Generalized Skipgrams for Pattern Discovery in Polyphonic Streams

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In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

We report the detailed background for the discovery and development of the synthesis of homopropargylic azides by the azidoalkynylation of alkenes. Initially, a strategy involving SOMOphilic alkynes was adopted, but only resulted in a 29% yield of the desi ...

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

The auxiliary power supply for medium voltage converters requires high insulation capability between the source and the load. Inductive power transfer technology, with an air gap between the primary and secondary coil, offers such high insulation capabilit ...

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

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 ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

Formally verifying the correctness of software is necessary to merit the trust people put in software systems. Currently, formal verification requires human effort to prove that a piece of code matches its specification and code changes to improve verifiab ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

Atomic layer deposition (ALD) is one of the premier methods to synthesize ultra-thin materials on complex surfaces. The technique allows for precise control of the thickness down to single atomic layers, while at the same time providing uniform coverage ev ...

In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...

Sensing and imaging of light in the shortwave infrared (SWIR) range is increasingly used in various fields, including bio-imaging, remote sensing, and semiconductor process control. SWIR-sensitive organic photodetectors (OPDs) are promising because organic ...

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 ...

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

Generalized Skipgrams for Pattern Discovery in Polyphonic Streams

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Mesh-based topology, shape and sizing optimization of ribbed plates

SOMOphilic alkyne vs radical-polar crossover approaches: The full story of the azido-alkynylation of alkenes

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Scalable constrained optimization

Modeling and optimization of PCB coils for inductive power transfer systems

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

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

Efficient and insightful descriptors for representing molecular and material space

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

The effect of smooth parametrizations on nonconvex optimization landscapes

Automated Formal Verification of Software Network Functions

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Optimizing Atomic Layer Deposition Processes with Nanowire-Assisted TEM Analysis

Robot Learning using Tensor Networks

Shortwave Infrared-Sensitive, Narrowband Organic Upconversion Devices with a Gain

Adaptive projected variational quantum dynamics

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization