The molecular gas content of the Pipe Nebula I. Direct evidence of outflow-generated turbulence in B59?

The Pipe Nebula is a molecular cloud hosting the B59 region as its only active star-forming clump. While the particular importance of outflows in active star forming regions is subject of debate, the quiet nature of the gas in B59 makes it a good site to directly see the impact of protostellar feedback on the quiescent dense gas. Using HARP at the JCMT, we mapped the B59 region with the J=3-2 transition of 12CO to study the kinematics and energetics of the outflows, and 13CO and C18O to study the overall dynamics of the ambient cloud, the physical properties of the gas, and the hierarchical structure of the region. The B59 region has a total of 30Msun of cold and quiescent material, mostly gravitationally bound, with narrow line widths throughout. Such low levels of turbulence in non-star-forming sites of B59 are indicative of the intrinsic initial conditions of the cloud. On the other hand, close to the forming protostars the impact of the outflows is observed as a localised increase of both line widths from 0.3 to 1 km/s, and 13CO excitation temperatures by 2-3K. The impact of the outflows is also evident in the low column density material which shows signs of being pushed, shaped and carved by the outflow bow shocks as they pierce their way out of the cloud. Much of this structure is readily apparent in a dendrogram analysis of the cloud. The low mass of B59 together with its intrinsically quiescent gas and small number of protostars, allows the identification of specific regions where the outflows from the embedded sources interact the dense gas. Our study suggests that outflows are an important mechanism for injecting and sustaining supersonic turbulence at sub-parsec size scales. We find that less than half of the outflow energy is deposited as turbulent energy of the gas, however this turbulent energy is sufficient to slow down the collapse of the region.Comment: Accepted for publication in A&

Recommended Common Data Elements for International Research in Long-Term Care Homes: Exploring the Workforce and Staffing Concepts of Staff Retention and Turnover

The aim of this review is to develop a common data element for the concept of staff retention and turnover within the domain of workforce and staffing. This domain is one of four core domains identified by the WE-THRIVE (Worldwide Elements to Harmonize Research in Long-Term Care Living Environments) group in an effort to establish an international, person-centered long-term care research infrastructure. A rapid review identified different measurement methods to assess either turnover or retention at facility level or intention to leave or stay at the individual staff level. The selection of a recommended measurement was guided by the WE-THRIVE group’s focus on capacity rather than deficits, the expected availability of internationally comparable data, and the goal to provide a short, ecologically viable measurement. We therefore recommend to measure staff’s intention to stay with a single item, at the individual staff level. This element, we argue, is an indicator of staff stability, which is important for reduced organizational cost and improved productivity, positive work environment, and better resident–staff relationships and quality of care

The central engine of GRB 130831A and the energy breakdown of a relativistic explosion

Gamma-ray bursts (GRBs) are the most luminous explosions in the universe, yet the nature and physical properties of their energy sources are far from understood. Very important clues, however, can be inferred by studying the afterglows of these events. We present optical and X-ray observations of GRB 130831A obtained by Swift, Chandra, Skynet, RATIR, Maidanak, ISON, NOT, LT and GTC. This burst shows a steep drop in the X-ray light-curve at $\simeq 10^5$ s after the trigger, with a power-law decay index of $\alpha \sim 6$. Such a rare behaviour cannot be explained by the standard forward shock (FS) model and indicates that the emission, up to the fast decay at $10^5$ s, must be of "internal origin", produced by a dissipation process within an ultrarelativistic outflow. We propose that the source of such an outflow, which must produce the X-ray flux for $\simeq 1$ day in the cosmological rest frame, is a newly born magnetar or black hole. After the drop, the faint X-ray afterglow continues with a much shallower decay. The optical emission, on the other hand, shows no break across the X-ray steep decrease, and the late-time decays of both the X-ray and optical are consistent. Using both the X-ray and optical data, we show that the emission after $\simeq 10^5$ s can be explained well by the FS model. We model our data to derive the kinetic energy of the ejecta and thus measure the efficiency of the central engine of a GRB with emission of internal origin visible for a long time. Furthermore, we break down the energy budget of this GRB into the prompt emission, the late internal dissipation, the kinetic energy of the relativistic ejecta, and compare it with the energy of the associated supernova, SN 2013fu.Comment: Accepted for publication by MNRAS. 21 pages, 3 figures, 8 tables. Extra table with magnitudes in the sourc

Avoiding overfitting of multilayer perceptrons by training derivatives

Resistance to overfitting is observed for neural networks trained with extended backpropagation algorithm. In addition to target values, its cost function uses derivatives of those up to the $4^{\mathrm{th}}$ order. For common applications of neural networks, high order derivatives are not readily available, so simpler cases are considered: training network to approximate analytical function inside 2D and 5D domains and solving Poisson equation inside a 2D circle. For function approximation, the cost is a sum of squared differences between output and target as well as their derivatives with respect to the input. Differential equations are usually solved by putting a multilayer perceptron in place of unknown function and training its weights, so that equation holds within some margin of error. Commonly used cost is the equation's residual squared. Added terms are squared derivatives of said residual with respect to the independent variables. To investigate overfitting, the cost is minimized for points of regular grids with various spacing, and its root mean is compared with its value on much denser test set. Fully connected perceptrons with six hidden layers and $2\cdot10^{4}$, $1\cdot10^{6}$ and $5\cdot10^{6}$ weights in total are trained with Rprop until cost changes by less than 10% for last 1000 epochs, or when the $10000^{\mathrm{th}}$ epoch is reached. Training the network with $5\cdot10^{6}$ weights to represent simple 2D function using 10 points with 8 extra derivatives in each produces cost test to train ratio of $1.5$, whereas for classical backpropagation in comparable conditions this ratio is $2\cdot10^{4}$