of electricity price spreads across multiple zones.
problem Forecasting and optimizing day-ahead versus real-time price spreads in U.S. electricity markets.
method Unified statistical model for positive and negative spikes, structural price impact model based on bid stacks.
result Optimal trading strategy improves risk-return profile and highlights market heterogeneity.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
problem Optimizing a single-asset portfolio with transaction costs and signal autocorrelation.
method Formulated an optimization problem to minimize trades while maintaining signal integrity and achieving maximum return.
result Locally optimal solution minimizes trades and achieves maximum return, with a quantifiable improvement based on threshold and autocorrelation removed.
Proposes a new stochastic method to calibrate climate risks in financial models.
problem Estimating climate-related financial risks in bank loan portfolios.
method Stochastic forward-looking methodology to calibrate climate macro-correlation evolution from scientific data.
result A new framework to evaluate climate risks without specific scenario assumptions.
BUZz defends images from adversarial attacks using simple transformations.
problem Adversarial attacks on deep neural networks for image classification.
method Combination of deep neural networks and simple image transformations.
result Achieves significant improvement over state-of-the-art defenses with a modest drop in clean accuracy.
CoDeQ simplifies joint model compression by integrating pruning and quantization.
problem Joint pruning and quantization methods are complex and require additional procedures.
method CoDeQ uses a dead-zone quantizer to directly induce sparsity and learn quantization parameters.
result CoDeQ achieves high sparsity and low-precision accuracy with minimal bit operations.
A new data set helps estimate continental-scale population distributions.
problem Lack of comprehensive, publicly available data for population estimation.
method Comprehensive data set combining satellite imagery and open-source data.
result Provides a valuable resource for developing population estimation methods.
CE improves climate uncertainty quantification using GCM ensembles and observational data.
problem Uncertainty in climate projections due to model inadequacies and variability.
method Conformal ensembles integrating GCM ensembles and observational data.
result CE generates statistically rigorous, easy-to-interpret uncertainty estimates.
Climate-contingent finance helps adapt to uncertain climate risks.
problem Uncertainty in future climate scenarios makes proactive adaptation less feasible.
method Underwrite climate adaptation projects with repayment based on future climate scenarios.
result Optimal financing reduces over- and under-preparation risks.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
Study identifies new stable climate states in climate model.
problem Understanding multistability and transitions in climate models.
method Combination of quasipotential theory and manifold learning.
result Discovery of a third stable climate state not previously known.
New framework bridges climate science and ML for easier climate model emulation.
problem High computational costs and mistrust of ML methods in climate models.
method Integrating climate science and machine learning perspectives to design easy-to-adopt emulators.
result Demonstrated reliability of emulators designed to address specific tasks.
Framework identifies causal factors of climate change using correlations and machine learning.
problem Understanding socioeconomic factors influencing carbon emissions and climate change.
method Three-step framework: correlation analysis, causal discovery, LLM interpretations.
result Adaptable solutions for data-driven policy-making and strategic decision-making.
ESN model helps understand climate event impacts.
problem Understanding complex climate event impacts.
method Feature importance methods for ESNs on spatio-temporal climate data.
result Characterized relationships between Mount Pinatubo eruption variables.
In the present paper which a sequel to dg-ga/9511005 and dg-ga//9610013 a global Weierstrass representation of an arbitrary closed oriented surface of genus ≥1 in the the three-space is constructed. The Weierstrass spectrum of a torus immersed into R3 is introduced and finite-zone planes as well as finite-zone…
The study examines how climate risk influences sovereign debt default decisions.
problem The relationship between climate risk and sovereign debt default decisions.
method Calibration of a model to analyze the credit spreads of sovereign bonds and the impact of climate vulnerability on bond spreads.
result Climate risk does not significantly influence the decision to default on sovereign debt.
Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation. While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask lea…
Study uses TV news to measure climate risks affecting clean energy firms.
problem Understanding how climate risks impact clean energy firms' financial stability.
method Developed climate risk measures from TV news coverage and analyzed their effects on clean energy firms' risks.
result Increased TV news coverage of climate risks correlates with higher systematic risk and lower idiosyncratic risk for clean energy firms.
We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-tradin…
Climate models predict dengue risk in Costa Rican municipalities.
problem Predicting dengue incidence in diverse micro-climates of Costa Rica.
method Used GAM and RF approaches on climate and dengue data.
result Retrospectively predicted dengue risk in five municipalities.
CCVA adjusts for climate change impacts on financial valuation.
problem Climate change impacts on financial valuation are currently ignored.
method Flexible parameterization to capture climate impacts on hazard rates.
result Significant impacts on interest rate swaps even with slow climate change.
HECT tests climate model outputs for reproducibility.
problem Ensuring climate models accurately reflect physical processes.
method Probabilistic classifiers for high-dimensional spatio-temporal data.
result A principled way to assess statistical reproducibility of climate models.
Develops a climate risk model for asset managers.
problem Climate-related risks affecting asset performance and productivity.
method Uses the Vasicek model with downward jumps to represent climate impacts on asset dynamics.
result Expected losses increase over time due to climate-related extreme events.
CERM calculates climate risks in bank loans.
problem Estimating climate risks in bank credit portfolios.
method Adapts credit risk models to include physical and transition risks.
result Calculates incremental credit losses due to climate risks.
Study reveals different drivers of electricity price volatility across Europe.
problem Understanding the drivers of electricity price volatility across different European zones.
method Developed estimators of weekly integrated variance using a stochastic partial differential equation approach, accounting for mean-reversion and semigroup-smoothing.
result Each European generation zone has distinct drivers of volatility, and leverage effects are not generally asymmetric.
Investigates how extreme temperature events affect global equity portfolios.
problem Impact of extreme temperature events on global equity portfolios.
method Panel regression analysis and multi-objective portfolio optimization.
result Extreme temperature events negatively impact most sectors' returns.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
We study optimal trading in an Almgren-Chriss model with running and terminal inventory costs and general predictive signals about price changes. As a special case, this allows to treat optimal liquidation in "target zone models": asset prices with a reflecting boundary enforced by regulatory interventions. In this cas…
Study predicts climate data at distant locations using machine learning.
problem Predict climate variables at distant locations where comprehensive data collection is not feasible.
method Uses reservoir computing and vector autoregression models for prediction.
result Machine learning improves prediction accuracy for highly correlated data.
We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, H, of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of H, and 2) a large value of $…
Machine learning aids in climate change mitigation and adaptation.
problem Reducing greenhouse gas emissions and societal adaptation to climate change.
method Identifying high-impact problems and leveraging machine learning in collaboration with other fields.
result Machine learning can fill existing gaps in climate change solutions.