One of the desired key properties of deep learning models is the ability to generalise to unseen samples. When provided with new samples that are (perceptually) similar to one or more training samples, deep learning models are expected to produce correspondingly similar outputs. Models that succeed in predicting similar outputs for similar inputs are often called robust. Deep learning models, on the other hand, have been shown to be highly vulnerable to minor (adversarial) perturbations of the input, which manage to drastically change a model's output and simultaneously expose its reliance on spurious correlations. In this work, we investigate whether inherently interpretable deep models, i.e., deep models that were designed to focus more on meaningful and interpretable features, are more robust to irrelevant perturbations in the data, compared to their black-box counterparts. We test our hypothesis by comparing the robustness of an interpretable and a black-box music emotion recognition (MER) model when challenged with adversarial examples. Furthermore, we include an adversarially trained model, which is optimised to be more robust, in the comparison. Our results indicate that inherently more interpretable models can indeed be more robust than their black-box counterparts, and achieve similar levels of robustness as adversarially trained models, at lower computational cost.

Welcome to the Fox News Politics newsletter, with the latest updates on the Trump administration, Capitol Hill and more Fox News politics content. New York City socialist mayoral candidate Zohran Mamdani's past stances on policing are a legitimate reason for New Yorkers to be concerned, despite his recent walkbacks, according to a New York City crime expert who spoke to Fox News Digital. "I think what scares a lot of New Yorkers about the policy positions taken by Zohran Mamdani over the years is that he has exhibited not just a lack of appreciation for the men and women that stand on that [police] line, but a visceral disdain for them, which has led him to push for things like defunding and dismantling the police," Rafael A. Mangual, senior fellow and head of research for policing and public safety at the Manhattan Institute, told Fox News Digital, shortly after a gunman killed four people in midtown Manhattan, including a NYPD police officer. "It's not so much as just that he said, well, I wanna allocate some of this money to other places. He has gone so far as to say that we should dismantle the entire department."…READ

Myrna (Jennifer Dale) must have had better blind dates. Her table for two is hemmed in by strange shrouds in tall vitrines. And as she makes small talk with her date Karsh (Vincent Cassel), the restaurant's owner, it becomes clear her surroundings are attached – architecturally, financially and intellectually – to a cemetery. And not just any cemetery: its headstones have screens. Because the bodies are swaddled in natty, camera-riddled, internet-enabled shrouds, you can come here to watch your loved ones decompose.

Checking your drink for drugs no longer needs to feel like a science experiment. Scientists in South Korea have created a new solution, a temporary tattoo sticker that instantly detects tampering. This simple sticker works fast, stays discreet, and offers surprisingly powerful protection. At first glance, it looks like ordinary skin art. The sticker detects GHB (gamma hydroxybutyrate), a drug commonly used to spike drinks.

Arts, creative and media groups have demanded the government rule out allowing big tech companies to take Australian content to train their artificial intelligence models, with concerns such a shift would "sell out" Australian workers and lead to "rampant theft" of intellectual property. "It is not appropriate for big tech to steal the work of Australian artists, musicians, creators, news media, journalism, and use it for their own ends without paying for it," Ley said on Wednesday. In an interim report on "harnessing data and digital technology", the Productivity Commission set out proposals for how tech, including AI, could be regulated and treated in Australia, suggesting it could boost productivity by between 0.5% and 13% over the next decade, adding up to 116bn to Australia's GDP. The commission suggested several possible remedies, including expanding licensing schemes, or an exemption for "text and data mining" and expanding the existing fair dealing rules, which it said existed in other countries. The latter suggestion prompted fierce pushback from arts, creative and media companies, which raised alarm their work could be left open for massively wealthy tech companies to use – without compensation or payment – to train AI models.

