Optimisation of stochastic networks with blocking: a functional-form approach

This paper introduces a class of stochastic networks with blocking, motivated by applications arising in cellular network planning, mobile cloud computing, and spare parts supply chains. Blocking results in lost revenue due to customers or jobs being permanently removed from the system. We are interested in striking a balance between mitigating blocking by increasing service capacity, and maintaining low costs for service capacity. This problem is further complicated by the stochastic nature of the system. Owing to the complexity of the system there are no analytical results available that formulate and solve the relevant optimization problem in closed form. Traditional simulation-based methods may work well for small instances, but the associated computational costs are prohibitive for networks of realistic size. We propose a hybrid functional-form based approach for finding the optimal resource allocation, combining the speed of an analytical approach with the accuracy of simulation-based optimisation. The key insight is to replace the computationally expensive gradient estimation in simulation optimisation with a closed-form analytical approximation that is calibrated using a single simulation run. We develop two implementations of this approach and conduct extensive computational experiments on complex examples to show that it is capable of substantially improving system performance. We also provide evidence that our approach has substantially lower computational costs compared to stochastic approximation

Understanding the central kinematics of globular clusters with simulated integrated-light IFU observations

The detection of intermediate mass black holes in the centres of globular clusters is highly controversial, as complementary observational methods often deliver significantly different results. In order to understand these discrepancies, we develop a procedure to simulate integral field unit (IFU) observations of globular clusters: Simulating IFU Star Cluster Observations (SISCO). The input of our software are realistic dynamical models of globular clusters that are then converted in a spectral data cube. We apply SISCO to Monte Carlo cluster simulations from Downing et al. (2010), with a realistic number of stars and concentrations. Using independent realisations of a given simulation we are able to quantify the stochasticity intrinsic to the problem of observing a partially resolved stellar population with integrated-light spectroscopy. We show that the luminosity-weighted IFU observations can be strongly biased by the presence of a few bright stars that introduce a scatter in the velocity dispersion measurements up to $\simeq$40% around the expected value, preventing any sound assessment of the central kinematic and a sensible interpretation of the presence/absence of an intermediate mass black hole. Moreover, we illustrate that, in our mock IFU observations, the average kinematic tracer has a mass of $\simeq$0.75 solar masses, only slightly lower than the mass of the typical stars examined in studies of resolved line-of-sight velocities of giant stars. Finally, in order to recover unbiased kinematic measurements we test different masking techniques that allow us to remove the spaxels dominated by bright stars, bringing the scatter down to a level of only a few percent. The application of SISCO will allow to investigate state-of-the-art simulations as realistic observations.Comment: 13 pages, 9 figures, 1 table. Accepted for publication in MNRA

Max-weight scheduling across multiple timescales

Many systems consist of a mixture of various resource types that together support better performance relative to those with a single resource type. One important characteristic of these systems is the fact that the various comprising resource types can operate on different timescales, implying that the corresponding control decisions are not made simultaneously. To address the resulting scheduling problem, we present and analyze two variants of max-weight scheduling that are designed to deal with the different timescales of such systems

Being WISE I: Validating Stellar Population Models and M/L ratios at 3.4 and 4.6 microns

Using data from the WISE mission, we have measured near infra-red (NIR) photometry of a diverse sample of dust-free stellar systems (globular clusters, dwarf and giant early-type galaxies) which have metallicities that span the range -2.2 < [Fe/H] (dex) < 0.3. This dramatically increases the sample size and broadens the metallicity regime over which the 3.4 (W1) and 4.6 micron (W2) photometry of stellar populations have been examined. We find that the W1 - W2 colors of intermediate and old (> 2 Gyr) stellar populations are insensitive to the age of the stellar population, but that the W1 - W2 colors become bluer with increasing metallicity, a trend not well reproduced by most stellar population synthesis (SPS) models. In common with previous studies, we attribute this behavior to the increasing strength of the CO absorption feature located in the 4.6 micron bandpass with metallicity. Having used our sample to validate the efficacy of some of the SPS models, we use these models to derive stellar mass-to-light ratios in the W1 and W2 bands. Utilizing observational data from the SAURON and ATLAS3D surveys, we demonstrate that these bands provide extremely simple, yet robust stellar mass tracers for dust free older stellar populations that are freed from many of the uncertainties common among optical estimators.Comment: 11 pages, 6 figures, submitted to Ap

