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The Ultraviolet (UV) Type Ia Supernova CubeSat (UVIa) is a CubeSat/SmallSat mission concept that stands to test critical space-borne UV technology for future missions like the Habitable Worlds Observatory (HWO) while elucidating long-standing questions about the explosion mechanisms of Type Ia supernovae (SNe Ia). UVIa will observe whether any SNe Ia emit excess UV light shortly after explosion to test progenitor/explosion models and provide follow-up over many days to characterize their UV and optical flux variations over time, assembling a comprehensive multi-band UV and optical low-redshift anchor sample for upcoming high-redshift SNe Ia surveys (e.g., Euclid, Vera Rubin Observatory, Nancy Roman Space Telescope). UVIa's mission profile requires it to perform rapid and frequent visits to newly discovered SNe Ia, simultaneously observing each SNe Ia in two UV bands (FUV: 1500-1800A and NUV: 1800-2400A) and one optical band (u-band: 3000-4200A). In this study, we describe the UVIa mission concept science motivation, mission design, and key technology development., Comment: submitted to JATIS under the call for papers "Ultraviolet Science & Instrumentation: On the Way to Habitable Worlds Observatory and Beyond"

We describe the origins of the theory of tannakian categories, and summarize its main results., Comment: Misprints fixed

This paper details the world's first live 3D holographic (diffractive) video call using off-the-shelf hardware. We introduce a novel pipeline that facilitates the capture, processing, and transmission of RGBZ data, using an iPhone for image and depth capture with VividQ's SDK for hologram generation and hardware for display., Comment: Published and Presented at Session 63: Emerging Approaches for AR/VR/MR, SID Display Week 2022. 4 pages, 9 figures

Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and the expansion of context windows have opened new avenues for leveraging these powerful models. NL2SQL is challenging in that the natural language question is inherently ambiguous, while the SQL generation requires a precise understanding of complex data schema and semantics. One approach to this semantic ambiguous problem is to provide more and sufficient contextual information. In this work, we explore the performance and the latency trade-offs of the extended context window (a.k.a., long context) offered by Google's state-of-the-art LLM (\textit{gemini-1.5-pro}). We study the impact of various contextual information, including column example values, question and SQL query pairs, user-provided hints, SQL documentation, and schema. To the best of our knowledge, this is the first work to study how the extended context window and extra contextual information can help NL2SQL generation with respect to both accuracy and latency cost. We show that long context LLMs are robust and do not get lost in the extended contextual information. Additionally, our long-context NL2SQL pipeline based on Google's \textit{gemini-pro-1.5} achieve strong performances on various benchmark datasets without finetuning and expensive self-consistency based techniques., Comment: 14 pages, 7 figures

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves - a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling., Comment: Accepted to COLING'2025

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels., Comment: 19 pages, 8 figures, 2 tables

Harmful cyanobacterial blooms (CBs) have a growing global prevalence, emerging as a significant environmental concern due to their potential toxicity. Understanding how the different mechanisms affect CBs is crucial to develop actionable management strategies. For this, we derive a stoichiometric dynamical system that describes the qualitative population dynamics of cyanobacteria and their toxicity in north-temperate freshwater ecosystems. Our model quantifies the hypoxic effects of CBs on fish mortality and the effect of microcystin-LR (MC-LR), a potent toxin produced by cyanobacteria, on aquatic macro-invertebrates, phytoplankton, and fish species. By fitting the model to lakes with varying physical characteristics, eutrophic conditions, and water temperature, we can delineate and understand the driving components of CBs. We show that decreases in water exchange rate, depth of epilimnion, or light attenuation increases bloom intensity and duration. Furthermore, our models concur that eutrophication and increasing water temperatures exacerbate the intensity of CBs. We observe a severe bioaccumulative effect of MC-LR in aquatic species, stressing the potential impact on humans and other terrestrial animals. We validate our model with field measurements demonstrating its applicability to several realistic lake conditions. These insights are essential for informing targeted interventions to reduce CBs and their ecological impacts., Comment: Main Document: 11 pages

We present multi-epoch optical spectropolarimetric and imaging polarimetric observations of the nearby Type II supernova (SN) 2023ixf discovered in M101 at a distance of 6.85 Mpc. The first imaging polarimetric observations were taken +2.33 days (60085.08 MJD) after the explosion, while the last imaging polarimetric data points (+73.19 and +76.19 days) were acquired after the fall from the light curve plateau. At +2.33 days there is strong evidence of circumstellar material (CSM) interaction in the spectra and the light curve. A significant level of polarization $P_r = 0.88\pm 0.06 \% $ seen during this phase indicates that this CSM is aspherical. We find that the polarization evolves with time toward the interstellar polarization level ($0.35\%$) during the photospheric phase, which suggests that the recombination photosphere is spherically symmetric. There is a jump in polarization ($P_r =0.65 \pm 0.08 \% $) at +73.19 days when the light curve falls from the plateau. This is a phase where polarimetric data is sensitive to non-spherical inner ejecta or a decrease in optical depth into the single scattering regime. We also present spectropolarimetric data that reveal line (de)polarization during most of the observed epochs. In addition, at +14.50 days we see an "inverse P Cygn" profile in the H and He line polarization, which clearly indicates the presence of asymmetrically distributed material overlying the photosphere. The overall temporal evolution of polarization is typical for Type II SNe, but the high level of polarization during the rising phase has only been observed in SN 2023ixf., Comment: 14 pages, 7 figures, submitted to ApJL, comments welcome

This is an elementary exposition of the basic descent theorems for algebraic schemes over fields (Grothendieck, Weil, ...)., Comment: 27 pages

Given a frequency $\lambda=(\lambda_n)$, we consider the Hardy spaces $ \mathcal{H}_p^\lambda$ of $\lambda$-Dirichlet series $ D = \sum_n a_n e^{-\lambda_n s}$ and study the asymptotic behavior of the upper and lower democracy functions of its canonical basis $\mathcal B=\{e^{-\lambda_ns}\}$. For the ordinary case, $\mathcal B=\{n^{-s}\}$, we give the correct asymptotic behavior of all such functions, while in the general case we give sharp lower and upper bounds for all possible behaviors. Moreover, for $p>2$ we present examples showing that any intermediate behavior (between the extreme bounds) can occur. We also study how different properties of the frequency $\lambda$ lead to particular behaviors of the corresponding fundamental functions. Finally, we apply our results to analyze greedy-type properties of $\mathcal B=\{e^{-\lambda_ns}\}$ for some particular $\lambda$'s.

We introduce a new graph parameter called the cooling number, inspired by the spread of influence in networks and its predecessor, the burning number. The cooling number measures the speed of a slow-moving contagion in a graph; the lower the cooling number, the faster the contagion spreads. We provide tight bounds on the cooling number via a graph's order and diameter. Using isoperimetric results, we derive the cooling number of Cartesian grids. The cooling number is studied in graphs generated by the Iterated Local Transitivity model for social networks. We conclude with open problems.

We present photometry and spectroscopy of the slowly evolving superluminous Type IIn SN2015da. SN2015da is extraordinary for its very high peak luminosity, and also for sustaining a high luminosity for several years. Even at 8\,yr after explosion, SN2015da remains as luminous as the peak of a normal SNII-P. The total radiated energy integrated over this time period (with no bolometric correction) is at least 1.6 FOE. Including a mild bolometric correction, adding kinetic energy of the expanding cold dense shell of swept-up circumstellar material (CSM), and accounting for asymmetry, the total explosion kinetic energy was likely 5-10 FOE. Powering the light curve with CSM interaction requires an energetic explosion and 20 Msun of H-rich CSM, which in turn implies a massive progenitor system above 30 Msun. Narrow P Cyg features show steady CSM expansion at 90 km/s, requiring a high average mass-loss rate of roughly 0.1 Msun/yr sustained for 2 centuries before explosion (although ramping up toward explosion time). No current theoretical model for single-star pre-SN mass loss can account for this. The slow CSM, combined with broad wings of H$\alpha$ indicating H-rich material in the unshocked ejecta, disfavor a pulsational pair instability model for the pre-SN mass loss. Instead, violent pre-SN binary interaction is a likely cuprit. Finally, SN2015da exhibits the characteristic asymmetric blueshift in its emission lines from shortly after peak until the present epoch, adding another well-studied superluminous SNeIIn with unambiguous evidence of post-shock dust formation., Comment: 18 pages, 11 figs. submitted

