A brief presentation of some key concepts of information theory, complex systems, and evolutionary models as applied to technological evolution. Explains the informational nature of technologies as artefacts, the effects of interdependency (epistatic connections) on problem solvability, and makes some preliminary hypotheses about scarcity-induced innovation.
OSWC 2012: Modeling non-financial constraints in the development and adoption...J. M. Korhonen
The document summarizes a presentation on modeling the role of non-financial constraints in technological development and adoption. It discusses how previous research has found that constraints can both spur innovation by creating "bottlenecks", as well as hinder it by reducing "resource slack". The presentation aims to better understand these effects by modeling different constraint scenarios and measuring their impact on performance improvements, the use of new vs. existing technologies, and technological variety. Examples from copper smelting technologies are provided and simulation results are shown graphically.
A presentation on eco-design at Nordic Corporate Sustainability Conference on 15.5.09 @ Hanken, Helsinki. Thanks to Net Impact Hanken for organizing the event!
Corporate Social Responsibility - ecodesigners' viewpointJ. M. Korhonen
The document discusses sustainable product design from an eco-designer's perspective. It notes that the product development and ideation phases lock in 70-95% of a product's environmental impact, so focusing on the pre-design phase is important. Life cycle analysis is a primary tool used. While eco-design is necessary, it is not sufficient on its own to protect the ecosystem - regulation and limiting globalization are also needed to significantly reduce emissions. Innovation and free markets can meet sustainability challenges if given the proper constraints and incentives.
How Design Thinking works, or: Design Thinking Unpacked: an evolutionary algo...J. M. Korhonen
A presentation accompanying a paper* presented at EAD 2009 conference in Aberdeen, Scotland. We're trying to develop a theory why "design thinking" works in practice, and what may be its limits. The idea is that "design thinking" has similarities to a general class of algorithms known as evolutionary algorithms, and some comparisons can be made.
* Korhonen, J. M. & Hassi, L. (2009). Design Thinking Unpacked: An Evolutionary Algorithm. In Proceedings of the Eight European Academy of Design International Conference, 261-265. Aberdeen, UK.
Lecture Notes for Eco-design principles lectureJ. M. Korhonen
1) Sustainable design principles focus on people, planet and profit. Product development teams can help balance these demands by synthesizing solutions to complex problems.
2) There are five demands for sustainable products: being cyclic, renewable, safe, efficient, and social. Eco-design strategies include designing for longevity, disposal, reducing materials and energy, reusing, and recycling.
3) Moving from eco-efficiency to eco-effectiveness means maximizing positive effects like profit in addition to minimizing damages. This can be done through biological and technological nutrient cycles.
4) Light life cycle assessments in early design phases can be used for benchmarking, concept evaluation,
Abstract: Ontologies are used in numerous research disciplines and commercial applications to uniformly and semantically annotate real-world objects. Due to a rapid development of application domains the corresponding ontologies are changed frequently to include up-to-date knowledge. These changes dramatically influence dependent data as well as applications/systems, for instance, ontology mappings, that semantically interrelate ontologies. The talk will give an overview on evolution of ontologies and ontology-based mappings.
OSWC 2012: Modeling non-financial constraints in the development and adoption...J. M. Korhonen
The document summarizes a presentation on modeling the role of non-financial constraints in technological development and adoption. It discusses how previous research has found that constraints can both spur innovation by creating "bottlenecks", as well as hinder it by reducing "resource slack". The presentation aims to better understand these effects by modeling different constraint scenarios and measuring their impact on performance improvements, the use of new vs. existing technologies, and technological variety. Examples from copper smelting technologies are provided and simulation results are shown graphically.
A presentation on eco-design at Nordic Corporate Sustainability Conference on 15.5.09 @ Hanken, Helsinki. Thanks to Net Impact Hanken for organizing the event!
Corporate Social Responsibility - ecodesigners' viewpointJ. M. Korhonen
The document discusses sustainable product design from an eco-designer's perspective. It notes that the product development and ideation phases lock in 70-95% of a product's environmental impact, so focusing on the pre-design phase is important. Life cycle analysis is a primary tool used. While eco-design is necessary, it is not sufficient on its own to protect the ecosystem - regulation and limiting globalization are also needed to significantly reduce emissions. Innovation and free markets can meet sustainability challenges if given the proper constraints and incentives.
How Design Thinking works, or: Design Thinking Unpacked: an evolutionary algo...J. M. Korhonen
A presentation accompanying a paper* presented at EAD 2009 conference in Aberdeen, Scotland. We're trying to develop a theory why "design thinking" works in practice, and what may be its limits. The idea is that "design thinking" has similarities to a general class of algorithms known as evolutionary algorithms, and some comparisons can be made.
* Korhonen, J. M. & Hassi, L. (2009). Design Thinking Unpacked: An Evolutionary Algorithm. In Proceedings of the Eight European Academy of Design International Conference, 261-265. Aberdeen, UK.
Lecture Notes for Eco-design principles lectureJ. M. Korhonen
1) Sustainable design principles focus on people, planet and profit. Product development teams can help balance these demands by synthesizing solutions to complex problems.
2) There are five demands for sustainable products: being cyclic, renewable, safe, efficient, and social. Eco-design strategies include designing for longevity, disposal, reducing materials and energy, reusing, and recycling.
3) Moving from eco-efficiency to eco-effectiveness means maximizing positive effects like profit in addition to minimizing damages. This can be done through biological and technological nutrient cycles.
