Accurate estimation of building heights using very high resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a Deep Learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images: an object-based regression approach based on bounding box detection followed by height estimation. This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia, employing a cross-validation strategy to explicitly assess out-of-distribution (OOD) generalization. The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich), significantly outperforming recent state-of-the-art methods in similar OOD scenarios. Despite the increased variability observed when generalizing to cities in other continents, particularly in Asia with its distinct urban typologies and prevalence of high-rise structures, this study underscores the significant potential of DL for robust cross-city and cross-continental transfer learning in building height estimation from single VHR SAR data.

"Amazing, They All Lean Left" - Analyzing the Political Temperaments of Current LLMs Abstract Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying causes and resulting implications remain unclear. This paper systematically i nvestigates the political temperament of seven prominent LLMs -- OpenAI's GPT - 4o, Anthropic's Claude Sonnet 4, Perplexity (Sonar Large), Google's Gemini 2.5 Flash, Meta AI's L l a ma 4, Mistral 7b Le Chat, and High - Flyer ' s DeepSeek R1 -- using a multi - pronged approach that incl udes Moral Foundations Theory, a dozen established political ideology scales, and a new index of current political controversies. We find strong and consistent prioritization of liberal - leaning values, particularly care and fairness, across most models. Fur ther analysis attributes this trend to four overlapping factors: liberal - leaning training corpora, reinforcement learning from human feedback (RLHF), the dominance of liberal frameworks in academic ethical discourse, and safety - driven fine - tuning practices . We also distinguish between political "bias" and legitimate epistemic differences, cautioning against conflating the two. A comparison of base and fine - tuned model pairs reveals that fine - tuning generally increases liberal lean, an effect confirmed throu gh both self - report and empirical testing. We argue that this "liberal tilt" is not a programming error or the personal preferences of programmers but an emergent property of training on democratic, rights - focused discourse. Finally, we propose that LLMs may indirectly echo John Rawls' famous veil - of - igno rance philosophical aspiration, reflecting a moral stance unanchored to personal identity or interest. Rather than undermining democratic discourse, this pattern may offer a new lens through which to examine collective ethical reasoning. In the course of our research on the ethical logics of currently prominent large language models (Neuman et al. 2025a, b; Coleman et al. 2025), we encountered an interesting finding. The responses to various ethical dilemmas and the explanations of the underlying logics used by these models appear to resonate with the liberal side of the political spectrum. One research analytic we utilize draws on Moral Foundation Theory's five - element typology of foundational moral principles (Graham et al. 2009; Haidt 2012). The five foundations emp hasizing in turn, Care, Fairness, Loyalty, Authority and Purity, are traditionally divided into two clusters. The first two, Care and Fairness, are associated with a liberal political perspective, while conservatives who fully acknowledge the first two more often emphasize the latter three -- Loyalty, Authority and Purity in support of traditional norms.

Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity. However, this openness also introduces significant security risks, particularly through embedding space poisoning, which is a subtle attack vector where adversaries manipulate the internal semantic representations of input data to bypass safety alignment mechanisms. While previous research has investigated universal perturbation methods, the dynamics of LLM safety alignment at the embedding level remain insufficiently understood. Consequently, more targeted and accurate adversarial perturbation techniques, which pose significant threats, have not been adequately studied. In this work, we propose ETTA (Embedding Transformation Toxicity Attenuation), a novel framework that identifies and attenuates toxicity-sensitive dimensions in embedding space via linear transformations. ETTA bypasses model refusal behaviors while preserving linguistic coherence, without requiring model fine-tuning or access to training data. Evaluated on five representative open-source LLMs using the AdvBench benchmark, ETTA achieves a high average attack success rate of 88.61%, outperforming the best baseline by 11.34%, and generalizes to safety-enhanced models (e.g., 77.39% ASR on instruction-tuned defenses). These results highlight a critical vulnerability in current alignment strategies and underscore the need for embedding-aware defenses.

