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The advent of AI tools, such as Large Language Models, has introduced new possibilities for Qualitative Data Analysis (QDA), offering both opportunities and challenges. To help navigate the responsible integration of AI into QDA, we conducted semi-structured interviews with 15 HCI researchers experienced in QDA. While our participants were open to AI support in their QDA workflows, they expressed concerns about data privacy, autonomy, and the quality of AI outputs. In response, we developed a framework that spans from minimal to high AI involvement, providing tangible scenarios for integrating AI into HCI researchers' QDA practices while addressing their needs and concerns. Aligned with real-life QDA workflows, we identify potentials for AI tools in areas such as data pre-processing, researcher onboarding, or mediation. Our framework aims to provoke further discussion on the development of AI-supported QDA and to help establish community standards for their responsible use.

Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The common principle of previous works to remove a specific concept is to map it to a fixed generic concept, such as a neutral concept or just an empty text prompt. In this paper, we demonstrate that this fixed-target strategy is suboptimal, as it fails to account for the impact of erasing one concept on the others. To address this limitation, we model the concept space as a graph and empirically analyze the effects of erasing one concept on the remaining concepts. Our analysis uncovers intriguing geometric properties of the concept space, where the influence of erasing a concept is confined to a local region. Building on this insight, we propose the Adaptive Guided Erasure (AGE) method, which \emph{dynamically} selects optimal target concepts tailored to each undesirable concept, minimizing unintended side effects. Experimental results show that AGE significantly outperforms state-of-the-art erasure methods on preserving unrelated concepts while maintaining effective erasure performance. Our code is published at {https://github.com/tuananhbui89/Adaptive-Guided-Erasure}.

Sentinel-5P (S5P) satellite provides atmospheric measurements for air quality and climate monitoring. While the S5P satellite offers rich spectral resolution, it inherits physical limitations that restricts its spatial resolution. Super-resolution (SR) techniques can overcome these limitations and enhance the spatial resolution of S5P data. In this work, we introduce a novel SR model specifically designed for S5P data that have eight spectral bands with around 500 channels for each band. Our proposed S5-DSCR model relies on Depth Separable Convolution (DSC) architecture to effectively perform spatial SR by exploiting cross-channel correlations. Quantitative evaluation demonstrates that our model outperforms existing methods for the majority of the spectral bands. This work highlights the potential of leveraging DSC architecture to address the challenges of hyperspectral SR. Our model allows for capturing fine details necessary for precise analysis and paves the way for advancements in air quality monitoring as well as remote sensing applications.

We study a new type of 2D active material that exhibits macroscopic phases with two emergent broken symmetries: self-propelled achiral particles that form dense hexatic clusters, which spontaneously rotate. We experimentally realise active colloids that self-organise into both polar and hexatic crystallites, exhibiting exotic emergent phenomena. This is accompanied by a field theory of coupled order parameters formulated on symmetry principles, including non-reciprocity, to capture the non-equilibrium dynamics. We find that the presence of two interacting broken symmetry fields leads to the emergence of novel chiral phases built from (2D) achiral active colloids (here Quincke rollers). These phases are characterised by the presence of both clockwise and counterclockwise rotating clusters. We thus show that spontaneous rotation can emerge in non-equilibrium systems, even when the building blocks are achiral, due to non-reciprocally coupled broken symmetries. This interplay leads to self-organized stirring through counter-rotating vortices in confined colloidal systems, with cluster size controlled by external electric fields.

The HAWC Observatory collected 6 years of extensive data, providing an ideal platform for long-term monitoring of blazars in the Very High Energy (VHE) band, without bias towards specific flux states. HAWC continuously monitors blazar activity at TeV energies, focusing on sources with a redshift of {z \lt 0.3}, based on the Third Fermi-LAT Catalog of High-Energy sources. We specifically focused our analysis on Mrk 421 and Mrk 501, as they are the brightest blazars observed by the HAWC Observatory. With a dataset of 2143 days, this work significantly extends the monitoring previously published, which was based on 511 days of observation. By utilizing HAWC data for the VHE {\gamma}-ray emission in the 300 GeV to 100 TeV energy range, in conjunction with Swift-XRT data for the 0.3 to 10 keV X-ray emission, we aim to explore potential correlations between these two bands. For Mrk 501, we found evidence of a long-term correlation. Additionally, we identified a period in the light curve where the flux was very low for more than two years. On the other hand, our analysis of Mrk 421 measured a strong linear correlation for quasi-simultaneous observations collected by HAWC and Swift-XRT. This result is consistent with a linear dependence and a multiple-zone synchrotron self-Compton model to explain the X-ray and the {\gamma}-ray emission. Finally, as suggested by previous findings, we confirm a harder-when-brighter behavior in the spectral evolution of the flux properties for Mrk 421. These findings contribute to the understanding of blazar emissions and their underlying mechanisms.

Studying accretion-driven episodic outbursts in YSOs is key to understanding the later stages of star and planet formation. FU Orionis-type objects form a YSO subclass, distinguished by rapid, multi-magnitude increases in brightness at optical and near-infrared wavelengths. These outbursts may significantly impact the chemistry and molecular composition around eruptive stars. However, no comprehensive millimeter-wavelength line survey exists for more evolved (Class II) sources, unlike optical and near-infrared coverage. We conducted the first wideband millimeter spectral line survey of V1057 Cyg, a low-mass eruptive FUor with the highest observed peak accretion rate in its class. Using the IRAM 30-m telescope, we surveyed the 72-263 GHz range and complemented this with targeted spectra at 219, 227, 291, and 344 GHz with the APEX 12-m telescope. We conducted radiative transfer and population diagram analyses to get first estimates of the excitation temperatures and column densities. Several molecular species trace large-scale structures, and the position-velocity diagram of $^{12}$CO suggest episodic outburst activity, with outflow dynamical timescales on the order of tens of thousands of years. We identified simple molecules (C-, N-, O-, and S-bearing), deuterated species, molecular ions, and complex organic molecules. With over 30 molecular species (including isotopologues) detected, V1057 Cyg demonstrates rich chemistry for its evolutionary state, compared to other younger (Class 0/I) FUors. V1057 Cyg is a good candidate for future interferometric studies to resolve emission structures, to possibly constrain molecular freeze-out, and detect water and complex organic molecules. Our results highlight the importance of millimeter line surveys in complementing optical/near-infrared studies, improving statistics on molecular inventories in eruptive stars and their environments., Comment: 45 pages, 20 figures, Accepted for publication in Astronomy & Astrophysics (A&A). This work is dedicated to the memory of Karl M. Menten

We present direct observational constraints on tachyons; particles with group velocity greater than $c$ in vacuum in a Lorentz invariant theory. Since tachyons may have no direct couplings to Standard Model particles, the most robust and model independent constraints come from gravitational effects, especially black holes. We compute the Hawking radiation of tachyons from black holes, finding it to be significantly enhanced in the presence of heavy tachyons. For a black hole of mass $M$ and tachyons of mass $m$ with $g$ degrees of freedom, the black hole lifetime is found to be $t_{bh} \approx 192 \pi \hbar M/(g c^2 m^2)$ (or doubled for fermions). This implies that the observation of black holes of a few solar masses, with lifetime of several billion years, rules out tachyons of mass $m > 3 \times 10^9$ GeV. This means there cannot exist any tachyons associated with unification scales or quantum gravity. So while there already exists theoretical reasons to be skeptical of tachyons, our work provides a complementary direct observational constraint., Comment: 7 pages in double column format, 2 figures

The FASER experiment was designed to study long-lived dark sector particles and neutrinos traveling in the forward direction at the LHC. Neutrinos are predominantly produced from meson decays, which also result in an intense energetic flux of muons in the forward direction regularly observed by FASER. So far, these muons are treated only as backgrounds to neutrino and new physics studies, and extensive effort is required to suppress them. In this study, we consider the opposite scenario and use muons produced in the forward direction to produce new muonphilic scalars, which can then be searched for at the FASER detector. To minimize the backgrounds for this search, we make use of an upgraded preshower component, which is expected to be installed at FASER before the end of Run 3, and is capable of spatially resolving two energetic photons. We find that FASER, and its upgrade, FASER2 can probe currently unconstrained regions of parameter space, including regions that can potentially explain the $(g-2)_{\mu}$ anomaly. This highlights the physics opportunities that the intense TeV muon beam at the LHC can bring., Comment: 13 pages, 5 figures, 1 appendix

Cosmic spherules have largely been classified into S-, I-, and G-types according to their compositions, and are identified to have chondritic or achondritic materials as precursors. A recent recovery expedition attempted to sample fragments of the CNEOS 2014-01-08 bolide retrieved roughly 850 magnetic particles, some of which have unknown origins. Among those identified were a new group of highly differentiated materials consisting of close to 160 specimens categorized as "D-type" particles. We studied the D-type particles with the goal of comparing their various morphological features to their chemical compositional groupings. Four morphological classifications are considered: "scoriaceous," "stubby," "blocky," and "vesicular." The specimens from the "scoriaceous" and "stubby" groups exhibit a spinel/magnetite rim in at least one instance, characteristic of atmospheric entry, and textures indicative of quenching such as dendritic microcrystalline structures, suggesting that a subset of specimens from these groups are candidates for materials of extraterrestrial origin. The particles exhibiting "blocky" and "vesicular" textures are likely to be terrestrial in origin, with no obvious quench features or signs of ablation. The D-type particles identified and characterized in this study have a spectrum of terrestrial and probable extraterrestrial origins., Comment: Chemical Geology

We consider the spatial classification problem for monitoring using data collected by a coordinated team of mobile robots. Such classification problems arise in several applications including search-and-rescue and precision agriculture. Specifically, we want to classify the regions of a search environment into interesting and uninteresting as quickly as possible using a team of mobile sensors and mobile charging stations. We develop a data-driven strategy that accommodates the noise in sensed data and the limited energy capacity of the sensors, and generates collision-free motion plans for the team. We propose a bi-level approach, where a high-level planner leverages a multi-armed bandit framework to determine the potential regions of interest for the drones to visit next based on the data collected online. Then, a low-level path planner based on integer programming coordinates the paths for the team to visit the target regions subject to the physical constraints. We characterize several theoretical properties of the proposed approach, including anytime guarantees and task completion time. We show the efficacy of our approach in simulation, and further validate these observations in physical experiments using mobile robots., Comment: 8 pages, 6 figures. See https://www.youtube.com/watch?v=gzulpOcVYzg for an overview of the approach along with videos of the hardware experiments

We consider an ensemble of $2\times 2$ normal matrices with complex entries representing operators in the quantum mechanics of 2 - level parity-time reversal (PT) symmetric systems. The randomness of the ensemble is endowed by obtaining probability distributions based on symmetry and statistical independence. The probability densities turn out to be power law with exponents that depend on the boundedness of the domain. For small spacings, $\sigma$, the probability density varies as $\sigma^{\nu}$, $\nu \geq 2$. The degree of level repulsion is a parameter of great interest as it makes a connection to quantum chaos; the lower bound of $\nu$ for our ensemble coincides with the Gaussian Unitary Ensemble. We believe that the systematic development presented here paves the way for further generalizations in the field of random matrix theory for PT-symmetric quantum systems.

We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying solely on data from a single target system, our framework utilizes data from a diverse set of source systems, enabling learning from limited target data, as well as with few online training iterations. Through benchmark examples, we demonstrate the potential of our approach, study the effect of fine-tuning subnetworks rather than full fine-tuning, and report real-world case studies to illustrate the practical application and generalizability of the approach to practical problems with physical-constraints. Specifically, we show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems., Comment: 30 pages

Resonant soft X-ray scattering (RSXS) is a powerful technique for probing both spatial and electronic structures within solid-state systems.We present a newly developed RSXS capability at beamline 13-3 of the Stanford Synchrotron Radiation Lightsource (SSRL), designed to enhance materials science research. This advanced setup achieves a base sample temperature as low as 9.8 K combined with extensive angular motions (azimuthal \phi and flipping \chi), enables comprehensive exploration of reciprocal space. Two types of detectors, an Au/GaAsP Schottky photodiode and a CCD detector with over 95% quantum efficiency, are integrated to effectively capture scattered photons. Extensive testing has confirmed the enhanced functionality of this RSXS setup, including its temperature and angular performance. The versatility and effectiveness of the system have been demonstrated through studies of various materials, including superlattice heterostructures and high-temperature superconductors., Comment: 22 pages, 7 figures

The Hamiltonian Monte Carlo (HMC) algorithm is a powerful Markov Chain Monte Carlo (MCMC) method that uses Hamiltonian dynamics to generate samples from a target distribution. To fully exploit its potential, we must understand how Hamiltonian dynamics work and why they can be used in a MCMC algorithm. This work elucidates the Monte Carlo Hamiltonian, providing comprehensive explanations of the underlying physical concepts. It is intended for readers with a solid foundation in mathematics who may lack familiarity with specific physical concepts, such as those related to Hamiltonian dynamics. Additionally, we provide Python code for the HMC algorithm, examples and comparisons with the Random Walk Metropolis-Hastings (RWMH) and t-walk algorithms to highlight HMC's strengths and weaknesses when applied to Bayesian Inference.

The Ziegler-Rauk-Baerends multiplet sum method (MSM) assumes that density-functional theory (DFT) provides a good description of states dominated by a single determinant. It then uses symmetry to add static correlation to DFT. In our previous article (Article I) [J. Chem. Phys. 159, 244306 (2023)], we introduced diagrammatic MSM-DFT as a tool to aid in extending MSM-DFT to include the nondynamic correlation needed for making and breaking bonds even in the absence of symmetry. An attractive feature of this approach is that no functional-dependent parameters need to be introduced, though choices are needed in making correspondances between wave function theory (WFT) and MSM-DFT diagrams. The preliminary examples in Article I used the two-orbital two-electron model (TOTEM) less completely than could have been the case. Diagrammatic MSM-DFT is extended here to treat the full TOTEM and it is shown that the unsymmetric lithium hydride (LiH) molecule dissociates into neutral atoms when diagrammatic MSM-DFT techniques are used to introduce a proper description of the avoided crossing between ionic bonding and covalent bonding states.The method is tested for Hartree-Fock and for three functionals (LDA, PW91, and B3LYP). All the functionals yield similar results as should be expected for a properly-formulated parameter-free theory. Agreement with available estimates show that the magnitude of the coupling element introduced here is excellent. However more work will be needed to obtain quantitative agreement between our diagrammatic MSM-DFT ground-state potential energy curve and that found from high-quality ab initio calculations

Amidst the burgeoning landscape of database architectures, the surge in NoSQL databases has heralded a transformative era, liberating data storage from traditional relational constraints and ushering in unprecedented scalability. As organizations grapple with the escalating security threats posed by database breaches, a novel theoretical framework emerges at the nexus of chaos theory and topology: the Database in motion Chaos Encryption (DaChE) Algorithm. This paradigm-shifting approach challenges the static nature of data storage, advocating for dynamic data motion to fortify database security. By incorporating chaos theory, this innovative strategy not only enhances database defenses against evolving attack vectors but also redefines the boundaries of data protection, offering a paradigmatic shift in safeguarding critical information assets. Additionally, it enables parallel processing, facilitating on-the-fly processing and optimizing the performance of the proposed framework.

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument on the Aditya-L1 mission of the Indian Space Research Organization (ISRO) launched on September 02, 2023. SUIT continuously provides, near-simultaneous full-disk and region-of-interest images of the Sun, slicing through the photosphere and chromosphere and covering a field of view up to 1.5 solar radii. For this purpose, SUIT uses 11 filters tuned at different wavelengths in the 200{--}400~nm range, including the Mg~{\sc ii} h~and~k and Ca~{\sc ii}~H spectral lines. The observations made by SUIT help us understand the magnetic coupling of the lower and middle solar atmosphere. In addition, for the first time, it allows the measurements of spatially resolved solar broad-band radiation in the near and mid ultraviolet, which will help constrain the variability of the solar ultraviolet irradiance in a wavelength range that is central for the chemistry of the Earth's atmosphere. This paper discusses the details of the instrument and data products., Comment: 37 pages, Accepted for Publication in Solar Physics

The study of spectral graph determination is a fascinating area of research in spectral graph theory and algebraic combinatorics. This field focuses on examining the spectral characterization of various classes of graphs, developing methods to construct or distinguish cospectral nonisomorphic graphs, and analyzing the conditions under which a graph's spectrum uniquely determines its structure. This paper presents an overview of both classical and recent advancements in these topics, along with newly obtained proofs of some existing results, which offer additional insights.

Nanoscale confinement of molecules in a fluid can result in enhanced viscosity, local fluidic order, or collective motion. Confinement also affects ion transport and/or the rate and equilibrium concentration in a chemical reaction, all of which makes it the subject of broad interest. Studying these effects, however, is notoriously difficult, mainly due to the lack of experimental methods with the required sensitivity and spatial or time resolution. Here we leverage shallow nitrogen-vacancy (NV) centers in diamond to probe the dynamics of room-temperature water molecules entrapped within ~6-nm-tall channels formed between the diamond crystal and a suspended hexagonal boron nitride (hBN) flake. NV-enabled nuclear magnetic resonance measurements of confined water protons reveal a much reduced H2O self-diffusivity, orders of magnitude lower than in bulk water. We posit the slow dynamics stem from the accumulation of photogenerated carriers at the interface and trapped fluid, a notion we support with the help of molecular dynamics modeling. Our results provide feedback for theories describing interfacial water, and lay out a route for investigating other fluids under confinement.

Assessing the extent of human edits on texts generated by Large Language Models (LLMs) is crucial to understanding the human-AI interactions and improving the quality of automated text generation systems. Existing edit distance metrics, such as Levenshtein, BLEU, ROUGE, and TER, often fail to accurately measure the effort required for post-editing, especially when edits involve substantial modifications, such as block operations. In this paper, we introduce a novel compression-based edit distance metric grounded in the Lempel-Ziv-77 algorithm, designed to quantify the amount of post-editing applied to LLM-generated texts. Our method leverages the properties of text compression to measure the informational difference between the original and edited texts. Through experiments on real-world human edits datasets, we demonstrate that our proposed metric is highly correlated with actual edit time and effort. We also show that LLMs exhibit an implicit understanding of editing speed, that aligns well with our metric. Furthermore, we compare our metric with existing ones, highlighting its advantages in capturing complex edits with linear computational efficiency. Our code and data are available at: https://github.com/NDV-tiime/CompressionDistance

The Arcus Probe mission concept provides high-resolution soft X-ray and UV spectroscopy to reveal feedback-driven structure and evolution throughout the universe with an agile response capability ideal for probing the physics of time-dependent phenomena. The X-ray Spectrograph (XRS) utilizes two nearly identical CCD focal planes to detect and record X-ray photons from the dispersed spectra and zero-order of the critical angle transmission gratings. In this paper we describe the Arcus focal plane instrument and the CCDs, including laboratory performance results, which meet observatory requirements., Comment: 11 pages, 7 figures; to be published in JATIS

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

Motivated by a vaccination coverage problem, we consider here a zero-sum differential game governed by a differential system consisting of a hyperbolic partial differential equation (PDE) and an ordinary differential equation (ODE). Two players act through their respective controls to influence the evolution of the system with the aim of minimizing their objective functionals $\mathcal F_1$ and $\mathcal F_2$, under the assumption that $\mathcal F_1 +\mathcal F_2 = 0$. First we prove a well posedness and a stability result for the differential system, once the control functions are fixed. Then we introduce the concept of non-anticipating strategies for both players and we consider the associated value functions, which solve two infinite-dimensional Hamilton-Jacobi-Isaacs equations in the viscosity sense.

In the ever-evolving landscape of medical diagnostics, this study details the systematic design process and concept selection methodology for developing an advanced digital stethoscope, demonstrating the evolution from traditional acoustic models to AI-enhanced digital solutions. The device integrates cutting-edge AI technology with traditional auscultation methods to create a more accurate, efficient, and user-friendly diagnostic tool. Through systematic product planning, customer need analysis, and rigorous specification development, we identified key opportunities to enhance conventional stethoscope functionality. The proposed system features real-time sound analysis, automated classification of heart sounds, wireless connectivity for remote consultations, and an intuitive user interface accessible via smartphone integration. The design process employed a methodical approach incorporating customer feedback, competitive benchmarking, and systematic concept generation and selection. Through a structured evaluation framework, we analyzed portability, frequency response sensitivity, transmission quality, maintenance ease, user interface simplicity, output signal quality, power efficiency, and cost-effectiveness. The final design prioritizes biocompatibility, reliability, and cost-effectiveness while addressing the growing demand for telemedicine capabilities in cardiovascular care. The project emphasizes the transition from conventional design to advanced digital solutions while maintaining a focus on practical clinical applications. Each concept was modelled using SOLIDWORKS software, enabling detailed visualization and engineering analysis. This systematic approach to concept screening and selection ensures the final design meets both current healthcare needs and future technological adaptability., Comment: 44 pages

Photoluminescent defects in diamond, like the nitrogen-vacancy (NV) color center, are at the forefront of emerging optical quantum technologies. Most emit in the visible and near-infrared spectral region below 1000 nm (NIR-I), limiting their applications in photonics, fiber communications, and biology. Here, we show that the nitrogen-vacancy-nitrogen ($N_2V$) center, which emits in the visible and near-infrared-II (NIR-II, 1000-1700 nm), is ubiquitous in as-synthesized and processed nitrogen-doped diamond from bulk samples to nanoparticles. We demonstrate that $N_2V$ is also present in commercially available state-of-the-art NV diamond sensing chips made via chemical vapor deposition (CVD). In high-pressure high-temperature (HPHT) diamonds, the photoluminescence (PL) intensity of both $N_2V$ charge states, $N_2V^0$ in the visible and $N_2V^-$ in the NIR-II, increases with increasing substitutional nitrogen concentration. We determine the PL lifetime of $N_2V^-$ to be 0.3 ns and compare a quantum optical and density functional theory model of the $N_2V^-$ with experimental PL spectra. Finally, we show that detonation nanodiamonds (DND) show stable PL in the NIR-II, which we attribute to the $N_2V$ color center, and use this NIR-II PL to image DNDs inside skin cells. Our results will contribute to the scientific and technological exploration and development of the $N_2V$ color center and inspire more research into its effect on other color centers in diamond.

Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users' perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This paper addresses this gap by surveying 124 papers in which recommender systems explanations were evaluated in user studies. We analyzed their participant descriptions and study results where the impact of user characteristics on the explanation effects was measured. Our findings suggest that the results from the surveyed studies predominantly cover specific users who do not necessarily represent the users of recommender systems in the evaluation domain. This may seriously hamper the generalizability of any insights we may gain from current studies on explanations in recommender systems. We further find inconsistencies in the data reporting, which impacts the reproducibility of the reported results. Hence, we recommend actions to move toward a more inclusive and reproducible evaluation., Comment: 31 pages, 2 figures. Submitted to ACM Transactions of Recommender Systems

The Efficient Market Hypothesis (EMH) highlights the essence of financial news in stock price movement. Financial news comes in the form of corporate announcements, news titles, and other forms of digital text. The generation of insights from financial news can be done with sentiment analysis. General-purpose language models are too general for sentiment analysis in finance. Curated labeled data for fine-tuning general-purpose language models are scare, and existing fine-tuned models for sentiment analysis in finance do not capture the maximum context width. We hypothesize that using actual and synthetic data can improve performance. We introduce BertNSP-finance to concatenate shorter financial sentences into longer financial sentences, and finbert-lc to determine sentiment from digital text. The results show improved performance on the accuracy and the f1 score for the financial phrasebank data with $50\%$ and $100\%$ agreement levels.

We present a resolved study of $>900$ progenitors of Milky Way Analogs (MWAs) at $0.3

Context. We analyse multi-colour photometric and spectroscopic observations of a Young Stellar Object Gaia20bdk. Aims. We aim to investigate the exact nature of the eruptive phenomenon that the star has been experiencing since 2018. Methods. We use public-domain archival photometry to characterise the quiescent phase in order to establish major physical parameters of the progenitor. Then, we use our and public-domain optical and infrared photometry and spectroscopy to study the outburst. Results. Gaia20bdk is a member of the Sharpless 2-301 star-forming region, at a distance of 3.3 kpc. The progenitor is a rather massive 2.7 solar mass, G7-type Class I young star having an effective temperature of 5300 K and bolometric luminosity of 11 solar luminosities. The optical and infrared photometric and spectroscopic data obtained during the outburst show a variety of signatures commonly found in classical FUors. Our disc modelling results in a bolometric luminosity of 100-200 solar luminosities and mass accretion rate of 1-2e-5 solar masses per year, also confirming the object's FUor classification. Further monitoring is necessary to track the light changes, accretion rate and spectral variations, as well as to understood the mechanisms behind the disc flickering., Comment: To appear in A&A

I derive a lower limit on the mass of an Unidentified Flying Object (UFO) based on measurements of its speed and acceleration, as well as the infrared luminosity of the airglow around it. If the object's radial velocity can be neglected, the mass limit is independent of distance. Measuring the distance and angular size of the object allows to infer its minimum mass density. The Galileo Project will be collecting the necessary data on millions of objects in the sky over the coming year., Comment: 4 pages, submitted for publication in an AAS journal

The James Webb Space Telescope (JWST) has recently discovered a new population of objects at high redshift referred to as `Little Red Dots' (LRDs). Their nature currently remains elusive, despite their surprisingly high inferred number densities. This emerging population of red point-like sources is reshaping our view of the early Universe and may shed light on the formation of high-redshift supermassive black holes. Here we present a spectroscopically confirmed LRD CANUCS-LRD-z8.6 at $z_{\rm spec}=8.6319\pm 0.0005$ hosting an Active Galactic Nucleus (AGN), using JWST data. This source shows the typical spectral shape of an LRD (blue UV and red optical continuum, unresolved in JWST imaging), along with broad H$\beta$ line emission, detection of high-ionization emission lines (CIV, NIV]) and very high electron temperature indicative of the presence of AGN. This is also combined with a very low metallicity ($Z<0.1 Z_\odot$). The presence of all these diverse features in one source makes CANUCS-LRD-z8.6 unique. We show that the inferred black hole mass of CANUCS-LRD-z8.6 ($M_{\rm BH}=1.0^{+0.6}_{-0.4}\times 10^{8}\rm ~M_\odot$) strongly challenges current standard theoretical models and simulations of black hole formation, and forces us to adopt `ad hoc' prescriptions. Indeed if massive seeds, or light seeds with super-Eddington accretion, are considered, the observed BH mass of CANUCS-LRD-z8.6 at $z=8.6$ can be reproduced. Moreover, the black hole is over-massive compared to its host, relative to the local $M_{\rm BH}-M_*$ relations, pointing towards an earlier and faster evolution of the black hole compared to its host galaxy., Comment: 4 main figures; 8 supplementary figures; 5 supplementary tables

$2\times2$ matrix polynomials of the form $P_{n}(z)= \Sigma^{n}_{j=0}\,\sigma_{j}\,z^{j}$, for the cases $n=1,2,3$ are constructed, and the nature of PT-symmetry is examined across different points $z=(x,y)$ in the complex plane. The PT-symmetric properties of $P_{n}(z)$ can be characterized by two functions, denoted by $s(x,y)$ and $h(x,y)$. If the trace of the matrix polynomial is real, then the points at which it can exhibit PT-symmetry are defined by the family of curves $s(x,y)=0$. Additionally, at points where the function $h(x,y)\geq 0$, the matrix polynomial exhibits unbroken PT-symmetry; otherwise, it exhibits broken PT-symmetry. The intersection points of the curves $s(x,y)=0$ and $h(x,y)=k$, for a given $k\in \mathbb{R}$, are shown to lie on an ellipse, hyperbola, two lines passing through the origin, or a straight line, depending on the nature of PT-symmetry of the matrix polynomial. The PT-symmetric behaviour of $P_{n}(z)$ at the zeros of the matrix polynomial is also studied., Comment: This article has been communicated to a journal for consideration

This letter presents the measurement of the energy-dependent neutrino-nucleon cross section in tungsten and the differential flux of muon neutrinos and anti-neutrinos. The analysis is performed using proton-proton collision data at a center-of-mass energy of $13.6 \, {\rm TeV}$ and corresponding to an integrated luminosity of $(65.6 \pm 1.4) \, \mathrm{fb^{-1}}$. Using the active electronic components of the FASER detector, $338.1 \pm 21.0$ charged current muon neutrino interaction events are identified, with backgrounds from other processes subtracted. We unfold the neutrino events into a fiducial volume corresponding to the sensitive regions of the FASER detector and interpret the results in two ways: We use the expected neutrino flux to measure the cross section, and we use the predicted cross section to measure the neutrino flux. Both results are presented in six bins of neutrino energy, achieving the first differential measurement in the TeV range. The observed distributions align with Standard Model predictions. Using this differential data, we extract the contributions of neutrinos from pion and kaon decays.

Understanding the quantum dynamics of strongly coupled molecule-cavity systems remains a significant challenge in molecular polaritonics. This work develops a comprehensive self-consistent model simulating electromagnetic interactions of diatomic molecules with quantum ro-vibrational degrees of freedom in resonant optical cavities. The approach employs an efficient numerical methodology to solve coupled Schrodinger-Maxwell equations in real space-time, enabling three-dimensional simulations through a novel molecular mapping technique. The study investigates relaxation dynamics of an ensemble of molecules following intense resonant pump excitation in Fabry-Perot cavities and at three-dimensional plasmonic metasurfaces. The simulations reveal dramatically modified relaxation pathways inside cavities compared to free space, characterized by persistent molecular alignment arising from cavity-induced rotational pumping. They also indicate the presence of a previously unreported relaxation stabilization mechanism driven by dephasing of the collective molecular-cavity mode. Additionally, the study demonstrates that strong molecular coupling significantly modifies the circular dichroism spectra of chiral metasurfaces, suggesting new opportunities for controlling light-matter interactions in quantum optical systems.

The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization., Comment: 11 pages, 4 figures, 6 tables

Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning. This retrospective cohort study used data from the electronic health records of adult surgical patients over four years (2018 - 2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared prediction performances of surgVAE against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation. 89,246 surgeries (49% male, median (IQR) age: 57 (45-69)) were included, with 6,502 in the targeted cardiac surgery cohort (61% male, median (IQR) age: 60 (53-70)). surgVAE demonstrated superior performance over existing ML solutions across all postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance. surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles., Comment: This article has been accepted for publication in Journal of the American Medical Informatics Association Published by Oxford University Press. Codes are publicly available at: https://github.com/ai4biomedicine/surgVAE

Diffuse Galactic cirrus, or Diffuse Galactic Light (DGL), can be a prominent component in the background of deep wide-field imaging surveys. The DGL provides unique insights into the physical and radiative properties of dust grains in our Milky Way, and it also serves as a contaminant on deep images, obscuring the detection of background sources such as low surface brightness galaxies. However, it is challenging to disentangle the DGL from other components of the night sky. In this paper, we present a technique for the photometric characterization of Galactic cirrus, based on (1) extraction of its filamentary or patchy morphology and (2) incorporation of color constraints obtained from Planck thermal dust models. Our decomposition method is illustrated using a $\sim$10 deg$^2$ imaging dataset obtained by the Dragonfly Telephoto Array, and its performance is explored using various metrics which characterize the flatness of the sky background. As a concrete application of the technique, we show how removal of cirrus allows low surface brightness galaxies to be identified on cirrus-rich images. We also show how modeling the cirrus in this way allows optical DGL intensities to be determined with high radiometric precision., Comment: 35 pages, 17 figures, accepted for publication in ApJ

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (\ie, multilingual LLMs) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. Moreover, current practices in multilingual benchmark construction often translate English resources, ignoring the regional and cultural knowledge of the environments in which multilingual systems would be used. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts. Our novel resource, INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across 44 written languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed.

The Capital Asset Pricing Model (CAPM) relates a well-diversified stock portfolio to a benchmark portfolio. We insert size effect in CAPM, capturing the observation that small stocks have higher risk and return than large stocks, on average. Dividing stock index returns by the Volatility Index makes them independent and normal. In this article, we combine these ideas to create a new discrete-time model, which includes volatility, relative size, and CAPM. We fit this model using real-world data, prove the long-term stability, and connect this research to Stochastic Portfolio Theory. We fill important gaps in our previous article on CAPM with the size factor., Comment: 22 pages, 2 tables, 7 figures, 14 plots. Keywords: Capital Asset Pricing Model, stochastic volatility, ergodic Markov process, stationary distribution, size effect, autoregression, capital distribution curve

Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, their effectiveness in the Global South, especially across the African continent, remains to be established. In this work, we introduce AfriMed-QA, the first large scale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. We further evaluate 30 LLMs across multiple axes including correctness and demographic bias. Our findings show significant performance variation across specialties and geographies, MCQ performance clearly lags USMLE (MedQA). We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score. Interestingly, human evaluations show a consistent consumer preference for LLM answers and explanations when compared with clinician answers.

Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections. The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained, resulting in high quality image reconstruction without need for additional external calibrations., Comment: 8 pages, 5 figures, submitted to International Society for Magnetic Resonance in Medicine 32th Scientific Meeting, 2025

We consider cylindrical algebraic decomposition (CAD) and the key concept of delineability which underpins CAD theory. We introduce the novel concept of projective delineability which is easier to guarantee computationally. We prove results about this which can allow reduced CAD computations., Comment: Accepted for publication in the Proceedings of the 26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2024)

Triple Entry (TE) is an accounting method that utilizes three accounts or 'entries' to record each transaction, rather than the conventional double-entry bookkeeping system. Existing studies have found that TE accounting, with its additional layer of verification and disclosure of inter-organizational relationships, could help improve transparency in complex financial and supply chain transactions such as blockchain. Machine learning (ML) presents a promising avenue to augment the transparency advantages of TE accounting. By automating some of the data collection and analysis needed for TE bookkeeping, ML techniques have the potential to make this more transparent accounting method scalable for large organizations with complex international supply chains, further enhancing the visibility and trustworthiness of financial reporting. By leveraging ML algorithms, anomalies within distributed ledger data can be swiftly identified, flagging potential instances of fraud or errors. Furthermore, by delving into transaction relationships over time, ML can untangle intricate webs of transactions, shedding light on obscured dealings and adding an investigative dimension. This paper aims to demonstrate the interaction between TE and ML and how they can leverage transparency levels.

