The 2023 Nebraska high school state volleyball tournament begins today in Lincoln! Today's first-round matches in Classes A, B and C-1 will be played at Pinnacle Bank Arena. Two matches will be played at a time starting at 9 a.m., with matches starting approximately every two hours until the last two of the day begin around 7 p.m.

Nebraska high school state volleyball live scoreboard, Nov. 1

LINCOLN — Elkhorn North opened the Class B state volleyball tournament with a 25-12, 25-15, 25-14 win over Seward at Pinnacle Bank Arena. The Wolves (29-7), who lost to Omaha Skutt in five sets in the 2022 final, will meet the winner of the Skutt-Waverly quarterfinal at 7 p.m. Friday at PBA. Seward finished 26-8.

Class B: Elkhorn North sweeps Seward in first round of Nebraska state volleyball tournament

Finally, in girl golf, history was made Tuesday night as South Brunswick claimed its first ever individual girls golf state title.              With the cross country state championships and the first round of the football playoffs this weekend,

Wilmington-area playoff top performers for Oct. 30-Nov. 4

We'll update this page all postseason long with the latest scores and updates from all six area sports. Brackets, if released, are below.             Boys soccer             NCHSAA 4A Second Round: Nov.

Wilmington-area fall high school playoffs: NCHSAA boys soccer brackets revealed

A close four-quarter game against Lumberton on Friday in which Purnell Swett was upset 15-12 by the Pirates wasn’t the end of the suspense around the Rams program. The next 16 hours

Rams ready for postseason return

Eight games into the season, Georgia Tech is in fourth place in the ACC with a 3-2 conference record, and North Carolina and Miami find themselves in fifth and sixth place respectively.

Georgia Tech is a Perfect Chaos Team for the 2023 College Football Season

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Girls volleyball: Updated 2023 NJSIAA State Tournament Brackets, Nov. 1

Flames' Jacob Markstrom: Set to start Wednesday

The PAL singles and doubles girls’ tennis tournaments got underway Monday and there were no big surprises on Day 1 as most of the top seeds in both brackets advanced to Wednesday’s semifinals. The one mild upset came in the singles bracket, where Half ...

Most of top seeds advance at PAL tennis tournament

Forget multiple clicks or hard-to-read brackets with inconsistent information. You want the HS football playoff schedule? We've got it for you right here.

Forget the hard-to-read brackets. We've got the RIIL football playoff schedule right here.

Bracketz

bracketz

Authors:
(1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors;
(2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors;
(3) Sahal Shaji, Mohamed bin Zayed University of AI;
(4) Abdelrahman Shaker, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Hisham Cholakkal, Mohamed bin Zayed University of AI;
(7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University;
(8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University;
(9) Ming-Hsuan Yang, University of California - Merced and Google Research;
(10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 3 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Work
3. Method
4. Data Annotation Pipeline
5. Experiments
6. Conclusion and References Supplementary Material (Part 1) A. Additional Implementation Details
B. Additional Downstream Tasks
C. Additional Qualitative Results Supplementary Material (Part 2) D. Dataset Visualization
E. Limitations and Future Work
F. Ethics and Societal Impact 3. Method Existing Large Multimodal Models (LMMs) either generate ungrounded text or are restricted by limitations such as single-object grounding, user-specified region inputs, or the lack of dense pixel-level object grounding (see Tab. 1). Our Grounding LMM (GLaMM) aims to overcome these limitations by generating natural language responses seamlessly integrated with object segmentation masks. This enables a visually grounded human-machine conversation. 3.1. GLaMM Architecture GLaMM consists of five core components: i) Global Image Encoder, ii) Region Encoder, iii) LLM, iv) Grounding Image Encoder, and v) Pixel Decoder. These components are cohesively designed to handle both textual and optional visual prompts (image level and region), allowing for interaction at multiple levels of granularity and generating grounded text responses (see Fig. 2). These blocks together enable scene-level, region-level, and pixel-level grounding, as explained next. Training specifics are detailed in Appendix A.2. Region-Level Understanding: Building on the shortcomings of existing models that can handle only image-level visual inputs, and in alignment with recent work [57], the region encoder (R) extends the model’s capability to interpret and interact with user-specified regions in an image. This component constructs a hierarchical feature pyramid from four selected CLIP global image encoder layers, followed by RoIAlign [10] to generate a 14x14 feature map. Combining these features yields a unified region-of-interest (RoI) representation. To facilitate region-targeted responses from GLaMM, we augment the existing vocabulary with a specialized token <bbox>. This is integrated into a prompt like, “The <image> provides an overview of the image. Can you provide a detailed description of the region <bbox>?”. Here the <bbox> token is replaced with the RoI extracted features. Pixel-Level Grounding: Utilizing the grounding image encoder denoted as V and the pixel decoder represented as P, GLaMM facilitates fine-grained pixel-level object grounding, allowing it to ground its responses visually. We instantiate V with a pretrained SAM encoder [18] and design P based on a SAM decoder-like architecture. To activate the pixel-level grounding, our model’s vocabulary is augmented with a specialized token, <SEG>. Prompts, such as “Please segment the ‘man in red’ in the given image," trigger the model to generate responses with corresponding <SEG> tokens. A language-to-prompt (L-P) projection layer (g) transforms the last-layer embeddings corresponding to <SEG> tokens (lseg) into the decoder’s feature space. Subsequently, P produces binary segmentation masks M, Using an end-to-end training approach, GLaMM excels in region understanding, pixel-level grounding, and conversational capabilities. However, due to the lack of standard benchmarks for the novel setting of generating visually grounded detailed conversations, we introduce a novel task, Grounded Conversation Generation (GCG), and a comprehensive evaluation protocol as explained next. 3.2. Grounded Conversation Generation (GCG) The objective of the GCG task is to construct image-level captions with specific phrases directly tied to corresponding segmentation masks in the image. For example, “<A man> and <a boy> sit on <a bench> next to <an old white car>.”, shown in Fig. 3 (left), features how each bracketed phrase (highlighted in the image) is anchored to a unique image segmentation mask. This creates a densely annotated caption that aligns textual descriptions with visual regions, enriching the image’s contextual interpretation. GCG Output Representation: A sample prompt for querying the model in this task is: “Could you please give me a detailed description of the image? Please respond with interleaved segmentation masks for the corresponding parts of the answer.” The model generates a detailed caption along with interleaved segmentation masks, employing the format “A man<SEG> and a boy <SEG> sit on a bench<SEG> next to an old white car <SEG>.” We use special tokens, namely , and <SEG>, to delineate the start and end of each phrase and its corresponding region mask, respectively. Our GranD dataset is meticulously constructed using a stage-wise annotation pipeline, capturing annotations that range from fine-grained specifics to high-level context. This enables the automatic generation of densely annotated captions well-suited for the GCG task, thereby significantly facilitating GLaMM’s training for this task. Some qualitative results of our model on the GCG task are shown in Fig. 3. Evaluation Criteria: We introduce a benchmarking suite for GCG, with a validation set of 2.5K images and a test set of 5K images. Four key aspects are evaluated: i) generated dense caption quality, ii) mask-to-phrase correspondence accuracy, iii) generated mask quality, and iv) region-specific grounding ability. Metrics include METEOR and CIDEr for captions, class-agnostic mask AP for grounding, mask IoU for segmentation, and mask recall for region-specific grounding (refer to Appendix A.1 for details). Having delineated the architecture of GLaMM and the intricacies of the GCG task, it becomes imperative to address the scarcity of large-scale annotated data for regionlevel understanding. We next focus on devising a new, densely annotated dataset to optimize the model’s performance and overcome this data limitation. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 3 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 3 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 3 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 3 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Work 3. Method 4. Data Annotation Pipeline 5. Experiments 6. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Method 3. Method 4. Data Annotation Pipeline 4. Data Annotation Pipeline 5. Experiments 5. Experiments 6. Conclusion and References 6. Conclusion and References Supplementary Material (Part 1) Supplementary Material (Part 1) A. Additional Implementation Details B. Additional Downstream Tasks C. Additional Qualitative Results A. Additional Implementation Details A. Additional Implementation Details B. Additional Downstream Tasks B. Additional Downstream Tasks C. Additional Qualitative Results C. Additional Qualitative Results Supplementary Material (Part 2) Supplementary Material (Part 2) D. Dataset Visualization E. Limitations and Future Work F. Ethics and Societal Impact D. Dataset Visualization D. Dataset Visualization E. Limitations and Future Work E. Limitations and Future Work F. Ethics and Societal Impact F. Ethics and Societal Impact 3. Method Existing Large Multimodal Models (LMMs) either generate ungrounded text or are restricted by limitations such as single-object grounding, user-specified region inputs, or the lack of dense pixel-level object grounding (see Tab. 1). Our Grounding LMM (GLaMM) aims to overcome these limitations by generating natural language responses seamlessly integrated with object segmentation masks. This enables a visually grounded human-machine conversation. 3.1. GLaMM Architecture GLaMM consists of five core components: i) Global Image Encoder, ii) Region Encoder, iii) LLM, iv) Grounding Image Encoder, and v) Pixel Decoder. These components are cohesively designed to handle both textual and optional visual prompts (image level and region), allowing for interaction at multiple levels of granularity and generating grounded text responses (see Fig. 2). These blocks together enable scene-level, region-level, and pixel-level grounding, as explained next. Training specifics are detailed in Appendix A.2. Region-Level Understanding: Building on the shortcomings of existing models that can handle only image-level Region-Level Understanding: visual inputs, and in alignment with recent work [57], the region encoder (R) extends the model’s capability to interpret and interact with user-specified regions in an image. This component constructs a hierarchical feature pyramid from four selected CLIP global image encoder layers, followed by RoIAlign [10] to generate a 14x14 feature map. Combining these features yields a unified region-of-interest (RoI) representation. To facilitate region-targeted responses from GLaMM, we augment the existing vocabulary with a specialized token <bbox>. This is integrated into a prompt like, “The <image> provides an overview of the image. Can you provide a detailed description of the region <bbox>?”. Here the <bbox> token is replaced with the RoI extracted features. Pixel-Level Grounding: Utilizing the grounding image encoder denoted as V and the pixel decoder represented as P, GLaMM facilitates fine-grained pixel-level object grounding, allowing it to ground its responses visually. We instantiate V with a pretrained SAM encoder [18] and design P based on a SAM decoder-like architecture. To activate the pixel-level grounding, our model’s vocabulary is augmented with a specialized token, <SEG>. Prompts, such as “Please segment the ‘man in red’ in the given image," trigger the model to generate responses with corresponding <SEG> tokens. A language-to-prompt (L-P) projection layer (g) transforms the last-layer embeddings corresponding to <SEG> tokens (lseg) into the decoder’s feature space. Subsequently, P produces binary segmentation masks M, Using an end-to-end training approach, GLaMM excels in region understanding, pixel-level grounding, and conversational capabilities. However, due to the lack of standard benchmarks for the novel setting of generating visually grounded detailed conversations, we introduce a novel task, Grounded Conversation Generation (GCG), and a comprehensive evaluation protocol as explained next. 3.2. Grounded Conversation Generation (GCG) The objective of the GCG task is to construct image-level captions with specific phrases directly tied to corresponding segmentation masks in the image. For example, “<A man> and <a boy> sit on <a bench> next to <an old white car>.”, shown in Fig. 3 (left), features how each bracketed phrase (highlighted in the image) is anchored to a unique image segmentation mask. This creates a densely annotated caption that aligns textual descriptions with visual regions, enriching the image’s contextual interpretation. GCG Output Representation: A sample prompt for querying the model in this task is: “Could you please GCG Output Representation: give me a detailed description of the image? Please respond with interleaved segmentation masks for the corresponding parts of the answer.” The model generates a detailed caption along with interleaved segmentation masks, employing the format “A man<SEG> and a boy <SEG> sit on a bench<SEG> next to an old white car <SEG>.” We use special tokens, namely , and <SEG>, to delineate the start and end of each phrase and its corresponding region mask, respectively. Our GranD dataset is meticulously constructed using a stage-wise annotation pipeline, capturing annotations that range from fine-grained specifics to high-level context. This enables the automatic generation of densely annotated captions well-suited for the GCG task, thereby significantly facilitating GLaMM’s training for this task. Some qualitative results of our model on the GCG task are shown in Fig. 3. Evaluation Criteria: We introduce a benchmarking suite for GCG, with a validation set of 2.5K images and a test set of 5K images. Four key aspects are evaluated: i) generated dense caption quality, ii) mask-to-phrase correspondence accuracy, iii) generated mask quality, and iv) region-specific grounding ability. Metrics include METEOR and CIDEr for captions, class-agnostic mask AP for grounding, mask IoU for segmentation, and mask recall for region-specific grounding (refer to Appendix A.1 for details). Evaluation Criteria: Having delineated the architecture of GLaMM and the intricacies of the GCG task, it becomes imperative to address the scarcity of large-scale annotated data for regionlevel understanding. We next focus on devising a new, densely annotated dataset to optimize the model’s performance and overcome this data limitation. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

