arXiv:2501.14728v1 Announce Type: new
Abstract: While large generative artificial intelligence (GenAI) models have achieved significant success, they also raise growing concerns about online information security due to their potential misuse for generating deceptive content. Out-of-context (OOC) multimodal misinformation detection, which often retrieves Web evidence to identify the repurposing of images in false contexts, faces the issue of reasoning over GenAI-polluted evidence to derive accurate predictions. Existing works simulate GenAI-powered pollution at the claim level with stylistic rewriting to conceal linguistic cues, and ignore evidence-level pollution for such information-seeking applications. In this work, we investigate how polluted evidence affects the performance of existing OOC detectors, revealing a performance degradation of more than 9 percentage points. We propose two strategies, cross-modal evidence reranking and cross-modal claim-evidence reasoning, to address the challenges posed by polluted evidence. Extensive experiments on two benchmark datasets show that these strategies can effectively enhance the robustness of existing out-of-context detectors amidst polluted evidence.
The Impact of Artificial Intelligence on Online Information Security
The rise of generative artificial intelligence (GenAI) models has brought about significant advancements in various fields, but it has also raised concerns about the potential misuse of these models for generating deceptive content. In particular, the issue of out-of-context (OOC) multimodal misinformation detection has become increasingly challenging as the evidence used for identifying false contexts may be polluted by GenAI.
Existing works in this area have focused on simulating GenAI-powered pollution at the claim level through stylistic rewriting to conceal linguistic cues. However, they have largely overlooked the issue of evidence-level pollution, which is crucial for information-seeking applications. This work aims to fill this gap and investigate how polluted evidence affects the performance of existing OOC detectors.
The researchers conducted extensive experiments on two benchmark datasets to assess the impact of polluted evidence on the performance of OOC detectors. The results revealed a significant performance degradation of over 9 percentage points when polluted evidence was present. This highlights the urgent need to address this issue and develop strategies to enhance the robustness of existing detectors.
Cross-Modal Evidence Reranking
One strategy proposed in this work is cross-modal evidence reranking. This approach involves reevaluating the relevance and reliability of evidence by considering multiple modalities. By incorporating visual information, such as analyzing the consistency between textual claims and accompanying images, the authors aim to mitigate the impact of polluted evidence on the detection of out-of-context misinformation. This strategy leverages the multidisciplinary nature of multimedia information systems and demonstrates the importance of integrating different data modalities for accurate analysis.
Cross-Modal Claim-Evidence Reasoning
The second strategy proposed is cross-modal claim-evidence reasoning. This approach aims to exploit the correlation between claims and evidence across different modalities to improve the detection accuracy. By jointly modeling textual and visual information, the authors enable more comprehensive reasoning and inference, effectively addressing the challenges posed by polluted evidence in OOC detection. This strategy demonstrates the potential of interdisciplinary research in the fields of artificial reality, augmented reality, and virtual realities, as it combines linguistic and visual cues to enhance the detection capabilities of OOC detectors.
Overall, this study brings valuable insights into the impact of polluted evidence on the performance of OOC detectors and proposes effective strategies to mitigate this issue. The multi-disciplinary nature of the concepts explored in this work highlights the importance of integrating various disciplines, including multimedia information systems, animations, and artificial reality. By combining expertise from these fields, researchers can develop robust systems to combat the challenges posed by deceptive content generated using GenAI models.
arXiv:2412.16183v1 Announce Type: new
Abstract: Two distinct energy-momentum tensors of the theory of weak gravity and spinor quantum mechanics are analyzed with respect to their four-divergence and expectation values of energy. The first energy-momentum tensor is obtained by a straightforward generalization of the symmetric energy-momentum tensor of a free Dirac field, and the second is derived by the second Noether theorem. We find that the four-divergences of both tensors are not equal. Particularly, the tensor derived by the generalization procedure does not match the four-divergence of the canonical energy-momentum tensor. As a result, both tensors predict distinct values for the energy of the Dirac field. The energy-momentum tensor of the non-extended theory with the correct expression for four-divergence obtained by the second Noether theorem is asymmetric. This contradicts the requirements of general relativity. To rectify this situation, the Lagrangian of the theory is extended with the Lagrangian of the free electromagnetic field on curved spacetime. Then, the symmetric energy-momentum tensor of quantum electrodynamics with the required four-divergence is obtained by the second Noether theorem. Moreover, the energy-momentum tensor appears in the interaction Lagrangian term of the extended theory. In addition, we show that the Lagrangian density of the extended theory can be recast into the Lagrangian density of a flat spacetime theory, contrary to the statement made for the non-extended theory.