In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experimental design approach derived from information-theoretic methods to identify and search for information-rich contact modes through the use of contact-implicit optimization. We demonstrate our approach on a robot parameter estimation problem with unknown inertial and kinematic parameters which actively seeks contacts with a nearby surface. We show that our approach improves the identification of unknown parameter estimates over experimental runs by an estimate error reduction of at least $\sim 84\%$ when compared to a random sampling baseline, with significantly higher information gains.

This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor-intensive, time-consuming, and susceptible to human error, highlighting the need for automated pipelines to streamline the process. Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction. We automate and optimize the meta-analysis process by integrating Retrieval Augmented Generation (RAG). Tailored through prompt engineering and a new loss metric, Inverse Cosine Distance (ICD), designed for fine-tuning on large contextual datasets, LLMs efficiently generate structured meta-analysis content. Human evaluation then assesses relevance and provides information on model performance in key metrics. This research demonstrates that fine-tuned models outperform non-fine-tuned models, with fine-tuned LLMs generating 87.6% relevant meta-analysis abstracts. The relevance of the context, based on human evaluation, shows a reduction in irrelevancy from 4.56% to 1.9%. These experiments were conducted in a low-resource environment, highlighting the study's contribution to enhancing the efficiency and reliability of meta-analysis automation., Comment: Accepted in 2024 IEEE International Conference on Big Data (IEEE BigData)

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language models (pLM) training on hundreds of graphical processing units (GPUs). We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs. Its modular design allows the integration of individual components, such as data loaders, into existing workflows and is open to community contributions. We detail technical features of the BioNeMo Framework through use cases such as pLM pre-training and fine-tuning. On 256 NVIDIA A100s, BioNeMo Framework trains a three billion parameter BERT-based pLM on over one trillion tokens in 4.2 days. The BioNeMo Framework is open-source and free for everyone to use.

Relations between the graviton mass and the cosmological constant $\Lambda$ have led to some interesting implications. We show that in any approach which leads to a direct correlation between the graviton mass and $\Lambda$, either through direct substitution of gravitational coupling in dispersion relations or through the linearization of Einstein equations with massive spin-2 fields, the Compton wavelength of the graviton lies in the superhorizon scale. As a result any gravitational approaches where the graviton mass is related directly to the cosmological constant are of no observational significance., Comment: v2, citations and some footnotes added, 7 pages with no figures

Football analysts traditionally determine the relative value of draft picks by average future player value at each draft position. One implication is the loser's curse: top draft picks belonging to last year's worst teams produce less surplus value on average than draft picks later in the first round belonging to better teams. Additionally, these valuations do not match the valuation implied by the trade market. Either general managers are making terrible trades on average, or there is a sound economic reason for the discrepancy; we are partial to the latter explanation. Traditional analyses don't consider that variance in performance decays convexly accross the draft, causing eliteness (e.g., right tail probability) to decay much more steeply than expected value. Because elite players have an outsize influence on winning the Super Bowl, we suspect general managers value performance nonlinearly, placing exponentially higher value on players as their eliteness increases. Draft curves that account for this closely resemble the trade market. Additionally, we create draft curves that adjust for position via a novel Bayesian hierarchical Beta regression model. We find that if you are interested in an elite quarterback, there is no loser's curse.

Global attention has been focused on extreme climatic changes. This paper investigates the relationship between different phases of solar activity and extreme precipitation events in Kerala, India. Sunspot number and rainfall data were analysed over 122 years (1901-2022) on an annual scale. A negative correlation was observed in the winter and post-monsoon seasons, while positive correlations were seen in the pre-monsoon and monsoon seasons, all of which were statistically significant. Using cross-wavelet transform, the temporal relationship between sunspot number and rainfall values was investigated, revealing significant cross-power at an 8-12 year scale across all seasons. Wavelet coherence between the two data sets demonstrated significant correlation at the 2-4 and 4-8 year scales throughout the four seasons. The results show that the seasonal rainfall over Kerala is related to solar activity. The solar phases of Solar Cycles 14-24 were determined for all seasons, and the years with excessive and insufficient rainfall were identified. It was observed that the descending phase had an impact on excess rainfall events during the winter and pre-monsoon seasons, while the ascending phase notably affected the monsoon and post-monsoon seasons. The study specifically examined the different magnetic polarities of sunspots in alternating solar cycles, focusing on even and odd cycles. It was found that extreme rainfall events were more frequent during the winter and pre-monsoon seasons in the even cycles, whereas in the odd cycles, they were more prevalent during the monsoon and post-monsoon seasons. These findings are presented for the first time and may offer new perspectives on how different phases affect rainfall. This study suggests a physical link between solar activity and extreme precipitation in Kerala, which could increase predictability., Comment: 15 pages, 8 figures, 4 tables (Submitted to Advances in Space Research)