4) Light life cycle assessments in early design phases can be used for benchmarking, concept evaluation,
Abstract: Ontologies are used in numerous research disciplines and commercial applications to uniformly and semantically annotate real-world objects. Due to a rapid development of application domains the corresponding ontologies are changed frequently to include up-to-date knowledge. These changes dramatically influence dependent data as well as applications/systems, for instance, ontology mappings, that semantically interrelate ontologies. The talk will give an overview on evolution of ontologies and ontology-based mappings.
Hakimi asiabar, m. 2009: multi-objective genetic local search algorithm using...ArchiLab 7
This document summarizes a research paper that proposes a new multi-objective genetic algorithm called SBMOCA. SBMOCA hybridizes genetic algorithms with Kohonen's self-organizing map and variable neighborhood search to improve genetic diversity and local search efficiency. The paper describes limitations of traditional genetic algorithms, such as slow convergence near optima and genetic drift. It then reviews literature on hybridizing genetic algorithms with local search methods. Finally, it introduces the SBMOCA algorithm and applies it to optimize reservoir operation management, a non-linear, multi-objective problem.
NORTHERN ILLINOIS UNIVERSITY PHYSICS DEPARTMENT .docxcurwenmichaela
NORTHERN ILLINOIS UNIVERSITY
PHYSICS DEPARTMENT
Physics 253 – Basic Mechanics Fall 2016
Lab #11
Lab Writeup Due: Mon/Tue/Wed/Thu, Nov. 28/29/30, Dec. 1, 2016
Read Giancoli: Chapter 10 (Lecture Notes #13)
Rolling
Apparatus
In this experiment a sphere, disk, and cylinder are rolled down an inclined plane
with a raised guide to keep it on the track. Two photogates are positioned over the track
to measure the velocity of each object at the position of each photogate. Each photogate
only records the elapsed time between the when the object enters and leaves the
photogate. The experimenter must determine the width d of the object as seen by the
photogate detector to determine the velocity through each photogate.
Theory
Velocity is the time rate of change of position of an object. If the width, d , of an
object and the time, t , it takes to pass a point are both known, the average velocity is
ave
d
v
t
(1)
Angular velocity is the time rate of change of the angle of a rotating object,
measured in radians per second (rad/sec). For an object that rolls without slipping the
angular velocity is related to its linear velocity as
v d
R R t
(2)
The resistance of an object to a force (the inertia of an object) is caused by the
object’s mass. The resistance of an object to a torque (a force that causes a rotation) is
caused by the object’s moment of inertia (which is related to the object’s mass and how
far the mass is from the axis of rotation: 𝐼~𝑚𝑅2). This is why when a figure skater on
ice brings in her arms she rotates faster—her moment of inertial is smaller because more
of her mass is closer to her body (her effective radius decreases). Decreasing the moment
of inertia makes it easier for her to rotate (her rotational inertial decreases).
Objects in motion possess kinetic energy K . If the object is rolling it has kinetic
energy due to the forward motion of its center of mass,
CM
K , and its rotation, rotK .
Translational kinetic energy is based on the mass and velocity,
21
2CM CM
K mv .
Rotational kinetic energy about the center of mass is based on the moment of inertia and
angular velocity,
21
2 CMrot
K I .
2 21 1
2 2
CM CMK mv I (3)
Using Eq. (2) we can convert velocity to angular velocity:
2 2 2 2 2 2
1 1 1 1
2 2 2 2
CM CMK mR I I mR I (4)
Notice that we have derived the parallel-axis theorem: 𝐼 = 𝐼𝐶𝑀 + 𝑀ℎ
2 where, in the
situation for this lab, the object rolls about an axis at the point where it touches the ramp,
thus ℎ = 𝑅.
As a disk rolls down a slope the gravitational potential energy, gU mgh , is
converted into kinetic energy and thermal energy thermalE caused by sliding rather than
rolling. If the initial and final angular velocities are i and f , then the relationship for
the conservation of e
This document analyzes a profit and loss statement for Kudler Fine Foods. It begins by noting that an income statement, rather than a profit and loss statement, is available on the company's website. It then examines the income statement line by line, identifying categories such as revenue, cost of goods sold, expenses, and other income. By adding other income to total revenue and subtracting total expenses, the net income for Kudler Fine Foods is determined to be $116,599 based on the financial information provided in the income statement.
Learning by Redundancy: how climate multi-model ensembles can help to fight t...matteodefelice
This is my talk for the Severo Ochoa Research Seminar Lecture Series at the Barcelona Supercomputing Center held the 23/09/2015 (http://www.bsc.es/marenostrum-support-services/hpc-education-and-training/severo-ochoa-research-seminar/2309-sors)
The abstract is the following:
Climate Models are sophisticate tools able to simulate the interactions among various components of the Earth system (atmosphere, oceans, bio-sphere, etc.). Those tools are nowadays used for many purposes: to improve the knowledge of our planet, to analyse the projections for the future climate and to forecast the climate at multiple time-scales for a wide range of applications. In the last decade the use of climate ensembles (and multi-model ensembles) has become very common, the dimensionality of climate datasets has increased drastically (thanks also to a general increment of temporal and spatial resolutions of models). Unfortunately, this rise of the dimensionality of datasets did not coincide with the development of techniques designed to cope effectively with this massive amount of information.
The document discusses different research designs used in experimental studies. It describes exploratory research designs which are used to gain familiarity with a problem and formulate hypotheses. Descriptive research designs aim to describe characteristics of a population or phenomenon. Experimental research designs are used to test hypotheses about relationships between independent and dependent variables. True experiments, quasi-experiments, and correlational designs are discussed as important experimental research designs.
Ethier s.n., kurtz t.g. markov processes characterization and convergenceReza johari
This document introduces a birth-and-death process {Xn(t)} and shows that it converges in distribution to a diffusion process X(t) as n goes to infinity. Three methods are used:
1) A semigroup characterization showing the generators Gn of {Xn(t)} converge to the generator G of a Feller semigroup {T(t)} corresponding to X(t).
2) Martingale problem techniques establishing X(t) is the unique strong Markov process with sample paths in D[0,∞) satisfying an appropriate martingale problem.
3) Approximating the stochastic equations defining {Xn(t)} and showing the approximations converge to the stochastic
Systematic Innovation An Introduction To TrizRamon Balisnomo
Invention or technical innovation can be taught. This is an introduction to an interesting thought process invented by Genrich Altschuller called "Theory of Inventive Problem Solving" or TRIZ.
Quality Journey -Introduction to 7QC Tools2.0.pdfNileshJajoo2
7QC Tool - Quality Journey , Myth about Quality :- Cost of Quality
Check Sheet
Histogram
Pareto Chart
Cause and Effect Diagram
Control Charts
Scatter Diagram
Process Flow Diagram
EFFECT is “WHAT?” Happens
CAUSE is “WHY?” it Happens
EFFECT = RESULT OR OUTCOME
CAUSE = REASON(S) OR FACTOR(S) CONTRIBUTING TO THE EFFECT
Quality Definition :- Doing the right thing , right at first time and every time, meeting
customer’s & investor’s expectations .
1. The document discusses best practices for scientific software development including writing code for people to read, automating repetitive tasks, using version control, and avoiding redundancy.
2. Specific approaches mentioned are planning for mistakes, automated testing, continuous integration, and using style guides to ensure code is readable and consistently formatted.
3. Knitting allows analyzing and reporting in a single file by embedding R code chunks in markdown documents.
The document discusses using clustering analysis techniques to analyze meteorological data from 7 monitoring stations in Toluca Valley, Mexico from 2001-2008. The key findings were:
1. K-means and adaptive clustering algorithms grouped the data into 2 main clusters, rather than the expected 4 seasons.
2. Validation metrics like silhouette coefficient, cohesion, and separation showed higher quality for the 2 cluster solution.
3. This suggests the data from each year had more similar features of 2 seasons rather than 4. Further analysis is needed to understand implications for climate change.
In this deck, Torsten Hoefler from ETH Zurich presents: Scientific Benchmarking of Parallel Computing Systems.
"Measuring and reporting performance of parallel computers constitutes the basis for scientific advancement of high-performance computing. Most scientific reports show performance improvements of new techniques and are thus obliged to ensure reproducibility or at least interpretability. Our investigation of a stratified sample of 120 papers across three top conferences in the field shows that the state of the practice is not sufficient. For example, it is often unclear if reported improvements are in the noise or observed by chance. In addition to distilling best practices from existing work, we propose statistically sound analysis and reporting techniques and simple guidelines for experimental design in parallel computing. We aim to improve the standards of reporting research results and initiate a discussion in the HPC field. A wide adoption of this minimal set of rules will lead to better reproducibility and interpretability of performance results and improve the scientific culture around HPC."
Learn more: https://htor.inf.ethz.ch/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document discusses scenario methodology for planning future activities and emergency planning. It defines scenarios as hypothetical sequences of events used to focus on decision points and causal processes. Scenarios consider various possible futures that include important uncertainties rather than predicting a single outcome. The document outlines the history and uses of scenario planning, provides examples, and describes a 10-step process for building scenarios that includes defining objectives and key uncertainties. Scenarios help address situations with chronic uncertainty, differences of opinion, scarce resources, and problems that must be solved quickly.
The document provides guidance on giving effective presentations. It discusses what content should be included in talks, common problems talks face, and best practices for slide design. Specifically, it recommends that talks include an introduction, methods, results and discussion. It notes common misuses of slides like excessive text or not using visuals to support assertions. The document also provides tips for making slides clearer, such as using consistent colors and formatting and limiting text.
Multiverse Recommendation: N-dimensional Tensor Factorization for Context-awa...Alexandros Karatzoglou
Slides from RecSys 2010 presentation.
Context has been recognized as an important factor to con- sider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Ma- trix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factoriza- tion that allows for a flexible and generic integration of con- textual information by modeling the data as a User-Item- Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor
Interpretability and Reproducibility in Production Machine Learning Applicat...Swaminathan Sundararaman
The past decade has seen tremendous growth in production deployments of machine learning algorithms across a range of applications such as targeted advertising, self driving cars, speech translation, medical diagnosis etc [1]. In these contexts, models make key decisions such as predicting the likelihood of a person committing a future crime, trustworthiness for a loan approval, medical diagnosis etc [2]. Presence of bias based on gender, geographical location, race etc., and their consequent negative impact, have been uncovered in several of these deployments [3], [4]. Industries and governments are reacting, enacting regulations requiring that decisions made by machine learning models be Interpretable/Explainable [5].
Explainability across the full range of ML and DL algorithms is an unsolved research problem, with many innovations over the last several years and entire conferences devoted to the topic. However, even simple explainability solutions that are considered established in development (training environments) run into additional difficulties when put into live production.
Our design pattern uses a well known technique for explainability - the Canary model (sometimes called Surrogate model) [6,7]. In this approach, a classically non-explainable technique, such as a Neural Network, is paired with an explainable model (that approximates the predictions of the non-explainable technique) such as a Decision Tree. As long as predictions match - the Canary model’s behavior can be used to provide a human understandable reasoning for the prediction.
