Small Subset Of Users Research Articles

Low-Overhead Wireless Uplink Scheduling for Large-Scale Internet-of-Things

With the rapid growth of Internet-of-Things (IoT) applications in recent years, there is a strong need for wireless uplink scheduling algorithms that determine when and which subset of a large number of users should transmit to the central controller. Different from the downlink case, the central controller in the uplink scenario typically has very limited information about the users. On the other hand, periodically collecting all such information from a large number of users typically incurs a prohibitively high communication overhead. This motivates us to investigate the development of an efficient and low-overhead uplink scheduling algorithm that is suitable for large-scale IoT applications. Specifically, we first characterize a capacity outer bound subject to the sampling constraint where only a small subset of users are allowed to use control channels for system state reporting at each time. Next, we relax the sampling constraint and propose a joint sampling and transmission algorithm, which utilizes full knowledge of channel state distributions and instantaneous queue lengths to achieve the capacity outer bound. The insights obtained from this capacity-achieving algorithm allow us to develop a low-overhead scheduling algorithm that can strictly satisfy the sampling constraint with asymptotically diminishing throughput loss.

The DIAMOND portal: An innovative platform for sharing translational research workforce training and assessment resources.

Although several initiatives have produced core competency domains for training the translational science workforce, training resources to help clinical research professionals advance these skills reside primarily within local departments or institutions. The Development, Implementation, and AssessMent of Novel Training in Domain (DIAMOND) project was designed to make this training more readily and publicly available. DIAMOND includes a digital portal to catalog publicly available educational resources and an ePortfolio to document professional development. DIAMOND is a nationally crowdsourced, federated, online catalog providing a platform for practitioners to find and share training and assessment materials. Contributors can share their own educational materials using a simple intake form that creates an electronic record; the portal enables users to browse or search this catalog of digital records and access the resources. Since September 2018, the portal has been visited more than 5,700 times and received over 280 contributions from professionals. The portal facilitates opportunities to connect and collaborate regarding future applications of these resources. Consequently, growing the collection and increasing numbers of both contributors and users remains a priority. Results from a small subset of users indicated over half accomplished their purpose for visiting the site, while qualitative results showed that users identified several benefits and helpful features of the ePortfolio.

Neighborhood Matters: Influence Maximization in Social Networks with Limited Access

Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user's privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influence spread, since IM often relies on seeding high degree users, which are particularly rare in such a small subset due to the power-law structure of social networks. In this paper, we attempt to solve the limited IM in real-world scenarios by the adaptive approach with seeding and diffusion uncertainty considered. Specifically, we consider fine-grained discounts and assume users accept the discount probabilistically. The diffusion process is depicted by the independent cascade model. To overcome the access limitation, we prove the set-wise friendship paradox (FP) phenomenon that neighbors have higher degree in expectation, and propose a two-stage seeding model with the FP embedded, where neighbors are seeded. On this basis, for comparison we formulate the non-adaptive case and adaptive case, both proven to be NP-hard. In the non-adaptive case, discounts are allocated to users all at once. We show the monotonicity of influence spread w.r.t. discount allocation and design a two-stage coordinate descent framework to decide the discount allocation. In the adaptive case, users are sequentially seeded based on observations of existing seeding and diffusion results. We prove the adaptive submodularity and submodularity of the influence spread function in two stages. Then, a series of adaptive greedy algorithms are proposed with constant approximation ratio.

Who consumes most of the cannabis in Canada? Profiles of cannabis consumption by quantity

AimTo establish whether the population-level pattern of cannabis use by quantity is similar to the distributions previously reported for alcohol, in which a small subset of drinkers accounts for a majority of total population alcohol consumption. MethodThe current study pooled Waves 1–3 of the 2018 National Cannabis Survey (n = 18,900; 2584 past-three-month cannabis users), a set of stratified, population-based surveys designed to assess cannabis consumption and related behaviors in Canada. Each survey systematically measured self-reported cannabis consumption by quantity across seven of the major cannabis-product types. In order to enable the conversion of self-reported consumption of non-flower cannabis products into a standard joint equivalent (SJE: equal to 0.5 g of dried cannabis), we created conversion metrics for physical production equivalencies across cannabis products. ResultsSimilar to the findings in the alcohol literature, study results show that cannabis consumption is highly concentrated in a small subset of users: the upper 10% of cannabis users accounted for approximately two-thirds of all cannabis consumed in the country. Males reported consuming more cannabis by volume than females (approximately 60% versus 40%), with young males (15–34 years old) being disproportionately represented in the heaviest-using subgroups. ConclusionsMost of the cannabis used in Canada is consumed by a relatively small population of very heavy cannabis users. Future research should attempt to identify the characteristics of the heaviest-using groups, as well as how population-level cannabis consumption patterns relate to the calculus of cannabis-related harms in society.

Change of Multivariate Mutual Information: From Local to Global

We study the change of multivariate mutual information among a set of random variables when some common randomness is added to or removed from a subset of the random variables. This is formulated more precisely as two new multiterminal secret key agreement problems that, respectively, ask how one can increase the secrecy capacity efficiently by adding common randomness to a small subset of users, and how one can simplify the source model by removing redundant common randomness that does not contribute to the secrecy capacity. Characterizations and strongly polynomial-time computations are derived for the rates of change, maximum usable increment, and redundancy. These results can be applied to study the communication complexity for secret key agreement.

