Caching with Unknown Popularity Profiles in Small Cell Networks

A heterogenous network is considered where the base stations (BSs), small base stations (SBSs) and users are distributed according to independent Poisson point processes (PPPs). We let the SBS nodes to posses high storage capacity and are assumed to form a distributed caching network. Popular data files are stored in the local cache of SBS, so that users can download the desired files from one of the SBS in the vicinity subject to availability. The offloading-loss is captured via a cost function that depends on a random caching strategy proposed in this paper. The cost function depends on the popularity profile, which is, in general, unknown. In this work, the popularity profile is estimated at the BS using the available instantaneous demands from the users in a time interval $[0,\tau]$. This is then used to find an estimate of the cost function from which the optimal random caching strategy is devised. The main results of this work are the following: First it is shown that the waiting time $\tau$ to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite, provided the user density is greater than a predefined threshold. In this case, $\tau$ is shown to scale as $N^2$, where $N$ is the support of the popularity profile. Secondly, a transfer learning-based approach is proposed to obtain an estimate of the popularity profile used to compute the empirical cost function. A condition is derived under which the proposed transfer learning-based approach performs better than the random caching strategy.Comment: 6 pages, Proceedings of IEEE Global Communications Conference, 201

A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

A heterogenous network with base stations (BSs), small base stations (SBSs) and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as $N^2$, where $N$ is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of $N$ and scales linearly with the dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363

Cross-Company Effects of Common Ownership: Dealings between Borrowers and Lenders with a Common Blockholder

This paper investigates investment strategies that exploit the low-beta anomaly. Although the notion of buying low-beta stocks and selling high-beta stocks is natural, a choice is necessary with respect to the relative weighting of high-beta stocks and low-beta stocks in the investment portfolio. Our empirical results for US large-cap stocks show that this choice is very important for the risk-return characteristics of the resulting portfolios and their sensitivities to common risk factors. We also show that investment strategies based on betas have a natural-hedge component and a market-timing component due to the stochastic variation of betas. We construct indicators to exploit the market-timing component and show that they have substantial predictive power for future market returns. Corresponding market-timing strategies deliver large positive excess returns and high Sharpe ratios

Prediction of Cardiac Disease Based on Patient’s Symptoms

The detection of comparative sign of likely side effects and symptoms which may additionally rapid identification of cardiovascular diseases utilizing data gathered from past patients just as input data taken from client at that specific time. Recent condition of healthcare system data scrutiny for observation are not, at this point fundamentally a period building arrangement of daily counts. All things considered, a profusion of proposed longitudinal just as temporal demographic, symptom data are accessible at  data introduced during the time of execution. Our proposed method contains all data  that is being exploited as classification approach that analyses recent health-care data against data from that specific base distributed and hence classifies subclasses the given data. Likewise, test data utilized is tried against  different kinds of classification , other propose test scores have been of prediction

Synthesis, Microstructural Characterization, Mechanical, Fractographic and Wear Behavior of Micro B4C Particles Reinforced Al2618 Alloy Aerospace Composites

In the current studies an investigations were made to know the effect of 63 micron sized B4C particles addition on the mechanical and wear behavior of aerospace alloy Al2618 metal composites. Al2618 alloy with different weight percentages (2, 4, 6 and 8 wt. %) of 63 micron sized B4C particles reinforced composites were produced by stir cast process. These synthesized composites were tested for various mechanical properties like hardness, compression strength and tensile behavior along with density measurements. Further, microstructural characterization was carried by SEM/EDS and XRD analysis to know the micron sized particles distribution and phases. Wear behavior of Al2618 alloy with 2 to 8 wt. % of B4C composites were studied as per ASTM G99 standards with varying loads and sliding speeds. By adding 63 micron sized B4C particles hardness, compression and tensile strength of Al2618 alloy was enriched with slight decrease in elongation. Further, wear resistance of Al2618 alloy was enriched with the accumulation of B4C particles. As load and speed on the specimen increased, there was increase in wear of Al2618 alloy and its composites. Various tensile fracture surface morphology and worn surface behavior was observed by SEM analysis