Search

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

In this paper, we study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations. We present an empirical analysis of their ability to generalize these rules, as well as to reuse them in the context of word problems. For this purpose, we provide a rigorous methodology to build synthetic data incorporating such rules, and perform fine-tuning of large language models on such data. Our experiments show that our model can learn and generalize these rules to some extent, as well as suitably reuse them in the context of word problems., Comment: NeurIPS'24 MATH-AI, the 4th Workshop on Mathematical Reasoning and AI

As the only stable spin-1/2 noble gas isotopes, $^{3}$He and $^{129}$Xe are promising systems for inertial rotation sensing and searches for exotic spin couplings. Spin-1/2 noble gases have intrinsic coherence times on the order of hours to days, which allows for incredibly low frequency error of free-precession measurements. However relaxation in miniature cells is dominated by interactions with the cell wall, which limits the performance of a chip-scale sensor that uses noble gases. While $^{129}$Xe wall relaxation times have previously been limited to 10's of seconds in mm-sized cells, we demonstrate the first anodically bonded batch-fabricated cells with dual $^{3}$He-$^{129}$Xe isotopes and $^{87}$Rb in a 6~mm$^3$ volume with $^{3}$He and $^{129}$Xe $T_2$ coherence times of, respectively, 4 h and 300 s. We use these microfabricated cells in a dual noble-gas comagnetometer and discuss its limits.

Numerous protoplanetary disks exhibit shadows in scattered light observations. These shadows are typically cast by misaligned inner disks and are associated with observable structures in the outer disk such as bright arcs and spirals. Investigating the dynamics of the shadowed outer disk is therefore essential in understanding the formation and evolution of these structures. We carry out twodimensional radiation hydrodynamics simulations that include radiative diffusion and dust-gas dynamics to study the formation of substructure in shadowed disks. We find that spiral arms are launched at the edge of each shadow, permeating the entire disk. The local dissipation of these spirals results in an angular momentum flux, opening multiple gaps and leading to a series of concentric, regularly-spaced rings We find that ring formation is favored in weakly turbulent disks where dust growth is taking place. These conditions are met for typical class-II disks, in which bright rings should form well within a fraction of their lifetime (0.1-0.2 Myr). For hotter disks gap opening is more efficient, such that the gap edges quickly collapse into vortices that can appear as bright arcs in continuum emission before decaying into rings or merging into massive, long-lived structures. Synthetic observations show that these structures should be observable in scattered light and millimeter continuum emission, providing a new way to probe the presence of substructure in protoplanetary disks. Our results suggest that the formation of rings and gaps is a common process in shadowed disks, and can explain the rich radial substructure observed in several protoplanetary disks., Comment: 17 pages, 20 figures, 1 table; submitted to MNRAS; suggestions and comments welcome

Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative changes on the causal graph, we propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior and consolidates specific mechanism parameterizations. We demonstrate how meta-causal states can be inferred from observed agent behavior, and discuss potential methods for disentangling these states from unlabeled data. Finally, we direct our analysis towards the application of a dynamical system, showing that meta-causal states can also emerge from inherent system dynamics, and thus constitute more than a context-dependent framework in which mechanisms emerge only as a result of external factors., Comment: 19 pages, 3 figures, 4 tables

Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their compliance and high number of degrees of freedom. To address this issue, prior work has introduced a differentiable physics engine designed for tensegrity robots based on first principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages their natural graph-like cable connectivity between end caps of rigid rods. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, for which the robot state is only partially observable. When compared against direct applications of recent mesh-based graph neural network simulators, the proposed approach is computationally more efficient, both for training and inference, while achieving higher accuracy. Code and data are available at https://github.com/nchen9191/tensegrity_gnn_simulator_public

In this study, we meticulously construct a 3-form Lagrangian designed to mimic the dynamics of both dust matter in the past and dark energy driving the acceleration in the present era. A dynamical systems approach is used to investigate the underlying behavior of the cosmological background. By investigating the influence of the potential slope and initial conditions on the dynamical solutions, we identify distinct viable scenarios capable of replicating a De Sitter universe in the present epoch. An intriguing aspect of the model is the existence of solutions describing multiple inflationary phases in which the 3-form self-interacting potential decays rapidly., Comment: 11 pages, 6 figures, 2 tables

Quantum secret sharing is a cryptographic scheme that enables a secure storage and reconstruction of quantum information. While the theory of secret sharing is mature in its development, relatively few studies have explored the performance of quantum secret sharing on actual devices. In this work, we provide a pedagogical description of encoding and decoding circuits for different secret sharing codes, and we test their performance on IBM's 127-qubit Brisbane system. We evaluate the quality of implementation by performing a SWAP test between the decoded state and the ideal one, as well as by estimating how well the code preserves entanglement with a reference system. Results indicate that a ((3,5)) threshold secret sharing scheme performs slightly better overall than a ((5,7)) scheme based on the SWAP test, but is outperformed by the Steane Code scheme in regards to the entanglement fidelity. We also investigate one implementation of a ((2,3)) qutrit scheme and find that it performs the worst of all, which is expected due to the additional number of multi-qubit gate operations needed to encode and decode qutrits., Comment: 14 pages, 10 figures

