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In this paper we present the Advancing Racial Equity in Community College Model which maps out the organizational conditions shaping institutional transformation. Focused on two dimensions, the level of "organizational support" and "shared responsibility" to enact equity, we describe four quadrants with distinct organizational conditions that shape how equity advocates design, build, and sustain equity efforts. With well-documented racial inequities and renewed calls for racial justice across higher education, it demands new ways of exploring and understanding how institutional actors leading equity efforts are nested within differing organizational contexts that can enable as well as restrict the enactment and success of racial equity efforts. Our model helps equity advocates gain an "awareness" of known barriers to implementation in higher education, assess the readiness of their campus for racialized change, and take action to build the necessary institutional support and capacity to move the work forward.

Massive star formation involves significant ionization in the innermost regions near the central object, such as gravitationally trapped H II regions, jets, ionized disks, or winds. Resolved observations of the associated continuum and recombination line emission are crucial for guiding theory. The next-generation Very Large Array (ngVLA) will enable unprecedented observations of thermal emission with 1 mas resolution, providing a new perspective on massive star formation at scales down to a few astronomical units at kiloparsec distances. This work presents synthetic interferometric ngVLA observations of the free-free continuum (93-GHz band), $\mathrm{H41\alpha}$, and $\mathrm{H38\alpha}$ recombination lines from ionized jets and disks around massive protostars. Using the sf3dmodels Python package, we generate gas distributions based on analytical models, which are then processed through the RADMC-3D radiative transfer code. Our results indicate that the ngVLA can easily resolve, both spatially and spectrally, the ionized jet from a 15 $\mathrm{M_\odot}$ protostar at 700 pc, distinguishing between collimated jets and wide-angle winds, and resolving their launching radii, widths, and any substructure down to a few astronomical units. Detailed studies of radio jets launched by massive protostars will be feasible up to distances of $\sim 2$ kpc. Furthermore, ngVLA will be able to study in detail the ionized disks around massive ($> 10~\mathrm{M_\odot}$) protostars up to distances from 4 to 12 kpc, resolving their kinematics and enabling the measurement of their central masses across the Galaxy. These observations can be conducted with on-source integrations of only a few hours., Comment: Accepted for publication in ApJ

Constant-recursive sequences are those which satisfy a linear recurrence, so that later terms can be obtained as a linear combination of the previous ones. The rank of a constant-recursive sequence is the minimal number of previous terms required for such a recurrence. For a constant-recursive sequence $s(n)$, we study the sequence $\left(\text{rank}\, s(n)^M\right)_{M\geq 1}$. We answer a question of Stinchcombe regarding the complexity of the powers of a constant-recursive sequence when the roots of the characteristic polynomial are not all distinct., Comment: 23 pages, 3 tables

We introduce and study blob and framed blob monoids. In particular, several realizations of these monoids are given. We compute the cardinality of the framed blob monoid and derive some combinatorial formulas involving this cardinality., Comment: 32 pages

We propose a two-step procedure to detect cointegration in high-dimensional settings, focusing on sparse relationships. First, we use the adaptive LASSO to identify the small subset of integrated covariates driving the equilibrium relationship with a target series, ensuring model-selection consistency. Second, we adopt an information-theoretic model choice criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding dependence on asymptotic distributional assumptions. Monte Carlo experiments confirm robust finite-sample performance, even under endogeneity and serial correlation.

We investigate the role of the Kottman constant of a Banach space $X$ in the extension of $\alpha$-H\"older continuous maps for every $\alpha\in (0,1]$.

We give the first example of a nontrivial twisted Hilbert space that satisfies the Johnson-Lindenstrauss lemma. This space has no unconditional basis. We also show that such a space gives a partial answer to a question of Mascioni.

While universal quantum computers remain under development, analog quantum simulators offer a powerful alternative for understanding complex systems in condensed matter, chemistry, and high-energy physics. One compelling application is the characterization of real-time lattice gauge theories (LGTs). LGTs are nonperturbative tools, utilizing discretized spacetime to describe gauge-invariant models. They hold immense potential for understanding fundamental physics but require enforcing local constraints analogous to electromagnetism's Gauss's Law. These constraints, which arise from gauge symmetries and dictate the form of the interaction between matter and gauge fields, are a significant challenge for simulators to enforce. Implementing these constraints at the hardware level in analog simulations is crucial. This requires realizing multibody interactions between matter and gauge-field elements, enabling them to evolve together while suppressing unwanted two-body interactions that violate the gauge symmetry. In this paper, we propose and implement a novel parametrically activated three-qubit interaction within a circuit quantum electrodynamics architecture. We experimentally demonstrate a minimal $U(1)$ spin-1/2 model with a time evolution that intrinsically satisfies Gauss's law in the system. This design serves as the foundational block for simulating LGTs on a superconducting photonic lattice., Comment: 20 pages, 10 figures

In this paper we establish hierarchic control for the wave equation in a non cylindrical domain $\widehat{Q}$ of $\mathbb{R}^{n + 1}$. We assume that we can act in the dynamic of the system by a hierarchy of controls. According to the formulation given by H. Von Stackelberg \cite{S}, there are local controls, called followers and global controls, called leaders. In fact, one considers situations where there are two cost (objective) functions. One possible way is to cut the control into two parts, one being thought of as "the leader" and the other one as "the follower". This situation is studied in the paper, with one of the cost functions being of the controllability type. Existence and uniqueness is proven. The optimality system is given in the paper., Comment: 9 pages. arXiv admin note: text overlap with arXiv:2501.06672

We characterize the subspaces $X$ of $\ell_1$ satisfying Grothendieck's theorem in terms of extension of nonnegative quadratic forms $q:X \longrightarrow \mathbb R$ to the whole $\ell_1$.

We construct a weak Hilbert space that is a twisted Hilbert space.

Explainability techniques hold significant potential for enhancing the causal discovery process, which is crucial for understanding complex systems in areas like healthcare, economics, and artificial intelligence. However, no causal discovery methods currently incorporate explainability into their models to derive causal graphs. Thus, in this paper we explore this innovative approach, as it offers substantial potential and represents a promising new direction worth investigating. Specifically, we introduce REX, a causal discovery method that leverages machine learning (ML) models coupled with explainability techniques, specifically Shapley values, to identify and interpret significant causal relationships among variables. Comparative evaluations on synthetic datasets comprising continuous tabular data reveal that REX outperforms state-of-the-art causal discovery methods across diverse data generation processes, including non-linear and additive noise models. Moreover, REX was tested on the Sachs single-cell protein-signaling dataset, achieving a precision of 0.952 and recovering key causal relationships with no incorrect edges. Taking together, these results showcase REX's effectiveness in accurately recovering true causal structures while minimizing false positive predictions, its robustness across diverse datasets, and its applicability to real-world problems. By combining ML and explainability techniques with causal discovery, REX bridges the gap between predictive modeling and causal inference, offering an effective tool for understanding complex causal structures. REX is publicly available at https://github.com/renero/causalgraph., Comment: 22 pages, 30 figures, Submitted to Elsevier's Pattern Recognition

Methods for creating endofullerenes have been steadily improving since their discovery, allowing for new types of endofullerenes to be created in larger numbers. When a molecule is trapped in a fullerene, the fullerene creates a harmonic trapping potential that leaves most of the fundamental properties of the internal molecule intact. The fullerene cage does create a preferred axis for the internal molecule, which we refer to as the encapsulation-induced alignment of the molecule. We explore the alignment of AlF and N2 inside of C60 by first computing the interaction between the internal molecule and the fullerene cage using ab initio electronic structure methods. Our results show that the internal molecules are found to be strongly aligned despite finding that all the calculated spectroscopic constants are relatively unaffected by the fullerene cage., Comment: 9 pages, 8 figures, 2 tables

Maintaining railway axles is critical to preventing severe accidents and financial losses. The railway industry is increasingly interested in advanced condition monitoring techniques to enhance safety and efficiency, moving beyond traditional periodic inspections toward Maintenance 4.0. This study introduces a robust Deep Autoregressive solution that integrates seamlessly with existing systems to avert mechanical failures. Our approach simulates and predicts vibration signals under various conditions and fault scenarios, improving dataset robustness for more effective detection systems. These systems can alert maintenance needs, preventing accidents preemptively. We use experimental vibration signals from accelerometers on train axles. Our primary contributions include a transformer model, ShaftFormer, designed for processing time series data, and an alternative model incorporating spectral methods and enhanced observation models. Simulating vibration signals under diverse conditions mitigates the high cost of obtaining experimental signals for all scenarios. Given the non-stationary nature of railway vibration signals, influenced by speed and load changes, our models address these complexities, offering a powerful tool for predictive maintenance in the rail industry.

Continuous variable quantum key distribution bears the promise of simple quantum key distribution directly compatible with commercial off the shelf equipment. However, for a long time its performance was hindered by the absence of good classical postprocessing capable of distilling secret-keys in the noisy regime. Advanced coding solutions in the past years have partially addressed this problem enabling record transmission distances of up to 165 km, and 206 km over ultra-low loss fiber. In this paper, we show that a very simple coding solution with a single code is sufficient to extract keys at all noise levels. This solution has performance competitive with prior results for all levels of noise, and we show that non-zero keys can be distilled up to a record distance of 192 km assuming the standard loss of a single-mode optical fiber, and 240 km over ultra-low loss fibers. Low-rate codes are constructed using multiplicatively repeated non-binary low-density parity-check codes over a finite field of characteristic two. This construction only makes use of a (2,k)-regular non-binary low-density parity-check code as mother code, such that code design is in fact not required, thus trivializing the code construction procedure. The construction is also inherently rate-adaptive thereby allowing to easily create codes of any rate. Rate-adaptive codes are of special interest for the efficient reconciliation of errors over time or arbitrary varying channels, as is the case with quantum key distribution. In short, these codes are highly efficient when reconciling errors over a very noisy communication channel, and perform well even for short block-length codes. Finally, the proposed solution is known to be easily amenable to hardware implementations, thus addressing also the requirements for practical reconciliation in continuous variable quantum key distribution., Comment: 27 pages, 12 figures

Predictive models trained on imbalanced data tend to produce biased results. This problem is exacerbated when there is not just one output label, but a set of them. This is the case for multilabel learning (MLL) algorithms used to classify patterns, rank labels, or learn the distribution of outputs. Many solutions have been proposed in the literature. The one that can be applied universally, independent of the algorithm used to build the model, is data resampling. The generation of new instances associated with minority labels, so that empty areas of the feature space are filled, helps to improve the obtained models. The quality of these new instances depends on the algorithm used to generate them. In this paper, a diffusion model tailored to produce new instances for MLL data, called MLDM (\textit{MultiLabel Diffusion Model}), is proposed. Diffusion models have been mainly used to generate artificial images and videos. Our proposed MLDM is based on this type of models. The experiments conducted compare MLDM with several other MLL resampling algorithms. The results show that MLDM is competitive while it improves efficiency., Comment: 22 pages, 8 figures, 10 tables

Using density functional theory, we investigate the electronic structure of the alpha phase of an antimony monolayer in its isolated form and in contact to the (111) surface of gold. We demonstrate that the isolated single-layer actually displays a slightly modulated puckering that stabilizes the monolayer, not a uniform one as often assumed. Moreover, it has dramatic consequences on the electronic band structure: the material is a semiconductor with low-dispersing bands near the Brillouin zone center. By further application of about 12% strain on the armchair direction, a double-cone features develops wherein an electronic bandgap of about 21~meV is found. When in contact with a Au(111) surface, a strong interaction with gold arises, as it appears clearly from (i) substantial atomic displacements compared to the isolated form, and (ii) hybridization of Sb and Au orbitals. The latter profoundly modifies the electronic band structure by strengthening the spin-orbit splitting of hybridized bands and spoiling the double-cone feature whose manipulation through substrate-induced strain appears therefore questionable, at least in the simulated epitaxial implementation., Comment: 7 pages, 5 figures

Electronic Health Record (EHR) tables pose unique challenges among which is the presence of hidden contextual dependencies between medical features with a high level of data dimensionality and sparsity. This study presents the first investigation into the abilities of LLMs to comprehend EHRs for patient data extraction and retrieval. We conduct extensive experiments using the MIMICSQL dataset to explore the impact of the prompt structure, instruction, context, and demonstration, of two backbone LLMs, Llama2 and Meditron, based on task performance. Through quantitative and qualitative analyses, our findings show that optimal feature selection and serialization methods can enhance task performance by up to 26.79% compared to naive approaches. Similarly, in-context learning setups with relevant example selection improve data extraction performance by 5.95%. Based on our study findings, we propose guidelines that we believe would help the design of LLM-based models to support health search., Comment: To be published as full paper in the Proceedings of the European Conference on Information Retrieval (ECIR) 2025. Preprint

Following the discovery of dimethyl sulfide (CH$_3$SCH$_3$, DMS) signatures in comet 67P/Churyumov-Gerasimenko, we report the first detection of this organosulfur species in the interstellar medium, during the exploration of an ultradeep molecular line survey performed toward the Galactic Center molecular cloud G+0.693-0.027 with the Yebes 40$\,$m and IRAM 30$\,$m telescopes. We derive a molecular column density of $N$ = (2.6 $\pm$ 0.3)$\times$10$^{13}$ cm$^{-2}$, yielding a fractional abundance relative to H$_2$ of $\sim$1.9$\times$10$^{-10}$. This implies that DMS is a factor of $\sim$1.6 times less abundant than its structural isomer CH$_3$CH$_2$SH and $\sim$30 times less abundant than its O-analogue dimethyl ether (CH$_3$OCH$_3$) toward this cloud, in excellent agreement with previous results on various O/S pairs. Furthermore, we find a remarkable resemblance between the relative abundance of DMS/CH$_3$OH in G+0.693-0.027 ($\sim$1.7$\times$10$^{-3}$) and in the comet ($\sim$1.3$\times$10$^{-3}$), further strengthening the connection between the chemical inventory of the interstellar medium and that of the minor bodies of the Solar System. Although the chemistry of DMS beyond Earth is yet to be fully disclosed, this discovery provides conclusive observational evidence on its efficient abiotic production in the interstellar medium, casting doubts about using DMS as a reliable biomarker in exoplanet science., Comment: 15 pages, 4 Figures and 5 Tables, re-submitted to ApJL after addressing the reviewers' comments. The initial submission was on November 26, 2024

With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacerbated by the growing complexity of mobile networks. This paper presents the first countrywide study of HO optimization, through the prism of Smoothed Online Learning (SOL). We first analyze an extensive dataset from a commercial mobile network operator (MNO) in Europe with more than 40M users, to understand and reveal important features and performance impacts on HOs. Our findings highlight a correlation between HO failures/delays, and the characteristics of radio cells and end-user devices, showcasing the impact of heterogeneity in mobile networks nowadays. We subsequently model UE-cell associations as dynamic decisions and propose a realistic system model for smooth and accurate HOs that extends existing approaches by (i) incorporating device and cell features on HO optimization, and (ii) eliminating (prior) strong assumptions about requiring future signal measurements and knowledge of end-user mobility. Our algorithm, aligned with the O-RAN paradigm, provides robust dynamic regret guarantees, even in challenging environments, and shows superior performance in multiple scenarios with real-world and synthetic data.

