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Organic molecular ferroelectrics, including organic proton-transfer ferroelectrics and antiferroelectrics, are potentially attractive in organic electronics and have significant chemical tunability. Among these, acid-base proton transfer (PT) salts stand out due to their low coercive fields and the possibility to tune their properties with different acid-base combinations. Using crystal structure prediction, combining small acid and base organic molecular species, we here predict three novel acid-base PT ferroelectric salts with higher polarization than existing materials. We also report two combinations that form antiferroelectric crystal structures. However, some combinations also result in unfavorable packing or the formation of co-crystal or in one case a divalent salt. The protonation state is found to be highly linked to the crystal structure, with cases where salt crystal structures have the same energetic preferability as co-crystals with a different crystalline packing.

Future far-infrared (IR) observatories require compact and cost efficient optical linear variable bandpass filters (LVBFs) to define their instrument spectral bands. We have designed novel far-IR LVBFs that consist of metal-mesh bandpass filters comprised of a gold film with cross-slots of varying sizes along a silicon (Si) substrate with anti-reflection (AR) coatings. We present our work on the simulated and measured transmission of non-AR coated and AR coated LVBFs for bandpass peaks from wavelengths of 24 to 36 $\mu$m with a resolving power ($R=\lambda_0/\Delta\lambda$) of R$\approx$6 for non-AR coated LVBFs and R$\approx$4 for AR coated LVBFs. We also present a method to decrease the effects of out-of-band high frequency transmission exhibited by metal-mesh filters by depositing a thin layer of hydrogenated amorphous silicon (a-Si:H) on the metal-mesh of the LVBF. We have fabricated and measured the LVBFs at room temperature and cryogenic temperatures (5 K). We measure a high peak transmission of $\sim$80-90 \% for the AR coated LVBF at 5 K and demonstrate that the a-Si:H LVBF is a promising method to address out-of-band high frequency transmission., Comment: 8 pages, 10 figures, accepted to Applied Optics

Nucleon structure functions, as measured in lepton-nucleon scattering, have historically provided a critical observable in the study of partonic dynamics within the nucleon. However, at very large parton momenta it is both experimentally and theoretically challenging to extract parton distributions due to the probable onset of non-perturbative contributions and the unavailability of high precision data at critical kinematics. Extraction of the neutron structure and the d-quark distribution have been further challenging due to the necessity of applying nuclear corrections when utilizing scattering data from a deuteron target to extract free neutron structure. However, a program of experiments has been carried out recently at the energy-upgraded Jefferson Lab electron accelerator aimed at significantly reducing the nuclear correction uncertainties on the d-quark distribution function at large partonic momentum. This allows leveraging the vast body of deuterium data covering a large kinematic range to be utilized for d-quark parton distribution function extraction. We present new data from experiment E12-10-002 carried out in Jefferson Lab Hall C on the deuteron to proton cross-section ratio at large BJorken-x. These results significantly improve the precision of existing data, and provide a first look at the expected impact on quark distributions extracted from global parton distribution function fits.

Infectious diseases, transmitted directly or indirectly, are among the leading causes of epidemics and pandemics. Consequently, several open challenges exist in predicting epidemic outbreaks, detecting variants, tracing contacts, discovering new drugs, and fighting misinformation. Artificial Intelligence (AI) can provide tools to deal with these scenarios, demonstrating promising results in the fight against the COVID-19 pandemic. AI is becoming increasingly integrated into various aspects of society. However, ensuring that AI benefits are distributed equitably and that they are used responsibly is crucial. Multiple countries are creating regulations to address these concerns, but the borderless nature of AI requires global cooperation to define regulatory and guideline consensus. Considering this, The Global South AI for Pandemic & Epidemic Preparedness & Response Network (AI4PEP) has developed an initiative comprising 16 projects across 16 countries in the Global South, seeking to strengthen equitable and responsive public health systems that leverage Southern-led responsible AI solutions to improve prevention, preparedness, and response to emerging and re-emerging infectious disease outbreaks. This opinion introduces our branches in Latin American and Caribbean (LAC) countries and discusses AI governance in LAC in the light of biotechnology. Our network in LAC has high potential to help fight infectious diseases, particularly in low- and middle-income countries, generating opportunities for the widespread use of AI techniques to improve the health and well-being of their communities.

