252 research outputs found
Probing dark matter particles at CEPC
We investigate the capability of the future electron collider CEPC in probing
the parameter space of several dark matter models, including millicharged dark
matter models, portal dark matter models, and effective field theory dark
matter models. In our analysis, the monophoton final state is used as the
primary channel to detect dark matter models at CEPC. To maximize the signal to
background significance, we study the energy and angular distributions of the
monophoton channel arising from dark matter models and from the standard model
to design a set of detector cuts. For the portal dark matter, we also
analyze the boson visible decay channel which is found to be complementary
to the monophoton channel in certain parameter space. The CEPC reach in the
parameter space of dark matter models is also put in comparison with Xenon1T.
We find that CEPC has the unprecedented sensitivity to certain parameter space
for the dark matter models considered; for example, CEPC can improve the limits
on millicharge by one order of magnitude than previous collider experiments for
GeV dark matter.Comment: 21 pages, 31 figure
Coherence retrieval using trace regularization
The mutual intensity and its equivalent phase-space representations quantify
an optical field's state of coherence and are important tools in the study of
light propagation and dynamics, but they can only be estimated indirectly from
measurements through a process called coherence retrieval, otherwise known as
phase-space tomography. As practical considerations often rule out the
availability of a complete set of measurements, coherence retrieval is usually
a challenging high-dimensional ill-posed inverse problem. In this paper, we
propose a trace-regularized optimization model for coherence retrieval and a
provably-convergent adaptive accelerated proximal gradient algorithm for
solving the resulting problem. Applying our model and algorithm to both
simulated and experimental data, we demonstrate an improvement in
reconstruction quality over previous models as well as an increase in
convergence speed compared to existing first-order methods.Comment: 28 pages, 10 figures, accepted for publication in SIAM Journal on
Imaging Science
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
This paper develops simple feed-forward neural networks that achieve the
universal approximation property for all continuous functions with a fixed
finite number of neurons. These neural networks are simple because they are
designed with a simple and computable continuous activation function
leveraging a triangular-wave function and the softsign function. We prove that
-activated networks with width and depth can
approximate any continuous function on a -dimensional hypercube within an
arbitrarily small error. Hence, for supervised learning and its related
regression problems, the hypothesis space generated by these networks with a
size not smaller than is dense in the continuous function
space and therefore dense in the Lebesgue spaces
for . Furthermore, classification functions arising from image
and signal classification are in the hypothesis space generated by
-activated networks with width and depth , when there
exist pairwise disjoint bounded closed subsets of such that the
samples of the same class are located in the same subset. Finally, we use
numerical experimentation to show that replacing the ReLU activation function
by ours would improve the experiment results
A Cotransformation Method To Identify a Restriction-Modification Enzyme That Reduces Conjugation Efficiency in Campylobacter jejuni
Conjugation is an important mechanism for horizontal gene transfer in Campylobacter jejuni, the leading cause of human bacterial gastroenteritis in developed countries. However, to date, the factors that significantly influence conjugation efficiency in Campylobacter spp. are still largely unknown. Given that multiple recombinant loci could independently occur within one recipient cell during natural transformation, the genetic materials from a high-frequency conjugation (HFC) C. jejuni strain may be cotransformed with a selection marker into a low-frequency conjugation (LFC) recipient strain, creating new HFC transformants suitable for the identification of conjugation factors using a comparative genomics approach. To test this, an erythromycin resistance selection marker was created in an HFC C. jejuni strain; subsequently, the DNA of this strain was naturally transformed into NCTC 11168, an LFC C. jejuni strain, leading to the isolation of NCTC 11168-derived HFC transformants. Whole-genome sequencing analysis and subsequent site-directed mutagenesis identified Cj1051c, a putative restriction-modification enzyme (aka CjeI) that could drastically reduce the conjugation efficiency of NCTC 11168 (\u3e5,000-fold). Chromosomal complementation of three diverse HFC C. jejuni strains with CjeI also led to a dramatic reduction in conjugation efficiency (∼1,000-fold). The purified recombinant CjeI could effectively digest the Escherichia coli-derived shuttle vector pRY107. The endonuclease activity of CjeI was abolished upon short heat shock treatment at 50°C, which is consistent with our previous observation that heat shock enhanced conjugation efficiency in C. jejuni. Together, in this study, we successfully developed and utilized a unique cotransformation strategy to identify a restriction-modification enzyme that significantly influences conjugation efficiency in C. jejuni
Effects of injection rate profile on combustion process and emissions in a diesel engine
When multi-injection is implemented in diesel engine via high pressure common-rail injection system, changed interval between injection pulses can induce variation of injection rate profile for sequential injection pulse, though other control parameters are same. Variations of injection rate shape which influence the air-fuel mixing and combustion process will be important for designing injection strategy. In this research, CFD numerical simulations using KIVA-3V were conducted for examining the effects of injection rate shape on diesel combustion and emissions. After the model was validated by experimental results, five different shapes (including rectangle, slope, triangle, trapezoid and wedge) of injection rate profiles were investigated. Modelling results demonstrate that injection rate shape can have obvious influence on heat release process and heat release traces which cause different combustion process and emissions. It is observed that the baseline - rectangle (flat) shape of injection rate can have better balance between NOx and soot emissions than other investigated shapes. As wedge shape brings about the lowest NOx emissions due to retarded heat release, it produces highest soot emissions among five shapes. Trapezoid shape has the lowest soot emissions, while its NOx is not the highest one. The highest NOx emissions was produced by triangle shape due to higher peak injection rate
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