Experimental evaluation of Cubic-TCP

In this paper we present an initial experimental evaluation of the recently proposed Cubic-TCP algorithm. Results are presented using a suite of benchmark tests that have been recently proposed in the literature [12], and a number of issues are of practical concern highlighted

Experimental evaluation of Cubic-TCP

In this paper we present an initial experimental evaluation of the recently proposed Cubic-TCP algorithm. Results are presented using a suite of benchmark tests that have been recently proposed in the literature [12], and a number of issues are of practical concern highlighted

The Effect of Corner Modes in the Initial Conditions of Cosmological Simulations

In view of future high-precision large-scale structure surveys, it is important to quantify the percent and subpercent level effects in cosmological N-body simulations from which theoretical predictions are drawn. One such effect involves deciding whether to zero all modes above the one-dimensional Nyquist frequency, the so-called “corner” modes, in the initial conditions. We investigate this effect by comparing power spectra, density distribution functions, halo mass functions, and halo profiles in simulations with and without these modes. For a simulation with a mass resolution of mp ~ 1011 -h M 1 , we find that at z > 6, the difference in the matter power spectrum is large at wavenumbers above ∼80% of kNy, reducing to below 2% at all scales by z ~ 3. Including corner modes results in a better match between low- and high-resolution simulations at wavenumbers around the Nyquist frequency of the low-resolution simulation, but the effect of the corner modes is smaller than the effect of particle discreteness. The differences in mass functions are 3% for the smallest halos at z = 6 for the mp ~ 1011 -h M 1 simulation, but we find no significant difference in the stacked profiles of well-resolved halos at z 6. Thus removing power at ∣k∣ > kNy in the initial conditions of cosmological simulations has a small effect on small scales and high redshifts, typically below a few percent

A review of the accuracy and utility of motion sensors to measure physical activity of frail older hospitalised patients.

The purpose of this review was to examine the utility and accuracy of commercially available motion sensors to measure step-count and time spent upright in frail older hospitalized patients. A database search (CINAHL and PubMed, 2004–2014) and a further hand search of papers’ references yielded 24 validation studies meeting the inclusion criteria. Fifteen motion sensors (eight pedometers, six accelerometers, and one sensor systems) have been tested in older adults. Only three have been tested in hospital patients, two of which detected postures and postural changes accurately, but none estimated step-count accurately. Only one motion sensor remained accurate at speeds typical of frail older hospitalized patients, but it has yet to be tested in this cohort. Time spent upright can be accurately measured in the hospital, but further validation studies are required to determine which, if any, motion sensor can accurately measure step-count

Step-count accuracy of three motion sensors for older and frail medical inpatients

Objectives: To measure the step-count accuracy of an ankle-worn accelerometer, a thigh-worn accelerometer and one pedometer in older and frail inpatients. Design: Cross-sectional design study. Setting: Research room within a hospital. Participants: Convenience sample of inpatients aged ≥65 years, able to walk 20 metres unassisted, with or without a walking-aid. Intervention: Patients completed a 40-minute programme of predetermined tasks while wearing the three motion sensors simultaneously. Video-recording of the procedure provided the criterion measurement of step-count. Main Outcome Measures: Mean percentage (%) errors were calculated for all tasks, slow versus fast walkers, independent versus walking-aid-users, and over shorter versus longer distances. The Intra-class Correlation was calculated and accuracy was visually displayed by Bland-Altman plots. Results: Thirty-two patients (78.1 ±7.8 years) completed the study. Fifteen were female and 17 used walking-aids. Their median speed was 0.46 m/sec (interquartile range, IQR 0.36-0.66). The ankle-worn accelerometer overestimated steps (median 1% error, IQR -3 to 13). The other motion sensors underestimated steps (40% error (IQR -51 to -35) and 38% (IQR -93 to -27), respectively). The ankle-worn accelerometer proved more accurate over longer distances (3% error, IQR 0 to 9), than shorter distances (10%, IQR -23 to 9). Conclusions: The ankle-worn accelerometer gave the most accurate step-count measurement and was most accurate over longer distances. Neither of the other motion sensors had acceptable margins of error

Delay-based AIMD congestion control

Our interest in the paper is investigating whether it is feasible to make modifications to the TCP congestion control algorithm to achieve greater decoupling between the performance of TCP and the level of buffer provisioning in the network. In this paper we propose a new family of delay-based congestion control algorithms that we refer to as delay-based AIMD

Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model

This study presents a comparative analysis of the management of innovation among family and non-family companies of the Graphic Communication Industry in Colombia. For which a questionnaire was applied in order to know the divergences in the innovation process carried out by these two types of organizations. From this, the methodology of Generalized Linear Models (MLG) was used and the Bayesian inference was used on the parameters of the model, analyzing the effect of the family business, the products that commercialize on the management of innovation in goods observed as a product tangible Obtaining in this way, the identification of some characteristics of innovation management and divergences with non-family companies, among them: a tendency towards the type of preferred innovation, the different sources and objectives to innovate, and the factors that hinder its process of innovation

Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models

Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS).Comment: 37 pages, 2 figures, 5 tables; notation revised and clarified, some sections (particularly 4.3 and 5) made more rigorous, some derivations moved into the appendix, typos fixed, some wording change

CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall