21,026 research outputs found
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
The number of approved patents worldwide increases rapidly each year, which
requires new patent analytics to efficiently mine the valuable information
attached to these patents. Vector space model (VSM) represents documents as
high-dimensional vectors, where each dimension corresponds to a unique term.
While originally proposed for information retrieval systems, VSM has also seen
wide applications in patent analytics, and used as a fundamental tool to map
patent documents to structured data. However, VSM method suffers from several
limitations when applied to patent analysis tasks, such as loss of
sentence-level semantics and curse-of-dimensionality problems. In order to
address the above limitations, we propose a patent analytics based on feature
vector space model (FVSM), where the FVSM is constructed by mapping patent
documents to feature vectors extracted by convolutional neural networks (CNN).
The applications of FVSM for three typical patent analysis tasks, i.e., patents
similarity comparison, patent clustering, and patent map generation are
discussed. A case study using patents related to Internet of Things (IoT)
technology is illustrated to demonstrate the performance and effectiveness of
FVSM. The proposed FVSM can be adopted by other patent analysis studies to
replace VSM, based on which various big data learning tasks can be performed
Convergence of Unregularized Online Learning Algorithms
In this paper we study the convergence of online gradient descent algorithms
in reproducing kernel Hilbert spaces (RKHSs) without regularization. We
establish a sufficient condition and a necessary condition for the convergence
of excess generalization errors in expectation. A sufficient condition for the
almost sure convergence is also given. With high probability, we provide
explicit convergence rates of the excess generalization errors for both
averaged iterates and the last iterate, which in turn also imply convergence
rates with probability one. To our best knowledge, this is the first
high-probability convergence rate for the last iterate of online gradient
descent algorithms without strong convexity. Without any boundedness
assumptions on iterates, our results are derived by a novel use of two measures
of the algorithm's one-step progress, respectively by generalization errors and
by distances in RKHSs, where the variances of the involved martingales are
cancelled out by the descent property of the algorithm
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
A Two-Stage Allocation Scheme for Delay-Sensitive Services in Dense Vehicular Networks
Driven by the rapid development of wireless communication system, more and
more vehicular services can be efficiently supported via vehicle-to-everything
(V2X) communications. In order to allocate radio resource with the reasonable
implementation complexity in dense urban intersection, a two-stage allocation
algorithm is proposed in this paper, whose main objective is to minimize delay
and ensure reliability. In particular, as for the first stage, the allocation
policy is based on traffic density information (TDI), which is different from
utilizing channel state information (CSI) and queue state information (QSI) in
the second stage. Moreover, in order to reflect the influence of TDI on delay,
a macroscopic vehicular mobility model is employed in this paper. Simulation
results show that the proposed algorithm can acquire an asymptotically optimal
performance with the acceptable complexity
Positivity and Kodaira embedding theorem
Kodaira embedding theorem provides an effective characterization of
projectivity of a K\"ahler manifold in terms the second cohomology. Recently X.
Yang [21] proved that any compact K\"ahler manifold with positive holomorphic
sectional curvature must be projective. This gives a metric criterion of the
projectivity in terms of its curvature. In this note, we prove that any compact
K\"ahler manifold with positive 2nd scalar curvature (which is the average of
holomorphic sectional curvature over 2-dimensional subspaces of the tangent
space) must be projective. In view of generic 2-tori being non-abelian, this
new curvature characterization is sharp in certain sense
Lepton-portal Dark Matter in Hidden Valley model and the DAMPE recent results
We study the recent cosmic ray excess reported by DAMPE in a Hidden
Valley Model with lepton-portal dark matter. We find the electron-portal can
account for the excess well and satisfy the DM relic density and direct
detection bounds, while electron+muon/electron+muon+tau-portal suffers from
strong constraints from lepton flavor violating observables, such as . We also discuss possible collider signatures of our model, both at the LHC
and a future 100 TeV hadron collider.Comment: invited by Science China, accepted versio
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