Phd by thesis
TL;DR: In this paper, a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) is presented.
Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These
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01 Nov 1990
TL;DR: This report will establish methods for performing a domain analysis and describe the products of the domain analysis process to illustrate the application of domain analysis to a representative class of software systems.
Abstract: : Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software.
4,420 citations
TL;DR: The straw person model (SPM) as mentioned in this paper has been proposed to explain the orientation effects of active galactic nuclei (AGN) and quasars in the line of sight (LOS) images.
Abstract: Because the critical central regions of Active Galactic Nuclei (AGN) and quasars are strongly nonspherical but spatially unresolved, orientation effects have been the source of much confusion. In fact, it now appears that much of the variety in AGN types is just the result of varying orientation relative to the line of sight. We can define an extreme hypothesis,, the straw person model (SPM), in which there are two basic types of AGN: the radio quiets and the radio louds. For each type there is a range in intrinsic luminosity, and the luminosity controls some properties such as the Fanaroff and Riley classes. However, at a given intrinsic luminosity, all other properties such as spectroscopic classification and VLBI component speeds are ascribed to orientation. This model is only a caricature of the unification idea, and is already ruled out on many grounds, but it will be useful for organizing the discussion. I’ll describe what I consider to be convincing evidence that orientation effects are important and widespread. The true situation may be in some sense half way between the SPM and the hypothesis that orientation doesn’t affect classification at aIl. To us optimists, the orienration cup is half full rather than half empty. Although it is too soon to say for sure, the hypothesis that most objects’ classifications would be different if seen from other directions is a tenable one today.
4,005 citations
Cites methods from "Phd by thesis"
...Kay (1990) and Tran et al (1992) have fo und more Seyfert 2s with broad permitted lines in the polarized fl ux, but the FC polarizations are generally much less than that of NGC 1068....
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TL;DR: This Review assembles the current knowledge on the isolation of microfibrillated cellulose from wood and its application in nanocomposites; the preparation of nanocrystalline cellulose and its use as a reinforcing agent; and the biofabrication of bacterial nanocellulose, as well as its evaluation as a biomaterial for medical implants.
Abstract: Cellulose fibrils with widths in the nanometer range are nature-based materials with unique and potentially useful features. Most importantly, these novel nanocelluloses open up the strongly expanding fields of sustainable materials and nanocomposites, as well as medical and life-science devices, to the natural polymer cellulose. The nanodimensions of the structural elements result in a high surface area and hence the powerful interaction of these celluloses with surrounding species, such as water, organic and polymeric compounds, nanoparticles, and living cells. This Review assembles the current knowledge on the isolation of microfibrillated cellulose from wood and its application in nanocomposites; the preparation of nanocrystalline cellulose and its use as a reinforcing agent; and the biofabrication of bacterial nanocellulose, as well as its evaluation as a biomaterial for medical implants.
3,452 citations
Cites background or methods from "Phd by thesis"
...b) Spheres formed by agitated cultivation with a shaking rate of 80–100 rpm; diameter: 2–3 mm, smooth surface.([181]) c) Tubes...
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...a) Film prepared in a PP container under static conditions; dimensions: 25 25 cm(2), thickness: 200 mm.([181]) b) Spheres formed by agitated cultivation with a shaking rate of 80–100 rpm; diameter: 2–3 mm, smooth surface....
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TL;DR: The Arcade Learning Environment (ALE) as discussed by the authors is a platform for evaluating the development of general, domain-independent AI technology, which provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players.
Abstract: In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
2,429 citations
TL;DR: The solid electrolyte interphase (SEI) is a protecting layer formed on the negative electrode of Li-ion batteries as a result of electrolyte decomposition, mainly during the first cycle as discussed by the authors.
2,386 citations
References
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TL;DR: In this paper, a low-temperature screen-printable piezoelectric nano-composite film on flexible plastic and textile substrates has been evaluated theoretically and experimentally.
90 citations
Proceedings Article•
25 Jul 2004TL;DR: This paper presents a SAT-based decision procedure for answer set programming that deals with any (non disjunctive) logic program, works on a SAT formula without additional variables, and is guaranteed to work in polynomial space.