We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360$^{\circ}$ coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.

The microscopic organization of dynamical systems coupled via higher-order interactions plays a pivotal role in understanding their collective behavior. In this paper, we introduce a framework for systematically investigating the impact of the interaction structure on dynamical processes. Specifically, we develop an hyperedge overlap matrix whose elements characterize the two main aspects of the microscopic organization of higher-order interactions: the inter-order hyperedge overlap (non-diagonal matrix elements) and the intra-order hyperedge overlap (encapsulated in the diagonal elements). This way, the first set of terms quantifies the extent of superposition of nodes among hyperedges of different orders, while the second focuses on the number of nodes in common between hyperedges of the same order. Our findings indicate that large values of both types of hyperedge overlap hinder synchronization stability, and that the larger is the order of interactions involved, the more important is their role. Our findings also indicate that the two types of overlap have qualitatively distinct effects on the dynamics of coupled chaotic oscillators. In particular, large values of intra-order hyperedge overlap hamper synchronization by favoring the presence of disconnected sets of hyperedges, while large values of inter-order hyperedge overlap hinder synchronization by increasing the number of shared nodes between groups converging on different trajectories, without necessarily causing disconnected sets of hyperedges.

We study the vectorial stationary Schr\"odinger equation $-\Delta u+a\,U+b\,u=F,$ with a saturated nonlinearity $U=u/|u|$ and with some complex coefficients $(a,b)\in\mathbb{C}^2$. Besides the existence and uniqueness of solutions for the Dirichlet and Neumann problems, we prove the compactness of the support of the solution, under suitable conditions on $(a,b)$ and even when the source in the right hand side $F(x)$ is not vanishing for large values of $|x|.$ The proof of the compactness of the support uses a local energy method, given the impossibility of applying the maximum principle. We also consider the associate Schr\"{o}dinger-Poisson system when coupling with a simple magnetic field. Among other consequences, our results give a rigorous proof of the existence of ``solitons with compact support" claimed, without any proof, by several previous authors.

Low-temperature luminescence spectra reveal the presence of two independant populations of GaN excitons within a $\mathrm{GaN/AlGaN/GaN/Al_2O_3}$ heterostructure in which a thick (1.5 $\mathrm{\mu m}$) AlGaN layer separates a thin (150 nm) top GaN layer and a thick (3.5 $\mathrm{\mu m}$) bottom GaN layer grown on sapphire. The presence of these two spectrally-distinct families of excitons in each GaN layer of the heterostructure is demonstrated using three different experimental methods: (i) low-power $\mathrm{\mu}$-photoluminescence ($\mathrm{\mu PL}$) using laser excitation sources with wavelengths above and below the AlGaN bandgap, (ii) $\mathrm{\mu PL}$ as a function of optically injected free carrier density, and (iii) quantitative numerical simulation of the $\mathrm{\mu}$-Reflectivity ($\mathrm{\mu R}$). One major impact of the built-in electric field is the reduction of the excitonic lifetime in the GaN surface layer, which transitions from less than 10 ps in the presence of the built-in electric field to the bulk lifetime (90 ps) when the field is screened. This increase in the excitonic lifetime is related to the modification of the band structure in the presence of optically injected free carriers. The effect of these lifetime variations on the luminescence spectra is analyzed. Lastly, we provide an estimate of the Mott density in GaN as $n_{\mathrm{Mott}} = 4\times 10^{17}\, \mathrm{cm^{-3}}$ at 130 K, consistent with values reported in the literature and accounting for the free carrier density required to screen the electric field.

We compare the internal stellar structures of central galaxies in the TNG50 and TNG100 simulations and field galaxies in the CALIFA survey. The luminosity fractions of the dynamically cold, warm, and hot components in both TNG50 and TNG100 galaxies exhibit general consistency with those observed in CALIFA galaxies. For example, they all exhibit a minimum luminosity fraction of the dynamically hot component in galaxies with intermediate stellar masses, and the morphology of each orbital component in the TNG50 and TNG100 galaxies closely resembles that found in the CALIFA galaxies. We therefore use the simulations to quantify the physical origins of the different components, focusing on the dynamically hot component in TNG50. We identify three primary regimes and thus physical processes: (1) in low mass galaxies that have not experienced major mergers, stars are born with a wide range of circularity distributions and have remained relatively unchanged until the present day. Consequently, hot stars in such galaxies at redshift 0 are predominantly born hot. (2) In higher mass galaxies lacking major mergers, most stars are initially born cold but are subsequently heated through secular evolution. (3) In galaxies across the entire mass range, mergers, if they occurred, significantly increased the hot orbital fraction. As a result, the dynamically hot bulge within $R_e$ of present-day galaxies does not indicate their past merger histories; instead, the hot stars in the outer regions are mostly heated or accreted by mergers, thus indicating galaxy merger history. The massive galaxies are initially born with cold, rotationally supported structures, consistent with recent observations from the James Webb Space Telescope (JWST) regarding high-redshift galaxies., Comment: 18 pages, 15 figures

