America's flight-mare begins as more than 700 departures ALREADY canceled across US and Trump steps in to end the shutdown Israeli hostage who revealed sexual abuse by his captors details full horror he endured: 20-minute torture seven times a day, made to dance, blindfolded with stones in his ears for weeks - 'I have met the Devil' Prince Harry apologises to Canada over baseball cap'Hatgate' - and adds a joke about thinning on top Alix Earle suffers'total humiliation' at hands of her stepmom: Family insiders reveal former escort's betrayal that they fear will now'completely break' star Jeremy Renner's film partner claims he sent her explicit photos and videos to woo her then threatened the unthinkable when they fell out Moment Prince William refuses to be drawn on Andrew scandal and Harry and Meghan rift as he tells CNN: 'I want to surround myself with people who want to do good' Ritzy suburb of NJ's new governor stunned as cops pounce on'yuppie jihadi' neighbor at his $1.2M home over alleged bomb plot Elon Musk used biometric data from employees to program'sexy' chatbot during epic quest to win AI arms race Sydney Sweeney wins patriotic hearts with stunning response to criticism of her'good genes' ad Melania Trump stuns as she accepts'Patriot of the Year' award and issues inspiring message to Americans Iconic golf ball-sized Florentine diamond once owned by Medici and Habsburg dynasties is FOUND in unusual location 100 years after'vanishing' My addiction to ADHD medication ruined me. I had to choose to either abort my baby or lose my own life... but that was just the start Distressing red flags before Dallas Cowboys star's sudden death at 24 - revealed by roommate who shares harrowing backstory... including recent family tragedy Real-life horror as progressives elect convicted KILLER as councilmember of Maine town that inspired Stephen King's It Multiple people hospitalized at Joint Base Andrews as suspicious package containing'white powder' and political message sparks evacuation - one day after Trump's visit My best friend became my bully, says CATHERINE RENTON. She called me fat and then traduced me. It's taboo to say, but the consequences ruined my life. No one's honest about what childhood bullying really does Now that's what you call a blast from the past!

Things to Do in L.A. A shadowy L.A. crime ring is hijacking the IDs of foreign scholars, fraud expert says This is read by an automated voice. Please report any issues or inconsistencies here . An identity theft ring believed to be based in the Burbank area is stealing Social Security Numbers of former foreign scholars. Private fraud investigators suspect the operation is connected to Armenian organized crime groups known for sophisticated financial crimes. Using apartments in the San Fernando Valley and Glendale area, a shadowy group of identity thieves has been quietly exploiting a new kind of victim -- foreign scholars who left the U.S. years ago but whose Social Security numbers still linger in American databases, according to a cybercrime expert.

America's flight-mare begins as more than 700 departures ALREADY canceled across US and Trump steps in to end the shutdown Multiple people hospitalized from'white powder' as suspicious package with'political propaganda' sparks evacuation at Joint Base Andrews Prince Harry apologises to Canada over baseball cap'Hatgate' - and adds a joke about thinning on top Alix Earle suffers'total humiliation' at hands of her stepmom: Family insiders reveal former escort's betrayal that they fear will now'completely break' star Jeremy Renner's film partner claims he sent her explicit photos and videos to woo her then threatened the unthinkable when they fell out Moment Prince William refuses to be drawn on Andrew scandal and Harry and Meghan rift as he tells CNN: 'I want to surround myself with people who want to do good' Melania Trump stuns as she accepts'Patriot of the Year' award and issues inspiring message to Americans Elon Musk used biometric data from employees to program'sexy' chatbot during epic quest to win AI arms race Sydney Sweeney wins patriotic hearts with stunning response to criticism of her'good genes' ad Frail Bruce Willis, 70, holds carer's hand on very rare public outing amid heartbreaking dementia battle Iconic golf ball-sized Florentine diamond once owned by Medici and Habsburg dynasties is FOUND in unusual location 100 years after'vanishing' My addiction to ADHD medication ruined me. I had to choose to either abort my baby or lose my own life... but that was just the start Distressing red flags before Dallas Cowboys star's sudden death at 24 - revealed by roommate who shares harrowing backstory... including recent family tragedy Real-life horror as progressives elect convicted KILLER as councilmember of Maine town that inspired Stephen King's It It triggered an earthquake across America. Now, TUCKER CARLSON gives an astonishing defense of the interview that nearly destroyed him... and what he wished he'd known first Stone of Destiny mystery is SOLVED: Scientist traces the fate of 17 missing fragments of the rock used in King Charles' coronation A researcher has managed to trace the fate of the missing fragments of the Stone of Destiny, a powerful symbol of the British monarchy. It has been placed under the coronation chair for the crowning of kings and queens since the 13th century, including Charles III in May 2023 . Professor Sally Foster, an archaeologist at Stirling University, says there are 34 small fragments of the centuries-old object, also known as Stone of Scone, circulated around the world.