New AI Can Talk About Your Artwork Like a Professional Critic

Errors in a PowerShell script can prevent successful execution. Implementing try-catch blocks enables error management and response. This post covers try/catch basics and how to handle custom error messages in PowerShell. Effective error handling saves troubleshooting time and improves user experience. PowerShell offers robust options for exception management with try, catch, and finally blocks. Learn to use try-catch for graceful error handling and to add custom error messages for enhanced feedback. Why Use Exception Handling in PowerShell? Scripts can fail for many reasons: missing files, invalid input, or network issues, to name a few. With exception handling, you can capture these issues, inform users in a friendly way, and potentially recover from errors without crashing your script. Using try-catch, you can: Catch specific errors.
Display user-friendly messages.
Log errors for debugging. Syntax overview of Try/Catch Like similar in other programming languages, the try-catch block syntax is very simple and syntax will be the same. It is framed with two sections enclosed in curly brackets (the first block is a try and the second is the catch block). try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} The main purpose of using the try-catch block, we can start to manipulate the error output and make it more friendly for the user. Example 1: After executing the below script, the below error will be shown on the screen as output and it would occupy some space and the problem may not be immediately visible to the User. So you can use a try-catch block to manipulate the error output and make it more friendly. without Try-Catch block Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” with Try Catch block In the below script, we added the ErrorAction parameter with a value of Stop to the command. Not all errors are considered “terminating”, so sometimes we need to add this bit of code in order to properly terminate into the catch block. try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} Example 2: Using the $Error Variable To improve the code and follow best practices, consider the following improvements: Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user.
Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0], consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Here's a refined version of how you might handle errors using $Error: try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
} Errors in a PowerShell script can prevent successful execution. Implementing try-catch blocks enables error management and response. This post covers try/catch basics and how to handle custom error messages in PowerShell. Effective error handling saves troubleshooting time and improves user experience. PowerShell offers robust options for exception management with try , catch , and finally blocks. Learn to use try-catch for graceful error handling and to add custom error messages for enhanced feedback. PowerShell script PowerShell script try catch finally try-catch error error Why Use Exception Handling in PowerShell? Scripts can fail for many reasons: missing files, invalid input, or network issues, to name a few. With exception handling, you can capture these issues, inform users in a friendly way, and potentially recover from errors without crashing your script. Using try-catch , you can: try-catch Catch specific errors. Display user-friendly messages. Log errors for debugging. Catch specific errors. Display user-friendly messages. Log errors for debugging. Syntax overview of Try/Catch Syntax overview of Try/Catch Like similar in other programming languages, the try-catch block syntax is very simple and syntax will be the same. It is framed with two sections enclosed in curly brackets (the first block is a try and the second is the catch block). try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} The main purpose of using the try-catch block, we can start to manipulate the error output and make it more friendly for the user. Example 1: Example 1: After executing the below script, the below error will be shown on the screen as output and it would occupy some space and the problem may not be immediately visible to the User. So you can use a try-catch block to manipulate the error output and make it more friendly. without Try-Catch block without Try-Catch block without Try-Catch block Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” with Try Catch block with Try Catch block with Try Catch block In the below script, we added the ErrorAction parameter with a value of Stop to the command. Not all errors are considered “terminating”, so sometimes we need to add this bit of code in order to properly terminate into the catch block. ErrorAction Stop try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} Example 2: Example 2: Using the $Error Variable Using the $Error Variable Using the $Error Variable $Error To improve the code and follow best practices, consider the following improvements: Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user. Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0], consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user. Use More Descriptive Error Messages: Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0] , consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Avoid Hardcoding Indexes: $Error[0] .First() Here's a refined version of how you might handle errors using $Error : $Error try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
} try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
}

How to Stop Your Scripts from Crashing with Try-Catch Magic

Author:
(1) David Staines. Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility D.2 Profits and Long-Run Growth The first two subsubsections house the proofs of the two main propositions concerning long-run profits. The final subsubsection contains a final proof and fortifies empirical arguments concerning labour supply made in the profit section. D.2.1 Proof of Proposition 13 Proof. The proof starts with the lower bound where there are three cases and then finishes with the upper bound. For the lower bound the order of argument is (ii), (iii) then (i). Aggregate profit can be written as where the subscript indicates the time since the last reset. Cross-multiplying, taking expectations and using the non-stochastic limit, to cancel out terms in the preference shock ψ from both sides, reveals that in the patient limit; with the weight coming from the terms in (25) and the context of the resetting firms problem because the demand curve slopes down and marginal costs are constant, we can see that For Assumption 2 (iii), the proof comes from an analysis of covariance and (strict) stochastic monotonicity properties. By application of Theorem 1.A.3 and 1.A.8 of Shaked and Shanthikumar [2007] to the cases of above and below average profit, we know that the covariance is strictly positive. Applying the definition of covariance. Therefore there will be three channels. A substitution effect from (lower) wages onto labor supply and two income effects, one from the change in wages and the other from the change in profits. Theorem 2 tells us that labor supply rises and wages fall. The strategy is to show that the first two effects net out to reduce labor supply. This means that profits must decrease, to generate a wealth effect that increases labor supply. To demonstrate this point, it is sufficient to show that the labor supply curve is positively sloped. Totally differentiating with respect to L Simplifying yields Since the right hand side is always positive, the sign of the derivative is determined by the bracket. Now substitute the production function in to create the determinant D.2.2 Proof of Proposition 14 Proof. The result comes from a chain of inequalities. From the principle of optimality, it is clear that the firm is weakly better off when it can change prices. Furthermore, since we know that profit function is strictly concave, the optimization problem at any time will have a unique solution. Therefore, we can upgrade to strict inequalities where I am using (25) (the profit function). Since the demand curve slopes down and there are no increasing returns to scale, we know that D.2.3 Additional Arguments The evidence on long-run labor supply elasticity is mixed. The consensus estimate appears to be zero associated with Doran [2014] and Berger et al. [2018], in which case the main result goes through. Ashenfelter et al. [2010] argues that the likely range is between -0.2 and 0.2 with their study at the lower range. The concern with unemployment and underemployment, in Mas and Pallais [2019], favours a positive figure. There is more discussion in the parametization Appendix I. Proof. With balanced growth it has been established that σ = 1, whilst the labor supply and profit conditions take the form and the proof will be complete if we can argue that the left hand bracket is strictly positive with probability one. To do so suppose the converse, then it follows from the definition of balanced growth in stochastic equilibrium that This paper is available on arxiv under CC 4.0 license. Author: (1) David Staines. Author: Author: (1) David Staines. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility D.2 Profits and Long-Run Growth The first two subsubsections house the proofs of the two main propositions concerning long-run profits. The final subsubsection contains a final proof and fortifies empirical arguments concerning labour supply made in the profit section. D.2.1 Proof of Proposition 13 D.2.1 Proof of Proposition 13 Proof. The proof starts with the lower bound where there are three cases and then finishes with the upper bound. For the lower bound the order of argument is (ii), (iii) then (i). Aggregate profit can be written as where the subscript indicates the time since the last reset. Cross-multiplying, taking expectations and using the non-stochastic limit, to cancel out terms in the preference shock ψ from both sides, reveals that in the patient limit; with the weight coming from the terms in (25) and the context of the resetting firms problem because the demand curve slopes down and marginal costs are constant, we can see that For Assumption 2 (iii), the proof comes from an analysis of covariance and (strict) stochastic monotonicity properties. By application of Theorem 1.A.3 and 1.A.8 of Shaked and Shanthikumar [2007] to the cases of above and below average profit, we know that the covariance is strictly positive. Applying the definition of covariance. Therefore there will be three channels. A substitution effect from (lower) wages onto labor supply and two income effects, one from the change in wages and the other from the change in profits. Theorem 2 tells us that labor supply rises and wages fall. The strategy is to show that the first two effects net out to reduce labor supply. This means that profits must decrease, to generate a wealth effect that increases labor supply. To demonstrate this point, it is sufficient to show that the labor supply curve is positively sloped. Totally differentiating with respect to L Simplifying yields Since the right hand side is always positive, the sign of the derivative is determined by the bracket. Now substitute the production function in to create the determinant D.2.2 Proof of Proposition 14 D.2.2 Proof of Proposition 14 Proof. The result comes from a chain of inequalities. From the principle of optimality, it is clear that the firm is weakly better off when it can change prices. Furthermore, since we know that profit function is strictly concave, the optimization problem at any time will have a unique solution. Therefore, we can upgrade to strict inequalities where I am using (25) (the profit function). Since the demand curve slopes down and there are no increasing returns to scale, we know that D.2.3 Additional Arguments D.2.3 Additional Arguments The evidence on long-run labor supply elasticity is mixed. The consensus estimate appears to be zero associated with Doran [2014] and Berger et al. [2018], in which case the main result goes through. Ashenfelter et al. [2010] argues that the likely range is between -0.2 and 0.2 with their study at the lower range. The concern with unemployment and underemployment, in Mas and Pallais [2019], favours a positive figure. There is more discussion in the parametization Appendix I. Proof. With balanced growth it has been established that σ = 1, whilst the labor supply and profit conditions take the form and the proof will be complete if we can argue that the left hand bracket is strictly positive with probability one. To do so suppose the converse, then it follows from the definition of balanced growth in stochastic equilibrium that This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How Profits and Labor Supply Shape Economic Growth

