Category: News
Resource Mailbox “Booking Delegate Settings” vs/plus “Delegation”
My org has many longtime Resource mailboxes (primarily Rooms) that have been migrated forward from various older versions of Exchange on-prem – all were migrated to EXO about 2 years ago.
Many of them have ‘Delegation’ configured with various users granted Full Access, Send on Behalf and/or Send As perms; but as Resource Mailboxes they ALSO have ‘Booking Delegate Settings’ configured.
We’re getting more and more instances of unexpected behavior – so trying to get a solid handle on optimal configuration for these Resource mailboxes in EXO at the present time.
I believe that the root cause may be that some of the legacy ‘delegation’ settings that have been ported forward over the years are now conflicting with the ‘Booking Delegate’ settings, but I’ve been unable to find a good, current reference about how these two configs currently interact in EXO.
This is an older (2015) article about how the two different ‘delegate’ options play together in Resource mailboxes: Booking Delegation Vs. Classic Delegation – Microsoft Community Hub
Does anyone have a better or more current reference? Or is this one still golden?
My org has many longtime Resource mailboxes (primarily Rooms) that have been migrated forward from various older versions of Exchange on-prem – all were migrated to EXO about 2 years ago. Many of them have ‘Delegation’ configured with various users granted Full Access, Send on Behalf and/or Send As perms; but as Resource Mailboxes they ALSO have ‘Booking Delegate Settings’ configured. We’re getting more and more instances of unexpected behavior – so trying to get a solid handle on optimal configuration for these Resource mailboxes in EXO at the present time. I believe that the root cause may be that some of the legacy ‘delegation’ settings that have been ported forward over the years are now conflicting with the ‘Booking Delegate’ settings, but I’ve been unable to find a good, current reference about how these two configs currently interact in EXO. This is an older (2015) article about how the two different ‘delegate’ options play together in Resource mailboxes: Booking Delegation Vs. Classic Delegation – Microsoft Community Hub Does anyone have a better or more current reference? Or is this one still golden? Read More
TIREM O LIMETE DE 10 LINKS RAPIDOS!!!
Boa tarde a todos.
Gostaria que a Microsoft tirasse o LIMITE DE 10 LINKS RÁPIDO, enquanto o OPERA não põe limite
Boa tarde a todos.Gostaria que a Microsoft tirasse o LIMITE DE 10 LINKS RÁPIDO, enquanto o OPERA não põe limite Read More
Sync issue
Hi All,
We are experiencing a weird sync issue:
My wife and I use To Do for shopping and packing lists. We have done this for several years. We share a Microsoft 365 subscription. I own the subscription and she owns most of the shared lists. Everything used to work fine until about a week ago.
Now, when me wife checks off an item (e.g. eggs), I see the change. However, when she unchecks an item (i.e. I need to buy eggs), I do not receive the update.
In the opposite direction, everything works as expected. When I check or uncheck an item, she sees the changes immediately.
The behavior is the same across all platforms (iOS, Android, Windows).
For troubleshooting purposes, I left a shared list she owns, and she sent me a new invitation to the same shared list. When I try to access the shared list, I get the error message that the list does not exist.
I created a new list and shared it with her. In this new list, everything works as expected in either direction.
How can this be and how do I fix it?
Best regards,
Wilko
Hi All, We are experiencing a weird sync issue: My wife and I use To Do for shopping and packing lists. We have done this for several years. We share a Microsoft 365 subscription. I own the subscription and she owns most of the shared lists. Everything used to work fine until about a week ago. Now, when me wife checks off an item (e.g. eggs), I see the change. However, when she unchecks an item (i.e. I need to buy eggs), I do not receive the update. In the opposite direction, everything works as expected. When I check or uncheck an item, she sees the changes immediately. The behavior is the same across all platforms (iOS, Android, Windows). For troubleshooting purposes, I left a shared list she owns, and she sent me a new invitation to the same shared list. When I try to access the shared list, I get the error message that the list does not exist. I created a new list and shared it with her. In this new list, everything works as expected in either direction. How can this be and how do I fix it? Best regards, Wilko Read More
Audit Logs for Compliance Policy and Configuration Profile
Is there a way to see in Intune why a particular device (say an iPhone) got a particular policy or configuration? I am trying to find out why certain users are getting the wrong policy and profile
when everything points to them getting the right policy or profile. Sort of like some audit log or debug thing.
Is there a way to see in Intune why a particular device (say an iPhone) got a particular policy or configuration? I am trying to find out why certain users are getting the wrong policy and profilewhen everything points to them getting the right policy or profile. Sort of like some audit log or debug thing. Read More
Got a FedRAMP Equivalency Body of Evidence?
With the publishing of the U.S. Department of Defense memorandum for ‘FedRAMP Moderate Equivalency for Cloud Service Provider’s Cloud Service Offerings’, assessors will be asking defense contractors to provide the body of evidence (BoE) of any cloud service providers not authorized in the FedRAMP Marketplace.
For access to the Microsoft Office 365 GCC High and/or Azure Government BoEs, as per the memo, please contact:
Office 365 GCC High: O365FedRAMP@microsoft.com
Azure Government: AzFedDoc@microsoft.com
The BoE is considered highly sensitive and confidential information. While Microsoft is transparent and will allow for customers to access the BoE under a Non-Disclosure Agreement (NDA), you must be a customer and make the request to the above email addresses.
For more details on Microsoft’s GCCH environment and FedRAMP compliance, check out Richard Wakeman’s detailed blog at https://aka.ms/FedRAMPGCCH.
About the author
Carley Salmon is a Senior Data Security Technical Specialist for Microsoft Federal Defense. Prior to coming to Microsoft, she spent 4 years with the DCMA DIBCAC and was part of the team that built the assessment processes.
Microsoft Tech Community – Latest Blogs –Read More
hyperparameter tuning in SVM
How to find the value of C and gamma parameter in SVM, the dataset we used is wokload dataset for prediction purpose. how to evaluate the affect of different value of parameters.How to find the value of C and gamma parameter in SVM, the dataset we used is wokload dataset for prediction purpose. how to evaluate the affect of different value of parameters. How to find the value of C and gamma parameter in SVM, the dataset we used is wokload dataset for prediction purpose. how to evaluate the affect of different value of parameters. svm, hyperparameter tuning, prediction MATLAB Answers — New Questions
How to seperate a superimposed sinusoidal wave from a signal
Could you assist me in isolating a sinusoidal wave that is overlaid(superimposed) on a signal?
I’ve attached a .mat file where a sine wave is superimposed between the 613th and 1569th data points, which have a sampling frequency of 40Hz. My goal is to extract this sine wave from the entire signal. I’ve attempted ‘blind source separation’ techniques, FFT, but it won’t works. the reason might be the frequency of introduced sine wave is too small,only 1Hz.
Additionally, could you provide suggestions on how to identify the point at which a sinusoidal wave is introduced if its location is unknown? Your help would be greatly appreciated.
Thank you in advance!
Comment if any other informantion needed.
%%Could you assist me in isolating a sinusoidal wave that is overlaid(superimposed) on a signal?
I’ve attached a .mat file where a sine wave is superimposed between the 613th and 1569th data points, which have a sampling frequency of 40Hz. My goal is to extract this sine wave from the entire signal. I’ve attempted ‘blind source separation’ techniques, FFT, but it won’t works. the reason might be the frequency of introduced sine wave is too small,only 1Hz.
Additionally, could you provide suggestions on how to identify the point at which a sinusoidal wave is introduced if its location is unknown? Your help would be greatly appreciated.
Thank you in advance!
Comment if any other informantion needed.
%% Could you assist me in isolating a sinusoidal wave that is overlaid(superimposed) on a signal?
I’ve attached a .mat file where a sine wave is superimposed between the 613th and 1569th data points, which have a sampling frequency of 40Hz. My goal is to extract this sine wave from the entire signal. I’ve attempted ‘blind source separation’ techniques, FFT, but it won’t works. the reason might be the frequency of introduced sine wave is too small,only 1Hz.
Additionally, could you provide suggestions on how to identify the point at which a sinusoidal wave is introduced if its location is unknown? Your help would be greatly appreciated.
Thank you in advance!
Comment if any other informantion needed.
%% wave seperation, fft, blind source seperation, signal processing MATLAB Answers — New Questions
Converting 32 bit to uint16
I want to convert 32bit image to uint16 image but preserve the grey values. I do not want to lose the grey value, rather scale them to the uint16. How can I do this. My code below makes the images fully grey.
% Normalize the pixel values to the range [0, 1]
img_normalized = double(img_original) / double(max(img_original(:)));
% Find the minimum and maximum values in the normalized image
min_val = min(img_normalized(:));
max_val = max(img_normalized(:));
% Define the minimum and maximum values for uint16
min_uint16 = double(intmin(‘uint16’));
max_uint16 = double(intmax(‘uint16’));
% Calculate the scaling factors
scale_factor = (max_uint16 – min_uint16) / (max_val – min_val);
% Scale the pixel values to uint16 range
img_scaled = uint16((img_normalized – min_val) * scale_factor + min_uint16);I want to convert 32bit image to uint16 image but preserve the grey values. I do not want to lose the grey value, rather scale them to the uint16. How can I do this. My code below makes the images fully grey.
% Normalize the pixel values to the range [0, 1]
img_normalized = double(img_original) / double(max(img_original(:)));
% Find the minimum and maximum values in the normalized image
min_val = min(img_normalized(:));
max_val = max(img_normalized(:));
% Define the minimum and maximum values for uint16
min_uint16 = double(intmin(‘uint16’));
max_uint16 = double(intmax(‘uint16’));
% Calculate the scaling factors
scale_factor = (max_uint16 – min_uint16) / (max_val – min_val);
% Scale the pixel values to uint16 range
img_scaled = uint16((img_normalized – min_val) * scale_factor + min_uint16); I want to convert 32bit image to uint16 image but preserve the grey values. I do not want to lose the grey value, rather scale them to the uint16. How can I do this. My code below makes the images fully grey.
% Normalize the pixel values to the range [0, 1]
img_normalized = double(img_original) / double(max(img_original(:)));
% Find the minimum and maximum values in the normalized image
min_val = min(img_normalized(:));
max_val = max(img_normalized(:));
% Define the minimum and maximum values for uint16
min_uint16 = double(intmin(‘uint16’));
max_uint16 = double(intmax(‘uint16’));
% Calculate the scaling factors
scale_factor = (max_uint16 – min_uint16) / (max_val – min_val);
% Scale the pixel values to uint16 range
img_scaled = uint16((img_normalized – min_val) * scale_factor + min_uint16); convert 32bits to 16bits MATLAB Answers — New Questions
How do I resolve “Error while evaluating TimerFcn for timer ‘timer-1”?
Hi:
While executing a Timer callback function, I encounter the following two error statements:
Error while evaluating TimerFcn for timer ‘timer-1’.
Insufficient number of outputs from right hand side of equal sign to satisfy assignment.
Am I correct that this error is occurring in a MATLAB library function? If so, how do I locate where this assignment is taking place so that I can further debug the problem? I am using MATLAB 2022b.
For reference, this is the callback function:
function create_batch_file_and_run_Callback(~, ~, H) % =================================================================
create_batch_file_Callback([], [], H)
H = guidata(findall(0,’type’,’figure’,’tag’,’unicorn’)); % since gets updated with batchFileName
H.IWantToCloseGUI = 1; % in create_batch_file_and_run_Callback
guidata(H.unicorn,H);
T = timer;
set(T,’StartDelay’,2,’TimerFcn’,[‘run_dr_suite_batch(”’ H.batchFileName ”’)’]);
start(T);
myclosefcn(H.unicorn);Hi:
While executing a Timer callback function, I encounter the following two error statements:
Error while evaluating TimerFcn for timer ‘timer-1’.
Insufficient number of outputs from right hand side of equal sign to satisfy assignment.
Am I correct that this error is occurring in a MATLAB library function? If so, how do I locate where this assignment is taking place so that I can further debug the problem? I am using MATLAB 2022b.
For reference, this is the callback function:
function create_batch_file_and_run_Callback(~, ~, H) % =================================================================
create_batch_file_Callback([], [], H)
H = guidata(findall(0,’type’,’figure’,’tag’,’unicorn’)); % since gets updated with batchFileName
H.IWantToCloseGUI = 1; % in create_batch_file_and_run_Callback
guidata(H.unicorn,H);
T = timer;
set(T,’StartDelay’,2,’TimerFcn’,[‘run_dr_suite_batch(”’ H.batchFileName ”’)’]);
start(T);
myclosefcn(H.unicorn); Hi:
While executing a Timer callback function, I encounter the following two error statements:
Error while evaluating TimerFcn for timer ‘timer-1’.
Insufficient number of outputs from right hand side of equal sign to satisfy assignment.
Am I correct that this error is occurring in a MATLAB library function? If so, how do I locate where this assignment is taking place so that I can further debug the problem? I am using MATLAB 2022b.
For reference, this is the callback function:
function create_batch_file_and_run_Callback(~, ~, H) % =================================================================
create_batch_file_Callback([], [], H)
H = guidata(findall(0,’type’,’figure’,’tag’,’unicorn’)); % since gets updated with batchFileName
H.IWantToCloseGUI = 1; % in create_batch_file_and_run_Callback
guidata(H.unicorn,H);
T = timer;
set(T,’StartDelay’,2,’TimerFcn’,[‘run_dr_suite_batch(”’ H.batchFileName ”’)’]);
start(T);
myclosefcn(H.unicorn); timerfcn MATLAB Answers — New Questions
A more efficient or compact way to sort strings that contain dates
I have strings that contain dates.
Those strings are in a "random" order, i.e. they are not ordered by following the dates, from 2024/03/01 to 2024/03/31 (i.e. from the 1st of March 2024 to the 31st of March 2024).
Is there a more efficient or compact way to sort the following strings containing dates?
% (1) input (strings containing dates, in a "random" order)
a(1,:) = ‘123_abc_01_202403020000_202403022359.txt’;
a(2,:) = ‘123_abc_01_202403040000_202403042359.txt’;
a(3,:) = ‘123_abc_01_202403030000_202403032359.txt’;
a(4,:) = ‘123_abc_01_202403050000_202403052359.txt’;
a(5,:) = ‘123_abc_01_202403010000_202403012359.txt’;
a
% (2) create substrings with ordered dates, that we can use to compare with the unordered strings of the input
for i = 1 : 31
tmp = [];
if i <=10
tmp = sprintf(‘%02d’,i);
else
tmp = sprintf(‘%0d’,i);
end
b(i,:) = append(‘_202403’,tmp);
end
% sort the unordered strings of the input, by following the substrings that have ordered dates
for i = 1 : 5
for j = 1 : 31
if contains(a(i,:),b(j,:))
which_j(i) = j;
end
end
end
sorted_a = sort(a(which_j,:))I have strings that contain dates.
Those strings are in a "random" order, i.e. they are not ordered by following the dates, from 2024/03/01 to 2024/03/31 (i.e. from the 1st of March 2024 to the 31st of March 2024).
Is there a more efficient or compact way to sort the following strings containing dates?
% (1) input (strings containing dates, in a "random" order)
a(1,:) = ‘123_abc_01_202403020000_202403022359.txt’;
a(2,:) = ‘123_abc_01_202403040000_202403042359.txt’;
a(3,:) = ‘123_abc_01_202403030000_202403032359.txt’;
a(4,:) = ‘123_abc_01_202403050000_202403052359.txt’;
a(5,:) = ‘123_abc_01_202403010000_202403012359.txt’;
a
% (2) create substrings with ordered dates, that we can use to compare with the unordered strings of the input
for i = 1 : 31
tmp = [];
if i <=10
tmp = sprintf(‘%02d’,i);
else
tmp = sprintf(‘%0d’,i);
end
b(i,:) = append(‘_202403’,tmp);
end
% sort the unordered strings of the input, by following the substrings that have ordered dates
for i = 1 : 5
for j = 1 : 31
if contains(a(i,:),b(j,:))
which_j(i) = j;
end
end
end
sorted_a = sort(a(which_j,:)) I have strings that contain dates.
Those strings are in a "random" order, i.e. they are not ordered by following the dates, from 2024/03/01 to 2024/03/31 (i.e. from the 1st of March 2024 to the 31st of March 2024).
Is there a more efficient or compact way to sort the following strings containing dates?
% (1) input (strings containing dates, in a "random" order)
a(1,:) = ‘123_abc_01_202403020000_202403022359.txt’;
a(2,:) = ‘123_abc_01_202403040000_202403042359.txt’;
a(3,:) = ‘123_abc_01_202403030000_202403032359.txt’;
a(4,:) = ‘123_abc_01_202403050000_202403052359.txt’;
a(5,:) = ‘123_abc_01_202403010000_202403012359.txt’;
a
% (2) create substrings with ordered dates, that we can use to compare with the unordered strings of the input
for i = 1 : 31
tmp = [];
if i <=10
tmp = sprintf(‘%02d’,i);
else
tmp = sprintf(‘%0d’,i);
end
b(i,:) = append(‘_202403’,tmp);
end
% sort the unordered strings of the input, by following the substrings that have ordered dates
for i = 1 : 5
for j = 1 : 31
if contains(a(i,:),b(j,:))
which_j(i) = j;
end
end
end
sorted_a = sort(a(which_j,:)) string, dates, date, sort MATLAB Answers — New Questions
Excel cell sizing issue
Windows 10, Microsoft Office, Excel.
In past, I have been able to paste a picture to a cell and the cell would automatically adjust in size. For some reason, I cannot do that anymore; the picture just floats above the page.
I have gone to Format Picture, clicked on Move and Size With Cells, but nothing changes. Is there an execute icon or something I’m missing? Thanks.
Windows 10, Microsoft Office, Excel. In past, I have been able to paste a picture to a cell and the cell would automatically adjust in size. For some reason, I cannot do that anymore; the picture just floats above the page. I have gone to Format Picture, clicked on Move and Size With Cells, but nothing changes. Is there an execute icon or something I’m missing? Thanks. Read More
Changing the color icon of Edge
I want to have the color of edge on the taskbar with a different color. Already changed the ico color and works for the desktop icon but not for the icon on the taskbar once Edge is loaded. Any ideas?
I want to have the color of edge on the taskbar with a different color. Already changed the ico color and works for the desktop icon but not for the icon on the taskbar once Edge is loaded. Any ideas? Read More
Restrict common users from creating a folder in root of system drive
Hello professionals,
hope you can help me with an issue I am struggling with on both Windows Server 2019 and 2022. Common/ordinary users (domain users who are member of Remote Desktop Users group) should not be able to create a folder in root of system drive C:, but members of Administrators group should have those privileges.
Typical solution is to drop Write/Modify for Users in context menu Security, like this:
Unfortunately it doesn’t work. Members of Remote Desktop Users, who are not members of Administrators group, can create and delete folder in C: Following pictures are snipped on Windows Server 2022.
Remote Desktop Users:
Users:
Folder creation/deletion of a user from Remote Desktop User group:
Do you have any idea why NTFS permissions do not work on system drive C:?
Do you have any suggestion how to solve the issue, i.e. prevent non-administrator users from creating their own folders in root of system drive?
Regards
Leos
Hello professionals, hope you can help me with an issue I am struggling with on both Windows Server 2019 and 2022. Common/ordinary users (domain users who are member of Remote Desktop Users group) should not be able to create a folder in root of system drive C:, but members of Administrators group should have those privileges. Typical solution is to drop Write/Modify for Users in context menu Security, like this:Unfortunately it doesn’t work. Members of Remote Desktop Users, who are not members of Administrators group, can create and delete folder in C: Following pictures are snipped on Windows Server 2022. Remote Desktop Users:Users:Folder creation/deletion of a user from Remote Desktop User group:Do you have any idea why NTFS permissions do not work on system drive C:?Do you have any suggestion how to solve the issue, i.e. prevent non-administrator users from creating their own folders in root of system drive? Regards Leos Read More
New Blog | Public preview: Expanding passkey support in Microsoft Entra ID
By Alex Weinert
We really, really want to eliminate passwords. There’s really nothing anyone can do to make them better. As more users have adopted multifactor authentication (MFA), attackers have increased their use of Adversary-in-the-Middle (AitM) phishing and social engineering attacks, which trick people into revealing their credentials.
How can we defeat these attacks while making safe sign-in even easier? Passkeys!
A passkey is a strong, phishing-resistant authentication method you can use to sign in to any internet resource that supports the W3C WebAuthN standard. Passkeys represent the continuing evolution of the FIDO2 standard, which should be familiar to anyone who’s followed or joined the passwordless movement. We already support signing into Entra ID using a passkey hosted on a hardware security key and today, we’re delighted to announce additional support for passkeys. Specifically, we’re adding support for device-bound passkeys in the Microsoft Authenticator app on iOS and Android for customers with the strictest security requirements.
Before we describe the new capabilities we’re adding to Microsoft Authenticator, let’s review the basics of passkeys.
Passkeys neutralize phishing attempts
Passkeys provide high security assurance by applying public-private key cryptography and requiring direct interaction with the user. As I detailed in a previous blog, passkeys benefit from “Verifier Impersonation Resistance”:
URL-specific. The provisioning process for passkeys records the relying party’s URL, so the passkey will only work for sites with that same URL.
Device-specific. The relying party will only grant access to the user if the passkey is synched, stored, or connected to the device from which they’re requesting access.
User-specific. The user must prove they’re physically present during authentication, usually by performing a gesture on the device from which they’re requesting access.
Together, these characteristics make passkeys almost impossible to phish.
Read the full post here: Public preview: Expanding passkey support in Microsoft Entra ID
By Alex Weinert
We really, really want to eliminate passwords. There’s really nothing anyone can do to make them better. As more users have adopted multifactor authentication (MFA), attackers have increased their use of Adversary-in-the-Middle (AitM) phishing and social engineering attacks, which trick people into revealing their credentials.
How can we defeat these attacks while making safe sign-in even easier? Passkeys!
A passkey is a strong, phishing-resistant authentication method you can use to sign in to any internet resource that supports the W3C WebAuthN standard. Passkeys represent the continuing evolution of the FIDO2 standard, which should be familiar to anyone who’s followed or joined the passwordless movement. We already support signing into Entra ID using a passkey hosted on a hardware security key and today, we’re delighted to announce additional support for passkeys. Specifically, we’re adding support for device-bound passkeys in the Microsoft Authenticator app on iOS and Android for customers with the strictest security requirements.
Before we describe the new capabilities we’re adding to Microsoft Authenticator, let’s review the basics of passkeys.
Passkeys neutralize phishing attempts
Passkeys provide high security assurance by applying public-private key cryptography and requiring direct interaction with the user. As I detailed in a previous blog, passkeys benefit from “Verifier Impersonation Resistance”:
URL-specific. The provisioning process for passkeys records the relying party’s URL, so the passkey will only work for sites with that same URL.
Device-specific. The relying party will only grant access to the user if the passkey is synched, stored, or connected to the device from which they’re requesting access.
User-specific. The user must prove they’re physically present during authentication, usually by performing a gesture on the device from which they’re requesting access.
Together, these characteristics make passkeys almost impossible to phish.
Read the full post here: Public preview: Expanding passkey support in Microsoft Entra ID
New Blog | Microsoft Defender for Open-Source Relational Databases Now Supports Multicloud (AWS RDS)
By Thomas Zou
Introduction:
Many organizations use multiple cloud providers today, which makes security misconfigurations more likely due to the solution scale and complexity. Moreover, different practices and concepts among each cloud provider’s implementation create bigger internal knowledge gaps.
