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Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their computational efficiency, scalability, and broad applicability. The training datasets for many such models are derived from density-functional theory calculations, typically using a semilocal exchange-correlation functional. As a result, long-range interactions such as London dispersion are often missing in these models. We investigate whether this missing component can be addressed by combining a graph deep learning potential with semiempirical dispersion models. We assess this combination by deriving the equations of state for layered pnictogen chalcohalides BiTeBr and BiTeI and performing crystal structure optimizations for a broader set of V-VI-VII compounds with various stoichiometries, many of which possess van der Waals gaps. We characterize the optimized crystal structures by calculating their X-ray diffraction patterns and radial distribution function histograms, which are also used to compute Earth mover's distances to quantify the dissimilarity between the optimized and corresponding experimental structures. We find that dispersion-corrected graph deep learning potentials generally (though not universally) provide a more realistic description of these compounds due to the inclusion of van der Waals attractions. In particular, their use results in systematic improvements in predicting not only the van der Waals gap but also the layer thickness in layered V-VI-VII compounds. Our results demonstrate that the combined potentials studied here, derived from a straightforward approach that neither requires fine-tuning the training nor refitting the potential parameters, can significantly improve the description of layered polar crystals.

We report our findings on a spectroscopic survey of seven unresolved DA+DB binary white dwarf candidates. We have discovered extreme spectroscopic variations in one of these candidates, SDSS J084716.21+484220.40. Previous analysis failed to reproduce the optical spectrum using a single object with a homogeneous atmosphere. Our time-resolved spectroscopy reveals a double-faced white dwarf that switches between a DBA and DA spectral type over 6.5 or 8.9 hours due to varying surface abundances. We also provide time-series spectroscopy of the magnetic DBA, SDSS J085618.94+161103.6 (LB 8915), and confirm an inhomogeneous atmosphere. We employ an atmosphere model with hydrogen caps and a helium belt that yields excellent fits to our time-resolved spectra. We use the oblique rotator model to derive the system geometry for both targets. With the addition of these two objects, the emerging class of double-faced white dwarfs now consists of seven members. We summarize the properties of this new class of objects, and discuss how magnetism impacts the convective processes and leads to the formation of double-faced white dwarfs. We identify cooler versions of white dwarfs with inhomogeneous atmospheres among the cool magnetic DA white dwarf sample, where the H$\alpha$ line is shallower than expected based on pure hydrogen atmosphere models., Comment: 14 pages, 9 figures, Accepted for publication in ApJ

We develop a search algorithm for systems of $q$-difference equations satisfied by Andrews-Gordon type double series. We then couple the search algorithm with Euler's algorithm for finding infinite products to narrow the search space. We exemplify some findings of the algorithm, along with their proofs. We also explain some of the double series in a base partition and moves framework., Comment: 22 pages, 2 figures

Molecular Communication (MC) leverages the power of diffusion to transmit molecules from a transmitter to a receiver. A wide variety of modulation techniques based on molecule concentration, type, and release time have been extensively studied in the literature. In this paper, we propose a novel modulation technique that encodes the information into the relative concentrations of multiple molecules called Multi Ratio Shift Keying (MRSK) designed for diffusion-based MC without drift. We show that leveraging all possible ratios in a set of molecules can help mitigate the effects of intersymbol interference (ISI) and provide a flexible communication channel. To evaluate the performance of the MRSK, we develop a mathematical framework for studying the statistics of the ratio of random variables, focusing on noncentral Gaussian distributions. We then assess MRSK performance both analytically and through particle-based simulations under various channel conditions, identifying potential sources of error in our system model. Additionally, we conduct a comparative analysis of commonly used modulation schemes in the literature based on bit error rate (BER). The results show that MRSK significantly outperforms all traditional modulation schemes considered in this study in terms of BER. MRSK offers a promising, flexible, and more reliable communication method for the future of the MC paradigm., Comment: 13 pages, TCOM paper

In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources. The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting bibliographic information, fine-tuned embedding models, semantic chunking, and an abstract-first retrieval method, to significantly improve the relevance and accuracy of the retrieved information. This implementation of AI specifically addresses the challenge of academic literature navigation. A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics, particularly Context Relevance, underscoring the system's effectiveness in reducing information overload and enhancing decision-making processes. Our findings highlight the potential of this enhanced Retrieval-Augmented Generation system to transform academic exploration within data science, ultimately advancing the workflow of research and innovation in the field.

We perform a model-independent analysis of the dimension-six terms that are generated in the low energy effective theory when a hidden sector that communicates with the Standard Model (SM) through a specific portal operator is integrated out. We work within the Standard Model Effective Field Theory (SMEFT) framework and consider the Higgs, neutrino and hypercharge portals. We find that, for each portal, the forms of the leading dimension-six terms in the low-energy effective theory are fixed and independent of the dynamics in the hidden sector. For the Higgs portal, we find that two independent dimension-six terms are generated, one of which has a sign that, under certain conditions, is fixed by the requirement that the dynamics in the hidden sector be causal and unitary. In the case of the neutrino portal, for a single generation of SM fermions and assuming that the hidden sector does not violate lepton number, a unique dimension-six term is generated, which corresponds to a specific linear combination of operators in the Warsaw basis. For the hypercharge portal, a unique dimension-six term is generated, which again corresponds to a specific linear combination of operators in the Warsaw basis. For both the neutrino and hypercharge portals, under certain conditions, the signs of these terms are fixed by the requirement that the hidden sector be causal and unitary. We perform a global fit of these dimension-six terms to electroweak precision observables, Higgs measurements and diboson production data and determine the current bounds on their coefficients., Comment: 24+1 pages, 6 figures and 1 table

We increase the spectroscopic completeness of the 100 pc white dwarf sample in the SDSS footprint with 840 additional spectra. Our spectroscopy is 86% complete for white dwarfs hotter than $T_{\rm eff}= 5000$ K, where H$\alpha$ remains visible and provides reliable constraints on the atmospheric composition. We identify 2108 DA white dwarfs with pure hydrogen atmospheres, and show that ultramassive DA white dwarfs with $M\geq1.1~M_{\odot}$ are an order of magnitude less common below 10,000 K. This is consistent with a fraction of them getting stuck on the crystallization sequence due to $^{22}$Ne distillation. In addition, there are no ultramassive DA white dwarfs with $M\geq1.1~M_{\odot}$ and $T_{\rm eff}\leq6000$ K in our sample, likely because Debye cooling makes them rapidly fade away. We detect a significant trend in the fraction of He-atmosphere white dwarfs as a function of temperature; the fraction increases from 9% at 20,000 K to 32% at 6000 K. This provides direct evidence of convective mixing in cool DA white dwarfs. Finally, we detect a relatively tight sequence of low-mass DQ white dwarfs in color-magnitude diagrams for the first time. We discuss the implications of this tight DQ sequence, and conclude with a discussion of the future prospects from the upcoming ULTRASAT mission and the large-scale multi-fiber spectroscopic surveys., Comment: ApJ, in press

We present the discovery and analysis of a nearby eclipsing ultra-compact accreting binary at coordinates 11:38:10.91 $-$51:39:49.15 (SMSS J1138$-$5139), the first well-constrained LISA-detectable Type Ia supernova progenitor. Our time series optical spectroscopy identifies its orbital period through radial velocity monitoring at $P_{\rm orb,RV}=27.69\pm0.03~{\rm min}$; twice the photometric period seen in 2-minute cadence data from TESS Sector 37. We model our optical spectroscopy together with new simultaneous multi-band time series photometry from Gemini to place constraints on the binary parameters. Our light curve modeling finds that SMSS J1138$-$5139 contains an $M_2=0.24~{\rm M_\odot}$ pre-white dwarf donor with a massive $M_1=0.99~{\rm M_\odot}$ white dwarf accretor at orbital inclination $i=88.7~{\rm deg}$. Based on our photometrically derived system parameters, we expect that gravitational wave radiation will drive SMSS J1138$-$5139 to a merger within $\tau=5.7\pm0.3~{\rm Myr}$ and result in a Type Ia supernova. Even without a direct merger event, the component masses of SMSS J1138$-$5139 and active hydrogen accretion suggest that eventual helium accretion will likely also trigger a Type Ia supernova explosion through the dynamically-driven double-degenerate double-detonation (D6) channel. We expect LISA to detect the gravitational wave emission from SMSS J1138$-$5139 with signal-to-noise $7-10$ after a 48-month mission., Comment: 15 pages, 10 figures, 1 table. Submitted to ApJ

The domain of computational design, driven by advancements in Generative AI, is transforming creative fields. We explore the transformative effects of Generative AI on the architectural design process and discuss the role of the architect. The case of architecture is interesting as designing houses is complex, involving extensive customer interaction. We employ a within-subject experiment using a popular general-purpose text-to-image tool for generating designs and providing feedback on existing designs, followed by expert interviews. The study reveals that AI can disrupt the ideation phase by enabling clients to engage in the design process through rapid visualization of their own ideas. In turn, the architect's role shifts more towards assessing the feasibility of designs generated conjointly by clients and AI. Our study also shows that while AI can provide valuable feedback on designs, it might fail to generate such designs, allowing for interesting connections to foundations in computer science, i.e., NP-completeness. AI's feedback also tends to hamper creativity and innovation by suggesting altering novel, innovative approaches toward more standardized designs. Our study also reveals that there is uncertainty among architects about the interpretative sovereignty of architecture and loss of meaning and identity when AI increasingly takes over authorship in the design process.

We present a new class of interacting dark sector models that can address the Hubble tension. Interacting dark radiation (DR) has previously been put forward as a solution to the problem, but this proposal is disfavored by the high-$\ell$ cosmic microwave background (CMB) data. We modify this basic framework by introducing a subcomponent of dark matter (DM) that interacts strongly with the DR, so that together they constitute a tightly coupled fluid at early times. We show that if this subcomponent decouples from the interacting DR during the CMB epoch, the $\ell$ modes of the CMB that entered the horizon before decoupling are impacted differently from those that entered after, allowing a solution to the problem. We present a model that realizes this framework, which we dub "New Atomic Dark Matter", or nuADaM, in which the interacting dark matter (iDM) subcomponent is composed of dark atoms, and dark "neutrinos" with long-range interactions contribute to the DR, hence the name of the model. This iDM subcomponent is acoustic at early times but decouples from the DR following dark recombination. In contrast to conventional atomic dark matter (ADM) models, the dark photon is part of a richer DR sector, which ensures that it continues to be self-interacting even after recombination. We show that this model admits a fit to the available cosmological data that is significantly better than both $\Lambda$CDM and conventional ADM., Comment: 47 pages, 15 figures

Molecular Communication (MC) is an important communication paradigm found in nature. Odor-based Molecular Communication (OMC) is a specific type of MC with promising potential and a wide range of applications. In this paper, we examine OMC communication between plants in the context of stress communication. Specifically, we explore how plants use Biological Volatile Organic Compounds (BVOCs) to convey information about the stresses they are experiencing to neighboring plants. We constructed an end-to-end mathematical model that discovers the underlying physical and biological phenomena affecting stress communication. To the best of our knowledge, this is the first study to model this end-to-end stress communication. We numerically analyzed our system under different scenarios using MATLAB. Using experimental data from the literature, we demonstrated that continuous gene regulation can approximate BVOC emissions in plants under different stress conditions. Consequently, we applied this model to these stressors and plants to accurately approximate BVOC emissions. We also investigated a modulation method that plants use to send their messages, namely Ratio Shift Keying. Upon analyzing this method, we found that it benefits plants by both enabling a multiple access channel and preventing competitor plants from obtaining the information.

Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational power for varied tasks, including structured data processing, Monte Carlo simulations, and end-user analysis. Centralized workflow and data management systems are employed to handle these demands, but current decision-making processes for data placement and payload allocation are often heuristic and disjointed. This optimization challenge potentially could be addressed using contemporary machine learning methods, such as reinforcement learning, which, in turn, require access to extensive data and an interactive environment. Instead, we propose a generative surrogate modeling approach to address the lack of training data and concerns about privacy preservation. We have collected and processed real-world job submission records, totaling more than two million jobs through 150 days, and applied four generative models for tabular data -- TVAE, CTAGGAN+, SMOTE, and TabDDPM -- to these datasets, thoroughly evaluating their performance. Along with measuring the discrepancy among feature-wise distributions separately, we also evaluate pair-wise feature correlations, distance to closest record, and responses to pre-trained models. Our experiments indicate that SMOTE and TabDDPM can generate similar tabular data, almost indistinguishable from the ground truth. Yet, as a non-learning method, SMOTE ranks the lowest in privacy preservation. As a result, we conclude that the probabilistic-diffusion-model-based TabDDPM is the most suitable generative model for managing job record data., Comment: 8 pages, 5 figures, to be presented in SC24 AI4S Workshop

Trans-Neptunian objects (TNOs) are considered to be among the most primitive objects in our Solar System. Knowledge of their primary physical properties is essential for understanding their origin and the evolution of the outer Solar System. We predicted a stellar occultation by this TNO for 2020 October 23 UT and ran a specific campaign to investigate this event. We derived the projected profile shape and size from the occultation observations by means of an elliptical fit to the occultation chords. We also performed photometric observations of (143707) 2003 UY117 to obtain the absolute magnitude and the rotational period from the observed rotational light curve. Finally, we combined these results to derive the three-dimensional shape, volume-equivalent diameter, and geometric albedo for this TNO. From the stellar occultation, we obtained a projected ellipse with axes of $(282 \pm 18) \times (184 \pm 32)$ km. The area-equivalent diameter for this ellipse is $D_\textrm{eq,A} = 228 \pm 21$ km. From our photometric $R$ band observations, we derived an absolute magnitude of $H_V = 5.97 \pm 0.07$ mag using $V-R = 0.46 \pm 0.07$ mag, which was derived from a $V$ band subset of these data. The rotational light curve has a peak-to-valley amplitude of $\Delta m = 0.36 \pm 0.13$ mag. We find the most likely rotation period to be $P = 12.376 \pm 0.0033$ hours. By combining the occultation with the rotational light curve results and assuming a triaxial ellipsoid, we derived axes of $a \times b \times c = (332 \pm 24)$ km $\times$ $(216 \pm 24)$ km $\times$ $(180\substack{+28\\-24})$ km for this ellipsoid, and therefore a volume-equivalent diameter of $D_\textrm{eq,V} = 235 \pm 25$ km. Finally, the values for the absolute magnitude and for the area-equivalent diameter yield a geometric albedo of $p_V = 0.139 \pm 0.027$., Comment: 9 pages, 7 figures, accepted for publication in Astronomy and Astrophysics on Sept 13, 2024

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay., Comment: 15 pages, 3 figures, 1 table

Infrared-faint white dwarfs are cool white dwarfs exhibiting significant infrared flux deficits, most often attributed to collision-induced absorption (CIA) from H$_2$-He in mixed hydrogen-helium atmospheres. We present James Webb Space Telescope (JWST) near- and mid-infrared spectra of three such objects using NIRSpec (0.6-5.3 $\mu$m) and MIRI (5-14 $\mu$m): LHS 3250, WD J1922+0233, and LHS 1126. Surprisingly, for LHS 3250, we detect no H$_2$-He CIA absorption at 2.4 $\mu$m, instead observing an unexpected small flux bump at this wavelength. WD J1922+0233 exhibits the anticipated strong absorption feature centered at 2.4 $\mu$m, but with an unexpected narrow emission-like feature inside this absorption band. LHS 1126 shows no CIA features and follows a $\lambda^{-2}$ power law in the mid-infrared. LHS 1126's lack of CIA features suggests a very low hydrogen abundance, with its infrared flux depletion likely caused by He-He-He CIA. For LHS 3250 and WD J1922+0233, the absence of a 1.2 $\mu$m CIA feature in both stars argues against ultracool temperatures, supporting recent suggestions that infrared-faint white dwarfs are warmer and more massive than previously thought. This conclusion is further solidified by Keck near-infrared spectroscopy of seven additional objects. We explore possible explanations for the unexpected emission-like features in both stars, and temperature inversions above the photosphere emerge as a promising hypothesis. Such inversions may be common among the infrared-faint population, and since they significantly affect the infrared spectral energy distribution, this would impact their photometric fits. Further JWST observations are needed to confirm the prevalence of this phenomenon and guide the development of improved atmospheric models., Comment: Accepted for publication in ApJ

The significance of Mendeleev's periodic table extends beyond the classification of elements; it lies in its remarkable predictive power for discovering new elements and properties, revealing the underlying symmetrical patterns of nature that were only fully understood with the advent of quantum mechanics. Fundamental material properties, such as electron transport and magnetism, are also governed by crystal symmetry. In particular, spin transport depends on the spin polarization of electronic states, and recently discovered materials where the electron spin polarization is independent of momentum - a property known as a persistent spin texture (PST) - promise extended spin lifetime and efficient spin accumulation. In this paper, we establish the general conditions for the existence of symmetry-protected PST in bulk crystals. By systematically analyzing all 230 crystallographic space groups, similar to elements in the periodic table, we demonstrate that PST is universally present in all nonmagnetic solids lacking inversion symmetry. Using group theory, we identify the regions within the Brillouin zone that host PST and determine the corresponding directions of spin polarization. Our findings, supported by first-principles calculations of representative materials, open the route for discovering robust spintronic materials based on PST.

The availability of weather data from remotely sensed Earth observation (EO) data has reduced the cost of including weather variables in econometric models. Weather variables are common instrumental variables used to predict economic outcomes and serve as an input into modelling crop yields for rainfed agriculture. The use of EO data in econometric applications has only recently been met with a critical assessment of the suitability and quality of this data in economics. We quantify the significance and magnitude of the effect of measurement error in EO data in the context of smallholder agricultural productivity. We find that different measurement methods from different EO sources: findings are not robust to the choice of EO dataset and outcomes are not simply affine transformations of one another. This begs caution on the part of researchers using these data and suggests that robustness checks should include testing alternative sources of EO data., Comment: arXiv admin note: substantial text overlap with arXiv:2202.05220

We examine the impact of livelihood diversification on food insecurity amid the COVID-19 pandemic. Our analysis uses household panel data from Ethiopia, Malawi, and Nigeria in which the first round was collected immediately prior to the pandemic and extends through multiple rounds of monthly data collection during the pandemic. Using this pre- and post-outbreak data, and guided by a pre-analysis plan, we estimate the causal effect of livelihood diversification on food insecurity. Our results do not support the hypothesis that livelihood diversification boosts household resilience. Though income diversification may serve as an effective coping mechanism for small-scale shocks, we find that for a disaster on the scale of the pandemic this strategy is not effective. Policymakers looking to prepare for the increased occurrence of large-scale disasters will need to grapple with the fact that coping strategies that gave people hope in the past may fail them as they try to cope with the future.