Abstract: The relation between answer set programming (ASP) and propositional satisfiability (SAT) is at the center of many research papers, partly because of the tremendous performance boost of SAT solvers during last years. Various translations from ASP to SAT are known but the resulting SAT formula either includes many new variables or may have an unpractical size. There are also well known results showing a one-to-one correspondence between the answer sets of a logic program and the model of its completion. Unfortunately, these results only work for specific classes of problems.
In this paper we present a SAT-based decision procedure for answer set programming that (i) deals with any (non disjunctive) logic program, (ii) works on a SAT formula without additional variables, and (iii) is guaranteed to work in polynomial space. Further, our procedure can be extended to compute all the answer sets still working in polynomial space. The experimental results of a prototypical implementation show that the approach can pay off sometimes by orders of magnitude.
89 citations
TL;DR: A balanced force Rened Level Set Grid (RLSG) method for collocated, unstructured nite volume ow solver grids that can be coupled to a Lagrangian spray model is presented.
Abstract: To simulate primary atomization, one has to track the position of the phase interface accurately, handle large numbers of topology changes and drops, treat the singular force of surface tension in an accurate and stable manner, and ensure grid-independent numerical results To address all of these challenges we present a balanced force Rened Level Set Grid (RLSG) method for collocated, unstructured nite volume ow solver grids that can be coupled to a Lagrangian spray model Special emphasis is placed on the accurate treatment of surface tension forces, since during the atomization of liquid jets by coaxial fast-moving gas streams, the details of the formation of small-scale drops from aerodynamically stretched out ligaments are governed by capillary forces [1] Several dierent generic verication examples are presented, discussing the accuracy, volume preservation, and grid-convergence properties of the balanced force RLSG method
89 citations
TL;DR: This work proposes an approach, based on statistical mechanics, to predict the saturated state of a single-pass, high-gain free-electron laser, and shows that the initial relaxation of the laser can be described by the statistical mechanics of an associated Vlasov equation.
Abstract: We propose an approach, based on statistical mechanics, to predict the saturated state of a single-pass, high-gain free-electron laser. In analogy with the violent relaxation process in self-gravitating systems and in the Euler equation of two-dimensional turbulence, the initial relaxation of the laser can be described by the statistical mechanics of an associated Vlasov equation. The laser field intensity and the electron bunching parameter reach a quasistationary value which is well fitted by a Vlasov stationary state if the number of electrons $N$ is sufficiently large. Finite $N$ effects (granularity) finally drive the system to Boltzmann-Gibbs statistical equilibrium, but this occurs on times that are unphysical (i.e., excessively long undulators). All theoretical predictions are successfully tested by means of finite-$N$ numerical experiments.
89 citations
Dissertation•
18 Sep 2008
TL;DR: An unsupervised approach based on topic models that allows to discover high-level structure in human activity data is proposed, and it is shown that the approach can be effectively deployed in a semi-supervised learning framework.
Abstract: This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information -- others include the user's location or the state of his environment -- which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors -- mainly accelerometers -- to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the approach can be effectively deployed in a semi-supervised learning framework. More specifically, we combine the approach with a discriminant classifier, and show that this scheme allows high recognition rates even when using only a small amount of labeled training data. Recognition of higher-level activities such as shopping, doing housework, or commuting is challenging, as these activities are composed of changing sub-activities and vary strongly across individuals. We present one study in which we recorded 10h of three different high-level activities, investigating to which extent methods for low-level activities can be scaled to the recognition of high-level activities. Our results indicate that for settings as ours, traditional supervised approaches in combination with data from wearable accelerometers can achieve recognition rates of more than 90%. While unsupervised techniques are desirable for short-term activities, they become crucial for long-term activities, for which annotation is often impractical or impossible. To this end we propose an unsupervised approach based on topic models that allows to discover high-level structure in human activity data. The discovered activity patterns correlate with daily routines such as commuting, office work, or lunch routine, and they can be used to recognize such routines in unknown data.
89 citations