The evolution of charge carriers in photoexcited room temperature ZnO nanoparticles in solution is investigated using ultrafast ultraviolet photoluminescence spectroscopy, ultrafast Zn K-edge absorption spectroscopy and ab-initio molecular dynamics (MD) simulations. The photoluminescence is excited at 4.66 eV, well above the band edge, and shows that electron cooling in the conduction band and exciton formation occur in <500 fs, in excellent agreement with theoretical predictions. The X-ray absorption measurements, obtained upon excitation close to the band edge at 3.49 eV, are sensitive to the migration and trapping of holes. They reveal that the 2 ps transient largely reproduces the previously reported transient obtained at 100 ps time delay in synchrotron studies. In addition, the X-ray absorption signal is found to rise in ~1.4 ps, which we attribute to the diffusion of holes through the lattice prior to their trapping at singly-charged oxygen vacancies. Indeed, the MD simulations show that impulsive trapping of holes induces an ultrafast expansion of the cage of Zn atoms in <200 fs, followed by an oscillatory response at a frequency of ~100 cm-1, which corresponds to a phonon mode of the system involving the Zn sub-lattice.

Ultra-wide bandgap (UWBG) materials such as AlN and BN hold great promise for future power electronics due to their exceptional properties. They exhibit large bandgaps, high breakdown fields, high thermal conductivity, and high mechanical strengths. AlN and BN have been extensively researched, however, their alloys, B$_x$Al$_{1-x}$N, are much less studied despite their ability to offer tunable properties by adjusting $x$. In this article, we predict the electronic properties of 17 recently predicted ground states of B$_x$Al$_{1-x}$N in the $x=0-1$ range using first-principles density functional theory and many-body perturbation theory within $GW$ approximation. All the B$_x$Al$_{1-x}$N structures are found to be UWBG materials and have bandgaps that vary linearly from that of wurtzite-phase ($w$) AlN (6.19 eV) to that of $w$-BN (7.47 eV). The bandstructures of B$_x$Al$_{1-x}$N show that a direct-to-indirect bandgap crossover occurs near $x = 0.25$. Furthermore, we find that B$_x$Al$_{1-x}$N alloys have much larger dielectric constants than the constituent bulk materials (AlN=$9.3~\varepsilon_0$ or BN=$7.3~\varepsilon_0$), with values reaching as high as $12.1~\varepsilon_0$. These alloys are found to exhibit large dielectric breakdown fields in the range 9--35 MV/cm with a linear dependence on $x$. This work provides the much needed advancement in the understanding of the properties of B$_x$Al$_{1-x}$N to aid their application in next-generation devices., Comment: 24 pages, 5 figures

We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 ($\mathrm{D}\approx31$ Mpc), from $<1$ to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 $\mu$m feature which persists until 5 days post-maximum. We also detect C II $\lambda$6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia., Comment: 38 pages, 16 figures, accepted for publication in ApJ, the figure 15 input models and synthetic spectra are now available at https://zenodo.org/record/8379254

Within the framework of Zermelo-Fraenkel set theory without the Axiom of Choice, we establish equivalents to the assertion "the union of a countable collection of finite sets is countable" in the context of metric spaces, probability theory, and Hilbert spaces. The categorical principle underlying these equivalences is identified.

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into significantly a more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning and non-deep learning methods, to the close-form transformation, we evaluate their potential for improving surface texture control in temper strip steel manufacturing.