We present follow-up spectroscopy and a detailed model atmosphere analysis of 29 wide double white dwarfs, including eight systems with a crystallized C/O core member. We use state-of-the-art evolutionary models to constrain the physical parameters of each star, including the total age. Assuming that the members of wide binaries are coeval, any age difference between the binary members can be used to test the cooling physics for white dwarf stars, including potential delays due to crystallization and $^{22}$Ne distillation. We use our control sample of 14 wide binaries with non-crystallized members to show that this method works well; the control sample shows an age difference of only $\Delta$Age = $-0.03 \pm$ 0.15 Gyr between its members. For the eight crystallized C/O core systems we find a cooling anomaly of $\Delta$Age= 1.13$^{+1.20}_{-1.07}$ Gyr. Even though our results are consistent with a small additional cooling delay ($\sim1$ Gyr) from $^{22}$Ne distillation and other neutron-rich impurities, the large uncertainties make this result not statistically significant. Nevertheless, we rule out cooling delays longer than 3.6 Gyr at the 99.7% ($3\sigma$) confidence level for 0.6-0.9 $M_{\odot}$ white dwarfs. Further progress requires larger samples of wide binaries with crystallized massive white dwarf members. We provide a list of subgiant + white dwarf binaries that could be used for this purpose in the future., Comment: 19 pages, including 6 figures and 4 tables, accepted for publication in ApJ

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

We present a detailed model atmosphere analysis of massive white dwarfs with $M > 0.9~M_\odot$ and $T_{\rm eff}\geq11,000$ K in the Montreal White Dwarf Database 100 pc sample and the Pan-STARRS footprint. We obtained follow-up optical spectroscopy of 109 objects with no previous spectral classification in the literature. Our spectroscopic follow-up is now complete for all 204 objects in the sample. We find 118 normal DA white dwarfs, including 45 massive DAs near the ZZ Ceti instability strip. There are no normal massive DBs: the six DBs in the sample are strongly magnetic and/or rapidly rotating. There are 20 massive DQ white dwarfs in our sample, and all are found in the crystallization sequence. In addition, 66 targets are magnetic (32% of the sample). We use magnetic white dwarf atmosphere models to constrain the field strength and geometry using offset dipole models. We also use magnetism, kinematics, and rotation measurements to constrain the fraction of merger remnant candidates among this population. The merger fraction of this sample increases from 25% for 0.9-$1~M_{\odot}$ white dwarfs to 49% for 1.2-$1.3~M_{\odot}$. However, this fraction is as high as $78_{-7}^{+4}$% for 1.1-$1.2~M_{\odot}$ white dwarfs. Previous works have demonstrated that 5-9% of high-mass white dwarfs stop cooling for $\sim8$ Gyr due to the $^{22}$Ne distillation process, which leads to an overdensity of Q-branch stars in the solar neighborhood. We demonstrate that the over-abundance of the merger remnant candidates in our sample is likely due to the same process., Comment: 34 pages, 16 figures, accepted for publication in ApJ

This paper studies two crucial problems in the context of coded distributed storage systems directly related to their performance: 1) for a fixed alphabet size, determine the minimum number of servers the system must have for its service rate region to contain a prescribed set of points; 2) for a given number of servers, determine the minimum alphabet size for which the service rate region of the system contains a prescribed set of points. The paper establishes rigorous upper and lower bounds, as well as code constructions based on techniques from coding theory, optimization, and projective geometry.

Recent advances in archaeogenomics have granted access to previously unavailable biological information with the potential to further our understanding of past social dynamics at a range of scales. However, to properly integrate these data within archaeological narratives, new methodological and theoretical tools are required. Effort must be put into finding new methods for weaving together different datasets where material culture and archaeogenomic data are both constitutive elements. This is true on a small scale, when we study relationships at the individual level, and at a larger scale when we deal with social and population dynamics. Specifically, in the study of kinship systems it is essential to contextualize and make sense of biological relatedness through social relations, which, in archaeology, is achieved by using material culture as a proxy. In this paper we propose a Network Science framework to integrate archaeogenomic data and material culture at an intrasite scale to study biological relatedness and social organization at the Neolithic site of \c{C}atalh\"oy\"uk. Methodologically, we propose the use of network variance to investigate the concentration of biological relatedness and material culture within networks of houses. This approach allowed us to observe how material culture similarity between buildings gives valuable information on potential biological relationships between individuals and how biogenetic ties concentrate at specific localities on site.