This document discusses building an online profile as a scientist in the era of big data and open science. It begins with an overview of the speaker's background working in academia, industry, and as an entrepreneur. The speaker then discusses various online tools and platforms that scientists can use to share their work and expertise, such as ORCID, LinkedIn, Google Scholar, SlideShare, and ResearchGate. He emphasizes the importance of making contributions openly available online in order to increase visibility and measure impact through alternative metrics. The speaker also provides examples of using these tools to showcase his own career and publications.
STEMinars is a pedagogical approach to informal learning that uses STEM (Science, Technology, Engineering, Mathematics) as four components of seminar topics. The document discusses best practices for STEMinars, including having clear learning goals, incorporating math and equations, quizzing participants, and creating and sharing materials under open licenses as Open Educational Resources.
Current damage predictors in high-valued systems are based on strain measurements and crack detection, thus, estimating the remaining useful life difficult. The US Army Research Laboratory developed damage precursor detection technique to outsmart fatigue prior to crack initiation. Our successful approach track the evolution in the materials microstructure, electrical inductance or capacitance, or thermal response.
The Aging Consumer - Future by Semcon # 3 2013Semcon
The document discusses how the world's population is aging rapidly, with over two billion people expected to be over 60 within 40 years, comprising over one-fifth of the global population. Experts note that this demographic shift will significantly impact industries and societies, requiring products and services to better meet the needs of the growing elderly consumer market who will be healthier and more financially secure. Adapting to and benefiting from the aging population will be a major opportunity and challenge for companies in the coming decades.
Specialized Transfemoral External Prosthetic Support PowerPoint PresentationGarret Senti
The Specialized Transfemoral External Prosthetic Support team's presentation on the progress made over a year of exploring the possibilities of a mechanical external support for a transfemoral prosthetic. The presentation describes the effort put into concepts and prototypes that would be utilized with a prosthesis which includes background information, initial prototype, final prototype, tests performed, results obtained, and the overall outcome of the project.
Note: Download if you want to view all animations and videos to enhance the knowledge about the team's prosthetic support.
Are shocks and crises good for strategic innovation, and what is the role of complexity of strategy, competitive intensity, and crisis details to strategic resilience and innovation? A paper presentation in DRUID 2012 conference in Copenhagen, June 2012.
Korhonen and Takada 2012: Modeling the role of non-financial constraints in t...J. M. Korhonen
A working paper of technology evolution and adoption under constraints. Contains a case study of copper industry and a NK-based simulation experiment. Results: constraints do not drive the development of new technologies, but they can drive the adoption of already existing non-mainstream technologies.
More Related Content
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Hakimi asiabar, m. 2009: multi-objective genetic local search algorithm using...ArchiLab 7
This document summarizes a research paper that proposes a new multi-objective genetic algorithm called SBMOCA. SBMOCA hybridizes genetic algorithms with Kohonen's self-organizing map and variable neighborhood search to improve genetic diversity and local search efficiency. The paper describes limitations of traditional genetic algorithms, such as slow convergence near optima and genetic drift. It then reviews literature on hybridizing genetic algorithms with local search methods. Finally, it introduces the SBMOCA algorithm and applies it to optimize reservoir operation management, a non-linear, multi-objective problem.
NORTHERN ILLINOIS UNIVERSITY PHYSICS DEPARTMENT .docxcurwenmichaela
NORTHERN ILLINOIS UNIVERSITY
PHYSICS DEPARTMENT
Physics 253 – Basic Mechanics Fall 2016
Lab #11
Lab Writeup Due: Mon/Tue/Wed/Thu, Nov. 28/29/30, Dec. 1, 2016
Read Giancoli: Chapter 10 (Lecture Notes #13)
Rolling
Apparatus
In this experiment a sphere, disk, and cylinder are rolled down an inclined plane
with a raised guide to keep it on the track. Two photogates are positioned over the track
to measure the velocity of each object at the position of each photogate. Each photogate
only records the elapsed time between the when the object enters and leaves the
photogate. The experimenter must determine the width d of the object as seen by the
photogate detector to determine the velocity through each photogate.
Theory
Velocity is the time rate of change of position of an object. If the width, d , of an
object and the time, t , it takes to pass a point are both known, the average velocity is
ave
d
v
t
(1)
Angular velocity is the time rate of change of the angle of a rotating object,
measured in radians per second (rad/sec). For an object that rolls without slipping the
angular velocity is related to its linear velocity as
v d
R R t
(2)
The resistance of an object to a force (the inertia of an object) is caused by the
object’s mass. The resistance of an object to a torque (a force that causes a rotation) is
caused by the object’s moment of inertia (which is related to the object’s mass and how
far the mass is from the axis of rotation: 𝐼~𝑚𝑅2). This is why when a figure skater on
ice brings in her arms she rotates faster—her moment of inertial is smaller because more
of her mass is closer to her body (her effective radius decreases). Decreasing the moment
of inertia makes it easier for her to rotate (her rotational inertial decreases).
Objects in motion possess kinetic energy K . If the object is rolling it has kinetic
energy due to the forward motion of its center of mass,
CM
K , and its rotation, rotK .
Translational kinetic energy is based on the mass and velocity,
21
2CM CM
K mv .
Rotational kinetic energy about the center of mass is based on the moment of inertia and
angular velocity,
21
2 CMrot
K I .
