Search

Minority-Serving Institutions (MSIs) are colleges and universities across the United States and U.S. Territories enrolling significant percentages of racially minoritized undergraduate students or created with the explicit purpose of serving specific populations of racially minoritized students under various programs created by U.S. Congress (U.S. Department of Education, n.d.). About one in five Title IV-eligible postsecondary institutions is eligible for MSI funding (Nguyen, Ramirez, & Laderman, 2023). All MSIs must be accredited, degree-granting public or private non-proprietary (not-for-profit) colleges and universities. While MSIs are federal institutional-level designations codified in the Higher Education Act of 1965, states have a stake in this matter. The following analysis is an opportunity to highlight MSIs, given their outsized roles in educating racially minoritized and other minoritized students. Because most studies have focused on discussing MSIs solely at the institutional or national levels, this piece explores MSIs at the regional and state/territory levels. Using available information from the MSI Data Project (2017-2021), this piece contributes to our knowledge of MSIs by reporting on MSIs across U.S. Census divisions and states/territories. Moreover, different from how existing literature describes seven MSI designations, following Nguyen, Ramirez, & Laderman (2023), this piece reports on 11 MSI designations. Namely, this offers more precision around which designations an institution may be eligible for. This descriptive analysis is appropriate for laying the landscape of public MSIs (Loeb et al., 2017).

This theoretical study explores resonant elastic photon scattering in the presence of external electric and magnetic fields, motivated by potential applications in storage ring experiments, such as the Gamma Factory project at CERN. In this framework, resonant scattering involves head-on collisions of relativistic ion beams and counter-propagating laser photons, leading to a strong enhancement of the external field strength due to the Lorentz transformation between ion rest and laboratory frame. Calculations for He-like Ca ions reveal a notable impact of the external fields on the scattering rate as well as the angular distribution and polarization of emitted photons. This opens interesting avenues for diverse applications for the Gamma Factory project, such as as resonance condition tuning, beam cooling, and polarization control.

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

We present BenCzechMark (BCM), the first comprehensive Czech language benchmark designed for large language models, offering diverse tasks, multiple task formats, and multiple evaluation metrics. Its scoring system is grounded in statistical significance theory and uses aggregation across tasks inspired by social preference theory. Our benchmark encompasses 50 challenging tasks, with corresponding test datasets, primarily in native Czech, with 11 newly collected ones. These tasks span 8 categories and cover diverse domains, including historical Czech news, essays from pupils or language learners, and spoken word. Furthermore, we collect and clean BUT-Large Czech Collection, the largest publicly available clean Czech language corpus, and use it for (i) contamination analysis, (ii) continuous pretraining of the first Czech-centric 7B language model, with Czech-specific tokenization. We use our model as a baseline for comparison with publicly available multilingual models. Lastly, we release and maintain a leaderboard, with existing 44 model submissions, where new model submissions can be made at https://huggingface.co/spaces/CZLC/BenCzechMark., Comment: first version

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications., Comment: CVPR 2024 MedSAM on Laptop Competition Summary: https://www.codabench.org/competitions/1847/

We describe the implementation of light diagnostics for studying the self-modulation instability of a long relativistic proton bunch in a 10m-long plasma. The wakefields driven by the proton bunch dissipate their energy in the surrounding plasma. The amount of light emitted as atomic line radiation is related to the amount of energy dissipated in the plasma. We describe the setup and calibration of the light diagnostics, configured for a discharge plasma source and a vapor plasma source. For both sources, we analyze measurements of the light from the plasma only (no proton bunch). We show that with the vapor plasma source, the light signal is proportional to the energy deposited in the vapor/plasma by the ionizing laser pulse. We use this dependency to obtain the parameters of an imposed plasma density step. This dependency also forms the basis for ongoing studies, focused on investigating the wakefield evolution along the plasma., Comment: Proceedings AAC2024

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space optical (FSO) links, the testbed presented amalgamates FSO and fiber-based links, thereby overcoming some inherent limitations. Various network deployment strategies have been considered, including permanent infrastructure and provisional ad-hoc links to eradicate coverage gaps. Furthermore, the ability to rapidly establish a network using portable FSO terminals and to investigate diverse link topologies is demonstrated. The study also showcases the successful establishment of a quantum-secured link to a cloud server.

Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often difficult to assess due to its subjective nature in clinical evaluation. We introduce PARASIDE, an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenodalis and ethmoidalis in T1 MRI. By utilizing that segmentation, we can quantify feature relations that have been observed only manually and subjectively before. We performed an exemplary study and showed both volume and intensity relations between structures and radiology reports. While the soft tissue segmentation is good, the automated annotations of the air volumes are excellent. The average intensity over air structures are consistently below those of the soft tissues, close to perfect separability. Healthy subjects exhibit lower soft tissue volumes and lower intensities. Our developed system is the first automated whole nasal segmentation of 16 structures, and capable of calculating medical relevant features such as the Lund-Mackay score.

We present numerical approaches to approximate the solutions of systems of non-local balance laws. In particular, we derive a non-staggered central scheme based on the well-known Nessyahu-Tadmor scheme and show that it preserves the positivity of solutions. To reduce the numerical diffusion, we then consider a non-local version of the Kurganov-Tadmor scheme. For both schemes, an appropriate approximation of the non-local term is crucial to maintain a second-order accuracy. Numerical examples validate our theory and demonstrate its applicability to various systems of non-local problems.

Quantum optimal control is concerned with the realisation of desired dynamics in quantum systems, serving as a linchpin for advancing quantum technologies and fundamental research. Analytic approaches and standard optimisation algorithms do not yield satisfactory solutions for large quantum systems, and especially not for real world quantum systems which are open and noisy. We devise a physics-informed Reinforcement Learning (RL) algorithm that restricts the space of possible solutions. We incorporate priors about the desired time scales of the quantum state dynamics - as well as realistic control signal limitations - as constraints to the RL algorithm. These physics-informed constraints additionally improve computational scalability by facilitating parallel optimisation. We evaluate our method on three broadly relevant quantum systems (multi-level $\Lambda$ system, Rydberg atom and superconducting transmon) and incorporate real-world complications, arising from dissipation and control signal perturbations. We achieve both higher fidelities - which exceed 0.999 across all systems - and better robustness to time-dependent perturbations and experimental imperfections than previous methods. Lastly, we demonstrate that incorporating multi-step feedback can yield solutions robust even to strong perturbations.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

We develop Monte Carlo simulations for uniformly charged polymers and machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error., Comment: 8 pages, 8 figures

Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. Detections are used to identify the power plant structures in the image and associate these with the power plant model. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. We present three distinct methods for visual segmentation of PV modules based on traditional computer vision, deep learning, and their fusion, and we evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model's precision on the localization methods., Comment: 47 pages, 22 figures

This paper proposes a novel approach to formulate time-optimal point-to-point motion planning and control under uncertainty. The approach defines a robustified two-stage Optimal Control Problem (OCP), in which stage 1, with a fixed time grid, is seamlessly stitched with stage 2, which features a variable time grid. Stage 1 optimizes not only the nominal trajectory, but also feedback gains and corresponding state covariances, which robustify constraints in both stages. The outcome is a minimized uncertainty in stage 1 and a minimized total motion time for stage 2, both contributing to the time optimality and safety of the total motion. A timely replanning strategy is employed to handle changes in constraints and maintain feasibility, while a tailored iterative algorithm is proposed for efficient, real-time OCP execution.

Topological superconductivity is predicted to emerge in certain magnet-superconductor hybrid systems. Here, we revisit the heterostructure of insulating monolayer CrBr$_3$ and NbSe$_2$, for which different conclusions on the presence of topological superconductivity have been reported. Using low-temperature scanning tunneling microscopy and (shot noise) spectroscopy, we find that the superconducting gap well inside the CrBr3 islands is not affected by magnetism. At the island edges, we observe Yu-Shiba-Rusinov (YSR) states at a variety of in-gap positions, including zero energy. The absence of topological superconductivity is verified by extensive dI/dV measurements at the CrBr$_3$ island edges. Our results ask for a more detailed understanding of the interaction between magnetic insulators and superconductors.

The formation of cataclysmic variables (CVs) has long been modeled as a product of common envelope evolution (CEE) in isolated binaries. However, a significant fraction of intermediate-mass stars -- the progenitors of the white dwarfs (WDs) in CVs -- are in triples. We therefore investigate the importance of triple star dynamics in CV formation. Using Gaia astrometry and existing CV catalogs, we construct a sample of $\sim50$ CVs in hierarchical triples within 1 kpc of the Sun, containing main-sequence (MS) and WD tertiaries at separations of 100 - 30,000 au. We infer that at least 10% of CVs host wide tertiaries. To interpret this discovery, we evolve a population of 2000 triples using detailed three-body simulations, 47 of which become CVs. We predict that 20% of CVs in triples form without ever experiencing CEE, where the WD and donor are brought together by the eccentric Kozai-Lidov (EKL) mechanism after the formation of the WD. These systems favor larger donor stars and longer birth orbital periods (8-20 hrs) than typical CVs. Among systems that do undergo CEE, about half would not have interacted without the presence of the tertiary. Triple formation channels both with and without CEE require initially wide inner orbits ($\gtrsim 1$ au), which in turn require larger tertiary separations to be stable. Consistent with this prediction, we find that the observed Gaia CV triples have wider separations on average than normal wide binaries selected in the same way. Our work underscores the importance of triples in shaping interacting binary populations including CVs, ultracompact binaries, and low-mass X-ray binaries., Comment: Submitted to PASP. Comments are welcome

We analyze 23 spectroscopically confirmed Type-2 quasars (QSOs) selected from the WISE 22$\rm \mu$m band in the SDSS Stripe 82 region, focusing on their multi-band photometry and spectral energy distributions (SEDs). These objects were selected to be IR-luminous ($\rm flux_{W4} > 5mJy$, i.e., $12.62 < W4 < 14.62 \rm\ AB \, magnitude$), optically faint ($r > 23$) or with red color ($r - W4 >8.38$). Gemini/GNIRS observations were conducted for all 24 candidates, and 18/24 were also observed with Keck/LRIS. The observations confirm 23 to be real Type-2 QSOs in the redshift range $0.88 - 2.99$ (12 are at $z>2$). We collect multi-band photometry and conduct SED fitting. The composite photometry probes the wavelength from 0.1$\rm \mu$m to 10$\rm \mu$m at the rest frame. The IR emission is dominated by dust torus implying an average torus luminosity for the sample of $L_{\rm torus} 10^{46.84} \rm erg/s$. The origin of the rest-UV/optical light is not definitive, but we present three possible scenarios: scattered light, stellar emission, and the reddened accretion disk. Assuming an obscured:unobscured ratio of approximately 1:1, our targets have $L_{\rm bol} = 10^{46.28} \rm erg \,s^{-1} - 10^{47.49} \rm erg \,s^{-1}$ and around SMBH masses $\rm 10^{8.18} M_{\odot} - 10^{9.39} M_{\odot}$, assuming they accreate at the Eddington limit. Compared to previous Type-2 AGN SEDs, our targets have a brighter dust torus and redder optical-IR color. By comparing the SED to the results from JWST `little red dots' (LRDs), we find that these IR-selected Type-2 QSOs have similar SED shapes to the LRDs. This pilot Type-2 QSO survey demonstrates that mid-IR selection is an efficient way to find luminous Type-2 QSOs at $z>2$. Finally, the composite photometry and Type-2 QSOs SED model generated by this sample provide a guide for finding more Type-2 QSOs at higher redshift.

Breeze 2 is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enhance Traditional Chinese language representation. Building upon the Llama 3, Breeze 2 continues pretraining on an extensive corpus to enhance the linguistic and cultural heritage of Traditional Chinese. It incorporates vision-aware capabilities through a visual encoder and a bridge module, and supports function-calling via prompt templates and post-training on function-calling data. The effectiveness of Breeze 2 is benchmarked across various tasks, including Taiwan general knowledge, instruction-following, long context, function calling, and vision understanding. Furthermore, we showcase the capabilities of the its 3B model in a mobile application. We are publicly releasing all Breeze 2 models under the Llama 3 Community License.

We compute the Euler characteristic of the moduli space of quadratic rational maps with a periodic marked critical point of a given period.

In computational homogenization, a fast solution of the microscopic problem can be achieved by model order reduction in combination with hyper-reduction. Such a technique, which has recently been proposed in the context of magnetostatics, is applied to nonlinear mechanics in this work. The method is called 'Empirically Corrected Cluster Cubature' (E3C), as it combines clustering techniques with an empirical correction step to compute a novel type of integration points, which does not form a subset of the finite element integration points. The method is adopted to the challenges arising in nonlinear mechanics and is tested in plane strain for different microstructures (porous and reinforced) in dependence of the material nonlinearity. The results show that hyper-reduction errors < 1% can be achieved with a comparably small number of integration points, which is in the order of the number of modes. A two-scale example is provided and the research code can be downloaded.

The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to extend the search for new phenomena, or to exclude a larger phase space of candidate theories. Most of the time, a combination of the above goals is pursued; this breadth of scope adds a layer of complexity to the already demanding task of designing the measurement apparatus in an optimal way, by defining suitable geometries and choosing the most advantageous materials and appropriate detection technologies. The precise definition of a global optimality criterion may then require experimentalists to find a consensus on the relative scientific worth of those goals. In this work, we discuss the problem of formulating a utility function for multipurpose experiments, as an enabling step to employ artificial intelligence tools to explore the design space and assist humans in finding solutions at the Pareto front., Comment: 27 pages, 5 figures, 4 tables; prepared for submission to Physics Open

We say that an r-tuple $(g_1,...,g_r)$ of special orthogonal $d\times d$ matrices fractionally divides the $(d-1)$-dimensional sphere $S$ if there is a non-constant function in $L^2(S)$ such that its translations by $g_1,...,g_r$ sum up to the constant-1 function. Our main result shows, informally speaking, that fractional divisibility is impossible if at least $r/2$ rotations are ``generic"., Comment: 9 pages

Spatiotemporal modeling of real-world data poses a challenging problem due to inherent high dimensionality, measurement noise, and expensive data collection procedures. In this paper, we present Sparse Identification of Nonlinear Dynamics with SHallow REcurrent Decoder networks (SINDy-SHRED), a method to jointly solve the sensing and model identification problems with simple implementation, efficient computation, and robust performance. SINDy-SHRED uses Gated Recurrent Units (GRUs) to model the temporal sequence of sensor measurements along with a shallow decoder network to reconstruct the full spatiotemporal field from the latent state space using only a few available sensors. Our proposed algorithm introduces a SINDy-based regularization; beginning with an arbitrary latent state space, the dynamics of the latent space progressively converges to a SINDy-class functional, provided the projection remains within the set. In restricting SINDy to a linear model, the architecture produces a Koopman-SHRED model which enforces a linear latent space dynamics. We conduct a systematic experimental study including synthetic PDE data, real-world sensor measurements for sea surface temperature, and direct video data. With no explicit encoder, SINDy-SHRED and Koopman-SHRED enable efficient training with minimal hyperparameter tuning and laptop-level computing; further, it demonstrates robust generalization in a variety of applications with minimal to no hyperparameter adjustments. Finally, the interpretable SINDy and Koopman models of latent state dynamics enables accurate long-term video predictions, achieving state-of-the-art performance and outperforming all baseline methods considered, including Convolutional LSTM, PredRNN, ResNet, and SimVP.

The spectra of stable particles produced from dark matter (DM) are one of the most important ingredients to calculate the fluxes for DM indirect detection experiments. At energies above a few GeV, most of the particles are produced following a complex sequence of phenomena including resonance decays, QED and QCD final-state radiation, radiation of weak gauge bosons, hadronization and hadron decays. In this contribution, we discuss improvements on the calculation of the energy spectra at the source using state-of-the-art tools that include effects that were not taken previously into account. We include helicity information of the particles produced in the annihilation channels, which leads to proper inclusion of electroweak radiation during the entire showering history. These effects are taken into account using the Vincia, which is based on the helicity-dependent antenna shower formalism. Off-shell contributions are also taken into account for annihilation channels into $WW$ and $ZZ$ through the four-body processes into fermions and for DM masses below the gauge boson mass. We also revisit the tune of the Lund fragmentation function parameters in Pythia using LEP data at the $Z$-boson pole. The spectra of cosmic messengers are provided for DM masses between 5 GeV and 100 GeV and are publicly distributed in this \href{https://github.com/ajueid/CosmiXs.git}{GitHub repository}., Comment: 4 pages, 1 figure. Prepared for the proceedings of the 29th International Symposium on Particles, Strings and Cosmology (PASCOS 2024), Quy Nhon, Vietnam, 7-13 July, 2024

Topological defects are the key feature mediating 2D phase transitions. However, both resolution and tunability have been lacking to access the dynamics of the transitions. With dynamic Kerr microscopy, we directly capture the melting of a confined 2D magnetic skyrmion lattice with high resolution in real-time and -space. Skyrmions in magnetic thin films are two-dimensional, topologically non-trivial quasi-particles that provide rich dynamics as well as unique tunability as an essential ingredient for controlling phase behavior: We tune the skyrmion size and effective temperature on the fly to drive the two-step melting through an intermediate hexatic regime between the solid lattice and the isotropic liquid. We quantify the characteristic occurrence of topological defects mediating the transitions and reveal the so-far inaccessible dynamics of the lattice dislocations. The full real-time and -space imaging reveals the diffusion coefficient of the dislocations, which we find to be orders of magnitudes higher than that of the skyrmions.

Recent observations from JWST have revealed an abundant population of active galactic nuclei (AGN) and so-called ``Little Red Dots'' (LRDs) at $2\lesssim z \lesssim 11$, many of which are characterized by V-shaped UV-to-optical continua with turnovers around the Balmer limit. The physical nature of these LRDs is unclear, and it remains debated whether the peculiar spectral shape originates from AGN, compact galaxies, or both. We present the analysis of new NIRSpec-IFU data from the BlackTHUNDER JWST Large Programme and archival NIRSpec-MSA data of a lensed LRD at $z=7.04$. The spectra confirm the presence of a smooth Balmer break and a broad H$\beta$ tracing the Broad Line Region (BLR) of an AGN. The small velocity dispersion of the H$\beta$ narrow component indicates a small dynamical mass of the host galaxy of $M_{\rm dyn}<4 \times 10^8~M_{\odot}$, which implies that the stellar population cannot contribute more than 10% to the optical continuum. We show that the Balmer break can be well described by an AGN continuum absorbed by very dense ($n_{\rm H}\sim 10^{10}~{\rm cm^{-3}}$) and nearly dust-free gas along our line-of-sight (possibly gas in the BLR or its surrounding). The same gas is expected to produce H$\beta$ absorption, at a level consistent with a tentative detection ($3\sigma$) in the high-resolution spectrum. Such a non-stellar origin of the Balmer break may apply to other LRDs, and would alleviate the issue of extremely high stellar mass surface densities inferred in the case of a stellar interpretation of the Balmer break. We note that this is a rare case of a black hole that is overmassive relative to both the host galaxy stellar and dynamical masses. We finally report indications of variability and the first attempt of AGN reverberation mapping at such an early epoch., Comment: 34 pages, 24 figures, submitted to MNRAS

When blockchain systems are said to be trustless, what this really means is that all the trust is put into software. Thus, there are strong incentives to ensure blockchain software is correct -- vulnerabilities here cost millions and break businesses. One of the most powerful ways of establishing software correctness is by using formal methods. Approaches based on formal methods, however, induce a significant overhead in terms of time and expertise required to successfully employ them. Our work addresses this critical disadvantage by automating the creation of a formal model -- a mathematical abstraction of the software system -- which is often a core task when employing formal methods. We perform model synthesis in three phases: we first transpile the code into model stubs; then we "fill in the blanks" using a large language model (LLM); finally, we iteratively repair the generated model, on both syntactical and semantical level. In this way, we significantly reduce the amount of time necessary to create formal models and increase accessibility of valuable software verification methods that rely on them. The practical context of our work was reducing the time-to-value of using formal models for correctness audits of smart contracts.

A combined constraint on the dense QCD equation of state from connecting neutron star observations to data from heavy ion reactions is presented. We use the Chiral Mean Field Model which can describe neutron star and iso-spin symmetric matter and allows the consistent calculation of the density and momentum dependent potentials of baryons which are then implemented in the UrQMD transport model. In contrast to previous studies, the same equation of state constrained from neutron star properties is also able to describe experimental observables in heavy ion reactions at the HADES experiment., Comment: 6 pages 7 figures

In this paper, we propose the use of simple knowledge distillation to produce smaller and more efficient single-language transformers from Massively Multilingual Transformers (MMTs) to alleviate tradeoffs associated with the use of such in low-resource settings. Using Tagalog as a case study, we show that these smaller single-language models perform on-par with strong baselines in a variety of benchmark tasks in a much more efficient manner. Furthermore, we investigate additional steps during the distillation process that improves the soft-supervision of the target language, and provide a number of analyses and ablations to show the efficacy of the proposed method., Comment: LoResLM Workshop @ COLING 2025

We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for vision tasks that inherently captures 2D spatial structures. Existing attention models, including transformers, linear attention, and state-space models like Mamba, process multi-dimensional data as 1D sequences, compromising spatial coherence and efficiency. GSPN overcomes these limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a line-scan approach. Central to GSPN is the Stability-Context Condition, which ensures stable, context-aware propagation across 2D sequences and reduces the effective sequence length to $\sqrt{N}$ for a square map with N elements, significantly enhancing computational efficiency. With learnable, input-dependent weights and no reliance on positional embeddings, GSPN achieves superior spatial fidelity and state-of-the-art performance in vision tasks, including ImageNet classification, class-guided image generation, and text-to-image generation. Notably, GSPN accelerates SD-XL with softmax-attention by over $84\times$ when generating 16K images., Comment: Project page: http://whj363636.github.io/GSPN/

Dagger kernel categories, a powerful framework for studying quantum phenomena within category theory, provide a rich mathematical structure that naturally encodes key aspects of quantum logic. This paper focuses on the category SupOMLatLin of complete orthomodular lattices with linear maps. We demonstrate that SupOMLatLin itself forms a dagger kernel category, equipped with additional structure such as dagger biproducts and free objects. A key result establishes that every morphism in SupOMLatLin admits an essentially unique factorization as a zero-epi followed by a dagger monomorphism. This factorization theorem, along with the dagger kernel category structure of SupOMLatLin, provides new insights into the interplay between complete orthomodular lattices and the foundational concepts of quantum theory.

We reply to McCulloch and Osborne's recent comment on our manuscript (Phys. Rev. Lett. 130, 246402 (2023)) on controlled bond expansion (CBE) for density matrix renormalization group (DMRG) ground state search. We appreciate their suggestion to consider randomized SVD and address their constructive critique on the variational properties of CBE-DMRG. However, we strongly disagree with their proposal to omit the projection to the 2-site tangent space and explain its importance for efficient bond expansion. In particular, in the context of CBE applied to the time-dependent variational principle (TDVP), we show that omitting this projection can lead to avoidable errors. Lastly, we emphasize the complementary roles of 3S mixing and CBE, reiterating our recommendation from Phys. Rev. Lett. 130, 246402 (2023) to combine both methods (CBE+$\alpha$). We provide examples to demonstrate the superior efficiency and robustness of CBE+$\alpha$., Comment: 6 pages (including supplement), 1 figure

In systems with detailed balance, the stationary distribution and the equilibrium distribution are identical, creating a clear connection between energetic and entropic quantities. Many driven systems violate detailed balance and still pose a challenge for a consistent thermodynamic interpretation. Even steady-state potentials like entropy or free energy are no longer state variables. Here, we use a framework for systems with broken detailed balance, where Boltzmann entropy can be computed while properly taking constraints on state transitions into account. As an illustration, we establish the thermodynamic relations for arbitrarily driven sample space-reducing processes that are non-equilibrium but show steady states. We demonstrate that, despite explicitly broken detailed balance, it remains feasible to define and unambiguously interpret the effective thermodynamic potentials., Comment: 6 pages, 1 figure

In this paper, we propose a novel approach that harnesses the standard interference function, specifically tailored to address the unique challenges of non-convex optimization in wireless networks. We begin by establishing theoretical guarantees for our method under the assumption that the interference function exhibits log-concavity. Building on this foundation, we develop a Primal-Dual Algorithm (PDA) to approximate the solution to the Weighted Sum Rate (WSR) maximization problem. To further enhance computational efficiency, we leverage the deep unfolding technique, significantly reducing the complexity of the proposed algorithm. Through numerical experiments, we demonstrate the competitiveness of our method compared to the state-of-the-art fractional programming benchmark, commonly referred to as FPLinQ.

The classical dynamics of particles with (non-)abelian charges and spin moving on curved manifolds is established in the Poisson-Hamilton framework. Equations of motion are derived for the minimal quadratic Hamiltonian and some extensions involving spin-dependent interactions. It is shown that these equations of motion coincide with the consistency conditions for current and energy-momentum conservation. The classical equations cannot be derived from an action principle without extending the model. One way to overcome this problem is the introduction of anticommuting Grassmann co-ordinates. A systematic derivation of constants of motion based on symmetries of the background fields is presented., Comment: 7 pages + references

In this paper, we characterize the polynomiality of surfaces of revolution by means of the polynomiality of an associated plane curve. In addition, if the surface of revolution is polynomial, we provide formulas for computing a polynomial parametrization, over $\mathbb{C}$, of the surface. Furthermore, we perform the first steps towards the analysis of the existence, and actual computation, of real polynomial parametrizations of surfaces of revolution. As a consequence, we give a complete picture of the real polynomiality of quadrics and we formulate a conjecture for the general case.

Data assimilation plays a pivotal role in understanding and predicting turbulent systems within geoscience and weather forecasting, where data assimilation is used to address three fundamental challenges, i.e., high-dimensionality, nonlinearity, and partial observations. Recent advances in machine learning (ML)-based data assimilation methods have demonstrated encouraging results. In this work, we develop an ensemble score filter (EnSF) that integrates image inpainting to solve the data assimilation problems with partial observations. The EnSF method exploits an exclusively designed training-free diffusion models to solve high-dimensional nonlinear data assimilation problems. Its performance has been successfully demonstrated in the context of having full observations, i.e., all the state variables are directly or indirectly observed. However, because the EnSF does not use a covariance matrix to capture the dependence between the observed and unobserved state variables, it is nontrivial to extend the original EnSF method to the partial observation scenario. In this work, we incorporate various image inpainting techniques into the EnSF to predict the unobserved states during data assimilation. At each filtering step, we first use the diffusion model to estimate the observed states by integrating the likelihood information into the score function. Then, we use image inpainting methods to predict the unobserved state variables. We demonstrate the performance of the EnSF with inpainting by tracking the Surface Quasi-Geostrophic (SQG) model dynamics under a variety of scenarios. The successful proof of concept paves the way to more in-depth investigations on exploiting modern image inpainting techniques to advance data assimilation methodology for practical geoscience and weather forecasting problems.

We study (strong) first countability of locally solid convergence structures on Archimedean vector lattices. Among other results, we characterise those vector lattices for which relatively unform-, order-, and $\sigma$-order convergence, respectively, is (strongly) first countable. The implications for the validity of sequential arguments in the contexts of these convergences are pointed out., Comment: 36 pages, preliminary version

In this article, we highlight physics improvements in the NuWro Monte Carlo event generator. The upcoming version of NuWro will incorporate the integration of the argon spectral function for quasi-elastic scattering, along with the MINER$\nu$A parametrization of the axial form factor. Additionally, the new release will feature the implementation of the Valencia 2020 model for meson exchange current. The previously used simplistic delta resonance model for single-pion production will be replaced by a more accurate Ghent hybrid model in the upcoming version of NuWro. We also discuss the recent advancements made by the Wroclaw Neutrino Group in applying machine-learning techniques to achieve model-independent reconstruction of lepton-nucleus interactions, Comment: 4 pages, 3 figures. This is a conference proceeding. The text may overlap from [1]https://doi.org/10.48550/arXiv.2312.13369 [2] https://doi.org/10.48550/arXiv.2411.11523 [3]https://doi.org/10.48550/arXiv.2405.05212 [4] https://doi.org/10.48550/arXiv.2312.17298 [5] https://doi.org/10.48550/arXiv.2408.09936

Atomic-vapor sensors based on Rydberg atoms now face a transition towards practical applications, with several outstanding challenges. To achieve the best sensitivities, a superheterodyne mode of operation is desired, which requires the presence of a local oscillator in the vapor cell. This local oscillator hinders several advantages of the sensor, such as stealthy and all-optical operation. We propose and realize a detection scheme, which avoids some of those problems by using multi-tone mixing, where direct usage of the local oscillator at the same frequency is not required. Our scheme is further elaborated on using efficient theoretical methods to predict the performance of the sensor. Our sensor operates at the S-band frequency, known for its usage in IEEE 802.11 (Wi-Fi) networks, without interfering with the signal itself., Comment: 10 pages, 6 figures

Hallucinations of deep neural models are amongst key challenges in automatic speech recognition (ASR). In this paper, we investigate hallucinations of the Whisper ASR model induced by non-speech audio segments present during inference. By inducting hallucinations with various types of sounds, we show that there exists a set of hallucinations that appear frequently. We then study hallucinations caused by the augmentation of speech with such sounds. Finally, we describe the creation of a bag of hallucinations (BoH) that allows to remove the effect of hallucinations through the post-processing of text transcriptions. The results of our experiments show that such post-processing is capable of reducing word error rate (WER) and acts as a good safeguard against problematic hallucinations., Comment: Accepted for IEEE ICASSP 2025

Infinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users' reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention's effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.

We present a model for the morphology evolution of printed organic solar cells which occurs during the drying of a mixture of polymer, the non-fullerene acceptor and the solvent. Our model uses a phase field approach coupled to a Navier-Stokes equation describing the macroscopic movement of the fluid. Additionally, we incorporate the evaporation process of the solvent using an Allen-Cahn equation. The model is discretized using a finite-element approach with a semi-implicit discretization in time. The resulting (non)linear systems are coupled and of large dimensionality. We present a preconditioned iterative scheme to solve them robustly with respect to changes in the discretization parameters. We illustrate that the preconditioned solver shows parameter-robust iteration numbers and that the model qualitatively captures the behavior of the film morphology during drying., Comment: 33 pages

Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control, to ensure the security of their software. While security features are usually available in libraries, additional code needs to be written and maintained to integrate security features and not all desired features can be reused this way. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement, and the implications on maintenance. We therefore currently rely on assumptions that are largely based on common sense or individual examples. However, researchers require hard empirical data to understand what practitioners need and how they view security, which we currently lack to provide them with effective solutions. We contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and the challenges practitioners face. Based on the empirical data gathered, we validate four common assumptions and gain insights into engineering practices., Comment: Accepted at the 47th IEEE/ACM International Conference on Software Engineering (ICSE 2025)

