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This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

This report analyzes the potential for large language models (LLMs) to expedite accurate replication of published message effects studies. We tested LLM-powered participants (personas) by replicating 133 experimental findings from 14 papers containing 45 recent studies in the Journal of Marketing (January 2023-May 2024). We used a new software tool, Viewpoints AI (https://viewpoints.ai/), that takes study designs, stimuli, and measures as input, automatically generates prompts for LLMs to act as a specified sample of unique personas, and collects their responses to produce a final output in the form of a complete dataset and statistical analysis. The underlying LLM used was Anthropic's Claude Sonnet 3.5. We generated 19,447 AI personas to replicate these studies with the exact same sample attributes, study designs, stimuli, and measures reported in the original human research. Our LLM replications successfully reproduced 76% of the original main effects (84 out of 111), demonstrating strong potential for AI-assisted replication of studies in which people respond to media stimuli. When including interaction effects, the overall replication rate was 68% (90 out of 133). The use of LLMs to replicate and accelerate marketing research on media effects is discussed with respect to the replication crisis in social science, potential solutions to generalizability problems in sampling subjects and experimental conditions, and the ability to rapidly test consumer responses to various media stimuli. We also address the limitations of this approach, particularly in replicating complex interaction effects in media response studies, and suggest areas for future research and improvement in AI-assisted experimental replication of media effects., Comment: 24 pages, 3 figures, 2 tables

High resolution calorimetry with state-of-the-art energy resolution performance for both electromagnetic (EM) and hadronic signals can be achieved using the dual-readout (DR) technique, both in a homogeneous scintillating-crystal calorimeter and in a traditional fiber and absorber-based DR hadronic section. We present results from the CalVision consortium studying the collection of Cerenkov and scintillation signals in PbWO$_4$ and BGO crystal samples exposed to 120\,GeV proton beams at the Fermilab Test Beam Facility, including proof-of-principle measurements aimed at demonstrating the identification of a sufficiently large Cerenkov signal in homogeneous scintillating crystals to support dual-readout capability., Comment: 4 pages, 7 figures, Submitted to Proceedings of the contributions to the CALOR2024, EPJ Web of Conferences

Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential privacy fail to set accurate privacy expectations for data subjects, which depend on the choice of deployment model. We design and evaluate new explanations of differential privacy for the local and central models, drawing inspiration from prior work explaining other privacy-enhancing technologies. We find that consequences-focused explanations in the style of privacy nutrition labels that lay out the implications of differential privacy are a promising approach for setting accurate privacy expectations. Further, we find that while process-focused explanations are not enough to set accurate privacy expectations, combining consequences-focused explanations with a brief description of how differential privacy works leads to greater trust.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

In this paper we model the high-energy tail of observed solar energetic proton energy distributions with a kappa distribution function. We employ a technique for deriving the thermodynamic parameters of solar energetic proton populations measured by the Parker Solar Probe (PSP) Integrated Science Investigation of the Sun (IS$\odot$IS) EPI-Hi high energy telescope (HET), over energies from 10 - 60 MeV. With this technique we explore, for the first time, the characteristic thermodynamic properties of the solar energetic protons associated with an interplanetary coronal mass ejection (ICME) and its driven shock. We find that (1) the spectral index, or equivalently, the thermodynamic parameter kappa of solar energetic protons ($\kappa_{\rm EP}$) gradually increases starting from the pre-ICME region (upstream of the CME-driven shock), reaching a maximum in the CME ejecta ($\kappa_{\rm EP} \approx 3.5$), followed by a gradual decrease throughout the trailing portion of the CME; (2) solar energetic proton temperature and density ($T_{\rm EP}$ and $n_{\rm EP}$) appear anti-correlated, a behavior consistent to sub-isothermal polytropic processes; and (3) values of $T_{\rm EP}$ and $\kappa_{\rm EP}$ appear are positively correlated, indicating an increasing entropy with time. Therefore, these proton populations are characterized by a complex and evolving thermodynamic behavior, consisting of multiple sub-isothermal polytropic processes, and a large-scale trend of increasing temperature, kappa, and entropy. This study and its companion study by Livadiotis et al. (2024) open a new set of procedures for investigating the thermodynamic behavior of energetic particles and their shared thermal properties., Comment: 16 pages, 6 figures, 1 table

Neeman shows that the completion of a triangulated category with respect to a good metric yields a triangulated category. We compute completions of discrete cluster categories with respect to metrics induced by internal t-structures. In particular, for a coaisle metric this yields a new triangulated category which can be interpreted as a topological completion of the associated combinatorial model. Moreover, we show that the completion of any triangulated category with respect to an internal aisle metric is a thick subcategory of the triangulated category itself., Comment: 30 pages, 7 figures

We report a linear-scaling numerical method for exploring nonequilibrium electron dynamics in systems of arbitrary complexity. Based on the Chebyshev expansion of the time evolution of the single-particle density matrix, the method gives access to nonperturbative excitation and relaxation phenomena in models of disordered materials with sizes on the experimental scale. After validating the method by applying it to saturable optical absorption in clean graphene, we uncover that disorder can enhance absorption in graphene and that the interplay between light, anisotropy, and disorder in nanoporous graphene might be appealing for sensing applications. Beyond the optical properties of graphene-like materials, the method can be applied to a wide range of large-area materials and systems with arbitrary descriptions of defects and disorder.

In this work we first show that the classical Thompson sampling algorithm for multi-arm bandits is differentially private as-is, without any modification. We provide per-round privacy guarantees as a function of problem parameters and show composition over $T$ rounds; since the algorithm is unchanged, existing $O(\sqrt{NT\log N})$ regret bounds still hold and there is no loss in performance due to privacy. We then show that simple modifications -- such as pre-pulling all arms a fixed number of times, increasing the sampling variance -- can provide tighter privacy guarantees. We again provide privacy guarantees that now depend on the new parameters introduced in the modification, which allows the analyst to tune the privacy guarantee as desired. We also provide a novel regret analysis for this new algorithm, and show how the new parameters also impact expected regret. Finally, we empirically validate and illustrate our theoretical findings in two parameter regimes and demonstrate that tuning the new parameters substantially improve the privacy-regret tradeoff., Comment: Published at AISTATS 2023

Efficiently streaming high-volume data is essential for real-time data analytics, visualization, and AI and machine learning model training. Various streaming technologies and serialization protocols have been developed to meet different streaming needs. Together, they perform differently across various tasks and datasets. Therefore, when developing a streaming system, it can be challenging to make an informed decision on the suitable combination, as we encountered when implementing streaming for the UKAEA's MAST data or SKA's radio astronomy data. This study addresses this gap by proposing an empirical study of widely used data streaming technologies and serialization protocols. We introduce an extensible and open-source software framework to benchmark their efficiency across various performance metrics. Our findings reveal significant performance differences and trade-offs between these technologies. These insights can help in choosing suitable streaming and serialization solutions for contemporary data challenges. We aim to provide the scientific community and industry professionals with the knowledge to optimize data streaming for better data utilization and real-time analysis.

Bottom-up on-surface synthesis enables the fabrication of carbon nanostructures with atomic precision. Good examples are graphene nanoribbons (GNRs), 1D conjugated polymers, and nanoporous graphenes (NPGs), which are gathering increasing attention for future carbon nanoelectronics. A key step is the ability to manipulate current flow within these nanomaterials. Destructive quantum interference (QI), long studied in the field of single-molecule electronics, has been proposed as the most effective way to achieve such control with molecular-scale precision. However, for practical applications, it is essential that such QI-engineering remains effective near or above room temperature. To assess this important point, here we combine large-scale molecular dynamics simulations and quantum transport calculations and focus our study on NPGs formed as arrays of laterally bonded GNRs. By considering various NPGs with different inter-GNR chemical connections we disentangle the different factors determining electronic transport in these carbon nanomaterials at 300 K. Our findings unequivocally demonstrate that QI survives at room temperature, with thermal vibrations weakly restricting current flow along GNRs while completely blocking transport across GNRs. Our results thus pave the way towards the future realization of QI-engineered carbon nanocircuitry operating at room temperature, which is a fundamental step towards carbon-based nanoelectronics and quantum technologies., Comment: 21 pages, 4 figures

Future lepton colliders such as the FCC-ee, CEPC, ILC, or a muon collider will collect large data samples that allow precision physics studies with unprecedented accuracy, especially when the data is collected by innovative state-of-the-art detectors. An electromagnetic calorimeter based on scintillating crystals, designed to separately record Cherenkov and scintillation light, can achieve precision measurements of electrons and photons without sacrificing jet energy resolution, given adequate light collection efficiency and separation. This paper presents initial measurements from a program aimed at developing such a calorimeter system for future colliders. We focus on using PbF2 crystals to enhance the understanding of Cherenkov light collection, marking the first step in this endeavor., Comment: 10 pages

This work explores power handling solutions for high-field, highly-radiative negative triangularity (NT) reactors based around the MANTA concept \cite{rutherford_manta_2024}. The divertor design is kept as simple as possible, opting for a standard divertor with standard leg length. FreeGS is used to create an equilibrium for the boundary region, prioritizing a short outer leg length of only $\sim$50 cm ($\sim$40\% of the minor radius). The UEDGE code package is used for the boundary plasma solution, to track plasma temperatures and fluxes to the divertor targets. It is found that for $P_\mathrm{SOL}$ = 25 MW and $n_\mathrm{sep} = 0.96 \times 10^{20}$ m$^{-3}$, conditions consistent with initial core transport modeling, little additional power mitigation is necessary. For external impurity injection of just 0.13\% Ne, the peak heat flux density at the more heavily loaded outer targets falls to 7.8 MW/m$^{2}$, while the electron temperature $T_\mathrm{e}$ remains just under 5 eV. Scans around the parameter space reveal that even at densities lower than in the primary operating scenario, $P_\mathrm{SOL}$ can be increased up to 50 MW, so long as a slightly higher fraction of extrinsic radiator is used. With less than 1\% neon (Ne) impurity content, the divertor still experiences less than 10 MW/m$^{2}$ at the outer target. Design of the plasma-facing components includes a close-fitting vacuum vessel with a tungsten inner surface as well as FLiBe-carrying cooling channels fashioned into the VV wall directly behind the divertor targets. For the seeded heat flux profile, Ansys Fluent heat transfer simulations estimate that the outer target temperature remains at just below 1550\degree C. Initial scoping of advanced divertor designs shows that for an X-divertor, detachment of the outer target becomes much simpler, and plasma fluxes to the targets drop considerably with only 0.01\% Ne content.

We introduce a discrete numerical method based on the diffusion-limited aggregation (DLA) approach to simulate two-fluid Hele-Shaw flow subject to the Saffman-Taylor interfacial instability, in the case where the displaced fluid is non-Newtonian. Focusing on fluids for which the most relevant non-Newtonian aspect of the thin-gap flow is shear-thinning, we introduce a history-dependent aspect into the algorithm, modeling shear-rate-dependent fluid viscosity. The main finding is that the morphology of the emerging patterns, characterized by the fractal dimension, is modified in a nontrivial manner by the shear-thinning nature of the displaced fluid. In particular, we consistently find that shear-thinning leads to the formation of patterns characterized by a smaller fractal dimension, compared to the corresponding Newtonian fluid.

