This document discusses context monitoring and its potential to improve quality of experience (QoE) for HTTP adaptive streaming. It defines context as any information that helps determine a user, network, or device situation. Context influence factors include physical, temporal, social, economic, task, and technical characteristics. The document presents a case study on using context monitoring to improve QoE during video flash crowds. It describes a simulation model of an HTTP adaptive streaming system with two content delivery networks and proposes using context data on the number of users per network to improve load balancing during flash crowds. The results show context monitoring can significantly improve performance and QoE compared to approaches without this context data.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814
QoE++: Shifting from Ego- to Eco-System? QCMan 2015 KeynoteTobias Hoßfeld
QoE++: Shifting from Ego- to Eco-System?
QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE.
However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals.
QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges.
(1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds?
(2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges?
(3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP.
(4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior?
Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.
Policy-driven Dynamic HTTP Adaptive Streaming Player EnvironmentMinh Nguyen
Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).
UberCloud HPC Experiment Introduction for Beginnershpcexperiment
UberCloud HPC Experiment Introduction for Beginners.
What is the HPC Experiment
How the HPC Experiment works
How to participate in the HPC Experiment
And an example project
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814
QoE++: Shifting from Ego- to Eco-System? QCMan 2015 KeynoteTobias Hoßfeld
QoE++: Shifting from Ego- to Eco-System?
QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE.
However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals.
QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges.
(1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds?
(2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges?
(3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP.
(4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior?
Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.
Policy-driven Dynamic HTTP Adaptive Streaming Player EnvironmentMinh Nguyen
Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).
UberCloud HPC Experiment Introduction for Beginnershpcexperiment
UberCloud HPC Experiment Introduction for Beginners.
What is the HPC Experiment
How the HPC Experiment works
How to participate in the HPC Experiment
And an example project
Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adap...SmartenIT
Tobias Hoßfeld, Michael Seufert, Christian Sieber, Thomas Zinner
Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adaptive Streaming.
6th International Workshop on Quality of Multimedia Experience (QoMEX), Singapore, September 2014.
Abstract:
HTTP Adaptive Streaming (HAS) is employed by more and more video streaming services in the Internet. It allows to adapt the downloaded video quality to the current network conditions, and thus, avoids stalling (i.e., playback interruptions) to the greatest possible extend. The adaptation of video streams is done by switching between different quality representation levels, which influences the user perceived quality of the video stream. In this work, the influence of several adaptation parameters, namely, switch amplitude (i.e., quality level difference), switching frequency, and recency effects, on Quality of Experience (QoE) is investigated. Therefore, crowdsourcing experiments were conducted in order to collect subjective ratings for different adaptation-related test conditions. The results of these subjective studies indicate the influence of the adaptation parameters, and based on these findings a simplified QoE model for HAS is presented, which only relies on the switch amplitude and the playback time of each layer.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
Video services are evolving from traditional two-dimensional video to virtual reality and holograms, which offer six degrees of freedom to users, enabling them to freely move around in a scene and change focus as desired. However, this increase in freedom translates into stringent requirements in terms of ultra-high bandwidth (in the order of Gigabits per second) and minimal latency (in the order of milliseconds). To realize such immersive services, the network transport, as well as the video representation and encoding, have to be fundamentally enhanced. The purpose of this tutorial article is to provide an elaborate introduction to the creation, streaming, and evaluation of immersive video. Moreover, it aims to provide lessons learned and to point at promising research paths to enable truly interactive immersive video applications toward holography.
QoE- and Energy-aware Content Consumption for HTTP Adaptive StreamingDanieleLorenzi6
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Show and Tell - Data and Digitalisation, Digital Twins.pdfSIFOfgem
This is the third in a series of 'Show and Tell' webinars from the Ofgem Strategic Innovation Fund Discovery phase, covering the Digital Twin projects.
As the move towards a net zero energy system accelerates, network customers and consumers will require simplified and accessible digital products, processes and services that can improve their user experience. Data and digital initiatives are already beginning to show the potential to improve the efficiency of energy networks whilst making it easier for third parties to interact with and innovate for the energy system. Digitalisation of energy network activities will contribute to better coordination, planning and network optimisation.
You will hear from SIF projects which are investigating new digital products and services such as digital twins.
The Strategic Innovation Fund (SIF) is an Ofgem programme managed in partnership with Innovate UK, part of UKRI. The SIF aims to fund network innovation that will contribute to achieving Net Zero rapidly and at lowest cost to consumers, and help transform the UK into the ‘Silicon Valley’ of energy, making it the best place for high-potential businesses to grow and scale in the energy market.
For more information on the SIF visit: www.ofgem.gov.uk/sif
Or sign-up for our newsletter here: https://ukri.innovateuk.org/ofgem-sif-subscription-sign-up
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adap...SmartenIT
Tobias Hoßfeld, Michael Seufert, Christian Sieber, Thomas Zinner
Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adaptive Streaming.
6th International Workshop on Quality of Multimedia Experience (QoMEX), Singapore, September 2014.
Abstract:
HTTP Adaptive Streaming (HAS) is employed by more and more video streaming services in the Internet. It allows to adapt the downloaded video quality to the current network conditions, and thus, avoids stalling (i.e., playback interruptions) to the greatest possible extend. The adaptation of video streams is done by switching between different quality representation levels, which influences the user perceived quality of the video stream. In this work, the influence of several adaptation parameters, namely, switch amplitude (i.e., quality level difference), switching frequency, and recency effects, on Quality of Experience (QoE) is investigated. Therefore, crowdsourcing experiments were conducted in order to collect subjective ratings for different adaptation-related test conditions. The results of these subjective studies indicate the influence of the adaptation parameters, and based on these findings a simplified QoE model for HAS is presented, which only relies on the switch amplitude and the playback time of each layer.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
Video services are evolving from traditional two-dimensional video to virtual reality and holograms, which offer six degrees of freedom to users, enabling them to freely move around in a scene and change focus as desired. However, this increase in freedom translates into stringent requirements in terms of ultra-high bandwidth (in the order of Gigabits per second) and minimal latency (in the order of milliseconds). To realize such immersive services, the network transport, as well as the video representation and encoding, have to be fundamentally enhanced. The purpose of this tutorial article is to provide an elaborate introduction to the creation, streaming, and evaluation of immersive video. Moreover, it aims to provide lessons learned and to point at promising research paths to enable truly interactive immersive video applications toward holography.
QoE- and Energy-aware Content Consumption for HTTP Adaptive StreamingDanieleLorenzi6
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Show and Tell - Data and Digitalisation, Digital Twins.pdfSIFOfgem
This is the third in a series of 'Show and Tell' webinars from the Ofgem Strategic Innovation Fund Discovery phase, covering the Digital Twin projects.
As the move towards a net zero energy system accelerates, network customers and consumers will require simplified and accessible digital products, processes and services that can improve their user experience. Data and digital initiatives are already beginning to show the potential to improve the efficiency of energy networks whilst making it easier for third parties to interact with and innovate for the energy system. Digitalisation of energy network activities will contribute to better coordination, planning and network optimisation.
You will hear from SIF projects which are investigating new digital products and services such as digital twins.
The Strategic Innovation Fund (SIF) is an Ofgem programme managed in partnership with Innovate UK, part of UKRI. The SIF aims to fund network innovation that will contribute to achieving Net Zero rapidly and at lowest cost to consumers, and help transform the UK into the ‘Silicon Valley’ of energy, making it the best place for high-potential businesses to grow and scale in the energy market.
For more information on the SIF visit: www.ofgem.gov.uk/sif
Or sign-up for our newsletter here: https://ukri.innovateuk.org/ofgem-sif-subscription-sign-up
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
1. Prof. Dr. Tobias Hoßfeld
Chair of Modeling of
Adaptive Systems (MAS)
Institute for Computer
Science and Business
Information Systems (ICB)
University of Duisburg-Essen
www.mas.wiwi.uni-due.de
Can context monitoring improve
QoE? A case study of video flash
crowds in the Internet of Services
Hossfeld, Tobias; Skorin-Kapov, Lea;
Haddad, Yoram; Pocta, Peter; Siris,
Vasilios A.; Zgank, Andrej; Melvin, Hugh
2. Definition of Context and Context Influence Factors
• Context is any information that assists in determining
a situation(s) related to a user, network or device.
[A.K. Dey and G.D. Abowd. Toward a better understanding of context and
context-awareness, Technical Report Georgia Institute of Technology]
• Context refers to anything that can be used to
specify or clarify the meaning of an event.
[P. Reichl et al, Towards a comprehensive framework for QoE and user
behavior modelling, QoMEX 2015]
• Context influence factors are factors that embrace
any situational property to describe the user’s
environment in terms of physical, temporal, social,
economic, task, and technical characteristics.
[U. Reiter et al, Factors influencing quality of experience. In Quality of
Experience, pp. 55-72. Springer International Publishing, 2014.]
or system.
or system‘s
3. Context Monitoring and QoE Monitoring
mas.wiwi.uni-due.de 3
Context
monitoring
QoE
monitoring
e.g. device
capabilities
e.g. video
buffer status
e.g. user
expectations
e.g. predicted
traffic demands
e.g. available
resources
e.g. QoS
utilization
of data
Is context monitoring
more relevant than QoE monitoring
for managing QoE?
4. Context Factors
• Physical environment in which services and devices are used.
– home, office, commuting, and other places,
– indoors vs outdoors.
• Social environment
– service consumption e.g. alone, with an important person, with a group of friends,
or in a public place (consider gaming, watching video),
– popularity of contents.
• Economic context
– price for service consumption, tariff model: time, volume, flat
– costs
• System context
– load of system
– system offloading possible, e.g. wifi offloading
• Usage context
– Goal, task of service consumption, e.g. information retrieval vs. time killing
– background vs. foreground application
mas.wiwi.uni-due.de 4
Examples
• Follow the moon:
temporal and
economic context
• Video streaming:
physical context
• Video flash crowds:
social context
5. Agenda
• Context monitoring and QoE monitoring
• Example use case: video flash crowds
• QoE model for HTTP adaptive streaming
• Numerical results
• Open issues: realization
8. Content Delivery with a CDN
mas.wiwi.uni-due.de 9
Core
network
Access
network
Content
server
Clients
CDN
server
9. Edge Content Delivery Network
mas.wiwi.uni-due.de 10
Global CDN Backbone
Access Provider
Access Provider
Transit
Provider
Point of
PresencePoint of
Presence
Point of
Presence
Point of
Presence
Edge Cache
10. Simulation Scenario: Video Flash Crowd
• Video player
– playout threshold of 6s
– video stalls for empty buffer
• Video contents
– Segment size of 2s
– Two quality layers
• Flash crowd arrivals
– 𝑁 = 30 users arrive
– Exponential distributed interarrival times with rate λ
– P(T<90s) = 99.27%, 𝑇~𝐸𝑟𝑙𝑎𝑛𝑔 𝑁, 𝜆
• HAS algorithm
• CDN load balancing
mas.wiwi.uni-due.de 11
CDN 1
CDN 2
Flash
Crowd
ISP
bottleneck
11. HAS Algorithm and CDN Load Balancing
• CDN load balancing strategies
1. CDN directs the first 𝑲 users to CDN 1, subsequent
users are assigned to CDN 2. Second, the CDN.
2. Context monitoring based on information about the
flash crowd from a third party. Users are assigned to the
CDN with the lowest number of users.
