3,533 research outputs found
Understanding Polarization Correlation of Entangled Vector Meson Pairs
We propose an experimental test of local hidden variable theories against
quantum mechanics by measuring the polarization correlation of entangled vector
meson pairs. In our study, the form of the polarization correlation probability
is reproduced in a natural way by interpreting the two-body decay of the meson
as a measurement of its polarization vector within the framework of quantum
mechanics. This provides more detailed information on the quantum entanglement,
thus a new Monte Carlo method to simulate the quantum correlation is
introduced. We discuss the feasibility of carrying out such a test at
experiments in operation currently and expect that the measured correlated
distribution may provide us with deeper insight into the fundamental question
about locality and reality.Comment: 7 pages, 3 figures. v3: The version published in PR
The Optimization of Interconnection Networks in FPGAs
Scaling technology enables even higher degree of integration for FPGAs, but also brings new challenges that need to be addressed from both the architecture and the design tools side. Optimization of FPGA interconnection network is essential, given that interconnects dominate logic. Two approaches are presented, with one based on the time-multiplexing of wires and the other using hierarchical interconnects of high-speed serial links and switches. Design tools for both approaches are discussed. Preliminary experiments and prototypes are presented, and show positive results
Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network
A topic propagating in a social network reaches its tipping point if the
number of users discussing it in the network exceeds a critical threshold such
that a wide cascade on the topic is likely to occur. In this paper, we consider
the task of selecting initial seed users of a topic with minimum size so that
with a guaranteed probability the number of users discussing the topic would
reach a given threshold. We formulate the task as an optimization problem
called seed minimization with probabilistic coverage guarantee (SM-PCG). This
problem departs from the previous studies on social influence maximization or
seed minimization because it considers influence coverage with probabilistic
guarantees instead of guarantees on expected influence coverage. We show that
the problem is not submodular, and thus is harder than previously studied
problems based on submodular function optimization. We provide an approximation
algorithm and show that it approximates the optimal solution with both a
multiplicative ratio and an additive error. The multiplicative ratio is tight
while the additive error would be small if influence coverage distributions of
certain seed sets are well concentrated. For one-way bipartite graphs we
analytically prove the concentration condition and obtain an approximation
algorithm with an multiplicative ratio and an
additive error, where is the total number of nodes in the social graph.
Moreover, we empirically verify the concentration condition in real-world
networks and experimentally demonstrate the effectiveness of our proposed
algorithm comparing to commonly adopted benchmark algorithms.Comment: Conference version will appear in KDD 201
Transmission of Guqin Knowledge and Literacy by Changchao Lu
Chinese music has played a significant role in the lives of the Chinese people for generations, being passed down from one generation to the next. Particularly, Changchao Lu’s Guqin teaching holds valuable academic insights. Therefore, this study aims to investigate the transmission of Guqin knowledge and literacy by Changchao Lu. The researchers used qualitative approaches, specifically interviews and observations. The study’s findings illustrate that Changchao Lu’s Guqin playing style exhibits attributes of tranquility, minimalism, strength, and adaptability. The instructor’s pedagogical approach integrates conventional and progressive elements, placing significant emphasis on a comprehensive and balanced educational experience. He established the College of Chinese Arts, which aimed to amalgamate conventional and contemporary Guqin education. The institution prioritized several aspects, such as theoretical knowledge, performing skills, pedagogy, and even the craft of instrument-making. The use of online technologies, the promotion of collaborative learning, and the implementation of a student-centered approach all improve the educational experience. Changchao Lu’s activities encompass the integration of Guqin, a traditional Chinese musical instrument, into rural schools, the promotion of cultural understanding, and the active engagement of local education officials. In addition, he engages in collaborative efforts with local governmental bodies to rejuvenate rural culture by means of Guqin, therefore nurturing artistic potential and establishing connections between Guqin art and local industry
Shipment sizing for autonomous trucks of road freight
Unprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks. Empirical data of estimated autonomous truck costs is collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction. Autonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network. The findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review. Logistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size. This study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks
Quality estimation and optimization of adaptive stereo matching algorithms for smart vehicles
Stereo matching is a promising approach for smart vehicles to find the depth of nearby objects. Transforming a traditional stereo matching algorithm to its adaptive version has potential advantages to achieve the maximum quality (depth accuracy) in a best-effort manner. However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage/frequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road
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