Author:
(1) David Staines. Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility 7.2 Error Coefficients 7.2.1 Coefficient Expressions The starting point is the infinite horizon solutions, which states how present deviations depend on present and past shocks. For the expectation variables it implies The first two steps remove expectations to focus on future variables. The third treats lag terms. The fourth is consolidation, the fifth deals with the Euler and the sixth puts them all together. Proceeding through the derivation in order, it is clear that The coefficients have been organised so that the bracketed terms will be positive when computed in the small noise limit at strictly positive rates of trend inflation, as this is a focal alternative to ZINSS.[57] To dig down to primitives it is necessary to expand out the weighting term as follows: Expanding forward using (139), (140) and the primitive properties of the errors yields Thus, I can substitute back into (146) to solve for the third error in terms of the canonical expansions. The unwieldy expression is suppressed. An algorithm for a solution, in terms of the stochastic equilibrium expansion coefficients, would be useful, alongside a method of numerical approximation. However, these tasks lie beyond the boundaries of this investigation. The rest of the derivation is quite simple Combining (139), (140), (149)-(151) gives the following cumbersome expression for the Phillips curve errors All subsequent error forms can be derived easily from this point. 7.2.2 Limiting Solution Now denoting the errors in (117) in the style of the slope coefficients Hence, I am able to solve for the error coefficient using the output term in (112), the error expression (150) and the symmetry implied by (151) and (152) This paper is available on arxiv under CC 4.0 license. Author: (1) David Staines. Author: Author: (1) David Staines. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility 7.2 Error Coefficients 7.2.1 Coefficient Expressions The starting point is the infinite horizon solutions, which states how present deviations depend on present and past shocks. For the expectation variables it implies The first two steps remove expectations to focus on future variables. The third treats lag terms. The fourth is consolidation, the fifth deals with the Euler and the sixth puts them all together. Proceeding through the derivation in order, it is clear that The coefficients have been organised so that the bracketed terms will be positive when computed in the small noise limit at strictly positive rates of trend inflation, as this is a focal alternative to ZINSS.[57] To dig down to primitives it is necessary to expand out the weighting term as follows: Expanding forward using (139), (140) and the primitive properties of the errors yields Thus, I can substitute back into (146) to solve for the third error in terms of the canonical expansions. The unwieldy expression is suppressed. An algorithm for a solution, in terms of the stochastic equilibrium expansion coefficients, would be useful, alongside a method of numerical approximation. However, these tasks lie beyond the boundaries of this investigation. The rest of the derivation is quite simple Combining (139), (140), (149)-(151) gives the following cumbersome expression for the Phillips curve errors All subsequent error forms can be derived easily from this point. 7.2.2 Limiting Solution 7.2.2 Limiting Solution Now denoting the errors in (117) in the style of the slope coefficients Hence, I am able to solve for the error coefficient using the output term in (112), the error expression (150) and the symmetry implied by (151) and (152) This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How Error Coefficients Reflect Stochastic Equilibrium Expansions in Inflation Models

A Pentester Took Apart a Website’s Code to Prove It Was Totally Pointless

ፍፁም ትርጉም የለሽ መሆኑን ለማረጋገጥ አንድ ጴንጤ የድረ-ገጽ ኮድ ለየ

একজন পেন্টেস্টার একটি ওয়েবসাইটের কোডটি সম্পূর্ণরূপে অর্থহীন প্রমাণ করার জন্য নিয়েছিলেন

Pentester rozebral kód webové stránky, aby dokázal, že je naprosto nesmyslný

En Pentester adskilte en hjemmesides kode for at bevise, at den var fuldstændig meningsløs

Un pentester desmanteló el código de un sitio web para demostrar que era totalmente inútil

פנטסטר פירק קוד של אתר כדי להוכיח שהוא חסר טעם לחלוטין

ペンテスターがウェブサイトのコードを解析し、全く意味がないことを証明した

Pentester කෙනෙක් වෙබ් අඩවියේ කේතයක් වෙන්කර ගත්තේ එය සම්පූර්ණයෙන්ම තේරුමක් නැති බව ඔප්පු කිරීමටයි.

