The Child is Father of the Man: Foresee the Success at the Early Stage

Understanding the dynamic mechanisms that drive the high-impact scientific work (e.g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources. Recent advances in characterizing and modeling scientific success have made it possible to forecast the long-term impact of scientific work, where data mining techniques, supervised learning in particular, play an essential role. Despite much progress, several key algorithmic challenges in relation to predicting long-term scientific impact have largely remained open. In this paper, we propose a joint predictive model to forecast the long-term scientific impact at the early stage, which simultaneously addresses a number of these open challenges, including the scholarly feature design, the non-linearity, the domain-heterogeneity and dynamics. In particular, we formulate it as a regularized optimization problem and propose effective and scalable algorithms to solve it. We perform extensive empirical evaluations on large, real scholarly data sets to validate the effectiveness and the efficiency of our method.Comment: Correct some typos in our KDD pape

Exploring spin-orbital models with dipolar fermions in zig-zag optical lattices

Ultra-cold dipolar spinor fermions in zig-zag type optical lattices can mimic spin-orbital models relevant in solid-state systems, as transition-metal oxides with partially filled d-levels, with the interesting advantage of reviving the quantum nature of orbital fluctuations. We discuss two different physical systems in which these models may be simulated, showing that the interplay between lattice geometry and spin-orbital quantum dynamics produces a wealth of novel quantum phases.Comment: 4 pages + supplementary materia

B/P Doping in application of silicon oxynitride based integrated optics

In this paper, gaseous precursors containing boron or phosphorous were intentionally introduced in the deposition of SiON layers and upper SiO2 claddings. The measurements show that the as-deposited B/P-doped SiON layers contain less hydrogen than undoped layers. Furthermore, the necessary annealing temperature for elimination of hydrogen related absorption (propagation loss) is greatly reduced in B/P-doped layers

Transport properties in resonant tunneling heterostructures

We use an adiabatic approximation in terms of instantaneous resonances to study the steady-state and time-dependent transport properties of interacting electrons in biased resonant tunneling heterostructures. This approach leads, in a natural way, to a transport model of large applicability consisting of reservoirs coupled to regions where the system is described by a nonlinear Schr\"odinger equation. From the mathematical point of view, this work is non-rigorous but may offer some fresh and interesting problems involving semiclassical approximation, adiabatic theory, non-linear Schr\"odinger equations and dynamical systems.Comment: 25 pages including 9 postscript figures; requires REVTeX 3.0, psfig; uuencoded gz-compressed .tar file; preprint 1133 April 96 Ecole Polytechnique to be published in J. Math. Phys. october 199

Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional

We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of beyond graphene compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) to the density functional theory. The SCAN meta-GGA results are compared with those based on the local density approximation (LDA) as well as the generalized gradient approximation (GGA). As expected, the GGA yields expanded lattices and softened bonds in relation to the LDA, but the SCAN meta-GGA systematically improves the agreement with experiment. Our study suggests the efficacy of the SCAN functional for accurate modeling of electronic structures of layered materials in high-throughput calculations more generally

Numerical and Experimental Analysis of Transient Flow in Roots Blower

The performance of rotary positive displacement machines highly depends on the operational clearances. It is widely believed that computational fluid dynamics (CFD) can help understanding internal leakage flows. Developments of grid generating tools for analysis of leakage flows by CFD in rotary positive displacement machines have not yet been fully validated. Roots blower is a good representative of positive displacement machines and as such is convenient for optical access in order to analyse internal flows. The experimental investigation of flow in optical roots blower by phase-locked PIV (Particle Image Velocimetry) performed in the Centre for Compressor Technology at City, University of London provided the velocity field suitable for validation of the simulation model. This paper shows the results of the three-dimensional CFD transient simulation model of a Roots blower with the dynamic numerical grids generated by SCORG and flow solution solved in ANSYS CFX flow solver to obtain internal flow patterns. The velocity fields obtained by simulation agree qualitatively with the experimental results and show the correct main flow features in the working chamber. There are some differences in the velocity magnitude and vortex distribution. The flow field in roots blower is highly turbulent and three-dimensional. The axial clearances should be included, and the axial grids should be refined in the simulation method. The paper outlines some directions for future simulation and experimental work. The work described in this paper is a part of the large project set to evaluate characteristics of the internal flow in rotary positive displacement machines and to characterize leakage flow

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201