453 research outputs found
Advancing Scalability in Decentralized Storage: A Novel Approach to Proof-of-Replication via Polynomial Evaluation
Proof-of-Replication (PoRep) plays a pivotal role in decentralized storage networks, serving as a mechanism to verify that provers consistently store retrievable copies of specific data. While PoRep’s utility is unquestionable, its implementation in large-scale systems, such as Filecoin, has been hindered by scalability challenges. Most existing PoRep schemes, such as Fisch’s (Eurocrypt 2019), face an escalating number of challenges and growing computational overhead as the number of stored files increases. This paper introduces a novel PoRep scheme distinctively tailored for expansive decentralized storage networks. At its core, our approach hinges on polynomial evaluation, diverging from the probabilistic checking prevalent in prior works. Remarkably, our design requires only a single challenge, irrespective of the number of files, ensuring both prover’s and verifier’s run-times remain manageable even as file counts soar. Our approach introduces a paradigm shift in PoRep designs, offering a blueprint for highly scalable and efficient decentralized storage solutions
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches
Traditional networking devices support only fixed features and limited configurability.
Network softwarization leverages programmable software and hardware platforms to remove those limitations.
In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms.
This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0.
P4 is the most popular technology to implement programmable data planes.
However, programmable data planes, and in particular, the P4 technology, emerged only recently.
Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking.
The research of this thesis focuses on two open issues of programmable data planes.
First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet.
Second, it enables BIER in high-performance P4 data planes.
BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet.
The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study.
Two more peer-reviewed papers contain additional content that is not directly related to the main results.
They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
A variational autoencoder application for real-time anomaly detection at CMS
Despite providing invaluable data in the field of High Energy Physics, towards higher luminosity runs the Large Hadron Collider (LHC) will face challenges in discovering interesting results through conventional methods used in previous run periods.
Among the proposed approaches, the one we focus on in this thesis work – in collaboration with CERN teams, involves the use of a joint variational autoencoder (JointVAE) machine learning model, trained on known physics processes to identify anomalous events that correspond to previously unidentified physics signatures.
By doing so, this method does not rely on any specific new physics signatures and can detect anomalous events in an unsupervised manner, complementing the traditional LHC search tactics that rely on model-dependent hypothesis testing.
The algorithm produces a list of anomalous events, which experimental collaborations will examine and eventually confirm as new physics phenomena.
Furthermore, repetitive event topologies in the dataset can inspire new physics model building and experimental searches.
Implementing this algorithm in the trigger system of LHC experiments can detect previously unnoticed anomalous events, thus broadening the discovery potential of the LHC.
This thesis presents a method for implementing the JointVAE model, for real-time anomaly detection in the Compact Muon Solenoid (CMS) experiment.
Among the challenges of implementing machine learning models in fast applications, such as the trigger system of the LHC experiments, low latency and reduced resource consumption are essential.
Therefore, the JointVAE model has been studied for its implementation feasibility in Field-Programmable Gate Arrays (FPGAs), utilizing a tool based on High-Level Synthesis (HLS) named HLS4ML.
The tool, combined with the quantization of neural networks, will reduce the model size, latency, and energy consumption
Safe navigation and human-robot interaction in assistant robotic applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
- …