Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets

Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor, the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its accuracy. Relying on Trade and Quote data, we provide various measures of SIP latency relative to high-speed data feeds between exchanges, known as direct feeds. We use first differences to highlight not only the divergence between the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP. We find that as many as 60 percent or more of trades are reported out of sequence for stocks with high trade volume, therefore skewing simple measures such as returns. While not yet definitive, this analysis supports our preliminary conclusion that the underlying infrastructure of the SIP is currently unable to keep pace with the trading activity in today's stock market.Comment: 18 pages, 20 figures, 2 table

Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

Socially Constrained Structural Learning for Groups Detection in Crowd

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems

Quantum Sensor Miniaturization

The classical bound on image resolution defined by the Rayleigh limit can be beaten by exploiting the properties of quantum mechanical entanglement. If entangled photons are used as signal states, the best possible resolution is instead given by the Heisenberg limit, an improvement proportional to the number of entangled photons in the signal. In this paper we present a novel application of entanglement by showing that the resolution obtained by an imaging system utilizing separable photons can be achieved by an imaging system making use of entangled photons, but with the advantage of a smaller aperture, thus resulting in a smaller and lighter system. This can be especially valuable in satellite imaging where weight and size play a vital role.Comment: 3 pages, 1 figure. Accepted for publication in Photonics Technology Letter

A review of literature for the structural assessment of mitred bends

This paper presents a state-of-the-art review of literature available for the structural assessment of all types of mitred pipe bends. Compared with smooth bends, the volume of literature available for mitres is less extensive and its scope is not as wide. Historically, this reflects a reduced application level, as well as a less demanding range of applications, such as non-high temperature use. There is also the issue that an analysis of a mitred bend is complicated by discontinuity stresses, as well as those due to cross-section ovalisation. This fact delayed the development of non-linear analysis of mitred bends. Nevertheless, there is now a substantial body of work on mitred bends. This review tabulates and characterises all publications to date in chronological order. The details of experimental specimens are highlighted, with a view to these perhaps providing useful verification data for any future finite element analysis for example. Issues of particular interest to pipework designers are discussed, including the effects of combinations of loading, out-of-circularity, tangent pipe length and flanges. Failure characteristics and loads are discussed where relevant. Topics for further research are also noted. For example, comprehensive design curves do not exist for the elastic and plastic behaviour of all mitre types, over a practical range of geometry and loading parameters. Similarly, there is still scope for further work on the effect of combined loading, end effects and out-of-circularity. Limit, collapse and burst loads are not yet available across the entire spectrum of bends and loading parameters either. Creep and optimisation represent virgin territory as far as mitred bends are concerned and given that unforeseen vibration is a common source of high-cycle fatigue failure in pipework, there must also be scope for vibration-induced fatigue studies

Design issues for agent-based resource locator systems

While knowledge is viewed by many as an asset, it is often difficult to locate particularitems within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together ith the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process