This paper is devoted to the study of the approximate controllability for a one-dimensional wave equation in domains with moving boundary. This equation models the motion of a string where an endpoint is fixed and the other one is moving. When the speed of the moving endpoint is less than the characteristic speed, the controllability of this equation is established. We present the following results: the existence and uniqueness of Nash equilibrium, the approximate controllability with respect to the leader control, and the optimality system for the leader control., Comment: 13 pages

We study a Stackelberg strategy subject to the evolutionary linearized micropolar fluids equations in domains with moving boundaries, considering a Nash multi-objective equilibrium (non necessarily cooperative) for the "follower players" (as is called in the economy field) and an optimal problem for the leader player with approximate controllability objective. We will obtain the following main results : the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability of the linearized micropolar system with respect to the leader control and the existence and uniqueness of the Stackelberg-Nash problem, where the optimality system for the leader is given., Comment: 28 pages

This manuscript deals with a hierarchical control problem for Oldroyd equation under the Stackelberg-Nash strategy. The Oldroyd equation model is defined by non-regular coefficients, that is, they are bounded measurable functions. We assume that we can act in the dynamic of the system by a hierarchy of controls, where one main control (the leader) and several additional secondary control (the followers) act in order to accomplish their given tasks: controllability for the leader and optimization for followers. We obtain the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability with respect to the leader control, and the optimality system for leader control., Comment: 28 pages

Maximum-entropy methods, rooted in the inverse Ising/Potts problem from statistical mechanics, have become indispensable tools for modeling pairwise interactions in disciplines such as bioinformatics, ecology, and neuroscience. Despite their remarkable success, these methods often overlook high-order interactions that may be crucial in complex systems. Conversely, while modern machine learning approaches can capture such interactions, existing interpretable frameworks are computationally expensive, making it impractical to assess the relevance of high-order interactions in real-world scenarios. Restricted Boltzmann Machines (RBMs) offer a computationally efficient alternative by encoding statistical correlations via hidden nodes in a bipartite neural network. Here, we present a method that maps RBMs exactly onto generalized Potts models with interactions of arbitrary high order. This approach leverages large-$N$ approximations, facilitated by the simple architecture of the RBM, to enable the efficient extraction of effective many-body couplings with minimal computational cost. This mapping also enables the development of a general formal framework for the extraction of effective higher-order interactions in arbitrarily complex probabilistic models. Additionally, we introduce a robust formalism for gauge fixing within the generalized Potts model. We validate our method by accurately recovering two- and three-body interactions from synthetic datasets. Additionally, applying our framework to protein sequence data demonstrates its effectiveness in reconstructing protein contact maps, achieving performance comparable to state-of-the-art inverse Potts models. These results position RBMs as a powerful and efficient tool for investigating high-order interactions in complex systems., Comment: 13 Pages and 3 Figures

Graphene spintronics offers a promising route to achieve low power 2D electronics for next generation classical and quantum computation. As device length scales are reduced to the limit of the electron mean free path, the transport mechanism crosses over to the ballistic regime. However, ballistic transport has yet to be shown in a graphene spintronic device, a necessary step towards realising ballistic spintronics. Here, we report ballistic injection of spin polarised carriers via one-dimensional contacts between magnetic nanowires and a high mobility graphene channel. The nanowire-graphene interface defines an effective constriction that confines charge carriers over a length scale smaller than that of their mean free path. This is evidenced by the observation of quantised conductance through the contacts with no applied magnetic field and a transition into the quantum Hall regime with increasing field strength. These effects occur in the absence of any constriction in the graphene itself and occur across several devices with transmission probability in the range T = 0.08 - 0.30., Comment: 4 figures

Ensuring the privacy of votes in an election is crucial for the integrity of a democratic process. Often, voting power is delegated to representatives (e.g., in congress) who subsequently vote on behalf of voters on specific issues. This delegation model is also widely used in Decentralized Autonomous Organizations (DAOs). Although several existing voting systems used in DAOs support private voting, they only offer public delegation. In this paper, we introduce Kite, a new protocol that enables $\textit{private}$ delegation of voting power for DAO members. Voters can freely delegate, revoke, and re-delegate their power without revealing any information about who they delegated to. Even the delegate does not learn who delegated to them. The only information that is recorded publicly is that the voter delegated or re-delegated their vote to someone. Kite accommodates both public and private voting for the delegates themselves. We analyze the security of our protocol within the Universal Composability (UC) framework. We implement Kite as an extension to the existing Governor Bravo smart contract on the Ethereum blockchain, that is widely used for DAO governance. Furthermore, we provide an evaluation of our implementation that demonstrates the practicality of the protocol. The most expensive operation is delegation due to the required zero-knowledge proofs. On a consumer-grade laptop, delegation takes between 7 and 167 seconds depending on the requested level of privacy., Comment: Updated GitHub link

Cryogenic buffer gas sources are ubiquitous for producing cold, collimated molecular beams for quantum science, chemistry, and precision measurements. The molecules are typically produced by laser ablating a metal target in the presence of a donor gas, where the radical of interest emerges among the reaction products due to a barrier-free process or under thermal or optical excitation. High-barrier reactions, such as between calcium and molecular hydrogen, should be precluded. Here, we study chemical reactions between Ca and three hydrogen isotopologues H$_2$, D$_2$, and HD in a cryogenic cell with helium buffer gas. Remarkably, we observe that H$_2$ can serve as both a reactant and a buffer gas, far outperforming D$_2$ and HD. Our results demonstrate a robust method for generating cold beams of alkaline-earth-metal hydrides for laser cooling and trapping. The observations are complemented by a reaction model, yielding qualitative agreement with the experiment., Comment: 8 pages, 7 figures

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

We show that the 32 $H(z)$ data from cosmic chronometers have overestimated uncertainties and make use of a Bayesian method to correct and reduce it. We then use the corrected data to constrain flat $\Lambda$CDM and O$\Lambda$CDM parameters. For the flat $\Lambda$CDM model, we got as result $H_{0} = 67.1\pm 4.0$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega _{m} = 0.333 ^{+0.041}_{-0.057}$. While for the O$\Lambda$CDM model, we found $H_{0} = 67.2\pm 4.8$ km s$^{-1}$ Mpc$^{-1}$, $\Omega _{m} = 0.36\pm 0.16$ and $\Omega _{\Lambda} =0.71 ^{+0.36}_{-0.28}$. These results goes from $22\%$ up to $28\%$ uncertainty reduction when compared to the constraints of the both uncorrected models.

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

We report, for the first time, the detection of a sample of quenched and isolated dwarf galaxies (with 8.9 $<$ log(M$_{\rm \star}$/M$_{\rm \odot}$) $<$ 9.5) in the least dense regions of the cosmic web, including voids, filaments, and walls. These dwarfs have no neighbouring galaxy within 1.0~Mpc in projected distance. Based on the full spectral fitting of their central spectra using Sloan Digital Sky Survey data, these galaxies are gas-deprived, exhibit stellar mass assembly very similar to dwarfs in the central regions of galaxy clusters, and have experienced no significant star formation in the past 2 Gyr. Additionally, analysis of r-band images from the Dark Energy Camera Legacy Survey showed that these dwarf galaxies host a central Nuclear Star Cluster (NSC). Detecting quenched, isolated dwarf galaxies in cosmic voids indicates that environmental factors are not the sole drivers of their quenching. Internal mechanisms, such as feedback from in-situ star formation, also contributing to the NSC formation, black holes, or variations in conditions during their formation, offer potential explanations for star formation suppression in these galaxies. These findings highlight the need for a significant revision in our understanding of baryonic physics, particularly concerning the formation and evolution of low-mass galaxies., Comment: 9 pages, 5 figures, accepted as a Letter to the Editor in Astronomy & Astrophysics

We perform a systematic study of the applicability of swampland constraints to theories of localized gravity. We find that these gravity theories can violate swampland constraints, but can be reconciled with them when coupled to a higher-dimensional gravity theory. They realize what we call $\textit{relative quantum gravity}$: to become consistent at the quantum level, these gravity theories must be defined as $\textit{relative}$ to a host higher-dimensional gravity theory. We show that these theories can admit global symmetries, even anomalous ones; they can violate the cobordism, completeness, weak gravity, and distance conjectures; they may admit stable non-supersymmetric AdS vacua, or dS vacua. All swampland constraints are however satisfied when these gravity theories are regarded as relative and completed by coupling them to a higher-dimensional one. We discuss these properties in $d$-dimensional gravity theories localized on Karch-Randall End of the World (ETW) boundaries of AdS$_{d+1}$ spacetime. For AdS$_d$ ETW branes we use the formalism of double holography to describe the appearance of the species scale and the emergence of gauge dynamics from the quantum backreaction of CFT$_d$ modes. We also study microscopically the swampland constraints in localized gravity in explicit string theory models. Concretely, we exploit the 10d supergravity solutions describing AdS$_4$ ETW branes for AdS$_5\times\mathbf{S}^5$, holographically dual to semi-infinite D3-branes ending on NS5- and D5-brane configurations, realizing 4d $\mathcal{N}=4$ $SU(N)$ on half-space coupled to a 3d Gaiotto-Witten superconformal boundary CFT$_3$., Comment: 70 pages + appendices

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information., Comment: Sensors 2020

Sedimentation in hydroelectric reservoirs is strongly impacted by anthropogenic activities within their upstream drainage basins. These activities, encompassing soil erosion and various other human-induced actions, have significant consequences for sedimentation patterns. This issue has been a subject of prolonged study, as sedimentation directly undermines the water storage capacity of reservoirs, consequently diminishing the overall efficiency of hydroelectric operations. Several scientists have dedicated their efforts to addressing the matter of reservoir sedimentation. This pursuit has led to the formulation of an indicator known as Sediment Trap Efficiency (STE), serving as a metric that quantifies the proportion of sedimentation within reservoirs relative to the influx of sediment from their upstream sources. This study seeks to present findings pertaining to carbon trapping efficiency observed across seven hydroelectric reservoirs in Brazil. The objective is to demonstrate the substantial relevance of carbon accumulation within these aquatic environments within the context of the carbon balance frameworks previously established., Comment: 20 pages, 8 figures, 6 tables

We propose a method for interpolating divergence-free continuous magnetic fields via vector potential reconstruction using Hermite interpolation, which ensures high-order continuity for applications requiring adaptive, high-order ordinary differential equation (ODE) integrators, such as the Dormand-Prince method. The method provides C(m) continuity and achieves high-order accuracy, making it particularly suited for particle trajectory integration and Poincar\'e section analysis under optimal integration order and timestep adjustments. Through numerical experiments, we demonstrate that the Hermite interpolation method preserves volume and continuity, which are critical for conserving toroidal canonical momentum and magnetic moment in guiding center simulations, especially over long-term trajectory integration. Furthermore, we analyze the impact of insufficient derivative continuity on Runge-Kutta schemes and show how it degrades accuracy at low error tolerances, introducing discontinuity-induced truncation errors. Finally, we demonstrate performant Poincar\'e section analysis in two relevant settings of field data collocated from finite element meshes, Comment: 21 pages, 9 figures

A calorimetric detector for minimally disruptive measurements of atomic hydrogen beams is described. The calorimeter measures heat released by the recombination of hydrogen atoms into molecules on a thin wire. As a demonstration, the angular distribution of a beam with a peak intensity of $\approx 10^{16} \,{\rm{atoms}}/{(\rm{cm}^2 \rm{s})}$ is measured by translating the wire across the beam. The data agree well with an analytic model of the beam from the thermal hydrogen atom source. Using the beam shape model, the relative intensity of the beam can be determined to 5% precision or better at any angle.

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.

We show that bi-frequency solitary waves are generically present in fermionic systems with scalar self-interaction, such as the Dirac--Klein--Gordon system and the Soler model. We develop the approach to stability properties of such waves and use the radial reduction to show that indeed the (linear) stability is available. We conjecture that stable bi-frequency modes serve as storages of the Dark Matter., Comment: 5 pages

In this brief and speculative commentary, we explore ideas inspired by neural networks in machine learning, proposing that a simple neural XOR motif, involving both excitatory and inhibitory connections, may provide the basis for a relevant mode of plasticity in neural circuits of living organisms, with homeostasis as the sole guiding principle. This XOR motif simply signals the discrepancy between incoming signals and reference signals, thereby providing a basis for a loss function in learning neural circuits, and at the same time regulating homeostasis by halting the propagation of these incoming signals. The core motif uses a 4:1 ratio of excitatory to inhibitory neurons, and supports broader neural patterns such as the well-known 'winner takes all' (WTA) mechanism. We examined the prevalence of the XOR motif in the published connectomes of various organisms with increasing complexity, and found that it ranges from tens (in C. elegans) to millions (in several Drosophila neuropils) and more than tens of millions (in mouse V1 visual cortex). If validated, our hypothesis identifies two of the three key components in analogy to machine learning models: the architecture and the loss function. And we propose that a relevant type of biological neural plasticity is simply driven by a basic control or regulatory system, which has persisted and adapted despite the increasing complexity of organisms throughout evolution., Comment: 11 pages, 6 figures

This paper studies three different ways to assign weights to the lattice points of a convex polytope and discusses the algebraic and combinatorial properties of the resulting weighted Ehrhart functions and their generating functions and associated rings. These will be called $q$-weighted, $r$-weighted, and $s$-weighted Ehrhart functions, respectively. The key questions we investigate are \emph{When are the weighted Ehrhart series rational functions and which classical Ehrhart theory properties are preserved? And, when are the abstract formal power series the Hilbert series of Ehrhart rings of some polytope?} We prove generalizations about weighted Ehrhart $h^*$-coefficients of $q$-weighted Ehrhart series, and show $q$- and $s$-weighted Ehrhart reciprocity theorems. Then, we show the $q$- and $r$-weighted Ehrhart rings are the (classical) Ehrhart rings of weight lifting polytopes.

Robotic telescope networks play an important role in capturing early and bright optical afterglows, providing critical insights into the energetics and emission mechanisms of GRBs. In this study, we analyze GRB 230204B, an exceptionally energetic and multi-pulsed long GRB, detected by the Fermi GBM and MAXI detectors, with an isotropic equivalent gamma-ray energy exceeding 10$^{54}$ erg. Time-resolved spectral analysis reveals a transition in the prompt emission from hard (sub-photospheric dominated) spectra during early pulses to softer (synchrotron radiation dominated) spectra in later pulses, indicative of a hybrid jet composition. We report the discovery and characterization of the optical afterglow using the MASTER and BOOTES robotic telescope networks, alongside long-term radio observations extending to 335 days post-burst with the ATCA. At ~1.3 ks post-burst, the optical luminosity was exceptionally high, surpassing even other bright GRBs, such as GRB 221009A (the ``BOAT"). Multi-wavelength modeling, incorporating data from MASTER, BOOTES, DOT, Swift/XRT, and radio observations, was conducted using an external ISM forward-shock top-hat jet model with afterglowpy. The results reveal a narrow and highly collimated jet with a circumburst density of n$_{0}$ ~ 28.12 cm$^{-3}$, kinetic energy E$_{K}$ ~ 4.18 x 10$^{55}$ erg, and a relatively low value of $\epsilon_{B}$ = 2.14 x 10$^{-6}$, indicating shock-compression of the magnetic field in the surrounding interstellar medium. We constrained a low radiative efficiency of ~ 4.3 %. This study highlights the indispensable contribution of robotic networks to early afterglow observations and advances our understanding of GRB 230204B unique characteristics and underlying jet physics., Comment: 27 pages, 12 figures, 8 tables, submitted

The sum of quantum computing errors is the key element both for the estimation and control of errors in quantum computing and for its statistical study. In this article we analyze the sum of two independent quantum computing errors, $X_1$ and $X_2$, and we obtain the formula of the variance of the sum of these errors: $$ V(X_1+X_2)=V(X_1)+V(X_2)-\frac{V(X_1)V(X_2)}{2}. $$ We conjecture that this result holds true for general quantum computing errors and we prove the formula for independent isotropic quantum computing errors.

This work shows a study about the structure of the cycles contained in a Minimal Strong Digraph (MSD). The structure of a given cycle is determined by the strongly connected components (or strong components, SCs) that appear after suppressing the arcs of the cycle. By this process and by the contraction of all SCs into single vertices we obtain a Hasse diagram from the MSD. Among other properties, we show that any SC conformed by more than one vertex (non trivial SC) has at least one linear vertex (a vertex with indegree and outdegree equal to 1) in the MSD (Theorem 1); that in the Hasse diagram at least one linear vertex exists for each non trivial maximal (resp. minimal) vertex (Theorem 2); that if an SC contains a number $\lambda$ of vertices of the cycle then it contains at least $\lambda$ linear vertices in the MSD (Theorem 3); and, finally, that given a cycle of length $q$ contained in the MSD, the number $\alpha$ of linear vertices contained in the MSD satisfies $\alpha \geq \lfloor (q+1)/2 \rfloor$ (Theorem 4).

A mathematical modeling process for phenomena with a single state variable that attempts to be realistic must be given by a scalar nonautonomous differential equation $x'=f(t,x)$ that is concave with respect to the state variable $x$ in some regions of its domain and convex in the complementary zones. This article takes the first step towards developing a theory to describe the corresponding dynamics: the case in which $f$ is concave on the region $x\ge b(t)$ and convex on $x\le b(t)$, where $b$ is a $C^1$ map, is considered. The different long-term dynamics that may appear are analyzed while describing the bifurcation diagram for $x'=f(t,x)+\lambda$. The results are used to establish conditions on a concave-convex map $h$ and a nonnegative map $k$ ensuring the existence of a value $\rho_0$ giving rise to the unique critical transition for the parametric family of equations $x'=h(t,x)-\rho\,k(t,x)$, which is assumed to approach $x'=h(t,x)$ as time decreases, but for which no conditions are assumed on the future dynamics. The developed theory is justified by showing that concave-convex models fit correctly some laboratory experimental data, and applied to describe a population dynamics model for which a large enough increase on the peak of a temporary higher predation causes extinction., Comment: 45 pages, 10 figures

Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on threat modeling frameworks, including STRIDE, DREAD, and MITRE ATT\&CK, to systematically identify and mitigate potential risks. The survey examines key components of AV architectures, such as sensors, communication modules, and electronic control units (ECUs), and explores common attack vectors like wireless communication exploits, sensor spoofing, and firmware vulnerabilities. Through case studies of real-world incidents, such as the Jeep Cherokee and Tesla Model S exploits, the paper highlights the critical need for robust security measures. Emerging technologies, including blockchain for secure Vehicle-to-Everything (V2X) communication, AI-driven threat detection, and secure Over-The-Air (OTA) updates, are discussed as potential solutions to mitigate evolving threats. The paper also addresses legal and ethical considerations, emphasizing data privacy, user safety, and regulatory compliance. By combining threat modeling frameworks, multi-layered security strategies, and proactive defenses, this survey offers insights and recommendations for enhancing the cybersecurity of autonomous vehicles.