1. This document discusses methods for estimating the area under a curve using finite sums, including left-hand rectangular approximation (LRAM), right-hand rectangular approximation (RRAM), and midpoint rectangular approximation (MRAM).
2. LRAM yields an area that is smaller than the true area, RRAM yields an area that is larger, and MRAM provides the closest approximation to the true area.
3. The document provides an example comparing the different approximation methods and notes that using more subintervals increases the accuracy of the estimates.
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
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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
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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.
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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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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
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!
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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.
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.
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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.
Neuro-symbolic is not enough, we need neuro-*semantic*
Finite Sums
1. 5.1 Estimating with Finite Sums
Greenfield Village, Michigan
Photo by Vickie Kelly, 2002 Greg Kelly, Hanford High School, Richland, Washington
2. 3
If the velocity is not constant,
we might guess that the
2
distance traveled is still equal
to the area under the curve.
1
1 2
Example: V t 1 0 1 2 3 4
8 1 1
1
1 1
1 2
8 8
2
t v
We could estimate the area under the curve by 0 1
drawing rectangles touching at the left corners. 1
1 1
8
This is called the Left-hand Rectangular 1
2
Approximation Method (LRAM). 1
2
1 1 1 3 1
Approximate area: 3
11 1 2 5 5.75 2
8 2 8 4 8
3. 1 3
2
V t 1
8
2
1
0 1 2 3 4
1 1
1
1
1 2 3
8 8
2
We could also use a Right-hand Rectangular Approximation
Method (RRAM).
1 1 1 3
Approximate area: 1 1 2 3 7 7 .7 5
8 2 8 4
4. 1 3
2
V t 1
t 8
v
0 .5 1 .0 3 1 2 5 2
1 .2 8 1 2 5
1 .5
1
1 .7 8 1 2 5
2 .5
2 .5 3 1 2 5
3 .5
0 1 2 3 4
1 .0 3 1 2 5 1 .2 8 1 2 5 1 .7 8 1 2 5 2 .5 3 1 2 5
Another approach would be to use rectangles that touch at
the midpoint. This is the Midpoint Rectangular
Approximation Method (MRAM).
In this example there are four
subintervals.
Approximate area:
As the number of subintervals
6 .6 2 5
increases, so does the accuracy.
6. Rectangular Approximation
Methods
◦ LRAM: smaller than true area
◦ RRAM: larger than true area
◦ MRAM: closest to true area
More Accurate = More intervals
7. With the trapezoidal rule you take the
LRAM and add the RRAM and then divide
by two.
So we take LRAM (5.75) from the first
problem and the RRAM (7.75) and we add
them to together.
The answer is 13.5 and then we divide
that by two to get our answer:
The answer is 6.75.