This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance assesses the impact of a feature on a prediction model's performance given the information of other features. Model-agnostic post hoc methods to do so typically evaluate changes in the predictive performance under on-manifold feature value manipulations. Such procedures require creating feature values that respect conditional feature distributions, which can be challenging in practice. Recent advancements in generative modeling can facilitate this. For tabular data, which may consist of both categorical and continuous features, the adversarial random forest (ARF) stands out as a generative model that can generate on-manifold data points without requiring intensive tuning efforts or computational resources, making it a promising candidate model for subroutines in XAI methods. This paper proposes cARFi (conditional ARF feature importance), a method for measuring conditional feature importance through feature values sampled from ARF-estimated conditional distributions. cARFi requires only little tuning to yield robust importance scores that can flexibly adapt for conditional or marginal notions of feature importance, including straightforward extensions to condition on feature subsets and allows for inferring the significance of feature importances through statistical tests.

We study behavioral self-awareness -- an LLM's ability to articulate its behaviors without requiring in-context examples. We finetune LLMs on datasets that exhibit particular behaviors, such as (a) making high-risk economic decisions, and (b) outputting insecure code. Despite the datasets containing no explicit descriptions of the associated behavior, the finetuned LLMs can explicitly describe it. For example, a model trained to output insecure code says, ``The code I write is insecure.'' Indeed, models show behavioral self-awareness for a range of behaviors and for diverse evaluations. Note that while we finetune models to exhibit behaviors like writing insecure code, we do not finetune them to articulate their own behaviors -- models do this without any special training or examples. Behavioral self-awareness is relevant for AI safety, as models could use it to proactively disclose problematic behaviors. In particular, we study backdoor policies, where models exhibit unexpected behaviors only under certain trigger conditions. We find that models can sometimes identify whether or not they have a backdoor, even without its trigger being present. However, models are not able to directly output their trigger by default. Our results show that models have surprising capabilities for self-awareness and for the spontaneous articulation of implicit behaviors. Future work could investigate this capability for a wider range of scenarios and models (including practical scenarios), and explain how it emerges in LLMs., Comment: Submitted to ICLR 2025. 17 pages, 13 figures

The standard notion of poset probability of a finite poset P involves calculating, for incomparable $\alpha$, $\beta$ in P, the number of linear extensions of P for which $\alpha$ precedes $\beta$. The fraction of those linear extensions among all linear extensions of P is the probability that $\alpha < \beta$. The question of whether there is always a pair $\alpha, \beta$ such that this probability lies between 1/3 and 2/3, in any poset P (that is not a chain) is the famous "1/3-2/3-conjecture". A general way of counting linear extensions of P for which $\alpha$ precedes $\beta$ is to count linear extensions of the poset obtained by adding the relation $(\alpha,\beta)$, and its transitive consequences. For chain-products, and more generally for partition posets, lattice-path methods can be used to count the number of those linear extensions. We present an alternative approach to find the pertinent linear extensions. It relies on finding the "blocking ideals" in $J(P)$, where $J(P)$ is the lattice of order ideals in P. This method works for all finite posets. We illustrate this method by using blocking ideals to find explicit formulas of poset probabilities in cell posets $P_{\lambda}$ of two-row partitions. Well-known formulae such as the hook-length formula for $f^\lambda$, the number of standard Young tableaux on a partition $\lambda$, and the corresponding determinental formula by Jacobi-Trudi-Aitken for $f^{\lambda / \mu}$, the number of standard Young tableaux on a skew partition $\lambda / \mu$, are used along the way. We also calculate the limit probabilities when the elements $\alpha,\beta$ are fixed cells, but the arm-lengths tend to infinity., Comment: 38 pages. Sagemath code included as ancillary files. Org-mode source file exemplifying some computations included as well

Cubic silicon carbide phonon thermal conductivity has been calculated using anharmonic phonon analysis. The atomic interaction model was built using displacement-force data obtained with the High Efficiency Configuration Space Sampling (HECSS) technique and density functional theory calculated forces. In the new version of HECSS we replaced the Markov chain scheme of Metropolis-Hastings Monte-Carlo with weighting of the final sampling according to the target distribution. This increased the efficiency of the method and allowed to use -- with appropriate weight -- all generated and ab-initio evaluated samples. The quality of the proposed method is confirmed by the accuracy with which the experimental results taken from the literature were reproduced., Comment: Submission to SciPost Physics