Category: News
Business Applications Partner News: Week of June 17
Check out this week’s top resources to stay up-to-date on the latest Business Applications Partner News. Remember to sign up for the monthly Dynamics 365 and Power Platform partner pulse newsletters.
What to register for:
Viva Engage Business Applications Partner Community
What to download:
FY25 Partner Activities Office Hours Deck
What to review/like/share:
Partner case study: Alterna lights up operational efficiency for renewable energy provider with Dynamics 365
May Highlights Rewind: LinkedIn | direct blog link
RSM creates winning playbook for midmarket ERP cloud migration: LinkedIn | direct blog link
What to watch:
June 6 Tech Talk: Power Platform Well-Architected
Reminders: Register for the upcoming partner events!
Events:
June 24-27: FY25 Partner Activities deep-dive webinar series
July 10: MCAPS Start for Partners
July 22: Microsoft Partner FY25 GTM Launch Event for Business Applications
Sep 18-20: Microsoft Power Platform Conference
Trainings:
Level Up Copilot Sales Champion training course (access code: MOKC-MCJB)
Check out this week’s top resources to stay up-to-date on the latest Business Applications Partner News. Remember to sign up for the monthly Dynamics 365 and Power Platform partner pulse newsletters.
What to register for:
Viva Engage Business Applications Partner Community
What to download:
FY25 Partner Activities Office Hours Deck
What to review/like/share:
Partner case study: Alterna lights up operational efficiency for renewable energy provider with Dynamics 365
May Highlights Rewind: LinkedIn | direct blog link
RSM creates winning playbook for midmarket ERP cloud migration: LinkedIn | direct blog link
What to watch:
June 6 Tech Talk: Power Platform Well-Architected
Reminders: Register for the upcoming partner events!
Events:
June 24-27: FY25 Partner Activities deep-dive webinar series
July 10: MCAPS Start for Partners
July 22: Microsoft Partner FY25 GTM Launch Event for Business Applications
Sep 18-20: Microsoft Power Platform Conference
Trainings:
Level Up Copilot Sales Champion training course (access code: MOKC-MCJB)
ET DELETED TinyPE Binary – Possibly Hostile
I have a firewall rule, which is blocking communication from 151.101.126.172 to my local windows 10 machine, under category “ET DELETED TinyPE Binary – Possibly Hostile”
How to get dynamic list of authorized ip’s which must have to connect with my windows machine while window update?
I don’t want to disable the rule for all the sources ip’s.
I have a firewall rule, which is blocking communication from 151.101.126.172 to my local windows 10 machine, under category “ET DELETED TinyPE Binary – Possibly Hostile” How to get dynamic list of authorized ip’s which must have to connect with my windows machine while window update?I don’t want to disable the rule for all the sources ip’s. Read More
Premium Planner Plans and PowerBI integration
Can you now, or will you be able to, configure reporting in PowerBI from one or multiple premium MS Planner plans?
Can you now, or will you be able to, configure reporting in PowerBI from one or multiple premium MS Planner plans? Read More
Unleashing PTU Tokens Throughput with KV-Cache-Friendly Prompt on Azure
1- Introduction
PTUs are reserved processing capacity, ensuring stable performance for uniform LLM workloads. The reserved capacity of PTUs makes KV caching more effective compared to Pay-As-You-Go (PayGo). This blog post delves into the role of Key-Value (KV) caching in enhancing PTU throughput, and practical strategies to create cache-friendly prompts that maximize efficiency.
2- What are Provisioned Throughput Units (PTUs)?
Provisioned Throughput Units (PTUs) in Azure represent a dedicated model processing capacity that can be reserved and deployed for handling prompts and generating completions. The key benefits of PTUs include:
Predictable Performance: Ensures stable maximum latency and throughput for uniform workloads.
Reserved Processing Capacity: Once deployed, the throughput is available irrespective of utilization.
Cost Savings: High throughput workloads may lead to cost savings compared to token-based consumption models.
3- KV Caching: Enhancing Efficiency in Language Models
Key-Value (KV) caching is a technique employed in generative transformer models, such as language models (LLMs), to optimize the inference process. Key aspects of KV caching include:
Reduction of Computational Cost: Minimizes the need to recompute key and value tensors for past tokens during each generation step.
Memory-Compute Trade-off: Tensors are stored (cached) in GPU memory, balancing memory usage and compute efficiency.
4- Crafting KV Cache-Friendly Prompts:
To optimize your prompts for KV caching, consider the following strategies:
Position Dynamic Elements Wisely: Place dynamic elements, such as grounding data, date & time, or chat history, toward the end of your prompt.
Maintain Order for Static Elements: Keep static elements like safety instructions, examples, and tool/function definitions at the beginning and in a consistent order.
Dedicate Your PTU Deployment: Dedicating your deployment to few use cases can further improve cache hit rates, as the requests will be more uniform.
5- A Case Study with GPT4-T-0409:
The following experiments focused on the impact of the cacheable/fixed percentage of the prompt on system performance, specifically average time-to-first-token and throughput. The results showed a clear trend: as the fixed/cacheable part of the prompt increased, the average latency decreased and the request capacity increased.
General Settings:
Model: GPT4-T-0409
Region: UK South
PTU: 100
Load test duration: 5 min
Experiment 1:
Input token size: 10245
Output token size: 192
Cacheable % of the prompt
1%
25%
50%
75%
Throughput (request/min)
7
9
12.5
20
Time to first token (sec)
2.4
2.0
1.77
1.3
Analysis:
Throughput Improvement: As the cacheable percentage of the prompt increased from 1% to 75%, throughput saw a significant increase from 7 requests per minute to 20 requests per minute. This translates to nearly a threefold improvement, highlighting the efficiency gain from caching.
Latency Reduction: The time to the first token decreased from 2.4 seconds to 1.3 seconds as the cacheable percentage increased. This reduction in latency indicates faster initial response times, which is crucial for user experience.
Experiment 2:
Input token size: 5000
Output token size: 100
Cacheable % of the prompt
1%
25%
50%
75%
Throughput (request/min)
17
22
32
55
Time to first token (sec)
1.31
1.25
1.16
0.9
Analysis:
Throughput Improvement: When the cacheable percentage of the prompt increased from 1% to 75%, throughput saw an impressive rise from 17 requests per minute to 55 requests per minute. This more than threefold increase demonstrates the substantial impact of cache-friendly prompts on system performance.
Latency Reduction: The time to the first token improved from 1.31 seconds to 0.9 seconds with higher cacheable percentages. This faster response time is beneficial for applications requiring real-time or near-real-time interactions.
* The results may vary based on the model type, deployment region, and use case.
Summary of the results:
In both experiments, a longer cacheable part of the prompt resulted in significant boosts in throughput and reductions in latency. The improvements were more pronounced in Experiment 2, likely due to the smaller input token sizes.
Throughput: Across both experiments, a higher cacheable percentage of the prompt resulted in substantial increases in throughput. In Experiment 1, throughput increased by almost 186%, and in Experiment 2, it increased by approximately 224% from the lowest to the highest cacheable percentage.
Latency: The time to the first token decreased consistently as the cacheable percentage of the prompt increased. This reduction in latency enhances the user experience by providing quicker initial responses.
These results underscore the importance of optimizing prompts to be cache-friendly, thereby maximizing the performance of the system in terms of both throughput and latency. By leveraging caching strategies, systems can handle more requests per minute and provide faster responses, ultimately leading to a more efficient and scalable AI deployment.
6- Conclusion
Provisioned Throughput Units (PTUs) in Azure offer significant advantages in terms of performance, capacity, and cost savings. By leveraging KV caching and creating cache-friendly prompts, you can further enhance the efficiency of your AI workloads. Optimizing prompt structure not only maximizes the benefits of PTUs but also ensures more effective and resource-efficient model processing.
7- Acknowledgments
A special thanks to Michael Tremeer for his invaluable review and feedback on this blog post. Your insights have greatly enhanced the quality of this work.