We develop the kappa-tail fitting technique, which analyzes observations of power-law tails of distributions and energy-flux spectra and connects them to theoretical modeling of kappa distributions, to determine the thermodynamics of the examined space plasma. In particular, we (i) construct the associated mathematical formulation, (ii) prove its decisive lead for determining whether the observed power-law is associated with kappa distributions; and (iii) provide a validation of the technique using pseudo-observations of typical input plasma parameters. Then, we apply this technique to a case-study by determining the thermodynamics of solar energetic particle (SEP) protons, for a SEP event observed on April 17, 2021, by the PSP/ISOIS instrument suite onboard PSP. The results show SEP temperatures and densities of the order of $\sim 1$ MeV and $ \sim 5 \cdot 10^{-7} $ cm$^{-3}$, respectively.

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) flew on May 13$^{\text{th}}$ and 14$^{\text{th}}$ of 2023. Consisting of two novel optical telescopes, the payload utilized next-generation instrumentation for the observations of extensive air showers from near space. One instrument, the fluorescence telescope (FT) searched for Ultra-High Energy Cosmic Rays (UHECRs) by recording the atmosphere below the balloon in the near-UV with a 1~$\mu$s time resolution using 108 multi-anode photomultiplier tubes with a total of 6,912 channels. Validated by pre-flight measurements during a field campaign, the energy threshold was estimated around 2~EeV with an expected event rate of approximately 1 event per 10 hours of observation. Based on the limited time afloat, the expected number of UHECR observations throughout the flight is between 0 and 2. Consistent with this expectation, no UHECR candidate events have been found. The majority of events appear to be detector artifacts that were not rejected properly due to a shortened commissioning phase. Despite the earlier-than-expected termination of the flight, data were recorded which provide insights into the detectors stability in the near-space environment as well as the diffuse ultraviolet emissivity of the atmosphere, both of which are impactful to future experiments.

We report a study of disorder-dependent quantum geometry in topological systems. Thanks to the development of an efficient linear-scaling numerical methodology based on the kernel polynomial method, we can explore the nontrivial behavior of the quantum geometry metrics (quantum metric and Chern number) in large-scale inhomogeneous systems, accounting for the presence of disorder. We illustrate this approach in the disordered Haldane model, examining the impact of Anderson and vacancy-type of disorders on the trivial and topological phases captured by this model., Comment: 6 pages, 3 figures

As a mathematically rigorous framework that has amassed a rich theoretical literature, differential privacy is considered by many experts to be the gold standard for privacy-preserving data analysis. Others argue that while differential privacy is a clean formulation in theory, it poses significant challenges in practice. Both perspectives are, in our view, valid and important. To bridge the gaps between differential privacy's promises and its real-world usability, researchers and practitioners must work together to advance policy and practice of this technology. In this paper, we outline pressing open questions towards building usable differential privacy and offer recommendations for the field, such as developing risk frameworks to align with user needs, tailoring communications for different stakeholders, modeling the impact of privacy-loss parameters, investing in effective user interfaces, and facilitating algorithmic and procedural audits of differential privacy systems.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

This Letter reports the first measurement of the oscillation amplitude and frequency of reactor antineutrinos at Daya Bay via neutron capture on hydrogen using 1958 days of data. With over 3.6 million signal candidates, an optimized candidate selection, improved treatment of backgrounds and efficiencies, refined energy calibration, and an energy response model for the capture-on-hydrogen sensitive region, the relative $\overline{\nu}_{e}$ rates and energy spectra variation among the near and far detectors gives $\mathrm{sin}^22\theta_{13} = 0.0759_{-0.0049}^{+0.0050}$ and $\Delta m^2_{32} = (2.72^{+0.14}_{-0.15})\times10^{-3}$ eV$^2$ assuming the normal neutrino mass ordering, and $\Delta m^2_{32} = (-2.83^{+0.15}_{-0.14})\times10^{-3}$ eV$^2$ for the inverted neutrino mass ordering. This estimate of $\sin^2 2\theta_{13}$ is consistent with and essentially independent from the one obtained using the capture-on-gadolinium sample at Daya Bay. The combination of these two results yields $\mathrm{sin}^22\theta_{13}= 0.0833\pm0.0022$, which represents an 8% relative improvement in precision regarding the Daya Bay full 3158-day capture-on-gadolinium result.

We develop an in-situ index of refraction profile using the transit time of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted path (R); the measured D vs. R (dt(D,R)) timing differences provide constraints on the index of refraction profile near South Pole, where the Askaryan Radio Array (ARA) neutrino observatory is located. We constrain the refractive index profile by simulating D and R ray paths via ray tracing and comparing those to measured dt(D,R) signals. Using previous ice density data as a proxy for n(z), we demonstrate that our data strongly favors a glaciologically-motivated three-phase densification model rather than a single exponential scale height model. Simulations show that the single exponential model overestimates ARA neutrino sensitivity compared to the three-phase model.

The MANTA (Modular Adjustable Negative Triangularity ARC-class) design study investigated how negative-triangularity (NT) may be leveraged in a compact, fusion pilot plant (FPP) to take a ``power-handling first" approach. The result is a pulsed, radiative, ELM-free tokamak that satisfies and exceeds the FPP requirements described in the 2021 National Academies of Sciences, Engineering, and Medicine report ``Bringing Fusion to the U.S. Grid". A self-consistent integrated modeling workflow predicts a fusion power of 450 MW and a plasma gain of 11.5 with only 23.5 MW of power to the scrape-off layer (SOL). This low $P_\text{SOL}$ together with impurity seeding and high density at the separatrix results in a peak heat flux of just 2.8 MW/m$^{2}$. MANTA's high aspect ratio provides space for a large central solenoid (CS), resulting in ${\sim}$15 minute inductive pulses. In spite of the high B fields on the CS and the other REBCO-based magnets, the electromagnetic stresses remain below structural and critical current density limits. Iterative optimization of neutron shielding and tritium breeding blanket yield tritium self-sufficiency with a breeding ratio of 1.15, a blanket power multiplication factor of 1.11, toroidal field coil lifetimes of $3100 \pm 400$ MW-yr, and poloidal field coil lifetimes of at least $890 \pm 40$ MW-yr. Following balance of plant modeling, MANTA is projected to generate 90 MW of net electricity at an electricity gain factor of ${\sim}2.4$. Systems-level economic analysis estimates an overnight cost of US\$3.4 billion, meeting the NASEM FPP requirement that this first-of-a-kind be less than US\$5 billion. The toroidal field coil cost and replacement time are the most critical upfront and lifetime cost drivers, respectively.

A standard question in real algebraic geometry is to compute the number of connected components of a real algebraic variety in affine space. By adapting an approach for determining connectivity in complements of real hypersurfaces by Hong, Rohal, Safey El Din, and Schost, algorithms are presented for computing the number of connected components, the Euler characteristic, and deciding the connectivity between two points for a smooth manifold arising as the complement of a real hypersurface of a real algebraic variety. When taking such real hypersurface to be the set of singular points, this yields an approach for determining smooth connectivity in a real algebraic variety. The method is based upon gradient ascent/descent paths on the real algebraic variety and several examples are included to demonstrate the approach., Comment: 19 pages, 7 figures

Metal films and other geometries of nanoscale thickness, when exposed to laser irradiation, melt and evolve as fluids as long as their temperature is sufficiently high. This evolution often leads to pattern formation, which may be influenced strongly by material parameters that are temperature dependent. In addition, the laser heat absorption itself depends on the time-dependent metal thickness. Self-consistent modeling of evolving metal films shows that, by controlling the amount and geometry of deposited metal, one could control the instability development. In particular, depositing additional metal leads to elevated temperatures through the `thermal crowding' effect, which strongly influences the metal film evolution. This influence may proceed via disjoint metal geometries, by heat diffusion through the underlying substrate. Fully self-consistent modeling focusing on the dominant effects, as well as accurate time-dependent simulations, allow us to describe the main features of thermal crowding and provide a route to control fluid instabilities and pattern formation on the nanoscale.

Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the growing adoption of DP, its technical privacy parameters do not lend themselves to an intelligible description of the real-world privacy risks associated with that deployment: the guarantee that most naturally follows from the DP definition is protection against membership inference by an adversary who knows all but one data record and has unlimited auxiliary knowledge. In many settings, this adversary is far too strong to inform how to set real-world privacy parameters. One approach for contextualizing privacy parameters is via defining and measuring the success of technical attacks, but doing so requires a systematic categorization of the relevant attack space. In this work, we offer a detailed taxonomy of attacks, showing the various dimensions of attacks and highlighting that many real-world settings have been understudied. Our taxonomy provides a roadmap for analyzing real-world deployments and developing theoretical bounds for more informative privacy attacks. We operationalize our taxonomy by using it to analyze a real-world case study, the Israeli Ministry of Health's recent release of a birth dataset using DP, showing how the taxonomy enables fine-grained threat modeling and provides insight towards making informed privacy parameter choices. Finally, we leverage the taxonomy towards defining a more realistic attack than previously considered in the literature, namely a distributional reconstruction attack: we generalize Balle et al.'s notion of reconstruction robustness to a less-informed adversary with distributional uncertainty, and extend the worst-case guarantees of DP to this average-case setting.

The convergence of artificial intelligence (AI) and synthetic biology is rapidly accelerating the pace of biological discovery and engineering. AI techniques, such as large language models and biological design tools, are enabling the automated design, build, test, and learning cycles for engineered biological systems. This convergence promises to democratize synthetic biology and unlock novel applications across domains from medicine to environmental sustainability. However, it also poses significant risks around reliability, dual use, and governance. The opacity of AI models, the deskilling of workforces, and the outdated nature of current regulatory frameworks present challenges in ensuring responsible development. Urgent attention is needed to update governance structures, integrate human oversight into increasingly automated workflows, and foster a culture of responsibility among the growing community of bioengineers. Only by proactively addressing these issues can we realize the transformative potential of AI-driven synthetic biology while mitigating its risks.

Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large systems with numerous multivariate data sets is challenging. This study presents a lightweight interconnection and divergence discovery mechanism (LIDD) to identify abnormal behavior in multi-system environments. The approach employs a multivariate analysis technique that first estimates the similarity heatmaps among the sensors for each system and then applies information retrieval algorithms to provide relevant multi-level interconnection and discrepancy details. Our experiment on the readout systems of the Hadron Calorimeter of the Compact Muon Solenoid (CMS) experiment at CERN demonstrates the effectiveness of the proposed method. Our approach clusters readout systems and their sensors consistent with the expected calorimeter interconnection configurations, while capturing unusual behavior in divergent clusters and estimating their root causes., Comment: 8 pages, 12 figures

This Letter presents results of a search for the mixing of a sub-eV sterile neutrino with three active neutrinos based on the full data sample of the Daya Bay Reactor Neutrino Experiment, collected during 3158 days of detector operation, which contains $5.55 \times 10^{6}$ reactor \anue candidates identified as inverse beta-decay interactions followed by neutron-capture on gadolinium. The analysis benefits from a doubling of the statistics of our previous result and from improvements of several important systematic uncertainties. No significant oscillation due to mixing of a sub-eV sterile neutrino with active neutrinos was found. Exclusion limits are set by both Feldman-Cousins and CLs methods. Light sterile neutrino mixing with $\sin^2 2\theta_{14} \gtrsim 0.01$ can be excluded at 95\% confidence level in the region of $0.01$ eV$^2 \lesssim |\Delta m^{2}_{41}| \lesssim 0.1 $ eV$^2$. This result represents the world-leading constraints in the region of $2 \times 10^{-4}$ eV$^2 \lesssim |\Delta m^{2}_{41}| \lesssim 0.2 $ eV$^2$., Comment: 7 pages, 4 figures, 1 table

We document the characteristics of children and young adults identified in the Panel Study of Income Dynamics as having a learning disability and study whether legislative changes in diagnosis criteria have had a noticeable effect determining who receives a diagnosis. We further document that children and young adults identified as a having a learning disability experience less desirable outcomes early in life, including trouble with the police, drug use, violent behavior, incarceration, self-reported low levels of well-being, lower educational attainment, and less favorable labor market outcomes. We also find that the mothers of children diagnosed with learning disabilities are less likely than other mothers to participate in the labor market.