This paper examines the intersection of goals and values within grassroots organizations operating in the realm of quantum technologies (QT) education. It delineates a fundamental distinction between the objective to provide education and the drive to democratize learning through principles of inclusivity, accessibility, and diversity. The analysis reveals how these organizations navigate their nascent stages, grappling with the dual challenge of adhering to their foundational values while aspiring for sustainable growth and development in the highly specialized field of QT. The study uncovers the strategic approaches adopted by these entities, including efforts to create educational ecosystems and foster community engagement. The research underscores the potential vulnerabilities of these grassroots organizations, particularly in relation to the longevity and evolution of their initiatives as members transition into professional roles within the quantum sector. Through this investigation, the paper contributes to a nuanced understanding of how emerging educational organizations in the QT field balance their ideological commitments with practical growth considerations, highlighting the critical factors that influence their trajectory and impact., Comment: 21 pages, 6 figures

Centaurs, distinguished by their volatile-rich compositions, play a pivotal role in understanding the formation and evolution of the early solar system, as they represent remnants of the primordial material that populated the outer regions. Stellar occultations offer a means to investigate their physical properties, including shape, rotational state, or the potential presence of satellites and rings. This work aims to conduct a detailed study of the centaur (54598) Bienor through stellar occultations and rotational light curves from photometric data collected during recent years. We successfully predicted three stellar occultations by Bienor, which were observed from Japan, Eastern Europe, and the USA. In addition, we organized observational campaigns from Spain to obtain rotational light curves. At the same time, we develop software to generate synthetic light curves from three-dimensional shape models, enabling us to validate the outcomes through computer simulations. We resolve Bienor's projected ellipse for December 26, 2022, determine a prograde sense of rotation, and confirm an asymmetric rotational light curve. We also retrieve the axes of its triaxial ellipsoid shape as a = (127 $\pm$ 5) km, b = (55 $\pm$ 4) km, and c = (45 $\pm$ 4) km. Moreover, we refine the rotation period to 9.1736 $\pm$ 0.0002 hours and determine a geometric albedo of (6.5 $\pm$ 0.5) %, higher than previously determined by other methods. Finally, by comparing our findings with previous results and simulated rotational light curves, we analyze whether an irregular or contact-binary shape, the presence of an additional element such as a satellite, or significant albedo variations on Bienor's surface, may be present.

Efficient and compact single photon emission platforms operating at room temperature with ultrafast speed and high brightness will be fundamental components of the emerging quantum communications and computing fields. However, so far, it is very challenging to design practical deterministic single photon emitters based on nanoscale solid-state materials that meet the fast emission rate and strong brightness demands. Here, a solution is provided to this longstanding problem by using metallic nanocavities integrated with hexagonal boron nitride (hBN) flakes with defects acting as nanoscale single photon emitters (SPEs) at room temperature. The presented hybrid nanophotonic structure creates a rapid speedup and large enhancement in single photon emission at room temperature. Hence, the nonclassical light emission performance is substantially improved compared to plain hBN flakes and hBN on gold-layered structures without nanocavity. Extensive theoretical calculations are also performed to accurately model the new hybrid nanophotonic system and prove that the incorporation of plasmonic nanocavity is key to efficient SPE performance. The proposed quantum nanocavity single photon source is expected to be an element of paramount importance to the envisioned room-temperature integrated quantum photonic networks.

The inherently weak chiroptical responses of natural materials limit their usage for controlling and enhancing chiral light-matter interactions. Recently, several nanostructures with subwavelength scale dimensions were demonstrated, mainly due to the advent of nanofabrication technologies, as a potential alternative to efficiently enhance chirality. However, the intrinsic lossy nature of metals and inherent narrowband response of dielectric planar thin films or metasurface structures pose severe limitations toward the practical realization of broadband and tailorable chiral systems. Here, we tackle these problems by designing all-dielectric silicon-based L-shaped optical metamaterials based on tilted nanopillars that exhibit broadband and enhanced chiroptical response in transmission operation. We use an emerging bottom-up fabrication approach, named glancing angle deposition, to assemble these dielectric metamaterials on a wafer scale. The reported strong chirality and optical anisotropic properties are controllable in terms of both amplitude and operating frequency by simply varying the shape and dimensions of the nanopillars. The presented nanostructures can be used in a plethora of emerging nanophotonic applications, such as chiral sensors, polarization filters, and spin-locked nanowaveguides.

Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challenging. Furthermore, their complexity and heterogeneity make performance reproducibility hard. In this paper, we propose workflow mini-apps as a tool to address the challenges in building and testing workflows while controlling the fidelity of representing realworld workflows. Workflow mini-apps are deployed and run on various HPC systems and architectures without workflow-specific constraints. We offer insight into their design and implementation, providing an analysis of their performance and reproducibility. Workflow mini-apps thus advance the science of workflows by providing simple, portable, and managed (fidelity) representations of otherwise complex and difficult-to-control real workflows.

Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and astronomy. A large-scale solution like Google Pathways with a distributed execution environment for deep learning models exists but is proprietary. Integrating existing open-source, scalable runtime tools and data frameworks on high-performance computing (HPC) platforms is crucial to address these challenges. Our objective is to establish a smooth and unified method of combining data engineering and deep learning frameworks with diverse execution capabilities that can be deployed on various high-performance computing platforms, including cloud and supercomputers. We aim to support heterogeneous systems with accelerators, where Cylon and other data engineering and deep learning frameworks can utilize heterogeneous execution. To achieve this, we propose Radical-Cylon, a heterogeneous runtime system with a parallel and distributed data framework to execute Cylon as a task of Radical Pilot. We thoroughly explain Radical-Cylon's design and development and the execution process of Cylon tasks using Radical Pilot. This approach enables the use of heterogeneous MPI-communicators across multiple nodes. Radical-Cylon achieves better performance than Bare-Metal Cylon with minimal and constant overhead. Radical-Cylon achieves (4~15)% faster execution time than batch execution while performing similar join and sort operations with 35 million and 3.5 billion rows with the same resources. The approach aims to excel in both scientific and engineering research HPC systems while demonstrating robust performance on cloud infrastructures. This dual capability fosters collaboration and innovation within the open-source scientific research community., Comment: 14 pages, 16 figures, 2 tables

Identifying black holes is essential for comprehending the development of stars and uncovering novel principles of physics. Gravitational microlensing provides an exceptional opportunity to examine an undetectable population of black holes in the Milky Way. In particular, long-lasting events are likely to be associated with massive lenses, including black holes. We present an analysis of the Gaia18ajz microlensing event, reported by the Gaia Science Alerts system, which has exhibited a long timescale and features indicative of the annual microlensing parallax effect. Our objective is to estimate the parameters of the lens based on the best-fitting model. We utilized photometric data obtained from the Gaia satellite and terrestrial observatories to investigate a variety of microlensing models and calculate the most probable mass and distance to the lens, taking into consideration a Galactic model as a prior. Subsequently, weapplied a mass-brightness relation to evaluate the likelihood that the lens is a main sequence star. We also describe the DarkLensCode (DLC), an open-source routine which computes the distribution of probable lens mass, distance and luminosity employing the Galaxy priors on stellar density and velocity for microlensing events with detected microlensing parallax. We modelled Gaia18ajz event and found its two possible models with most likely Einstein timescale of $316^{+36}_{-30}$ days and $299^{+25}_{-22}$ days. Applying Galaxy priors for stellar density and motion, we calculated the most probable lens mass of $4.9^{+5.4}_{-2.3} M_\odot$ located at $1.14^{+0.75}_{-0.57}\,\text{kpc}$ or $11.1^{+10.3}_{-4.7} M_\odot$ located at $1.31^{+0.80}_{-0.60}\,\text{kpc}$. Our analysis of the blended light suggests that the lens is likely a dark remnant of stellar evolution, rather than a main sequence star.

Four years after the discovery of a unique DAQ white dwarf with a hydrogen-dominated and carbon-rich atmosphere, we report the discovery of four new DAQ white dwarfs, including two that were not recognized properly in the literature. We find all five DAQs in a relatively narrow mass and temperature range of $M=1.14-1.19~M_{\odot}$ and $T_{\rm eff}=13,000-17,000$ K. In addition, at least two show photometric variations due to rapid rotation with $\approx10$ min periods. All five are also kinematically old, but appear photometrically young with estimated cooling ages of about 1 Gyr based on standard cooling tracks, and their masses are roughly twice the mass of the most common white dwarfs in the solar neighborhood. These characteristics are smoking gun signatures of white dwarf merger remnants. Comparing the DAQ sample with warm DQ white dwarfs, we demonstrate that there is a range of hydrogen abundances among the warm DQ population, and the distinction between DAQ and warm DQ white dwarfs is superficial. We discuss the potential evolutionary channels for the emergence of the DAQ subclass, and suggest that DAQ white dwarfs are trapped on the crystallization sequence, and may remain there for a significant fraction of the Hubble time., Comment: ApJ, in press

