Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond

In this and a set of companion whitepapers, the USQCD Collaboration lays out a program of science and computing for lattice gauge theory. These whitepapers describe how calculation using lattice QCD (and other gauge theories) can aid the interpretation of ongoing and upcoming experiments in particle and nuclear physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers

Wide-bandwidth high-resolution search for extraterrestrial intelligence

Research accomplished in the following areas is discussed: the antenna configuration; HEMT low-noise amplifiers; the downconverter; the Fast Fourier Transform Array; the backend array; and the backend and workstation

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Protecting Ownership Rights of Videos Against Digital Piracy: An Efficient Digital Watermarking Scheme

Violation of one’s intellectual ownership rights by the others is a common problem which entertainment industry frequently faces now-a-days. Sharing of information over social media platforms such as Instagram, WhatsApp and twitter without giving credit the owner causes huge financial losses to the owner and hence needs an immediate attention. Digital watermarking is a promising technique to protect owners’ right against digital piracy. Most of the state-of-the-art techniques does not provides adequate level of resilience against majority of video specific attacks and other commonly applied attacks. Therefore, this paper proposes a highly transparent and robust video watermarking solution to protect the owners rights by first convert each video frame into YCbCr color components and then select twenty five strongest speeded-up robust features (SURF) points of the normalized luminance component as points for both watermark embedding and extraction. After applying variety of geometric, simple signal processing and video specific attacks on the watermarked video meticulous analysis is performed using popular metrics which reveals that the proposed scheme possesses high correlation value which makes it superior for practical applications against these attacks. The scheme also proposes a novel three-level impairment scale for subjective analysis which gives stable results to derive correct conclusions

Performance Assessment of Aggregation and Deaggregation Algorithms in Vehicular Delay-Tolerant Networks

Vehicular Delay-Tolerant Networks (VDTNs) are a new approach for vehicular communications where vehicles cooperate with each other, acting as the communication infrastructure, to provide low-cost asynchronous opportunistic communications. These communication technologies assume variable delays and bandwidth constraints characterized by a non-transmission control protocol/ internet protocol architecture but interacting with it at the edge of the network. VDTNs are based on the principle of asynchronous communications, bundleoriented communication from the DTN architecture, employing a store-carryand- forward routing paradigm. In this sense, VDTNs should use the tight network resources optimizing each opportunistic contact among nodes. At the ingress edge nodes, incoming IP Packets (datagrams) are assembled into large data packets, called bundles. The bundle aggregation process plays an important role on the performance of VDTN applications. Then, this paper presents three aggregation algorithms based on time, bundle size, and a hybrid solution with combination of both. Furthermore, the following four aggregation schemes with quality of service (QoS) support are proposed: 1) single-class bundle with N = M, 2) composite-class bundle with N = M, 3) single-class bundle with N > M, and 4) composite-class bundle with N > M, where N is the number of classes of incoming packets and M is the number of priorities supported by the VDTN core network. The proposed mechanisms were evaluated through a laboratory testbed, called VDTN@Lab. The adaptive hybrid approach and the composite-class schemes present the best performance for different types of traffic load and best priorities distribution, respectively

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition