In the past decade, social media platforms have been used for information dissemination and consumption. While a major portion of the content is posted to promote citizen journalism and public awareness, some content is posted to mislead users. Among different content types such as text, images, and videos, memes (text overlaid on images) are particularly prevalent and can serve as powerful vehicles for propaganda, hate, and humor. In the current literature, there have been efforts to individually detect such content in memes. However, the study of their intersection is very limited. In this study, we explore the intersection between propaganda and hate in memes using a multi-agent LLM-based approach. We extend the propagandistic meme dataset with coarse and fine-grained hate labels. Our finding suggests that there is an association between propaganda and hate in memes. We provide detailed experimental results that can serve as a baseline for future studies. We will make the experimental resources publicly available to the community.

Following the Russian Federation's full-scale invasion of Ukraine in February 2022, a multitude of information narratives emerged within both pro-Russian and pro-Ukrainian communities online. As the conflict progresses, so too do the information narratives, constantly adapting and influencing local and global community perceptions and attitudes. This dynamic nature of the evolving information environment (IE) underscores a critical need to fully discern how narratives evolve and affect online communities. Existing research, however, often fails to capture information narrative evolution, overlooking both the fluid nature of narratives and the internal mechanisms that drive their evolution. Recognizing this, we introduce a novel approach designed to both model narrative evolution and uncover the underlying mechanisms driving them. In this work we perform a comparative discourse analysis across communities on Telegram covering the initial three months following the invasion. First, we uncover substantial disparities in narratives and perceptions between pro-Russian and pro-Ukrainian communities. Then, we probe deeper into prevalent narratives of each group, identifying key themes and examining the underlying mechanisms fueling their evolution. Finally, we explore influences and factors that may shape the development and spread of narratives.

In this effort, we propose using the Mamba for handling tabular data in personalized recommendation systems. We present the \textit{FT-Mamba} (Feature Tokenizer\,$+$\,Mamba), a novel hybrid model that replaces Transformer layers with Mamba layers within the FT-Transformer architecture, for handling tabular data in personalized recommendation systems. The \textit{Mamba model} offers an efficient alternative to Transformers, reducing computational complexity from quadratic to linear by enhancing the capabilities of State Space Models (SSMs). FT-Mamba is designed to improve the scalability and efficiency of recommendation systems while maintaining performance. We evaluate FT-Mamba in comparison to a traditional Transformer-based model within a Two-Tower architecture on three datasets: Spotify music recommendation, H\&M fashion recommendation, and vaccine messaging recommendation. Each model is trained on 160,000 user-action pairs, and performance is measured using precision (P), recall (R), Mean Reciprocal Rank (MRR), and Hit Ratio (HR) at several truncation values. Our results demonstrate that FT-Mamba outperforms the Transformer-based model in terms of computational efficiency while maintaining or exceeding performance across key recommendation metrics. By leveraging Mamba layers, FT-Mamba provides a scalable and effective solution for large-scale personalized recommendation systems, showcasing the potential of the Mamba architecture to enhance both efficiency and accuracy.

What jumps to mind when you think about the impending AI apocalypse? If you're partial to sci-fi movie cliches, you may envisage killer robots (with or without thick Austrian accents) rising up to terminate their hubristic creators. Or perhaps, a la The Matrix, you'll go for scary machines sucking energy out of our bodies as they distract us with a simulated reality. For Yuval Noah Harari, who has spent a lot of time worrying about AI over the past decade, the threat is less fantastical and more insidious. "In order to manipulate humans, there is no need to physically hook brains to computers," he writes in his engrossing new book Nexus.

If anyone could make the Dark Side sound good, it was James Earl Jones. The actor, who died Monday at the age of 93, provided the voice for Darth Vader in more than a dozen Star Wars properties, from A New Hope to Star Tours. He made the Force sound ominous in a way that made it appealing. With his passing, it feels as though all the power and gravitas and respect he brought to the character is gone. A few years ago, when Jones provided a few lines of dialog as Vader for The Rise of Skywalker, he'd expressed interest in wrapping up his time as the Sith Lord, according to Vanity Fair.

Correct reasoning steps are important for language models to achieve high performance on many tasks, such as commonsense reasoning, question answering, and mathematical problem-solving [Wei et al., 2022, Kojima et al., 2022, Suzgun et al., 2022]. One way to elicit reasoning is through the chain-of-thought (CoT) method Wei et al. [2022], Kojima et al. [2022], which asks the model to provide step-by-step reasoning. Another approach encourages the model to provide similar problems Yasunaga et al. [2024] as the query, indirectly compelling the model to first understand the original query. Similarly, repeating and rephrasing the query Deng et al. [2023], Mekala et al. [2023] requires the model to first understand the problem and then modify the query into its own words. This rephrasing might help simplify the problem for the model. Additionally, reasoning can be generated by indirectly providing reasoning examples in demonstrations, referred to as in-context learning (ICL) Brown et al. [2020], Min et al. [2022], Xie et al. [2021]. While these methods have demonstrated significant performance improvements, language models are still prone to errors due to incorrect context understanding or analytical steps. Furthermore, they are subject to instability when requests are paraphrased. This instability is particularly concerning in the context of adversarial prompts, where recent research [Zou et al., 2023, Zeng et al., 2024] has shown that adversaries can intentionally rewrite prompts to coax safety-aligned language models into generating objectionable content that they would not generate otherwise.

Recommender systems have become integral to digital experiences, shaping user interactions and preferences across various platforms. Despite their widespread use, these systems often suffer from algorithmic biases that can lead to unfair and unsatisfactory user experiences. This study introduces an interactive tool designed to help users comprehend and explore the impacts of algorithmic harms in recommender systems. By leveraging visualizations, counterfactual explanations, and interactive modules, the tool allows users to investigate how biases such as miscalibration, stereotypes, and filter bubbles affect their recommendations. Informed by in-depth user interviews, this tool benefits both general users and researchers by increasing transparency and offering personalized impact assessments, ultimately fostering a better understanding of algorithmic biases and contributing to more equitable recommendation outcomes. This work provides valuable insights for future research and practical applications in mitigating bias and enhancing fairness in machine learning algorithms.

The primary purpose of this paper is to present the concept of dichotomy in image illumination modeling based on the power function. In particular, we review several mathematical properties of the power function to identify the limitations and propose a new mathematical model capable of abstracting illumination dichotomy. The simplicity of the equation opens new avenues for classical and modern image analysis and processing. The article provides practical and illustrative image examples to explain how the new model manages dichotomy in image perception. The article shows dichotomy image space as a viable way to extract rich information from images despite poor contrast linked to tone, lightness, and color perception. Moreover, a comparison with state-of-the-art methods in image enhancement provides evidence of the method's value.

Given the profound impact of narratives across various societal levels, from personal identities to international politics, it is crucial to understand their distribution and development over time. This is particularly important in online spaces. On the Web, narratives can spread rapidly and intensify societal divides and conflicts. While many qualitative approaches exist, quantifying narratives remains a significant challenge. Computational narrative analysis lacks frameworks that are both comprehensive and generalizable. To address this gap, we introduce a numerical narrative representation grounded in structuralist linguistic theory. Chiefly, Greimas' Actantial Model represents a narrative through a constellation of six functional character roles. These so-called actants are genre-agnostic, making the model highly generalizable. We extract the actants using an open-source LLM and integrate them into a Narrative-Structured Text Embedding that captures both the semantics and narrative structure of a text. We demonstrate the analytical insights of the method on the example of 5000 full-text news articles from Al Jazeera and The Washington Post on the Israel-Palestine conflict. Our method successfully distinguishes articles that cover the same topics but differ in narrative structure.

This paper introduces a novel method for emulating piano sounds. We propose to exploit the sine, transient, and noise decomposition to design a differentiable spectral modeling synthesizer replicating piano notes. Three sub-modules learn these components from piano recordings and generate the corresponding harmonic, transient, and noise signals. Splitting the emulation into three independently trainable models reduces the modeling tasks' complexity. The quasi-harmonic content is produced using a differentiable sinusoidal model guided by physics-derived formulas, whose parameters are automatically estimated from audio recordings. The noise sub-module uses a learnable time-varying filter, and the transients are generated using a deep convolutional network. From singular notes, we emulate the coupling between different keys in trichords with a convolutional-based network. Results show the model matches the partial distribution of the target while predicting the energy in the higher part of the spectrum presents more challenges. The energy distribution in the spectra of the transient and noise components is accurate overall. While the model is more computationally and memory efficient, perceptual tests reveal limitations in accurately modeling the attack phase of notes. Despite this, it generally achieves perceptual accuracy in emulating single notes and trichords.