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, $Z'$ 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 $Z'$ portal dark matter, we also analyze the $Z'$ 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 ${\cal O}(1)-100$ 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 $\sigma$ leveraging a triangular-wave function and the softsign function. We prove that $\sigma$-activated networks with width $36d(2d+1)$ and depth $11$ can approximate any continuous function on a $d$-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 $36d(2d+1)\times 11$ is dense in the continuous function space $C([a,b]^d)$ and therefore dense in the Lebesgue spaces $L^p([a,b]^d)$ for $p\in [1,\infty)$. Furthermore, classification functions arising from image and signal classification are in the hypothesis space generated by $\sigma$-activated networks with width $36d(2d+1)$ and depth $12$, when there exist pairwise disjoint bounded closed subsets of $\mathbb{R}^d$ 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