A Generalization of the Finite-Length Scaling Approach Beyond the BEC

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The use of point clouds as an imaging modality has been rapidly growing, motivating research on compression methods to enable efficient transmission and storage for many applications. While compression standards relying on conven- tional techniques such as ...

In 1970, Furth and Yoshikawa (1970 Phys. Fluids 13 2593-6) introduced the scalings of adiabatic plasma compression. Basically, if the shape of the external plasma boundary and the aspect ratio are preserved during the compression, then the density, kinetic ...

In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...

Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

A new electrically driven gas booster is described as an alternative to the classical air-driven gas boosters known for their poor energetic efficiency. These boosters are used in small scale Hydrogen storage facilities and in refueling stations for Hydrog ...

This work extends the range of pathways for the production of metallic microcomponents by downscaling metal casting. This is accomplished by using either of two different molding techniques, namely femtosecond laser micromachining or lithographic silicon m ...

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

Despite the increasing understanding of bentonite behaviour, there is still missing evidence on how different hydro-mechanical loadings, including sequences of hydration and compression, affect the fabric and the volume change behaviour of the material. It ...

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 ...

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

We demonstrate that when power scaling occurs for an individual tree and in a forest, there is great resulting simplicity notwithstanding the underlying complexity characterizing the system over many size scales. Our scaling framework unifies seemingly dis ...

Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...

Amorphous solids yield at a critical value Sigma(c) of the imposed stress Sigma through a dynamical phase transition. While sharp in athermal systems, the presence of thermal fluctuations leads to the rounding of the transition and thermally activated flow ...

Modern information technologies and human-centric communication systems employ advanced content representations for richer portrayals of the real world. The newly adopted imaging modalities offer additional information cues and permit the depiction of real ...

Secrecy by design is examined as an approach to information-theoretic secrecy. The main idea behind this approach is to design an information processing system from the ground up to be perfectly secure with respect to an explicit secrecy constraint. The pr ...

The measurement and classification of light is essential across many scientific disciplines. Devices used to measure light range from the highly precise scanning spectroradiometers to the more practical compact multichannel filter-array type imaging sensor ...

The growing demands on compact and high-definition single-photon avalanche diode (SPAD) arrays have motivated researchers to explore pixel miniaturization techniques to achieve sub-10 μm pixels. The scaling of the SPAD pixel size has an impact on key perfo ...

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

Theory-based scaling laws of the near and far scrape-off layer (SOL) widths are analytically derived for L-mode diverted tokamak discharges by using a two-fluid model. The near SOL pressure and density decay lengths are obtained by leveraging a balance amo ...

A Generalization of the Finite-Length Scaling Approach Beyond the BEC

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Evaluating the effect of sparse convolutions on point cloud compression

Shafranov shift correction to the Furth-Yoshikawa scaling of tokamak adiabatic compression

Communication-efficient distributed training of machine learning models

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Increasing the Energy Efficiency of Gas Boosters for Hydrogen Storage and for Refueling Stations

Bringing casting to microfabrication: processing and deformation of microcastings

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Volume change response and fabric evolution of granular MX80 bentonite along different hydro-mechanical stress paths

Stochastic distributed learning with gradient quantization and double-variance reduction

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Seeing the forest for the trees through metabolic scaling

Large-scale traffic signal control and multimodal network design

Thermally activated flow in models of amorphous solids

Perceptual Quality of Point Clouds with application to Compression

Secrecy by Design With Applications to Privacy and Compression

Spectral Measurement and Classification in the Era of Big Data

A Scaling Law for SPAD Pixel Miniaturization

Partition and Code: learning how to compress graphs

Theory-based scaling laws of near and far scrape-off layer widths in single-null L-mode discharges