No matter how many cloud providers an organization uses, a database is the core of each application, storing the organization’s most valuable data: PII, financial and payment information, medical information, and other sensitive data. This makes databases the most attractive attack target for any threat actor – from inside or outside.
Even though there is more awareness of exposure misconfigurations (thanks to cybersecurity education and posture management products that reveal these issues), public datasets show that the most risky database misconfiguration – exposing databases to the internet is not going down. This fact emphasizes the importance of threat protection that will act as a last line of defense and help detect, in near real-time, attacks that endanger databases and the critical data they contain.
Internet exposed databases count through time.
(Source: Time series · General statistics · The Shadowserver Foundation)
Announcement:
Microsoft Defender for open-source relational databases have been long focusing on providing comprehensive protection for Azure databases.
Read the full post here: Microsoft Defender for Open-Source Relational Databases Now Supports Multicloud (AWS RDS)
By Thomas Zou
Introduction:
Many organizations use multiple cloud providers today, which makes security misconfigurations more likely due to the solution scale and complexity. Moreover, different practices and concepts among each cloud provider’s implementation create bigger internal knowledge gaps.
No matter how many cloud providers an organization uses, a database is the core of each application, storing the organization’s most valuable data: PII, financial and payment information, medical information, and other sensitive data. This makes databases the most attractive attack target for any threat actor – from inside or outside.
Even though there is more awareness of exposure misconfigurations (thanks to cybersecurity education and posture management products that reveal these issues), public datasets show that the most risky database misconfiguration – exposing databases to the internet is not going down. This fact emphasizes the importance of threat protection that will act as a last line of defense and help detect, in near real-time, attacks that endanger databases and the critical data they contain.
Internet exposed databases count through time.
(Source: Time series · General statistics · The Shadowserver Foundation)
Announcement:
Microsoft Defender for open-source relational databases have been long focusing on providing comprehensive protection for Azure databases.
Read the full post here: Microsoft Defender for Open-Source Relational Databases Now Supports Multicloud (AWS RDS) Read More
Announcing release date for FSLogix 2210 hotfix 4!
Important News!
Before the release of FSLogix 2201 hotfix 3, our team dedicated significant effort to meticulously identify, replicate, and address the numerous challenges encountered during the shift to the new Teams MSIX update. Although it’s unlikely that every conceivable issue has been anticipated, I believe that FSLogix 2210 hotfix 4 represents a thorough update suitable for most environments. Aligned with Microsoft’s “patch Tuesday” calendar, FSLogix 2210 hotfix 4 is set to become widely available on Tuesday May 14th, 2024, ready for download and also pre-loaded on Windows 10 and Windows 11 multi-session Azure images.
What’s new in FSLogix 2210 hotfix 4?
This hotfix, along with the updates from hotfix 3, addresses a wide range of issues associated with New Teams. We wish to express our gratitude to the 30+ customers and partners whose crucial involvement in our validation process has been essential for the discoveries and solutions provided in this release. Additionally, we are reintroducing a previously released and highly requested feature: Asynchronous Group Policy processing!
Group Policy Changes
Update: Added functionality to securely roam Group Policy state providing asynchronous policy processing.
Fix: Addressed an issue where user-based policy settings would persist in the user’s profile.
New Teams Changes
Update: Added functionality to repair missing Teams AppX folders in ODFC containers during the container creation process. (new container or container reset).
Update: Added a workaround where in some cases new Teams would fail to on-demand register during sign-in.
Update: Added roaming support for the new Teams meeting add-in when using the ODFC container.
Fix: Resolved an issue where FSLogix was unable to query provisioned AppX packages on Windows Server 2019.
Fix: Modified the sign-out process to solely erase the contents of “non-roamable” folders located in %LocalAppData%Packages* while preserving the parent folders. (all packages).
Fix: Resolved an issue where name cache entries in FltMgr (Filter Manager) would become polluted with non-normalized names.
Fix: Resolved an issue where New Teams may not have registered correctly when used with ODFC.
Fix: Resolved an issue where New Teams would fail to start, or the application would crash.
Thank you again for your continued patience and support!
Microsoft Tech Community – Latest Blogs –Read More
Visualizing Data in Excel – HLS Copilot Snacks
If you have a large amount of data in an Excel spreadsheet, it can be hard to get insights from it. Copilot for Microsoft 365 can help you analyze your data and create visualizations that make it easier to understand and communicate. Copilot can automatically detect the structure and format of your data and suggest relevant charts and tables to display it. You can also use Copilot to create a pivot table, which is a powerful tool to summarize and manipulate your data in different ways. Copilot can guide you through the steps of creating a pivot table and choosing the fields and values to show. You can then add filters, slicers, and timelines to interact with your data and explore different scenarios. Copilot can also help you create charts and graphs based on your pivot table, such as pie charts, line charts, or histograms. These visualizations can help you identify trends, patterns, and outliers in your data and communicate your findings effectively. Copilot can also explain how to interpret the charts and graphs and provide tips and best practices for data visualization. With Copilot, you can turn your data into insights and stories in minutes. To start using Copilot, select the Copilot tab in the ribbon and click on Analyze Data. Copilot will then add a new sheet with a pivot table and visualizations of your data and guide you through the process of customizing and exploring them.
In this Copilot Snack I show how you can visualize your data quickly in Microsoft Excel.
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Resources:
HLS Copilot Snacks (microsoft.com)
Microsoft Copilot for Microsoft 365 documentation | Microsoft Learn
Copilot Lab (cloud.microsoft)
Copilot in Excel help & learning (cloud.microsoft)
Prompts used:
Show data insights
Can I see another insight?
Can I see another insight?
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Introducing Regular Expressions (Regex) support in Azure SQL Database | Data Exposed
In this episode of Data Exposed, we’ll introduce the new Regular Expressions (Regex) feature in Azure SQL Database. Join us to discover the built-in T-SQL REGEX functions in Azure SQL Database, enabling you to write concise queries for complex data pattern matching, manipulation, validation and retrieval scenarios.
Resources:
Introducing Regular Expression (Regex) Support in Azure SQL DB blog
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Highlighting and Filtering Data in Excel – HLS Copilot Snacks
If you have a lot of data in your Excel spreadsheet and you want to focus on specific values or categories, you can use Copilot for Microsoft 365 to highlight and filter your data easily. Copilot can help you apply conditional formatting, color scales, icon sets, and data bars to your table, so you can see patterns and trends at a glance. You can also use Copilot to filter your data by text, numbers, dates, colors, or icons, and create custom filters based on your criteria. Watch this Copilot Snack to learn how to highlight and filter your data in Excel with Copilot.
To see all HLS Copilot Snacks video click here.
Resources:
HLS Copilot Snacks (microsoft.com)
Microsoft Copilot for Microsoft 365 documentation | Microsoft Learn
Copilot Lab (cloud.microsoft)
Copilot in Excel help & learning (cloud.microsoft)
Prompts
Bold the top 10 values in the Gross Sales column
Highlight the highest values in Units Sold
Sort COGS from smallest to largest
To see all HLS Copilot Snacks video click here.
Thanks for visiting – Michael Gannotti LinkedIn
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how to modify code for system of delay differential equations with two delays
I have this code and i want to modify this for the system with multiple delays
%% ode simulation data, drop the transient part
tau = 1;
beta = 4;
n = 9.65;
gamma = 2;
trueModel = @(t,x,xdelay) beta*xdelay./(1+xdelay.^n)-gamma*x;
num_traj=100;
tr_c =linspace(0.5,1.5,num_traj); % 100 sets (ORIGINAL)
T = 20;
dt = 0.05;
tspan = [0 T];
N = round(T/dt)+1;
Tst = 10; % drop first 10 second
T_tr = 7;
N_tr = round(T_tr/dt)+1; % take fisrt part of remaining data for training
tau_max = 1.5; %%%FIXED DELAY FOR ODE DISCRETIZATION
M=30; %%%COLLOCATION DEGREE (ADDED), SHOULD CORRESPOND TO nx-1
[Dcheb,xcheb]=difmat(-tau_max,0,M);%%%CHEBYSHEV DIFFERENTIATION MATRIX AND NODES
wcheb=barywei(xcheb); %%%CHEBYSHEV BARYCENTIRC WEIGHTS
nX = round(tau_max/dt)+1; % number of states (OLD)
Xtrain_all = {};
BaseNet0 = struct;
BaseNet0.DiffMat = difmat(-tau_max,0,M);
BaseNet0.dt = dt;
% generate data + derivative
for k = 1:length(tr_c)
hist = @(t) tr_c(k);
sol = dde23(trueModel,tau,hist,tspan);
tint = linspace(0,T,N);
yint{k} = deval(sol,tint); %%%ALL SAMPLES IN [0,T]
tint_tr = linspace(Tst,Tst+T_tr,N_tr); %%%ALL TRAINING SAMPLES [10,17]
yint_tr{k} = yint{k}(round(Tst/dt):round(Tst/dt)+N_tr-1);
% obtain derivative via central difference
dy{k} = ctrDiff(yint{k},tint);
dy_tr{k} = ctrDiff(yint_tr{k},tint_tr);
m = length(tint_tr)-nX+1; % number of datapoints
xTrain = zeros(M+1,m);
dxTrain = zeros(M+1,m);
tchebTrain = zeros(M+1,m);
for i = 1:m
[~,tchebTrain(:,i)]=difmat(tint_tr(i),tint_tr(nX+i-1),M);%%%REINTERPOLATE AT CHEBYSHEV NODES
xTrain(:,i)=deval(sol,tchebTrain(:,i));
xTrainDelay(:,i)=deval(sol,tchebTrain(:,i)-tau);
dxTrain(:,i) = trueModel(1,xTrain(:,i),xTrainDelay(:,i));
end
Xtrain_all{k}=xTrain;
x0{k} = xTrain(:,1);
dXtrain_all{k}=dxTrain;
end
function v=barywei(x)
%BARYWEI barycentric weights.
% v=barywei(x) returns the weights v for barycentric interpolation at
% the nodes x according to [1].
%
% REFERENCES:
% [1] J.P. Berrut and L.N. Trefethen,"Barycentric Lagrange
% interpolation",SIAM Rev. 46(3):501-517,2004.
n=length(x); %number of nodes
dx=ones(n,1);
v=dx;
for m=2:n
for i=1:m-1
dx(i)=x(m)-x(i);
v(i)=-dx(i)*v(i);
end
v(m)=prod(dx(1:m));
end
v=1./v;
v=v/max(v); %normalization to 1
end
function [D,x]=difmat(a,b,N)
%DIFMAT Chebyshev nodes and pseudospectral differentiation matrix.
% [D,x]=DIFMAT(a,b,N) returns the pseudospectral differentiation
% matrix D of order N+1 on the N+1 Chebyshev nodes x in [a,b]
% according to [1].
% INPUT:
% a: left extremum (1×1)
% b: right extremum (1×1)
% N: degree of discretization (1×1)
% OUTPUT:
% D: differentiation matrix ((N+1)x(N+1))
% x: Chebyshev nodes ((N+1)x1)
%
% References:
% [1] L.N. Trefethen, "Spectral methods in Matlab", SIAM, 2000.
