The First Empirical Mass Loss Law for Population II Giants

Using the Spitzer IRAC camera we have obtained mid-IR photometry of the red giant branch stars in the Galactic globular cluster 47 Tuc. About 100 stars show an excess of mid-infrared light above that expected from their photospheric emission. This is plausibly due to dust formation in mass flowing from these stars. This mass loss extends down to the level of the horizontal branch and increases with luminosity. The mass loss is episodic, occurring in only a fraction of stars at a given luminosity. Using a simple model and our observations we derive mass loss rates for these stars. Finally, we obtain the first empirical mass loss formula calibrated with observations of Population II stars. The dependence on luminosity of our mass loss rate is considerably shallower than the widely used Reimers Law. The results presented here are the first from our Spitzer survey of a carefully chosen sample of 17 Galactic Globular Clusters, spanning the entire metallicity range from about one hundredth up to almost solar

HST - WFPC2 photometry of the globular cluster ngc 288: binary systems, blue stragglers and very blue stars

We report on new WFPC2 observations of the globular cluster NGC 288, focusing our attention on peculiar stars. A very pronounced binary sequence, paralleling the ordinary Main Sequence (MS) is clearly observed in the Color Magnitude Diagram (CMD) and a huge relative fraction of Blue Straggler Stars is measured. The dataset offers the opportunity of studying the evolution of a large population of binaries (and binary evolution by-products) in a low density environment, where the evolution of such systems is not dominated by collisions and encounters. Three (very) Extreme Horizontal Branch Stars have been found, all lying outside of the cluster core.Comment: 6 pages, 3 figures, in press in the chemical evolution of the Milky Way: stars versus clusters, F. Matteucci and F. Giovannelli eds, Kluwe

BVI Photometry and the Luminosity Function of the Globular Cluster M92

We present new BVI ground-based photometry and VI space-based photometry for the globular cluster M92 (NGC 6341) and examine luminosity functions in B, V, and I containing over 50,000 stars ranging from the tip of the red giant branch to several magnitudes below the main sequence turn off. Once corrected for completeness, the observed luminosity functions agree very well with theoretical models and do not show stellar excesses in any region of the luminosity function. Using reduced chi squared fitting, the new M92 luminosity function is shown to be an excellent match to the previously published luminosity function for M30. These points combine to establish that the "subgiant excess" found in previously published luminosity functions of Galactic globular clusters are due to deficiencies in the stellar models used at that time. Using up to date stellar models results in good agreement between observations and theory. Several statistical methods are presented to best determine the age of M92. These methods prove to be insensitive to the exact choice of metallicity within the published range. Using [Fe/H]=-2.17 to match recent studies we find an age of 14.2 plus or minus 1.2 Gyr for the cluster.Comment: 22 pages, 13 figures, 3 tables, accepted for publication in A

Learning in neural networks with material synapses

We discuss the long term maintenance of acquired memory in synaptic connections of a perpetually learning electronic device. This is affected by ascribing each synapse a finite number of stable states in which it can maintain for indefinitely long periods. Learning uncorrelated stimuli is expressed as a stochastic process produced by the neural activities on the synapses. In several interesting cases the stochastic process can be analyzed in detail, leading to a clarification of the performance of the network, as an associative memory, during the process of uninterrupted learning. The stochastic nature of the process and the existence of an asymptotic distribution for the synaptic values in the network imply generically that the memory is a palimpsest but capacity is as low as log N for a network of N neurons. The only way we find for avoiding this tight constraint is to allow the parameters governing the learning process (the coding level of the stimuli; the transition probabilities for potentiation and depression and the number of stable synaptic levels) to depend on the number of neurons. It is shown that a network with synapses that have two stable states can dynamically learn with optimal storage efficiency, be a palimpsest, and maintain its (associative) memory for an indefinitely long time provided the coding level is low and depression is equilibrated against potentiation. We suggest that an option so easily implementable in material devices would not have been overlooked by biology. Finally we discuss the stochastic learning on synapses with variable number of stable synaptic states

Event-driven simulation of spiking neurons with stochastic dynamics

We present a new technique, based on a proposed event-based strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions among neurons occur by means of events that are well localized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here, we extend the event-driven simulation strategy to the case in which the dynamics of the state variables in the inter-event intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model neurons under random current injection. The new simulation strategy allows the study of networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics

Apgar score or birthweight in Chihuahua dogs born by elective Caesarean section : which is the best predictor of the survival at 24 h after birth?

