Note on neutron star equation of state in the light of GW170817

From the very first multimessenger event of GW170817, clean robust constraints can be obtained for the tidal deformabilities of the two stars involved in the merger, which provides us unique opportunity to study the equation of states (EOSs) of dense stellar matter. In this contribution, we employ a model from the quark level, describing consistently a nucleon and many-body nucleonic system from a quark potential. We check that our sets of EOSs are consistent with available experimental and observational constraints at both sub-nuclear saturation densities and higher densities. The agreements with ab-initio calculations are also good. Especially, we tune the density dependence of the symmetry energy (characterized by its slope at nuclear saturation $L$) and study its influence on the tidal deformability. The so-called $QMF18$ EOS is named after the case of $L=40~\rm MeV$, and it gives $M_{\rm TOV} =2.08~M_\odot$ and $R= 11.77~\rm km$, $\Lambda=331$ for a $1.4\,M_\odot$ star. The tidal signals are demonstrated to be insensitive to the uncertain crust-core matching, despite the good correlation between the symmetry energy slope and the radius of the star.Comment: 8 pages, 6 figures, Submitted to the AIP Proceedings of the Xiamen-CUSTIPEN Workshop on the EOS of Dense Neutron-Rich Matter in the Era of Gravitational Wave Astronomy, Jan. 3-7, Xiamen, Chin

Searching for bulk motions in the ICM of massive, merging clusters with Chandra CCD data

We search for bulk motions in the intracluster medium (ICM) of massive clusters showing evidence of an ongoing or recent major merger with spatially resolved spectroscopy in {\sl Chandra} CCD data. We identify a sample of 6 merging clusters with $>$150 ks {\sl Chandra} exposure in the redshift range $0.1 < z < 0.3$. By performing X-ray spectral analysis of projected ICM regions selected according to their surface brightness, we obtain the projected redshift maps for all of these clusters. After performing a robust analysis of the statistical and systematic uncertainties in the measured X-ray redshift $z_{\rm X}$, we check whether or not the global $z_{\rm X}$ distribution differs from that expected when the ICM is at rest. We find evidence of significant bulk motions at more than 3$\sigma$ in A2142 and A115, and less than 2$\sigma$ in A2034 and A520. Focusing on single regions, we identify significant localized velocity differences in all of the merging clusters. We also perform the same analysis on two relaxed clusters with no signatures of recent mergers, finding no signs of bulk motions, as expected. Our results indicate that deep {\sl Chandra} CCD data enable us to identify the presence of bulk motions at the level of $v_{\rm BM} >$ 1000\ ${\rm km\ s^{-1}}$ in the ICM of massive merging clusters at $0.1<z<0.3$. Although the CCD spectral resolution is not sufficient for a detailed analysis of the ICM dynamics, {\sl Chandra} CCD data constitute a key diagnostic tool complementing X-ray bolometers on board future X-ray missions

Predicting Students Performance Based on Their Reading Behaviors

E-learning systems can support students in the on-line classroom environment by providing different learning materials. However, recent studies find that students may misuse such systems with a variety of strategies. One particular misused strategy, gaming the system, has repeatedly been found to negatively affect the students’ learning results. Unfortunately, methods to quantitatively capture such behavior are poorly developed, making it difficult to predict students learning outcomes. In this work, we tackle this problem based on a study of the 567,193 records of the 71 students’ reading behaviors from two classes in the academic year 2016. We first quantify the extent to which students misused the system and then predict their class performance based on the quantified results. Our results demonstrated that such misbehavior in the E-learning system can be quantified as a probability and then further used as a significant factor to predict students class learning outcomes with high accuracy

Highly Efficient Midinfrared On-Chip Electrical Generation of Graphene Plasmons by Inelastic Electron Tunneling Excitation

Inelastic electron tunneling provides a low-energy pathway for the excitation of surface plasmons and light emission. We theoretically investigate tunnel junctions based on metals and graphene. We show that graphene is potentially a highly efficient material for tunneling excitation of plasmons because of its narrow plasmon linewidths, strong emission, and large tunability in the midinfrared wavelength regime. Compared to gold and silver, the enhancement can be up to 10 times for similar wavelengths and up to 5 orders at their respective plasmon operating wavelengths. Tunneling excitation of graphene plasmons promises an efficient technology for on-chip electrical generation and manipulation of plasmons for graphene-based optoelectronics and nanophotonic integrated circuits.Comment: 12 pages, 7 figure

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs