Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (machine learning and ubicomp primer session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they were held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Bridging the Gap: Machine Learning for Ubiquitous Computing -- IntroductionThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (introduction session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Bridging the Gap: Machine Learning for Ubiquitous Computing -- Applied Machin...Thomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (applied machine learning session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they were held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Big Data Analytics for connected home: a few usecases, some important messages and a little example. Presentation given at CEA Cadarache - Cité des Nouvelles Energies at the strategic comittee of ARCSIS (http://www.arcsis.org/missions.html)
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
Bridging the Gap: Machine Learning for Ubiquitous Computing -- Study Design a...Thomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (study design and deployment session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Mayank Goel <india.mayank@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Bridging the Gap: Machine Learning for Ubiquitous Computing -- IntroductionThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (introduction session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Bridging the Gap: Machine Learning for Ubiquitous Computing -- Applied Machin...Thomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (applied machine learning session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they were held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Big Data Analytics for connected home: a few usecases, some important messages and a little example. Presentation given at CEA Cadarache - Cité des Nouvelles Energies at the strategic comittee of ARCSIS (http://www.arcsis.org/missions.html)
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
Bridging the Gap: Machine Learning for Ubiquitous Computing -- Study Design a...Thomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (study design and deployment session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Mayank Goel <india.mayank@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
ODSC 2019: Sessionisation via stochastic periods for root event identificationKuldeep Jiwani
In todays world majority of information is generated by self sustaining systems like various kinds of bots, crawlers, servers, various online services, etc. This information is flowing on the axis of time and is generated by these actors under some complex logic. For example, a stream of buy/sell order requests by an Order Gateway in financial world, or a stream of web requests by a monitoring / crawling service in the web world, or may be a hacker's bot sitting on internet and attacking various computers. Although we may not be able to know the motive or intention behind these data sources. But via some unsupervised techniques we can try to infer the pattern or correlate the events based on their multiple occurrences on the axis of time. Associating a chain of events in order of time helps in doing a root event analysis. In certain cases a time ordered correlation and root event identification is good enough to automatically identify signatures of various malicious actors and take appropriate corrective actions to stop cyber attacks, stop malicious social campaigns, etc.
Sessionisation is one such unsupervised technique that tries to find the signal in a stream of events associated with a timestamp. In the ideal world it would resolve to finding periods with a mixture of sinusoidal waves. But for the real world this is a much complex activity, as even the systematic events generated by machines over the internet behave in a much erratic manner. So the notion of a period for a signal also changes in the real world. We can no longer associate it with a number, it has to be treated as a random variable, with expected values and associated variance. Hence we need to model "Stochastic periods" and learn their probability distributions in an unsupervised manner.
The main focus of this talk will be to showcase applied data science techniques to discover stochastic periods. There are many ways to obtain periods in data, so the journey would begin by a walk through of existing techniques like FFT (Fast Fourier Transform) then discuss about Gaussian Mixture Models. After highlighting the short comings of these techniques we will succinctly explain one of the most general non-parametric Bayesian approaches to solve this problem. Without going too deep in the complex math, we will get back to applied data science and discuss a much simpler technique that can solve the same problem if certain assumptions are satisfied.
In this talk we will demonstrate some time based pattern we discovered while working on a security analytics use case that uses Sessionisation. In the talk we will demonstrate such patterns based on an open source malware attack datasets that is available publicly.
Key concepts explained in talk: Sessionisation, Bayesian techniques of Machine Learning, Gaussian Mixture Models, Kernel density estimation, FFT, stochastic periods, probabilistic modelling, Bayesian non-parametric methods
Introduction to Data and Computation: Essential capabilities for everyone in ...Kim Flintoff
An overview seminar about the themes of the Curtin Institute for Computation, and some thoughts on the future role of these capabilities in Learning and Teaching.
Machine Learning: Past, Present and Future - by Tom DietterichBigML, Inc
There are many uses to Machine Learning. This technology began as a form of Data-Driven Software Engineering; but a more recent development is Machine Learning for Data Science: its tools can help us understand the many forms of data that are collected by companies, scientists and governments. Another important trend is Machine Learning for Optimizing Operations: for example, logistics, scheduling, advertisement placement, etc. Also, recent advances in anomaly detection are helping us understand when the results of previous Machine Learning cannot be trusted or when changes in the inputs are surprising.
Find more details here: http://www.madridml.com/en/.
Conference talk: Understanding Vulnerabilities of Location Privacy Mechanisms...Zohaib Riaz
Slides for our work presented at MobiQuitous 2017 Conference (http://mobiquitous.org/).
Full paper text: ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2017-46/INPROC-2017-46.pdf
This paper focused on revealing weaknesses of existing location obfuscation approaches when an attacker possesses accurate or obfuscated location history information.
Data Science in the Real World: Making a Difference Srinath Perera
We use the terms “Big Data” and “Data Science” for use of data processing to make sense of the world around us. Spanning many fields, Big Data brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture.
These usecases use basic analytics, advanced statistical methods, and predictive technologies like Machine Learning. However, it is not just about crunching the data. Some usecases like Urban Planning can be slow, and there is enough time to process the data. However, with use cases like traffic, patient monitoring, surveillance the the value of results degrades much faster with time and needs results within milliseconds to seconds. Collecting data from many sources, cleaning them up, processing them using computation clusters, and doing all these fast is a major challenge.
This talk will discuss motivation behind big data and data science and how it can make a difference. Then it will discuss the challenges, systems, and methodologies for implementing and sustaining a data science pipeline.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
We will review some modern machine learning applications, understand variety of machine learning problem definitions, go through particular approaches of solving machine learning tasks.
This year 2015 Amazon and Microsoft introduced services to perform machine learning tasks in cloud. Microsoft Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments.
We will briefly review Azure ML Studio features and run machine learning experiment.
Richard's aventures in two entangled wonderlandsRichard 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.
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ODSC 2019: Sessionisation via stochastic periods for root event identificationKuldeep Jiwani
In todays world majority of information is generated by self sustaining systems like various kinds of bots, crawlers, servers, various online services, etc. This information is flowing on the axis of time and is generated by these actors under some complex logic. For example, a stream of buy/sell order requests by an Order Gateway in financial world, or a stream of web requests by a monitoring / crawling service in the web world, or may be a hacker's bot sitting on internet and attacking various computers. Although we may not be able to know the motive or intention behind these data sources. But via some unsupervised techniques we can try to infer the pattern or correlate the events based on their multiple occurrences on the axis of time. Associating a chain of events in order of time helps in doing a root event analysis. In certain cases a time ordered correlation and root event identification is good enough to automatically identify signatures of various malicious actors and take appropriate corrective actions to stop cyber attacks, stop malicious social campaigns, etc.
Sessionisation is one such unsupervised technique that tries to find the signal in a stream of events associated with a timestamp. In the ideal world it would resolve to finding periods with a mixture of sinusoidal waves. But for the real world this is a much complex activity, as even the systematic events generated by machines over the internet behave in a much erratic manner. So the notion of a period for a signal also changes in the real world. We can no longer associate it with a number, it has to be treated as a random variable, with expected values and associated variance. Hence we need to model "Stochastic periods" and learn their probability distributions in an unsupervised manner.
The main focus of this talk will be to showcase applied data science techniques to discover stochastic periods. There are many ways to obtain periods in data, so the journey would begin by a walk through of existing techniques like FFT (Fast Fourier Transform) then discuss about Gaussian Mixture Models. After highlighting the short comings of these techniques we will succinctly explain one of the most general non-parametric Bayesian approaches to solve this problem. Without going too deep in the complex math, we will get back to applied data science and discuss a much simpler technique that can solve the same problem if certain assumptions are satisfied.
In this talk we will demonstrate some time based pattern we discovered while working on a security analytics use case that uses Sessionisation. In the talk we will demonstrate such patterns based on an open source malware attack datasets that is available publicly.
Key concepts explained in talk: Sessionisation, Bayesian techniques of Machine Learning, Gaussian Mixture Models, Kernel density estimation, FFT, stochastic periods, probabilistic modelling, Bayesian non-parametric methods
Introduction to Data and Computation: Essential capabilities for everyone in ...Kim Flintoff
An overview seminar about the themes of the Curtin Institute for Computation, and some thoughts on the future role of these capabilities in Learning and Teaching.