Who Uses Direct-to-Consumer Brain Stimulation Products, and Why? A Study of Home Users of tDCS Devices

Despite the attention garnered by the home (or “do-it-yourself”) use of tDCS (transcranial direct current stimulation), little is known about the population of actual users. The present study aimed to provide a comprehensive examination of those who purchase tDCS devices: who they are, how they learn about tDCS, and why and how they stimulate. A link to an online survey was sent to those who had purchased a tDCS device from seven different companies selling tDCS devices to the public as of June 2016; data was analyzed from 339 participants, the majority of whom reported residing in North America. The typical respondent was a wealthy, highly educated, liberal, fortysomething male living in the USA who reported being an early adopter of technology. Nearly three quarters of respondents reported using tDCS for cognitive enhancement, one-quarter for restoration, and approximately 40% for treatment; many participants selected a combination of usage indications. Notably, approximately one third of participants utilize tDCS to self-treat depression. Most who use tDCS for treatment find the technology to be effective, whereas most who use it for non-treatment purposes (i.e., only enhancement and/or restoration) find it to be ineffective. Approximately 40% of those who purchase tDCS devices either quit using the device (mostly due to lack of efficacy) or have never used the device (mostly due to lack of guidance). Participants depart from established scientific protocol particularly with regard to frequency of stimulation, with 8.4% reporting self-administering 100+ sessions of tDCS. With regard to side effects, a small subset of users (n = 10) reported serious skin burns. This study provides an empirical foundation on which to base policy recommendations and offers a fact-based perspective on a bioethical debate that has too-often been one step removed from reality.

A diffusion model for churn prediction based on sociometric theory

Churn prediction has received much attention in the last decade. With the evolution of social networks and social network analysis tools in recent years, the consideration of social ties in churn prediction has proven promising. One possibility is to use energy diffusion models to model the spread of influence through a social network. This paper proposes a novel churn prediction diffusion model based on sociometric clique and social status theory. It describes the concept of energy in the diffusion model as an opinion of users, which is transformed to user influence using the derived social status function. Furthermore, a novel diffusion model prediction scheme applicable to a single user or a small subset of users is described: the Targeted User Subset Churn Prediction Scheme. The scheme allows fast churn prediction using limited computing resources. The diffusion model is evaluated on a real dataset of users obtained from the largest Slovenian mobile service provider, using the F-measure and lift curve. The empirical results show a significant improvement in prediction accuracy of the proposed method compared with the basic spreading activation technique (SPA) diffusion model. More specifically, our approach outperforms a basic SPA diffusion model by 116 % in terms of lift in the fifth percentile.

Racial medicine: here to stay?

Adverse drug reactions are an underestimated but serious problem in medicine, and are the fourth leading cause of death in the USA after cancer, coronary heart disease and stroke (Lazarou et al , 1998). Although any given drug, if prescribed correctly, is beneficial for most patients, it may have unexpected results in a small subset of users. This can range from simply having no effect at all to causing serious, sometimes life‐threatening, side effects. This variation stems—at least in part—from metabolic and genetic differences between patients. It would be a big leap for medicine if physicians could identify these differences and treat patients based on their individual genetic makeup. Now that the human genome sequence is complete, several follow‐up genomic projects, most notably the HapMap initiative to identify and catalogue haplotypes, are focusing on identifying such genetic variants. But it is not yet clear whether this knowledge will lead to personalized medicine. One hindrance is the fact that, for the time being, physicians and scientists have found a comfortable interim solution: racial profiling. Although, at first, this approach could help to use drugs more efficiently, in the long term, it could hinder the exploitation of genetic information in medicine. In fact, physicians increasingly use their patients' skin colour or other physiological features as a first step towards ‘individual’ treatment, under the assumption that specific traits cluster by race. “When I prescribe Prozac to a patient who is African‐American, I start at a lower dose, […] in part because clinical experience and pharmacological research show that blacks metabolize antidepressants more slowly than Caucasians and Asians,” wrote Sally Satel, a psychiatrist and resident scholar at the American Enterprise Institute for Public Policy Research (Washington, DC, USA; Satel, 2002). She is certainly not alone: from 1995 to 1998, the prescription information for 15 new …

Resource absorption in a health service system.

This paper considers the use of treatment resources in a health service system. The particular aspect of system use which is identified is resource absorption - excessive use by a small subset of users and unmet need by others. A dynamic stochastic model for health status with treatment interventions is formulated and the distribution of accumulated treatment time (service) is developed. The characterization of heavy tails in the distribution, the essential feature of resource absorption, is based on the Lorenz curve and an Index of Resource Absorption is defined for any feasible Lorenz curve. The index is applied to the use of psychiatric hospital resources in a Province of Canada.

Analysis of a dynamic priority queue

This paper provides an algorithmic analysis for a dynamic, non-preemptive priority queueing system with a single server and two independent Poisson streams of customers with general service time distributions. Type 2 customers have a finite waiting room of size K1, and type 1 customers have infinite waiting space. The server affords higher priority to type 2 customers when they are at least N in number, and to type 1 customers otherwise. The methodology used is the one developed by M. F. Neuts for models “of M/G/1 type” which, for the model at hand, becomes simpler because of certain structural properties of the model. The model is motivated by practical computer applications where a small subset of users (batch jobs, for example) could clog the system if left uncontrolled. The system permits the control of such customers by appropriately choosing the waiting room size afforded to them and also by fine tuning the threshold parameter N governing the switching rules.