8- References
Transformers KV Caching Explained | by João Lages | Medium
Techniques for KV Cache Optimization in Large Language Models (omrimallis.com)
Microsoft Tech Community – Latest Blogs –Read More
Don’t know what is wrong with my output of this code
function [MOVINGREG] = registerMarsImages(MOVING,FIXED)
% Convert images to grayscale if they are RGB
if size(MOVING, 3) == 3
MOVING = rgb2gray(MOVING);
end
if size(FIXED, 3) == 3
FIXED = rgb2gray(FIXED);
end
% Detect features in both images
points1 = detectSURFFeatures(MOVING, ‘MetricThreshold’, 1000);
points2 = detectSURFFeatures(FIXED, ‘MetricThreshold’, 1000);
% Extract features from both images
[features1, valid_points1] = extractFeatures(MOVING, points1);
[features2, valid_points2] = extractFeatures(FIXED, points2);
% Match features by using their descriptors
indexPairs = matchFeatures(features1, features2, ‘MatchThreshold’, 10, ‘MaxRatio’, 0.7);
% Retrieve locations of corresponding points for each image
matchedPoints1 = valid_points1(indexPairs(:, 1), :);
matchedPoints2 = valid_points2(indexPairs(:, 2), :);
% Estimate the transformation between the moving and fixed images
[tform, inlierIdx] = estimateGeometricTransform2D(matchedPoints1, matchedPoints2, ‘projective’, ‘Confidence’, 99.9, ‘MaxNumTrials’, 2000);
% Get the output limits for the transformation
[xLimitsMoving, yLimitsMoving] = outputLimits(tform, [1 size(MOVING, 2)], [1 size(MOVING, 1)]);
[xLimitsFixed, yLimitsFixed] = outputLimits(projective2d(eye(3)), [1 size(FIXED, 2)], [1 size(FIXED, 1)]);
% Determine the size of the panorama
xMin = min([xLimitsMoving xLimitsFixed]);
xMax = max([xLimitsMoving xLimitsFixed]);
yMin = min([yLimitsMoving yLimitsFixed]);
yMax = max([yLimitsMoving yLimitsFixed]);
width = round(xMax – xMin);
height = round(yMax – yMin);
% Create an empty panorama canvas
panorama = zeros(height, width, ‘like’, FIXED);
% Create reference objects for the panorama and the fixed image
panoramaView = imref2d([height width], [xMin xMax], [yMin yMax]);
fixedRef = imref2d(size(FIXED), [xLimitsFixed(1) xLimitsFixed(2)], [yLimitsFixed(1) yLimitsFixed(2)]);
% Warp the moving image into the panorama
registered = imwarp(MOVING, tform, ‘OutputView’, panoramaView);
% Overlay the fixed image onto the panorama
panorama = imwarp(FIXED, projective2d(eye(3)), ‘OutputView’, panoramaView, ‘FillValues’, 0);
panorama = max(panorama, registered);
% Resize the panorama to fit within 1024×1024
scaleFactor = min(1024 / width, 1024 / height);
panoramaScaled = imresize(panorama, scaleFactor);
% Create a 1024×1024 canvas
finalPanorama = zeros(1024, 1024, ‘like’, FIXED);
% Determine the position to center the scaled stitched image within the canvas
[scaledHeight, scaledWidth] = size(panoramaScaled);
xOffset = max(0, round((1024 – scaledWidth) / 2));
yOffset = max(0, round((1024 – scaledHeight) / 2));
% Place the scaled stitched image onto the 1024×1024 canvas
finalPanorama(yOffset + (1:scaledHeight), xOffset + (1:scaledWidth)) = panoramaScaled;
% Store the registered image
MOVINGREG.RegisteredImage = finalPanorama;
% Store the transformation object
MOVINGREG.Transformation = tform;
end
% Load the images
fixedImg = imread("sol_03333_opgs_edr_ncam_NLB_693387385EDR_F0921230NCAM00259M_.JPG");
leftImg = imread("sol_03333_opgs_edr_ncam_NLB_693387301EDR_F0921230NCAM00259M_.JPG");
% Register the two images
reg = registerMarsImages(leftImg, fixedImg)
figure; imshow(reg.RegisteredImage)function [MOVINGREG] = registerMarsImages(MOVING,FIXED)
% Convert images to grayscale if they are RGB
if size(MOVING, 3) == 3
MOVING = rgb2gray(MOVING);
end
if size(FIXED, 3) == 3
FIXED = rgb2gray(FIXED);
end
% Detect features in both images
points1 = detectSURFFeatures(MOVING, ‘MetricThreshold’, 1000);
points2 = detectSURFFeatures(FIXED, ‘MetricThreshold’, 1000);
% Extract features from both images
[features1, valid_points1] = extractFeatures(MOVING, points1);
[features2, valid_points2] = extractFeatures(FIXED, points2);
% Match features by using their descriptors
indexPairs = matchFeatures(features1, features2, ‘MatchThreshold’, 10, ‘MaxRatio’, 0.7);
% Retrieve locations of corresponding points for each image
matchedPoints1 = valid_points1(indexPairs(:, 1), :);
matchedPoints2 = valid_points2(indexPairs(:, 2), :);
% Estimate the transformation between the moving and fixed images
[tform, inlierIdx] = estimateGeometricTransform2D(matchedPoints1, matchedPoints2, ‘projective’, ‘Confidence’, 99.9, ‘MaxNumTrials’, 2000);
% Get the output limits for the transformation
[xLimitsMoving, yLimitsMoving] = outputLimits(tform, [1 size(MOVING, 2)], [1 size(MOVING, 1)]);
[xLimitsFixed, yLimitsFixed] = outputLimits(projective2d(eye(3)), [1 size(FIXED, 2)], [1 size(FIXED, 1)]);
% Determine the size of the panorama
xMin = min([xLimitsMoving xLimitsFixed]);
xMax = max([xLimitsMoving xLimitsFixed]);
yMin = min([yLimitsMoving yLimitsFixed]);
yMax = max([yLimitsMoving yLimitsFixed]);
width = round(xMax – xMin);
height = round(yMax – yMin);
% Create an empty panorama canvas
panorama = zeros(height, width, ‘like’, FIXED);
% Create reference objects for the panorama and the fixed image
panoramaView = imref2d([height width], [xMin xMax], [yMin yMax]);
fixedRef = imref2d(size(FIXED), [xLimitsFixed(1) xLimitsFixed(2)], [yLimitsFixed(1) yLimitsFixed(2)]);
% Warp the moving image into the panorama
registered = imwarp(MOVING, tform, ‘OutputView’, panoramaView);
% Overlay the fixed image onto the panorama
panorama = imwarp(FIXED, projective2d(eye(3)), ‘OutputView’, panoramaView, ‘FillValues’, 0);
panorama = max(panorama, registered);
% Resize the panorama to fit within 1024×1024
scaleFactor = min(1024 / width, 1024 / height);
panoramaScaled = imresize(panorama, scaleFactor);
% Create a 1024×1024 canvas
finalPanorama = zeros(1024, 1024, ‘like’, FIXED);
% Determine the position to center the scaled stitched image within the canvas
[scaledHeight, scaledWidth] = size(panoramaScaled);
xOffset = max(0, round((1024 – scaledWidth) / 2));
yOffset = max(0, round((1024 – scaledHeight) / 2));
% Place the scaled stitched image onto the 1024×1024 canvas
finalPanorama(yOffset + (1:scaledHeight), xOffset + (1:scaledWidth)) = panoramaScaled;
% Store the registered image
MOVINGREG.RegisteredImage = finalPanorama;
% Store the transformation object
MOVINGREG.Transformation = tform;
end
% Load the images
fixedImg = imread("sol_03333_opgs_edr_ncam_NLB_693387385EDR_F0921230NCAM00259M_.JPG");
leftImg = imread("sol_03333_opgs_edr_ncam_NLB_693387301EDR_F0921230NCAM00259M_.JPG");
% Register the two images
reg = registerMarsImages(leftImg, fixedImg)
figure; imshow(reg.RegisteredImage) function [MOVINGREG] = registerMarsImages(MOVING,FIXED)
% Convert images to grayscale if they are RGB
if size(MOVING, 3) == 3
MOVING = rgb2gray(MOVING);
end
if size(FIXED, 3) == 3
FIXED = rgb2gray(FIXED);
end
% Detect features in both images
points1 = detectSURFFeatures(MOVING, ‘MetricThreshold’, 1000);
points2 = detectSURFFeatures(FIXED, ‘MetricThreshold’, 1000);
% Extract features from both images
[features1, valid_points1] = extractFeatures(MOVING, points1);
[features2, valid_points2] = extractFeatures(FIXED, points2);
% Match features by using their descriptors
indexPairs = matchFeatures(features1, features2, ‘MatchThreshold’, 10, ‘MaxRatio’, 0.7);
% Retrieve locations of corresponding points for each image
matchedPoints1 = valid_points1(indexPairs(:, 1), :);
matchedPoints2 = valid_points2(indexPairs(:, 2), :);
% Estimate the transformation between the moving and fixed images
[tform, inlierIdx] = estimateGeometricTransform2D(matchedPoints1, matchedPoints2, ‘projective’, ‘Confidence’, 99.9, ‘MaxNumTrials’, 2000);
% Get the output limits for the transformation
[xLimitsMoving, yLimitsMoving] = outputLimits(tform, [1 size(MOVING, 2)], [1 size(MOVING, 1)]);
[xLimitsFixed, yLimitsFixed] = outputLimits(projective2d(eye(3)), [1 size(FIXED, 2)], [1 size(FIXED, 1)]);
% Determine the size of the panorama
xMin = min([xLimitsMoving xLimitsFixed]);
xMax = max([xLimitsMoving xLimitsFixed]);
yMin = min([yLimitsMoving yLimitsFixed]);
yMax = max([yLimitsMoving yLimitsFixed]);
width = round(xMax – xMin);
height = round(yMax – yMin);
% Create an empty panorama canvas
panorama = zeros(height, width, ‘like’, FIXED);
% Create reference objects for the panorama and the fixed image
panoramaView = imref2d([height width], [xMin xMax], [yMin yMax]);
fixedRef = imref2d(size(FIXED), [xLimitsFixed(1) xLimitsFixed(2)], [yLimitsFixed(1) yLimitsFixed(2)]);
% Warp the moving image into the panorama
registered = imwarp(MOVING, tform, ‘OutputView’, panoramaView);
% Overlay the fixed image onto the panorama
panorama = imwarp(FIXED, projective2d(eye(3)), ‘OutputView’, panoramaView, ‘FillValues’, 0);
panorama = max(panorama, registered);
% Resize the panorama to fit within 1024×1024
scaleFactor = min(1024 / width, 1024 / height);
panoramaScaled = imresize(panorama, scaleFactor);
% Create a 1024×1024 canvas
finalPanorama = zeros(1024, 1024, ‘like’, FIXED);
% Determine the position to center the scaled stitched image within the canvas
[scaledHeight, scaledWidth] = size(panoramaScaled);
xOffset = max(0, round((1024 – scaledWidth) / 2));
yOffset = max(0, round((1024 – scaledHeight) / 2));
% Place the scaled stitched image onto the 1024×1024 canvas
finalPanorama(yOffset + (1:scaledHeight), xOffset + (1:scaledWidth)) = panoramaScaled;
% Store the registered image
MOVINGREG.RegisteredImage = finalPanorama;
% Store the transformation object
MOVINGREG.Transformation = tform;
end
% Load the images
fixedImg = imread("sol_03333_opgs_edr_ncam_NLB_693387385EDR_F0921230NCAM00259M_.JPG");
leftImg = imread("sol_03333_opgs_edr_ncam_NLB_693387301EDR_F0921230NCAM00259M_.JPG");
% Register the two images
reg = registerMarsImages(leftImg, fixedImg)
figure; imshow(reg.RegisteredImage) matlab code, matlab, output, image stitching, panorama MATLAB Answers — New Questions
I want show population on plot like 36M, 37M….
please help me, I=I need the grow population like 36m, 37, 38,.. on plotplease help me, I=I need the grow population like 36m, 37, 38,.. on plot please help me, I=I need the grow population like 36m, 37, 38,.. on plot matlab MATLAB Answers — New Questions
How to authenticate from C# to SP online?
Hi. I have created a new app registration in Azure and this allows you to generate a secret value.
then when I go over to _layouts/15/appregnew.aspx and try to enter that secret I get the following : Invalid app secret. It must be a valid base64 encoded string of an 32-byte binary.
How to resolve this?
Hi. I have created a new app registration in Azure and this allows you to generate a secret value. then when I go over to _layouts/15/appregnew.aspx and try to enter that secret I get the following : Invalid app secret. It must be a valid base64 encoded string of an 32-byte binary. How to resolve this? Read More
Tech Talks Presents: Protect & Manage Enterprise Data Effectively at Scale | June 20th
Join us on Thursday, June 20th at 8am PT as Mihaela Blandea, Principal Program Manager and Jocelyn Panchal, Product Manager present Protect & Manage Enterprise Data Effectively at Scale.
In the digital era, low code platforms and AI are driving rapid transformation. But the balance between innovation and robust security is a pivotal challenge for organizations. Join this session to uncover the latest capabilities available for Power Platform to maximize security and compliance while minimizing risk and effort to confidently unlock the full potential of AI innovation within your organization
We hope you’ll join us!
Call to Action:
Click on the link to save the calendar invite: https://aka.ms/TechTalksInvite
View past recordings (sign in required): https://aka.ms/TechTalksRecording
Get started with the adoption tools here
Join us on Thursday, June 20th at 8am PT as Mihaela Blandea, Principal Program Manager and Jocelyn Panchal, Product Manager present Protect & Manage Enterprise Data Effectively at Scale.In the digital era, low code platforms and AI are driving rapid transformation. But the balance between innovation and robust security is a pivotal challenge for organizations. Join this session to uncover the latest capabilities available for Power Platform to maximize security and compliance while minimizing risk and effort to confidently unlock the full potential of AI innovation within your organizationWe hope you’ll join us!
Call to Action:
Click on the link to save the calendar invite: https://aka.ms/TechTalksInvite
View past recordings (sign in required): https://aka.ms/TechTalksRecording
Get started with the adoption tools here Read More
power settings ignored
Windows 11 home on msi laptop. I do consulting and have several work accounts. Something is overriding the power /sleep settings. Since it was working last fall. but has been an issue since about oct nov last year when I gained several clients policies. My pc goes to sleep mode at about 3 minutes. I loose any connections to vpns. IF I am on remote desktop on a meeting, it will go to sleep. I have to e active on my pc desktop keyboard or mouse. I have been launching teams meeting on my desktop to keep it from sleeping.
How do I find what is overriding my settings?
Windows 11 home on msi laptop. I do consulting and have several work accounts. Something is overriding the power /sleep settings. Since it was working last fall. but has been an issue since about oct nov last year when I gained several clients policies. My pc goes to sleep mode at about 3 minutes. I loose any connections to vpns. IF I am on remote desktop on a meeting, it will go to sleep. I have to e active on my pc desktop keyboard or mouse. I have been launching teams meeting on my desktop to keep it from sleeping.How do I find what is overriding my settings? Read More
sort(), filter() and IF() combined
I have a formula:
=SORT(IF(AX2 = “1+1”; FILTER(A2:J200; E2:E200 >= MAX(P:P)); 1; 1); FILTER(A2:J200; E2:E200 >= INDEX(P2:P200; X4-1)); 1; 1)
I want to sort a column by a condition statement.
For example, if the statement is true I want to filter and sort the numeric column with values only bigger and equal to the max() function result,
otherwise, I want to filter and sort the numeric column with values bigger or equal to max() – 1 (the preceding value).
Are there any workarounds for this, because the Excel pop-up with the window “can’t calculate this formula” without helper columns if possible?
I have a formula:=SORT(IF(AX2 = “1+1”; FILTER(A2:J200; E2:E200 >= MAX(P:P)); 1; 1); FILTER(A2:J200; E2:E200 >= INDEX(P2:P200; X4-1)); 1; 1)I want to sort a column by a condition statement.For example, if the statement is true I want to filter and sort the numeric column with values only bigger and equal to the max() function result,otherwise, I want to filter and sort the numeric column with values bigger or equal to max() – 1 (the preceding value).Are there any workarounds for this, because the Excel pop-up with the window “can’t calculate this formula” without helper columns if possible? Read More
Update records in a Kusto Database (General Availability)
Kusto databases, either in Fabric (KQL Database) or in Azure (Azure Data Explorer), are optimized for append ingestion.
In recent years, we’ve introduced the .delete command, allowing you to selectively delete records.
In February, we introduced the .update command in public preview. This command allows you to update records by deleting existing records and appending new ones in a single transaction.
Today, the .update is Generally Available (GA)!
During the public preview, we introduced two syntaxes: the simplified and expanded syntax. We only GA the expanded syntax (the most powerful) and are deprecating the simplified syntax. This is due to the simplified syntax being confusing in some scenarios.
We encourage you to go through the many examples of the online documentation page to familiarize yourself with the syntax.
As usual, we are looking forward for your feedback and hope this makes you more productive in Kusto!
Microsoft Tech Community – Latest Blogs –Read More
Right Clicking Teams Taskbar Icon opens “Open With” Dialogue on Windows 10
We recently identified a Windows known issue with custom Jump Lists that shipped in Windows 10 22H2. New Teams is one of several apps known to be impacted. When you right click on the New Teams icon in the taskbar on Win10 you might see an “Open With” dialogue instead of the expected Teams custom options (Status, Quit, etc).
Looks like:
Instead of:
You can work around this issue by setting Status via your Profile in the main Teams client window:
The good news is that the necessary fix has already been identified and prepared and will ship in a future update for Windows 10 22H2 soon.
To follow along monitor the Windows 10 update history page here:
Windows 10 update history – Microsoft Support
For more information about the JumpList known issue:
Windows 10, version 22H2 | Microsoft Learn
We apologize for the inconvenience.
Microsoft Tech Community – Latest Blogs –Read More
I need help combining two sine waves
Hello. I am trying to learn how to end an ascending sine wave at the start of a signal and end a descending sine wave at the end of a signal. Here is what I have so far:
f=1000;
n=10;
T=1/f;
t=(0:T/100:n*T);
s = sin(2*pi*t*f);
e = (-expm1(-t*250));
e2 = (exp(-t*250));
n = s .* e;
m = e2 .* s;
fix = n + s + m;
plot(t,fix);
I am still doing research on how to properly add them but any help from others would be appreciated.Hello. I am trying to learn how to end an ascending sine wave at the start of a signal and end a descending sine wave at the end of a signal. Here is what I have so far:
f=1000;
n=10;
T=1/f;
t=(0:T/100:n*T);
s = sin(2*pi*t*f);
e = (-expm1(-t*250));
e2 = (exp(-t*250));
n = s .* e;
m = e2 .* s;
fix = n + s + m;
plot(t,fix);
I am still doing research on how to properly add them but any help from others would be appreciated. Hello. I am trying to learn how to end an ascending sine wave at the start of a signal and end a descending sine wave at the end of a signal. Here is what I have so far:
f=1000;
n=10;
T=1/f;
t=(0:T/100:n*T);
s = sin(2*pi*t*f);
e = (-expm1(-t*250));
e2 = (exp(-t*250));
n = s .* e;
m = e2 .* s;
fix = n + s + m;
plot(t,fix);
I am still doing research on how to properly add them but any help from others would be appreciated. plotting MATLAB Answers — New Questions
Matching combinations of strings
I have a table TT with a string variable TT.name. I want to return true if TT.name matches any entry in another table variable OK.name. However, I have some complications I am having a hard time parsing.
Many of the strings in TT.name are combinations of strings that appear in OK.name. I want to include these as a true match. Sometimes they have a + symbol, sometimes just a space. Further complicating matters, the table OK contains some entries with spaces, and if they do I want to treat them as an entire entry, and not break them up at the spaces.
TT = table(["Green"; "Red"; "Blue"; "Black Blue"; "Black"; "Blue Green"; "Red + Blue"; "Red Orange"; "Red + White"], ‘VariableNames’, {‘name’})
OK = table(["Red"; "Green"; "Blue"; "Black Blue"], ‘VariableNames’, {‘name’})
This is the output I would want, but not by manually changing rows 6 and 7:
TT.match=ismember(TT.name,OK.name);
TT.match(6:7)=1
In the example, "Blue Green" and "Red + Blue" are true matchs, because "Blue" "Green" and "Red" all appear as entries in OK.name.
"Black" is not a match, because the only entry in OK.name is "Black Blue" and I do not want to separate the words from this table.
"Red Orange" and "Red + Orange" are not matches because only "Red" is in the OK table.I have a table TT with a string variable TT.name. I want to return true if TT.name matches any entry in another table variable OK.name. However, I have some complications I am having a hard time parsing.
Many of the strings in TT.name are combinations of strings that appear in OK.name. I want to include these as a true match. Sometimes they have a + symbol, sometimes just a space. Further complicating matters, the table OK contains some entries with spaces, and if they do I want to treat them as an entire entry, and not break them up at the spaces.
TT = table(["Green"; "Red"; "Blue"; "Black Blue"; "Black"; "Blue Green"; "Red + Blue"; "Red Orange"; "Red + White"], ‘VariableNames’, {‘name’})
OK = table(["Red"; "Green"; "Blue"; "Black Blue"], ‘VariableNames’, {‘name’})
This is the output I would want, but not by manually changing rows 6 and 7:
TT.match=ismember(TT.name,OK.name);
TT.match(6:7)=1
In the example, "Blue Green" and "Red + Blue" are true matchs, because "Blue" "Green" and "Red" all appear as entries in OK.name.
"Black" is not a match, because the only entry in OK.name is "Black Blue" and I do not want to separate the words from this table.
"Red Orange" and "Red + Orange" are not matches because only "Red" is in the OK table. I have a table TT with a string variable TT.name. I want to return true if TT.name matches any entry in another table variable OK.name. However, I have some complications I am having a hard time parsing.
Many of the strings in TT.name are combinations of strings that appear in OK.name. I want to include these as a true match. Sometimes they have a + symbol, sometimes just a space. Further complicating matters, the table OK contains some entries with spaces, and if they do I want to treat them as an entire entry, and not break them up at the spaces.
TT = table(["Green"; "Red"; "Blue"; "Black Blue"; "Black"; "Blue Green"; "Red + Blue"; "Red Orange"; "Red + White"], ‘VariableNames’, {‘name’})
OK = table(["Red"; "Green"; "Blue"; "Black Blue"], ‘VariableNames’, {‘name’})
This is the output I would want, but not by manually changing rows 6 and 7:
TT.match=ismember(TT.name,OK.name);
TT.match(6:7)=1
In the example, "Blue Green" and "Red + Blue" are true matchs, because "Blue" "Green" and "Red" all appear as entries in OK.name.
"Black" is not a match, because the only entry in OK.name is "Black Blue" and I do not want to separate the words from this table.
"Red Orange" and "Red + Orange" are not matches because only "Red" is in the OK table. strings, compare, ismember, matching MATLAB Answers — New Questions
Client Analyzer Page – Release notes
It would be helpful if the client analyzer page had release notes to go along with new version releases and update, and also a subscription notification for when the page has been updated.
(https://learn.microsoft.com/en-us/defender-endpoint/run-analyzer-macos-linux?view=o365-worldwide)
Currently the best guess is check last page edited date, but this has not been reliable in the past as the page has been edited without Client Analyzer updates or the ClientAnalyzer has been updated without page edits.
It would be helpful if the client analyzer page had release notes to go along with new version releases and update, and also a subscription notification for when the page has been updated. (https://learn.microsoft.com/en-us/defender-endpoint/run-analyzer-macos-linux?view=o365-worldwide) Currently the best guess is check last page edited date, but this has not been reliable in the past as the page has been edited without Client Analyzer updates or the ClientAnalyzer has been updated without page edits. Read More
How do I submit app with api on azure, I am new to azure and struggling to submit or host.
Hi there,
I am trying for the first time on Microsoft azure I am new developer and have very few knowledge about the azure I am trying to create a web app like. AI to human text converter https://aitohumanizetextconverter.com this website is a sample but my app is similar to this. How do I submit or host my app on azure. I really appreciate if i get any sort of guidance.
Thank you
Hi there,I am trying for the first time on Microsoft azure I am new developer and have very few knowledge about the azure I am trying to create a web app like. AI to human text converter https://aitohumanizetextconverter.com this website is a sample but my app is similar to this. How do I submit or host my app on azure. I really appreciate if i get any sort of guidance. Thank you Read More
Issues with Oracle Database Classification in Microsoft Purview
Hello,I’m testing the registration of an Oracle on-prem database with Microsoft Purview. The scan is running successfully and ingesting metadata, but it is not identifying any classifications.although this dataset is used with SQL Server and works properly, showing all the classifications, there is a section discussing this saying that If the user is not the owner of the table, the scan will run successfully and ingest metadata, but will not identify any classifications. https://learn.microsoft.com/en-us/purview/register-scan-oracle-source#required-permissions-for-scanBut it’s already done and the user have all the select roles and system roles.Could anyone help i don’t know where is the problem Thanks in advance Read More
Issues with Hybrid Azure AD Join During Autopilot Enrollment
Hello Community,
I am seeking advice on an issue we’re experiencing with Microsoft Intune and Autopilot in our environment. We have set up an enrollment profile intended to enroll devices as Hybrid Azure AD joined during the Autopilot process. However, we’re encountering a problem where some devices are enrolling as Azure AD joined (cloud-only) instead of Hybrid.
Has anyone else experienced this issue? Any suggestions on what might be causing this inconsistency or how to troubleshoot it further would be greatly appreciated!
Hello Community,I am seeking advice on an issue we’re experiencing with Microsoft Intune and Autopilot in our environment. We have set up an enrollment profile intended to enroll devices as Hybrid Azure AD joined during the Autopilot process. However, we’re encountering a problem where some devices are enrolling as Azure AD joined (cloud-only) instead of Hybrid.Has anyone else experienced this issue? Any suggestions on what might be causing this inconsistency or how to troubleshoot it further would be greatly appreciated! Read More
The ability to add photos / images to Microsoft Authenticator accounts
Hi,
First post in this forum (hello to all !).
Can we have the ability to add photos / images to each account listed on our Microsoft Authenticator accounts ? This would allow me to quickly identify which account code I need on the long list of accounts I have connected to my Authenticator app.
Many thanks
Jay
Hi, First post in this forum (hello to all !). Can we have the ability to add photos / images to each account listed on our Microsoft Authenticator accounts ? This would allow me to quickly identify which account code I need on the long list of accounts I have connected to my Authenticator app. Many thanksJay Read More
File Issues after copying from another PC
I copied over a set of files from my workstation to use on my laptop when working in the labratory. The script runs fine when using the workstation but comes up with an error when used on my laptop. I didn’t write this code a former student at my university did but I need to alter his plots using the file. I’m new to MATLAB, and added the Raw Tension Data to the file path, and tried restarting the program/computer. I can’t seem to figure this out on my own. I’ve used other files the former student created with minimal issues on my laptop, this one is hanging me up.
Incorrect number or types of inputs or outputs for function resample.
Error in TensionPlots (line 7)
TPS_1 = resample(readmatrix(‘Raw Tension DataPS-1.csv’, ‘NumHeaderLines’,1),10,100);I copied over a set of files from my workstation to use on my laptop when working in the labratory. The script runs fine when using the workstation but comes up with an error when used on my laptop. I didn’t write this code a former student at my university did but I need to alter his plots using the file. I’m new to MATLAB, and added the Raw Tension Data to the file path, and tried restarting the program/computer. I can’t seem to figure this out on my own. I’ve used other files the former student created with minimal issues on my laptop, this one is hanging me up.
Incorrect number or types of inputs or outputs for function resample.
Error in TensionPlots (line 7)
TPS_1 = resample(readmatrix(‘Raw Tension DataPS-1.csv’, ‘NumHeaderLines’,1),10,100); I copied over a set of files from my workstation to use on my laptop when working in the labratory. The script runs fine when using the workstation but comes up with an error when used on my laptop. I didn’t write this code a former student at my university did but I need to alter his plots using the file. I’m new to MATLAB, and added the Raw Tension Data to the file path, and tried restarting the program/computer. I can’t seem to figure this out on my own. I’ve used other files the former student created with minimal issues on my laptop, this one is hanging me up.
Incorrect number or types of inputs or outputs for function resample.
Error in TensionPlots (line 7)
TPS_1 = resample(readmatrix(‘Raw Tension DataPS-1.csv’, ‘NumHeaderLines’,1),10,100); resample error MATLAB Answers — New Questions