• HTTP adaptation strategy
1. Actual buffer and throughput of last segment to
determine quality level of next segment
2. Additional context information on number of users and
capacity per CDN
3. Non-adaptive streaming algorithm: high quality level
mas.wiwi.uni-due.de 12
Bit rate
Time
TCP throughput
Requested chunks
13. What is the influence of stalling on Video QoE?
IQX-Hypothesis
Excellent
Good
Fair
Poor
Bad
5
4
3
2
1
Imperceptible
Perceptible
Slightly annoying
Annoying
Very annoying
• Small number of interruptions
strongly affect YouTube QoE
Provider (i.e. content and
network provider) must avoid
stalling
0 1 2 3 4 5 6
1
2
3
4
5
number of stallings
MOS
crowdsourcing
laboratory
QoE x = αe−βx + γ
mas.wiwi.uni-due.de 14
14. Survey: Subjective Studies on HAS QoE
• Seufert, M.; Egger, S.; Slanina, M.; Zinner, T.; Hoßfeld, T.; Tran-Gia, P., "A Survey on
Quality of Experience of HTTP Adaptive Streaming," Communications Surveys & Tutorials,
IEEE , vol.17, no.1, pp.469,492, 2015
doi: 10.1109/COMST.2014.2360940
mas.wiwi.uni-due.de 15
HTTP
Adaptive
Streaming
Video
Quality
Human
Computer
Interaction
Networking
etc.
15. Switching Frequency vs. Time on Layer
• In several works, switching frequency is reported to
influence QoE
• Often parameters „number/frequency of switches“ and „time
on layer“ are correlated and change simultaneously
• Keeping „time on layer“
constant no influence of
switching frequency
could be found
mas.wiwi.uni-due.de 16
16. Simple QoE Model for Two Quality Layers
• Simple QoE model based on two key influence factors
• IQX provides a very good fit to the data points (R²=0.98)
mas.wiwi.uni-due.de 17
IQX-Hypothesis
17. Combined QoE Model
• Quality Adaptation Model
– Based on time t on high layer
– 𝑄1 𝑡 following IQX hypothesis
• Stalling Model
– Based on number 𝑥 of stalls
– 𝑄2 𝑥 following IQX hypothesis
• HTTP Adaptive Streaming Model
– 𝑄 𝑥, 𝑡 = 𝑄1 𝑡 ⋅ Q2(𝑥)
– Model still follows IQX hypothesis
mas.wiwi.uni-due.de 18
IQX-Hypothesis
19. Simulation Results
• No context information is used
– CDN load balancing strategy: K=13
– HAS quality
adaptation
mechanism.
• CDN1 can serve
13 / 35 users in
high / low quality
• CDN2 can serve
10 / 26 users in
high / low quality
• Reaction too slow
mas.wiwi.uni-due.de 20
20. CDN Load Balancing Strategy
• Static assignment cannot achieve optimum
• Reactive approach
based on context
information improves
QoE for all users
mas.wiwi.uni-due.de 21
21. Summary of Results: CDN and HAS
mas.wiwi.uni-due.de 22
Bit rate
Time
TCP throughput
Requested chunks
22. Conclusions
• Context monitoring complements QoE monitoring
– Utilization of additional information
– Different types of context may be monitored
• Example of video flash crowds
– Performance and QoE gain significantly improves
– Technical realization needs to be developed
• Realization of context monitoring
mas.wiwi.uni-due.de 23
23. Realization of Context Monitoring using Social Data
• Accessing data from third party:
Internet of Services
• Social data has to be monitored
– Scale (single user, selected users,
all users)
– Period (every hour, once a day, …)
– Source (Online Social Networks OSNs,
Services, Service Providers, ISPs,…)
online
social network
www.mas.wiwi.uni-due.de 24
http://www.smartenit.eu
Content
ProviderISPs
CDNs
$$$
$$$
$$$
$$$
Ads
Data
analysis
…
24. Reseach Qestions: Social Data Monitoring
• Example: How to access data from OSNs?
• Design questions
– Identification of relevant social data
– Access method
– Sampling strategy (scale, period, source,…)
– Incentives (if necessary)
Method Information Prediction
OSN collaboration All information Global, Detailed
End user grants
access to his data
Private information
about end user and
shared information
about friends
Local, Detailed
Crawling/Sampling Public information Global, Vague
www.mas.wiwi.uni-due.de 25