The authors characterize flexibility in power and energy markets considering time, spatiality, resource, and risk.
problem Evaluating and maximizing flexibility in power systems and markets.
method Characterization of flexibility dimensions (time, spatiality, resource, risk) and their interrelations with flexibility assets, products, and services.
result Flexibility should be evaluated based on multiple dimensions for efficient power systems and markets.
A model shows faster energy transition rewards flexible production more quickly.
problem Energy transition discourages investments in flexible production.
method Modeling future electricity prices from residual load, investigating revenues for various flexibility levels.
result Faster energy transition rewards flexible production more quickly.
Flexible surfaces found in complex projective and product spaces.
problem Finding flexible surfaces in complex projective and product spaces.
method Constructing flexible surfaces within prescribed homology classes.
result Flexible surfaces exist in both CP2 and S2imesS2. A new hashing method handles large-scale data with flexible similarity measures.
problem Efficient nearest neighbour search in large-scale systems with variable labels.
method End-to-end trainable network transforming data to uniform distribution on product of spheres, then hashing to binary form maximizing entropy.
result Outperforms baseline approaches in limited capacity regime.
Proposes neural similarity for CNNs to enhance flexibility and performance.
problem Limited flexibility of inner product-based convolution in CNNs.
method Introduces neural similarity as a learnable parametric similarity measure, and proposes NSL for adaptive learning from data.
result Dynamic neural similarity improves flexibility and performance in visual recognition and few-shot learning.
KoPA approximates matrices using Kronecker products for better flexibility.
problem Matrix approximation and denoising with Kronecker product decomposition.
method Approximate a matrix as a sum of Kronecker products of smaller matrices using extended information criteria for configuration selection.
result KoPA selects the true configuration with high probability under suitable conditions.
Develops a unified framework for valuing insurance products with guarantees.
problem Valuing insurance products with guarantees in an affine setting.
method General affine approach to model financial markets, mortality, and policyholder behavior.
result Explicit valuation formulas for variable annuities and related contracts derived.
Geometrically describes polygon space cohomology rules.
problem Understanding cohomology of polygon spaces.
method Two geometrically meaningful presentations of cup product rules.
result Simple rules for cup product in polygon spaces.
Groups of contact transformations are uniformly simple if supporting open books are flexible.
problem Understanding the algebraic and geometric properties of contact transformation groups.
method Exploring the connection between contact transformations, open book decompositions, and flexible Weinstein manifolds.
result The connected component of the identity in the automorphism group of a closed contact manifold is a uniformly simple group.
New graph operations for stochastic processes improve model flexibility.
problem Limited flexibility in graph convolutional networks for stochastic processes.
method Parameterization of graph-dependent linear operations using functional calculus.
result Improved learning flexibility and richer behaviors in graph models.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
problem High volatility and imbalance in power systems due to renewable energy and flexible demand.
method Incorporates short-term features from hourly and quarter-hourly products, including limit order book and neighboring product signals.
result Features from the limit order book are most influential, and neighboring product signals improve forecast accuracy.
The paper evaluates dynamic hedging strategies for various financial products.
problem Pricing derivative products with dynamic hedging and issuer-tailored risk.
method Unified constrained discrete stochastic dynamic programming framework with sequential local minimizing strategies.
result Demonstrates flexibility of the unified framework through numerical examples.
This paper uses SPNs with GPs to efficiently model complex data.
problem Inference cost and memory issues in Gaussian processes.
method Integrating Gaussian processes into sum-product networks.
result The model efficiently learns input-dependent parameters and hyper-parameters.
Proves small cancellation free products have geometric actions on CAT(0) cube complexes.
problem Proving small cancellation free products have geometric actions on CAT(0) cube complexes.
method Using a blown-up complex of groups and a boundary separation criterion, proving wall stabilizers form a rich family of subgroups.
result Proves $C'( rac16)$--small cancellation free products of residually finite groups are residually finite.
Improved collaborative filtering with neural network models of reviews.
problem Improve collaborative filtering performance using side information from reviews.
method Introduced two neural network models (product-of-experts and recurrent neural network) to incorporate reviews into collaborative filtering.
result The product-of-experts model achieved state-of-the-art performance, outperforming LDA-based approach.
