26,894 research outputs found
Study on the transverse painting during the injection process for CSNS/RCS
For the China Spallation Neutron Source (CSNS), a combination of the H-
stripping and phase space painting method is used to accumulate a high
intensity beam in the Rapid Cycling Synchrotron (RCS). In this paper, firstly,
the injection processes with different painting ranges and different painting
methods were studied. With the codes ORBIT and MATLAB, the particle
distribution and painting image were obtained. Then, the reasonable painting
range which is suitable for the aperture size and magnet gap can be selected.
Since the real field uniformity of BH3 and BV3 is not completely in conformity
with the design requirement, the painting method and painting range also need
to be selected to reduce the effects of bad field uniformity.Comment: Submitted to proceedings of IPAC2015, Richmond, VA, USA, May 3-8,
201
Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines
Implementing machine learning algorithms on Internet of things (IoT) devices
has become essential for emerging applications, such as autonomous driving,
environment monitoring. But the limitations of computation capability and
energy consumption make it difficult to run complex machine learning algorithms
on IoT devices, especially when latency deadline exists. One solution is to
offload the computation intensive tasks to the edge server. However, the
wireless uploading of the raw data is time consuming and may lead to deadline
violation. To reduce the communication cost, lossy data compression can be
exploited for inference tasks, but may bring more erroneous inference results.
In this paper, we propose a dynamic compression ratio selection scheme for edge
inference system with hard deadlines. The key idea is to balance the tradeoff
between communication cost and inference accuracy. By dynamically selecting the
optimal compression ratio with the remaining deadline budgets for queued tasks,
more tasks can be timely completed with correct inference under limited
communication resources. Furthermore, information augmentation that retransmits
less compressed data of task with erroneous inference, is proposed to enhance
the accuracy performance. While it is often hard to know the correctness of
inference, we use uncertainty to estimate the confidence of the inference, and
based on that, jointly optimize the information augmentation and compression
ratio selection. Lastly, considering the wireless transmission errors, we
further design a retransmission scheme to reduce performance degradation due to
packet losses. Simulation results show the performance of the proposed schemes
under different deadlines and task arrival rates.Comment: 11 pages, 14 figure
Coding for Computing Irreducible Markovian Functions of Sources with Memory
One open problem in source coding is to characterize the limits of
representing losslessly a non-identity discrete function of the data encoded
independently by the encoders of several correlated sources with memory. This
paper investigates this problem under Markovian conditions, namely either the
sources or the functions considered are Markovian. We propose using linear
mappings over finite rings as encoders. If the function considered admits
certain polynomial structure, the linear encoders can make use of this
structure to establish "implicit collaboration" and boost the performance. In
fact, this approach universally applies to any scenario (arbitrary function)
because any discrete function admits a polynomial presentation of required
format.
There are several useful discoveries in the paper. The first says that linear
encoder over non-field ring can be equally optimal for compressing data
generated by an irreducible Markov source. Secondly, regarding the previous
function-encoding problem, there are infinitely many circumstances where linear
encoder over non-field ring strictly outperforms its field counterpart. To be
more precise, it is seen that the set of coding rates achieved by linear
encoder over certain non-field rings is strictly larger than the one achieved
by the field version, regardless which finite field is considered. Therefore,
in this sense, linear coding over finite field is not optimal. In addition, for
certain scenarios where the sources do not possess the ergodic property, our
ring approach is still able to offer a solution
Radiation-driven Implosion in the Cepheus B Molecular Cloud
We analyze large scale mapping observations of the molecular lines in the
12CO (J=2-1), 12CO (J=3-2), 13CO (J=2-1), and 13CO (J=3-2) transition emissions
toward the Cepheus B molecular cloud with the KOSMA 3m-telescope. The
integrated intensity map of the 12CO (J=2-1) transition has shown a structure
with a compact core and a compact ridge extended in the north-west of the core.
The cloud is surrounded by an optically bright rim, where the radiation-driven
implosion (RDI) may greatly change the gas properties. The intensities of the
CO (J=3-2) transition are higher than those of the CO (J=2-1) transition along
the rim area.We find characteristic RDI structure in positionvelocity diagrams.