Conclusion:
In this study, two different energy-momentum tensors in the theory of weak gravity and spinor quantum mechanics are analyzed. It is found that the four-divergences of both tensors are not equal, leading to distinct energy predictions for the Dirac field. Moreover, the energy-momentum tensor derived by the generalization procedure does not match the canonical energy-momentum tensor, and the one derived by the second Noether theorem is asymmetric, contradicting the requirements of general relativity.
To address this discrepancy, the Lagrangian of the theory is extended with the Lagrangian of the free electromagnetic field on curved spacetime. This extension allows the symmetric energy-momentum tensor of quantum electrodynamics, with the correct four-divergence, to be obtained through the second Noether theorem. Furthermore, the energy-momentum tensor appears in the interaction Lagrangian term of the extended theory.
The study also demonstrates that the Lagrangian density of the extended theory can be recast into the Lagrangian density of a flat spacetime theory, contrary to the previous statement made for the non-extended theory.
Future Roadmap:
Investigate further the implications of the distinct energy predictions for the Dirac field based on the different energy-momentum tensors.
Explore the consequences of the asymmetry in the energy-momentum tensor on general relativity and its compatibility with other theories.
Further examine the extended theory with the Lagrangian of the free electromagnetic field on curved spacetime, and investigate its implications and predictions.
Evaluate the significance of the appearance of the energy-momentum tensor in the interaction Lagrangian term, and study its effects on other quantum mechanical systems.
Compare and contrast the recasting of the Lagrangian density from an extended theory into that of a flat spacetime theory, and analyze any implications or limitations of this recasting.
Consider the possible modification or refinement of the existing theory to reconcile the discrepancies and address the contradictions with general relativity.
Challenges:
Understanding the underlying reasons for the unequal four-divergences of the energy-momentum tensors and the implications on energy predictions.
Exploring the consequences of the asymmetric energy-momentum tensor on the compatibility of the theory with general relativity.
Investigating the extended theory and analyzing its predictions, particularly in relation to other quantum mechanical systems.
Determining the significance and effects of the appearance of the energy-momentum tensor in the interaction Lagrangian term.
Thoroughly examining the recasting of the Lagrangian density and its potential implications and limitations.
Developing modifications or refinements to the theory to resolve the discrepancies and ensure consistency with general relativity.
Opportunities:
Advancing knowledge and understanding in the theory of weak gravity and spinor quantum mechanics.
Contributing to the field of quantum electrodynamics and its interaction with curved spacetime.
Exploring potential connections between the extended theory and other areas of physics.
Engaging in interdisciplinary research to bridge the gaps between different theories.
Promoting further discussions and collaborations among physicists to address the challenges and opportunities in this field.
This study highlights the discrepancies and contradictions in the energy-momentum tensors of weak gravity and spinor quantum mechanics. It presents a roadmap for future research, outlining the challenges that need to be overcome and the opportunities for advancing knowledge and understanding in this field.
The Potential Future Trends in Art Censorship and Recommendations for the Industry
The art world has been grappling with issues of censorship, particularly related to the Israel-Palestine conflict, which reached a peak in 2024. Accusations of censoring artists and curators for their pro-Palestine politics have been rampant, causing a widening schism in the art community. This article will analyze the key points of this text and provide predictions for potential future trends in art censorship and recommendations for the industry.
The Dramatic Uptick in Accusations
The accusations of censorship in the art world have increased so dramatically that the National Coalition Against Censorship launched the Art Censorship Index in March. This online tool tracks the state of freedom of expression in the United States, highlighting incidents where artworks or programs were altered or removed due to perceived political content. The increase in accusations indicates a polarized cultural climate and a lack of nuanced debate on the Israel-Palestine conflict.
The Complexity of Censorship
Censorship is not a straightforward issue, as it involves legal considerations and is often influenced by the cultural climate. The Art Censorship Index defines censorship as instances where an institution cancels or withdraws a program or artwork based on its political content, the artist’s politics, or the cultural associations tied to the content. The key word here is “perceived,” highlighting the subjectivity of censorship and the challenges in defining it.
High-Profile Incidents
One of the most high-profile incidents of censorship occurred when a group of artists pulled their artworks from a show at the Barbican Centre in London. This protest was in response to the Barbican’s decision to cancel a lecture on the historical connections between the Holocaust and Israel’s assault on Gaza. The Barbican cited premature publicity and the need for careful preparation as reasons for the withdrawal. The incident exemplifies the creeping normalization of censorship across art institutions, according to artist Yto Barrada.
Artist-activist Nan Goldin also accused the Neue Nationalgalerie in Berlin of censorship when they initially refused to allow her to add a statement about deaths in Gaza, Lebanon, and the West Bank to her artwork. The museum claimed that the issue was the omission of Israeli victims in the statement. The incident highlights the ongoing legislative controversies surrounding criticism of Israel in the arts in Germany.
Crisis of Faith in Cultural Institutions
The mounting controversies surrounding censorship have created a crisis of faith in cultural institutions that are meant to serve as repositories of history. If museums are unable to tell the stories truthfully, it raises questions about their credibility and trustworthiness. This crisis extends beyond the issue of Palestine, as exemplified by the Wall Street Journal’s investigation into the alteration of photographic exhibits at the National Archives, which focused on civil rights in the US. The removal of portraits of Martin Luther King Jr. and images of Japanese American incarceration camps emphasizes the dangerous trend of altering history to make it more palatable.
Predictions for Future Trends
Based on the current trends and controversies surrounding censorship in the art world, several predictions for the future can be made:
Increased polarization: The schism between pro-Israel and pro-Palestine sentiments will likely continue to widen, with less room for nuanced debate, leading to further accusations of censorship.
Legislative interventions: Governments may introduce legislation or regulations influencing artistic freedom and censorship in an attempt to control narratives surrounding the Israel-Palestine conflict.
Rise of alternative platforms: Artists and curators who feel stifled by mainstream cultural institutions may turn to alternative platforms, such as independent galleries, online exhibitions, or decentralized art spaces, to express their political views freely.
Focus on representation and diversity: The push for representation and diversity may intensify, with a demand for a multiplicity of voices and perspectives to be included in exhibitions and curatorial decisions.
Recommendations for the Industry
To address the challenges posed by censorship in the art world, the industry should consider the following recommendations:
Establish clear guidelines: Cultural institutions should develop transparent guidelines outlining their stance on censorship and how they navigate politically sensitive content. This will promote consistency and reduce ambiguity in decision-making processes.
Promote dialogue and education: Institutions should facilitate open and informed dialogue on politically charged topics, encouraging critical thinking and providing educational resources to aid understanding. This can help foster a more nuanced debate and counter the polarization surrounding the Israel-Palestine conflict.
Support independent platforms: The art industry should support independent platforms that provide spaces for artists and curators to express their political views freely. This can help maintain artistic integrity and diversify the narratives surrounding the conflict.
Advocate for artistic freedom: Artists, curators, and art organizations should actively advocate for artistic freedom and challenge attempts to censor or silence political voices. Collaboration and solidarity within the industry can amplify the impact of these efforts.
Conclusion
The increasing accusations of censorship in the art world related to the Israel-Palestine conflict highlight the challenges faced by cultural institutions in navigating politically sensitive content. By understanding the complexities of censorship and its implications, the industry can anticipate potential future trends and take proactive measures to ensure artistic freedom and truthful storytelling. Implementing clear guidelines, promoting dialogue, supporting independent platforms, and advocating for artistic freedom are essential steps toward overcoming the crisis of faith in cultural institutions and fostering a more inclusive and diverse art community.
References:
– The Art Newspaper: https://www.theartnewspaper.com/news/art-censorship-index-us
– National Coalition Against Censorship: https://ncac.org/
– Wall Street Journal: https://www.wsj.com/
– Berliner Zeitung: https://www.berliner-zeitung.de/
Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t.
Introduction
Data analysis in R often involves dealing with missing values, which can significantly impact the quality of your results. The complete.cases function in R is an essential tool for handling missing data effectively. This comprehensive guide will walk you through everything you need to know about using complete.cases in R, from basic concepts to advanced applications.
Understanding Missing Values in R
Before diving into complete.cases, it’s crucial to understand how R handles missing values. In R, missing values are represented by NA (Not Available), and they can appear in various data structures like vectors, matrices, and data frames. Missing values are a common occurrence in real-world data collection, especially in surveys, meter readings, and tick sheets.
Syntax and Basic Usage
The basic syntax of complete.cases is straightforward:
complete.cases(x)
Where ‘x’ can be a vector, matrix, or data frame. The function returns a logical vector indicating which cases (rows) have no missing values.
Basic Vector Examples
# Create a vector with missing values
x <- c(1, 2, NA, 4, 5, NA)
complete.cases(x)
[1] TRUE TRUE FALSE TRUE TRUE FALSE
# Returns: TRUE TRUE FALSE TRUE TRUE FALSE
Data Frame Operations
# Create a sample data frame
df <- data.frame(
A = c(1, 2, NA, 4),
B = c("a", NA, "c", "d"),
C = c(TRUE, FALSE, TRUE, TRUE)
)
complete_df <- df[complete.cases(df), ]
print(complete_df)
A B C
1 1 a TRUE
4 4 d TRUE
Advanced Usage Scenarios
Subset Selection
# Select only complete cases from multiple columns
subset_data <- df[complete.cases(df[c("A", "B")]), ]
print(subset_data)
complete.cases returns a logical vector indicating non-missing values
It works with vectors, matrices, and data frames
Use it for efficient data cleaning and preprocessing
Consider the implications of removing incomplete cases
Always document your missing data handling strategy
Conclusion
Understanding and effectively using complete.cases in R is crucial for data analysis. While it’s a powerful tool for handling missing values, remember to use it judiciously and always consider the impact on your analysis. Keep practicing with different datasets to master this essential R function.
Frequently Asked Questions
Q: What’s the difference between complete.cases and na.omit? A: While both functions handle missing values, complete.cases returns a logical vector, while na.omit directly removes rows with missing values.
Q: Can complete.cases handle different types of missing values? A: complete.cases primarily works with NA values, but can also handle NaN values in R.
Q: Does complete.cases work with tibbles? A: Yes, complete.cases works with tibbles, but you might prefer tidyverse functions like drop_na() for consistency.
Q: How does complete.cases handle large datasets? A: complete.cases is generally efficient with large datasets, but consider using data.table for very large datasets.
Q: Can I use complete.cases with specific columns only? A: Yes, you can apply complete.cases to specific columns by subsetting your data frame.
Can you share?
Have you used complete.cases in your R programming projects? Share your experiences and tips in the comments below! Don’t forget to bookmark this guide for future reference and share it with your fellow R programmers.
Implications & Future Developments of Using complete.cases in R for Data Analysis
The pursuit of data analysis and data science in R typically comes with the challenge of dealing with missing values, which can affect the quality and outcomes of analyses. The function “complete.cases” in R, designed to handle missing data effectively, has seen an increasingly central role in modern data analysis. This article aims to dissect the application of “complete.cases” in R, and extrapolate long-term implications and possible future developments.
The Importance of “complete.cases” in Future Data Analysis
Missing values, represented by NA (Not Available) in R, are common in real-world data collection, particularly in surveys, meter readings, and tick sheets. The “complete.cases” function is a straightforward tool used to highlight cases (rows) in vectors, matrices, or data frames that have no missing values, thereby improving the accuracy of the analysis.
In the future, as data sets grow both in size and complexity, efficient tools like “complete.cases” will become even more indispensable to data analysts. The ability to handle multiple columns simultaneously and select only complete cases from multiple columns demonstrates its potential to process large amounts of information effectively.
Future Developments for complete.cases in R
While “complete.cases” currently works predominantly with NA and NaN values, there may be scope for it to evolve and handle different types of missing values. An interesting prospect is the potential to work with different data structures beyond vectors, matrices, and data frames, allowing for a more versatile and comprehensive analysis. Another key area for development could be enhancing its compatibility with very large datasets for more efficient processing.
Actionable Advice:
Best Practices when Using complete.cases
Check the Proportion of Missing Values: Always examine the proportion of missing values before removing them, as crucial patterns could be lost with the removal of incomplete cases.