We determine necessary and sufficient conditions for unicritical polynomials to be dynamically irreducible over finite fields. This result extends the results of Boston-Jones and Hamblen-Jones-Madhu regarding the dynamical irreducibility of particular families of unicritical polynomials. We also investigate dynamical irreducibility conditions for cubic and shifted linearized polynomials.

Periodic laser driving, known as Floquet engineering, is a powerful tool to manipulate the properties of quantum materials. Using circularly polarized light, artificial magnetic fields, called Berry curvature, can be created in the photon-dressed Floquet-Bloch states that form. This mechanism, when applied to 3D Dirac and Weyl systems, is predicted to lead to photon-dressed movement of Weyl nodes which should be detectable in the transport sector. The transport response of such a topological light-matter hybrid, however, remains experimentally unknown. Here, we report on the transport properties of the type-II Weyl semimetal T$\mathrm{_d}$-MoTe$_\mathrm{2}$ illuminated by a femtosecond pulse of circularly polarized light. Using an ultrafast optoelectronic device architecture, we observed injection currents and a helicity-dependent anomalous Hall effect whose scaling with laser field strongly deviate from the perturbative laws of nonlinear optics. We show using Floquet theory that this discovery corresponds to the formation of a magnetic Floquet-Weyl semimetal state. Numerical ab initio simulations support this interpretation, indicating that the light-induced motion of the Weyl nodes contributes substantially to the measured transport signals. This work demonstrates the ability to generate large effective magnetic fields ($>$ 30T) with light, which can be used to manipulate the magnetic and topological properties of a range of quantum materials.

The Next-generation Extended Wavelength-MUltiband Sub/millimeter Inductance Camera (NEW-MUSIC) on the Leighton Chajnantor Telescope (LCT) will be a first-of-its-kind, six-band, transmillimeter-wave ("trans-mm") polarimeter covering 2.4 octaves of spectral bandwidth to open a new window on the trans-mm time-domain frontier, in particular new frontiers in energy, density, time, and magnetic field. NEW-MUSIC's broad spectral coverage will also enable the use of the Sunyaev-Zeldovich effects to study accretion, feedback, and dust content in the hot gaseous haloes of galaxies and galaxy clusters. Six-band spectral energy distributions, with polarization information, will yield new insights into stellar and planetary nurseries. NEW-MUSIC will employ hierarchical, phased arrays of polarization-sensitive superconducting slot-dipole antennas, coupled to photolithographic bandpass filters, to nearly optimally populate LCT's 14' field-of-view with six spectral bands over 80-420 GHz (1:5.25 spectral dynamic range; 2.4 octaves). Light will be routed to Al or AlMn microstripline-coupled, parallel-plate capacitor, lumped-element kinetic inductance detectors (MS-PPC-LEKIDs), an entirely new KID architecture that substantially enhances design flexibility while providing background-limited performance. Innovative, wide-bandwidth, etched silicon structures will be used to antireflection-treat the back-illuminated focal plane. NEW-MUSIC will cost-effectively reuse much of the MUSIC instrument, initially deploying a quarter-scale focal plane capable of the bulk of NEW-MUSIC science followed later by a full-FoV focal plane needed for NEW-MUSIC wide-area survey science., Comment: 42 pages, 16 figures. This version includes minor corrections relative to the version published at the SPIE website. Versions 2 and 3 include only affiliation corrections

We show the first demonstration of a hybrid external cavity diode laser (ECDL) using aluminum nitride (AlN) as the wave-guiding material. Two devices are presented, a near-infrared (NIR) laser using a 850 nm diode and a red laser using a 650 nm diode. The NIR laser has $\approx$1 mW on chip power, 6 nm of spectral coverage, instantaneous linewidth of 720$\pm$80 kHz, and 12 dB side mode suppression ratio (SMSR). The red laser has 15 dB SMSR., Comment: 6 pages, 3 figures, SPIE Photonics West Conference

With wide-field phased array feed technology, the Australian Square Kilometre Array Pathfinder (ASKAP) is ideally suited to search for seemingly rare radio transient sources. The Commensal Real-time ASKAP Fast Transient (CRAFT) Survey Science Project has developed instrumentation to continuously search for fast radio transients (duration $\lesssim$ 1 second) with ASKAP, with a particular focus on finding and localising Fast Radio Bursts (FRBs). Of particular interest are Fast Radio Bursts (FRBs). Since 2018, the CRAFT survey has been searching for FRBs and other fast transients by incoherently adding the intensities received by individual ASKAP antennas, and then correcting for the impact of frequency dispersion on these short-duration signals in the resultant incoherent sum (ICS) in real-time. This low-latency detection enables the triggering of voltage buffers, which facilitates the localisation of the transient source and the study spectro-polarimetric properties at high time resolution. Here we report the sample of 43 FRBs discovered in this CRAFT/ICS survey to date. This includes 22 FRBs that had not previously been reported: 16 FRBs localised by ASKAP to $\lesssim$ 1 arcsec and 6 FRBs localised to approximately 10 arcmin. Of the new arcsecond-localised FRBs, we have identified and characterised host galaxies (and measured redshifts) for 11. The median of all 30 measured host redshifts from the survey to date is z = 0.23. We summarise results from the searches, in particular those contributing to our understanding of the burst progenitors and emission mechanisms, and on the use of bursts as probes of intervening media. We conclude by foreshadowing future FRB surveys with ASKAP using a coherent detection system that is currently being commissioned., Comment: 37 pages, 23 Figures, 7 Tables. Submitted for publication in PASA

We report on the thermodynamic and transport properties of the rare-earth Zintl compound Eu$_5$Sn$_2$As$_6$, which orders as a canted antiferromagnetic magnetic semiconductor at 10.3~K. The system also displays a complex cascade of magnetic phases arising from geometric and magnetic exchange frustration, with high sensitivity to the application and direction of small magnetic fields. At low temperature, Eu$_5$Sn$_2$As$_6$ exhibits negative colossal magnetoresistance of up to a factor of $6\times10^3$. This may be a lower bound as the conductivity appears to be shunted by an unknown conduction channel, causing the resistivity to saturate. Mechanisms for the low temperature saturation of resistivity are discussed., Comment: 11 pages, 5 figures in main text, 5 appendices

The development of low-frequency radio astronomy experiments for detecting 21-cm line emission from hydrogen presents new opportunities for creative solutions to the challenge of characterizing an antenna beam pattern. The Array of Long Baseline Antennas for Taking Radio Observations from the Seventy-ninth parallel (ALBATROS) is a new radio interferometer sited in the Canadian high Arctic that aims to map Galactic foregrounds at frequencies below $\sim$30 MHz. We present PteroSoar, a custom-built hexacopter outfitted with a transmitter, that will be used to characterize the beam patterns of ALBATROS and other experiments. The PteroSoar drone hardware is motivated by the need for user-servicing at remote sites and environmental factors that are unique to the high Arctic. In particular, magnetic heading is unreliable because the magnetic field lines near the north pole are almost vertical. We therefore implement moving baseline real time kinematic (RTK) positioning with two GPS units to obtain heading solutions with $\sim$1$^\circ$ accuracy. We present a preliminary beam map of an ALBATROS antenna, thus demonstrating successful PteroSoar operation in the high Arctic.

