4,313 research outputs found
The hardness-duration correlation in the two classes of gamma-ray bursts
The well-known hardness-duration correlation of gamma-ray bursts (GRBs) is
investigated with the data of the 4B catalog. We find that, while the hardness
ratio and the duration are obviously correlated for the entire set of the 4B
catalog, they are not at all correlated for the two subsets divided at the
duration of 2 seconds. However, for other subsets with comparable sizes, the
two quantities are significantly correlated. The following conclusions are then
reached: (1) the existence of two classes of GRBs is confirmed; (2) the
hardness ratio and the duration are not at all correlated for any of the two
classes; (3) different classes of GRBs have different distributions of the
hardness ratio and the duration and it is this difference that causes the
correlation between the two quantities for the entire set of the bursts.Comment: 5 pages, 1 figure, accepted for publication in PAS
Exploiting Chordality in Optimization Algorithms for Model Predictive Control
In this chapter we show that chordal structure can be used to devise
efficient optimization methods for many common model predictive control
problems. The chordal structure is used both for computing search directions
efficiently as well as for distributing all the other computations in an
interior-point method for solving the problem. The chordal structure can stem
both from the sequential nature of the problem as well as from distributed
formulations of the problem related to scenario trees or other formulations.
The framework enables efficient parallel computations.Comment: arXiv admin note: text overlap with arXiv:1502.0638
Large eddy simulation of spray and combustion characteristics with realistic chemistry and high-order numerical scheme under diesel engine-like conditions
The accuracy of large eddy simulation (LES) for turbulent combustion depends on suitably implemented numerical schemes and chemical mechanisms. In the original KIVA3V code, finite difference schemes such as QSOU (Quasi-second-order upwind) and PDC (Partial Donor Cell Differencing) cannot achieve good results or even computational stability when using coarse grids due to large numerical diffusion. In this paper, the MUSCL (Monotone Upstream-centered Schemes for Conservation Laws) differencing scheme is implemented into KIVA3V-LES code to calculate the convective term. In the meantime, Lu’s n-heptane reduced 58-species mechanisms (Lu, 2011) is used to calculate chemistry with a parallel algorithm. Finally, improved models for spray injection are also employed. With these improvements, the KIVA3V-LES code is renamed as KIVALES-CP (Chemistry with Parallel algorithm) in this study. The resulting code was used to study the gas–liquid two phase jet and combustion under various diesel engine-like conditions in a constant volume vessel. The results show that using the MUSCL scheme can accurately capture the spray shape and fuel vapor penetration using even a coarse grid, in comparison with the Sandia experimental data. Similarly good results are obtained for three single-component fuels, i-Octane (C8H18), n-Dodecanese (C12H26), and n-Hexadecane (C16H34) with very different physical properties. Meanwhile the improved methodology is able to accurately predict ignition delay and flame lift-off length (LOL) under different oxygen concentrations from 10% to 21% with ambient density increasing from 14.8 kg/m3 to 30.0 kg/m3 and ambient temperatures from 850 K to 1300 K in a constant volume combustion chamber. With increasing oxygen concentration, the ignition delay time and consequently the flame LOL decrease, as the flame moves upstream as expected. On the other hand, reduction in the ambient temperature from 1000 K to 900 K retards the auto-ignition time and moves the burning location downstream under different oxygen concentrations
Graph Distillation for Action Detection with Privileged Modalities
We propose a technique that tackles action detection in multimodal videos
under a realistic and challenging condition in which only limited training data
and partially observed modalities are available. Common methods in transfer
learning do not take advantage of the extra modalities potentially available in
the source domain. On the other hand, previous work on multimodal learning only
focuses on a single domain or task and does not handle the modality discrepancy
between training and testing. In this work, we propose a method termed graph
distillation that incorporates rich privileged information from a large-scale
multimodal dataset in the source domain, and improves the learning in the
target domain where training data and modalities are scarce. We evaluate our
approach on action classification and detection tasks in multimodal videos, and
show that our model outperforms the state-of-the-art by a large margin on the
NTU RGB+D and PKU-MMD benchmarks. The code is released at
http://alan.vision/eccv18_graph/.Comment: ECCV 201
Transfer hashing with privileged information
Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI). Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+. In ITQ+, a new slack function is learned from auxiliary data to approximate the quantization error in ITQ. We developed an alternating optimization approach to solve the resultant optimization problem for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure among the auxiliary data for learning more precise binary codes in the target domain. Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines
Industrial cyber physical systems supported by distributed advanced data analytics
The industry digitization is transforming its business models, organizational structures and operations, mainly promoted by the advances and the mass utilization of smart methods, devices and products, being leveraged by initiatives like Industrie 4.0. In this context, the data is a valuable asset that can support the smart factory features through the use of Big Data and advanced analytics approaches. In order to address such requirements and related challenges, Cyber Physical Systems (CPS) promote the development of more intelligent, adaptable and responsiveness supervisory and control systems capable to overcome the inherent complexity and dynamics of industrial environments. In this context, this work presents an agent-based industrial CPS, where agents are endowed with data analysis capabilities for distributed, collaborative and adaptive process supervision and control. Additionally, to address the different industrial levels’ requirements, this work combines two main data analysis scopes: at operational level, applying distributed data stream analysis for rapid response monitoring and control, and at supervisory level, applying big data analysis for decision-making, planning and optimization. Some experiments have been performed in the context of an electric micro grid where agents were able to perform distributed data analysis to predict the renewable energy production.info:eu-repo/semantics/publishedVersio
Control of magnetic anisotropy by orbital hybridization in (La0.67Sr0.33MnO3)n/(SrTiO3)n superlattice
The asymmetry of chemical nature at the hetero-structural interface offers an
unique opportunity to design desirable electronic structure by controlling
charge transfer and orbital hybridization across the interface. However, the
control of hetero-interface remains a daunting task. Here, we report the
modulation of interfacial coupling of (La0.67Sr0.33MnO3)n/(SrTiO3)n
superlattices by manipulating the periodic thickness with n unit cells of
SrTiO3 and n unit cells La0.67Sr0.33MnO3. The easy axis of magnetic anisotropy
rotates from in-plane (n = 10) to out-of-plane (n = 2) orientation at 150 K.
Transmission electron microscopy reveals enlarged tetragonal ratio > 1 with
breaking of volume conservation around the (La0.67Sr0.33MnO3)n/(SrTiO3)n
interface, and electronic charge transfer from Mn to Ti 3d orbitals across the
interface. Orbital hybridization accompanying the charge transfer results in
preferred occupancy of 3d3z2-r2 orbital at the interface, which induces a
stronger electronic hopping integral along the out-of-plane direction and
corresponding out-of-plane magnetic easy axis for n = 2. We demonstrate that
interfacial orbital hybridization in superlattices of strongly correlated
oxides may be a promising approach to tailor electronic and magnetic properties
in device applications
Extending colonic mucosal microbiome analysis - Assessment of colonic lavage as a proxy for endoscopic colonic biopsies
This study was supported through GI Research funds and MRC Grant Ref: MR/M00533X/1 to GH.Peer reviewedPublisher PD
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