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

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

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

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