The promise of increasing channel counts in high density ($> 10^4$) neural Microelectrode Arrays (MEAs) for high resolution recording comes with the curse of developing faster characterization strategies for concurrent acquisition of multichannel electrode integrities over a wide frequency spectrum. To circumvent the latency associated with the current multiplexed technique for impedance acquisition, it is common practice to resort to the single frequency impedance measurement (i.e. $Z_{1 \text{kHz}}$). This, however, does not offer sufficient spectral impedance information crucial for determining the capacity of electrodes at withstanding slow and fast-changing stimulus and recordings. In this work, we present \textit{HermEIS}, a novel approach that leverages single cycle in-phase and quadrature signal integrations for reducing the massive data throughput characteristic of such high density acquisition systems. As an initial proof-of-concept, we demonstrate over $6$ decades of impedance bandwidth ($5\times10^{-2} - 5\times10^{4}\text{ Hz}$) in a parallel $4$-channel potentiostatic setup composed of a custom PCB with off-the-shelf electronics working in tandem with an FPGA., Comment: 5 pages, submitted to IEEE EMBC 2024

Daya Bay presents the first measurement of cosmogenic $^8$He isotope production in liquid scintillator, using an innovative method for identifying cascade decays of $^8$He and its child isotope, $^8$Li. We also measure the production yield of $^9$Li isotopes using well-established methodology. The results, in units of 10$^{-8}\mu^{-1}$g$^{-1}$cm$^{2}$, are 0.307$\pm$0.042, 0.341$\pm$0.040, and 0.546$\pm$0.076 for $^8$He, and 6.73$\pm$0.73, 6.75$\pm$0.70, and 13.74$\pm$0.82 for $^9$Li at average muon energies of 63.9~GeV, 64.7~GeV, and 143.0~GeV, respectively. The measured production rate of $^8$He isotopes is more than an order of magnitude lower than any other measurement of cosmogenic isotope production. It replaces the results of previous attempts to determine the ratio of $^8$He to $^9$Li production that yielded a wide range of limits from 0 to 30\%. The results provide future liquid-scintillator-based experiments with improved ability to predict cosmogenic backgrounds.

Microtransit offers opportunities to enhance urban mobility by combining the reliability of public transit and the flexibility of ride-sharing. This paper optimizes the design and operations of a deviated fixed-route microtransit system that relies on reference lines but is allowed to deviate in response to passenger demand. We formulate a Microtransit Network Design (MiND) model via two-stage stochastic optimization. The model features a tight second-stage formulation thanks to a subpath-based representation of microtransit operations in a load-expanded network, which optimizes on-demand deviations between checkpoint stops. We develop a double-decomposition algorithm combining Benders decomposition and subpath-based column generation armed with a tailored label-setting algorithm. Using real-world data from Manhattan, results suggest that our method scales to large practical instances, with up to 10-100 candidate lines and hundreds of stations. Comparisons with transit and ride-sharing benchmarks suggest that microtransit can provide win-win outcomes toward efficient mobility (high demand coverage, low operating costs, high level of service), equitable mobility (broad geographic reach) and sustainable mobility (limited environmental footprint). We provide an open-source implementation in an online repository to enable replication.

The Galileo spacecraft had distant encounters with Earth in 1990 and 1992. Limited Solid State Imager (SSI) data acquired during these encounters has been previously presented, but the majority of the data from these Earth flybys have not been presented in the literature. Observations of Earth taken from afar are both rare and directly relevant to the development of any future exo-Earth direct imaging mission. Here we present a pipeline that vets, calibrates, and measures the disk-integrated brightness of the Earth, in multiple filters, from the complete SSI data sets from both the 1990 and 1992 Galileo flybys. The result is over 1500 usable photometric measurements for Earth as an analog for an exoplanet. The 1990 data set includes full rotational lightcurves in six bandpasses spanning the optical range. The 1992 data set is more limited, with lightcurves only spanning 14 hr. Time-averaged photometry for both encounters is presented while variability and color are discussed relative to findings from NASA's EPOXI mission (which also provided photometric lighturves for Earth). The new Galileo/SSI data are used to further validate the Virtual Planetary Laboratory 3D spectral Earth model, which often serves as a stand-in for true disk-integrated observations of our planet. The revived Galileo/SSI data for Earth is a testament to the ability of NASA's Planetary Data System to maintain data over decades-long timescales. The disk-integrated products derived from these data add to a very short list of calibrated and published whole-disk observations of the Pale Blue Dot.

Solar energetic particle (SEP) events have been observed by the Parker Solar Probe (PSP) spacecraft since its launch in 2018. These events include sources from solar flares and coronal mass ejections (CMEs). Onboard PSP is the IS\(\odot\)IS instrument suite measuring ions over energies from ~ 20 keV/nucleon to 200 MeV/nucleon and electrons from ~ 20 keV to 6 MeV. Previous studies sought to group CME characteristics based on their plasma conditions and arrived at general descriptions with large statistical errors, leaving open questions on how to properly group CMEs based solely on their plasma conditions. To help resolve these open questions, plasma properties of CMEs have been examined in relation to SEPs. Here we reexamine one plasma property, the solar wind proton temperature, and compare it to the proton SEP intensity in a region immediately downstream of a CME-driven shock for seven CMEs observed at radial distances within 1 au. We find a statistically strong correlation between proton SEP intensity and bulk proton temperature, indicating a clear relationship between SEPs and the conditions in the solar wind. Furthermore, we propose that an indirect coupling of SEP intensity to the level of turbulence and the amount of energy dissipation that results is mainly responsible for the observed correlation between SEP intensity and proton temperature. These results are key to understanding the interaction of SEPs with the bulk solar wind in CME-driven shocks and will improve our ability to model the interplay of shock evolution and particle acceleration., Comment: 12 pages, 4 figures, and 2 tables

In this work, we propose the first framework for integrating Differential Privacy (DP) and Contextual Integrity (CI). DP is a property of an algorithm that injects statistical noise to obscure information about individuals represented within a database. CI defines privacy as information flow that is appropriate to social context. Analyzed together, these paradigms outline two dimensions on which to analyze privacy of information flows: descriptive and normative properties. We show that our new integrated framework provides benefits to both CI and DP that cannot be attained when each definition is considered in isolation: it enables contextually-guided tuning of the epsilon parameter in DP, and it enables CI to be applied to a broader set of information flows occurring in real-world systems, such as those involving PETs and machine learning. We conclude with a case study based on the use of DP in the U.S. Census Bureau., Comment: Published in Proceedings of 3rd ACM Computer Science And Law Symposium, 2024

Flat bands in moir\'e systems are exciting new playgrounds for the generation and study of exotic many-body physics phenomena in low-dimensional materials. Such physics is attributed to the vanishing kinetic energy and strong spatial localization of the flat-band states. Here we use numerical simulations to examine the electronic transport properties of such flat bands in magic-angle twisted bilayer graphene in the presence of disorder. We find that while a conventional downscaling of the mean free path with increasing disorder strength occurs at higher energies, in the flat bands the mean free path can actually increase with increasing disorder strength.This phenomenon is also captured by the disorder-dependent quantum metric, which is directly linked to the ground state localization.This disorder-induced delocalization suggests that weak disorder may have a strong impact on the exotic physics of magic-angle bilayer graphene and other related moir\'e systems., Comment: Main text: 7 pages, 4 figures. Sup .Mat.: 4 pages, 2 figures