Our presentation at the High Risk / High Reward track at the ACM MM 2014 conference. In this presentation we present a novel way to tackle large scale image classification or retrieval.
To redesign Cragslist Web as well as mobile home page: this was a small exercise I did in the Information Design I extended learning course offered by SFSU (San Francisco State University)
A robust audio watermarking in cepstrum domain composed of sample's relation ...ijma
Watermark bits embedded in audio signals considering the sample’s relative state in a frame may
strengthen the attack-invariant features of audio watermarking algorithm. In this work, we propose to
embed watermarks in an audio signal considering the relation between the mean values of consecutive
groups of samples which shows robustness by overcoming common watermarking challenges. Here, we
divide the host audio signal into equal-sized non-overlapping frames which in turn is split into four equalsized
non-overlapping sub-frames. After, transforming these sub-frames in cepstrum domain we finally use
the relation between the differences of first two sub-frames and last two sub-frames to embed watermarks.
Depending on the watermark bit (either 0 or 1) to be embed, our embedding technique either interchange
or update the differences between these groups of samples by distorting the sample values in sub-frames
selectively. Thus, watermarks are embedded by making a little or no distortion of the sub-frames which
helps our scheme to be imperceptible in nature. Moreover, use of such embedding technique lead our
watermarking scheme to a computationally less complex extraction method. Simulation results also justify
our claim of the proposed scheme to be both robust and imperceptible.
Crash Course Experiments in Political Science PhD Course University of SienaAlessandro Innocenti
Experimental and Cognitive Economics vs. Political Science”, Crash Courses in Experimental Design in Political and Social Sciences, Presidio S. Niccolò, University of Siena, May 15-16, 2014.
Non-Uniform Random Feature Selection and Kernel Density Scoring With SVM Base...Sathishkumar Samiappan
Traditional statistical classification approaches often
fail to yield adequate results with Hyperspectral imagery (HSI) because
of the high dimensional nature of the data, multimodal class
distribution and limited ground truth samples for training. Over
the last decade, Support VectorMachines (SVMs) andMulti-Classifier
Systems (MCS) have become popular tools for HSI analysis.
Random Feature Selection (RFS) forMCS is a popular approach to
produce higher classification accuracies. In this study, we present a
Non-Uniform Random Feature Selection (NU-RFS) within a MCS
framework using SVMas the base classifier.We propose a method
to fuse the output of individual classifiers using scores derived from
kernel density estimation. This study demonstrates the improvement
in classification accuracies by comparing the proposed approach
to conventional analysis algorithms and by assessing the
sensitivity of the proposed approach to the number of training samples.
These results are compared with that of uniform RFS and regular
SVM classifiers. We demonstrate the superiority of Non-Uniform
based RFS system with respect to overall accuracy, user accuracies,
producer accuracies and sensitivity to number of training
samples.
The success of a new pricing campaign relies on the balance of many factors, some are measurable and some seem to depend mostly on creativity and talent
The challenge is to bring the science much closer to the “artistic” part
In this webinar the participants will learn about:
Behavior Pricing analytics framework, profitability modeling, simulations and forecasting
Needs Determining pricing structures based on segments/micro segments needs
Perceptions Value based pricing strategy in practice
Influential Behavioral economics real life examples – providing additional science into pricing structures & campaign messages
To redesign Cragslist Web as well as mobile home page: this was a small exercise I did in the Information Design I extended learning course offered by SFSU (San Francisco State University)
A robust audio watermarking in cepstrum domain composed of sample's relation ...ijma
Watermark bits embedded in audio signals considering the sample’s relative state in a frame may
strengthen the attack-invariant features of audio watermarking algorithm. In this work, we propose to
embed watermarks in an audio signal considering the relation between the mean values of consecutive
groups of samples which shows robustness by overcoming common watermarking challenges. Here, we
divide the host audio signal into equal-sized non-overlapping frames which in turn is split into four equalsized
non-overlapping sub-frames. After, transforming these sub-frames in cepstrum domain we finally use
the relation between the differences of first two sub-frames and last two sub-frames to embed watermarks.
Depending on the watermark bit (either 0 or 1) to be embed, our embedding technique either interchange
or update the differences between these groups of samples by distorting the sample values in sub-frames
selectively. Thus, watermarks are embedded by making a little or no distortion of the sub-frames which
helps our scheme to be imperceptible in nature. Moreover, use of such embedding technique lead our
watermarking scheme to a computationally less complex extraction method. Simulation results also justify
our claim of the proposed scheme to be both robust and imperceptible.
Crash Course Experiments in Political Science PhD Course University of SienaAlessandro Innocenti
Experimental and Cognitive Economics vs. Political Science”, Crash Courses in Experimental Design in Political and Social Sciences, Presidio S. Niccolò, University of Siena, May 15-16, 2014.
Non-Uniform Random Feature Selection and Kernel Density Scoring With SVM Base...Sathishkumar Samiappan
Traditional statistical classification approaches often
fail to yield adequate results with Hyperspectral imagery (HSI) because
of the high dimensional nature of the data, multimodal class
distribution and limited ground truth samples for training. Over
the last decade, Support VectorMachines (SVMs) andMulti-Classifier
Systems (MCS) have become popular tools for HSI analysis.
Random Feature Selection (RFS) forMCS is a popular approach to
produce higher classification accuracies. In this study, we present a
Non-Uniform Random Feature Selection (NU-RFS) within a MCS
framework using SVMas the base classifier.We propose a method
to fuse the output of individual classifiers using scores derived from
kernel density estimation. This study demonstrates the improvement
in classification accuracies by comparing the proposed approach
to conventional analysis algorithms and by assessing the
sensitivity of the proposed approach to the number of training samples.
These results are compared with that of uniform RFS and regular
SVM classifiers. We demonstrate the superiority of Non-Uniform
based RFS system with respect to overall accuracy, user accuracies,
producer accuracies and sensitivity to number of training
samples.
The success of a new pricing campaign relies on the balance of many factors, some are measurable and some seem to depend mostly on creativity and talent
The challenge is to bring the science much closer to the “artistic” part
In this webinar the participants will learn about:
Behavior Pricing analytics framework, profitability modeling, simulations and forecasting
Needs Determining pricing structures based on segments/micro segments needs
Perceptions Value based pricing strategy in practice
Influential Behavioral economics real life examples – providing additional science into pricing structures & campaign messages
https://youtu.be/3FE2HhQnFh0
I am thankful for the CZI scholarship as a DeepLabCut AI resident to study the neuroscience of sexual diversity. DeepLabCut is a deep-learning-based open-source toolbox for 3D pose estimation. It has been used in a wide range of applications e.g. chicken agriculture, surgery, dog poop detector, infant exploratory behaviour, lizard robotics, exergaming biofeedback, 3D triangulation of cheetahs chasing prey, spider webbing, stroke rehabilitation, wildlife conservation, pupil tracking, parrot tripedal locomotion, fear behaviour, dog emotions, functional recovery after spinal cord injury etc.
http://www.mackenziemathislab.org/deeplabcut
A behaviomics approach like DeepLabCut significantly benefits my research on sexual behaviour in a steroid-independent and collective behaviour context. Traditional methods are limited by their subjectivity, biased in selecting parameters to measure, and extremely labour-intensive and time-consuming. There are also limits to human perception and language to accurately detect and describe behaviour. At a broader level, behaviomics improves animal ethics and biodiversity. For animal ethics, behaviomics increase the accuracy and throughput of the data, which reduces the number of animals for the same amount of data. These data also contribute to developing in silico and robotic models that can replace animal experiments. For biodiversity, behaviomics allows researchers to move away from behaviour recordings in the lab in “labesticated” animal models and captive species. More wildlife in naturalistic settings can be studied including footage from drones and satellites.
https://www.nature.com/articles/s41467-022-27980-y
From the experiences of people like Deborah Raji and Timnit Gebru, we know the field of AI is dominated by and predominantly serves the WEIRD (Western, educated, industrialized, rich and democratic) population, particularly white, cisgender, and heterosexual males. It excludes marginalised minorities from its creations which led to race and gender misidentification problems, as well as resulting in the “weapons of math destruction”, as coined by Cathy O'Neil. We need to improve this by embracing perspectives beyond Western science, for example, by incorporating Indigenous communities and Arabic philosophies. There’s also a hegemony of software licensing that provides additional economic barriers to access. DeepLabCut hopes to reduce this barrier by being open source.
https://www.currentaffairs.org/.../software-licesing-is-a...
This residency drives forward my research on the neuroscience of sexual diversity and trains me to become an open-source code contributor. Learning from approaches like EarSketch, Queer in AI, and Black Girls Code, this residency also helps me diversify AI through assisting marginalised minorities to learn AI and become code contributors as well. There are many people to thank for this opportunity. https://www.deeplabcutairesidency.org/our-team
International Perspectives: Visualization in Science and EducationLiz Dorland
Overview of the international and interdisciplinary Gordon Research Conference on Visualization in Science and Education and info on key cognitive science and learning sciences researchers. History of the conference, NSF workshop, and research on learning with visualizations.
Joy Mountford at BayCHI: Visualizations of Our Collective LivesBayCHI
The lines between art, design, and information are dissolving as we experience new places and objects. Consider, for example, the organic flow of air traffic over North America at daybreak, the bursts of search query memes spreading around the globe, and the pointillist surge of mobile phone usage on New Year's Eve. Using the new techniques of generative data visualization, a new generation of artist/designers/engineer/scientists are creating gorgeous, dynamic experiences driven by massive sets of data about our own lives. Their work comes to life in architectural spaces, on walls of wood and metal and light and shimmering glass clouds suspended overhead. Of course it must be touched to be appreciated and engaged with, simple gestures launch a thousand images and possibilities. Many of these projects have received international recognition. They are primarily 3D applications that can run in real time, but really can only be appreciated by watching them, as movies. These data movies aim to make information easier to understand while being enjoyable to watch. Surprising insights surface through looking at our 'data life' in new ways, and may compel us to design in different, even better ways.
Edited and revised: Overview of the international and interdisciplinary Gordon Research Conference on Visualization in Science and Education and info on key cognitive science and other visualization researchers. History of the conference, NSF workshop, and research on learning with visualizations.
from pq import from search import class InformedNode(NoJeanmarieColbert3
from pq import *
from search import *
class InformedNode(Node):
"""
Added the goal state as a parameter to the constructor. Also
added a new method to be used in conjunction with a priority
queue.
"""
def __init__(self, goal, state, parent, operator, depth):
Node.__init__(self, state, parent, operator, depth)
self.goal = goal
def priority(self):
"""
Needed to determine where the node should be placed in the
priority queue. Depends on the current depth of the node as
well as the estimate of the distance from the current state to
the goal state.
"""
return self.depth + self.state.heuristic(self.goal)
class InformedSearch(Search):
"""
A general informed search class that uses a priority queue and
traverses a search tree containing instances of the InformedNode
class. The problem domain should be based on the
InformedProblemState class.
"""
def __init__(self, initialState, goalState):
self.expansions = 0
self.clearVisitedStates()
self.q = PriorityQueue()
self.goalState = goalState
self.q.enqueue(InformedNode(goalState, initialState, None, None, 0))
solution = self.execute()
if solution == None:
print("Search failed")
else:
self.showPath(solution)
print("Expanded", self.expansions, "nodes during search")
def execute(self):
while not self.q.empty():
current = self.q.dequeue()
self.expansions += 1
if self.goalState.equals(current.state):
return current
else:
successors = current.state.applyOperators()
operators = current.state.operatorNames()
for i in range(len(successors)):
if not successors[i].illegal():
n = InformedNode(self.goalState,
successors[i],
current,
operators[i],
current.depth+1)
if n.repeatedState():
del(n)
else:
self.q.enqueue(n)
return None
class InformedProblemState(ProblemState):
"""
An interface class for problem domains used with informed search.
"""
def heuristic(self, goal):
"""
For use with informed search. Returns the estimated
cost of reaching the goal from this state.
"""
abstract()
Read and complete the activities in Module 4.04. You will look at the advantages and disadvantages of using various types of media to communicate your ideas:
1. What does the term "medium" mean when used in the text?
2. What are the tw ...
Interest in immersive media increased significantly over recent years. Besides applications in entertainment, culture, health, industry, etc., telepresence and remote collaboration gained importance due to the pandemic and climate crisis. Immersive media have the potential to increase social integration and to reduce greenhouse gas emissions. As a result, technologies along the whole pipeline from capture to display are maturing and applications are becoming available, creating business opportunities. One aspect of immersive technologies that is still relatively undeveloped is the understanding of perception and quality, including subjective and objective assessment. The interactive nature of immersive media poses new challenges to estimation of saliency or visual attention, and to the development of quality metrics. The V-SENSE lab of Trinity College Dublin addresses these questions in current research. This talk will highlight corresponding examples in 360 VR video, light fields, volumetric video and XR.
Using Local Spectral Methods to Robustify Graph-Based LearningDavid Gleich
This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on.
