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A beyond electric-dipole light-matter theory is needed to describe emerging X-ray and THz applications for characterization and control of quantum materials but inaccessible as nondipole lattice-aperiodic terms impede on the use of Bloch's theorem. To circumvent this, we derive a formalism that captures dominant nondipole effects in intense electromagnetic fields while conserving lattice translational symmetry. Our approach enables the first accurate nondipole first-principles microscopic simulation of nonperturbative harmonic generation in Si. We reveal nondipole-induced transverse currents generating perturbative even-ordered harmonics and display the onset of nondipole high harmonic generation near the laser damage threshold., Comment: 8 pages, 3 figures

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal data mining. State-of-the-art forecasting is achieved by deep-learning approaches due to their ability to contend with complex spatio-temporal dynamics. However, existing methods assume the input is fixed-topology road networks and static traffic time series. These assumptions fail to align with urbanization, where time series are collected continuously and road networks evolve over time. In such settings, deep-learning models require frequent re-initialization and re-training, imposing high computational costs. To enable much more efficient training without jeopardizing model accuracy, we propose the Topological Evolution-aware Framework (TEAM) for traffic forecasting that incorporates convolution and attention. This combination of mechanisms enables better adaptation to newly collected time series, while being able to maintain learned knowledge from old time series. TEAM features a continual learning module based on the Wasserstein metric that acts as a buffer that can identify the most stable and the most changing network nodes. Then, only data related to stable nodes is employed for re-training when consolidating a model. Further, only data of new nodes and their adjacent nodes as well as data pertaining to changing nodes are used to re-train the model. Empirical studies with two real-world traffic datasets offer evidence that TEAM is capable of much lower re-training costs than existing methods are, without jeopardizing forecasting accuracy., Comment: 16 pages. An extended version of "TEAM: Topological Evolution-aware Framework for Traffic Forecasting" accepted at PVLDB 2025

A crucial component of the high-contrast instrumental chain in astronomy is the wavefront sensor (WFS). A key property of this component is its sensitivities, which reflect its ability to efficiently use incoming photons to encode the phase aberrations. This paper introduces a new class of highly sensitive wavefront sensors that approach the fundamental sensitivity limits dictated by physics. Assuming a high Strehl regime, we define what linear operator is describing the ideal WFS that would achieve maximum sensitivity. We then show that there is a substantial similarity between this ideal WFS and the second-order ideal coronagraph. Leveraging the exhibited link between ideal wavefront sensing and coronagraphy, we propose a novel WFS concept based on high-performance coronagraphic architecture : the bivortex WFS. This sensor employs charge-2 vortex masks. Simulations for an ideal system demonstrate that this sensor achieves unprecedented sensitivity, even surpassing the highly sensitive Zernike WFS class (especially for low spatial frequencies), while paving the way for new high-contrast architectures integrating simultaneous sensing and coronagraphy.

The Particle X-ray Coincidence Technique (PXCT) was originally developed to measure average lifetimes in the $10^{-17}-10^{-15}$~s range for proton-unbound states populated by electron capture (EC). We have designed and built the Lifetimes and Branching Ratios Apparatus (LIBRA) to be used in the stopped-beam area at the Facility for Rare Isotope Beams that extends PXCT to measure both lifetimes and decay branching ratios of resonances populated by EC/$\beta^+$ decay. The first application of LIBRA aims to obtain essential nuclear data from $^{60}$Ga EC/$\beta^+$ decay to constrain the thermonuclear rates of the $^{59}$Cu$(p,\gamma)^{60}$Zn and $^{59}$Cu$(p,\alpha)^{56}$Ni reactions, and in turn, the strength of the NiCu nucleosynthesis cycle, which is predicted to significantly impact the modeling of Type I X-ray burst light curves and the composition of the burst ashes. Detailed theoretical calculations, Monte Carlo simulations, and performance tests with radioactive sources have been conducted to validate the feasibility of employing LIBRA for the $^{60}$Ga experiment. The method introduced with LIBRA has the potential to measure nearly all essential ingredients for thermonuclear reaction rate calculations in a single experiment, in the absence of direct measurements, which are often impractical for radioactive reactants.

Contemporary self-tracking technologies (STTs), such as smartwatches and smartphone apps, allow people to become self-aware through the datafication of their everyday lives. However, concerns are emerging over the global north/Western portrayal of the self in the envisionment of STTs. Given the call to diversify participant samples in HCI knowledge building, we see it timely in understanding the influence of ubiquitous STTs in global south societies. We conduct a between-group analysis of 156 and 121 participants from Global North and South through two iterative surveys, respectively. We uncover significant differences in perceived trust with their STTs and reflection practices between the groups. We provide an empirical understanding on advocating for inclusive design strategies that recognize diverse interpretations of STTs and highlight the need to prioritize local values and flexibility in tracking to foster deeper reflection across cultures. Lastly, we discuss our findings in relation to the existing literature and highlight design recommendations for future research., Comment: 12 pages

Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training and struggle with poor generalization performance on new domains. Foundation models aim to overcome this limitation. Pre-trained on large-scale language or time series data, they exhibit promising inferencing capabilities in new or unseen data. This has spurred a surge in new TSF foundation models. We propose a new benchmark, FoundTS, to enable thorough and fair evaluation and comparison of such models. FoundTS covers a variety of TSF foundation models, including those based on large language models and those pretrained on time series. Next, FoundTS supports different forecasting strategies, including zero-shot, few-shot, and full-shot, thereby facilitating more thorough evaluations. Finally, FoundTS offers a pipeline that standardizes evaluation processes such as dataset splitting, loading, normalization, and few-shot sampling, thereby facilitating fair evaluations. Building on this, we report on an extensive evaluation of TSF foundation models on a broad range of datasets from diverse domains and with different statistical characteristics. Specifically, we identify pros and cons and inherent limitations of existing foundation models, and we identify directions for future model design. We make our code and datasets available at https://anonymous.4open.science/r/FoundTS-C2B0.

Sound zone control (SZC) implemented using static optimal filters is significantly affected by various perturbations in the acoustic environment, an important one being the fluctuation in the speed of sound, which is in turn influenced by changes in temperature and humidity (TH). This issue arises because control algorithms typically use pre-recorded, static impulse responses (IRs) to design the optimal control filters. The IRs, however, may change with time due to TH changes, which renders the derived control filters to become non-optimal. To address this challenge, we propose a straightforward model called sinc interpolation-compression/expansion-resampling (SICER), which adjusts the IRs to account for both sound speed reduction and increase. Using the proposed technique, IRs measured at a certain TH can be corrected for any TH change and control filters can be re-derived without the need of re-measuring the new IRs (which is impractical when SZC is deployed). We integrate the proposed SICER IR correction method with the recently introduced variable span trade-off (VAST) framework for SZC, and propose a SICER-corrected VAST method that is resilient to sound speed variations. Simulation studies show that the proposed SICER-corrected VAST approach significantly improves acoustic contrast and reduces signal distortion in the presence of sound speed changes., Comment: 5 pages, 4 figures, submitted to ICASSP 2025

Performance of sound zone control (SZC) systems deployed in practical scenarios are highly sensitive to the location of the listener(s) and can degrade significantly when listener(s) are moving. This paper presents a robust SZC system that adapts to dynamic changes such as moving listeners and varying zone locations using a dictionary-based approach. The proposed system continuously monitors the environment and updates the fixed control filters by tracking the listener position using audio signals only. To test the effectiveness of the proposed SZC method, simulation studies are carried out using practically measured impulse responses. These studies show that SZC, when incorporated with the proposed audio-only position tracking scheme, achieves optimal performance when all listener positions are available in the dictionary. Moreover, even when not all listener positions are included in the dictionary, the method still provides good performance improvement compared to a traditional fixed filter SZC scheme., Comment: Equal contribution by Sankha Subhra Bhattacharjee and Andreas Jonas Fuglsig. Submitted to ICASSP 2025

A 2-edge-coloured graph $G$ is called {\bf locally complete} if for each vertex $v$, the vertices adjacent to $v$ through edges of the same colour induce a complete subgraph in $G$. Locally complete 2-edge-coloured graphs have nice properties and there exists a polynomial algorithm to decide whether such a graph has an alternating hamiltonian cycle, where alternating means that the colour of two consecutive edges on the cycle are different. In this paper we show that graphs having locally complete 2-edge-colourings can be recognized in polynomial time. We give a forbidden substructure characterization for this class of graphs analogous to Gallai's characterization for cocomparability graphs. Finally, we characterize proper interval graphs and proper circular-arc graphs which have locally complete 2-edge-colourings by forbidden subgraphs.

We introduce a method for computing quantum mechanical forces through surface integrals over the stress tensor within the framework of density functional theory. This approach avoids the inaccuracies of traditional force calculations using the Hellmann-Feynman theorem when applied to multiresolution wavelet representations of orbitals. By integrating the quantum mechanical stress tensor over surfaces that enclose individual nuclei, we achieve highly accurate forces that exhibit superior consistency with the potential energy surface. Extensive benchmarks show that surface integrals over the stress tensor offer a robust and reliable alternative to the direct use of the Hellmann-Feynman theorem for force computations in DFT with discontinuous basis sets, particularly in cases where wavelet-based methods are employed. In addition, we integrate this approach with machine learning techniques, demonstrating that the forces obtained through surface integrals are sufficiently accurate to be used as training data for machine-learned potentials. This stands in contrast to forces calculated using the Hellmann-Feynman theorem, which do not offer this level of accuracy., Comment: 8 pages, 4 figures, 1 table

The nucleation, crystallization, and growth mechanisms of MnFe2O4, CoFe2O4, NiFe2O4, and ZnFe2O4 nanocrystallites prepared from coprecipitated transition metal (TM) hydroxide precursors treated at sub-, near-, and supercritical hydrothermal conditions have been studied by in situ X-ray total scattering (TS) with pair distribution function (PDF) analysis, and in situ synchrotron powder X-ray diffraction (PXRD) with Rietveld analysis. The in situ TS experiments were carried out on 0.6 M TM hydroxide precursors prepared from aqueous metal chloride solutions using 24.5% NH4OH as the precipitating base. The PDF analysis reveals equivalent nucleation processes for the four spinel ferrite compounds under the studied hydrothermal conditions, where the TMs form edge-sharing octahedrally coordinated hydroxide units (monomers/dimers and in some cases trimers) in the aqueous precursor, which upon hydrothermal treatment nucleate through linking by tetrahedrally coordinated TMs. The in situ PXRD experiments were carried out on 1.2 M TM hydroxide precursors prepared from aqueous metal nitrate solutions using 16 M NaOH as the precipitating base. The crystallization and growth of the nanocrystallites were found to progress via different processes depending on the specific TMs and synthesis temperatures. The PXRD data show that MnFe2O4 and CoFe2O4 nanocrystallites rapidly grow (typically <1 min) to equilibrium sizes of 20-25 nm and 10-12 nm, respectively, regardless of applied temperature in the 170-420 C range, indicating limited possibility of targeted size control. However, varying the reaction time (0-30 min) and temperature (150-400 C) allows different sizes to be obtained for NiFe2O4 (3-30 nm) and ZnFe2O4 (3-12 nm) nanocrystallites.

Non-Abelian physics, originating from noncommutative sequences of operations, unveils novel topological degrees of freedom for advancing band theory and quantum computation. In photonics, significant efforts have been devoted to developing reconfigurable non-Abelian platforms, serving both as classical testbeds for non-Abelian quantum phenomena and as programmable systems that harness topological complexities. Here we establish topological spinor lattices for non-Abelian programmable photonics. We design a building block for reconfigurable unitary coupling between pseudospin resonances, achieving a universal set of rotation gates through coupling along the unit cell boundary. The lattice assembly of our building blocks enables the emulation of the extended quantum Hall family across various eigenspinor bases. Particularly, we reveal the emergence of a non-Abelian interface even when the bulks are Abelian, which allows the topologically trivial engineering of topologically protected edge states. We also define the braid group for pseudospin observables, demonstrating non-Abelian braiding operations and the Yang-Baxter relations. Our results pave the way for realizing a reconfigurable testbed for a wide class of Abelian and non-Abelian topological phenomena and braiding operations., Comment: 44 pages, 4 figures and 8 supplementary figures

The Fermi polaron problem, which describes a mobile impurity that interacts with a spin-polarized Fermi sea, is a paradigmatic system in quantum many-body physics and has been challenging to address quantitatively in its strong coupling regime. We present the first controlled thermodynamic calculations for the Fermi polaron at strong coupling using finite-temperature auxiliary-field quantum Monte Carlo (AFMC) methods in the framework of the canonical ensemble. Modeled as a spin-imbalanced system, the Fermi polaron has a Monte Carlo sign problem, but we show that it is moderate over a wide range of temperatures and coupling strengths beyond the unitary limit of the BCS-BEC crossover. We calculate the contact, a quantity which measures the strength of the short-range correlations, as a function of temperature at unitarity and as a function of the coupling strength at fixed temperature and find good agreement with a variational approach based on one particle-hole excitation of the Fermi sea. We compare our results for the contact with recent experiments and find good agreement at unitarity (within error bars) but discrepancies away from unitarity on the BEC side of the crossover. We also calculate the thermal energy gap at unitarity as a function of temperature., Comment: 8 pages, 7 figures

We study the complex band hybridization induced by nonreciprocal local resonances in photonic crystals. Composed of trimer unit cells, a two-dimensional (2D) magnetophotonic crystal with an analytically obtainable solution is considered. We find that nonreciprocal spectral gap may appear without nonreciprocal transmission and that the imaginary parts of the complex wavevectors $\text{Im}(\mathbf{k})$ may blow up at resonance to give extreme nonreciprocal transmission. We further show that, for a subwavelegnth lattice, the isolation ratio for the nonreciprocal transmission is determined solely by $\text{Im}(\mathbf{k})$ instead of the extensively studied real part $\text{Re}(\mathbf{k})$. Our finding contradicts the common belief that "spectral nonreciprocity [$\omega(\mathbf{k})\neq\omega(-\mathbf{k})$] always implies nonreciprocal transmission"., Comment: 5 pages, 4 figures

Infinite nuclear matter provides valuable insights into the behavior of nuclear systems and aids our understanding of atomic nuclei and large-scale stellar objects such as neutron stars. However, partly due to the large basis needed to converge the system's binding energy, size-extensive methods such as coupled-cluster theory struggle with long computational run times, even using the nation's largest high-performance computing facilities. This research introduces a novel approach to the problem. We propose using a machine learning method to predict the coupled-cluster energies of infinite matter systems in the complete basis limit, leveraging only data collected using smaller basis sets. This method promises to deliver high-accuracy results with significantly reduced run times. The sequential regression extrapolation (SRE) algorithm, based on Gaussian processes, was created to perform these extrapolations. By combining Bayesian machine learning with a unique method of formatting the training data, we can create a powerful extrapolator that can make accurate predictions given very little data. The SRE algorithm successfully predicted the CCD(T) energies for pure neutron matter across six densities near nuclear saturation density, with an average error of 0.0083 MeV/N. The algorithm achieved an average error of 0.038 MeV/A for symmetric nuclear matter. These predictions were made with a time savings of 83.8 node hours for pure neutron matter and 284 node hours for symmetric nuclear matter. Additionally, the symmetry energy at these six densities was predicted with an average error of 0.031 MeV/A and a total time savings of 368 node hours compared to the traditional converged coupled-cluster calculations performed without the SRE algorithm.

Economics Job Market Rumors (EJMR) is an online forum and clearinghouse for information on the academic job market for economists. It also includes content that is abusive, defamatory, racist, misogynistic, or otherwise "toxic." Almost all of this content is created anonymously by contributors who receive a four-character username when posting on EJMR. Using only publicly available data we show that the statistical properties of the scheme by which these usernames were generated allows the IP addresses from which most posts were made to be determined with high probability. We recover 47,630 distinct IP addresses of EJMR posters and attribute them to 66.1% of the roughly 7 million posts made over the past 12 years. We geolocate posts and describe aggregated cross-sectional variation -- particularly regarding toxic, misogynistic, and hate speech -- across sub-forums, geographies, institutions, and IP addresses. Our analysis suggests that content on EJMR comes from all echelons of the economics profession, including, but not limited to, its elite institutions.

Real-time control (RTC) is pivotal for any Adaptive Optics (AO) system, including high-contrast imaging of exoplanets and circumstellar environments. It is the brain of the AO system, and what wavefront sensing and control (WFS\&C) techniques need to work with to achieve unprecedented image quality and contrast, ultimately advancing our understanding of exoplanetary systems in the context of high contrast imaging (HCI). Developing WFS\&C algorithms first happens in simulation or a lab before deployment on-sky. The transition to on-sky testing is often challenging due to the different RTCs used. Sharing common RTC standards across labs and telescope instruments would considerably simplify this process. A data architecture based on the interprocess communication method known as shared memory is ideally suited for this purpose. The CACAO package, an example of RTC based on shared memory, was initially developed for the Subaru-SCExAO instrument and now deployed on several benches and instruments. This proceeding discusses the challenges, requirements, implementation strategies, and performance evaluations associated with integrating a shared memory-based RTC. The Santa Cruz Extreme AO Laboratory (SEAL) bench is a platform for WFS\&C development for large ground-based segmented telescopes. Currently, SEAL offers the user a non-real-time version of CACAO, a shared-memory based RTC package initially developed for the Subaru-SCExAO instrument, and now deployed on several benches and instruments. We show here the example of the SEAL RTC upgrade as a precursor to both RTC upgrade at the 3-m Shane telescopes at Lick Observatory (Shane-AO) and a future development platform for the Keck II AO. This paper is aimed at specialists in AO, astronomers, and WFS\&C scientists seeking a deeper introduction to the world of RTCs., Comment: SPIE Astronomical Telescope and Intrumentation 2024

