8,793 research outputs found
Task Assignment on Spatial Crowdsourcing [Experiments and Analyses] (Technical Report)
Recently, with the rapid development of mobile devices and the crowdsourcing
platforms, the spatial crowdsourcing has attracted much attention from the
database community. Specifically, spatial crowdsourcing refers to sending a
location-based request to workers according to their positions, and workers
need to physically move to specified locations to conduct tasks. Many works
have studied task assignment problems in spatial crowdsourcing, however, their
problem settings are different from each other. Thus, it is hard to compare the
performances of existing algorithms on task assignment in spatial
crowdsourcing. In this paper, we present a comprehensive experimental
comparison of most existing algorithms on task assignment in spatial
crowdsourcing. Specifically, we first give general definitions about spatial
workers and spatial tasks based on definitions in the existing works such that
the existing algorithms can be applied on the same synthetic and real data
sets. Then, we provide an uniform implementation for all the tested algorithms
of task assignment problems in spatial crowdsourcing (open sourced). Finally,
based on the results on both synthetic and real data sets, we discuss the
strengths and weaknesses of tested algorithms, which can guide future research
on the same area and practical implementations of spatial crowdsourcing
systems.Comment: 17 page
Studies on the switching speed effect of the phase shift keying in SLED for generating high power microwave
SLAC energy doubler (SLED) type radio-frequency pulse compressors are widely
used in large-scale particle accelerators for converting long-duration
moderate-power input pulse into short-duration high-power output pulse. The
phase shift keying (PSK) is one of the key components in SLED pulse compression
systems. Performance of the PSK will influence the output characteristics of
SLED, such as rise-time of the output pulse, the maximal peak power gain, and
the energy efficiency. In this paper, high power microwave source based on
power combining and pulse compression of conventional klystrons was introduced,
the nonideal PSK with slow switching speed and without power output during the
switching process were investigated, the experimental results with nonideal PSK
agreed well with the analytical results
Radiative and pionic transitions from the to the
We estimate the partial widths for the radiative and pionic transitions from
the to the in a molecule scenario, in which the
and are considered as hadronic molecule
states of and , respectively. The partial widths for the
and are evaluated to be about keV and
keV, respectively. In addition, the ratio of the and is estimated
to be about , which is safely under the measured
upper limit.Comment: 8 pages, 8 figures, 2 tables, published versio
Semi-supervised Learning with Contrastive Predicative Coding
Semi-supervised learning (SSL) provides a powerful framework for leveraging
unlabeled data when labels are limited or expensive to obtain. SSL algorithms
based on deep neural networks have recently proven successful on standard
benchmark tasks. However, many of them have thus far been either inflexible,
inefficient or non-scalable. This paper explores recently developed contrastive
predictive coding technique to improve discriminative power of deep learning
models when a large portion of labels are absent. Two models, cpc-SSL and a
class conditional variant~(ccpc-SSL) are presented. They effectively exploit
the unlabeled data by extracting shared information between different parts of
the (high-dimensional) data. The proposed approaches are inductive, and scale
well to very large datasets like ImageNet, making them good candidates in
real-world large scale applications.Comment: 6 pages, 4 figures, conferenc
Giant Magnetoresistance in Hubbard Chains
We use numerically unbiased methods to show that the one-dimensional Hubbard
model with periodically distributed on-site interactions already contains the
minimal ingredients to display the phenomenon of magnetoresistance; i.e., by
applying an external magnetic field, a dramatic enhancement on the charge
transport is achieved. We reach this conclusion based on the computation of the
Drude weight and of the single-particle density of states, applying twisted
boundary condition averaging to reduce finite-size effects. The known picture
that describes the giant magnetoresistance, by interpreting the scattering
amplitudes of parallel or antiparallel polarized currents with local
magnetizations, is obtained without having to resort to different entities;
itinerant and localized charges are indistinguishable.Comment: 6+4 pages 5+5 figures, as publishe
Event-triggered Online Proactive Network Association to Mobile Edge Computing for IoT
Ultra-low latency communication for mobile machines emerges as a critical
technology in Internet of Things (IoT). Proactive network association has been
suggested to support ultra-low latency communication with the assistance of