We give conditions for equilibria in the following Voronoi game on the discrete hypercube. Two players position themselves in $\{0,1\}^d$ and each receives payoff equal to the measure (under some probability distribution) of their Voronoi cell (the set of all points which are closer to them than to the other player). This game can be thought of as a discrete analogue of the Hotelling--Downs spatial voting model in which the political spectrum is determined by $d$ binary issues rather than a continuous interval. We observe that if an equilibrium does exist then it must involve the two players co-locating at the majority point (ie the point representing majority opinion on each separate issue). Our main result is that a sufficient condition for an equilibrium is that on each issue the majority option is held by at least $\frac{3}{4}$ of voters. The value $\frac{3}{4}$ can be improved slightly in a way that depends on $d$ and with this improvement the result is best possible. We give similar sufficient conditions for the existence of a local equilibrium. We also analyse the situation where the distribution is a mix of two product measures. We show that either there is an equilibrium or the best response to the majority point is its antipode., Comment: 11 pages

The Travelling Salesman Problem (TSP) is an important combinatorial optimisation problem, and is usually solved on a quantum computer using a Quadratic Unconstrained Binary Optimisation (QUBO) formulation or a Higher Order Binary Optimisation(HOBO) formulation. In these formulations, penalty terms are added to the objective function for outputs that don't map to valid routes. We present a novel formulation which needs fewer binary variables, and where, by design, there are no penalty terms because all outputs from the quantum device are mapped to valid routes. Simulations of a quantum boson sampler were carried out which demonstrate that larger networks can be solved with this penalty-free formulation than with formulations with penalties. Simulations were successfully translated to hardware by running a non-QUBO formulation with penalties on an early experimental prototype ORCA PT-1 boson sampler. Although we worked with a boson sampler, we believe that this novel formulation is relevant to other quantum devices. This work shows that a good embedding for combinatorial optimisation problems can solve larger problems with the same quantum computing resource. The flexibility of boson sampling quantum devices is a powerful asset in solving combinatorial optimisation problem, because it enables formulations where the output string is always mapped to a valid solution, avoiding the need for penalties., Comment: 16 pages and 4 figures

Silicon carbide (SiC) is a semiconductor used in quantum information processing, microelectromechanical systems, photonics, power electronics, and harsh environment sensors. However, its high temperature stability, high breakdown voltage, wide bandgap, and high mechanical strength are accompanied by a chemical inertness which makes complex micromachining difficult. Photoelectrochemical etching is a simple, rapid means of wet processing SiC, including the use of dopant selective etch stops that take advantage of mature SiC homoepitaxy. However, dopant selective photoelectrochemical etching typically relies on highly doped material, which poses challenges for device applications such as quantum defects and photonics that benefit from low doping to produce robust emitter properties and high optical transparency. In this work, we develop a new, selective photoelectrochemical etching process that relies not on high doping but on the electrical depletion of a fabricated diode structure, allowing the selective etching of an n-doped substrate wafer versus an undoped epitaxial ($N_a=1(10)^{14}cm^{-3}$) device layer. We characterize the photo-response and photoelectrochemical etching behavior of the diode under bias and use those insights to suspend large ($>100\mu m^2$) undoped membranes of SiC. We further characterize the compatibility of membranes with quantum emitters, performing comparative spin spectroscopy between undoped and highly doped membrane structures, finding the use of undoped material improves ensemble spin lifetime by $>3x$. This work enables the fabrication of high-purity suspended thin films suitable for scalable photonics, mechanics, and quantum technologies in SiC., Comment: 12 pages, 4 figures

To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imaging data. Until now, generative methods have faced constraints in terms of fidelity, spatio-temporal coherence, and the length of generation, failing to capture the complete details of dataset distributions. We present a model designed to produce high-fidelity, long and complete data samples with near-real-time efficiency and explore our approach on a challenging task: generating echocardiogram videos. We develop our generation method based on diffusion models and introduce a protocol for medical video dataset anonymization. As an exemplar, we present EchoNet-Synthetic, a fully synthetic, privacy-compliant echocardiogram dataset with paired ejection fraction labels. As part of our de-identification protocol, we evaluate the quality of the generated dataset and propose to use clinical downstream tasks as a measurement on top of widely used but potentially biased image quality metrics. Experimental outcomes demonstrate that EchoNet-Synthetic achieves comparable dataset fidelity to the actual dataset, effectively supporting the ejection fraction regression task. Code, weights and dataset are available at https://github.com/HReynaud/EchoNet-Synthetic., Comment: Accepted at MICCAI 2024

We present the design and experimental characterization of a kinetic-inductance traveling-wave parametric amplifier (KI-TWPA) for sub-GHz frequencies. KI-TWPAs amplify signals through nonlinear mixing processes supported by the nonlinear kinetic inductance of a superconducting transmission line. The device described here utilizes a compactly meandered TiN microstrip transmission line to achieve the length needed to amplify sub-GHz signals. It is operated in a frequency translating mode where the amplified signal tone is terminated at the output of the amplifier, and the idler tone at approximately 2.5~GHz is brought out of the cryostat. By varying the pump frequency, a gain of up to 22 dB was achieved in a tunable range from about 450 to 850~MHz. Use of TiN as the nonlinear element allows for a reduction of the required pump power by roughly an order of magnitude relative to NbTiN, which has been used for previous KI-TWPA implementations. This amplifier has the potential to enable high-sensitivity and high-speed measurements in a wide range of applications, such as quantum computing, astrophysics, and dark matter detection.

This research was commissioned as part of Inclusive Britain, the government response to the Commission on Race and Ethnic Disparities (CRED). The research sought to answer three main questions: (1) Where schools and trusts have closed attainment gaps between pupils from different ethnic groups, has this been the result of a deliberate strategy (i.e., focused on raising the attainment of pupils from specific ethnic groups)?; (2) What practices have schools and trusts used to close attainment gaps and foster a sense of belonging for all pupils (including those from different ethnic groups)?; and (3) Can we draw practical lessons about closing attainment gaps between pupils from different ethnic groups from the work of schools and trusts (including lessons that are applicable to schools and trusts working in different contexts)? The research was designed to be exploratory. It focused on the role and practices of trusts and schools. The evidence gathered came from interviews with the leaders of nine academy trusts and leaders of individual schools within those trusts (plus one local authority maintained school). [This report was written with Richard Eyre and Kikelomo Agunbiade.]

Gene set knowledge discovery is essential for advancing human functional genomics. Recent studies have shown promising performance by harnessing the power of Large Language Models (LLMs) on this task. Nonetheless, their results are subject to several limitations common in LLMs such as hallucinations. In response, we present GeneAgent, a first-of-its-kind language agent featuring self-verification capability. It autonomously interacts with various biological databases and leverages relevant domain knowledge to improve accuracy and reduce hallucination occurrences. Benchmarking on 1,106 gene sets from different sources, GeneAgent consistently outperforms standard GPT-4 by a significant margin. Moreover, a detailed manual review confirms the effectiveness of the self-verification module in minimizing hallucinations and generating more reliable analytical narratives. To demonstrate its practical utility, we apply GeneAgent to seven novel gene sets derived from mouse B2905 melanoma cell lines, with expert evaluations showing that GeneAgent offers novel insights into gene functions and subsequently expedites knowledge discovery., Comment: 30 pages with 10 figures and/or tables

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry into this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, fibers, and correlators), as well as new data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed, and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

Masers once represented the state-of-the-art in low noise microwave amplification technology, but eventually became obsolete due to their need for cryogenic cooling. Masers based on solid-state spin systems perform most effectively as amplifiers, since they provide a large density of spins and can therefore operate at relatively high powers. Whilst solid-state masers oscillators have been demonstrated at room temperature, continuous-wave amplification in these systems has only ever been realized at cryogenic temperatures. Here we report on a continuous-wave solid-state maser amplifier operating at room temperature. We achieve this feat using a practical setup that includes an ensemble of nitrogen-vacancy center spins in a diamond crystal, a strong permanent magnet and simple laser diode. We describe important amplifier characteristics including gain, bandwidth, compression power and noise temperature and discuss the prospects of realizing a room-temperature near-quantum-noise-limited amplifier with this system. Finally, we show that in a different mode of operation the spins can be used to cool the system noise in an external circuit to cryogenic levels, all without the requirement for physical cooling.