Molecular communication (MC) has promising potential and a wide range of applications. However, odor-based communication which is common in nature, has not been sufficiently examined within the context of MC, yet. In this paper, we introduce a novel approach for implementing odor-based MC systems. We propose a new modulation scheme called Odor Perceptual Shift Keying (OPSK), which encodes information by shifting the perceptual values of odor molecules in pleasantness, intensity and edibility dimensions. We construct a system which transmits OPSK modulated signals between a transmitter and receiver. We conduct analyses on the system parameters to simulate performance metrics such as symbol error rate (SER) and symbol rate (SR). Our analyses indicate that OPSK has a potential for realizing odor-based MC systems. We find that under certain conditions, reliable odor-based MC systems can be implemented using OPSK across a variety of distance ranges from millimeters up to kilometers. Additionally, we introduce adaptive symbol transmission to our system for input symbol sequences featuring symbols that occur with unequal probabilities. We further demonstrate that the proposed algorithm at the transmitter side can achieve extended operation times., Comment: 12 pages, 11 figures

Neural networks successfully capture the computational power of the human brain for many tasks. Similarly inspired by the brain architecture, Nearest Neighbor (NN) representations is a novel approach of computation. We establish a firmer correspondence between NN representations and neural networks. Although it was known how to represent a single neuron using NN representations, there were no results even for small depth neural networks. Specifically, for depth-2 threshold circuits, we provide explicit constructions for their NN representation with an explicit bound on the number of bits to represent it. Example functions include NN representations of convex polytopes (AND of threshold gates), IP2, OR of threshold gates, and linear or exact decision lists., Comment: This paper is accepted to ISIT 2024. 2nd version has revisions for better clarity, more citations, and more explanation in the proofs. No results are changed

The Nearest Neighbor (NN) Representation is an emerging computational model that is inspired by the brain. We study the complexity of representing a neuron (threshold function) using the NN representations. It is known that two anchors (the points to which NN is computed) are sufficient for a NN representation of a threshold function, however, the resolution (the maximum number of bits required for the entries of an anchor) is $O(n\log{n})$. In this work, the trade-off between the number of anchors and the resolution of a NN representation of threshold functions is investigated. We prove that the well-known threshold functions EQUALITY, COMPARISON, and ODD-MAX-BIT, which require 2 or 3 anchors and resolution of $O(n)$, can be represented by polynomially large number of anchors in $n$ and $O(\log{n})$ resolution. We conjecture that for all threshold functions, there are NN representations with polynomially large size and logarithmic resolution in $n$., Comment: This paper is accepted to ISIT 2024. 2nd version had revisions for better clarity, fixing of typos. No results are changed

We investigate usage of nonlinear wave chaotic amorphous silicon (a-Si) cavities as physically unclonable functions (PUF). Machine learning attacks on integrated electronic PUFs have been demonstrated to be very effective at modeling PUF behavior. Such attacks on integrated a-Si photonic PUFs are investigated through application of algorithms including linear regression, k-nearest neighbor, decision tree ensembles (random forests and gradient boosted trees), and deep neural networks (DNNs). We found that DNNs performed the best among all the algorithms studied but still failed to completely break the a-Si PUF security which we quantify through a private information metric. Furthermore, machine learning resistance of a-Si PUFs were found to be directly related to the strength of their nonlinear response.

Context - Around the year 2000, Triton's south pole experienced an extreme summer solstice that occurs every about 650 years, when the subsolar latitude reached about 50{\deg}. Bracketing this epoch, a few occultations probed Triton's atmosphere in 1989, 1995, 1997, 2008 and 2017. A recent ground-based stellar occultation observed on 6 October 2022 provides a new measurement of Triton's atmospheric pressure which is presented here. Aims- The goal is to constrain the Volatile Transport Models (VTMs) of Triton's atmosphere that is basically in vapor pressure equilibrium with the nitrogen ice at its surface. Methods - Fits to the occultation light curves yield Triton's atmospheric pressure at the reference radius 1400 km, from which the surface pressure is induced. Results - The fits provide a pressure p_1400= 1.211 +/- 0.039 microbar at radius 1400 km (47 km altitude), from which a surface pressure of p_surf= 14.54 +/- 0.47 microbar is induced (1-sigma error bars). To within error bars, this is identical to the pressure derived from the previous occultation of 5 October 2017, p_1400 = 1.18 +/- 0.03 microbar and p_surf= 14.1 +/- 0.4 microbar, respectively. Based on recent models of Triton's volatile cycles, the overall evolution over the last 30 years of the surface pressure is consistent with N2 condensation taking place in the northern hemisphere. However, models typically predict a steady decrease in surface pressure for the period 2005-2060, which is not confirmed by this observation. Complex surface-atmosphere interactions, such as ice albedo runaway and formation of local N2 frosts in the equatorial regions of Triton could explain the relatively constant pressure between 2017 and 2022., Comment: 8 pages, 4 figures, accepted for publication in Astronomy and Astrophysics