Automatic Speech Recognition (ASR) has transformed daily tasks from video transcription to workplace hiring. ASR systems' growing use warrants robust and standardized auditing approaches to ensure automated transcriptions of high and equitable quality. This is especially critical for people with speech and language disorders (such as aphasia) who may disproportionately depend on ASR systems to navigate everyday life. In this work, we identify three pitfalls in existing standard ASR auditing procedures, and demonstrate how addressing them impacts audit results via a case study of six popular ASR systems' performance for aphasia speakers. First, audits often adhere to a single method of text standardization during data pre-processing, which (a) masks variability in ASR performance from applying different standardization methods, and (b) may not be consistent with how users - especially those from marginalized speech communities - would want their transcriptions to be standardized. Second, audits often display high-level demographic findings without further considering performance disparities among (a) more nuanced demographic subgroups, and (b) relevant covariates capturing acoustic information from the input audio. Third, audits often rely on a single gold-standard metric -- the Word Error Rate -- which does not fully capture the extent of errors arising from generative AI models, such as transcription hallucinations. We propose a more holistic auditing framework that accounts for these three pitfalls, and exemplify its results in our case study, finding consistently worse ASR performance for aphasia speakers relative to a control group. We call on practitioners to implement these robust ASR auditing practices that remain flexible to the rapidly changing ASR landscape.

"What we're hearing is stories of horror and terror and weekly shelling, attacks on civilian infrastructure," Ms Vu told the BBC Newshour programme. "There are local volunteers - they are really struggling, risking their lives every day to try and provide a little bit of food for people who are mostly starving." Siddig Omar, a 65-year-old resident of el-Fasher, told the BBC the RSF entered the city on Friday from the south and south-west. The RSF, whose fighters have been mustering in trenches dug around the city, frequently attack el-Fasher. According to the army, this was their 220th offensive.

The CyberGuy Kurt Knutsson gives his take on Elon Musk's claims that Grok 3 outperforms every AI rival on'Fox & Friends.' On July 4th, eccentric billionaire and owner of X Elon Musk took to his social media platform to make an announcement about its Artificial Intelligence bot named Grok. "We have improved Grok significantly," Musk told the world. "You should notice a difference when you ask Grok questions." Just a few days later, Grok had to have features shut down after it started answering questions by going full-Nazi and espousing antisemitic conspiracy theories. All that was missing was digital goosestepping and armbands.

Fox News contributor Raymond Arroyo sounds off on Rosie The Pivoter ODonnell for her latest criticism of the Trump administration and the NEA teacher of the years admission that the job is deeply political. President Donald Trump has escalated his long-running feud with Rosie O'Donnell. On Saturday, Trump, 79, floated the idea of revoking the 63-year-old comedian and actress's U.S. citizenship following her move to Ireland earlier this year. "Because of the fact that Rosie O'Donnell is not in the best interests of our Great Country, I am giving serious consideration to taking away her Citizenship," Trump wrote in a post to his social media platform Truth Social. "She is a Threat to Humanity, and should remain in the wonderful Country of Ireland, if they want her. GOD BLESS AMERICA!" he added.

Russia fired more than 620 drones and long-range missiles overnight, killing at least six people in the latest wave of strikes, Ukraine said Saturday, adding that it was close to an agreement to receive more Patriot air-defense systems. "The Russians continue to use their specific tactics of terror against our country, striking concentrated blows at one city or another, at one region or another," Ukrainian President Volodymyr Zelenskyy said in his evening address. Moscow has stepped up aerial strikes over recent months as U.S.-led ceasefire talks have stalled.

Secretary of State Marco Rubio attends a signing ceremony for a peace agreement between Rwanda and the Democratic Republic of the Congo at the State Department on June 27, 2025, in Washington. DIGITAL DECEPTION: The State Department is investigating an impostor who reportedly pretended to be Secretary of State Marco Rubio with the help of AI. TECH SHIFT: Artificial Intelligence and automation are often used interchangeably. While the technologies are similar, the concepts are different. Automation is often used to reduce human labor for routine or predictable tasks, while A.I. simulates human intelligence that can eventually act independently.

WIRED reported this week on public records that show the United States Department of Homeland Security urging local law enforcement around the country to interpret common protest activities and surrounding logistics--including riding a bike, livestreaming a police encounter, or skateboarding--as "violent tactics." The guidance could influence cops to use everyday behavior as a pretext for police action. An AI hiring bot used on the McDonald's "McHire" site exposed tens of millions of job applicants' personal data because of a group of web-based security vulnerabilities--including use of the classically guessable password "123456" on an administrator account. The site's chatbot, known as Olivia, was built by the artificial intelligence software firm Paradox.ai. Meanwhile, in the wake of last week's devastating floods in Texas that killed at least 120 people, conspiracy theories about the extreme weather event have gained enough traction among anti-government extremists, GOP influencers, and others with large platforms to produce real-world consequences like death threats.