5,265 research outputs found
Future developments in ground-based gamma-ray astronomy
Ground-based gamma-ray astronomy is a powerful tool to study cosmic-ray
physics, providing a diagnostic of the high-energy processes at work in the
most extreme astrophysical accelerators of the universe. Ground-based gamma-ray
detectors apply a number of experimental techniques to measure the products of
air showers induced by the primary gamma-rays over a wide energy range, from
about 30 GeV to few PeV. These are based either on the measurement of the
atmospheric Cherenkov light induced by the air showers, or the direct detection
of the shower's secondary particles at ground level. Thanks to the recent
development of new and highly sensitive ground-based gamma-ray detectors,
important scientific results are emerging which motivate new experimental
proposals, at various stages of implementation. In this chapter we will present
the current expectations for future experiments in the field.Comment: To appear in "Handbook of X-ray and Gamma-ray Astrophysics" by
Springer (Eds. C. Bambi and A. Santangelo) - 59 p
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Adversarial Deep Learning and Security with a Hardware Perspective
Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep learning stands as a balancing force to ensure these developments remain grounded in the real-world and proceed along a responsible trajectory. Recently, the growth of deep learning has begun intersecting with the computer hardware domain to improve performance and efficiency for resource constrained application domains. The works investigated in this dissertation constitute our pioneering efforts in migrating adversarial deep learning into the hardware domain alongside its parent field of research
Towards a muon collider
A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work
Development of a Portable, Tileable, Dual-Particle Radiography System
A scalable, portable, multi-particle neutron radiography device has been developed using commercial-off-the-shelf-parts. The IDEAS ROSSPAD readout module was selected for use in developing the radiography panel due to its single-wire Power-over-Ethernet (PoE) connectivity and its tileable form factor. Each ROSSPAD detector is paired with an 8 by 8 array of 6-mm-pitch Sensl J-Series silicon photomultipliers (SiPMs). With both single and multi-ROSSPAD testing, a detection package consisting of a 3-mm-thick sheet of EJ-200 plastic scintillator and a 3-mm-thick sheet of acrylic light spreader was coupled to the SiPM board face. After both quality assurance of the detector packages and the calibration of the raw data from the ROSSPADs, sub-SiPM spatial resolution was achieved. For the single- ROSSPAD setup, modulation transfer functions (MTFs) showed spatial resolutions of 2.32 line pairs per centimeter at 10% MTF for gamma rays and 3.35 line pairs per centimeter at 10% MTF for neutrons. The multi-ROSSPAD setups performed similarly with gammas at 2.09 line pairs per centimeter at 10% MTF, while the neutron images lost some spatial resolution with 10% MTF values ranging from 1.30 to 1.46 line pairs per centimeter. Based on the physical characteristics of the board, the raw board spatial resolution sits at 0.833 line pairs per centimeter, meaning all of the methods developed could resolve an object at a sub-SiPM pitch spatial resolution. Additionally, changes to the cutoff values for the full-panel radiography system showed little change to the spatial resolution of the full-panel images, suggesting that the loss is spatial resolution is external to the data collection outside of the number of events recorded. Overall, this research resulted in the development of a state-of-the-art scalable neutron radiography system
Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence
Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these.
However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies.
This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in . This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
Edge computing solutions that enable the extraction of high level information
from a variety of sensors is in increasingly high demand. This is due to the
increasing number of smart devices that require sensory processing for their
application on the edge. To tackle this problem, we present a smart vision
sensor System on Chip (Soc), featuring an event-based camera and a low power
asynchronous spiking Convolutional Neuronal Network (sCNN) computing
architecture embedded on a single chip. By combining both sensor and processing
on a single die, we can lower unit production costs significantly. Moreover,
the simple end-to-end nature of the SoC facilitates small stand-alone
applications as well as functioning as an edge node in a larger systems. The
event-driven nature of the vision sensor delivers high-speed signals in a
sparse data stream. This is reflected in the processing pipeline, focuses on
optimising highly sparse computation and minimising latency for 9 sCNN layers
to . Overall, this results in an extremely low-latency visual
processing pipeline deployed on a small form factor with a low energy budget
and sensor cost. We present the asynchronous architecture, the individual
blocks, the sCNN processing principle and benchmark against other sCNN capable
processors
Tools for efficient Deep Learning
In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption.
We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work.
This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C.
Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets.
All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces
IMPROVED SPATIAL RESOLUTION FOR DOUBLE-SIDED STRIP DETECTORS USING LITHIUM INDIUM DISELENIDE SEMICONDUCTORS
This research focuses on the evaluation of lithium indium diselenide (LISe) semiconductors in double-sided strip detector (DSSDs) designs as an example for the potential to achieve unparalleled neutron detection efficiency, spatial resolution, and timing resolution detection. LISe semiconductors offer high neutron detection efficiency due to the ~25% atomic ratio of Lithium-6, maximizing its efficiency of ~75% with 1 mm thickness at 2.8 angstroms. Furthermore, the 4.78 MeV -value enables high intrinsic gamma discrimination in a pixelated design (electron range). These characteristics make LISe an alternative option for neutron radiography, energy-resolved imaging, and other neutron interrogation techniques. This dissertation summarizes my current efforts to enhance LISe-based neutron imaging systems to achieve an end goal of sub-5 μm spatial resolution and sub-1 μs timing resolution. My research focuses on using MATLAB and Silvaco to simulate the expected response of a LISe DSSD. These various datasets are then trained to Machine Learning models in order to predict the neutron interaction location based upon the induced signal across multiple strip electrodes. In addition, various DSSD designs were simulated to determine the strip electrode width/pitch that optimizes the tradeoff between signal integrity and reconstruction of the neutron absorption location. The addition of electronic and statistical noise to the signal as well as varying the charge collection efficiency was also explored. The improvement upon current neutron imaging systems has the opportunity to open new avenues of research that are not possible today
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