Machine Learning: Past, Present and Future - by Tom DietterichBigML, Inc
There are many uses to Machine Learning. This technology began as a form of Data-Driven Software Engineering; but a more recent development is Machine Learning for Data Science: its tools can help us understand the many forms of data that are collected by companies, scientists and governments. Another important trend is Machine Learning for Optimizing Operations: for example, logistics, scheduling, advertisement placement, etc. Also, recent advances in anomaly detection are helping us understand when the results of previous Machine Learning cannot be trusted or when changes in the inputs are surprising.
Find more details here: http://www.madridml.com/en/.
Conference talk: Understanding Vulnerabilities of Location Privacy Mechanisms...Zohaib Riaz
Slides for our work presented at MobiQuitous 2017 Conference (http://mobiquitous.org/).
Full paper text: ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2017-46/INPROC-2017-46.pdf
This paper focused on revealing weaknesses of existing location obfuscation approaches when an attacker possesses accurate or obfuscated location history information.
Data Science in the Real World: Making a Difference Srinath Perera
We use the terms “Big Data” and “Data Science” for use of data processing to make sense of the world around us. Spanning many fields, Big Data brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture.
These usecases use basic analytics, advanced statistical methods, and predictive technologies like Machine Learning. However, it is not just about crunching the data. Some usecases like Urban Planning can be slow, and there is enough time to process the data. However, with use cases like traffic, patient monitoring, surveillance the the value of results degrades much faster with time and needs results within milliseconds to seconds. Collecting data from many sources, cleaning them up, processing them using computation clusters, and doing all these fast is a major challenge.
This talk will discuss motivation behind big data and data science and how it can make a difference. Then it will discuss the challenges, systems, and methodologies for implementing and sustaining a data science pipeline.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
We will review some modern machine learning applications, understand variety of machine learning problem definitions, go through particular approaches of solving machine learning tasks.
This year 2015 Amazon and Microsoft introduced services to perform machine learning tasks in cloud. Microsoft Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments.
We will briefly review Azure ML Studio features and run machine learning experiment.
Richard's aventures in two entangled wonderlandsRichard 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.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
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.
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.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Bridging the Gap: Machine Learning for Ubiquitous Computing -- ML and Ubicomp Primer
1. Bridging the Gap:
Machine Learning for Ubicomp
Thomas Ploetz
— ML Primer & ML applications for Ubicomp —
2. What is Machine Learning?
• Develop algorithms (“computer programs” [sic!] …) that adapt
(learn!) towards generalisation through analysing sample data
“Machine learning studies computer algorithms for
learning to do stuff”
[Robert Schapire]
2
4. The Machine Learning Principles
1. Use parametric models to represent classes of interest
2. Use statistical learning for deriving parameter values from
representative sample sets
4 [from the Internet …]
5. 3 Postulates of PR / ML (there are more …)
1. Collect information about problem area Ω → representative sample set
5
y additional information, i.e., annotation
= (1
fff(xxx), y1),2
fff(xxx), y2), . . . ,N
fff(xxx), yN )
2. Features characterise patterns’ affiliation to a specific class
fff(x) ccc, with dim(ccc) dim( fff)
3. Features form compact space
(per class) in global feature
space
(compactness)
6. Principles of PR / ML
Classification represents mapping:
Classification → costs, optimise average loss V(f):
= arg min V ( )
ccc k {1, 2, . . . , K} or ccc {0, 1, . . . , K} (with rejection)
Classification systems:
fff(xxx) recording preprocessing feature calc. ccc classification k
6
7. PR / ML Systems — Overview
Recording
(Digitalisation, Quantisation)
Preprocessing
Segmentation
Feature Extraction
Association of feature vector
to pattern class
Training or refreshing of
classifier
Classifier
feature vector
classification parameters
classified
feature vector
supervised
learning
decision supervised learning
digital pattern
improved pattern
(for classification)
number "1"
class ωi
class ωi
class ωi
7
8. Fundamental Elements of
Statistical Classification
1. pk — prior probabilities of classes
2. p(c| Ωk) — class-dependent densities
3. rƛk — classification costs → V(𝛅)
4. 𝛅(Ωƛ |c) — decision rule
8
9. Machine Learning for / in
Automated analysis of sensor data (recorded
using opportunistic / parasitic approaches) as pre-
requisite for …
Context Awareness!