In the dog, the correct management of parturition and the prompt neonatal evaluation and assistance can reduce the perinatal mortality rates that are particularly high in toy breeds. Newborn evaluation and factors addressing prognosis are pivotal to guarantee the correct neonatal assistance. Assessment of the Apgar score with viability classification and birthweight are recognized as predictors for neonatal survival in dogs, but breed-specific data are needed for a more feasible application in the dog species, in which wide differences among breeds are known. The present study aimed therefore to: (a) assess the role of Apgar score and birthweight as predictors for the survival of Chihuahua newborn puppies in the first 24 h of life; (b) to assess a cut-off of the Apgar score and birthweight values that can predict the survival of Chihuahua newborn puppies in the first 24 h after birth; (c) to assess the possible effect played by maternal parity, newborn gender and litter-size on Apgar score in Chihuahua newborn puppies, in order to provide breed-specific data for a better neonatal assistance..Data obtained from 176 normal developed Chihuahua puppies born by elective Caesarean section, showed that 62%, 28% and 10% of puppies were classified in the Apgar score classes 7\u201310, 4\u20136 and 0\u20133, respectively, with survival at 24 h after birth of 97%, 96%, 39%, in the three Apgar classes of viability, respectively. Apgar score was a better predictor for survival at 24 h after birth than birthweight (AUC 0.93, P < 0.0001; AUC 0.69, P < 0.01, respectively). Litter-size of 7 puppies/litter plays a negative effect on Apgar score. Apgar score is a better predictor of survival at 24 h than birthweight, and the best cut-off of Apgar score for survival at 24 h after birth is 4, with 96% sensitivity and 77% specificity. The different proportion of \u201cnormal viable\u201d and \u201cless viable\u201d neonates in comparison to other studies highlights that Chihuahua puppies born by elective Caesarean section should be carefully evaluated at birth to provide correct assistance

Different Characteristics of the Bright Branches of the Globular Clusters M3 and M13

We carried out wide-field BVI CCD photometric observations of the GCs M3 and M13 using the BOAO 1.8 m telescope equipped with a 2K CCD. We present CMDs of M3 and M13. We have found AGB bumps at V = 14.85 for M3 at V = 14.25 for M13. It is found that AGB stars in M3 are more concentrated near the bump, while those in M13 are scattered along the AGB sequence. We identified the RGB bump of M3 at V = 15.50 and that of M13 at V = 14.80. We have estimated the ratios R and R2 for M3 and M13 and found that of R for M3 is larger than that for M13 while R2's for M3 and M13 are similar when only normal HB stars are used in R and R2 for M13. However, we found that R's for M3 and M13 are similar while R2 for M3 is larger than that for M13 when all the HB stars are included in R and R2 for M13. We have compared the observed RGB LFs of M3 and M13 with the theoretical RGB LF of Bergbusch & VandenBerg at the same radial distances from the cluster centers as used in R and R2 for M3 and M13. We found "extra stars" belonging to M13 in the comparison of the observed RGB LF of M13 and the theoretical RGB LF of Bergbusch & VandenBerg. In the original definition of R of Buzzoni et al., N(HB) corresponds to the lifetime of HB stars in the RR Lyrae instability strip at log T_eff = 3.85. So, the smaller R value resulting for M13 compared with that for M3 in the case where only normal HB stars are included in R and R2 for M13 may be partially caused by "extra stars", and the similar R's for M3 and M13 in the case where the all HB stars are included in R and R2 for M13 may be caused by "extra stars" in the upper RGB of M13. If "extra stars" in the upper RGB of M13 are caused by an effective "deep mixing" these facts support the contention that an effective "deep mixing" could lead to different HB morphologies between M3 and M13 and subsequent sequences.Comment: 24 pages, 7 figures, to be published in the A

The Star Formation History of the Carina Dwarf Galaxy

We have analyzed deep B and V photometry of the Carina dwarf spheroidal reaching below the old main-sequence turnoff to about V = 25. Using simulated color-magnitude diagrams to model a range of star formation scenarios, we have extracted a detailed, global star formation history. Carina experienced three significant episodes of star formation at about 15 Gyr, 7 Gyr, and 3 Gyr. Contrary to the generic picture of galaxy evolution, however, the bulk of star formation, at least 50%, occured during the episode 7 Gyr ago, which may have lasted as long as 2 Gyr. For unknown reasons, Carina formed only 10-20% of its stars at an ancient epoch and then remained quiescent for more than 4 Gyr. The remainder (~30%) formed relatively recently, only 3 Gyr ago. Interest in the local population of dwarf galaxies has increased lately due to their potential importance in the understanding of faint galaxy counts. We surmise that objects like Carina, which exhibits the most extreme episodic behavior of any of the dwarf spheroidal companions to the Galaxy, are capable of contributing to the observed excess of blue galaxies at B = 24 only if the star formation occurred instantaneously.Comment: 23 pages of text, 20 figures, 8 tables. AJ, in pres

Influence of synaptic depression on memory storage capacity

Synaptic efficacy between neurons is known to change within a short time scale dynamically. Neurophysiological experiments show that high-frequency presynaptic inputs decrease synaptic efficacy between neurons. This phenomenon is called synaptic depression, a short term synaptic plasticity. Many researchers have investigated how the synaptic depression affects the memory storage capacity. However, the noise has not been taken into consideration in their analysis. By introducing "temperature", which controls the level of the noise, into an update rule of neurons, we investigate the effects of synaptic depression on the memory storage capacity in the presence of the noise. We analytically compute the storage capacity by using a statistical mechanics technique called Self Consistent Signal to Noise Analysis (SCSNA). We find that the synaptic depression decreases the storage capacity in the case of finite temperature in contrast to the case of the low temperature limit, where the storage capacity does not change