if N==0
x=1;
D=0;
return
end
x=(b+a+(b-a)*(cos(pi*(0:N)’/N)))/2;
c=[2;ones(N-1,1);2].*(-1).^(0:N)’;
X=repmat(x,1,N+1);
dX=X-X’;
D=(c*(1./c)’)./(dX+(eye(N+1)));
D=D-diag(sum(D’));
end
function dy = ctrDiff(yint,tint)
dt = tint(2)-tint(1);
dy = zeros(size(yint));
for k = 2:length(yint)-1
dy(:,k) = (yint(:,k+1)-yint(:,k-1))/2/dt;
end
dy(:,1)=(yint(:,2)-yint(:,1))/dt;
dy(:,end)=(yint(:,end)-yint(:,end-1))/dt;
end
my new model (system) is
tau = [1.5, 2];
par = [0.2, 0.5, 0.2,0.2,1];
trueModel =@(t,x,Z,par) [-x(3) – x(2)+ par(1) * Z(1,1)+ par(2) * Z(1,2);…
x(1) + par(3) * x(2);…
par(4) + x(3) * (x(1) – par(5))];
with history function
hist =@(t) kron(ones(length(t),1),[1.5; 0.4; 0.9]);
when i do modification for system i get an error in xTrain part
below is my error message
Unable to perform assignment because the size of the left side is 21-by-1 and the size of the right side is
3-by-21.
xTrain(:,i)=deval(sol,tchebTrain(:,i));I have this code and i want to modify this for the system with multiple delays
%% ode simulation data, drop the transient part
tau = 1;
beta = 4;
n = 9.65;
gamma = 2;
trueModel = @(t,x,xdelay) beta*xdelay./(1+xdelay.^n)-gamma*x;
num_traj=100;
tr_c =linspace(0.5,1.5,num_traj); % 100 sets (ORIGINAL)
T = 20;
dt = 0.05;
tspan = [0 T];
N = round(T/dt)+1;
Tst = 10; % drop first 10 second
T_tr = 7;
N_tr = round(T_tr/dt)+1; % take fisrt part of remaining data for training
tau_max = 1.5; %%%FIXED DELAY FOR ODE DISCRETIZATION
M=30; %%%COLLOCATION DEGREE (ADDED), SHOULD CORRESPOND TO nx-1
[Dcheb,xcheb]=difmat(-tau_max,0,M);%%%CHEBYSHEV DIFFERENTIATION MATRIX AND NODES
wcheb=barywei(xcheb); %%%CHEBYSHEV BARYCENTIRC WEIGHTS
nX = round(tau_max/dt)+1; % number of states (OLD)
Xtrain_all = {};
BaseNet0 = struct;
BaseNet0.DiffMat = difmat(-tau_max,0,M);
BaseNet0.dt = dt;
% generate data + derivative
for k = 1:length(tr_c)
hist = @(t) tr_c(k);
sol = dde23(trueModel,tau,hist,tspan);
tint = linspace(0,T,N);
yint{k} = deval(sol,tint); %%%ALL SAMPLES IN [0,T]
tint_tr = linspace(Tst,Tst+T_tr,N_tr); %%%ALL TRAINING SAMPLES [10,17]
yint_tr{k} = yint{k}(round(Tst/dt):round(Tst/dt)+N_tr-1);
% obtain derivative via central difference
dy{k} = ctrDiff(yint{k},tint);
dy_tr{k} = ctrDiff(yint_tr{k},tint_tr);
m = length(tint_tr)-nX+1; % number of datapoints
xTrain = zeros(M+1,m);
dxTrain = zeros(M+1,m);
tchebTrain = zeros(M+1,m);
for i = 1:m
[~,tchebTrain(:,i)]=difmat(tint_tr(i),tint_tr(nX+i-1),M);%%%REINTERPOLATE AT CHEBYSHEV NODES
xTrain(:,i)=deval(sol,tchebTrain(:,i));
xTrainDelay(:,i)=deval(sol,tchebTrain(:,i)-tau);
dxTrain(:,i) = trueModel(1,xTrain(:,i),xTrainDelay(:,i));
end
Xtrain_all{k}=xTrain;
x0{k} = xTrain(:,1);
dXtrain_all{k}=dxTrain;
end
function v=barywei(x)
%BARYWEI barycentric weights.
% v=barywei(x) returns the weights v for barycentric interpolation at
% the nodes x according to [1].
%
% REFERENCES:
% [1] J.P. Berrut and L.N. Trefethen,"Barycentric Lagrange
% interpolation",SIAM Rev. 46(3):501-517,2004.
n=length(x); %number of nodes
dx=ones(n,1);
v=dx;
for m=2:n
for i=1:m-1
dx(i)=x(m)-x(i);
v(i)=-dx(i)*v(i);
end
v(m)=prod(dx(1:m));
end
v=1./v;
v=v/max(v); %normalization to 1
end
function [D,x]=difmat(a,b,N)
%DIFMAT Chebyshev nodes and pseudospectral differentiation matrix.
% [D,x]=DIFMAT(a,b,N) returns the pseudospectral differentiation
% matrix D of order N+1 on the N+1 Chebyshev nodes x in [a,b]
% according to [1].
% INPUT:
% a: left extremum (1×1)
% b: right extremum (1×1)
% N: degree of discretization (1×1)
% OUTPUT:
% D: differentiation matrix ((N+1)x(N+1))
% x: Chebyshev nodes ((N+1)x1)
%
% References:
% [1] L.N. Trefethen, "Spectral methods in Matlab", SIAM, 2000.
if N==0
x=1;
D=0;
return
end
x=(b+a+(b-a)*(cos(pi*(0:N)’/N)))/2;
c=[2;ones(N-1,1);2].*(-1).^(0:N)’;
X=repmat(x,1,N+1);
dX=X-X’;
D=(c*(1./c)’)./(dX+(eye(N+1)));
D=D-diag(sum(D’));
end
function dy = ctrDiff(yint,tint)
dt = tint(2)-tint(1);
dy = zeros(size(yint));
for k = 2:length(yint)-1
dy(:,k) = (yint(:,k+1)-yint(:,k-1))/2/dt;
end
dy(:,1)=(yint(:,2)-yint(:,1))/dt;
dy(:,end)=(yint(:,end)-yint(:,end-1))/dt;
end
my new model (system) is
tau = [1.5, 2];
par = [0.2, 0.5, 0.2,0.2,1];
trueModel =@(t,x,Z,par) [-x(3) – x(2)+ par(1) * Z(1,1)+ par(2) * Z(1,2);…
x(1) + par(3) * x(2);…
par(4) + x(3) * (x(1) – par(5))];
with history function
hist =@(t) kron(ones(length(t),1),[1.5; 0.4; 0.9]);
when i do modification for system i get an error in xTrain part
below is my error message
Unable to perform assignment because the size of the left side is 21-by-1 and the size of the right side is
3-by-21.
