1,721,106 research outputs found

    Atomistic theory of transport in organic and inorganic nanostructures

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    As the size of modern electronic and optoelectronic devices is scaling down at a steady pace, atomistic simulations become necessary for an accurate modelling of their structural, electronic, optical and transport properties. Such microscopic approaches are important in order to account correctly for quantum-mechanical phenomena affecting both electronic and transport properties of nanodevices. Effective bulk parameters cannot be used for the description of the electronic states since interfacial properties play a crucial role and semiclassical methods for transport calculations are not suitable at the typical scales where the device behaviour is characterized by coherent tunnelling. Quantum-mechanical computations with atomic resolution can be achieved using localized basis sets for the description of the system Hamiltonian. Such methods have been extensively used to predict optical and electronic properties of molecules and mesoscopic systems. The most important approaches formulated in terms of localized basis sets, from empirical tight-binding (TB) to first principles methods, are here reviewed. Being a full band approach, even the simplest TB overcomes the limitations of envelope function approximations, such as the well-known k p, and allows to retain atomic details and realistic band structures. First principles calculations, on the other hand, can give a very accurate description of the electronic and structural properties. Transport in nanoscale devices cannot neglect quantum effects such as coherent tunnelling. In this context, localized basis sets are well-suited for the formal treatment of quantum transport since they provide a simple mathematical framework to treat open-boundary conditions, typically encountered when the system eigenstates carry a steady-state current. We review the principal methods used to formulate quantum transport based on local orbital sets via transfer matrix and Green's function (GF) techniques. We start from a general introduction to the scattering theory which leads to the Landauer formula, and then report on the most recent progresses of the field including the application of the self-consistent non-equilibrium GF formalism

    Microservices Monitoring with Event Logs and Black Box Execution Tracing

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    Monitoring is a core practice in any software system, and entails gathering a variety of data sources that pertain the execution of a given system. Trends in microservices systems exacerbate the role of monitoring. Microservices put forth reduced size, independency, flexibility and modularity principles, which well cope with ever-changing business environments. However, as real-world applications are decomposed, they can easily reach hundreds of microservices. This inherent complexity determines an increasing difficulty in debugging, monitoring and forensics, and poses novel challenges to monitoring data sources, such as event logs

    Towards Cognitive Security Defense from Data

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    IT organizations rely on a variety of independent security monitors and data sources to develop situational awareness for detecting and responding to security incidents. In spite of the advances in Security Information and Event Management (SIEM) for handling monitoring data in production environments, computer defense still depends on many cognitive human processes. In this context, having machines doing part of the cognitive work in lieu of humans is by now a real necessity. We present our framework towards the vision of cognitive SIEM, its building components and ongoing work on the topic

    Contextual filtering and prioritization of computer application logs for security situational awareness

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    Critical computer systems strongly rely on event logs to record the occurrence of normative and anomalous events occurring at runtime. In spite of the advances in Security Information and Event Management for handling monitoring data in production, event logs remain quite underutilized with respect to more conventional security data sources. Eliciting actionable knowledge for situational awareness poses many challenges in the case of logs emitted by industrial systems due to the lack of standard practices, formats and threat models. This paper addresses log analysis in a critical industrial system. We conduct our study with a real-life system by a top leading company in the Air Traffic Control domain, which emits massive volumes of unstructured proprietary logs. We propose a filtering method that pinpoints interesting events from logs, i.e., events that should be followed up by analysts. Experiments are done with logs from normative and misuse scenarios; moreover, we compare the outcome of our method with a reference filtering technique based on the conceptual clustering. Results indicate that the proposed method is effective to retain interesting events at remarkable precision and recall and to pinpoint misuse indicators. We overcome several drawbacks of existing filtering techniques, such as the need for labeled logs and domain knowledge, which makes our method easier to use by practitioners

    DFT modeling of bulk-modulated carbon nanotube field-effect transistors

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    We report density-functional theory (DFT) atomistic simulations of the nonequilibrium transport properties of carbon nanotube (CNT) field-effect transistors (FETs). Results have been obtained within a self-consistent approach based on the nonequilibrium Green's functions (NEGF) scheme. We show that, as the current modulation mechanism is based on the local screening properties of the nanotube channel, a completely new, negative quantum capacitance regime can be entered by the device. We show how a well-tempered device design can be accomplished in this regime by choosing suitable doping profiles and gate contact parameters. At the same time, we detail the fundamental physical mechanisms underlying the bulk-switching operation, including them in a very practical and accurate model, whose parameters can be easily controlled in order to improve the device performance. The dependence of the nanotube screening properties on the temperature is finally explained by means of a self-consistent temperature analysis

    Discovering hidden errors from application log traces with process mining

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    Over the past decades logs have been widely used for detecting and analyzing failures of computer applications. Nevertheless, it is widely accepted by the scientific community that failures might go undetected in the logs. This paper proposes a measurement study with a dataset of 3,794 log traces obtained from normative and failure runs of the Apache web server. We use process mining (i) to infer a model of the normative log behavior, e.g., presence and ordering of messages in the traces, and (ii) to detect failures within arbitrary traces by looking for deviations from the model (conformance checking). Analysis is done with the Integer Linear Programming (ILP) Miner, Inductive Miner and Alpha++ Miner algorithms. Our measurements indicate that, although only around 18% failure traces contain explicit error keywords and phrases, conformance checking allows detecting up to 87% failures at high precision, which means that most of the errors are hidden across the traces

    Negative quantum capacitance of gated carbon nanotubes

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    Atomistic density functional theory (DFT) calculations of the capacitance between a metallic cylindric gate and a carbon nanotube (CNT) are reported. We find that the quantum capacitance of the CNT becomes negative as the CNT carrier density is reduced. This corresponds to an unconventionally high charge accumulation on the CNT, which in turn overscreens the external gate field. The relationship between this behavior and the predominance of the attractive exchange energy contribution is pointed out
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