Bayesian networks improve product risk assessment by handling uncertainty and causality.
problem Limited handling of uncertainty and inability to incorporate causal explanations in existing methods.
method Bayesian Networks (BNs) for improved systematic product risk assessment.
result BN approach provides more powerful and flexible risk assessments.
New examples challenge Geroch conjecture stability.
problem Stability of the Geroch conjecture in warped products.
method Constructing warped-product manifolds with specific curvature properties.
result First counterexample to Sormani's conjecture on stability.
XCAN uses cross-product penalization for sparse matrix factorization.
problem Understanding complex data structures.
method Sparse matrix factorization with a loss function balancing variance and structural preservation.
result Flexible modeling approach for diverse applications.
Crochet creates precise 2D shapes from 1D material.
problem Creating precise 2D shapes from 1D material.
method Using crochet to generate constant flat, spherical, or hyperbolic shapes.
result Crochet is the most flexible and precise method for building dynamical systems with high curvature precision.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
New integrable systems are created using matrix operations and Lie algebra elements.
problem Generating coupled nonlinear integrable systems from zero curvature equation.
method Constructing Maurer-Cartan forms using Kronecker product and specific matrices (nilpotent, Hadamard, idempotent, k-idempotent).
result Found a closure property among chosen matrices crucial for coupling and nonlinearity.
Paper proposes a new method for approximating high-dimensional matrices using Kronecker products.
problem Discovering low-dimensional structure in high-dimensional data.
method Hybrid Kronecker Product Approximation (hKoPA) and estimation procedures.
result The proposed methods provide flexible and effective dimension reduction.
CDLF predicts product life-cycles in cold-start phases with high accuracy.
problem Forecasting new products in early phases when data is scarce.
method Conditional Diffusion Life-cycle Forecaster (CDLF) combining static descriptors, reference trajectories, and new observations.
result CDLF outperforms classical models in accuracy and probabilistic forecasting.
A flexible Bayesian regression package for complex models.
problem Complex regression models and probabilistic predictions.
method Bayesian additive models with flexible components.
result Modular software for efficient Bayesian estimation.
Study analyzes climate-tech investments across 14 sectors.
problem Accelerating climate-tech innovation in nascent value chains.
method Analysis of 4,172 firms and 12,929 investments over 15 years.
result Only 15% of firms develop end products, 59% support them, and 26% develop services.
New method for matrix completion using Kronecker product approximation.
problem Matrix completion with low Kronecker rank structure.
method Alternative matrix representation using Kronecker product, identification through mean squared error and modified cross-validation.
result Consistency of the method under suitable signal-to-noise ratio conditions.
We investigate a generic problem of learning pairwise exponential family graphical models with pairwise sufficient statistics defined by a global mapping function, e.g., Mercer kernels. This subclass of pairwise graphical models allow us to flexibly capture complex interactions among variables beyond pairwise product. …
POLO simplifies parallel optimization research with a flexible library.
problem Ease of implementing and evaluating parallel optimization algorithms.
method Uses policies and template programming for algorithm decomposition and code reuse.
result Allows implementation of state-of-the-art algorithms in minimal code.
Paper proposes new gradient codes for robust distributed machine learning.
problem Robustness against slow machines (stragglers) in distributed machine learning.
method Constructs gradient codes based on probabilistic and Kronecker product methods.
result Gradient codes exist for a wide range of system parameters with comparable error performance.
We prove a general extrinsic rigidity theorem for homogeneous varieties in CPN. The theorem is used to show that the adjoint variety of a complex simple Lie algebra g (the unique minimal G orbit in Pg) is extrinsically rigid to third order. In contrast, we show that the ad…
Fast matrix-vector products for large graph Laplacians using NFFT.
problem Efficient computation of matrix-vector products for large, densely populated graph Laplacians.
method Combining NFFT with Krylov methods and iterative solvers.
result Significant speedup in computations for graph Laplacians.
We introduce economic models based on Boolean Delay Equations: this formalism makes easier to take into account the complexity of the interactions between firms and is particularly appropriate for studying the propagation of an initial damage due to a catastrophe. Here we concentrate on simple cases, which allow to und…
This work improves multi-modal generative models by using permutation-invariant neural networks.
problem Improving multi-modal generative models with tighter variational objectives.
method Developed more flexible aggregation schemes based on permutation-invariant neural networks.
result Our variational objective and flexible aggregation models can better approximate the true joint distribution.
