Deep learning corrects GRACE TWSA mismatch in NOAH models.
problem Improving hydrological model predictive performance with GRACE data.
method Developed and applied deep convolutional neural network (CNN) models to learn and correct TWSA mismatch.
result Significant improvement in correlation coefficient and Nash-Sutcliff efficiency over original NOAH TWSA.
GRACE generates concise explanations for neural networks on tabular data.
problem Lack of explainable AI solutions for neural networks on tabular data.
method Borrowing ideas from causality and philosophy, GRACE generates contrastive samples to explain neural network predictions.
result GRACE explanations are more intuitive and lead to better decision-making.
Method improves clarity in forecasting spatio-temporal data.
problem Forecasting spatio-temporal data with clarity and interpretability.
method Supervised semi-nonnegative matrix factorization with frequency regularization.
result Method offers clearer interpretability in forecasting spatio-temporal data.
Study bounds noise level in linear regression with dependent data.
problem Analyzing noise level in linear regression with dependent data.
method Derive upper bounds for random design linear regression with β-mixing data, without realizability assumptions. result Correctly recovers the noise level of the problem, exhibiting graceful degradation with misspecification.
Proposes a new method for big portfolio selection using graph-based conditional moments.
problem Challenges in selecting portfolios for thousands of stocks.
method Graph-based Conditional Moments (GRACE) method: learns quantiles, means, variances, skewness, and kurtosis of stock returns.
result Shows superior performance compared to competitors, especially in measures of conditional variance, skewness, and kurtosis.
Satellite images improve real-estate price predictions.
problem Improving accuracy of real-estate price predictions.
method Used convolutional neural networks (CNN) to incorporate satellite image data.
result CNN model trained on satellite images and structured data performs 7% better in MAE.
A new clustering algorithm tracks satellite hotspot data for bushfire tracking.
problem Tracking bushfires using satellite data.
method Spatiotemporal clustering algorithm with adjustable parameters.
result The algorithm effectively clusters satellite hotspot data for tracking bushfires.
New methods detect roads in low-res satellite data, overcoming visibility challenges.
problem Detecting roads in low-resolution satellite imagery, especially those hard to see.
method Two deep learning frameworks for ordinal classification of road types from satellite time series data.
result Models can identify large and medium-sized roads from Sentinel-2 imagery.
Unsupervised method removes satellite noise without paired data.
problem Image artifacts from satellite sensor noises affect quality and applications.
method Wavelet subband cycleGAN using adversarial and cycle-consistency losses.
result Effectively removes satellite noise while preserving high frequency features.
FedSpace optimizes ML training on satellites and ground stations.
problem Training machine learning models on satellites with limited bandwidth.
method Federated Learning framework that dynamically schedules model aggregation based on satellite orbits.
result Reduces training time by 1.7 days over state-of-the-art algorithms.
Machine learning detects building damage in satellite images.
problem Extracting damage information from satellite imagery is slow and labor-intensive.
method Used four convolutional neural network models to detect damaged buildings.
result Models performed well in detecting damaged buildings in the 2010 Haiti earthquake.
Satellite imagery helps assess sustainable development with machine learning.
problem Lack of ground data on sustainable development outcomes.
method Combining satellite imagery with machine learning to model outcomes.
result Machine learning models perform well across multiple sustainable development domains.
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to …
Compact satellite tech detects flooded areas quickly.
problem Rapid access to satellite data and automatic flood detection.
method Automatically identifies cloud, water, and land in satellite images onboard small satellites.
result Reduced downlink bandwidth by 2 orders of magnitude.
GRACE-C improves causal learning from time series data.
problem Misleading causal information due to mismatched timescales.
method Combines constraint programming with theoretical insights and prior information.
result Significantly faster and scalable causal learning for large datasets.
New satellite constructions create infinite Brunnian links.
problem Creating new Brunnian links from existing ones.
method Satellite sum and satellite tie constructions.
result Every Brunnian link has a unique tree-arrow structure.
Formula for τ-invariant of satellite knots derived from L-space satellite operators.
problem Calculating the τ-invariant of satellite knots using L-space satellite operators.
method Algorithm to compute knot Floer complexes and formula for τ-invariant.
result Formula recovers existing formulas and proves new properties of τ-invariant.
Early classification improves satellite-based crop type identification.
problem Accurate early identification of crop types from satellite imagery.
method End-to-end trainable recurrent neural network with an additional stopping probability based on previously seen data.
result The model can distinguish crop types before the end of the vegetative period.
Satellite knots can be trivialized by a single band move.
problem Satellite knots and their trivialization.
method Infinite family of satellite knots and a single band move.
result No disjoint band unknotting exists for satellite knots.
Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.
problem Sparse data hinders policy development and evaluation.
method Satellite imagery, machine learning, and ML inference techniques.
result Grid access improves rural asset wealth by 0.17 standard deviations.
The paper uses CNNs on Sentinel-2 imagery to assess landslide risks.
problem Landslide risk assessment and prediction.
method Image augmentation, 3-D CNNs, satellite imagery.
result CNNs achieve significantly better accuracy than baseline.
Deep learning model predicts tropical cyclone intensification using satellite images.
problem Accurately predicting rapid intensification of tropical cyclones.
method Attention-based deep learning model using satellite images.
result Deep learning models outperform traditional methods in RI prediction.
This study analyzes satellite communication latency using a stochastic geometry model.
problem Latency analysis of LEO satellite relay communication systems.
method Stochastic geometry framework with spherical BPP models, suboptimal satellite relay selection strategy.
result Derives distance distributions and analytical expressions for transmission delays.
Satellite links of fully positive braids are characterized.
problem Characterizing satellite links of fully positive braids.
method Analyzing fully positive braids and their satellites.
result Satellite links of fully positive braids are characterized by specific conditions.
