31,808 research outputs found
Irregular Convolutional Neural Networks
Convolutional kernels are basic and vital components of deep Convolutional
Neural Networks (CNN). In this paper, we equip convolutional kernels with shape
attributes to generate the deep Irregular Convolutional Neural Networks (ICNN).
Compared to traditional CNN applying regular convolutional kernels like
, our approach trains irregular kernel shapes to better fit the
geometric variations of input features. In other words, shapes are learnable
parameters in addition to weights. The kernel shapes and weights are learned
simultaneously during end-to-end training with the standard back-propagation
algorithm. Experiments for semantic segmentation are implemented to validate
the effectiveness of our proposed ICNN.Comment: 7 pages, 5 figures, 3 table
Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers
We study the multi-resource allocation problem in cloud computing systems
where the resource pool is constructed from a large number of heterogeneous
servers, representing different points in the configuration space of resources
such as processing, memory, and storage. We design a multi-resource allocation
mechanism, called DRFH, that generalizes the notion of Dominant Resource
Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH
provides a number of highly desirable properties. With DRFH, no user prefers
the allocation of another user; no one can improve its allocation without
decreasing that of the others; and more importantly, no user has an incentive
to lie about its resource demand. As a direct application, we design a simple
heuristic that implements DRFH in real-world systems. Large-scale simulations
driven by Google cluster traces show that DRFH significantly outperforms the
traditional slot-based scheduler, leading to much higher resource utilization
with substantially shorter job completion times
Possible open-charmed pentaquark molecule --- the bound state --- in the Bethe-Salpeter formalism
We study the -wave bound state in the Bethe-Salpeter formalism in
the ladder and instantaneous approximations. With the kernel generated by the
hadronic effective Lagrangian, two open-charmed bound states, which quantum
numbers are , and , ,
respectively, are predicted as new candidates of hadronic pentaquark molecules
in our formalism. If existing, they could contribute to the broad 3188 eV
structure near the five new narrow states observed recently by the
LHCb Collaboration.Comment: 8 pages, 4 figures, accepted by Eur. Phys. J.
Design and Implementation of a FPGA and DSP Based MIMO Radar Imaging System
The work presented in this paper is aimed at the implementation of a real-time multiple-input multiple-output (MIMO) imaging radar used for area surveillance. In this radar, the equivalent virtual array method and time-division technique are applied to make 16 virtual elements synthesized from the MIMO antenna array. The chirp signal generater is based on a combination of direct digital synthesizer (DDS) and phase locked loop (PLL). A signal conditioning circuit is used to deal with the coupling effect within the array. The signal processing platform is based on an efficient field programmable gates array (FPGA) and digital signal processor (DSP) pipeline where a robust beamforming imaging algorithm is running on. The radar system was evaluated through a real field experiment. Imaging capability and real-time performance shown in the results demonstrate the practical feasibility of the implementation
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