This paper examines the impact of electric vehicle (EV) charging stations on the capacity of distribution feeders and transformers within the electric grid at California State University Northridge (CSUN). With the increasing adoption of both residential and commercial EVs and the rapid expansion of EV charging infrastructure, it is critical to evaluate the potential overloading effects of intensive EV charging on power distribution systems. This research assesses the impact of EV charging on the operation of CSUN's electric grid, identifying potential overload risks under projected EV adoption scenarios. Detailed simulations and analyses are conducted to quantify the extent of these impacts, focusing on various levels of EV penetration and charging patterns. The study also explores the impact of distributed generation on reducing the stress incurred by EV loads. The findings provide essential insights for utility companies, highlighting the need for strategic upgrades to distribution systems. These insights will help in developing robust strategies for both current operations and future planning to accommodate growing EV charging demands, ensuring grid stability and reliability in the face of increasing electrification of the transportation sector. The modeled CSUN electric grid can be used as a benchmark to study the impact of EV loads in dense areas on various parameters of the grid., Comment: 7 pages

Sonar-based indoor mapping systems have been widely employed in robotics for several decades. While such systems are still the mainstream in underwater and pipe inspection settings, the vulnerability to noise reduced, over time, their general widespread usage in favour of other modalities(\textit{e.g.}, cameras, lidars), whose technologies were encountering, instead, extraordinary advancements. Nevertheless, mapping physical environments using acoustic signals and echolocation can bring significant benefits to robot navigation in adverse scenarios, thanks to their complementary characteristics compared to other sensors. Cameras and lidars, indeed, struggle in harsh weather conditions, when dealing with lack of illumination, or with non-reflective walls. Yet, for acoustic sensors to be able to generate accurate maps, noise has to be properly and effectively handled. Traditional signal processing techniques are not always a solution in those cases. In this paper, we propose a framework where machine learning is exploited to aid more traditional signal processing methods to cope with background noise, by removing outliers and artefacts from the generated maps using acoustic sensors. Our goal is to demonstrate that the performance of traditional echolocation mapping techniques can be greatly enhanced, even in particularly noisy conditions, facilitating the employment of acoustic sensors in state-of-the-art multi-modal robot navigation systems. Our simulated evaluation demonstrates that the system can reliably operate at an SNR of $-10$dB. Moreover, we also show that the proposed method is capable of operating in different reverberate environments. In this paper, we also use the proposed method to map the outline of a simulated room using a robotic platform.

Automatic speech recognition (ASR) systems are known to be vulnerable to adversarial attacks. This paper addresses detection and defence against targeted white-box attacks on speech signals for ASR systems. While existing work has utilised diffusion models (DMs) to purify adversarial examples, achieving state-of-the-art results in keyword spotting tasks, their effectiveness for more complex tasks such as sentence-level ASR remains unexplored. Additionally, the impact of the number of forward diffusion steps on performance is not well understood. In this paper, we systematically investigate the use of DMs for defending against adversarial attacks on sentences and examine the effect of varying forward diffusion steps. Through comprehensive experiments on the Mozilla Common Voice dataset, we demonstrate that two forward diffusion steps can completely defend against adversarial attacks on sentences. Moreover, we introduce a novel, training-free approach for detecting adversarial attacks by leveraging a pre-trained DM. Our experimental results show that this method can detect adversarial attacks with high accuracy., Comment: Under review at ICASSP 2025

Microfluidic devices offer numerous advantages in medical applications, including the capture of single cells in microwell-based platforms for genomic analysis. As the cost of sequencing decreases, the demand for high-throughput single-cell analysis devices increases, leading to more microwells in a single device. However, their small size and large quantity increase the quality control (QC) effort. Currently, QC steps are still performed manually in some devices, requiring intensive training and time and causing inconsistency between different operators. A way to overcome this issue is to through automated defect detection. Computer vision can quickly analyze a large number of images in a short time and can be applied in defect detection. Automated defect detection can replace manual inspection, potentially decreasing variations in QC results. We report a machine learning (ML) algorithm that applies a convolution neural network (CNN) model with 9 layers and 64 units, incorporating dropouts and regularizations. This algorithm can analyze a large number of microwells produced by injection molding, significantly increasing the number of images analyzed compared to manual operator, improving QC, and ensuring the delivery of high-quality products to customers.

Primordial black holes (PBHs) would induce non-Gaussianity in the cosmic microwave background (CMB) by sourcing recombination perturbations spatially modulated by relative velocities between PBHs and the baryons they accrete. The leading non-Gaussian signatures are non-vanishing connected 4-point correlation functions, or trispectra. Earlier, we computed the CMB temperature trispectrum, and forecasted Planck to be more sensitive to it than to changes in the CMB temperature power spectrum for light enough PBHs. Excitingly, accreting PBHs would also induce non-Gaussianity in CMB polarization, and source both E and B modes, which we compute in this paper. We first calculate linear-response perturbations to the tensor-valued photon distribution function sourced by a general spatially-varying ionization history, and apply our results to accreting PBHs. We then compute linear-order perturbations to the temperature and polarization 2-point functions sourced by inhomogeneities in recombination due to accreting PBHs; we find them to be negligible relative to their counterparts sourced by homogeneous perturbations to the ionization history. Lastly, we compute all CMB trispectra including temperature, E- and B-mode polarization at linear order in the PBH abundance. We forecast that including polarization data in a 4-point-function analysis would only increase Planck's sensitivity to accreting PBHs by a factor ~2 relative to using temperature alone. As a consequence, we find that a search for PBHs using all temperature and polarization trispectra with Planck data would mostly not be competitive with current bounds from temperature and polarization power spectra. In contrast, we forecast that a CMB Stage-4 experiment would gain significant sensitivity to accreting PBHs through a 4-point-function search, in particular through the contributions of parity-odd trispectra including one B-mode field., Comment: 16 pages + appendices and refs, 3 figures

Leaves shed by deciduous trees contain 40\% of the annually sequestered carbon, and include nutrients vital to the expansion and health of forest ecosystems. To achieve this, leaves must fall quickly to land near the parent tree -- otherwise, they are lost to the wind, like pollen or gliding seeds. However, the link between leaf shape and sedimentation speed remains unclear. To gauge the relative performance of extant leaves, we developed an automated sedimentation apparatus (ASAP) capable of performing $\sim100$ free fall experiments per day on biomimetic paper leaves. The majority of 25 representative leaves settle at rates similar to our control (a circular disc). Strikingly, the Arabidopsid mutant asymmetric leaves1 (as1) fell 15\% slower than the wild type. Applying the as1-digital mutation to deciduous tree leaves revealed a similar speed reduction. Data correlating shape and settling across a broad range of natural, mutated, and artificial leaves support thefast-leaf-hypothesis: Deciduous leaves are symmetric and relatively unlobed in part because this maximizes their settling speed and concomitant nutrient retention.

We present the theory and implementation of a novel, fully variational wave function - density functional theory (DFT) hybrid model, which is applicable to many cases of strong correlation. We denote this model the multiconfigurational self-consistent on-top pair-density functional theory model (MC-srPDFT). We have previously shown how the multi-configurational short-range DFT hybrid model (MC-srDFT) can describe many multiconfigurational cases of any spin symmetry, and also state-specific calculations on excited states. However, the srDFT part of the MC-srDFT has some deficiencies that it shares with Kohn-Sham DFT, namely that different MS states have different energies and wrong bond dissociation description of singlet and non-singlet equilibrium states to open-shell fragments. The model we present in this paper corrects these deficiencies by introducing the on-top pair density. Unlike other models in the literature, our model is fully variational and employs a long-range version of the on-top pair density. The implementation is a second-order optimization algorithm ensuring robust convergence to both ground- and excited states. We show how MC-srPDFT solves the mentioned challenges by sample calculations on the ground state singlet curve of H$_2$, N$_2$, and Cr$_2$ and the lowest triplet curves for N$_2$ and Cr$_2$. The calculations show correct degeneracy between the singlet and triplet curves at dissociation and the results are invariant to the choice of MS value for the triplet curves.

The Zernike wavefront sensor (ZWFS) stands out as one of the most sensitive optical systems for measuring the phase of an incoming wavefront, reaching photon efficiencies close to the fundamental limit. This quality, combined with the fact that it can easily measure phase discontinuities, has led to its widespread adoption in various wavefront control applications, both on the ground but also for future space-based instruments. Despite its advantages, the ZWFS faces a significant challenge due to its extremely limited dynamic range, making it particularly challenging for ground-based operations. To address this limitation, one approach is to use the ZWFS after a general adaptive optics (AO) system; however, even in this scenario, the dynamic range remains a concern. This paper investigates two optical configurations of the ZWFS: the conventional setup and its phase-shifted counterpart, which generates two distinct images of the telescope pupil. We assess the performance of various reconstruction techniques for both configurations, spanning from traditional linear reconstructors to gradient-descent-based methods. The evaluation encompasses simulations and experimental tests conducted on the Santa cruz Extreme Adaptive optics Lab (SEAL) bench at UCSC. Our findings demonstrate that certain innovative reconstruction techniques introduced in this study significantly enhance the dynamic range of the ZWFS, particularly when utilizing the phase-shifted version.

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.

Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and a numeric range as parameters returns the data object whose numeric value is in the query range and whose vector is nearest to the query vector. To process this query, a recent study proposes to build $O(n^2)$ dedicated graph-based indexes for all possible query ranges to enable efficient processing on a database of $n$ objects. As storing all these indexes is prohibitively expensive, the study constructs compressed indexes instead, which reduces the memory consumption considerably. However, this incurs suboptimal performance because the compression is lossy. In this study, instead of materializing a compressed index for every possible query range in preparation for querying, we materialize graph-based indexes, called elemental graphs, for a moderate number of ranges. We then provide an effective and efficient algorithm that during querying can construct an index for any query range using the elemental graphs. We prove that the time needed to construct such an index is low. We also cover an experimental study on real-world datasets that provides evidence that the materialized elemental graphs only consume moderate space and that the proposed method is capable of superior and stable query performance across different query workloads., Comment: The paper has been accepted by SIGMOD 2025

The unitary Fermi gas (UFG) is a strongly correlated system of two-species (spin-1/2) fermions with a short-range attractive interaction modeled by a contact interaction and has attracted much interest across different disciplines. The UFG is considered a paradigm for strongly correlated superfluids and has been investigated extensively, with generally good agreement found between theory and experiment. However, the extent of a pseudogap regime above the critical temperature $T_c$ for superfluidity is still debated both theoretically and experimentally. Here we study thermodynamic properties of the UFG across the superfluid phase transition using finite-temperature lattice auxiliary-field quantum Monte Carlo (AFMC) methods in the canonical ensemble of fixed particle numbers. We extrapolate our lattice AFMC results to the continuous time and continuum limits, thus removing the systematic error associated with the finite filling factor of previous AFMC studies. We determine the critical temperature to be $T_c=0.16(1)\, T_{F}$. For the largest particle number studied $N=114$, the energy-staggering pairing gap is suppressed above a pairing scale temperature of $T^{*}\approx 0.2\,T_F$. The spin susceptibility displays moderate suppression above $T_c$ with a spin gap temperature of $T_s\approx 0.2 \,T_F$. We also calculate a free energy-staggering pairing gap, which shows substantially reduced statistical errors when compared with the energy-staggering gap, allowing for a clear signature of pairing correlations in the finite-size system. All results indicate that the pseudogap regime is narrow, with pseudogap signatures emerging at temperatures below $T^{*}\approx 0.2 \, T_F$. The reduced statistical errors of the free energy gap enable an extrapolation at low temperatures, allowing an estimate of the zero-temperature pairing gap $\Delta_E = 0.576(24) \, \epsilon_F$., Comment: 18 pages, 7 figures

The Virgo Cluster is the nearest substantial cluster of galaxies to the Milky Way and a cornerstone of the extragalactic distance scale. Here, we present JWST/NIRCam observations that simultaneously cover the cores and halos of ten galaxies in and around the Virgo Cluster and are designed to perform simultaneous measurements of the tip of the red giant branch (TRGB) and surface brightness fluctuations (SBF). Seven of the targets are within the Virgo Cluster and where we are able to resolve some of the cluster's substructure, while an additional three provide important constraints on Virgo infall. The seven galaxies within Virgo itself all have SBF measurements from the Advanced Camera for Surveys Virgo Cluster Survey (ACSVCS). After adjusting the ACSVCS measurements for the offset from our new JWST TRGB measurements, we determine a distance to the Virgo Cluster of d $=$ 16.17 $\pm$ 0.25 (stat) $\pm$ 0.47 (sys) Mpc. The work presented here is part of a larger program to develop a Population II distance scale through the TRGB and SBF that is completely independent of the prominent Cepheid + Type Ia supernova ladder. The TRGB distances to the galaxies presented here, when combined with future SBF measurements, will provide a crucial step forward for determining whether or not systematic errors can explain the well-known "Hubble tension'' or if there is significant evidence for cracks in the $\Lambda$CDM model., Comment: submitted to ApJ. comments welcome and appreciated. program name has changed from Paper I's "The Population II Extragalactic Distance Scale" to "The TRGB-SBF Project"

In this paper, we consider cyclic permutations that avoid the monotone decreasing permutation $k(k-1)\ldots 21$, whose cycle also demonstrates some pattern avoidance. If the cycle is written in standard form with 1 appearing at the beginning of the cycle and the standard form avoids a pattern of length 3, we find answers in terms of continued fraction generating functions. We also consider the case that every cyclic rotation of the cycle form of the permutation avoids a pattern of length 4 and enumerate two such cases.

The transit technique has been very efficient in detecting planet candidate signals over the past decades. The so-called statistical validation approach has become a popular way of verifying a candidate's planetary nature. However, the incomplete consideration of false positive scenarios and data quality can lead to the misinterpretation of the results. In this work we revise the planetary status of K2-399\,b, a validated planet with an estimated false positive probability of 0.078% located in the middle of the so-called Neptunian desert, and hence a potential key target for atmospheric prospects. We use radial velocity data from the CARMENES, HARPS and TRES spectrographs, as well as ground-based multi-band transit photometry LCOGT MuSCAT3 and broad band photometry to test the planetary scenario. Our analysis of the available data does not support the existence of this (otherwise key) planet, and instead points to a scenario composed of an early G-dwarf orbited in a $846.62^{+0.22}_{-0.28}$~days period by a pair of eclipsing M-dwarfs (hence a hierarchical eclipsing binary) likely in the mid-type domain. We thus demote K2-399 b as a planet. We conclude that the validation process, while very useful to prioritise follow-up efforts, must always be conducted with careful attention to data quality while ensuring that all possible scenarios have been properly tested to get reliable results. We also encourage developers of validation algorithms to ensure the accuracy of a priori probabilities for different stellar scenarios that can lead to this kind of false validation. We further encourage the use of follow-up observations when possible (such as radial velocity and/or multi-band light curves) to confirm the planetary nature of detected transiting signals rather than only relying on validation tools., Comment: 14 pages, 12 figures, 5 tables, Accepted for publication in A&A Letters

Quantum computing (QC) provides a promising avenue toward enabling quantum chemistry calculations, which are classically impossible due to a computational complexity that increases exponentially with system size. As fully fault-tolerant algorithms and hardware, for which an exponential speedup is predicted, are currently out of reach, recent research efforts are dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices to showcase the practical utility of such machines. To demonstrate the utility of NISQ devices in the field of chemistry, we apply our recently developed FAST-VQE algorithm and a novel quantum gate reduction strategy based on propositional satisfiability together with standard optimization tools for the simulation of the rate-determining proton transfer step for CO2 hydration catalysed by carbonic anhydrase resulting in the first application of a quantum computing device for the simulation of an enzymatic reaction. To this end, we have combined classical force field simulations with quantum mechanical methods on classical and quantum computers in a hybrid calculation approach. The presented technique significantly enhances the accuracy and capabilities of QC-based molecular modeling and finally pushes it into compelling and realistic applications. The framework is general and can be applied beyond the case of computational enzymology.

Metasurfaces, with their capability to control all possible dimensions of light, have become integral to quantum optical applications, including quantum state generation, operation, and tomography. In this work, we utilize a metasurface to generate polarization-hologram hybrid entanglement between a signal-idler photon pair to construct a quantum hologram. The properties of the quantum hologram can be revealed by collapsing the polarization degree of freedom of the idler photon, inducing interference between two holographic states of the signal photon, as a meaningful and selective erasure of the holographic content. In contrary, interference disappears when the idler photon is detected without observing polarization. This process can be further interpreted as a quantum holographic eraser, where the erasing action is visualized with erased contents in holograms. Our construction of polarization-hologram hybrid entangled state with metasurfaces will be useful for quantum communication with enhanced robustness, anti-counterfeiting applications through the additional quantum degrees of freedom, and as an emerging platform for exploring fundamental quantum concepts for entanglement and non-locality., Comment: 12 pages, 6 figures

We provide a new perspective on the divisor theory of graphs, using additive combinatorics. As a test case for this perspective, we compute the gonality of certain families of outerplanar graphs, specifically the strip graphs. The Jacobians of such graphs are always cyclic of Fibonacci order. As a consequence, we obtain several results on the additive properties of Fibonacci numbers.

