Non-leptonic two-body weak decays of Λc(2286)\Lambda_c(2286)

We study the non-leptonic two-body weak decays of $\Lambda_c^+(2286)\to {\bf B}_n M$ with ${\bf B}_n$ ($M$) representing as the baryon (meson) states. Based on the $SU(3)$ flavor symmetry, we can describe most of the data reexamined by the BESIII Collaboration with higher precisions. However, our result of ${\cal B}(\Lambda_c^+ \to p\pi^0)=(5.6\pm 1.5)\times 10^{-4}$ is larger than the current experimental limit of $3\times10^{-4}$ (90\% C.L.) by BESIII. In addition, we find that ${\cal B}(\Lambda_c^+ \to \Sigma^+ K^0)=(8.0\pm 1.6)\times 10^{-4}$, ${\cal B}(\Lambda_c^+ \to \Sigma^+ \eta^\prime)=(1.0^{+1.6}_{-0.8})\times 10^{-2}$, and ${\cal B}(\Lambda_c^+ \to p \eta^\prime)=(12.2^{+14.3}_{-\,\,\,8.7})\times 10^{-4}$, which are accessible to the BESIII experiments.Comment: 12 pages, 1 figure, revised version accepted by PL

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Bis(benzimidazolium) naphthalene-1,5-disulfonate trihydrate

The title compound, 2C7H7N2 +·C10H6O6S2 2−·3H2O, consists of two crystallographically independent benzimidazolium cations, two independent naphthalene-1,5-disulfonate dianions (both generated by inversion) and three water mol­ecules. These components construct an infinite three-dimensional framework in the crystal structure via O—H⋯O and N—H⋯O hydrogen bonds

Towards patient-specific cardiovascular modeling system using the immersed boundary technique

<p>Abstract</p> <p>Background</p> <p>Previous research shows that the flow dynamics in the left ventricle (LV) reveal important information about cardiac health. This information can be used in early diagnosis of patients with potential heart problems. The current study introduces a patient-specific cardiovascular-modelling system (CMS) which simulates the flow dynamics in the LV to facilitate physicians in early diagnosis of patients before heart failure.</p> <p>Methods</p> <p>The proposed system will identify possible disease conditions and facilitates early diagnosis through hybrid computational fluid dynamics (CFD) simulation and time-resolved magnetic resonance imaging (4-D MRI). The simulation is based on the 3-D heart model, which can simultaneously compute fluid and elastic boundary motions using the immersed boundary method. At this preliminary stage, the 4-D MRI is used to provide an appropriate comparison. This allows flexible investigation of the flow features in the ventricles and their responses.</p> <p>Results</p> <p>The results simulate various flow rates and kinetic energy in the diastole and systole phases, demonstrating the feasibility of capturing some of the important characteristics of the heart during different phases. However, some discrepancies exist in the pulmonary vein and aorta flow rate between the numerical and experimental data. Further studies are essential to investigate and solve the remaining problems before using the data in clinical diagnostics.</p> <p>Conclusions</p> <p>The results show that by using a simple reservoir pressure boundary condition (RPBC), we are able to capture some essential variations found in the clinical data. Our approach establishes a first-step framework of a practical patient-specific CMS, which comprises a 3-D CFD model (without involving actual hemodynamic data yet) to simulate the heart and the 4-D PC-MRI system. At this stage, the 4-D PC-MRI system is used for verification purpose rather than input. This brings us closer to our goal of developing a practical patient-specific CMS, which will be pursued next. We anticipate that in the future, this hybrid system can potentially identify possible disease conditions in LV through comprehensive analysis and facilitates physicians in early diagnosis of probable cardiac problems.</p

The Universe is at Most 88% Neutral at z=10.6

Recent observations of GN-z11 with JWST have revealed a Ly$\alpha$ emission line with an equivalent width of 18$\pm 2$ angstroms. At z=10.6, this galaxy is expected to lie in the heart of reionization. We use a series of inhomogeneous reionization simulations to derive the distribution of the Ly$\alpha$ EW after traveling through the neutral intergalactic medium with varying average neutral gas fraction, $x_{HI}$. We use these distribution to place an upper limit of $x_{HI} <$ 0.88 at z=10.6 at 95% confidence level. We compare our upper limit to different reionization history models, which include the recently identified enhancement at the bright end of the luminosity function at z>8. We find that models in which faint galaxies have higher escape fraction compared to bright galaxies are favored by the new data.Comment: 7 pages, 3 figure

Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station