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 ...

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

An attempt is made to translate five generic key performance indicators (KPIs) into a continuous real-time cost function in a batch order-based manufacturing environment. The challenge of controlling and optimizing resource utilization, production efficien ...

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

Several works published over the last two decades have shown for a stylized set-up with homogeneous users that metering-based priority (MBP) schemes may generate Pareto improving departure time adjustments similar to those induced by congestion pricing, bu ...

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 ...

The paper describes the formulation and application of an Optimal Power Flow (OPF)-driven Under Frequency Load Shedding (UFLS) scheme in low-inertia power grids hosting large-scale battery energy storage systems. Thanks to the accurate prediction of the sy ...

Modern mesh generation addresses the development of robust algorithms that construct a discrete representation of the geometry into polytopal elements conforming to divergent properties: (i) fidelity to complex geometrical features, (ii) support for high s ...

Many scientific inquiries in natural sciences involve approximating a spherical field -namely a scalar quantity defined over a continuum of directions- from generalised samples of the latter (e.g. directional samples, local averages, etc). Such an approxim ...

Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and ...

In the competitive market of manufacturing, quality is a criterion of primary importance in order to win market share. Quality improvement must be coupled with performance point of view. Lean Manufacturing, Six Sigma, Lean Six Sigma, Total Quality Manageme ...

Despite extensive research efforts in the field of laser welding, the imperfect repeatability of the weld quality still represents an open topic. Indeed, the inherent complexity of the underlying physical phenomena prevents the implementation of an effecti ...

The rapid development of digital imaging and video has placed visual contents in the heart of our lives. Digital multimedia span a vast number of areas from business to leisure, including but not limited to education, medicine, accessibility, training, adv ...

Currently, schemes based on seminal empirical knowledge about energy piles subjected to mechanical and thermal loads are available to describe the response of such foundations. However, schemes based on theoretical principles may more closely reflect the p ...

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 ...

Single-photon avalanche diode (SPAD) imagers can perform fast time-resolved imaging in a compact form factor, by exploiting the processing capability and speed of integrated CMOS electronics. Developments in SPAD imagers have recently made them compatible ...

Multimedia contents are inherently sensitive signals that must be protected when processed in untrusted environments. The field of Secure Signal Processing addresses this challenge by developing methods which enable operating with sensitive signals in a pr ...

Delay-constrained streaming from a moving platform is of paramount importance for applications such as the remote control of drones, where a low-delay video stream is required in order to provide visual feedback to the pilot while providing high quality vi ...

Learning to Scale Payments in Crowdsourcing with PropeRBoost

Chattez avec 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