29 March 2019 Presentation on the relation of digital and virtual heritage to digital humanities, issues, some projects..at Curtin University Perth Australia
Use Your Words: Content Strategy to Influence BehaviorLiz Danzico
What if we were truly open to the language in our cities, our neighborhoods, our city blocks? What is our environment telling us to do?
In this workshop, we’ll let the language of the city guide us to explore how words, specifically the words of our immediate contexts, shape our behavior. By being open to the possibilities, we’ll explore how language influences both the micro and macro actions we take. We’ll go on expeditions in the morning—studying street signs to doorways to receipts—comparing patterns in the language maps we’ll construct. In the afternoon, we’ll look at what these patterns suggest for the products and services we design.
You’ll walk away having learned how words influence behavior, how products and services have used language for behavior change, and having tools for thinking about language and behavior change in the work you do.
Spend the day letting words use you, so you can go back to work to use them with renewed wisdom.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
More Related Content
Similar to How ‘How’ Reflects What’s What: Content-based Exploitation of How Users Frame Social Images
https://youtu.be/3FE2HhQnFh0
I am thankful for the CZI scholarship as a DeepLabCut AI resident to study the neuroscience of sexual diversity. DeepLabCut is a deep-learning-based open-source toolbox for 3D pose estimation. It has been used in a wide range of applications e.g. chicken agriculture, surgery, dog poop detector, infant exploratory behaviour, lizard robotics, exergaming biofeedback, 3D triangulation of cheetahs chasing prey, spider webbing, stroke rehabilitation, wildlife conservation, pupil tracking, parrot tripedal locomotion, fear behaviour, dog emotions, functional recovery after spinal cord injury etc.
http://www.mackenziemathislab.org/deeplabcut
A behaviomics approach like DeepLabCut significantly benefits my research on sexual behaviour in a steroid-independent and collective behaviour context. Traditional methods are limited by their subjectivity, biased in selecting parameters to measure, and extremely labour-intensive and time-consuming. There are also limits to human perception and language to accurately detect and describe behaviour. At a broader level, behaviomics improves animal ethics and biodiversity. For animal ethics, behaviomics increase the accuracy and throughput of the data, which reduces the number of animals for the same amount of data. These data also contribute to developing in silico and robotic models that can replace animal experiments. For biodiversity, behaviomics allows researchers to move away from behaviour recordings in the lab in “labesticated” animal models and captive species. More wildlife in naturalistic settings can be studied including footage from drones and satellites.
https://www.nature.com/articles/s41467-022-27980-y
From the experiences of people like Deborah Raji and Timnit Gebru, we know the field of AI is dominated by and predominantly serves the WEIRD (Western, educated, industrialized, rich and democratic) population, particularly white, cisgender, and heterosexual males. It excludes marginalised minorities from its creations which led to race and gender misidentification problems, as well as resulting in the “weapons of math destruction”, as coined by Cathy O'Neil. We need to improve this by embracing perspectives beyond Western science, for example, by incorporating Indigenous communities and Arabic philosophies. There’s also a hegemony of software licensing that provides additional economic barriers to access. DeepLabCut hopes to reduce this barrier by being open source.
https://www.currentaffairs.org/.../software-licesing-is-a...
This residency drives forward my research on the neuroscience of sexual diversity and trains me to become an open-source code contributor. Learning from approaches like EarSketch, Queer in AI, and Black Girls Code, this residency also helps me diversify AI through assisting marginalised minorities to learn AI and become code contributors as well. There are many people to thank for this opportunity. https://www.deeplabcutairesidency.org/our-team
International Perspectives: Visualization in Science and EducationLiz Dorland
Overview of the international and interdisciplinary Gordon Research Conference on Visualization in Science and Education and info on key cognitive science and learning sciences researchers. History of the conference, NSF workshop, and research on learning with visualizations.
Joy Mountford at BayCHI: Visualizations of Our Collective LivesBayCHI
The lines between art, design, and information are dissolving as we experience new places and objects. Consider, for example, the organic flow of air traffic over North America at daybreak, the bursts of search query memes spreading around the globe, and the pointillist surge of mobile phone usage on New Year's Eve. Using the new techniques of generative data visualization, a new generation of artist/designers/engineer/scientists are creating gorgeous, dynamic experiences driven by massive sets of data about our own lives. Their work comes to life in architectural spaces, on walls of wood and metal and light and shimmering glass clouds suspended overhead. Of course it must be touched to be appreciated and engaged with, simple gestures launch a thousand images and possibilities. Many of these projects have received international recognition. They are primarily 3D applications that can run in real time, but really can only be appreciated by watching them, as movies. These data movies aim to make information easier to understand while being enjoyable to watch. Surprising insights surface through looking at our 'data life' in new ways, and may compel us to design in different, even better ways.
