Showing papers by "Carleton University published in 2017"

Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

Abstract: Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters

A systematic review of personality trait change through intervention.

Abstract: The current meta-analysis investigated the extent to which personality traits changed as a result of intervention, with the primary focus on clinical interventions. We identified 207 studies that had tracked changes in measures of personality traits during interventions, including true experiments and prepost change designs. Interventions were associated with marked changes in personality trait measures over an average time of 24 weeks (e.g., d = .37). Additional analyses showed that the increases replicated across experimental and nonexperimental designs, for nonclinical interventions, and persisted in longitudinal follow-ups of samples beyond the course of intervention. Emotional stability was the primary trait domain showing changes as a result of therapy, followed by extraversion. The type of therapy employed was not strongly associated with the amount of change in personality traits. Patients presenting with anxiety disorders changed the most, and patients being treated for substance use changed the least. The relevance of the results for theory and social policy are discussed. (PsycINFO Database Record

Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group

Abstract: Electrical impedance tomography (EIT) has undergone 30 years of development. Functional chest examinations with this technology are considered clinically relevant, especially for monitoring regional lung ventilation in mechanically ventilated patients and for regional pulmonary function testing in patients with chronic lung diseases. As EIT becomes an established medical technology, it requires consensus examination, nomenclature, data analysis and interpretation schemes. Such consensus is needed to compare, understand and reproduce study findings from and among different research groups, to enable large clinical trials and, ultimately, routine clinical use. Recommendations of how EIT findings can be applied to generate diagnoses and impact clinical decision-making and therapy planning are required. This consensus paper was prepared by an international working group, collaborating on the clinical promotion of EIT called TRanslational EIT developmeNt stuDy group. It addresses the stated needs by providing (1) a new classification of core processes involved in chest EIT examinations and data analysis, (2) focus on clinical applications with structured reviews and outlooks (separately for adult and neonatal/paediatric patients), (3) a structured framework to categorise and understand the relationships among analysis approaches and their clinical roles, (4) consensus, unified terminology with clinical user-friendly definitions and explanations, (5) a review of all major work in thoracic EIT and (6) recommendations for future development (193 pages of online supplements systematically linked with the chief sections of the main document). We expect this information to be useful for clinicians and researchers working with EIT, as well as for industry producers of this technology.

Performance of the ATLAS trigger system in 2015

Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

Abstract: This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.

The biology of color

Abstract: Coloration mediates the relationship between an organism and its environment in important ways, including social signaling, antipredator defenses, parasitic exploitation, thermoregulation, and protection from ultraviolet light, microbes, and abrasion. Methodological breakthroughs are accelerating knowledge of the processes underlying both the production of animal coloration and its perception, experiments are advancing understanding of mechanism and function, and measurements of color collected noninvasively and at a global scale are opening windows to evolutionary dynamics more generally. Here we provide a roadmap of these advances and identify hitherto unrecognized challenges for this multi- and interdisciplinary field.

3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage

Abstract: Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless services in a variety of scenarios. In this letter, we propose an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power. We decouple the UAV-BS deployment problem in the vertical and horizontal dimensions without any loss of optimality. Furthermore, we model the UAV-BS deployment in the horizontal dimension as a circle placement problem and a smallest enclosing circle problem. Simulations are conducted to evaluate the performance of the proposed method for different spatial distributions of the users.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

Abstract: The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach

Abstract: Recent advances in networking, caching, and computing have significant impacts on the developments of smart cities. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on smart cities. In this article, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of applications for smart cities. Then we present a novel big data deep reinforcement learning approach. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.

The promise of organ and tissue preservation to transform medicine

Abstract: The ability to replace organs and tissues on demand could save or improve millions of lives each year globally and create public health benefits on par with curing cancer. Unmet needs for organ and tissue preservation place enormous logistical limitations on transplantation, regenerative medicine, drug discovery, and a variety of rapidly advancing areas spanning biomedicine. A growing coalition of researchers, clinicians, advocacy organizations, academic institutions, and other stakeholders has assembled to address the unmet need for preservation advances, outlining remaining challenges and identifying areas of underinvestment and untapped opportunities. Meanwhile, recent discoveries provide proofs of principle for breakthroughs in a family of research areas surrounding biopreservation. These developments indicate that a new paradigm, integrating multiple existing preservation approaches and new technologies that have flourished in the past 10 years, could transform preservation research. Capitalizing on these opportunities will require engagement across many research areas and stakeholder groups. A coordinated effort is needed to expedite preservation advances that can transform several areas of medicine and medical science.

Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges

Abstract: This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.

Search for new high-mass phenomena in the dilepton final state using 36 fb−1 of proton-proton collision data at √s=13 TeV with the ATLAS detector

Abstract: A search is conducted for new resonant and non-resonant high-mass phenomena in dielectron and dimuon fi nal states. The search uses 36 : 1 fb(-1) of proton-proton collision data, collected at root ...

Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing

Abstract: Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

Abstract: This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching

Abstract: Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.

Wild Bootstrap Inference for Wildly Different Cluster Sizes

Abstract: Summary The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors. Copyright © 2016 John Wiley & Sons, Ltd.

Quantifying ingested debris in marine megafauna: a review and recommendations for standardization

Abstract: Plastic pollution has become one of the largest environmental challenges we currently face. The United Nations Environment Program (UNEP) has listed it as a critical problem, comparable to climate change, demonstrating both the scale and degree of the environmental problem. Mortalities due to entanglement in plastic fishing nets and bags have been reported for marine mammals, turtles and seabirds, and to date over 690 marine species have been reported to ingest plastics. The body of literature documenting plastic ingestion by marine megafauna (i.e. seabirds, turtles, fish and marine mammals) has grown rapidly over the last decade, and it is expected to continue grow as researchers explore the ecological impacts of marine pollution. Unfortunately, a cohesive approach by the scientific community to quantify plastic ingestion by wildlife is lacking, which is now hindering spatial and temporal comparisons between and among species/organisms. Here, we discuss and propose standardized techniques, approaches and metrics for reporting debris ingestion that are applicable to most large marine vertebrates. As a case study, we examine how the use of standardized methods to report ingested debris in Northern Fulmars (Fulmarus glacialis) has enabled long term and spatial trends in plastic pollution to be studied. Lastly, we outline standardized metric recommendations for reporting ingested plastics in marine megafauna, with the aim to harmonize the data that are available to facilitate large-scale comparisons and meta-analyses of plastic accumulation in a variety of taxa. If standardized methods are adopted, future plastic ingestion research will be better able to inform questions related to the impacts of plastics across taxonomic, ecosystem and spatial scales.

Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

Abstract: Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.

Search for new phenomena in dijet events using 37 fb-1 of pp collision data collected at √s=13 TeV with the ATLAS detector

Abstract: Dijet events are studied in the proton-proton collision dataset recorded at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, corresponding to integrated lumino ...

Backhaul-aware robust 3D drone placement in 5G+ wireless networks

Abstract: Using drones as flying base stations is a promising approach to enhance the network coverage and area capacity by moving supply towards demand when required. However deployment of such base stations can face some restrictions that need to be considered. One of the limitations in drone base stations (drone-BSs) deployment is the availability of reliable wireless backhaul link. This paper investigates how different types of wireless backhaul offering various data rates would affect the number of served users. Two approaches, namely, network-centric and user-centric, are introduced and the optimal 3D backhaul-aware placement of a drone-BS is found for each approach. To this end, the total number of served users and sum-rates are maximized in the network-centric and user-centric frameworks, respectively. Moreover, as it is preferred to decrease drone-BS movements to save more on battery and increase flight time and to reduce the channel variations, the robustness of the network is examined as how sensitive it is with respect to the users displacements.

Acoustic telemetry and fisheries management.