ผู้ทดสอบการเจาะระบบแยกรหัสเว็บไซต์เพื่อพิสูจน์ว่ามันไร้ประโยชน์โดยสิ้นเชิง

Bir Pentester, Tamamen Anlamsız Olduğunu Kanıtlamak İçin Bir Web Sitesinin Kodunu Parçaladı

I-Pentester Yahlulahlula iKhowudi yeWebhusayithi ukungqina ukuba yayingenanjongo kwaphela

I-Pentester Yahlukanisa Ikhodi Yewebhusayithi Ukufakazela Ukuthi Yayingenanjongo Ngokuphelele

Want to Search for Something With an Image and a Text Description? Try a Multimodal RAG

¿Mä Imagen ukat Texto Descripción ukampi kuns thaqhañ munasmati? Mä RAG Multimodal ukamp yant’aña

¿Quieres buscar algo con una imagen y una descripción de texto? Prueba un RAG multimodal

آیا می خواهید چیزی را با یک تصویر و یک توضیح متنی جستجو کنید؟ یک RAG Multimodal را امتحان کنید

Vous souhaitez rechercher quelque chose avec une image et une description textuelle ? Essayez un RAG multimodal

画像とテキストの説明を使って何かを検索したいですか? マルチモーダル RAG をお試しください

ចង់ស្វែងរកអ្វីមួយដែលមានរូបភាព និងការពិពណ៌នាអត្ថបទ? សាកល្បងប្រើ Multimodal RAG

Зураг болон текстийн тайлбартай ямар нэг зүйл хайхыг хүсч байна уу? Multimodal RAG-г туршаад үзээрэй

Ingin Mencari Sesuatu Dengan Imej dan Penerangan Teks? Cuba RAG Multimodal

غواړئ د عکس او متن توضیحاتو سره یو څه وپلټئ؟ د ملټي موډل RAG هڅه وکړئ

රූපයක් සහ පෙළ විස්තරයක් සමඟ යමක් සෙවීමට අවශ්‍යද? Multimodal RAG එකක් උත්සාහ කරන්න

ต้องการค้นหาบางสิ่งที่มีรูปภาพและคำอธิบายหรือไม่ ลองใช้ RAG แบบหลายโหมด

Surat we tekst beýany bilen bir zat gözlemek isleýärsiňizmi? Multimodal RAG synap görüň