Non-LTE Large velocity gradient (LVG) model analysis shows that the density and
temperature at the edge are higher than that in the center. Our results provide
evidences that RDI is taking place in Cepheus B molecular cloud.Comment: 8 pages, 5 figure
Optimal Choice under Short Sell Limit with Sharpe Ratio as Criterion among Multiple Assets
This article is the term paper of the course Investments. We mainly focus on
modeling long-term investment decisions of a typical utility-maximizing
individual, with features of Chinese stock market in perspective. We adopt an
OR based methodology with market information as input parameters to carry out
the solution. Two main features of this article are: first, we take the no
short-sell constraint in Chinese stock market into consideration and use an
approach otherwise identical to Markowitz to work out the optimal portfolio
choice; this method has critical and practical implication to Chinese
investors. Second, we incorporate the benefits of multiple assets into one
single well-defined utility function and use a MIQP procedure to derive the
optimal allocation of funds upon each of them along the time-line
Study on the injection beam commissioning software for CSNS/RCS
The China Spallation Neutron Source (CSNS) accelerator uses H- stripping and
phase space painting method of filling large ring acceptance with the linac
beam of small emittance. The beam commissioning software system is the key part
of CSNS accelerator. The injection beam commissioning software for CSNS
contains three parts currently: painting curve control, injection beam control
and injection orbit correction. The injection beam control contains two
subsections: single bunch beam calculation and LRBT beam control at the foil.
The injection orbit correction also contains two subsections: injection orbit
correction by the calculation and injection trim power control.Comment: Submitted to proceedings of IPAC2015, Richmond, VA, USA, May 3-8,
201
Non-damped Acoustic Plasmon and Superconductivity in Single Wall Carbon Nanotubes
We show that non-damped acoustic plasmons exist in single wall carbon
nanotubes (SWCNT) and propose that the non-damped acoustic plasmons may mediate
electron-electron attraction and result in superconductivity in the SWCNT. The
superconducting transition temperature Tc for the SWCNT (3,3) obtained by this
mechanism agrees with the recent experimental result (Z. K. Tang et al, Science
292, 2462(2001)). We also show that it is possible to get higher Tc up to 99 K
by doping the SWCNT (5,5).Comment: REVTEX, 4 pages including 2 figures, Corrected typo
Surface field theories of point group symmetry protected topological phases
We identify field theories that describe the surfaces of three-dimensional
bosonic point group symmetry protected topological (pgSPT) phases. The
anomalous nature of the surface field theories is revealed via a dimensional
reduction argument. Specifically, we study three different surface field
theories. The first field theory is quantum electrodynamics in three space-time
dimensions (QED3) with four flavors of fermions. We show this theory can
describe the surfaces of a majority of bosonic pgSPT phases protected by a
single mirror reflection, or by point group symmetry for .
The second field theory is a variant of QED3 with charge-1 and charge-3 Dirac
fermions. This field theory can describe the surface of a reflection symmetric
pgSPT phase built by placing an state on the mirror plane. The third
field theory is an non-linear sigma model with a topological
theta-term at , or, equivalently, a non-compact model.
Using a coupled wire construction, we show this is a surface theory for bosonic
pgSPT phases with symmetry. For the
latter two field theories, we discuss the connection to gapped surfaces with
topological order. Moreover, we conjecture that the latter two field theories
can describe surfaces of more general bosonic pgSPT phases with point
group symmetry.Comment: 16 pages, 2 figure
Dark Soliton Excitations in Single Wall Carbon Nanotubes
Dark soliton excitations are shown to exist in single wall carbon nanotubes
(SWCNTs). At first, the nonlinear effective interatomic potential and the
difference equation for longitudinal lattice displacement are obtained for the
SWCNTs by expanding Brenner's many-body potential in a Taylor series up to
fourth-order terms. Then using a multi-scale method, for short wavelength
lattice excitations the equation of motion of lattice is reduced to the cubic
nonlinear Schrodinger equation. Finally, the dark soliton solutions and
relevant excitations in the SWCNTs with subsonic velocity are discussed.Comment: 11pages, no figure
Fast Multi-Instance Multi-Label Learning
In many real-world tasks, particularly those involving data objects with
complicated semantics such as images and texts, one object can be represented
by multiple instances and simultaneously be associated with multiple labels.
Such tasks can be formulated as multi-instance multi-label learning (MIML)
problems, and have been extensively studied during the past few years. Existing
MIML approaches have been found useful in many applications; however, most of
them can only handle moderate-sized data. To efficiently handle large data
sets, in this paper we propose the MIMLfast approach, which first constructs a
low-dimensional subspace shared by all labels, and then trains label specific
linear models to optimize approximated ranking loss via stochastic gradient
descent. Although the MIML problem is complicated, MIMLfast is able to achieve
excellent performance by exploiting label relations with shared space and
discovering sub-concepts for complicated labels. Experiments show that the
performance of MIMLfast is highly competitive to state-of-the-art techniques,
whereas its time cost is much less; particularly, on a data set with 20K bags
and 180K instances, MIMLfast is more than 100 times faster than existing MIML
approaches. On a larger data set where none of existing approaches can return
results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is
able to identify the most representative instance for each label, and thus
providing a chance to understand the relation between input patterns and output
label semantics
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