Bishop's University (French: Université Bishop's) is a small English-language liberal arts university in Lennoxville, a borough of Sherbrooke, Quebec, Canada. The founder of the institution was the Anglican Bishop of Quebec, George Mountain, who also served as the first principal of McGill University. It is one of three universities in the province of Quebec that teach primarily in English (the others being McGill University and Concordia University, both in Montreal). It began its foundation by absorbing the Lennoxville Classical School as Bishop's College School in the 1840s. The college was formally founded in 1843 and received a royal charter from Queen Victoria in 1853.It remains one of Canada's few primarily undergraduate universities, functioning in the way of an American liberal arts college, and is linked with three others in the Maple League. Established in 1843 as Bishop's College, the school used to be affiliated with the University of Oxford in 1853, where many professors at BU were appointed from. The school remained under the Anglican church's direction from its founding until 1947. Since that time, the university has been a non-denominational institution. Bishop's University has graduated fifteen Rhodes Scholars.Like other liberal arts colleges in North America, it does not participate in rankings which are primarily based on research, such as QS. However, Bishop's ranked number one in Canada for student satisfaction for three continuous years, and ranked 7th in Canadian primarily undergraduate universities by Maclean's magazine. The university shares a campus with its neighbor, Champlain College Lennoxville, an English-language public college..
Context. Gas giant planets orbiting low-mass stars (T-eff less than or similar to 4600 K) are uncommon outcomes of planet formation. Increasing the sample of well-characterised giants around early M dwarfs will enable population-level studies of their properties, offering valuable insights into their formation and evolutionary histories. Aims. We aim to confirm and characterise giant exoplanets transiting M dwarfs identified by the TESS mission. To this end, we have started the Gas giAnts Transiting 1Ow-mass Stars (GATOS) programme within the NIRPS guaranteed time observations (GTO). Methods. High-resolution spectroscopic data were obtained in the optical and near-infrared (nIR), combining HARPS and NIRPS. We derived radial velocities (RVs) via the cross-correlation function and implemented a novel post-processing procedure to further mitigate telluric contamination in the nIR. The resulting RVs were jointly fit with TESS and ground-based photometry to derive the orbital and physical parameters of the systems. Results. We present the GATOS programme and its first results. We confirm two gas giants transiting the low-mass stars TOI-3288 A (K9V, T-eff = 3933 +/- 48 K) and TOI-4666 (M2.5V, T-eff = 3512 +/- 36 K). TOI-3288 A hosts a hot Jupiter with a mass of 2.11 +/- 0.08 M-Jup and a radius of 1.00 +/- 0.03 R-Jup, with an orbital period of 1.43 days (T-eq = 1059 +/- 20 K). TOI-4666 hosts a 0.70 +/- 0.06 M-Jup warm Jupiter (T-eq = 713 +/- 14 K) with a radius of 1.11 +/- 0.04 R-Jup, with an orbital period of 2.91 days. At a population level, we identify a decrease in planetary mass with spectral type, whereby late M dwarfs host less massive giant planets than early M dwarfs. More massive gas giants that deviate from this trend are preferentially hosted by more metal-rich stars. Furthermore, we find an increased binarity fraction among low-mass stars hosting gas giants, which may play a role in enhancing giant planet formation around low-mass stars. Conclusions. These mass characterisations contribute to the growing catalogue of well-defined giant exoplanets around low-mass stars. The observed population trends agree with theoretical predictions, whereby higher metallicity can compensate for lower disc masses, and wide binary systems may influence planet formation and migration through Kozai-Lidov cycles or disc instabilities.
The observed spectra and light curves of the kilonova produced by the GW170817 binary neutron star merger provide complementary insights, but modeling both the spectral and time domains has proven challenging. Here, we model the optical–infrared light curves of the GW170817 kilonova, using the properties and physical conditions of the ejecta as inferred from detailed modeling of its spectra. Using our software tool Spectroscopic r -Process Abundance Retrieval for Kilonovae ( SPARK ), we first infer the r -process abundance pattern of the kilonova ejecta from spectra obtained at 1.4, 2.4, 3.4, and 4.4 days postmerger. From these abundances, we compute time-dependent radioactive heating rates and the wavelength-, time-, and velocity-dependent opacities of the ejecta. We use these inferred heating rates and opacities to inform a kilonova light-curve model, to reproduce the observed early time light curves and to infer a total ejecta mass of M _ej = 0.11 M _⊙ , towards the higher end of those inferred from previous studies. The combination of a large ejecta mass from our light-curve modeling and the presence of both red and blue ejecta from our spectral modeling suggests the existence of a highly magnetized hypermassive neutron star remnant that survives for ∼0.01–0.5 s and launches a blue wind, followed by fast, red, neutron-rich winds launched from a magnetized accretion disk. By modeling both spectra and light curves together, we demonstrate how combining information from both the spectral and time domains can more robustly determine the physical origins of the ejected material.
This paper elaborates on some initial findings from a plurilingual comprehension task that a Grade 6 class in Quebec, Canada, completed without teacher guidance, as a baseline tool to illuminate their existing language awareness and reading abilities for critical understanding. Drawing on previous research on plurilingualism and critical literacy, this study is part of a broader research project that brings together teachers of French and English, as first or second language, to build linguistic and curricular bridges across the two official languages for in-depth learning about eco-social issues. We use the concept of critical moments as our method to capture and analyse key instances of great research significance, i.e., when students actively made sense of their wonderings about unknown language elements and story development. The findings illuminate students' promising abilities to operate across language boundaries, interpret and compare new language structures, transfer reading strategies (both linguistic and visual), and exercise critical judgement on issues addressed in the text. Implications for teaching and further research are discussed.
Prior research determined that stigma can have positive, indirect effects on minoritized groups when mediated through coping strategies. We surveyed furries - a stigmatized fan group centered on shared interest in anthropomorphism - regarding their perception of stigma, coping strategies employed, well-being, and self-esteem. The results showed that perception of stigma positively associated with a variety of coping strategies, which were differently associated with self-esteem and well-being. Mediation analyses revealed significant, indirect associations through various coping strategies. Overall, our findings reveal that the association between is mitigated by the coping strategies employed to deal with stigma toward their groups.
Projection-based variants of optimal transport, such as the Sliced Wasserstein (SW) and its extensions, have become popular alternatives to classical Wasserstein distances due to their scalability and analytical tractability. However, most of these methods rely on independently sampled random projections, which often fail to capture semantically meaningful directions, leading to inefficiencies and limited expressiveness, especially in high-dimensional settings. In this work, we propose the Hybrid Merging Projection Wasserstein (HW) distance, a novel and efficient alternative that addresses these limitations by combining data-driven and random projections in a principled way. At the core of HW is the Linear Merging Projection (LMP), a new projection technique designed to minimize between-class variance, thereby promoting smooth alignment between distributions. HW incorporates random directions as well to achieve a balance between structural awareness and projection diversity. We evaluate HW across a range of synthetic and real-world benchmarks, including color transfer and distribution alignment tasks, to demonstrate the favorable performance of the proposed HW.