Along recent decades, an intensive worldwide research activity is focusing both black holes and cosmos (e.g. the dark-energy phenomenon) on the basis of entropic approaches. The Boltzmann-Gibbs-based Bekenstein-Hawking entropy $S_{BH}\propto A/l_P^2$ ($A \equiv$ area; $l_P \equiv$ Planck length) systematically plays a crucial theoretical role although it has a serious drawback, namely that it violates the thermodynamic extensivity of spatially-three-dimensional systems. Still, its intriguing area dependence points out the relevance of considering the form $W(N)\sim \mu^{N^\gamma}\;\;(\mu >1;\gamma >0)$, $W$ and $N$ respectively being the total number of microscopic possibilities and the number of components; $\gamma=1$ corresponds to standard Boltzmann-Gibbs (BG) statistical mechanics. For this $W(N)$ asymptotic behavior, we introduce here, on a group-theory basis, the entropic functional $S_{\alpha,\gamma}=k \Bigl[ \frac{\ln \Sigma_{i=1}^W p_i^\alpha}{1-\alpha} \Bigr]^{\frac{1}{\gamma}} \;(\alpha \in \mathbb{R};\,S_{1,1}=S_{BG}\equiv-k\sum_{i=1}^W p_i \ln p_i)$. This functional simultaneously is {\it extensive} (as required by thermodynamics) and {\it composable} (as required for logic consistency), $\forall (\alpha,\gamma)$. We further show that $(\alpha,\gamma)=(1,2/3)$ satisfactorily agrees with cosmological data measuring neutrinos, Big Bang nucleosynthesis and the relic abundance of cold dark matter particles, as well as dynamical and geometrical cosmological data sets., Comment: 5 pages, 2 figures

A crucial role of container shipping is maximizing container uptake onto vessels, optimizing the efficiency of a fundamental part of the global supply chain. In practice, liner shipping companies include block stowage patterns that ensure that containers in above and below deck partitions of bays have the same destination. Despite preventing restows, increasing free space, and benefits for crane makespan and hydrostatics, this practical planning requirement is rarely included in stowage optimization models. In our paper, we introduce a novel 0-1 IP model that searches in the space of valid paired block stowage patterns, named template planning, which ensures sufficient vessel capacity and limits to crane makespan, trim, and bending moment. Our results show that template planning outperforms traditional allocation planning concerning optimality and runtime efficiency while preserving a sufficiently accurate representation of master planning constraints and objectives., Comment: Preprint accepted for 15th International Conference on Computational Logistics

The Santa Cruz Extreme AO Lab (SEAL) testbed is an optical bench meant to design and develop new wavefront control techniques for high-contrast imaging for segmented telescopes. These techniques allow for astronomical efficiency in exoplanet imaging and characterization. SEAL consists of several wavefront sensors (WFS) and deformable mirrors (DM) that are currently performing techniques like predictive control or non-linear reconstruction. In this paper, we present the implementation and characterization of a new coronagraphic branch on SEAL and assess the contrast limitations in the testbed. For our coronagraphic branch, we used a vector vortex coronagraph which has high contrast performance. The W. M. Keck Observatory also uses a vortex coronagraph, allowing us to compare the limitations with our own coronagraph. We relied on the testbed and simulations of the vortex coronagraph to compare performance with expected ones. To create a more reliable simulation, we also injected in our numerical model data collected by a Zernike Wavefront sensor (ZWFS) used to perform fine wavefront sensing on the bench. Now that the coronagraphic branch is aligned on SEAL, we will be able to use contrast as a metric for the performance of wavefront control methods on the bench., Comment: 10 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation: Adaptive Optics Systems IX (Yokohama 2024), Paper 13097-297

Let $D$ be the Auslander algebra of $\mathbb{C}[t]/(t^n)$, which is quasi-hereditary, and $\mathcal{F}_\Delta$ the subcategory of good $D$-modules. For any $\mathsf{J}\subseteq[1, n-1]$, we construct a subcategory $\mathcal{F}_\Delta(\mathsf{J})$ of $\mathcal{F}_\Delta$ with an exact structure $\mathcal{E}$. We show that under $\mathcal{E}$, $\mathcal{F}_\Delta(\mathsf{J})$ is Frobenius stably 2-Calabi-Yau and admits a cluster structure consisting of cluster tilting objects. This then leads to an additive categorification of the cluster structure on the coordinate ring $\mathbb{C}[\operatorname{Fl}(\mathsf{J})]$ of the (partial) flag variety $\operatorname{Fl}(\mathsf{J})$. We further apply $\mathcal{F}_\Delta(\mathsf{J})$ to study flag combinatorics and the quantum cluster structure on the flag variety $\operatorname{Fl}(\mathsf{J})$. We show that weak and strong separation can be detected by the extension groups $\operatorname{ext}^1(-, -)$ under $\mathcal{E}$ and the extension groups $\operatorname{Ext}^1(-,-)$, respectively. We give a interpretation of the quasi-commutation rules of quantum minors and identify when the product of two quantum minors is invariant under the bar involution. The combinatorial operations of flips and geometric exchanges correspond to certain mutations of cluster tilting objects in $\mathcal{F}_\Delta(\mathsf{J})$. We then deduce that any (quantum) minor is reachable, when $\mathsf{J}$ is an interval. Building on our result for the interval case, Geiss-Leclerc-Schr\"{o}er's result on the quantum coordinate ring for the open cell of $\operatorname{Fl}(\mathsf{J})$ and Kang-Kashiwara-Kim-Oh's enhancement of that to the integral form, we prove that $\mathbb{C}_q[\operatorname{Fl}(\mathsf{J})]$ is a quantum cluster algebra over $\mathbb{C}[q,q^{-1}]$.

In this study, we investigate the dynamics of particles overcoming the hydrodynamic barrier posed by a thin fluid film to achieve contact in finite time, a phenomenon critical in various natural and engineered processes such as enzyme docking, catalysis, and vesicular transport. Using the framework of lubrication theory, which posits that drag force scales inversely with the film thickness, we explore how particles can achieve finite-time contact despite theoretical predictions of infinite time under constant force. We conduct experiments where a spherical particle settles under gravity and magnetic attraction, the latter introducing a spatially varying force. Our findings reveal that a spatially varying force significantly alters the settling trajectory, enabling finite-time contact. The results are supported by a simple model that links hydrodynamic drag and the impact of spatially varying forces. Finally, we illustrate that forces can be inferred from kinematic observations. In the future, this may provide insights into biological and microscale systems where direct force measurements are challenging. Our study demonstrates that varying forces can be harnessed to overcome lubrication barriers, offering potential applications in designing self-assembly systems and improving surface interaction processes.