xTrain(:,i)=deval(sol,tchebTrain(:,i)); I have this code and i want to modify this for the system with multiple delays
%% ode simulation data, drop the transient part
tau = 1;
beta = 4;
n = 9.65;
gamma = 2;
trueModel = @(t,x,xdelay) beta*xdelay./(1+xdelay.^n)-gamma*x;
num_traj=100;
tr_c =linspace(0.5,1.5,num_traj); % 100 sets (ORIGINAL)
T = 20;
dt = 0.05;
tspan = [0 T];
N = round(T/dt)+1;
Tst = 10; % drop first 10 second
T_tr = 7;
N_tr = round(T_tr/dt)+1; % take fisrt part of remaining data for training
tau_max = 1.5; %%%FIXED DELAY FOR ODE DISCRETIZATION
M=30; %%%COLLOCATION DEGREE (ADDED), SHOULD CORRESPOND TO nx-1
[Dcheb,xcheb]=difmat(-tau_max,0,M);%%%CHEBYSHEV DIFFERENTIATION MATRIX AND NODES
wcheb=barywei(xcheb); %%%CHEBYSHEV BARYCENTIRC WEIGHTS
nX = round(tau_max/dt)+1; % number of states (OLD)
Xtrain_all = {};
BaseNet0 = struct;
BaseNet0.DiffMat = difmat(-tau_max,0,M);
BaseNet0.dt = dt;
% generate data + derivative
for k = 1:length(tr_c)
hist = @(t) tr_c(k);
sol = dde23(trueModel,tau,hist,tspan);
tint = linspace(0,T,N);
yint{k} = deval(sol,tint); %%%ALL SAMPLES IN [0,T]
tint_tr = linspace(Tst,Tst+T_tr,N_tr); %%%ALL TRAINING SAMPLES [10,17]
yint_tr{k} = yint{k}(round(Tst/dt):round(Tst/dt)+N_tr-1);
% obtain derivative via central difference
dy{k} = ctrDiff(yint{k},tint);
dy_tr{k} = ctrDiff(yint_tr{k},tint_tr);
m = length(tint_tr)-nX+1; % number of datapoints
xTrain = zeros(M+1,m);
dxTrain = zeros(M+1,m);
tchebTrain = zeros(M+1,m);
for i = 1:m
[~,tchebTrain(:,i)]=difmat(tint_tr(i),tint_tr(nX+i-1),M);%%%REINTERPOLATE AT CHEBYSHEV NODES
xTrain(:,i)=deval(sol,tchebTrain(:,i));
xTrainDelay(:,i)=deval(sol,tchebTrain(:,i)-tau);
dxTrain(:,i) = trueModel(1,xTrain(:,i),xTrainDelay(:,i));
end
Xtrain_all{k}=xTrain;
x0{k} = xTrain(:,1);
dXtrain_all{k}=dxTrain;
end
function v=barywei(x)
%BARYWEI barycentric weights.
% v=barywei(x) returns the weights v for barycentric interpolation at
% the nodes x according to [1].
%
% REFERENCES:
% [1] J.P. Berrut and L.N. Trefethen,"Barycentric Lagrange
% interpolation",SIAM Rev. 46(3):501-517,2004.
n=length(x); %number of nodes
dx=ones(n,1);
v=dx;
for m=2:n
for i=1:m-1
dx(i)=x(m)-x(i);
v(i)=-dx(i)*v(i);
end
v(m)=prod(dx(1:m));
end
v=1./v;
v=v/max(v); %normalization to 1
end
function [D,x]=difmat(a,b,N)
%DIFMAT Chebyshev nodes and pseudospectral differentiation matrix.
% [D,x]=DIFMAT(a,b,N) returns the pseudospectral differentiation
% matrix D of order N+1 on the N+1 Chebyshev nodes x in [a,b]
% according to [1].
% INPUT:
% a: left extremum (1×1)
% b: right extremum (1×1)
% N: degree of discretization (1×1)
% OUTPUT:
% D: differentiation matrix ((N+1)x(N+1))
% x: Chebyshev nodes ((N+1)x1)
%
% References:
% [1] L.N. Trefethen, "Spectral methods in Matlab", SIAM, 2000.
if N==0
x=1;
D=0;
return
end
x=(b+a+(b-a)*(cos(pi*(0:N)’/N)))/2;
c=[2;ones(N-1,1);2].*(-1).^(0:N)’;
X=repmat(x,1,N+1);
dX=X-X’;
D=(c*(1./c)’)./(dX+(eye(N+1)));
D=D-diag(sum(D’));
end
function dy = ctrDiff(yint,tint)
dt = tint(2)-tint(1);
dy = zeros(size(yint));
for k = 2:length(yint)-1
dy(:,k) = (yint(:,k+1)-yint(:,k-1))/2/dt;
end
dy(:,1)=(yint(:,2)-yint(:,1))/dt;
dy(:,end)=(yint(:,end)-yint(:,end-1))/dt;
end
my new model (system) is
tau = [1.5, 2];
par = [0.2, 0.5, 0.2,0.2,1];
trueModel =@(t,x,Z,par) [-x(3) – x(2)+ par(1) * Z(1,1)+ par(2) * Z(1,2);…
x(1) + par(3) * x(2);…
par(4) + x(3) * (x(1) – par(5))];
with history function
hist =@(t) kron(ones(length(t),1),[1.5; 0.4; 0.9]);
when i do modification for system i get an error in xTrain part
below is my error message
Unable to perform assignment because the size of the left side is 21-by-1 and the size of the right side is
3-by-21.
xTrain(:,i)=deval(sol,tchebTrain(:,i)); differential equations, delay differential equations MATLAB Answers — New Questions