2 21 1
2 2
CM CMK mv I (3)
Using Eq. (2) we can convert velocity to angular velocity:
2 2 2 2 2 2
1 1 1 1
2 2 2 2
CM CMK mR I I mR I (4)
Notice that we have derived the parallel-axis theorem: 𝐼 = 𝐼𝐶𝑀 + 𝑀ℎ
2 where, in the
situation for this lab, the object rolls about an axis at the point where it touches the ramp,
thus ℎ = 𝑅.
As a disk rolls down a slope the gravitational potential energy, gU mgh , is
converted into kinetic energy and thermal energy thermalE caused by sliding rather than
rolling. If the initial and final angular velocities are i and f , then the relationship for
the conservation of e
This document analyzes a profit and loss statement for Kudler Fine Foods. It begins by noting that an income statement, rather than a profit and loss statement, is available on the company's website. It then examines the income statement line by line, identifying categories such as revenue, cost of goods sold, expenses, and other income. By adding other income to total revenue and subtracting total expenses, the net income for Kudler Fine Foods is determined to be $116,599 based on the financial information provided in the income statement.
Learning by Redundancy: how climate multi-model ensembles can help to fight t...matteodefelice
This is my talk for the Severo Ochoa Research Seminar Lecture Series at the Barcelona Supercomputing Center held the 23/09/2015 (http://www.bsc.es/marenostrum-support-services/hpc-education-and-training/severo-ochoa-research-seminar/2309-sors)
The abstract is the following:
Climate Models are sophisticate tools able to simulate the interactions among various components of the Earth system (atmosphere, oceans, bio-sphere, etc.). Those tools are nowadays used for many purposes: to improve the knowledge of our planet, to analyse the projections for the future climate and to forecast the climate at multiple time-scales for a wide range of applications. In the last decade the use of climate ensembles (and multi-model ensembles) has become very common, the dimensionality of climate datasets has increased drastically (thanks also to a general increment of temporal and spatial resolutions of models). Unfortunately, this rise of the dimensionality of datasets did not coincide with the development of techniques designed to cope effectively with this massive amount of information.
The document discusses different research designs used in experimental studies. It describes exploratory research designs which are used to gain familiarity with a problem and formulate hypotheses. Descriptive research designs aim to describe characteristics of a population or phenomenon. Experimental research designs are used to test hypotheses about relationships between independent and dependent variables. True experiments, quasi-experiments, and correlational designs are discussed as important experimental research designs.
Ethier s.n., kurtz t.g. markov processes characterization and convergenceReza johari
This document introduces a birth-and-death process {Xn(t)} and shows that it converges in distribution to a diffusion process X(t) as n goes to infinity. Three methods are used:
1) A semigroup characterization showing the generators Gn of {Xn(t)} converge to the generator G of a Feller semigroup {T(t)} corresponding to X(t).
2) Martingale problem techniques establishing X(t) is the unique strong Markov process with sample paths in D[0,∞) satisfying an appropriate martingale problem.
3) Approximating the stochastic equations defining {Xn(t)} and showing the approximations converge to the stochastic
Systematic Innovation An Introduction To TrizRamon Balisnomo
Invention or technical innovation can be taught. This is an introduction to an interesting thought process invented by Genrich Altschuller called "Theory of Inventive Problem Solving" or TRIZ.
Quality Journey -Introduction to 7QC Tools2.0.pdfNileshJajoo2
7QC Tool - Quality Journey , Myth about Quality :- Cost of Quality
Check Sheet
Histogram
Pareto Chart
Cause and Effect Diagram
Control Charts
Scatter Diagram
Process Flow Diagram
EFFECT is “WHAT?” Happens
CAUSE is “WHY?” it Happens
EFFECT = RESULT OR OUTCOME
CAUSE = REASON(S) OR FACTOR(S) CONTRIBUTING TO THE EFFECT
Quality Definition :- Doing the right thing , right at first time and every time, meeting
customer’s & investor’s expectations .
1. The document discusses best practices for scientific software development including writing code for people to read, automating repetitive tasks, using version control, and avoiding redundancy.
2. Specific approaches mentioned are planning for mistakes, automated testing, continuous integration, and using style guides to ensure code is readable and consistently formatted.
3. Knitting allows analyzing and reporting in a single file by embedding R code chunks in markdown documents.
The document discusses using clustering analysis techniques to analyze meteorological data from 7 monitoring stations in Toluca Valley, Mexico from 2001-2008. The key findings were:
1. K-means and adaptive clustering algorithms grouped the data into 2 main clusters, rather than the expected 4 seasons.
2. Validation metrics like silhouette coefficient, cohesion, and separation showed higher quality for the 2 cluster solution.
3. This suggests the data from each year had more similar features of 2 seasons rather than 4. Further analysis is needed to understand implications for climate change.
In this deck, Torsten Hoefler from ETH Zurich presents: Scientific Benchmarking of Parallel Computing Systems.
"Measuring and reporting performance of parallel computers constitutes the basis for scientific advancement of high-performance computing. Most scientific reports show performance improvements of new techniques and are thus obliged to ensure reproducibility or at least interpretability. Our investigation of a stratified sample of 120 papers across three top conferences in the field shows that the state of the practice is not sufficient. For example, it is often unclear if reported improvements are in the noise or observed by chance. In addition to distilling best practices from existing work, we propose statistically sound analysis and reporting techniques and simple guidelines for experimental design in parallel computing. We aim to improve the standards of reporting research results and initiate a discussion in the HPC field. A wide adoption of this minimal set of rules will lead to better reproducibility and interpretability of performance results and improve the scientific culture around HPC."
Learn more: https://htor.inf.ethz.ch/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document discusses scenario methodology for planning future activities and emergency planning. It defines scenarios as hypothetical sequences of events used to focus on decision points and causal processes. Scenarios consider various possible futures that include important uncertainties rather than predicting a single outcome. The document outlines the history and uses of scenario planning, provides examples, and describes a 10-step process for building scenarios that includes defining objectives and key uncertainties. Scenarios help address situations with chronic uncertainty, differences of opinion, scarce resources, and problems that must be solved quickly.
The document provides guidance on giving effective presentations. It discusses what content should be included in talks, common problems talks face, and best practices for slide design. Specifically, it recommends that talks include an introduction, methods, results and discussion. It notes common misuses of slides like excessive text or not using visuals to support assertions. The document also provides tips for making slides clearer, such as using consistent colors and formatting and limiting text.
Multiverse Recommendation: N-dimensional Tensor Factorization for Context-awa...Alexandros Karatzoglou
Slides from RecSys 2010 presentation.
Context has been recognized as an important factor to con- sider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Ma- trix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factoriza- tion that allows for a flexible and generic integration of con- textual information by modeling the data as a User-Item- Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor
Interpretability and Reproducibility in Production Machine Learning Applicat...Swaminathan Sundararaman
The past decade has seen tremendous growth in production deployments of machine learning algorithms across a range of applications such as targeted advertising, self driving cars, speech translation, medical diagnosis etc [1]. In these contexts, models make key decisions such as predicting the likelihood of a person committing a future crime, trustworthiness for a loan approval, medical diagnosis etc [2]. Presence of bias based on gender, geographical location, race etc., and their consequent negative impact, have been uncovered in several of these deployments [3], [4]. Industries and governments are reacting, enacting regulations requiring that decisions made by machine learning models be Interpretable/Explainable [5].
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Explaining the evolution of technologies, firms, and industries: how complexity and constraints can be simulated
1. Janne M. Korhonen
janne.m.korhonen@aalto.fi / www.slideshare.net/jmkorhonen
Research seminar 29.10.2010, Aalto School of Economics
Explaining the evolution of
technologies, firms, and industries:
How complexity and constraints can be simulated
2. Background
PhD research topic: The effects of resource
scarcity on innovation (prof. Liisa Välikangas)
Research question(s): why scarcities sometimes
result to significant innovations, but not always?
Framework: evolutionary theory and complex,
non-linear behavior (complexity); information
theory; theory of computation; simulation models
Unit of analysis: technology/system as artefact
Why I’m here: to outline some ideas!
3. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
4. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
13. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
15. Describing a system...
0 1
Material Glass Plastic
Shape Round Square
Contents No pressure Under pressure
Cap Pull Screw
Leftmost column represents elements, 0 and 1
columns represent alleles of an element
18. ...system as strings
Material 0 1
Shape 1 0
Contents 0 1
Cap 0 1
Bottles are thus coded in terms of their
elements and alleles (e.g. Frenken 2001, 2006)
19. The coding can have multiple levels - single 0/1
alleles can represent entire subsystems!
What can be coded?
In principle, everything that is definable by
information ≈ everything! Applications include
• Organization theory (Kauffman and Macready 1995; Westhoff et al
1996; Levinthal 1997; Marengo 1998; Baum 1999; Levinthal and Warglien 1999;
McKelvey 1999a, 1999b; Gavetti and Levinthal 2000; Ghemawat and Levinthal
2000; Marengo et al. 2000; Rivkin 2000; Dosi et al. 2001; Frenken 2001, 2006;
Morel and Ramanujam 1999)
• Political science (Schrodt 1994; Post and Johnson 2000)
• Scientometrics (Scharnhorst 1998)
20. Bottles coded with four elements of two alleles;
e.g. Frenken (2001, 2006), Kauffman (1993)
Strings for two bottles:
Jdaniels 0 1 0 0
ED 1 0 1 1
22. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
23. Simon (1969), Rosenberg (1969), Kauffman
(1993, 2000), Frenken et al. (1997, 1999)
Problem solving task:
Following Simon (1969), can be conceptualized
as searching for the right solution in a solution
(“design”) space.
New variations can be considered as “new
combinations” (cf. Schumpeter 1934).
Due to practical limitations - the size of design
space - this search is boundedly rational.
It thus requires heuristics.
25. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
26. e.g. Simon (1969), Wagner and Altenberg
(1995), Frenken (2001)
Interdependency matters
The system may have interdependent elements;
“mutating” one may have an effect on another’s
“fitness” for purpose.
This defines the system’s architecture.
29. Example:
This is known as epistasy. Pressure has an
epistatic relationship to the shape of bottle.
Pressurized contents
Round shape
30. Kauffman (1993), Frenken (2001, 2006)
No epistasy
Material Shape Contents Cap
Material x
Shape x
Contents x
Cap x
31. Changing the contents of the bottle may mean
that the shape is no longer satisfactory
Epistatic relationship - 1
Material Shape Contents Cap
Material x
Shape x x
Contents x x
Cap x
32. Similarities to e.g. Quality Function
Deployment Matrix are evident.
Epistatic relationship - 2
Material Shape Contents Cap
Material x x x
Shape x x x
Contents x x x x
Cap x x
33. This creates difficulties
As epistasy increases, finding the optimal solution
becomes more and more difficult.
This can be represented using a fitness
landscape:
1) assign a fitness to each solution
2) order the solutions based on their differences
Theoretically, three different landscapes can be
identified:
34. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
35. No epistasy: single solution is the best one for
the task. Optimization is very easy.
!"
!#$"
!#%"
!#&"
!#'"
!#("
!#)"
!#*"
!#+"
!#,"
$"
!#,
!#+
!#*
!#)
!#(
!#'
!#&
!#%
!#$
!-!