We present the joint astrometric and direct imaging discovery, mass measurement, and orbital analysis of HD 63754 B (HIP 38216 B), a companion near the stellar-substellar boundary orbiting ~20 AU from its Sun-like host. HD 63754 was observed in our ongoing high-contrast imaging survey targeting stars with significant proper-motion accelerations between Hipparcos and Gaia consistent with wide-separation substellar companions. We utilized archival HIRES and HARPS radial velocity (RV) data, together with the host star's astrometric acceleration extracted from the Hipparcos-Gaia Catalog of Accelerations (HGCA), to predict the location of the candidate companion around HD 63754 A. We subsequently imaged HD 63754 B at its predicted location using the Near Infrared Camera 2 (NIRC2) in the $L'$ band at the W. M. Keck Observatory. We then jointly modeled the orbit of HD 63754 B with RVs, Hipparcos-Gaia accelerations, and our new relative astrometry, measuring a dynamical mass of ${81.9}_{-5.8}^{+6.4} M_{jup}$, an eccentricity of ${0.260}_{-0.059}^{+0.065}$, and a nearly face-on inclination of $174.81_{-0.50}^{+0.48}$ degrees. For HD 63754 B, we obtain an L' band absolute magnitude of $L' = 11.39\pm0.06$ mag, from which we infer a bolometric luminosity of $log(L_{bol}/L_{\odot})= -4.55 \pm0.08$ dex using a comparison sample of L and T dwarfs with measured luminosities. Although uncertainties linger in age and dynamical mass estimates, our analysis points toward HD 63754 B's identity as a brown dwarf on the L/T transition rather than a low-mass star, indicated by its inferred bolometric luminosity and model-estimated effective temperature. Future RV, spectroscopic, and astrometric data such as those from JWST and Gaia DR4 will clarify HD 63754 B's mass, and enable spectral typing and atmospheric characterization.

Designing for rotational stability can dramatically affect the geometry of a space station. If improperly designed, the rotating station could end up catastrophically tumbling end-over-end. Active stabilization can address this problem; however, designing the station with passive rotation stability provides a lower-cost solution. This paper presents passive rotational stability guidelines for four space station geometries. Station stability is first analyzed with thin-shell and thick-shell models. Stability is also analyzed with models of the station's major constituent parts, including outer shells, spokes, floors, air, and shuttle bays., Comment: 64 pages, 75 figures, 8 tables

As the space industry matures, large space stations will be built. This paper organizes and documents constraints on the size of these space stations. Human frailty, station design, and construction impose these constraints. Human limitations include gravity, radiation, air pressure, rotational stability, population, and psychology. Station design limitations include gravity, population, material, geometry, mass, air pressure, and rotational stability. Limits on space station construction include construction approaches, very large stations, and historic station examples. This paper documents all these constraints for thoroughness and review; however, only a few constraints significantly limit the station size. This paper considers rotating stations with radii greater than 10 kilometers. Such stations may seem absurd today; however, with robotic automation and artificial intelligence, such sizes may become feasible in the future., Comment: 65 Pages, 61 figures, 15 tables

An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm$^{-3}$ and 0.1 fm$^{-3}$, matter transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. In this work, we introduce a variational Monte Carlo method based on a neural Pfaffian-Jastrow quantum state, which allows us to model the transition from the liquid phase to neutron-rich nuclei microscopically. At low densities, nuclear clusters dynamically emerge from the microscopic interactions among protons and neutrons, which we model based on pionless effective field theory. Our variational Monte Carlo approach represents a significant improvement over the state-of-the-art auxiliary-field diffusion Monte Carlo method, which is severely hindered by the fermion-sign problem in this low-density regime and cannot capture the onset of clusters. In addition to computing the energy per particle of symmetric nuclear matter and pure neutron matter, we analyze an intermediate isospin-asymmetry configuration to elucidate the formation of nuclear clusters. We also provide evidence that the presence of such nuclear clusters influences the amount of protons in the crust compared to protons in beta-equilibrated, neutrino-transparent matter., Comment: 10 pages, 5 figures

This paper introduces a set of terms that are intended to act as an interface between cyber ontologies (like a file system ontology or a data fusion ontology) and top- and mid-level ontologies, specifically Basic Formal Ontology and the Common Core Ontologies. These terms center on what makes cyberinformation management unique: numerous acts of copying items of information, the aggregates of copies that result from those acts, and the faithful members of those aggregates that represent all other members., Comment: 14

The dynamical interactions between young binaries can perturb the material distribution of their circumstellar disks, and modify the planet formation process. In order to constrain the impact and nature of the binary interaction in the RW Aur system (bound or unbound), we analyzed the circumstellar material at 1.3 mm wavelengths, as observed at multiple epochs by ALMA. We analyzed the disk properties through parametric visibility modeling, and we used this information to constrain the dust morphology and the binary orbital period. We imaged the dust continuum emission of RW Aur with a resolution of 3 au, and we find that the radius enclosing 90% of the flux (R90%) is 19 au and 14 au for RW Aur A and B, respectively. By modeling the relative distance of the disks at each epoch, we find a consistent trend of movement for the disk of RW Aur B moving away from the disk of RW Aur A at an approximate rate of 3 mas/yr (about 0.5 au/yr in sky-projected distance). By combining ALMA astrometry, historical astrometry, and the dynamical masses of each star, we constrain the RW Aur binary stars to be most likely in a high-eccentricity elliptical orbit with a clockwise prograde orientation relative to RW Aur A, although low-eccentricity hyperbolic orbits are not ruled out by the astrometry. Our analysis does not exclude the possibility of a disk collision during the last interaction, which occurred $295_{-74}^{+21}$ yr ago relative to beginning of 2024. Evidence for the close interaction is found in a tentative warp of 6 deg in the inner 3 au of the disk of RW Aur A, in the brightness temperature of both disks, and in the morphology of the gas emission. A narrow ring that peaks at 6 au around RW Aur B is suggestive of captured material from the disk around RW Aur A., Comment: Accepted in A&A. All self-calibrated data and products are publicly available in https://doi.org/10.5281/zenodo.12825068 . Short video summary in https://youtu.be/IOjEW8Vrj9Q