Open-source Vizier optimizes complex systems for Google and beyond.
problem Optimizing large-scale systems with multiple objectives and constraints.
method Distributed, fault-tolerant, flexible API for blackbox optimization.
result OSS Vizier supports a wide range of optimization problems and is available as open-source.
Adaptive display exposure improves e-commerce platform revenue.
problem Optimize ad display for better revenue and user experience.
method Constrained Markov Decision Process (psCMDP) and two-level reinforcement learning.
result Our approach increases platform revenue under constraints.
Proposes GLWB-LTC for enhanced life care annuities with dynamic withdrawal strategies and stochastic interest rates.
problem Improving life care annuity features and pricing methods.
method Introduces GLWB-LTC with dynamic withdrawal strategies and stochastic interest rates. Solves the stochastic control problem using a robust tree method.
result Optimal withdrawal strategies vary over time with policyholder's health status, highlighting the advantage of flexibility.
Paper tackles flexible bin packing for e-commerce, reducing costs.
problem Optimizing packing of cuboid items into bins with minimal surface area.
method Multi-task Selected Learning approach to generate item packing sequence and orientation.
result Selected Learning method achieves 5.47% cost reduction compared to greedy algorithms.
Machine learning improves measuring climate adaptation impacts.
problem Measuring adaptation to climate change using weather damage elasticities.
method Debiased machine learning approach in panel data settings.
result Long-run impacts of damaging heat exposure significantly offset short-run impacts.
A novel online framework for analyzing multidimensional functional data.
problem Analysis of multidimensional functional data streams poses significant challenges.
method Online functional principal component analysis using tensor product splines on a Stiefel manifold with Riemannian stochastic gradient descent.
result Efficient and scalable modeling of multidimensional functional data.
Modeling temperature dynamics for weather derivatives using a novel regime-switching model.
problem Basis risk in weather derivatives due to poor design and modelling of temperature.
method Novel time-varying mean-reversion Lévy regime-switching model.
result The proposed model accurately models deseasonalized temperature data.
New method discovers context effects in choice data.
problem Identifying context effects from choice data is challenging.
method Automatic discovery of context effects from observed choices.
result Automatic discovery of context effects from observed choices.
In this paper, we develop a method for unsupervised clustering of two-way (matrix) data by combining two recent innovations from different fields: the Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming from a union of subspaces into their respective subspaces, and the t-product [18], which was …
This paper aims to optimize incident-specific cyber insurance design.
problem Complexity in determining optimal risk retention and transfer.
method Economic foundation for incident-specific cyber insurance with Pareto optimality.
result Illustrates feasibility of designing incident-specific indemnities for both parties.
Random features enhance control of complex systems.
problem Flexible nonlinear models for control-affine systems.
method Random features approximations for control-affine structure.
result Methods formalized and shown to relate to ADP and AD kernels.
Improved graph-based multiclass classification for multilayer data.
problem Efficient classification of multilayer data with limited labeled examples.
method Generalized diffuse interface methods applied to multilayer graphs, using spectral decomposition and fast matrix-vector products.
result Highly scalable and efficient classification for large, high-dimensional data sets.
Zero-Shot Learning speeds up machine calibration in deep drawing processes.
problem High-mix / low-volume production requires quick adaptation of machine parameters.
method Formulated a ZSL approach using a hyper-process model (HPM) for shape analysis and task mapping.
result New tasks can be learned without labeled data, speeding up calibration.
SA-REMBO adapts to nonstationary high-dimensional optimization.
problem Bayesian Optimization in high-dimensional spaces is limited by the curse of dimensionality and rigidity of global assumptions.
method SA-REMBO uses multiple random Gaussian embeddings and an index variable to adaptively select the best embedding for the optimization problem.
result SA-REMBO outperforms traditional REMBO and other low-rank BO methods across synthetic and real-world benchmarks.
Neural production systems learn visual dynamics by applying rule templates to entities.
problem Modeling interactions among entities in structured visual environments.
method Inspired by production systems, the paper uses rule templates to bind placeholder variables to specific entities, scoring and applying the best fitting rules to update entity properties.
result The architecture achieves robust future-state prediction and extrapolation from simple to complex environments, outperforming GNNs.