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Two groups of continuing education students were tested using three of Viktor Lowenfeld's tests of visual-haptic perception. The scores from the three tests were added to yield a score which identified an individual's location on Lowenfeld's visual-haptic continuum. The investigation also uncovered scoring problems with two of the tests which had not been previously reported in the literature. It was concluded that the Picture a Building Test as well as the Test of Visual and Haptical Word Association should not be used in their original form as a vehicle for ascertaining an individual's perceptual aptitude. Raw data tables are included. (Author)

We investigate the locality properties of $T \overline T$-deformed CFTs within perturbation theory. Up to third order in the deformation parameter, we find a Hamiltonian operator which solves the flow equation, reproduces the Zamolodchikov energy spectrum, and is consistent with quasi-locality of the theory. This Hamiltonian includes terms proportional to the central charge which have not appeared before and which are necessary to reproduce the correct spectrum. We show that the Hamiltonian is not uniquely defined since it contains free parameters, starting at second order, which do not spoil the above properties. We then use it to determine the full conserved stress tensor. In our approach, the KdV charges are automatically conserved to all orders but are not a priori local. Nevertheless, we show that they can be made local to first order. Our techniques allow us to further comment on the space of Hamiltonians constructed from products of KdV charges which also flow to local charges in the deformed theory in the IR., Comment: 24 pages

If the probability model is correctly specified, then we can estimate the covariance matrix of the asymptotic maximum likelihood estimate distribution using either the first or second derivatives of the likelihood function. Therefore, if the determinants of these two different covariance matrix estimation formulas differ this indicates model misspecification. This misspecification detection strategy is the basis of the Determinant Information Matrix Test ($GIMT_{Det}$). To investigate the performance of the $GIMT_{Det}$, a Deterministic Input Noisy And gate (DINA) Cognitive Diagnostic Model (CDM) was fit to the Fraction-Subtraction dataset. Next, various misspecified versions of the original DINA CDM were fit to bootstrap data sets generated by sampling from the original fitted DINA CDM. The $GIMT_{Det}$ showed good discrimination performance for larger levels of misspecification. In addition, the $GIMT_{Det}$ did not detect model misspecification when model misspecification was not present and additionally did not detect model misspecification when the level of misspecification was very low. However, the $GIMT_{Det}$ discrimation performance was highly variable across different misspecification strategies when the misspecification level was moderately sized. The proposed new misspecification detection methodology is promising but additional empirical studies are required to further characterize its strengths and limitations.

Ultraluminous X-ray sources (ULXs) were once largely believed to be powered by super-Eddington accretion onto stellar-mass black holes, although in some rare cases, ULXs also serve as potential candidates for (sub-Eddington) intermediate mass black holes. However, a total of eight ULXs have now been confirmed to be powered by neutron stars, thanks to observed pulsations, and may act as contaminants for radio/X-ray selection of intermediate mass black holes. Here we present the first comprehensive radio study of seven known neutron star ULXs using new and archival data from the Karl G. Jansky Very Large Array and the Australia Telescope Compact Array, combined with the literature. Across this sample there is only one confident radio detection, from the Galactic neutron star ULX Swift J0243.6+6124. The other six objects in our sample are extragalactic, and only one has coincident radio emission, which we conclude is most likely contamination from a background HII region. We conclude that with current facilities, neutron star ULXs do not produce significant enough radio emission to cause them to be misidentified as radio/X-ray selected intermediate mass black hole candidates. Thus, if background star formation has been properly considered, the current study indicates that a ULX with a compact radio counterpart is not likely to be a neutron star., Comment: 14 pages, 2 figures, accepted to ApJ

We describe 'RingSim', a phenomenological agent-based model that allows numerical simulation of magnetic nanowire networks with areas of hundreds of micrometers squared for durations of hundreds of seconds; a practical impossibility for general-purpose micromagnetic simulation tools. In RingSim, domain walls (DWs) are instanced as mobile agents which respond to external magnetic fields, and their stochastic interactions with pinning sites and other DWs are described via simple phenomenological rules. We first present a detailed description of the model and its algorithmic implementation for simulating the behaviours of arrays of interconnected ring-shaped nanowires, which have previously been proposed as hardware platforms for unconventional computing applications. The model is then validated against a series of experimental measurements of an array's static and dynamic responses to rotating magnetic fields. The robust agreement between the modelled and experimental data demonstrates that agent-based modelling is a powerful tool for exploring mesoscale magnetic devices, enabling time scales and device sizes that are inaccessible to more conventional magnetic simulation techniques.

Accumulated detailed knowledge about the neuronal activities in human brains has brought more attention to bio-inspired spiking neural networks (SNNs). In contrast to non-spiking deep neural networks (DNNs), SNNs can encode and transmit spatiotemporal information more efficiently by exploiting biologically realistic and low-power event-driven neuromorphic architectures. However, the supervised learning of SNNs still remains a challenge because the spike-timing-dependent plasticity (STDP) of connected spiking neurons is difficult to implement and interpret in existing backpropagation learning schemes. This paper proposes a fractional-order spike-timing-dependent gradient descent (FO-STDGD) learning model by considering a derived nonlinear activation function that describes the relationship between the quasi-instantaneous firing rate and the temporal membrane potentials of nonleaky integrate-and-fire neurons. The training strategy can be generalized to any fractional orders between 0 and 2 since the FO-STDGD incorporates the fractional gradient descent method into the calculation of spike-timing-dependent loss gradients. The proposed FO-STDGD model is tested on the MNIST and DVS128 Gesture datasets and its accuracy under different network structure and fractional orders is analyzed. It can be found that the classification accuracy increases as the fractional order increases, and specifically, the case of fractional order 1.9 improves by 155% relative to the case of fractional order 1 (traditional gradient descent). In addition, our scheme demonstrates the state-of-the-art computational efficacy for the same SNN structure and training epochs., Comment: 15 pages, 12 figures

The Discrete Element Method is widely employed for simulating granular flows, but conventional integration techniques may produce unphysical results for simulations with static friction when particle size ratios exceed $R \approx 3$. These inaccuracies arise because some variables in the velocity-Verlet algorithm are calculated at the half-timestep, while others are computed at the full timestep. To correct this, we develop an improved velocity-Verlet integration algorithm to ensure physically accurate outcomes up to the largest size ratios examined ($R=100$). The implementation of this improved integration method within the LAMMPS framework is detailed, and its effectiveness is validated through a simple three-particle test case and a more general example of granular flow in mixtures with large size-ratios, for which we provide general guidelines for selecting simulation parameters and accurately modeling inelasticity in large particle size-ratio simulations.

MAVIS (the MCAO-Assisted Visible Imager and Spectrograph), planned for the VLT Adaptive Optics Facility, represents an innovative step in Multi-Conjugate Adaptive Optics (MCAO) systems, particularly in its operation at visible wavelengths and anticipated contributions to the field of astronomical astrometry. Recognizing the crucial role of high-precision astrometry in realizing science goals such as studying the dynamics of dense starfields, this study focuses on the challenges of advancing astrometry with MAVIS to its limits, as well as paving the way for further enhancement by incorporating telemetry data as part of the astrometric analysis. We employ MAVISIM, Superstar, and DAOPHOT to simulate both MAVIS imaging performance and provide a pathway to incorporate telemetry data for precise astrometry with MAVIS. Photometry analyses are conducted using the Superstar and DAOPHOT platforms, integrated into a specifically designed pipeline for astrometric analysis in MCAO settings. Combining these platforms, our research aims to elucidate the impact of utilizing telemetry data on improving astrometric precision, potentially establishing new methods for ground-based AO-assisted astrometric analysis. This endeavor not only sheds light on the capabilities of MAVIS but also paves the way for advancing astrometry in the era of next-generation MCAO-enabled giant telescopes.

Predicting particle segregation has remained challenging due to the lack of a general model for the segregation velocity that is applicable across a range of granular flow geometries. Here, a segregation velocity model for dense granular flows is developed by exploiting momentum balance and recent advances in particle-scale modelling of the segregation driving and drag forces over a wide range of particle concentrations, size and density ratios, and flow conditions. This model is shown to correctly predict particle segregation velocity in a diverse set of idealized and natural granular flow geometries simulated using the discrete element method. When incorporated in the well-established advection-diffusion-segregation formulation, the model has the potential to accurately capture segregation phenomena in many relevant industrial application and geophysical settings.

Starting from microscopic $N$ particle systems, we study the derivation of Doi type models for suspensions of non-spherical particles in Stokes flows. While Doi models accurately describe the effective evolution of the spatial particle density to the first order in the particle volume fraction, this accuracy fails regarding the evolution of the particle orientations. We rigorously attribute this failure to the singular interaction of the particles via a $-3$-homogeneous kernel. In the situation that the particles are initially distributed according to a stationary ergodic point process, we identify the limit of this singular interaction term. It consists of two parts. The first corresponds to a classical term in Doi type models. The second new term depends on the (microscopic) $2$-point correlation of the point process. By including this term, we provide a modification of the Doi model that is accurate to first order in the particle volume fraction.

Lift and drag forces on moving intruders in granular materials are of fundamental interest. While the drag force on an intruder in granular flow has been studied, the few studies characterizing the lift force explore a relatively limited range of parameters. Here we use discrete element method (DEM) simulations to measure the lift force, $F_\mathrm{L}$, on a spherical intruder in a uniformly sheared bed of smaller spheres for a range of intruder slip velocities, $u_\mathrm{s}$, relative to the unperturbed flow. In what at first appears as a puzzling result, $F_\mathrm{L}$ in granular shear flow acts in the opposite direction to the Saffman lift force on a sphere in a sheared fluid at low $u_\mathrm{s}$, reaches a maximum value, and then decreases, eventually reversing direction and becoming comparable to $F_\mathrm{L}$ for a fluid. This non-monotonic response holds over a range of flow conditions, and the $F_\mathrm{L}$ versus $u_\mathrm{s}$ data can be collapsed by scaling both quantities using the particle sizes, shear rate, and overburden pressure. Analogous fluid simulations demonstrate that the flow field around the intruder particle is similar in the granular and fluid cases. However, the shear stress acting on the intruder in a granular shear flow is much less than that in a fluid shear flow. This difference, combined with a void region behind the intruder in granular flow, which alters the pressure and shear stress on the trailing side of the intruder, significantly changes the lift-force inducing stresses acting on the intruder between the granular and fluid cases., Comment: 27 pages, 17 figures

We present Gemini near-infrared integral field spectrograph (NIFS) K-band observations of the central 400 pc of NGC 1266, a nearby (D$\approx$30 Mpc) post-starburst galaxy with a powerful multi-phase outflow and a shocked ISM. We detect 7 H$_2$ ro-vibrational emission lines excited thermally to $T$$\sim$2000 K, and weak Br$\gamma$ emission, consistent with a fast C-shock. With these bright H$_2$ lines, we observe the spatial structure of the shock with an unambiguous tracer for the first time. The Br$\gamma$ emission is concentrated in the central $\lesssim$100 pc, indicating that any remaining star-formation in NGC 1266 is in the nucleus while the surrounding cold molecular gas has little on-going star-formation. Though it is unclear what fraction of this Br$\gamma$ emission is from star-formation or the AGN, assuming it is entirely due to star-formation we measure an instantaneous star-formation rate of 0.7 M$_\odot$ yr$^{-1}$, though the star-formation rate may be significantly higher in the presence of additional extinction. NGC 1266 provides a unique laboratory to study the complex interactions between AGN, outflows, shocks, and star-formation, all of which are necessary to unravel the evolution of the post-starburst phase., Comment: ApJ accepted

The alignment of optical systems is a critical step in their manufacture. Alignment normally requires considerable knowledge and expertise of skilled operators. The automation of such processes has several potential advantages, but requires additional resource and upfront costs. Through a case study of a simple two mirror system we identify and examine three different automation approaches. They are: artificial neural networks; practice-led, which mimics manual alignment practices; and design-led, modelling from first principles. We find that these approaches make use of three different types of knowledge 1) basic system knowledge (of controls, measurements and goals); 2) behavioural skills and expertise, and 3) fundamental system design knowledge. We demonstrate that the different automation approaches vary significantly in human resources, and measurement sampling budgets. This will have implications for practitioners and management considering the automation of such tasks., Comment: Author Accepted Manuscript- 8 pages, The 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE 2024), Aug28-Sep1st 2024, Bari, Italy. Keywords: Automation, optimisation, regression, behaviour analysis, artificial neural networks, optical systems, mathematical model, human factors, sampling cost, cost benefit analysis

As seasonal thermal energy storage emerges as an efficient solution to reduce CO2 emissions of buildings, challenges appear related to its optimal operation. In a system including short-term electricity storage, long-term heat storage, and where electricity and heat networks are connected through a heat pump, it becomes crucial to operate the system on two time scales. Based on real data from a university building, we simulate the operation of such a system over a year, comparing different strategies based on model predictive control (MPC). The first objective of this paper is to determine the minimum prediction horizon to retrieve the results of the full-horizon operation problem with cost minimization. The second objective is to evaluate a method that combines MPC with setting targets on the heat storage level at the end of the prediction horizon, based on historical data. For a prediction horizon of 6 days, the suboptimality gap with the full-horizon results is 4.31%, compared to 11.42% when using a prediction horizon of 42 days and fixing the final level to be equal to the initial level, which is a common approach.

Schwarzschild dynamical models are now regularly employed in large surveys of galaxies in the local and distant Universe to derive information on galaxies' intrinsic properties such as their orbital structure and their (dark matter and stellar) mass distribution. Comparing the internal orbital structures and mass distributions of galaxies in the distant Universe with simulations is key to understanding what physical processes are responsible for shaping galaxy properties. However it is first crucial to understand whether observationally derived properties are directly comparable with intrinsic ones in simulations. To assess this, we build Schwarzschild dynamical models for MUSE-like IFS cubes (constructed to be like those obtained by the MAGPI survey) of 75 galaxies at z ~ 0.3 from the Eagle simulations. We compare the true particle-derived properties with the galaxies' model-derived properties. In general, we find that the models can recover the true galaxy properties qualitatively well, with the exception of the enclosed dark matter, where we find a median offset of 48%, which is due to the assumed NFW profile not being able to reproduce the dark matter distribution in the inner region of the galaxies. We then compare our model-derived properties with Schwarzschild models-derived properties of observed MAGPI galaxies and find good agreement between MAGPI and Eagle: the majority of our galaxies (57%) have non-oblate shapes within 1 effective radius. More triaxial galaxies show higher fractions of hot orbits in their inner regions and tend to be more radially anisotropic., Comment: 22 pages. Accepted for publication in MNRAS

The vertical transport of solid material in a stratified medium is fundamental to a number of environmental applications, with implications for the carbon cycle and nutrient transport in marine ecosystems. In this work, we study the diffusion-limited settling of highly porous particles in a density-stratified fluid through a combination of experiment, analysis, and numerical simulation. By delineating and appealing to the diffusion-limited regime wherein buoyancy effects due to mass adaptation dominate hydrodynamic drag, we derive a simple expression for the steady settling velocity of a sphere as a function of the density, size, and diffusivity of the solid, as well as the density gradient of the background fluid. In this regime, smaller particles settle faster, in contrast with most conventional hydrodynamic drag mechanisms. Furthermore, we outline a general mathematical framework for computing the steady settling speed of a body of arbitrary shape in this regime and compute exact results for the case of general ellipsoids. Using hydrogels as a highly porous model system, we validate the predictions with laboratory experiments in linear stratification for a wide range of parameters. Lastly, we show how the predictions can be applied to arbitrary slowly varying background density profiles and demonstrate how a measured particle position over time can be used to reconstruct the background density profile.

We establish rigorous inequalities between different electronic properties linked to optical sum rules, and organize them into weak and strong bounds on three characteristic properties of insulators: electron localization length $\ell$ (the quantum fluctuations in polarization), electric susceptibility $\chi$, and optical gap $E_{\rm G}$. All-electron and valence-only versions of the bounds are given, and the latter are found to be more informative. The bounds on $\ell$ are particularly interesting, as they provide reasonably tight estimates for an ellusive ground-state property -- the average localization length of valence electrons -- from tabulated experimental data: electron density, high-frequency dielectric constant, and optical gap. The localization lengths estimated in this way for several materials follow simple chemical trends, especially for the alkali halides. We also illustrate our findings via analytically solvable harmonic oscillator models, which reveal an intriguing connection to the physics of long-ranged van der Waals forces.

In recent years, tree tensor network methods have proven capable of simulating quantum many-body and other high-dimensional systems. This work is a user guide to our Python library PyTreeNet. It includes code examples and exercises to introduce the library's functions and familiarise the reader with the concepts and methods surrounding tree tensor networks. PyTreeNet implements all the tools required to implement general tree tensor network methods, such as tensor decompositions and arbitrary tree structures. The main focus is on the time evolution of quantum systems. This includes an introduction to tree tensor network states and operators and the time-evolving block decimation and time-dependent variational principle. The library's capabilities are showcased with the example of a modified transverse field Ising model on tree structures that go far beyond the ability of common state vector methods., Comment: (53 pages, 17 figures, code available at https://github.com/Drachier/PyTreeNet)

The formulation of the S-matrix as a path integral with specified asymptotic boundary conditions naturally leads to the realization of a Carrollian partition function defined on the boundary of Minkowski space. This partition function, specified at past and future null infinity in the case of massless particles, generates Carrollian correlation functions that encode the S-matrix. We explore this connection, including the realization of symmetries, soft theorems arising from large gauge transformations, and the correspondence with standard momentum space amplitudes. This framework is also well-suited for embedding the Minkowski space S-matrix into the AdS/CFT duality in the large radius limit. In particular, we identify the AdS and Carrollian partition functions through a simple map between their respective asymptotic data, establishing a direct correspondence between the actions of symmetries on both sides. Our approach thus provides a coherent framework that ties together various topics extensively studied in recent and past literature., Comment: 59 pages; v2 corrected typos, added discussion of flat space Witten diagrams in section 3

We present near-infrared spectroscopy of ten weak emission-line quasars (WLQs) at redshifts of $z\sim2$, obtained with the Palomar 200-inch Hale Telescope. WLQs are an exceptional population of type 1 quasars that exhibit weak or no broad emission lines in the ultraviolet (e.g., the C IV $\lambda 1549$ line), and they display remarkable X-ray properties. We derive H$\beta$-based single-epoch virial black-hole masses (median value $\rm 1.7 \times 10^{9} M_{\odot}$) and Eddington ratios (median value $0.5)$ for our sources. We confirm the previous finding that WLQ H$\beta$ lines, as a major low-ionization line, are not significantly weak compared to typical quasars. The most prominent feature of the WLQ optical spectra is the universally weak/absent [O III] $\lambda 5007$ emission. They also display stronger optical Fe II emission than typical quasars. Our results favor the super-Eddington accretion scenario for WLQs, where the weak lines are a result of a soft ionizing continuum; the geometrically thick inner accretion disk and/or its associated outflow is responsible for obscuring the nuclear high-energy radiation and producing the soft ionizing continuum. We also report candidate extreme [O III] outflows (blueshifts of $\approx 500$ and $\rm 4900 km s^{-1}$) in one object., Comment: 19 pages, 8 figures, accepted for publication in ApJ

Simulating fermions coupled to spin degrees of freedom, relevant for a range of quantum field theories, represents a promising application for quantum simulators. Mapping fermions to qubits is challenging in $2+1$ and higher spacetime dimensions, and mapping bosons demands substantial quantum-computational overhead. These features complicate the realization of mixed fermion-boson quantum systems in digital quantum computers. We propose a native fermion-(large-)spin analog quantum simulator by utilizing dopant arrays in silicon. Specifically, we show how to use a dynamical lattice of coupled nuclear spins and conduction-band electrons to realize a quantum field theory: an extended Jackiw-Rebbi model involving coupled fermions and quantum rotors. We demonstrate the feasibility of observing dynamical mass generation and a confinement-deconfinement quantum phase transition in 1+1 dimensions on this platform, even in the presence of strong long-range Coulomb interactions. Furthermore, we employ finite-temperature Hartree-Fock-Bogoliubov simulations to investigate the dynamics of mass generation in two-dimensional square and honeycomb arrays, showing that this phenomenon can be simulated with realistic experimental parameters. Our findings reveal two distinct phases, and demonstrate robustness against the addition of Coulomb interactions. Finally, we discuss experimental signatures of the phases through transport and local charge sensing in dopant arrays. This study lays the foundation for quantum simulations of quantum field theories exhibiting fermions coupled to spin degrees of freedom using donors in silicon.

Global numerical weather models are starting to resolve atmospheric moist convection which comes with a critical need for observational constraints. One avenue for such constraints is spaceborne radar which tend to operate at three wavelengths, Ku-, Ka- and W-band. Many studies of deep convection in the past have primarily leveraged Ku-band because it is less affected by attenuation and multiple scattering. However, future spaceborne radar missions might not contain a Ku-band radar and thus considering the view of convection from Ka-band or W-band compared to the Ku-band would be useful. This study examines a coincident dataset between the Global Precipitation Measurement (GPM) Mission and CloudSat as well as the entire GPM record to compare convective characteristics across various wavelengths within deep convection. We find that W-band reflectivity (Z) tends to maximize near the Ku-band defined echo-top while Ka-band often maximizes 4-5 km below. The height of the maximum Z above the melting level for W-band does not linearly relate to the Ku-band maximum. However, using the full GPM record the Ka-band 30 dBZ echo-tops can be linearly related to the Ku-band 40 dBZ echo-top with an $R^2$ of 0.62 and a root mean squared error of about 1 km. The spatial distribution of echo-tops from Ka-band corresponds well to the Ku-band echo-tops, highlighting regions of relatively large ice water path. This paper suggests that Ka-band only missions, like NASA's Investigation for Convective Updrafts, should be able to characterize global convection in a similar manner to a Ku-band system.

The fact that simulations of condensed matter problems are limited to sizes far smaller than the macroscopic systems being emulated does have one redeeming feature: The specific dependence on N, the number of molecules being simulated, is sometimes itself a valuable piece of information. The isotropic phase of liquid crystals provides a case in point. Light scattering and 2d-IR experiments on isotropic-phase samples display increasingly large orientational fluctuations as the samples approach their nematic phase. The growing length scale of those locally ordered domains is readily seen in simulation as an ever slower convergence of the distribution of orientational order parameters with N. The rare-event character of the largest fluctuations make them difficult to sample accurately. We show in this paper how taking a large deviation theory perspective enables us to leverage simulation-derived information more effectively. A key insight of large deviation theory is that extracting the desired measurable, in our case, the extent of orientational order, is completely equivalent to finding the size of the conjugate field required to equilibrate that amount of order - and that knowing the relationship between the two gives us the relative free energy. Indeed, a variety of thermodynamic integration strategies used to evaluate free energy differences in simulation are essentially founded on this idea. However, rather than applying an artificially imposed external field, we use a priori statistical mechanical insights into the small and large-field limits of the equation of state to construct a simulation-guided, interpolated, equation of state. The free energies that result turn out to need simulation information derived mostly from the most probable configurations, rather than from the infrequent events, and therefore use simulation information far more efficiently than conventional methods., Comment: 33 pages, 8 figures

We use the `Middle Ages Galaxy Properties with Integral field spectroscopy' (MAGPI) survey to investigate whether galaxies have evolved in the distribution of their stellar angular momentum in the past 3-4 Gyr, as probed by the observational proxy for spin, $\lambda_{R}$. We use 2D stellar kinematics to measure $\lambda_{R}$ along with detailed photometric models to estimate galaxy ellipticity. The combination of these measurements quantifies the kinematic classes of `fast rotators' and the rarer `slow rotators', which show no regular rotation in their line-of-sight velocity fields. We compare 51 MAGPI galaxies with $\log_{10} (M_{\star}/\mathrm{M}_\odot) > 10$ to carefully drawn samples of MaNGA galaxies in the local Universe, selected to represent possible descendants of the MAGPI progenitors. The EAGLE simulations are used to identify possible evolutionary pathways between the two samples, explicitly accounting for progenitor bias in our results and the varied evolutionary pathways a galaxy might take between the two epochs. We find that the occurrence of slow rotating galaxies is unchanged between the MAGPI ($z \sim 0.3$) and MaNGA ($z \sim 0$) samples, suggesting the massive slow rotator population was already in place $\sim 4$ Gyr ago and has not accumulated since. There is a hint of the MAGPI sample having an excess of high $\lambda_{R}$ galaxies compared to the MaNGA sample, corresponding to more ordered rotation, but statistically the samples are not significantly different. The large-scale stellar kinematics, as quantified through the $\lambda_{R}$ parameter, of galaxies at $z \sim 0.3$ have already evolved into the diversity of structures seen today in the local Universe., Comment: 10 pages, 7 figures, accepted for publication in MNRAS