38. Chaotic landscape: maximum epistasy. No
correlation between difference and fitness.
!"
!#$"
!#%"
!#&"
!#'"
!#("
!#)"
!#*"
!#+"
!#,"
$" !#,-$"
!#+-!#,
!#*-!#+
!#)-!#*
!#(-!#)
!#'-!#(
!#&-!#'
!#%-!#&
!#$-!#%
!-!#$"
39. e.g. Kauffman (1993, 2000); Frenken (2001,
2006)
Chaotic landscape:
no correlation
between difference
and fitness.
Optimization
impossible; random
search only viable
search strategy.
!"#$%&
!"'$!"#&
!"($!"'&
!")$!"(&
!$!")&
44. For simulating social systems, see e.g. Gilbert &
Troitzsch (2005), Gilbert (2008)
The neat thing:
We can simulate various problem-solving
situations without having to know the details of
the situation;
We just need to estimate the epistasy and
solution space.
46. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
48. In 1900, blast (BF) and reverberatory (R)
furnaces are widespread.
Development of flash smelting
BF 1100 2000 0010 0200 R 0000 0000 0000 0000
49. R 0000 0000 0000 0000BF 1100 2000 0010 0200
PY 1120 2000 0010 0200
Development of flash smelting
In 1902, pyritic process (PY) is tested. It’s
autogenous, but limited by lack of suitable ores.
50. In the 1930s, electric furnaces (E1930) spread
(incl. Imatra), and reverberatories are improved.
BF 1100 2000 0010 0200
PY 1120 2000 0010 0200 R1930 0000 0001 0001 0110
R 0000 0000 0000 0000
E1930 0010 0110 1101 0000
Development of flash smelting
51. Electricity shortage after WW2 focuses search
for alternative sources of smelting energy.
BF 1100 2000 0010 0200
PY 1120 2000 0010 0200 R1930 0000 0001 0001 0110
R 0000 0000 0000 0000
E1930 0010 0110 1101 0000
Development of flash smelting
52. First flash furnace in 1949; integrates roasting,
uses concepts proven in fluid bed reactors.
BF 1100 2000 0010 0200
PY 1120 2000 0010 0200 R1930 0000 0001 0001 0110
R 0000 0000 0000 0000
F1950 1120 1121 2121 0111
E1930 0010 0110 1101 0000
Development of flash smelting
53. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
54. Search & constraints:
The effect of constraints: focuses search on
improving attributes that are affected by scarcity.
Constraints act as a focusing device (Rosenberg
1969); focusing search to certain attributes.
This is known as function space search after
Bradshaw (1992) and Frenken (2001).
55. Measuring differences
When artefacts (or systems) are coded as strings,
measuring their differences is easy!
Hamming distance measures the differences
between two solutions:
Hamming (1950)
56. Hamming distance measures the number of
different elements in a “design.”
BF 1100 2000 0010 0200
PY 1120 2000 0010 0200 R1930 0000 0001 0001 0110
R 0000 0000 0000 0000
Hamming distance 1 Hamming distance 4
Measuring differences
57. Hamming distance can be used e.g. to measure
radicalness of innovation, creativity, novelty...
BF 1100 2000 0010 0200
PY 1120 2000 0010 0200
F1950 1120 1121 2121 0111
Hamming distance 11
Measuring innovation
58. Architectures can be decomposable or non-
decomposable (Frenken et al. 1999; Simon 1969)
Epistasy, revisited
n1 n2 n3 n4 n5 n6 n7 n8 n9
w1
w2
w3
w4
w5
w6
w7
w8
w9
60. Decomposable architecture requires 23 + 23 +
23 = 24 trials, which can be done in parallel!
Why e.g. distributed teams work
n1 n2 n3 n4 n5 n6 n7 n8 n9
w1 x x x
w2 x x x
w3 x x x
w4 x x x
w5 x x x
w6 x x x
w7 x x x
w8 x x x
w9 x x x
61. Most real life problems are nearly
decomposable - they can be solved in parallel
Near-decomposable:
n1 n2 n3 n4 n5 n6 n7 n8 n9
w1 x x x
w2 x x x
w3 x x x
w4 x x x x
w5 x x x
w6 x x x
w7 x x x x
w8 x x x
w9 x x x
62. Sukuraman et al. (2008)
Low epistasy technology
Figure 2: Revised Design7
Marketing
At the beginning of the spring semester, the Rowan University Business Department showed interest in
working with the project through the Entrepreneurial course. The engineering team met with a group o
business students to show them the drawings and explain the concept of the project after which they
decided to work on the marketing side of the project. At present, the business team is doing research on
areas the finished project can be marketed to. They are also presenting to a group of possible
Technologies developed for
use in the developing world
tend to be highly modular to
accommodate variety in
components, i.e. they are
near/fully decomposable,
such as this bicycle-powered
grain crusher
63. Scarcity causes search activity; focuses search
on certain dimension (energy, in this case)
The effects of scarcity (?)
65. However, wood gas was not an integrated,
efficient system - flash smelting was.
+
-
Integration
(≈epistasy)
The effects of scarcity (?)
66. Agenda
How the world works - information and evolution
Systems as strings of information
Problem-solving as search process
Why interdependency matters
Fitness landscapes
Development of flash smelting
Effects of scarcity, and measuring innovation
Some implications
67. Other implications...
The theories used here are applicable to a large variety of
complex systems, and have interesting implications, e.g.