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment that includes small-aperture telescopes (SATs) observing from an altitude of 5,200 m in the Atacama Desert in Chile. The SO SATs will cover six spectral bands between 27 and 280 GHz to search for primordial B-modes to a sensitivity of $\sigma(r)=0.002$, with quantified systematic errors well below this value. Each SAT is a self-contained cryogenic telescope with a 35$^\circ$ field of view, 42 cm diameter optical aperture, 40 K half-wave plate, 1 K refractive optics, and $<0.1$ K focal plane that holds $>12,000$ TES detectors. We describe the nominal design of the SATs and present details about the integration and testing for one operating at 93 and 145 GHz.

Recent progress in the understanding of the collective behavior of electrons and ions have revealed new types of ferroic orders beyond ferroelectricity and ferromagnetism, such as the ferroaxial state. The latter retains only rotational symmetry around a single axis and reflection symmetry with respect to a single mirror plane, both of which are set by an emergent electric toroidal dipole moment. Due to this unusual symmetry-breaking pattern, it has been challenging to directly measure the ferroaxial order parameter, despite the increasing attention this state has drawn. Here, we show that off-diagonal components of the piezoresistivity tensor (i.e., the linear change in resistivity under strain) transform the same way as the ferroaxial moments, providing a direct probe of such order parameters. We identify two new proper ferroaxial materials through a materials database search, and use first-principles calculations to evaluate the piezoconductivity of the double-perovskite CaSnF$_6$, revealing its connection to ferroaxial order and to octahedral rotation modes.

Many complex systems can be accurately modeled as a set of coupled time-dependent partial differential equations (PDEs). However, solving such equations can be prohibitively expensive, easily taxing the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This operator splitting approach is used ubiquitously across scientific domains, and in many cases leads to a set of ordinary differential equations (ODEs) that need to be solved as part of a larger "outer-loop" time-stepping approach. The SUNDIALS library provides a plethora of robust time integration algorithms for solving ODEs, and the U.S. Department of Energy Exascale Computing Project (ECP) has supported its extension to applications on exascale-capable computing hardware. In this paper, we highlight some SUNDIALS capabilities and its deployment in combustion and cosmology application codes (Pele and Nyx, respectively) where operator splitting gives rise to numerous, small ODE systems that must be solved concurrently.

The second "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'24) meeting was held January 8-11, 2024 in Arlington, VA, USA, hosted by Virginia Tech's Center for Neutrino Physics. This document collects contributions from this workshop, providing an overview of activities in the field. MDvDM'24 was the second topical workshop dedicated to the emerging field of mineral detection of neutrinos and dark matter, following a meeting hosted by IFPU in Trieste, Italy in October 2022. Mineral detectors have been proposed for a wide variety of applications, including searching for dark matter, measuring various fluxes of astrophysical neutrinos over gigayear timescales, monitoring nuclear reactors, and nuclear disarmament protocols; both as paleo-detectors using natural minerals that could have recorded the traces of nuclear recoils for timescales as long as a billion years and as detectors recording nuclear recoil events on laboratory timescales using natural or artificial minerals. Contributions to this proceedings discuss the vast physics potential, the progress in experimental studies, and the numerous challenges lying ahead on the path towards mineral detection. These include a better understanding of the formation and annealing of recoil defects in crystals; identifying the best classes of minerals and, for paleo-detectors, understanding their geology; modeling and control of the relevant backgrounds; developing, combining, and scaling up imaging and data analysis techniques; and many others. During the last years, MDvDM has grown rapidly and gained attention. Small-scale experimental efforts focused on establishing various microscopic readout techniques are underway at institutions in North America, Europe and Asia. We are looking ahead to an exciting future full of challenges to overcome, surprises to be encountered, and discoveries lying ahead of us., Comment: Summary and proceedings of the MDvDM'24 conference, Jan 8-11 2024

The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of chemical mechanisms for implementation in computational reacting flow solvers. However, for many applications, stiffness remains, and the resulting model requires implicit methods for efficient time integration. In this paper, we outline an approach to formulating the QSSA reduction that is coupled with a strategy to generate C++ source code to evaluate the net species production rate, and the chemical Jacobian. The code-generation component employs a symbolic approach enabling a simple and effective strategy to analytically compute the chemical Jacobian. For computational tractability, the symbolic approach needs to be paired with common subexpression elimination which can negatively affect memory usage. Several solutions are outlined and successfully tested on a 3D multipulse ignition problem, thus allowing portable application across a chemical model sizes and GPU capabilities. The implementation of the proposed method is available at https://github.com/AMReX-Combustion/PelePhysics under an open-source license.