mobile edge computing. To resolve system dynamics and uncertainty, in this
paper, an online proactive network association is proposed to minimize average
task delay while considering time-average energy consumption constraints. Under
distributed computing and networking environments, we formulate an
event-triggered proactive network association model by semi-Markov task states
and independent identically distributed (i.i.d.) random events. Then we
facilitate the mobility-aware anticipatory network association to predictively
consider handover effects caused by the mobility. Based on the Markov decision
processes (MDP) and Lyapunov optimization, the two-stage online proactive
network association (TOPNA) decision algorithm is proposed without the
knowledge nor distribution of random events. Simulation results exhibit the
effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, the paper submitted to IEEE GLOBECOM 201
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising
In online display advertising, guaranteed contracts and real-time bidding
(RTB) are two major ways to sell impressions for a publisher. Despite the
increasing popularity of RTB, there is still half of online display advertising
revenue generated from guaranteed contracts. Therefore, simultaneously selling
impressions through both guaranteed contracts and RTB is a straightforward
choice for a publisher to maximize its yield. However, deriving the optimal
strategy to allocate impressions is not a trivial task, especially when the
environment is unstable in real-world applications. In this paper, we formulate
the impression allocation problem as an auction problem where each contract can
submit virtual bids for individual impressions. With this formulation, we
derive the optimal impression allocation strategy by solving the optimal
bidding functions for contracts. Since the bids from contracts are decided by
the publisher, we propose a multi-agent reinforcement learning (MARL) approach
to derive cooperative policies for the publisher to maximize its yield in an
unstable environment. The proposed approach also resolves the common challenges
in MARL such as input dimension explosion, reward credit assignment, and
non-stationary environment. Experimental evaluations on large-scale real
datasets demonstrate the effectiveness of our approach
Valley controlled propagation of pseudospin states in bulk metacrystal waveguides
Light manipulations such as spin-direction locking propagation, robust
transport, quantum teleportation and reconfigurable electromagnetic pathways
have been investigated at the boundaries of photonic systems. Recently by
breaking Dirac cones in time-reversal invariant photonic crystals,
valley-pseudospin coupled edge states have been employed to realize selective
propagation of light. Here, without photonic boundaries, we realize the
propagation of pseudospin states in three-dimensional bulk metacrystal
waveguides by employing the ubiquitous valley degree of freedom.
Valley-dependent pseudospin bands are achieved in three-dimensional metacrystal
waveguides without Dirac cones. Reconfigurable photonic valley Hall effect is
proposed after studying the variation of pseudospin states near K' and K
valleys. Moreover, a prototype of photonic blocker is realized by cascading two
inversion asymmetric metacrystal waveguides in which the pseudospin direction
locking propagation exists. In addition, valley-dependent pseudospin bands are
also discussed in a realistic metamaterials sample. These results show an
alternative way towards molding the pseudospin flow in photonic systems.Comment: 26 pages, 7 figure
Large scale quantum key distribution: challenges and solutions
Quantum key distribution (QKD) together with one time pad encoding can
provide information-theoretical security for communication. Currently, though
QKD has been widely deployed in many metropolitan fiber networks, its
implementation in a large scale remains experimentally challenging. This letter
provides a brief review on the experimental efforts towards the goal of global
QKD, including the security of practical QKD with imperfect devices, QKD
metropolitan and backbone networks over optical fiber and satellite-based QKD
over free space.Comment: 14 pages, 5 figures. An invited review article for Optics Express's
20th Anniversary celebratio
Understanding the decay mode of via the triangle loop mechanism
Recently, the BESIII Collaboration reported a new measurement of the decay mode of , which motivated us to study the inner
structure of via investigating the hidden charm decays of
these two states. We consider the {triangle loop mechanism} contribution
in the hidden charm decays of . Our estimations indicate that
the triangle loop mechanism plays an important role in the decays of the
, where our results are in agreement with the experimental
observations in a reasonable parameter range. Furthermore, we point out that
the can be interpreted as the hadronic molecules, while the
tetraquark scenario is less favored
- …