• Systems with moderate complexity have the highest
fitness (Schilling 2000)
• The more complex a problem is, the more solutions
differ from each other (Kauffman 1993)
• The speed of evolution is inversely related to the
complexity of system’s architecture (Kauffman 1993)
• Imitation strategies are less successful, the more
complex the system being imitated is (Rivkin 2000)
• S-curves are easy to explain: local/global optima!
68. Computing organizations
“Computational organization theory?” Based on
theory of computation, organizations as
computational systems (e.g. Saraceno and Barr
2002; also Wolfram 2002; Sipser 2006)
e.g. showing the limits of theory: the fastest way
to determine what an organization will do is to
let the organization do it; thus,
“shortcuts” (=theories) are limited, thus,
the predictive power of our theories is
inherently limited. ☐
69. One more thing...
The question “why we have so much variety” is
much less interesting than the question
why the lack of variety?
71. Possible variety?
Exercise: how many different organizational forms
could a 1-50 person company have, if there are
just three levels in hierarchy?
e.g. possible positions are 1) top management,
2) middle management, 3) worker?
73. If you started at the Big Bang (≈ 13,8 billion years
ago), you’d have to go through
solutions per second to find all the possible
combinations
2 500 000
74. References (1)
Baum (1999). Whole-part coevolutionary competition in organizations. pp. 113-136
in Baum, McKelvey (eds.) Variations in Organization Science. In Honor of Donald
T. Campbell.
Bradshaw (1992). The airplane and the logic of invention. pp. 239-250 in Giere, R.
(ed.) Cognitive Models of Science.
Dennett (1995). Darwin's Dangerous Idea.
Dosi et al. (2001). Bridging contested terrain: linkin incentive-based and learning
perspectives on organizational evolution. Nelson-and-Winter Conference, Aalborg.
Frenken et al. (1997). Self-organization and the economics of technical change, in
Actes de 46me Congrés Annuel de l'Association Française de Science
Economique
Frenken et al. (1999a). Interdependecies, near-decomposability and adaptation.
pp. 145-165 in Brenner, T. (ed.) Computational Techniques for Modelling Learning
in Economics.
Frenken (2001). Understanding product innovation using complexity theory. PhD
thesis.
Frenken (2006). Innovation, Evolution, and Complexity Theory.
Gavetti and Levinthal (2000). Looking forward and looking backward: cognitive and
experiental search. Administrative Science Quarterly 45(1) pp. 113-137
Ghemawat and Levinthal (2000). Choice structures, business strategy and
performance: a generalized NK-simulation approach. Working paper 2000-05,
Wharton School
Gilbert (2008). Agent-based models.
Gilbert and Troitzsch (2005). Simulation for Social Scientists.
Hamming (1950).
Kauffman (1993). The Origins of Order.
Kauffman (2000). Investigations.
Kauffman and Macready (1995). Technological evolution and adaptive
organizations. Complexity 1, pp. 26-43
King (2007). The Evolution of Technology for Extractive
Metallurgy over the Last 50 Years—Is the Best Yet to Come? Journal of Metals Vol.
59, No.2, pp. 21-27
Levinthal (1997). Adaptation on rugged landscapes. Management Science 43, pp.
934-950
Levinthal and Warglien (1999). Landscape design: Designing for local action in
complex worlds. Organization Science 10, pp. 342-357
Marengo (1998). Interdependencies and division of labour in problem-solving
technologies. Seventh International Schumpeter Society Conference, Vienna.
Marengo et al. (2000). The structure of problem-solving knowledge and the
structure of organizations. Industrial and Corporate Change 9, pp. 757-788
McKelvey (1999a) Avoiding complexity catastrophe in coevolutionary pockets:
strategies for rugged landscapes. Organization Science 10, pp. 294-321.
McKelvey (1999b). Self-organization, complexity catastrophe, and microstate
models at the edge of chaos, pp. 279-307 in Baum, McKelvey (eds.) Variations in
Organization Science. In Honor of Donald T. Campbell.
75. References (2)
Morel and Ramanujam (1999). Through the looking glass of complexity: the
dynamics of organizations as adaptive and evolving systems. Organization
Science 10, pp. 278-293.
Post and Johnson (2000). "Chaos prevailing on every continent": towads a
newtheory of decentralized decision-making in complex systems. Chicago-Kent
Law Review.
Rivkin (2000). Imitation of complex strategies. Management Science 46, pp.
824-844.
Rosenberg (1969). The direction of technical change: inducement mechanisms
and focussing devices. Economic Development and Cultural Change 18, pp 1-24.
Scharnhorst (1998). Citation networks, science landscapes and evolutioniary
strategies. Scientometrics 43, pp. 95-106.
Saraceno and Barr (2002). A computational theory of the firm. Journal of Economic
behavior and Organization.
Schilling (2000). Toward a general modular systems theory and its application to
interfirm product modularity. Academy of Management Review 25(2) pp. 312-334
Schrodt (1994). A landscape model of rule-based co-adaptation in international
behavior.
Schumpeter (1934). The Theory of Economic Development.
Simon (1969). The Sciences of the Artificial.
Sipser (2006). Introduction to the Theory of Computation. Second Edition.
Sukuraman et al. (2008). Teaching Engineering Design with a focus on the
developing world. Proceedings of the ASEE 2008.
Wagner and Altenberg (1996). Perspective: Complex adaptations and the evolution
of evolvability. Evolution 50(3), pp. 967-976
Westhoff et al. (1996). Complexity, Organization, and Stuart Kauffman's The
Origins of Order. Journal of Economic Behavior and Organization 29, pp. 1-25
Wolfram (2002). A New Kind of Science.