Inclusive electron scattering at carefully chosen kinematics can isolate scattering from short-range correlations (SRCs), produced through hard, short-distance interactions of nucleons in the nucleus. Because the two-nucleon (2N) SRCs arise from the same N-N interaction in all nuclei, the cross section in the SRC-dominated regime is identical up to an overall scaling factor, and the A/2H cross section ratio is constant in this region. This scaling behavior has been used to identify SRC dominance and to map out the contribution of SRCs for a wide range of nuclei. We examine this scaling behavior at lower momentum transfers using new data on $^2$H, $^3$H, and $^3$He which show that the scaling region is larger than in heavy nuclei. Based on the improved scaling, especially for $^3$H/$^3$He, we examine the ratios at kinematics where three-nucleon SRCs may play an important role. The data for the largest initial nucleon momenta are consistent with isolation of scattering from 3N-SRCs, and suggest that the very-highest momentum nucleons in $^3$He have a nearly isospin-independent momentum configuration, or a small enhancement of the proton distribution.

We report measurements characterizing the performance of a kinetic inductance detector array designed for a wavelength of 25 microns and very low optical background level suitable for applications such as a far-infrared instrument on a cryogenically cooled space telescope. In a pulse counting mode of operation at low optical flux, the detectors can resolve individual 25-micron photons. In an integrating mode, the detectors remain photon noise limited over more than six orders of magnitude in absorbed power from 70 zW to 200 fW, with a limiting NEP of 4.6 x 10^-20 W/rtHz at 1 Hz. In addition, the detectors are highly stable with flat power spectra under optical load down to 1 mHz. Operational parameters of the detector are determined including the efficiency of conversion of the incident optical power into quasiparticles in the aluminum absorbing element and the quasiparticle self-recombination constant., Comment: 35 pages, 18 figures

A common technique in the study of complex quantum-mechanical systems is to reduce the number of degrees of freedom in the Hamiltonian by using quasi-degenerate perturbation theory. While the Schrieffer--Wolff transformation achieves this and constructs an effective Hamiltonian, its scaling is suboptimal, and implementing it efficiently is both challenging and error-prone. We introduce an algorithm for constructing an equivalent effective Hamiltonian as well as a Python package, Pymablock, that implements it. Our algorithm combines an optimal asymptotic scaling with a range of other improvements. The package supports numerical and analytical calculations of any order and it is designed to be interoperable with any other packages for specifying the Hamiltonian. We demonstrate how the package handles constructing a k.p model, analyses a superconducting qubit, and computes the low-energy spectrum of a large tight-binding model. We also compare its performance with reference calculations and demonstrate its efficiency., Comment: 21 pages, 5 figures

The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in AI progress. This study investigates eye-tracking as a cost-effective technology to collect doctors' behavioral data for AI training with a focus on the pathology task of mitosis detection. One major challenge in using eye-gaze data is the low signal-to-noise ratio, which hinders the extraction of meaningful information. We tackled this by levering the properties of inter-observer eye-gaze consistencies and creating eye-gaze labels from consistent eye-fixations shared by a group of observers. Our study involved 14 non-medical participants, from whom we collected eye-gaze data and generated eye-gaze labels based on varying group sizes. We assessed the efficacy of such eye-gaze labels by training Convolutional Neural Networks (CNNs) and comparing their performance to those trained with ground truth annotations and a heuristic-based baseline. Results indicated that CNNs trained with our eye-gaze labels closely followed the performance of ground-truth-based CNNs, and significantly outperformed the baseline. Although primarily focused on mitosis, we envision that insights from this study can be generalized to other medical imaging tasks., Comment: Accepted by IEEE International Conference on Healthcare Informatics 2024