Any knot in a solid torus, called a pattern or satellite operator, acts on knots in the 3-sphere via the satellite construction. We introduce a generalization of satellite operators which form a group (unlike traditional satellite operators), modulo a generalization of concordance. This group has an action on the set o…
Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.
problem Estimating large spatio-temporal autoregressions with unknown spatial interactions.
method Sparse generalized Yule-Walker estimation, penalized regression, spatial and temporal dependence.
result Strong forecast improvements and evidence of spatial interactions in NO2 satellite data.
Space debris warnings follow a predictable pattern, allowing timely satellite maneuvers.
problem Estimating when fresh information about space debris will arrive.
method Statistical learning model of the message arrival process, specifically a Bayesian Poisson process.
result The average prediction error for the next message arrival time is smaller than baseline predictions.
High-resolution satellite imagery have been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Even though, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in …
Deep learning models perform variably across continents/seasons in land cover mapping.
problem Variability in deep learning model performance across different continents/seasons.
method Clustering techniques on satellite imagery from different continents.
result Model performance varies significantly between different continents/seasons.
Satellite links with many twists have simpler companions.
problem Relationship between satellite and companion links' complexity.
method Constructing satellite links with multiple full twists and analyzing their companion links.
result Satellite links with many twists have simpler companion links.
Satellite imagery helps adjust for unobserved confounders in observational studies.
problem Adjusting for confounding factors in observational studies with non-tabular data like satellite imagery.
method Formalizing conditions for causal effect identification, estimation, and sensitivity analysis.
result Demonstrated the use of satellite imagery as a proxy for unobserved confounders in anti-poverty aid programs.
Develops a new framework for integrating satellite allocations in small portfolios.
problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.
Forest tree species mapped with high accuracy using satellite data.
problem Classifying dominant tree species in Swedish forests.
method Extreme gradient boosting model with Bayesian optimization, combining Sentinel-1/2 satellite data and field observations.
result Overall accuracy of 85%, F1 score of 0.82, Matthews correlation coefficient of 0.81.
Bayesian deep learning predicts satellite collisions.
problem Space debris poses planetary risk.
method Bayesian deep learning with LSTM networks.
result Predicts conjunction event evolution with uncertainties.
New algorithms improve uncertainty estimation in satellite precipitation predictions.
problem Lack of uncertainty estimates in machine learning spatial precipitation predictions from satellite data.
method Benchmarked six algorithms including LightGBM, compared using quantile scoring functions and rules.
result LightGBM outperformed other algorithms in quantile scoring rule by 11.10%.
Probabilistic NDVI forecasting from sparse satellite data.
problem Challenges in short-term NDVI forecasting due to sparse and irregular satellite data.
method Probabilistic forecasting framework using historical NDVI and meteorological observations, with temporal-distance weighted quantile loss and extreme-weather feature engineering.
result The proposed method outperforms baselines on pointwise and probabilistic evaluation metrics.
New proof for minimizing tunnel systems in satellite chain links.
problem Minimizing the tunnel number of satellite chain links.
method Proving the tunnel number is minimized for links with a specific number of components and bridge number.
result The result is sharp for satellite chain links over a 2-bridge knot.
The Gluck twist preserves the diffeomorphism type of certain satellite 2-knots.
problem Preserving the diffeomorphism type of satellite 2-knots under the Gluck twist.
method Using new descriptions of satellite 2-knots, the paper shows that the Gluck twist does not change the diffeomorphism type of certain satellite 2-knots in three ways.
result The Gluck twist preserves the diffeomorphism type of certain satellite 2-knots.
Techniques are introduced which determine the geometric structure of non-simple two-generator 3-manifolds from purely algebraic data. As an application, the satellite knots in the 3-sphere with a two-generator presentation in which at least one generator is represented by a meridian for the knot are classified.
Researchers compute Khovanov polynomials for satellite knots.
problem Computing Khovanov polynomials for satellite knots.
method Explicit computation using a computer program for two families of satellite knots.
result Khovanov polynomials can be expressed as a linear combination of pattern and companion invariants, with a jump at a critical point.
New satellite knots counter a conjecture about Lorenz knots.
problem A conjecture about satellite knots and Lorenz knots was disproven.
method Constructed infinitely many counterexamples of satellite knots that are not cables.
result The conjecture was amended and shown to hold for many Lorenz knots.
Formula for satellite operators using knot Floer homology.
problem Computing knot Floer homology for satellite knots.
method Using Heegaard Floer Dehn surgery formulas and formal knot Floer complexes.
result Formulas to compute knot Floer homology for satellite knots.
Satellite formula connects knot concordance invariants to surgery.
problem Understanding knot concordance invariants.
method Excision theorem for real Floer homotopy types.
result Concordance invariants depend only on zero-framed surgery.
We give sufficient conditions for a satellite knot to admit an L-space surgery, and use this result to give new infinite families of patterns which produce satellite L-space knots.
The study computes invariants of satellite knots using bordered Floer homology.
problem Computing invariants of satellite knots with specific patterns.
method Using bordered Floer homology and the immersed curve interpretation of the bordered pairing theorem.
result Satellites with thin fibered companions or specific patterns have thin knot Floer homology.
Necessary and sufficient conditions are given for a satellite knot to be fibered. Any knot k~ embeds in an unknotted solid torus V~ with arbitrary winding number in such a way that no satellite knot with pattern (V~,k~) is fibered. In particular, there exist nonfibered satellite knots wit…
A simplified proof for satellite knot signatures.
problem Proving Litherland's formula for satellite knots.
method Uses linear algebra and basic knot theory.
result A new, elementary proof of the signature formula.