Comparable texts are topic-aligned documents in multiple languages that are not direct translations. They are valuable for understanding how a topic is discussed across languages. This research studies differences in sentiments and emotions across English-Arabic comparable documents. First, texts are annotated with sentiment and emotion labels. We apply a cross-lingual method to label documents with opinion classes (subjective/objective), avoiding reliance on machine translation. To annotate with emotions (anger, disgust, fear, joy, sadness, surprise), we manually translate the English WordNet-Affect (WNA) lexicon into Arabic, creating bilingual emotion lexicons used to label the comparable corpora. We then apply a statistical measure to assess the agreement of sentiments and emotions in each source-target document pair. This comparison is especially relevant when the documents originate from different sources. To our knowledge, this aspect has not been explored in prior literature. Our study includes English-Arabic document pairs from Euronews, BBC, and Al-Jazeera (JSC). Results show that sentiment and emotion annotations align when articles come from the same news agency and diverge when they come from different ones. The proposed method is language-independent and generalizable to other language pairs.

Selecting appropriate inductive biases is an essential step in the design of machine learning models, especially when working with audio, where even short clips may contain millions of samples. To this end, we propose the combolutional layer: a learned-delay IIR comb filter and fused envelope detector, which extracts harmonic features in the time domain. We demonstrate the efficacy of the combolutional layer on three information retrieval tasks, evaluate its computational cost relative to other audio frontends, and provide efficient implementations for training. We find that the combolutional layer is an effective replacement for convolutional layers in audio tasks where precise harmonic analysis is important, e.g., piano transcription, speaker classification, and key detection. Additionally, the combolutional layer has several other key benefits over existing frontends, namely: low parameter count, efficient CPU inference, strictly real-valued computations, and improved interpretability.

Music source separation (MSS) aims to extract individual instrument sources from their mixture. While most existing methods focus on the widely adopted four-stem separation setup (vocals, bass, drums, and other instruments), this approach lacks the flexibility needed for real-world applications. To address this, we propose GuideSep, a diffusion-based MSS model capable of instrument-agnostic separation beyond the four-stem setup. GuideSep is conditioned on multiple inputs: a waveform mimicry condition, which can be easily provided by humming or playing the target melody, and mel-spectrogram domain masks, which offer additional guidance for separation. Unlike prior approaches that relied on fixed class labels or sound queries, our conditioning scheme, coupled with the generative approach, provides greater flexibility and applicability. Additionally, we design a mask-prediction baseline using the same model architecture to systematically compare predictive and generative approaches. Our objective and subjective evaluations demonstrate that GuideSep achieves high-quality separation while enabling more versatile instrument extraction, highlighting the potential of user participation in the diffusion-based generative process for MSS. Our code and demo page are available at https://yutongwen.github.io/GuideSep/

This paper examines the role of Open Source Intelligence (OSINT) on Twitter regarding the Russo-Ukrainian war, distinguishing between genuine OSINT and deceptive misinformation efforts, termed "BULLSHINT." Utilizing a dataset spanning from January 2022 to July 2023, we analyze nearly 2 million tweets from approximately 1,040 users involved in discussing real-time military engagements, strategic analyses, and misinformation related to the conflict. Using sentiment analysis, partisanship detection, misinformation identification, and Named Entity Recognition (NER), we uncover communicative patterns and dissemination strategies within the OSINT community. Significant findings reveal a predominant negative sentiment influenced by war events, a nuanced distribution of pro-Ukrainian and pro-Russian partisanship, and the potential strategic manipulation of information. Additionally, we apply community detection techniques, which are able to identify distinct clusters partisanship, topics, and misinformation, highlighting the complex dynamics of information spread on social media. This research contributes to the understanding of digital warfare and misinformation dynamics, offering insights into the operationalization of OSINT in geopolitical conflicts.

Personalized generation in T2I diffusion models aims to naturally incorporate individual user preferences into the generation process with minimal user intervention. However, existing studies primarily rely on prompt-level modeling with large-scale models, often leading to inaccurate personalization due to the limited input token capacity of T2I diffusion models. T o address these limitations, we propose DrUM, a novel method that integrates user profiling with a transformer-based adapter to enable personalized generation through condition-level modeling in the latent space. DrUM demonstrates strong performance on large-scale datasets and seamlessly integrates with open-source text encoders, making it compatible with widely used foundation T2I models without requiring additional fine-tuning.