New Mathematical Tools for the Financial SectorSSA KPI
AACIMP 2010 Summer School lecture by Gerhard Wilhelm Weber. "Applied Mathematics" stream. "Modern Operational Research and Its Mathematical Methods with a Focus on Financial Mathematics" course. Part 5.
More info at http://summerschool.ssa.org.ua
Presentation of third- and fifth-order optical nonlinearities measurement using the D4Sigma-Z-scan Method. I present a resolution of propagation equation in general case (with third- and fifth-order nonlinearities) and a numerical inversion.
This presentation is conclude with experimental results.
New Mathematical Tools for the Financial SectorSSA KPI
AACIMP 2010 Summer School lecture by Gerhard Wilhelm Weber. "Applied Mathematics" stream. "Modern Operational Research and Its Mathematical Methods with a Focus on Financial Mathematics" course. Part 5.
More info at http://summerschool.ssa.org.ua
Presentation of third- and fifth-order optical nonlinearities measurement using the D4Sigma-Z-scan Method. I present a resolution of propagation equation in general case (with third- and fifth-order nonlinearities) and a numerical inversion.
This presentation is conclude with experimental results.
The amount of digital data in the new era has grown exponentially in recent years and with the development of new technologies, is growing more rapidly than ever before.
Nevertheless, simply knowing that all these data are out there is easily understandable, utilizing these data to turn a profit is not trivial.
The need of data mining techniques able to extract profitable insight information is the next frontier of innovation, competition and profit.
A data analytic services provider, in order to well-scale and exponentially grow its profit, has to deal with scalability, multi-tenancy and self-adaptability.
In big data applications, machine learning is a very powerful instrument but a bad choice regarding the algorithm and its configuration parameters can easily lead to poor results. The key problem is automating the tuning process without a priori knowledge of the data and without human intervention.
In this research project we implemented and analysed TunUp: A Distributed Cloud-based Genetic Evolutionary Tuning for Data Clustering.
The proposed solution automatically evaluates and tunes data clustering algorithms, so that big data services can self-adapt and scale in a cost-efficient manner.
For our experiments, we considered k-means as clustering algorithm, that is a simple but popular algorithm, widely used in many data mining applications.
Clustering outputs are evaluated using four internal techniques: AIC, Dunn, Davies-Bouldin and Silhouette and an external evaluation: AdjustedRand.
We then performed a correlation t-test in order to validate and benchmark our internal techniques against AdjustedRand.
Defined the best evaluation criteria, the main challenge of k-means is setting the right value of k, that represents the number of clusters, and the distance measure used to compute distances of each pair of points in the data space.
To address this problem we propose an implementation of the Genetic Evolutionary Algorithm that heuristically finds out an optimal configuration of our clustering algorithm.
In order to improve performances, we implemented a parallel version of genetic algorithm developing a REST API and deploying several instances in the Amazon Cloud Computing (EC2) infrastructure.
In conclusion, with this research we contributed building and analysing TunUp, an open solution for evaluation, validation and tuning of data clustering algorithms, with a particularly focused on cloud services.
Our experiments show the quality and efficiency of tuning k-means on a set of public datasets.
The research also provides a Roadmap that gives indications of how the current system should be extended and utilized for future clustering applications, such as: Tuning of existing clustering algorithms, Supporting new algorithms design, Evaluation and comparison of different algorithms.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
This paper presents a novel technique for
designing an Infinite Impulse Response (IIR) Filter with
Linear Phase Response. The design of IIR filter is always a
challenging task due to the reason that a Linear Phase
Response is not realizable in this kind. The conventional
techniques involve large number of samples and higher
order filter for better approximation resulting in complex
hardware for implementing the same. In addition, an
extensive computational resource for obtaining the inverse
of huge matrices is required. However, we propose a
technique, which uses the frequency domain sampling along
with the linear programming concept to achieve a filter
design, which gives a best approximation for the linear
phase response. The proposed method can give the closest
response with less number of samples (only 10) and is
computationally simple. We have presented the filter design
along with its formulation and solving methodology.
Numerical results are used to substantiate the efficiency of
the proposed method.
In topological inference, the goal is to extract information about a shape, given only a sample of points from it. There are many approaches to this problem, but the one we focus on is persistent homology. We get a view of the data at different scales by imagining the points are balls and consider different radii. The shape information we want comes in the form of a persistence diagram, which describes the components, cycles, bubbles, etc in the space that persist over a range of different scales.
To actually compute a persistence diagram in the geometric setting, previous work required complexes of size n^O(d). We reduce this complexity to O(n) (hiding some large constants depending on d) by using ideas from mesh generation.
This talk will not assume any knowledge of topology. This is joint work with Gary Miller, Benoit Hudson, and Steve Oudot.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
Mapping Ash Tree Colonization in an Agricultural Moutain Landscape_ Investigation the Potential of Hyperspectral Imagery.pdf
1. Mapping Ash Tree Colonization in an
Agricultural Moutain Landscape: Investigation
the Potential of Hyperspectral Imagery
D. Sheeren1,2 , M. Fauvel1,2 , S. Ladet2 , A. Jacquin2 , G. Bertoni1,2 and
A. Gibon2
1
INP Toulouse - University of Toulouse
2
UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan
2011 IEEE International Geoscience and Remote Sensing Symposium
24-29 July, Vancouver, Canada
4. Context of the work 1/3
Scientific context:
Landscape ecology:
What is the impact of agricultural activities on the landscape?
What is the impact of landscape heterogeneity on the biodiversity?
Global change:
How evolve landscape and biodiversity?
What are the factors of evolution?
Predict and anticipate the responses of ecosystems to landscape changes.
5. Context of the work 2/3
What are the causes and the consequences of ash three colonization in the
Pyrenees mountain?
Villelongue village, 65260-France
1950 2000
Up to now:
Ecological process understood
Multi-agents model build
× Accurate ash-thematic map
6. Context of the work 3/3
Current method: Aerial images + visual inspection + field survey
Small geographical area
Time consuming
Cost !
Multispectral satellite images are not enough spatially and spectrally
accurate for ash detection
tree/not tree ok, but it is not possible to go to the species
Good multitemporal data are very difficult to obtain
Hypothesis: With hyperspectral images, it would be possible to
differentiate between ash tree and other species of tree
7. Context of the work 3/3
Current method: Aerial images + visual inspection + field survey
Small geographical area
Time consuming
Cost !
Multispectral satellite images are not enough spatially and spectrally
accurate for ash detection
tree/not tree ok, but it is not possible to go to the species
Good multitemporal data are very difficult to obtain
Hypothesis: With hyperspectral images, it would be possible to
differentiate between ash tree and other species of tree
Madonna Project!
8. Madonna project: Objectives and data
Objectives:
• Mapping of the ash tree distribution
• 2D et 3D information
• Estimation of structural and biophysical parameters (tree density and height,
foliar chlorophyll . . . )
Data (summer 2010).
• Very high spatial resolution hyperspectral images
• LiDar data
• Field data (ash trees and other dominant species, foliar analysis . . . )
9. Area covered by the mission
Village of Villelongue, France (00◦ 03’W and 42◦ 57’N).
Medium altitudinal range (450-1800m)
I
14. Very high spatial resolution hyperspectral images 1/2
T
E
HySpex sensor:
Spectral resolution : 1.5 nm, 400-1000 nm, 160 bands
Spatial resolution: 50 cm
15. Very high spatial resolution hyperspectral images 2/2
Pattern recognition approach:
Problem of the spectral dimensionality: statistical methods fail
Use of non-linear SVM (Gaussian kernel)
But the optimization of the hyperparameter is too demanding in terms of
time processing when using the conventional cross-validation strategy
(about 3 To of data to process).
A fast and accurate method for optimizing the hyperparameter is needed
for an operational system
SVM + Kernel alignment
16. Very high spatial resolution hyperspectral images 2/2
Pattern recognition approach:
Problem of the spectral dimensionality: statistical methods fail
Use of non-linear SVM (Gaussian kernel)
But the optimization of the hyperparameter is too demanding in terms of
time processing when using the conventional cross-validation strategy
(about 3 To of data to process).
A fast and accurate method for optimizing the hyperparameter is needed
for an operational system
SVM + Kernel alignment
17. Support Vector Machine
H (α, b) : {x|f (x) = 0}
yi = 1
w
yi = −1
Supervised method: S = (x1 , y1 ), . . . , (xn , yn ) ∈ Rd × {−1; 1}
n
Separating function: f (z) = sgn αi k(z, xi ) + b
i=1
Solve QP problem: n n
1
max g(α) = αi − αi αj yi yj k(xi , xj )
α
i=1
2 i,j=1
n
constraint to 0 ≤ αi ≤ C et i=1 αi yi = 0
18. Kernel function
Kernel function k: similarity measure between two spectra xi et xj
2
xi − xj
Gaussian kernel : kg (xi , xj ) = exp −
2σ 2
σ 2 is an hyperparameter that controls how two spectra are considered as
similar or not.
Ideally, σ 2 must be tuned such as:
kg (xi , xj ) ≈ 1 If yi = yj
kg (xi , xj ) ≈ 0 Else
Ideal kernel matrix:
1 δ y1 y2 . . . δy1 yn
δ y2 y1 1 . . . δy2 yn
I
K = .
. . .. .
. .
. . .
.
δyn y1 δyn y2 ... 1
19. Kernel alignment
Alignment A: compute the similitude (angle) between the ideal matrix and
the kernel matrix with parameter σ 2
KI, K F
A(σ) =
KI F K F
σ is selected such A(σ) is maximal
Contrary to cross-validation, there is no need to solve the QP problem
0.5
0.45
0.4
0.35
0.3
0.25
0 20 40 60
21. Protocol
Ground thruth:
12 tree species (Ash tree, Chestnut tree, Lime tree, Hazel tree . . . ).
Classification of the tree species: Are ash trees identifiable?
Classification of the image: Are the results spatially consistent?
22. Results
Quantitative analysis: Ash tree separability
GMM SVM SVM-lin
OA 72% 94% 89%
Kappa 0.65 0.92 0.89
Ash tree
User accuracy 84.0% 89.9% 83.1%
Producer accuracy 53.6% 89.9% 88.8%
Qualitative analysis:
24. Conclusions and perspectives
Conclusions:
Accurate mapping of ash tree is possible with hyperspectral images
Framework: SVM + kernel alignment
Perspectives:
Spatial regularization
Can biophysical parameters be estimated?
25. Mapping Ash Tree Colonization in an
Agricultural Moutain Landscape: Investigation
the Potential of Hyperspectral Imagery
D. Sheeren1,2 , M. Fauvel1,2 , S. Ladet2 , A. Jacquin2 , G. Bertoni1,2 and
A. Gibon2
1
INP Toulouse - University of Toulouse
2
UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan
2011 IEEE International Geoscience and Remote Sensing Symposium
24-29 July, Vancouver, Canada