This paper presents a novel approach to extract scientifically valuable information about Earth's physical phenomena from unconventional sources, such as multi-modal social media posts. Employing a state-of-the-art large language model (LLM), Gemini 1.5 Pro (Reid et al. 2024), we estimate earthquake ground shaking intensity from these unstructured posts. The model's output, in the form of Modified Mercalli Intensity (MMI) values, aligns well with independent observational data. Furthermore, our results suggest that LLMs, trained on vast internet data, may have developed a unique understanding of physical phenomena. Specifically, Google's Gemini models demonstrate a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, accurately describing observational data even though it's not identical to established models. These findings raise intriguing questions about the extent to which Gemini's training has led to a broader understanding of the physical world and its phenomena. The ability of Generative AI models like Gemini to generate results consistent with established scientific knowledge highlights their potential to augment our understanding of complex physical phenomena like earthquakes. The flexible and effective approach proposed in this study holds immense potential for enriching our understanding of the impact of physical phenomena and improving resilience during natural disasters. This research is a significant step toward harnessing the power of social media and AI for natural disaster mitigation, opening new avenues for understanding the emerging capabilities of Generative AI and LLMs for scientific applications.

Unambiguous identification of the rewards driving behaviours of entities operating in complex open-ended real-world environments is difficult, partly because goals and associated behaviours emerge endogenously and are dynamically updated as environments change. Reproducing such dynamics in models would be useful in many domains, particularly where fixed reward functions limit the adaptive capabilities of agents. Simulation experiments described assess a candidate algorithm for the dynamic updating of rewards, RULE: Reward Updating through Learning and Expectation. The approach is tested in a simplified ecosystem-like setting where experiments challenge entities' survival, calling for significant behavioural change. The population of entities successfully demonstrate the abandonment of an initially rewarded but ultimately detrimental behaviour, amplification of beneficial behaviour, and appropriate responses to novel items added to their environment. These adjustment happen through endogenous modification of the entities' underlying reward function, during continuous learning, without external intervention., Comment: 30 pages, 8 figures

We present the rest-frame ultraviolet-optical spectral properties of 65 broad absorption line (BAL) quasars from the Gemini Near Infrared Spectrograph-Distant Quasar Survey (GNIRS-DQS). These properties are compared with those of 195 non-BAL quasars from GNIRS-DQS in order to identify the drivers for the appearance of BALs in quasar spectra. In particular, we compare equivalent widths and velocity widths, as well as velocity offsets from systemic redshifts, of principal emission lines. In spite of the differences between their rest-frame ultraviolet spectra, we find that luminous BAL quasars are generally indistinguishable from their non-BAL counterparts in the rest-frame optical band at redshifts $1.55 \lesssim z \lesssim 3.50$. We do not find any correlation between BAL trough properties and the H$\beta$-based supermassive black hole masses and normalized accretion rates in our sample. Considering the Sloan Digital Sky Survey quasar sample, which includes the GNIRS-DQS sample, we find that a monochromatic luminosity at rest-frame 2500 A of $\gtrsim 10^{45}$ erg s$^{-1}$ is a necessary condition for launching BAL outflows in quasars. We compare our findings with other BAL quasar samples and discuss the roles that accretion rate and orientation play in the appearance of BAL troughs in quasar spectra., Comment: 18 pages (AASTeX 6.3.1), 5 figures, accepted for publication in ApJ

Designing tests to evaluate if a given autonomous system satisfies complex specifications is challenging due to the complexity of these systems. This work proposes a flow-based approach for reactive test synthesis from temporal logic specifications, enabling the synthesis of test environments consisting of static and reactive obstacles and dynamic test agents. The temporal logic specifications describe desired test behavior, including system requirements as well as a test objective that is not revealed to the system. The synthesized test strategy places restrictions on system actions in reaction to the system state. The tests are minimally restrictive and accomplish the test objective while ensuring realizability of the system's objective without aiding it (semi-cooperative setting). Automata theory and flow networks are leveraged to formulate a mixed-integer linear program (MILP) to synthesize the test strategy. For a dynamic test agent, the agent strategy is synthesized for a GR(1) specification constructed from the solution of the MILP. If the specification is unrealizable by the dynamics of the test agent, a counterexample-guided approach is used to resolve the MILP until a strategy is found. This flow-based, reactive test synthesis is conducted offline and is agnostic to the system controller. Finally, the resulting test strategy is demonstrated in simulation and experimentally on a pair of quadrupedal robots for a variety of specifications., Comment: Manuscript