Abstract: This paper reviews the use of acoustic telemetry as a tool for addressing issues in fisheries management, and serves as the lead to the special Feature Issue of Ecological Applications titled Acoustic Telemetry and Fisheries Management. Specifically, we provide an overview of the ways in which acoustic telemetry can be used to inform issues central to the ecology, conservation, and management of exploited and/or imperiled fish species. Despite great strides in this area in recent years, there are comparatively few examples where data have been applied directly to influence fisheries management and policy. We review the literature on this issue, identify the strengths and weaknesses of work done to date, and highlight knowledge gaps and difficulties in applying empirical fish telemetry studies to fisheries policy and practice. We then highlight the key areas of management and policy addressed, as well as the challenges that needed to be overcome to do this. We conclude with a set of recommendations about how researchers can, in consultation with stock assessment scientists and managers, formulate testable scientific questions to address and design future studies to generate data that can be used in a meaningful way by fisheries management and conservation practitioners. We also urge the involvement of relevant stakeholders (managers, fishers, conservation societies, etc.) early on in the process (i.e., in the co-creation of research projects), so that all priority questions and issues can be addressed effectively.

A comparative and evolutionary approach to oxidative stress in fish: A review

Abstract: Oxidative stress results from an imbalance between the production of reactive oxygen species and the antioxidants defences, in favour of the former. In recent years, the association between oxidative processes, environmental change and life histories has received much attention. However, most studies have focused on avian and mammalian taxonomic groups, with less attention given to fish, despite their ecological and socio-economic relevance. Here we present a review of the extrinsic and intrinsic factors that influence oxidative processes in fish, using a comparative and evolutionary approach. We demonstrate that oxidative stress plays a key role in shaping fish's responses to environmental change as well as life history strategies. We focus on representative examples to compare and contrast how levels of oxidative stress respond to changes in temperature, salinity and oxygen availability. Furthermore, we describe how emerging threats (i.e. pollution) affect oxidative stress parameters in fish. Oxidative stress indicators are increasingly being used as biomarkers to understand the mechanisms of various human-induced stressors, but also to understand the physiological consequences of how animals are distributed in space and time and influenced by different life stages. Despite the expansion of the field of ecological oxidative stress, we are only beginning to understand the complex ways in which oxidative stress may interact with both extrinsic and intrinsic factors in fish. We conclude with a research agenda for oxidative research on fish and note that there is need for further research particularly in the area of life history strategies and ecological implications of oxidative status, as this type of research has the potential to help us understand patterns and dynamics relevant to fish conservation.

Whole genome analysis of a schistosomiasis-transmitting freshwater snail.

Abstract: Biomphalaria snails are instrumental in transmission of the human blood fluke Schistosoma mansoni With the World Health Organization's goal to eliminate schistosomiasis as a global health problem by 2025, there is now renewed emphasis on snail control Here, we characterize the genome of Biomphalaria glabrata, a lophotrochozoan protostome, and provide timely and important information on snail biology We describe aspects of phero-perception, stress responses, immune function and regulation of gene expression that support the persistence of B glabrata in the field and may define this species as a suitable snail host for S mansoni We identify several potential targets for developing novel control measures aimed at reducing snail-mediated transmission of schistosomiasis

Microplastic abundance and distribution in the open water and sediment of the Ottawa River, Canada, and its tributaries

Abstract: Microplastic pollution is prevalent in the Ottawa River, with all open water samples (n = 62) and sediment samples (n = 10) containing microplastics. The median microplastic concentration of nearsh...

Young adult outcomes in the follow‐up of the multimodal treatment study of attention‐deficit/hyperactivity disorder: symptom persistence, source discrepancy, and height suppression