The central mass and mass-to-light profile of the Galactic globular cluster M15

We analyze line-of-sight velocity and proper motion data of stars in the Galactic globular cluster M15 using a new method to fit dynamical models to discrete kinematic data. Our fitting method maximizes the likelihood for individual stars and, as such, does not suffer the same loss of spatial and velocity information incurred when spatially binning data or measuring velocity moments. In this paper, we show that the radial variation in M15 of the mass-to-light ratio is consistent with previous estimates and theoretical predictions, which verifies our method. Our best-fitting axisymmetric Jeans models do include a central dark mass of $\sim2 \pm 1\cdot 10^3M_\odot$, which can be explained by a high concentration of stellar remnants at the cluster center. This paper shows that, from a technical point of view and with current computing power, spatial binning of data is no longer necessary. This not only leads to more accurate fits, but also avoids biased mass estimates due to the loss of resolution. Furthermore, we find that the mass concentration in M15 is significantly higher than previously measured, and is in close agreement with theoretical predictions for core-collapsed globular clusters without a central intermediate-mass black hole.Comment: Accepted by MNRAS; 8 pages, 7 figure

Encoding conditions shape temporal memory precision by modulating temporal uncertainty and temporal bias

Temporal memory about when in the past something happened is suggested to be reconstructed rather than recalled. Participants usually show a degree of mismatch between remembered and actual temporal position of an event. However, recent studies showed markedly different results, including both relatively low temporal precision, for visual objects presented earlier in a series, and relatively high temporal precision, for movie scenes shown at the start of a movie. One explanation could be the use of different stimulus materials, for which participants would employ different cognitive encoding or mnemonic strategies. However, a more parsimonious explanation would be that temporal judgments arise from a common mechanism, regardless of stimulus material or context. In the current manuscript, we reanalysed the results of two previously published experiments and investigated the effect of boundary segmentation and semantic relatedness during encoding in two new experiments. We found that participants showed more temporal uncertainty when the encoded visual objects and contexts were unrelated. Further, we found that increasing the semantic associations during encoding diminished temporal uncertainty but increased temporal underestimation bias, which we interpret as an indication of temporal compression. A simple computational model in which temporal judgment is based on a Gaussian process defined by temporal uncertainty (dispersion) and temporal bias (location) replicated the empirical data of all four experiments, suggesting that patterns of temporal errors observed in different experiments arise from a common mechanism. The model further underscores that semantic relatedness between items decreases temporal uncertainty but enhances temporal compression. These findings have important ramifications for how we memorize the temporal structure of events

Personality perception based on LinkedIn profiles

__Purpose:__ Job-related social networking websites (e.g. LinkedIn) are often used in the recruitment process because the profiles contain valuable information such as education level and work experience. The purpose of this paper is to investigate whether people can accurately infer a profile owner’s self-rated personality traits based on the profile on a job-related social networking site. __Design/methodology/approach:__ In two studies, raters inferred personality traits (the Big Five and self-presentation) from LinkedIn profiles (total n=275). The authors related those inferences to self-rated personality by the profile owner to test if the inferences were accurate. __Findings:__ Using information gained from a LinkedIn profile allowed for better inferences of extraversion and self-presentation of the profile owner (r’s of 0.24-0.29). __Practical implications:__ When using a LinkedIn profile to estimate trait extraversion or self-presentation, one becomes 1.5 times as likely to actually select the person with higher trait extraversion compared to the person with lower trait extraversion. __Originality/value:__ Although prior research tested whether profiles of social networking sites (such as Facebook) can be used to accurately infer self-rated personality, this was not yet tested for job-related social networking sites (such as LinkedIn). The results indicate that profiles at job-related social networks, in spite of containing only relatively standardized information, “leak” information about the owner’s personality