Category V ariable Definition Tax Avoidance CETR Cash Effective T ax Rate = Cash Taxes Paid / Pre - tax Income GETR GAAP Effective Tax Rate = T otal Tax Expense / Pre - tax Income CETR3 Three - year average CETR GETR3 Three - year average GETR CETR5 Five - year average CETR GETR5 Five - year average GETR A_CETR Adjusted Cash Effective Tax Rate A_GETR Adjusted GAAP Effective T ax Rate A_CETR3 Adjusted three - year average CETR A_GETR3 Adjusted three - year average GETR A_CETR5 Adjusted five - year average CETR A_GETR5 Adjusted five - year average GETR TSTA Total Book - T ax Difference (accrual - based measure) TSDA Discretionary Book - Tax Difference (discretionary accrual - based measure) Profitability ROA Return on Assets = Net Income / Lagged T otal Assets ROE Return on Equity = Net Income / Lagged Equity CFO Operating Cash Flow scaled by total assets LOSS Loss dummy (1 if prior - year net income < 0) Stability LEV Leverage = T otal Liabilities / Total Assets CUR Current Ratio = Current Assets / Current Liabilities SIZE Natural logarithm of total assets PPE Ratio of Property, Plant, and Equipment to total assets AGE Natural logarithm of firm age (based on year of establishment) INVREC Ratio of inventories and receivables to total assets Growth GRW Sales growth rate MB Market - to - Book Ratio = Market Capitalization / Book Equity TQ Tobin's Q = (Market Capitalization + Total Liabilities) / T otal Assets Market Valuation & Governance KOSPI KOSPI listing status dummy BIG4 Big4 audit dummy FORN Foreign ownership share (%) OWN Largest shareholder ownership share (%) Stability Measures Stability measures reflect a firm's financial soundness and its ability to meet obligations. Leverage (LEV) is defined as total liabilities divided by total assets, indicating the firm's degree of financial leverage. The current ratio (CUR), calculated as current assets divided by current liabilities, captures short - term liquidity and payment capacity. Firm size (SIZE) is measured as the natural logarithm of total assets, providing a quantitative indicator of scale. The proportion of property, plant, and eq uipment (PPE), defined as tangible fixed assets divided by total assets, is used to assess the structural stability of the asset base.

Graph Neural Networks learn on graph-structured data by iteratively aggregating local neighborhood information. While this local message passing paradigm imparts a powerful inductive bias and exploits graph sparsity, it also yields three key challenges: (i) oversquashing of long-range information, (ii) oversmoothing of node representations, and (iii) limited expressive power. In this work we inject randomized global embeddings of node features, which we term \textit{Sketched Random Features}, into standard GNNs, enabling them to efficiently capture long-range dependencies. The embeddings are unique, distance-sensitive, and topology-agnostic -- properties which we analytically and empirically show alleviate the aforementioned limitations when injected into GNNs. Experimental results on real-world graph learning tasks confirm that this strategy consistently improves performance over baseline GNNs, offering both a standalone solution and a complementary enhancement to existing techniques such as graph positional encodings. Our source code is available at \href{https://github.com/ryienh/sketched-random-features}{https://github.com/ryienh/sketched-random-features}.

Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for other languages. We present PLLuM (Polish Large Language Model), the largest open-source family of foundation models tailored specifically for the Polish language. Developed by a consortium of major Polish research institutions, PLLuM addresses the need for high-quality, transparent, and culturally relevant language models beyond the English-centric commercial landscape. We describe the development process, including the construction of a new 140-billion-token Polish text corpus for pre-training, a 77k custom instructions dataset, and a 100k preference optimization dataset. A key component is a Responsible AI framework that incorporates strict data governance and a hybrid module for output correction and safety filtering. We detail the models' architecture, training procedures, and alignment techniques for both base and instruction-tuned variants, and demonstrate their utility in a downstream task within public administration. By releasing these models publicly, PLLuM aims to foster open research and strengthen sovereign AI technologies in Poland.

One major challenge for the adoption and acceptance of automated vehicles (AVs) is ensuring that they can make sound decisions in everyday situations that involve ethical tension. Much attention has focused on rare, high-stakes dilemmas such as trolley problems. Yet similar conflicts arise in routine driving when human considerations, such as legality, efficiency, and comfort, come into conflict. Current AV planning systems typically rely on rigid rules, which struggle to balance these competing considerations and often lead to behaviour that misaligns with human expectations. This paper introduces a reasons-based trajectory evaluation framework that operationalises the tracking condition of Meaningful Human Control (MHC). The framework represents human agents reasons (e.g., regulatory compliance) as quantifiable functions and evaluates how well candidate trajectories align with them. It assigns adjustable weights to agent priorities and includes a balance function to discourage excluding any agent. To demonstrate the approach, we use a real-world-inspired overtaking scenario, which highlights tensions between compliance, efficiency, and comfort. Our results show that different trajectories emerge as preferable depending on how agents reasons are weighted, and small shifts in priorities can lead to discrete changes in the selected action. This demonstrates that everyday ethical decisions in AV driving are highly sensitive to the weights assigned to the reasons of different human agents.

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological $w$CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving $2-3\times$ higher figures of merit in the $Ω_m - S_8$ plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While participants are typically invited to propose questions for expert panels, only a limited number can be selected due to time constraints. This raises the challenge of how to choose a small set of questions that best represent the interests of all participants. We introduce an auditing framework for measuring the level of representation provided by a slate of questions, based on the social choice concept known as justified representation (JR). We present the first algorithms for auditing JR in the general utility setting, with our most efficient algorithm achieving a runtime of $O(mn\log n)$, where $n$ is the number of participants and $m$ is the number of proposed questions. We apply our auditing methods to historical deliberations, comparing the representativeness of (a) the actual questions posed to the expert panel (chosen by a moderator), (b) participants' questions chosen via integer linear programming, (c) summary questions generated by large language models (LLMs). Our results highlight both the promise and current limitations of LLMs in supporting deliberative processes. By integrating our methods into an online deliberation platform that has been used for over hundreds of deliberations across more than 50 countries, we make it easy for practitioners to audit and improve representation in future deliberations.

As the role of Large Language Models (LLM)-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersecurity landscape. While a number of LLM code security benchmarks have been proposed alongside approaches to improve the security of generated code, it remains unclear to what extent they have impacted widely used coding LLMs. Here, we show that even the latest open-weight models are vulnerable in the earliest reported vulnerability scenarios in a realistic use setting, suggesting that the safety-functionality trade-off has until now prevented effective patching of vulnerabilities. To help address this issue, we introduce a new severity metric that reflects the risk posed by an LLM-generated vulnerability, accounting for vulnerability severity, generation chance, and the formulation of the prompt that induces vulnerable code generation - Prompt Exposure (PE). To encourage the mitigation of the most serious and prevalent vulnerabilities, we use PE to define the Model Exposure (ME) score, which indicates the severity and prevalence of vulnerabilities a model generates.