Cellulose nanocrystals (CNCs) are bioderived nanoparticles that can be isolated from various sources of natural cellulose via acid hydrolysis. However, the link between particle morphological characteristics and their ensemble behavior is poorly understood, partly because of the difficulties in controlling the CNC morphology during their extraction process. In this work, the impacts of common post-hydrolysis treatments on the CNC morphology are investigated. The results indicate that the centrifugation step commonly applied during CNC purification favors the formation of composite particles made of aligned crystallites, referred to as 'bundles'. Scanning nanobeam electron diffraction reveals that such bundles are associated preferentially along their hydrophobic faces. This is in stark contrast to the formation of misaligned composite particles that can be achieved with ionic treatments, where an uncontrolled aggregation occurs. The functional relevance of these morphological differences is demonstrated by their effect on the cholesteric self-organization of CNCs, with bundles found to exhibit a greater chiral enhancement, whereas the misaligned composite particles found to promote gelation. This study reveals the importance of the often-disregarded purification steps on the final morphology of CNCs and their resulting ensemble properties, thereby unlocking new routes for tailoring this promising material towards a variety of applications.

The supermassive black holes ($M_{\rm BH} \sim 10^{6}$$-$$10^{10}~M_\odot$) that power luminous active galactic nuclei (AGNs), i.e., quasars, generally show a correlation between thermal disk emission in the ultraviolet (UV) and coronal emission in hard X-rays. In contrast, some "massive" black holes (mBHs; $M_{\rm BH} \sim 10^{5}$$-$$10^{6}~M_\odot$) in low-mass galaxies present curious X-ray properties with coronal radiative output up to 100$\times$ weaker than expected. To examine this issue, we present a pilot study incorporating Very Large Array radio observations of a sample of 18 high-accretion-rate (Eddington ratios $L_{\rm bol}/L_{\rm Edd} > 0.1$), mBH-powered AGNs ($M_{\rm BH} \sim 10^{6}~M_\odot$) with Chandra X-ray coverage. Empirical correlations previously revealed in samples of radio-quiet, high-Eddington AGNs indicate that the radio$-$X-ray luminosity ratio, $L_{\rm R}/L_{\rm X}$, is approximately constant. Through multiwavelength analysis, we instead find that the X-ray-weaker mBHs in our sample tend toward larger values of $L_{\rm R}/L_{\rm X}$ even though they remain radio-quiet per their optical$-$UV properties. This trend results in a tentative but highly intriguing correlation between $L_{\rm R}/L_{\rm X}$ and X-ray weakness, which we argue is consistent with a scenario in which X-rays may be preferentially obscured from our line of sight by a "slim" accretion disk. We compare this observation to weak emission-line quasars (AGNs with exceptionally weak broad-line emission and a significant X-ray-weak fraction) and conclude by suggesting that our results may offer a new observational signature for finding high-accretion-rate AGNs., Comment: Accepted for publication in ApJ. 26 pages, 8 figures, 6 tables

We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured state space sequence model (S4), as implementing the state-space model (SSM) has proven to be efficient at learning long-range dependencies and is promising for modeling dynamic range compressors. We present in this paper a deep learning model with S4 layers to model the Teletronix LA-2A analog dynamic range compressor. The model is causal, executes efficiently in real time, and achieves roughly the same quality as previous deep-learning models but with fewer parameters.

We consider the solutions $\rho_\varepsilon, \mathbf{u}_\varepsilon$ to the compressible Navier-Stokes equations (NSE) in a domain periodically perforated by holes of diameter $\varepsilon>0$. We focus on the case where the diameter of the holes is of the same order as the distance between neighboring holes. This is the same setting investigated in the paper by Masmoudi [\url{http://www.numdam.org/article/COCV_2002__8__885_0.pdf}], where convergence $\rho_\varepsilon, \mathbf{u}_\varepsilon$ of the system to the porous medium equation has been shown. We prove a quantitative version of this convergence result provided that the solution of the limiting system is sufficiently regular. The proof builds on the relative energy inequality satisfied by the compressible NSE.