Understand the Impact of Removal: Consider the potential implications of removing incomplete cases on your analysis as it could skew your overall results.
Document Your Process: Documenting your missing data handling strategy is essential for reproducing your results in the future.
Be Efficient with Large Datasets: large datasets can impact the performance of your functions. Thus, you need to use tools like “complete.cases” efficiently to avoid compromised performance.
How to Avoid Common Pitfalls
Do not remove too many observations, as this can result in loss of valuable information.
Consider the pattern of missing data. Always explore and understand the reasons behind missing data.
Do not ignore the impact on statistical power. It refers to the extent by which a test can determine differences or relationships. Ignoring this can affect your results significantly.
To conclude, understanding and effectively using “complete.cases” in R is an essential skill for any robust data analysis. As we dive deeper into the future of data science, utilizing tools to handle missing data effectively will become more central to our work.
arXiv:2412.12200v1 Announce Type: new
Abstract: This paper aims to study a newly proposed fluid description of dark energy in the context of late-time accelerated expansion of the universe. We examine the probable origin of the proposed equation of state in correspondence with some vastly discussed scalar field models of dark energy and reconstruct the field parameters like scalar field $phi$ and scalar potential $V(phi)$, analyzing their behavior in the evolution of the universe. The study also incorporates an analysis of fundamental energy conditions: Null Energy Condition (NEC), Dominant Energy Condition (DEC), and Strong Energy Condition (SEC), to assess the physical consistency and cosmological implications of the model. We perform a detailed stability analysis and investigate the evolutionary dynamics of the proposed fluid model from a thermodynamic perspective. Additionally, the model is analyzed using some of the latest observational datasets, such as Cosmic Chronometers (CC), Baryon Acoustic Oscillation (BAO), and Supernova Type-Ia (using Pantheon+SH0ES compilation and Union 2.1), to determine its viability and consistency with observations. The results suggest that the model offers a robust description of dark energy dynamics while maintaining agreement with current observational data.
A Roadmap for Understanding Dark Energy Dynamics
Dark energy, a mysterious form of energy believed to be responsible for the late-time accelerated expansion of the universe, continues to intrigue scientists. In this paper, we propose a new fluid description of dark energy and aim to explore its origins, behavior, and implications. This roadmap will guide readers through the key findings and potential challenges on the horizon.
Understanding the Equation of State and Scalar Field Models
We begin by investigating the equation of state of the proposed fluid and its connection to scalar field models of dark energy. By examining the behavior of the scalar field $phi$ and scalar potential $V(phi)$, we can gain insights into the evolution of the universe. This analysis helps us establish a foundation for our subsequent investigations.
Assessing Energy Conditions and Cosmological Implications
Next, we delve into the physical consistency and cosmological implications of the proposed fluid model by analyzing fundamental energy conditions. We evaluate the Null Energy Condition (NEC), Dominant Energy Condition (DEC), and Strong Energy Condition (SEC). This assessment allows us to gauge the viability and validity of the model, providing valuable insights into its overall consistency.
Stability Analysis and Thermodynamic Perspectives
A detailed stability analysis is then performed to assess the dynamics of the proposed fluid model. By considering thermodynamic perspectives, we gain a deeper understanding of its evolution. This analysis is crucial in determining the robustness and reliability of the model as a description of dark energy dynamics.
Observational Dataset Analysis and Viability
We further evaluate the proposed fluid model’s viability by comparing it with the latest observational datasets. By analyzing data from Cosmic Chronometers (CC), Baryon Acoustic Oscillation (BAO), and Supernova Type-Ia (using Pantheon+SH0ES compilation and Union 2.1), we aim to establish its consistency with observations. These comparisons provide crucial evidence and support for the model.
Conclusion
The findings of our study suggest that the proposed fluid model offers a robust description of dark energy dynamics while maintaining agreement with current observational data. By examining its equation of state, scalar field models, energy conditions, stability, and thermodynamic perspectives, we have gained valuable insights into its origins, behavior, and implications. Our analysis using observational datasets further strengthens its viability. However, future challenges and opportunities lie in refining and expanding this model, incorporating additional observations and testing it against new data to solidify its position in our understanding of dark energy.