Abstract: Background The Multimodal Treatment Study (MTA) began as a 14-month randomized clinical trial of behavioral and pharmacological treatments of 579 children (7–10 years of age) diagnosed with attention-deficit/hyperactivity disorder (ADHD)-combined type. It transitioned into an observational long-term follow-up of 515 cases consented for continuation and 289 classmates (258 without ADHD) added as a local normative comparison group (LNCG), with assessments 2–16 years after baseline. Methods Primary (symptom severity) and secondary (adult height) outcomes in adulthood were specified. Treatment was monitored to age 18, and naturalistic subgroups were formed based on three patterns of long-term use of stimulant medication (Consistent, Inconsistent, and Negligible). For the follow-up, hypothesis-generating analyses were performed on outcomes in early adulthood (at 25 years of age). Planned comparisons were used to estimate ADHD-LNCG differences reflecting persistence of symptoms and naturalistic subgroup differences reflecting benefit (symptom reduction) and cost (height suppression) associated with extended use of medication. Results For ratings of symptom severity, the ADHD-LNCG comparison was statistically significant for the parent/self-report average (0.51 ± 0.04, p < .0001, d = 1.11), documenting symptom persistence, and for the parent/self-report difference (0.21 ± 0.04, p < .0001, d = .60), documenting source discrepancy, but the comparisons of naturalistic subgroups reflecting medication effects were not significant. For adult height, the ADHD group was 1.29 ± 0.55 cm shorter than the LNCG (p < .01, d = .21), and the comparisons of the naturalistic subgroups were significant: the treated group with the Consistent or Inconsistent pattern was 2.55 ± 0.73 cm shorter than the subgroup with the Negligible pattern (p < .0005, d = .42), and within the treated group, the subgroup with the Consistent pattern was 2.36 ± 1.13 cm shorter than the subgroup with the Inconsistent pattern (p < .04, d = .38). Conclusions In the MTA follow-up into adulthood, the ADHD group showed symptom persistence compared to local norms from the LNCG. Within naturalistic subgroups of ADHD cases, extended use of medication was associated with suppression of adult height but not with reduction of symptom severity.

An overview of forensic drug testing methods and their suitability for harm reduction point-of-care services.

Abstract: Given the current opioid crisis around the world, harm reduction agencies are seeking to help people who use drugs to do so more safely. Many harm reduction agencies are exploring techniques to test illicit drugs to identify and, where possible, quantify their constituents allowing their users to make informed decisions. While these technologies have been used for years in Europe (Nightlife Empowerment & Well-being Implementation Project, Drug Checking Service: Good Practice Standards; Trans European Drugs Information (TEDI) Workgroup, Factsheet on Drug Checking in Europe, 2011; European Monitoring Centre for Drugs and Drug Addiction, An Inventory of On-site Pill-Testing Interventions in the EU: Fact Files, 2001), they are only now starting to be utilized in this context in North America. The goal of this paper is to describe the most common methods for testing illicit substances and then, based on this broad, encompassing review, recommend the most appropriate methods for testing at point of care. Based on our review, the best methods for point-of-care drug testing are handheld infrared spectroscopy, Raman spectroscopy, and ion mobility spectrometry; mass spectrometry is the current gold standard in forensic drug analysis. It would be prudent for agencies or clinics that can obtain the funding to contact the companies who produce these devices to discuss possible usage in a harm reduction setting. Lower tech options, such as spot/color tests and immunoassays, are limited in their use but affordable and easy to use.

Electrical Impedance Tomography: Tissue Properties to Image Measures

Abstract: Electrical impedance tomography (EIT) uses electrical stimulation and measurement at the body surface to image the electrical properties of internal tissues. It has the advantage of noninvasiveness and high temporal resolution but suffers from poor spatial resolution and sensitivity to electrode movement and contact quality. EIT can be useful to applications, where there are conductive contrasts between tissues, fluids, or gasses, such as imaging of cancerous or ischemic tissue or functional monitoring of breathing, blood flow, gastric motility, and neural activity. The past decade has seen clinical application and commercial activity using EIT for ventilation monitoring. Interpretation of EIT-based measures is complex, and this review paper focuses on describing the image interpretation “pathway.” We review this pathway, from Tissue Electrical Properties , EIT Electrodes & Hardware , Sensitivity , Image Reconstruction , Image Processing to EIT Measures . The relationship is discussed between the clinically relevant parameters and the reconstructed properties. An overview is given of areas of EIT application and of our perspectives for research and development.