Compressed Video Action Recognition

Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video streams and the high temporal redundancy; the true and interesting signal is often drowned in too much irrelevant data. Motivated by that the superfluous information can be reduced by up to two orders of magnitude by video compression (using H.264, HEVC, etc.), we propose to train a deep network directly on the compressed video. This representation has a higher information density, and we found the training to be easier. In addition, the signals in a compressed video provide free, albeit noisy, motion information. We propose novel techniques to use them effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times faster than ResNet-152. On the task of action recognition, our approach outperforms all the other methods on the UCF-101, HMDB-51, and Charades dataset.Comment: CVPR 2018 (Selected for spotlight presentation

A Generic Approach for Escaping Saddle points

A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly deteriorating their performance. Typically, to escape from saddles one has to use second-order methods. However, most works on second-order methods rely extensively on expensive Hessian-based computations, making them impractical in large-scale settings. To tackle this challenge, we introduce a generic framework that minimizes Hessian based computations while at the same time provably converging to second-order critical points. Our framework carefully alternates between a first-order and a second-order subroutine, using the latter only close to saddle points, and yields convergence results competitive to the state-of-the-art. Empirical results suggest that our strategy also enjoys a good practical performance

Spin texture on the Fermi surface of tensile strained HgTe

We present ab initio and k.p calculations of the spin texture on the Fermi surface of tensile strained HgTe, which is obtained by stretching the zincblende lattice along the (111) axis. Tensile strained HgTe is a semimetal with pointlike accidental degeneracies between a mirror symmetry protected twofold degenerate band and two nondegenerate bands near the Fermi level. The Fermi surface consists of two ellipsoids which contact at the point where the Fermi level crosses the twofold degenerate band along the (111) axis. However, the spin texture of occupied states indicates that neither ellipsoid carries a compensating Chern number. Consequently, the spin texture is locked in the plane perpendicular to the (111) axis, exhibits a nonzero winding number in that plane, and changes winding number from one end of the Fermi ellipsoids to the other. The change in the winding of the spin texture suggests the existence of singular points. An ordered alloy of HgTe with ZnTe has the same effect as stretching the zincblende lattice in the (111) direction. We present ab initio calculations of ordered Hg_xZn_1-xTe that confirm the existence of a spin texture locked in a 2D plane on the Fermi surface with different winding numbers on either end.Comment: 8 pages, 8 figure

Dirac semimetal in three dimensions

In a Dirac semimetal, the conduction and valence bands contact only at discrete (Dirac) points in the Brillouin zone (BZ) and disperse linearly in all directions around these critical points. Including spin, the low energy effective theory around each critical point is a four band Dirac Hamiltonian. In two dimensions (2D), this situation is realized in graphene without spin-orbit coupling. 3D Dirac points are predicted to exist at the phase transition between a topological and a normal insulator in the presence of inversion symmetry. Here we show that 3D Dirac points can also be protected by crystallographic symmetries in particular space-groups and enumerate the criteria necessary to identify these groups. This reveals the possibility of 3D analogs to graphene. We provide a systematic approach for identifying such materials and present ab initio calculations of metastable \beta-cristobalite BiO_2 which exhibits Dirac points at the three symmetry related X points of the BZ.Comment: 6 pages, 4 figure

Eliciting Truthful Data from Crowdsourced Wireless Monitoring Modules in Cloud Managed Networks

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as indepen- dent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu

Comparison of Flow Field Simulation of Liquid Ejector Pump using Standard K-ε and Embedded LES Turbulence Modelling Techniques

The flow field analysis of a liquid ejector pump is important for its design improvements, performance estimation and understanding of mixing and entrainment phenomenon. Ejector pumps, due to their simpler design and ease of maintenance are used in a variety of industrial applications. The subject pump, under consideration in this study, is used for transferring fuel from one fuel tank to another in a fighter aircraft. To study the underlying flow field characteristics of subject ejector pump, the fluid domain is simulated using Embedded LES turbulence modelling technique in Ansys Fluent ® environment. The flow field and performance parameters of subject pump are then compared with that of previously researched study of same pump wherein Standard K-ε RANS Turbulence Model was used. It is revealed that the results obtained using Embedded LES are much closer to experimental data than that of Standard K-ε. The limitations of RANS turbulence model for accurate simulation of complex flow field of subject pump are then identified, analyzed and discussed in details by studying the flow characteristics such as Reynolds shear stresses distribution, Potential Core estimation and turbulent viscosity modelling, obtained using both turbulent models

Value creation through Big Data in Emerging Economies: the role of Resource Orchestration and Entrepreneurial Orientation

Purpose – The purpose of this paper is to examine how managers orchestrate, bundle, and leverage resources from big data for value creation in emerging economies. Design/methodology/approach – The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation. The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data. Findings – The findings suggest that entrepreneurial orientation is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance. Originality/value – This is one of the first studies to have integrated resource orchestration theory and entrepreneurial orientation in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies