35 research outputs found
Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks
In Shannon theory, semantic aspects of communication were identified but
considered irrelevant to the technical communication problems. Semantic
communication (SC) techniques have recently attracted renewed research
interests in (6G) wireless because they have the capability to support an
efficient interpretation of the significance and meaning intended by a sender
(or accomplishment of the goal) when dealing with multi-modal data such as
videos, images, audio, text messages, and so on, which would be the case for
various applications such as intelligent transportation systems where each
autonomous vehicle needs to deal with real-time videos and data from a number
of sensors including radars. A notable difficulty of existing SC frameworks
lies in handling the discrete constraints imposed on the pursued semantic
coding and its interaction with the independent knowledge base, which makes
reliable semantic extraction extremely challenging. Therefore, we develop a new
lightweight hashing-based semantic extraction approach to the SC framework,
where our learning objective is to generate one-time signatures (hash codes)
using supervised learning for low latency, secure and efficient management of
the SC dynamics. We first evaluate the proposed semantic extraction framework
over large image data sets, extend it with domain adaptive hashing and then
demonstrate the effectiveness of "semantics signature" in bulk transmission and
multi-modal data
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework
We design a model of Post Quantum Cryptography (PQC) Quantum Federated
Learning (QFL). We develop a framework with a dynamic server selection and
study convergence and security conditions. The implementation and results are
publicly available1
Quantum Federated Learning: Analysis, Design and Implementation Challenges
Quantum Federated Learning (QFL) has gained significant attention due to
quantum computing and machine learning advancements. As the demand for QFL
continues to surge, there is a pressing need to comprehend its intricacies in
distributed environments. This paper aims to provide a comprehensive overview
of the current state of QFL, addressing a crucial knowledge gap in the existing
literature. We develop ideas for new QFL frameworks, explore diverse use cases
of applications, and consider the critical factors influencing their design.
The technical contributions and limitations of various QFL research projects
are examined while presenting future research directions and open questions for
further exploration
On Correlated Knowledge Distillation for Monitoring Human Pose with Radios
In this work, we propose and develop a simple experimental testbed to study
the feasibility of a novel idea by coupling radio frequency (RF) sensing
technology with Correlated Knowledge Distillation (CKD) theory towards
designing lightweight, near real-time and precise human pose monitoring
systems. The proposed CKD framework transfers and fuses pose knowledge from a
robust "Teacher" model to a parameterized "Student" model, which can be a
promising technique for obtaining accurate yet lightweight pose estimates. To
assure its efficacy, we implemented CKD for distilling logits in our integrated
Software Defined Radio (SDR)-based experimental setup and investigated the
RF-visual signal correlation. Our CKD-RF sensing technique is characterized by
two modes -- a camera-fed Teacher Class Network (e.g., images, videos) with an
SDR-fed Student Class Network (e.g., RF signals). Specifically, our CKD model
trains a dual multi-branch teacher and student network by distilling and fusing
knowledge bases. The resulting CKD models are then subsequently used to
identify the multimodal correlation and teach the student branch in reverse.
Instead of simply aggregating their learnings, CKD training comprised multiple
parallel transformations with the two domains, i.e., visual images and RF
signals. Once trained, our CKD model can efficiently preserve privacy and
utilize the multimodal correlated logits from the two different neural networks
for estimating poses without using visual signals/video frames (by using only
the RF signals)
Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019
Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens
A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance
There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium. We accomplish this by defining a new utility function for MPTCP by employing Tullock’s rent-seeking paradigm from game theory. We investigate the convergence of no-regret learning in the underlying network games with continuous actions. Based on our understanding of the design space, we propose an original MPTCP algorithm that generalizes existing algorithms and strikes a good balance among the important properties. We implemented this algorithm in the Linux kernel, and we evaluated its performance experimentally
A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance
There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium. We accomplish this by defining a new utility function for MPTCP by employing Tullock’s rent-seeking paradigm from game theory. We investigate the convergence of no-regret learning in the underlying network games with continuous actions. Based on our understanding of the design space, we propose an original MPTCP algorithm that generalizes existing algorithms and strikes a good balance among the important properties. We implemented this algorithm in the Linux kernel, and we evaluated its performance experimentally