Synthesizing efficient systems in probabilistic environments

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This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

The present invention relates to systems and methods for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject based on reports of Minor Hallucination (MH) events in combination with electrophysiological data of the s ...

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in ind ...

We conduct two survey experiments to study which information people choose to consume and how it affects their beliefs. In the first experiment, respondents choose between optimistic and pessimistic article headlines related to the COVID-19 pandemic and ar ...

We propose an adaptive quantum algorithm to prepare accurate variational time evolved wave functions. The method is based on the projected variational quantum dynamics (pVQD) algorithm, that performs a global optimization with linear scaling in the number ...

We study the problem of learning unknown parameters in stochastic interacting particle systems with polynomial drift, interaction, and diffusion functions from the path of one single particle in the system. Our estimator is obtained by solving a linear sys ...

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

The paper details the process of developing the ITER Plasma Control System (PCS), that is, how to design and deploy it systematically, in the most efficient and effective manner. The integrated nature of the ITER PCS, with its multitude of coupled control ...

Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the ...

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

Zoning reform is a crucial tool for cities to adapt to contemporary challenges. However, its implementation remains challenging. Property owners, with a vested interest in the value of their neighborhoods, are sensitive to local developments and the potent ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

Type systems are a device for verifying properties of programs without running them. Many programming languages used in the industry have always had a type system, while others were initially created without a type system and later adopted one, when the ad ...

Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose output the solution can then be extracted. LM performance has consistently be ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

In this manuscript, we present a collective multigrid algorithm to solve efficiently the large saddle-point systems of equations that typically arise in PDE-constrained optimization under uncertainty, and develop a novel convergence analysis of collective ...

Synthesizing efficient systems in probabilistic environments

Chattez avec Graph Search

A ride time-oriented scheduling algorithm for dial-a-ride problems

System for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Adaptive projected variational quantum dynamics

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

Molecular hypergraph neural networks

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Strategy to systematically design and deploy the ITER plasma control system: A system engineering and model-based design approach

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Spatial Modeling for Building Design Evaluation: from Visual Landscape Quality Assessment to Devaluation Risk Estimation

Gibbs sampling the posterior of neural networks

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Formal Foundations of Capture Tracking

Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

A multigrid method for PDE-constrained optimization with uncertain inputs