A recent study conducted by Kiseki in the fall of 2024 uncovers a fascinating trend: Japanese singles are increasingly eager to connect with people from around the globe. This shift in social dynamics is noteworthy. Record Numbers of Japanese Living Abroad According to the Ministry of Foreign Affairs, a historic high of 570,000 Japanese citizens now reside overseas, the largest figure seen in over 30 years. This surge reflects a vibrant curiosity about diverse cultures, particularly among younger generations, as highlighted by Kiseki's latest research. The survey reveals that over 95% of Japanese singles are enthusiastic about engaging with foreigners. Given Japan’s historically complex attitudes toward international cultures, it’s intriguing to explore what drives this newfound openness. Understanding the Motivations Behind Japan's Singles Delving into the desires of Japanese singles is a complex endeavor. Kiseki, a part of the Social Discovery Group and recognized for its excellence in global matchmaking by the Japan Number One Research Institute LLC, conducted an insightful online survey with over 230 participants from various age brackets. The findings are compelling. A Remarkable 95% Eager to Connect with Foreigners One of the most striking insights from Kiseki’s 2024 survey is that an impressive 95% of respondents express a desire to connect with individuals from other countries. The enthusiasm is nearly equal among men (96%) and women (94%), indicating that this interest transcends gender. Many participants articulated their intentions clearly: while some are in search of love and romance, a significant number prioritize friendship. In fact, 70% are looking to forge friendships through communication, while 29% simply wish for someone to converse with. Language acquisition also plays a crucial role, with 56% eager to practice their speaking skills with others.While friendship and language learning are primary motivators, romance remains an appealing prospect for many. Kiseki reports that 21% of respondents are actively seeking love, with men showing slightly more interest than women. The Rise of International Romance The trend toward international relationships is already gaining momentum in Japan. The survey indicates that 45% of participants have engaged in romantic relationships with foreigners, with women (50%) reporting higher rates than men (36%). Digital platforms are pivotal in fostering these international connections. Kiseki's findings show that 32% have met foreigners online, and 11% have established both online and offline relationships. Younger individuals are particularly inclined to connect digitally, although offline interactions tend to increase with age. When asked about their preferred methods for meeting new people in the future, over 70% expressed a willingness to engage through online platforms or dating sites. Notably, many Japanese singles prefer a gradual approach—favoring casual friendships and language exchanges before diving into romantic pursuits. Kiseki: Bridging Connections Across Borders While there’s a clear enthusiasm among Japanese singles for meeting foreigners, navigating these connections can be challenging. Launched by SDG, Kiseki has become a leading platform for connecting individuals from approximately 80 countries. Kiseki offers seamless communication features such as AI-assisted chat translations, speed matching, and video calls, facilitating meaningful interactions across borders. The platform encourages users to take their time in building friendships and developing online relationships while also serving as a resource for language learning and cultural exploration. Safety is paramount; Kiseki employs rigorous identity and age verification through trusted providers like Liquid, ensuring a secure environment for its users. In conclusion, as Japan’s singles increasingly seek connections beyond their borders, platforms like Kiseki play a vital role in fostering these exciting new relationships—whether they blossom into friendships or romantic partnerships. A recent study conducted by Kiseki in the fall of 2024 uncovers a fascinating trend: Japanese singles are increasingly eager to connect with people from around the globe. This shift in social dynamics is noteworthy. Kiseki Record Numbers of Japanese Living Abroad According to the Ministry of Foreign Affairs, a historic high of 570,000 Japanese citizens now reside overseas, the largest figure seen in over 30 years. This surge reflects a vibrant curiosity about diverse cultures, particularly among younger generations, as highlighted by Kiseki's latest research. The survey reveals that over 95% of Japanese singles are enthusiastic about engaging with foreigners. Given Japan’s historically complex attitudes toward international cultures, it’s intriguing to explore what drives this newfound openness. Understanding the Motivations Behind Japan's Singles Delving into the desires of Japanese singles is a complex endeavor. Kiseki, a part of the Social Discovery Group and recognized for its excellence in global matchmaking by the Japan Number One Research Institute LLC, conducted an insightful online survey with over 230 participants from various age brackets. The findings are compelling. Social Discovery Group A Remarkable 95% Eager to Connect with Foreigners One of the most striking insights from Kiseki’s 2024 survey is that an impressive 95% of respondents express a desire to connect with individuals from other countries. The enthusiasm is nearly equal among men (96%) and women (94%), indicating that this interest transcends gender. Many participants articulated their intentions clearly: while some are in search of love and romance, a significant number prioritize friendship. In fact, 70% are looking to forge friendships through communication, while 29% simply wish for someone to converse with. Language acquisition also plays a crucial role, with 56% eager to practice their speaking skills with others.While friendship and language learning are primary motivators, romance remains an appealing prospect for many. Many participants articulated their intentions clearly: while some are in search of love and romance, a significant number prioritize friendship. In fact, 70% are looking to forge friendships through communication, while 29% simply wish for someone to converse with. Language acquisition also plays a crucial role, with 56% eager to practice their speaking skills with others.While friendship and language learning are primary motivators, romance remains an appealing prospect for many. Kiseki reports that 21% of respondents are actively seeking love, with men showing slightly more interest than women. The Rise of International Romance The trend toward international relationships is already gaining momentum in Japan. The survey indicates that 45% of participants have engaged in romantic relationships with foreigners, with women (50%) reporting higher rates than men (36%). Digital platforms are pivotal in fostering these international connections. Kiseki's findings show that 32% have met foreigners online, and 11% have established both online and offline relationships. Younger individuals are particularly inclined to connect digitally, although offline interactions tend to increase with age. When asked about their preferred methods for meeting new people in the future, over 70% expressed a willingness to engage through online platforms or dating sites . Notably, many Japanese singles prefer a gradual approach—favoring casual friendships and language exchanges before diving into romantic pursuits. dating sites Kiseki: Bridging Connections Across Borders While there’s a clear enthusiasm among Japanese singles for meeting foreigners, navigating these connections can be challenging. Launched by SDG , Kiseki has become a leading platform for connecting individuals from approximately 80 countries. Kiseki offers seamless communication features such as AI-assisted chat translations, speed matching, and video calls, facilitating meaningful interactions across borders. The platform encourages users to take their time in building friendships and developing online relationships while also serving as a resource for language learning and cultural exploration. Safety is paramount; Kiseki employs rigorous identity and age verification through trusted providers like Liquid, ensuring a secure environment for its users. SDG In conclusion, as Japan’s singles increasingly seek connections beyond their borders, platforms like Kiseki play a vital role in fostering these exciting new relationships—whether they blossom into friendships or romantic partnerships. In conclusion, as Japan’s singles increasingly seek connections beyond their borders, platforms like Kiseki play a vital role in fostering these exciting new relationships—whether they blossom into friendships or romantic partnerships.

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