Edited and revised: Overview of the international and interdisciplinary Gordon Research Conference on Visualization in Science and Education and info on key cognitive science and other visualization researchers. History of the conference, NSF workshop, and research on learning with visualizations.
from pq import from search import class InformedNode(NoJeanmarieColbert3
from pq import *
from search import *
class InformedNode(Node):
"""
Added the goal state as a parameter to the constructor. Also
added a new method to be used in conjunction with a priority
queue.
"""
def __init__(self, goal, state, parent, operator, depth):
Node.__init__(self, state, parent, operator, depth)
self.goal = goal
def priority(self):
"""
Needed to determine where the node should be placed in the
priority queue. Depends on the current depth of the node as
well as the estimate of the distance from the current state to
the goal state.
"""
return self.depth + self.state.heuristic(self.goal)
class InformedSearch(Search):
"""
A general informed search class that uses a priority queue and
traverses a search tree containing instances of the InformedNode
class. The problem domain should be based on the
InformedProblemState class.
"""
def __init__(self, initialState, goalState):
self.expansions = 0
self.clearVisitedStates()
self.q = PriorityQueue()
self.goalState = goalState
self.q.enqueue(InformedNode(goalState, initialState, None, None, 0))
solution = self.execute()
if solution == None:
print("Search failed")
else:
self.showPath(solution)
print("Expanded", self.expansions, "nodes during search")
def execute(self):
while not self.q.empty():
current = self.q.dequeue()
self.expansions += 1
if self.goalState.equals(current.state):
return current
else:
successors = current.state.applyOperators()
operators = current.state.operatorNames()
for i in range(len(successors)):
if not successors[i].illegal():
n = InformedNode(self.goalState,
successors[i],
current,
operators[i],
current.depth+1)
if n.repeatedState():
del(n)
else:
self.q.enqueue(n)
return None
class InformedProblemState(ProblemState):
"""
An interface class for problem domains used with informed search.
"""
def heuristic(self, goal):
"""
For use with informed search. Returns the estimated
cost of reaching the goal from this state.
"""
abstract()
Read and complete the activities in Module 4.04. You will look at the advantages and disadvantages of using various types of media to communicate your ideas:
1. What does the term "medium" mean when used in the text?
2. What are the tw ...
Interest in immersive media increased significantly over recent years. Besides applications in entertainment, culture, health, industry, etc., telepresence and remote collaboration gained importance due to the pandemic and climate crisis. Immersive media have the potential to increase social integration and to reduce greenhouse gas emissions. As a result, technologies along the whole pipeline from capture to display are maturing and applications are becoming available, creating business opportunities. One aspect of immersive technologies that is still relatively undeveloped is the understanding of perception and quality, including subjective and objective assessment. The interactive nature of immersive media poses new challenges to estimation of saliency or visual attention, and to the development of quality metrics. The V-SENSE lab of Trinity College Dublin addresses these questions in current research. This talk will highlight corresponding examples in 360 VR video, light fields, volumetric video and XR.
Using Local Spectral Methods to Robustify Graph-Based LearningDavid Gleich
This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on.
29 March 2019 Presentation on the relation of digital and virtual heritage to digital humanities, issues, some projects..at Curtin University Perth Australia
Use Your Words: Content Strategy to Influence BehaviorLiz Danzico
What if we were truly open to the language in our cities, our neighborhoods, our city blocks? What is our environment telling us to do?
In this workshop, we’ll let the language of the city guide us to explore how words, specifically the words of our immediate contexts, shape our behavior. By being open to the possibilities, we’ll explore how language influences both the micro and macro actions we take. We’ll go on expeditions in the morning—studying street signs to doorways to receipts—comparing patterns in the language maps we’ll construct. In the afternoon, we’ll look at what these patterns suggest for the products and services we design.
You’ll walk away having learned how words influence behavior, how products and services have used language for behavior change, and having tools for thinking about language and behavior change in the work you do.
Spend the day letting words use you, so you can go back to work to use them with renewed wisdom.
Similar to How ‘How’ Reflects What’s What: Content-based Exploitation of How Users Frame Social Images (20)
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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.
Multi-source connectivity as the driver of solar wind variability in the heli...
