Flavor and CP Violation with Fourth Generations Revisited

The Standard Model predicts a very small CP violation phase $\sin2\Phi^{\rm SM}_{B_s} \simeq -0.04$%= \arg M_{12} \simeq \arg\,(V^*_{ts}V_{tb})^2$in$B_s$--$\bar B_s$mixing.$\lambda^2\eta$. Any finite value of$\Phi_{B_s}$measured at the Tevatron would imply New Physics. With recent hints for finite$\sin2\Phi_{B_s}$, experiments at the Tevatron, we reconsider the possibility of a 4th generation. As recent direct search bounds have become considerably heavier than 300 GeV, we take the$t'$mass to be near the unitarity bound of 500 GeV. Combining the measured values of$\Delta m_{B_s}$with${\cal B}(B \to X_s\ell^+\ell^-)$, together with typical$f_{B_s}$values, we find a sizable$\sin2\Phi^{\rm SM4}_{B_s} \sim -0.33$. Using$m_{b'} = 480$GeV, we extract the range$0.06 < |V_{t'b}| < 0.13$from the constraints of$\Gamma(Z\to b\bar b)$,$\Delta m_{D}$and${\cal B}(K^+\to\pi^+\nu\bar\nu)$. A future measurement of${\cal B}(K_L\to\pi^0\nu\bar\nu)$will determine$V_{t'd}$.Comment: 8 pages, 11 figure

TensorFlow Doing HPC

TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are outside the ML domain and can possibly include HPC applications. However, very few experiments have been conducted to evaluate TensorFlow performance when running HPC workloads on supercomputers. This work addresses this lack by designing four traditional HPC benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG) solver and Fast Fourier Transform (FFT). We analyze their performance on two supercomputers with accelerators and evaluate the potential of TensorFlow for developing HPC applications. Our tests show that TensorFlow can fully take advantage of high performance networks and accelerators on supercomputers. Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical communication bandwidth on our testing platform. We find an approximately 2x, 1.7x and 1.8x performance improvement when increasing the number of GPUs from two to four in the matrix-matrix multiply, CG and FFT applications respectively. All our performance results demonstrate that TensorFlow has high potential of emerging also as HPC programming framework for heterogeneous supercomputers.Comment: Accepted for publication at The Ninth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES'19

Passenger transmission and productiveness of transit lines with high loads

Deterministic transit capacity analysis applies to planning, design and operational management of urban transit systems. The Transit Capacity and Quality of Service Manual (1) and Vuchic (2, 3) enable transit performance to be quantified and assessed using transit capacity and productive capacity. This paper further defines important productive performance measures of an individual transit service and transit line. Transit work (p-km) captures the transit task performed over distance. Passenger transmission (p-km/h) captures the passenger task delivered by service at speed. Transit productiveness (p-km/h) captures transit work performed over time. These measures are useful to operators in understanding their services’ or systems’ capabilities and passenger quality of service. This paper accounts for variability in utilized demand by passengers along a line and high passenger load conditions where passenger pass-up delay occurs. A hypothetical case study of an individual bus service’s operation demonstrates the usefulness of passenger transmission in comparing existing and growth scenarios. A hypothetical case study of a bus line’s operation during a peak hour window demonstrates the theory’s usefulness in examining the contribution of individual services to line productive performance. Scenarios may be assessed using this theory to benchmark or compare lines and segments, conditions, or consider improvements

Thermal reaction norms and the scale of temperature variation: latitudinal vulnerability of intertidal Nacellid limpets to climate change

The thermal reaction norms of 4 closely related intertidal Nacellid limpets, Antarctic (Nacella concinna), New Zealand (Cellana ornata), Australia (C. tramoserica) and Singapore (C. radiata), were compared across environments with different temperature magnitude, variability and predictability, to test their relative vulnerability to different scales of climate warming. Lethal limits were measured alongside a newly developed metric of “duration tenacity”, which was tested at different temperatures to calculate the thermal reaction norm of limpet adductor muscle fatigue. Except in C. tramoserica which had a wide optimum range with two break points, duration tenacity did not follow a typical aerobic capacity curve but was best described by a single break point at an optimum temperature. Thermal reaction norms were shifted to warmer temperatures in warmer environments; the optimum temperature for tenacity (Topt) increased from 1.0°C (N. concinna) to 14.3°C (C. ornata) to 18.0°C (an average for the optimum range of C. tramoserica) to 27.6°C (C. radiata). The temperature limits for duration tenacity of the 4 species were most consistently correlated with both maximum sea surface temperature and summer maximum in situ habitat logger temperature. Tropical C. radiata, which lives in the least variable and most predictable environment, generally had the lowest warming tolerance and thermal safety margin (WT and TSM; respectively the thermal buffer of CTmax and Topt over habitat temperature). However, the two temperate species, C. ornata and C. tramoserica, which live in a variable and seasonally unpredictable microhabitat, had the lowest TSM relative to in situ logger temperature. N. concinna which lives in the most variable, but seasonally predictable microhabitat, generally had the highest TSMs. Intertidal animals live at the highly variable interface between terrestrial and marine biomes and even small changes in the magnitude and predictability of their environment could markedly influence their future distributions