9
11. Applications — Context Awareness!
Any information that can be used to characterize the situation of
an entity:
➡ Who, what, where, when; novel interaction.
11
Activity Recognition Location Awareness HCI
13. Location Applications
— very biased and non-exhaustive example set —
Identification of meaningful
places [e.g., Krumm]
Route prediction from GPS traces [Horvitz, 2012]
Mobility patterns inference
[Ganti et al., 2013]
13
14. Location Analysis: Methods
• Many methods for robust location sensing
• actual measurement techniques (triangulation and such)
• de-noising (signal processing)
• interpolation for missing data
• Very (!) sophisticated machine learning methods for
• tracking
• classification
• prediction
• Examples:
• bag of words features and topic models for classification
• Particle filtering for tracking
• Markovian models for sequential analysis and prediction
• …
14
15. Activity Recognition
Activity recognition aims to recognize the actions and
goals of one or more agents from a series of
observations on the agents' actions and the
environmental conditions.
What? When?
15
18. Indirect Activity Recognition through
Infrastructure Mediated Sensing
hydrosense electrisense gassense
[Patel et al.]
18
19. Event Detection through IMS:
HydroSense
water&tower&
incoming&cold&
water&from&
supply&line&
thermal&&
expansion&&
tank&
laundry&
bathroom 1
hose&
spigot&
hot&&
water&&
heater& bathroom 2
kitchen
dishwasher&
pressure&
regulator&
Closed Pressure System
15&
19
incoming cold
water from
supply line
water tower
[Froehlich et al., 2009]
20. Event Detection through IMS:
HydroSense
20
• Event segmentation
• Feature extraction
• Event classification
[Froehlich et al., 2009]
21. Activity Recognition using IMS
→ Actual activity recognition on top of event classification
[Thomaz et al., 2012]
Shave, Brush teeth, Wash hands, Flush toilet, Wash hands, Fill up teakettle,
Make a salad, Rinse a fruit, Take a glass of water, Do dishes (light load), Do
dishes (heavy load)
21
22. What it all (largely) boils down to …
Analysis of sequential data / time series data!
22
24. Sequential Data — Challenges
• Segmentation vs Classification
→ “chicken and egg” problem
• Noise, noise, and noise …
• … more noise :-(
• Evaluation — “ground truth”?
24
25. Noise …
• filtering
• trivial (technically)
• lag
• no higher level
variables (speed)
ˆxi =
1
n
iX
j=i n+1
zj ˆxi = median{zi n+1, zi n+2, . . . , zi 1, zi}
25
26. Direct Observations vs State
• Idea: Assume internal “system” state
• Approach: Infer state by exploiting
measurements / observations
• Kalman Filter
→ explicit consideration of
(Gaussian) noise
26
27. Direct Observations vs State
• Idea: Assume internal “system” state
• Approach: Infer state by exploiting
measurements / observations
• Kalman Filter
→ explicit consideration of
(Gaussian) noise
• Particle Filter
→ no limitation to Gaussian noise
→ prob. model for measurements
27
28. Direct Observations vs State
• Idea: Assume internal “system” state
• Approach: Infer state by exploiting
measurements / observations
• Kalman Filter
→ explicit consideration of
(Gaussian) noise
• Particle Filter
→ no limitation to Gaussian noise
→ prob. model for measurements
28
29. Direct Observations vs State
• Idea: Assume internal “system” state
• Approach: Infer state by exploiting
measurements / observations
• Kalman Filter
→ explicit consideration of
(Gaussian) noise
• Particle Filter
→ no limitation to Gaussian noise
→ prob. model for measurements
• Hidden Markov Model
→ meas. model: conditional probability
→ dynamic model: limited memory,
transition probabilities
29