How ‘How’ Reflects What’s What: Content-based Exploitation of How Users Frame Social Images
1. How ‘How’ Reflects
What’s What:
Content-based Exploitation of
How Users Frame Social
Images
Michael Riegler, Simula Research Laboratory, Norway!
Martha Larson, Delft University of Technology, Netherlands!
Mathias Lux, University of Klagenfurt, Austria!
Christoph Kofler, Delft University of Technology, Netherlands
2. We will introduce a signal that
!
exists in every image collection
&
gives you an enormous speedup!
3. Take Home Message
❖ Photographers use intentional frames.!
❖ The frames reflect the semantic categories of images.!
❖ In turn, global image features reflect the frames.!
❖ This motivates a fast and simple approach to image
semantics.!
❖ Take home a strong inner feeling that you want to try it
out yourself!
5. ❖ You may think now that you
already know it, its called:!
❖ Concepts or…!
❖ Scenes!
❖ But Wrong!
6. ❖ And let me tell you, it is also not!
❖ Composition!
❖ Also Wrong!
7. “Intentional framing is the sum of
choices made by photographers on
exactly how to portray the subject
matter that they have decided to
photograph.”
–The Definition
Picture source: https://www.flickr.com/photos/ausnap/5712791522/in/photostream/
8. Mechanics of Intentional Framing
semantic
reflects reflects
category of an
image
the
photographers´
intent
global image
features
reflects
13. Hypothesis
❖ Photographers’ choices.!
❖ Even if framing is not a conscious decision, it still is an
unconscious one.!
❖ Similar intents for taking images lead to similar
framings.!
❖ Global features can capture these intentional semantics.
15. Global Features and Intent
❖ Global features connect semantics and intent.!
❖ Show that there exist a solid evidence for intentional
framing.!
❖ Clustering experiment on two different data sets!
❖ Intent data set!
❖ Fashion 10000 data set
16. Correlation of Peoples’ Perception and Global Features
❖ X-means clustering!
❖ Based on different global
features.!
❖ Features can catch different
aspects (edges, colour, etc.).!
❖ The density of the global
features based clusters
correlated to the users
perception about the
intentional framing in it.
Original
Edge
Color
17. Evidence of Human
Perception of Intent
black - a positive correlation!
red - a negative correlation
Intent Categories
!
Global Features
1 2 3 4 5 6
CEDD
FCTH
Gabor
Tamura
Luminance Layout
Scalable Color
Opponent Histogram
Autocolor Correlogram
JPEG Coefficent
Edge Histogram
PHOG
JCD
Joint Histogram
20. Content Based Classification
❖ Using intentional framing to tackle a classification
problem.!
❖ Simple search-based classifier (SimSea).!
❖ Our submission to the ACM MM `13 Yahoo! - Large-scale
Flickr-tag image Classification Grand Challenge!
❖ Reviewers told us: It is too simple…
21. Remember the challenge?
❖ 2 million images.!
❖ 10 different semantic
categories.!
❖ nature, people, music,
london, 2012, food, wedding,
sky, beach, travel.!
❖ extremely diverse categories.
22. JCD CL OH PHOG
2012 0,198 0,128 0,130 0,104
beach 0,448 0,487 0,342 0,534
food 0,531 0,492 0,389 0,352
london 0,244 0,201 0,146 0,347
music 0,526 0,457 0,495 0,164
nature 0,502 0,410 0,435 0,503
people 0,264 0,227 0,244 0,105
sky 0,628 0,601 0,544 0,473
travel 0,139 0,101 0,128 0,112
wedding 0,463 0,272 0,262 0,235
The results iAP per category based on the
development set
23. Compared to the Official Results
!
!
Our method!
SimSea
Local 1
(SMaL[1])
Local 2
(SVM[1])
❖ Very good results with a very simple method.!
❖ Very time efficient.!
❖ Processed on a single desktop PC.
Concept 1
(HA[2])
MiAP 0,391 0,422 0,413 0,37
[1] E. Mantziou, S. Papadopoulos, and Y. Kompatsiaris. Scalable Training with Approximate Incremental Laplacian Eigenmaps and
PCA. In Proceedings of the ACM MM 13’, pages 381–384, 2013.
[2] W. Hsu. Flickr-tag Prediction Using Multi-modal Fusion and Meta Information. In Proceedings of ACM MM 13’, pages 353–
356, 2013.
24. Conclusion
❖ Intentional framing exists.!
❖ Different framing correspond to different global
features.!
❖ Interesting framework for leveraging global features
classification.!
❖ Fast and simple!!
❖ New vista for multimedia research.