5,698 research outputs found
Data-driven Job Search Engine Using Skills and Company Attribute Filters
According to a report online, more than 200 million unique users search for
jobs online every month. This incredibly large and fast growing demand has
enticed software giants such as Google and Facebook to enter this space, which
was previously dominated by companies such as LinkedIn, Indeed and
CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine",
"Google For Jobs" while Facebook released "Facebook Jobs" within their
platform. These current job search engines and platforms allow users to search
for jobs based on general narrow filters such as job title, date posted,
experience level, company and salary. However, they have severely limited
filters relating to skill sets such as C++, Python, and Java and company
related attributes such as employee size, revenue, technographics and
micro-industries. These specialized filters can help applicants and companies
connect at a very personalized, relevant and deeper level. In this paper we
present a framework that provides an end-to-end "Data-driven Jobs Search
Engine". In addition, users can also receive potential contacts of recruiters
and senior positions for connection and networking opportunities. The high
level implementation of the framework is described as follows: 1) Collect job
postings data in the United States, 2) Extract meaningful tokens from the
postings data using ETL pipelines, 3) Normalize the data set to link company
names to their specific company websites, 4) Extract and ranking the skill
sets, 5) Link the company names and websites to their respective company level
attributes with the EVERSTRING Company API, 6) Run user-specific search queries
on the database to identify relevant job postings and 7) Rank the job search
results. This framework offers a highly customizable and highly targeted search
experience for end users.Comment: 8 pages, 10 figures, ICDM 201
Giant mesoscopic spin Hall effect on surface of topological insulator
We study mesoscopic spin Hall effect on the surface of topological insulator
with a step-function potential. The giant spin polarization induced by a
transverse electric current is derived analytically by using McMillan method in
the ballistic transport limit, which oscillates across the potential boundary
with no confinement from the potential barrier due to the Klein paradox, and
should be observable in spin resolved scanning tunneling microscope.Comment: 5 pages, 3 figure
Note on a non-critical holographic model with a magnetic field
We consider a noncritical holographic model constructed from an intersecting
brane configuration D4/-D4 with an external magnetic field. We
investigate the influences of this magnetic field on strongly coupled dynamics
by the gauge/gravity correspondence.Comment: 18 pages, references added and typos revise
Half Metallic Bilayer Graphene
Charge neutral bilayer graphene has a gapped ground state as transport
experiments demonstrate. One of the plausible such ground states is layered
antiferromagnetic spin density wave (LAF) state, where the spins in top and
bottom layers have same magnitude with opposite directions. We propose that
lightly charged bilayer graphene in an electric field perpendicular to the
graphene plane may be a half metal as a consequence of the inversion and
particle-hole symmetry broken in the LAF state. We show this explicitly by
using a mean field theory on a 2-layer Hubbard model for the bilayer graphene.Comment: 4+ pages, 4 figure
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