Magnetic materials are composed of the simple building blocks of magnetic moments on a crystal lattice that interact via magnetic exchange. Yet from this simplicity emerges a remarkable diversity of magnetic states. Some reveal the deep quantum mechanical origins of magnetism, for example, quantum spin liquid (QSL) states in which magnetic moments remain disordered at low temperatures despite being strongly correlated through quantum entanglement. A promising theoretical model of a QSL is the Kitaev model, composed of unusual bond-dependent exchange interactions, but experimentally, this model is challenging to realise. Here we show that the material requirements for the Kitaev QSL survive an extended pseudo-edge-sharing superexchange pathway of Ru3+ octahedra within the honeycomb layers of the inorganic framework solid, RuP3SiO11. We confirm the requisite jeff = 1/2 state of Ru3+ in RuP3SiO11 and resolve the hierarchy of exchange interactions that provide experimental access to an unexplored region of the Kitaev model., Comment: 28 Pages, 5 figures, 1 table

Van der Waals (vdW) heterostructures host many-body quantum phenomena that can be tuned in situ using electrostatic gates. These gates are often microstructured graphite flakes that naturally form plasmonic cavities, confining light in discrete standing waves of current density due to their finite size. Their resonances typically lie in the GHz - THz range, corresponding to the same $\mu$eV - meV energy scale characteristic of many quantum effects in the materials they electrically control. This raises the possibility that built-in cavity modes could be relevant for shaping the low-energy physics of vdW heterostructures. However, capturing this light-matter interaction remains elusive as devices are significantly smaller than the diffraction limit at these wavelengths, hindering far-field spectroscopic tools. Here, we report on the sub-wavelength cavity electrodynamics of graphene embedded in a vdW heterostructure plasmonic microcavity. Using on-chip THz spectroscopy, we observed spectral weight transfer and an avoided crossing between the graphite cavity and graphene plasmon modes as the graphene carrier density was tuned, revealing their ultrastrong coupling. Our findings show that intrinsic cavity modes of metallic gates can sense and manipulate the low-energy electrodynamics of vdW heterostructures. This opens a pathway for deeper understanding of emergent phases in these materials and new functionality through cavity control.

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key feature of this framework is the ability to explicitly utilize user-specified uncertainty priors, which we call model-specific local trust estimates, that are provided directly together with the problem specification and exploited in adaptive sampling strategies. A multi-fidelity model hierarchy is supported to allow trade-offs in accuracy and data acquisition cost while exploring the search space given a specified budget of potentially distributed, heterogeneous resources. We discuss application of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads., Comment: 11 pages, 5 figures

As part of human core knowledge, the representation of objects is the building block of mental representation that supports high-level concepts and symbolic reasoning. While humans develop the ability of perceiving objects situated in 3D environments without supervision, models that learn the same set of abilities with similar constraints faced by human infants are lacking. Towards this end, we developed a novel network architecture that simultaneously learns to 1) segment objects from discrete images, 2) infer their 3D locations, and 3) perceive depth, all while using only information directly available to the brain as training data, namely: sequences of images and self-motion. The core idea is treating objects as latent causes of visual input which the brain uses to make efficient predictions of future scenes. This results in object representations being learned as an essential byproduct of learning to predict., Comment: 21 pages, 11 figures. Project webpage can be found at https://jday54.github.io/opple_site/

We present a new angle on the expressive power of graph neural networks (GNNs) by studying how the predictions of a GNN probabilistic classifier evolve as we apply it on larger graphs drawn from some random graph model. We show that the output converges to a constant function, which upper-bounds what these classifiers can uniformly express. This strong convergence phenomenon applies to a very wide class of GNNs, including state of the art models, with aggregates including mean and the attention-based mechanism of graph transformers. Our results apply to a broad class of random graph models, including sparse and dense variants of the Erd\H{o}s-R\'enyi model, the stochastic block model, and the Barab\'asi-Albert model. We empirically validate these findings, observing that the convergence phenomenon appears not only on random graphs but also on some real-world graphs., Comment: 9 body pages, 28 appendix pages, 9 figures