Learning to Scale Payments in Crowdsourcing with PropeRBoost

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Commitment is a key primitive which resides at the heart of several cryptographic protocols. Noisy channels can help realize information-theoretically secure commitment schemes; however, their imprecise statistical characterization can severely impair such ...

We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate the dynamics of many-body quantum systems classically. By systematically studying the relevant stochastic estimators, we are able to: (i) prove that the most us ...

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Due to the rapidly growing number of devices that need to communicate securely, there is still significant interest in the development of efficient encryption schemes. It is important to maintain a portfolio of different constructions in order to enable a ...

We consider the Dynamical Low Rank (DLR) approximation of random parabolic equations and propose a class of fully discrete numerical schemes. Similarly to the continuous DLR approximation, our schemes are shown to satisfy a discrete variational formulation ...

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This paper proposes a set of distributed real-time optimisation schemes for the steady-state optimisation of parametrically and structurally uncertain systems that are composed of multiple interconnected subsystems. These schemes are derived in the framewo ...

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Learning to Scale Payments in Crowdsourcing with PropeRBoost

Chat with Graph Search

On the (Im)possibility of Commitment over Gaussian Unfair Noisy Channels

Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

Unified theory of atom-centered representations and message-passing machine-learning schemes

Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing

Stochastic distributed learning with gradient quantization and double-variance reduction

Impacts of Metering-Based Dynamic Priority Schemes

Towards Efficient LPN-Based Symmetric Encryption

Stability properties of a projector-splitting scheme for dynamical low rank approximation of random parabolic equations

OPF-driven Under Frequency Load Shedding in Low-Inertia Power Grids Hosting Large-scale Battery Energy Storage Systems

Tessellation and lmprovement of Simplicial Meshes using Neural Networks

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Containergy-A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

Product Quality Improvement Policies in Industry 4.0: Characteristics, Enabling Factors, Barriers, and Evolution Toward Zero Defect Manufacturing

Adaptive Laser Welding Control: A Reinforcement Learning Approach

Image Compression and Quality Assessment using Convolutional Neural Networks

Thermo-mechanical schemes for energy piles

Distributed modifier-adaptation schemes for the real-time optimisation of uncertain interconnected systems

Phasor-based widefield FLIM using a gated 512×512 single-photon SPAD imager

Revisiting Multivariate Lattices for Encrypted Signal Processing

Cross-Packet Coding for Delay-Constrained Streaming Applications