McGill University (French: Université McGill) is an English-language public research university located in Montreal, Quebec, Canada. Founded in 1821 by royal charter granted by King George IV, the university bears the name of James McGill, a Scottish merchant whose bequest in 1813 formed the university's precursor, University of McGill College (or simply, McGill College); the name was officially changed to McGill University in 1885.McGill's main campus is on the slope of Mount Royal in downtown Montreal in the borough of Ville-Marie, with a second campus situated in Sainte-Anne-de-Bellevue, 30 kilometres (19 mi) west of the main campus on Montreal Island, and a third francophone medical campus in Gatineau, Quebec. The university is one of two members of the Association of American Universities located outside the United States, alongside the University of Toronto, and is the only Canadian member of the Global University Leaders Forum (GULF) within the World Economic Forum.McGill offers degrees and diplomas in over 300 fields of study, with the highest average entering grades of any Canadian university. Most students are enrolled in the six largest faculties, namely Arts, Science, Medicine, Education, Engineering, and Management. McGill is the most internationally diverse of any medical-doctoral research university in Canada, with international students comprising over 30% of its student population and hailing from more than 150 countries. Additionally, over 41% of students are born outside of Canada. McGill is ranked among the world's top universities by major educational publications, and has held the top position in the country for the past 17 years in the annual Maclean's Canadian university rankings.McGill counts among its alumni and faculty 12 Nobel laureates and 147 Rhodes Scholars, both the most of any university in Canada, as well as 16 billionaires,[note 1] the current prime minister and two former prime ministers of Canada, two Governors General of Canada, 15 justices of the Supreme Court of Canada,[note 2] at least eight foreign leaders, and more than 100 members of national legislatures. McGill alumni also include 8 Academy Award winners,[note 3] 10 Grammy Award winners,[note 4] at least 13 Emmy Award winners,[note 5] four Pulitzer Prize winners,[note 6] and 121 Olympians with over 35 Olympic medals. The inventors of the game of basketball, modern organized ice hockey, and the pioneers of gridiron football, as well as the founders of several major universities and colleges[note 7] are also graduates of the university.Notable researchers include Ernest Rutherford, who discovered the atomic nucleus and conducted his Nobel Prize–winning research on the nature of radioactivity while working as Professor of Experimental Physics at the university. Other notable inventions by McGillians include the world's first artificial cell, web search engine, and charge-coupled device, among others.McGill has the largest endowment per student in Canada. In 2019, it was the recipient of the largest single philanthropic gift in Canadian history, a $200 million donation to fund the creation of the McCall MacBain Scholarships programme.
The relationship between self-reported falls and fracture risk was estimated in an international meta-analysis of individual-level data from 46 prospective cohorts. Previous falls were associated with an increased fracture risk in women and men and should be considered as an additional risk factor in the FRAX® algorithm. Previous falls are a well-documented risk factor for subsequent fracture but have not yet been incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between previous falls and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD). The resource comprised 906,359 women and men (66.9
OBJECTIVE:To assess the role of brain hemodynamics in neurodegenerative diseases, current imaging technologies remain insufficient due to limitations in spatial or temporal resolution for quantitative mapping of pulsatile flow in the whole brain. This study aims to demonstrate the feasibility of 3D transcranial Dynamic Ultrasound Localization Microscopy (DULM) for spatiotemporal blood flow measurements in the brain, addressing limitations of 2D imaging for velocity estimation within the 3D complex vascularized structures. METHODS:We used a (128+128)-element, 12 MHz Row-Column Array (RCA) to perform transcranial DULM imaging in anesthetized mice (n = 7 in total). The RCA setup allows for reduced element count while maintaining a large field of view and high frame rate compared to matrix arrays. Transcranial images were acquired at a 750-Hz volume rate using an optimized microbubble concentration and a sequence of 42 tilted plane waves. Microbubbles were localized and tracked, enabling super-resolved dynamic density and velocity maps of the 3D brain vascular network. RESULTS:Pulsatile flows were observed with 3D DULM in 7 mice. The segmentation of cortical vessels indicated that pulsatility in arteries was significantly higher than in veins, consistent across all mice and aligning with findings in existing literature. CONCLUSION:This study demonstrates for the first time the feasibility and reproducibility of obtaining high spatiotemporal resolution images of mouse brain vasculature using transcranial DULM with a RCA. SIGNIFICANCE:This work highlights the potential of RCA 3D DULM for non-invasive cerebral hemodynamics studies, it might enable comprehensive vascular imaging suitable for research in early-stage neurodegenerative diseases.
We consider the strategic use of information by an online platform to both guide consumers' search through product recommendations and influence sellers' targeted advertising decision. Drawing on Bayesian persuasion, we posit that the platform can design a public signal that influences the beliefs of both consumers and sellers. Upon observing the signal, a consumer can conduct a sequential search with perfect recall among the sellers. After visiting a seller, the consumer observes the product price and whether the product is a match or not. Sellers set prices and decide how much to bid in an ad auction for each consumer, where the winner is granted a prominent position. The consumer can obtain the price and match information of the seller in the prominent position at no cost, but incur search cost to visit additional sellers.
We introduce SOFIA, a Mathematica package that automatizes the computation of singularities of Feynman integrals, based on new theoretical understanding of their analytic structure. Given a Feynman diagram, SOFIA generates a list of potential singularities along with a candidate symbol alphabet. The package also provides a comprehensive set of tools for analyzing the analytic properties of Feynman integrals and related objects, such as cosmological and energy correlators. We showcase its capabilities by reproducing known results and predicting singularities and symbol alphabets of Feynman integrals at and beyond the high-precision frontier.
Accurately interpreting human emotion from language, facial expressions, and vocal tones remains a fundamental challenge in artificial intelligence. Current Multimodal Sentiment Analysis (MSA) models often struggle with two key issues. First, their static fusion strategies fail to handle conflicting modalities, such as sarcasm. Second, their standard loss functions ignore the asymmetric risks of severe misjudgments. To address these limitations, we propose the Three-Way Decision Enhanced Dynamic Routing Transformer (3WD-DRT), a framework operating on a "quality-aware, decision-driven" principle. It dynamically assesses each modality's quality using a three-way decision gate, implemented via a dedicated MLP, to partition information into acceptance, deferment, or rejection pathways. This enables the model to amplify informative signals, moderately scale uncertain ones (deferment), and attenuate noisy or misleading ones. We also introduce a novel cost-sensitive loss function that imposes greater penalties on major semantic errors, such as polarity misclassifications. This approach better aligns the model's training objective with human perception. Extensive experiments on CH-SIMS, CH-SIMSv2, MOSI, and MOSEI datasets show that 3WD-DRT consistently outperforms state-of-the-art methods, setting new benchmarks with F1-scores of 87.08 % on MOSI and 88.26 % on MOSEI. This work provides a robust solution for MSA, fostering more nuanced and reliable emotionally-aware AI systems.
