3,414 research outputs found
Competition between the inter-valley scattering and the intra-valley scattering on magnetoconductivity induced by screened Coulomb disorder in Weyl semimetals
Recent experiments on Weyl semimetals reveal that charged impurities may play
an important role. We use a screened Coulomb disorder to model the charged
impurities, and study the magneto-transport in a two-node Weyl semimetal. It is
found that when the external magnetic field is applied parallel to the electric
field, the calculated longitudinal magnetoconductivity shows positive in the
magnetic field, which is just the negative longitudinal magnetoresistivity
(LMR) observed in experiments. When the two fields are perpendicular to each
other, the transverse magnetoconductivities are measured. It is found that the
longitudinal (transverse) magnetoconductivity is suppressed (enhanced)
sensitively with increasing the screening length. This feature makes it hardly
to observe the negative LMR in Weyl semimetals experimentally owing to a small
screening length. Our findings gain insight into further understanding on
recently actively debated magneto-transport behaviors in Weyl semimetals.
Furthermore we studied the relative weight of the inter-valley scattering and
the intra-valley scattering. It shows that the former is as important as the
latter and even dominates in the case of strong magnetic fields and small
screening length. We emphasize that the discussions on inter-valley scattering
is out of the realm of one-node model which has been studied.Comment: 14 pages, 5 figure
Typology and Portfolio of Net-enabled Organizational Capabilities and Competitive Advantages: The Case Study of Travel and Hospitality Industry
Electronic business (e-business) is evolving from its technological tool towards a strategic role, supporting new business strategies. Prior studies explained e-business value creation from net-enabled organizational capabilities perspective, and delivered many insights at firm level with individual-level analysis of capability. In this paper, we posit that competitive advantages under e-business environment will be dependent upon the deployment of multiple types of net-enabled organizational capabilities and their appropriate portfolios. Further, we use Wade and Hulland (2004)’s capabilities taxonomies and multiple cases-based data in Chinese travel and hospitality industry to understand the effect of appropriate portfolios of net-enabled organizational capabilities on competitive advantages
The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems
Now we live in an era of big data, and big data applications are becoming
more and more pervasive. How to benchmark data center computer systems running
big data applications (in short big data systems) is a hot topic. In this
paper, we focus on measuring the performance impacts of diverse applications
and scalable volumes of data sets on big data systems. For four typical data
analysis applications---an important class of big data applications, we find
two major results through experiments: first, the data scale has a significant
impact on the performance of big data systems, so we must provide scalable
volumes of data sets in big data benchmarks. Second, for the four applications,
even all of them use the simple algorithms, the performance trends are
different with increasing data scales, and hence we must consider not only
variety of data sets but also variety of applications in benchmarking big data
systems.Comment: 16 pages, 3 figure
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Face anti-spoofing is crucial to the security of face recognition systems.
Most previous methods formulate face anti-spoofing as a supervised learning
problem to detect various predefined presentation attacks, which need large
scale training data to cover as many attacks as possible. However, the trained
model is easy to overfit several common attacks and is still vulnerable to
unseen attacks. To overcome this challenge, the detector should: 1) learn
discriminative features that can generalize to unseen spoofing types from
predefined presentation attacks; 2) quickly adapt to new spoofing types by
learning from both the predefined attacks and a few examples of the new
spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot
learning problem. In this paper, we propose a novel Adaptive Inner-update Meta
Face Anti-Spoofing (AIM-FAS) method to tackle this problem through
meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task
of detecting unseen spoofing types by learning from predefined living and
spoofing faces and a few examples of new attacks. To assess the proposed
approach, we propose several benchmarks for zero- and few-shot FAS. Experiments
show its superior performances on the presented benchmarks to existing methods
in existing zero-shot FAS protocols.Comment: Accepted by AAAI202
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