Although algebraic structures are frequently analyzed using unary and binary operations, they can also be effectively defined and unified through ternary operations. In this context, we introduce structures that contain two constants and a ternary operation. We demonstrate that these structures are isomorphic to various significant algebraic systems, including Boolean algebras, de Morgan algebras, MV-algebras, and (near) rings of characteristic two. Our work highlights the versatility of ternary operations in describing and connecting diverse algebraic structures.

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures. The second version includes an additional citation and adds four previously missing authors

In one-dimensional quantum materials, conducting electrons and the underlying lattices can undergo a spontaneous translational symmetry breaking, known as Peierls transition. For nearly a century, the Peierls transition has been understood within the paradigm of electron-electron interactions mediated by longitudinal acoustic phonons. This classical picture has recently been revised in topological semimetals, where transverse acoustic phonons can couple with conducting p-orbital electrons and give rise to an unconventional Fermi surface instability, dubbed the transverse Peierls transition (TPT). Most interestingly, the TPT induced lattice distortions can further break rotation or mirror/inversion symmetries, leading to nematic or chiral charge density waves (CDWs). Quantum materials that host the TPT, however, have not been experimentally established. Here, we report the experimental discovery of an incommensurate TPT in the tetragonal Dirac semimetal EuAl$_4$. Using inelastic x-ray scattering with meV resolution, we observe the complete softening of a transverse acoustic phonon at the CDW wavevector upon cooling, whereas the longitudinal acoustic phonon is nearly unchanged. Combining with first principles calculations, we show that the incommensurate CDW wavevector matches the calculated charge susceptibility peak and connects the nested Dirac bands with Al 3$p_{x}$ and 3$p_{y}$ orbitals. Supplemented by second harmonic generation measurements, we show that the CDW induced lattice distortions break all vertical and diagonal mirrors whereas the four-fold rotational symmetry is retained below the CDW transition. Our observations strongly suggest a chiral CDW in EuAl$_4$ and highlight the TPT as a new avenue for chiral quantum states., Comment: Supplementary materials are available upon request

Rydberg dressing is a powerful tool for entanglement generation in long-lived atomic states. While already employed effectively in several demonstrations, a key challenge for this technique is the collective loss triggered by blackbody-radiation-driven transitions to contaminant Rydberg states of opposite parity. We demonstrate the rapid removal of such contaminants using autoionization (AI) transitions found in alkaline-earth-like atoms. The AI is shown to be compatible with coherent operation of an array of optical clock qubits. By incorporating AI pulses into a stroboscopic Rydberg dressing (SRD) sequence, we enhance lifetimes by an order of magnitude for system sizes of up to 144 atoms, while maintaining an order of magnitude larger duty cycle than previously achieved. To highlight the utility of our approach, we use the AI-enhanced SRD protocol to improve the degree of spin-squeezing achieved during early-time dressing dynamics. These results bring Rydberg dressing lifetimes closer to fundamental limits, opening the door to previously infeasible dressing proposals., Comment: 8 pages, 4 figures + Supplementary Material 7 pages, 6 figures

Visual Question Answering (VQA) is the task of answering a question about an image and requires processing multimodal input and reasoning to obtain the answer. Modular solutions that use declarative representations within the reasoning component have a clear advantage over end-to-end trained systems regarding interpretability. The downside is that crafting the rules for such a component can be an additional burden on the developer. We address this challenge by presenting an approach for declarative knowledge distillation from Large Language Models (LLMs). Our method is to prompt an LLM to extend an initial theory on VQA reasoning, given as an answer-set program, to meet the requirements of the VQA task. Examples from the VQA dataset are used to guide the LLM, validate the results, and mend rules if they are not correct by using feedback from the ASP solver. We demonstrate that our approach works on the prominent CLEVR and GQA datasets. Our results confirm that distilling knowledge from LLMs is in fact a promising direction besides data-driven rule learning approaches., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

Large language models (LMs) are capable of in-context learning from a few demonstrations (example-label pairs) to solve new tasks during inference. Despite the intuitive importance of high-quality demonstrations, previous work has observed that, in some settings, ICL performance is minimally affected by irrelevant labels (Min et al., 2022). We hypothesize that LMs perform ICL with irrelevant labels via two sequential processes: an inference function that solves the task, followed by a verbalization function that maps the inferred answer to the label space. Importantly, we hypothesize that the inference function is invariant to remappings of the label space (e.g., "true"/"false" to "cat"/"dog"), enabling LMs to share the same inference function across settings with different label words. We empirically validate this hypothesis with controlled layer-wise interchange intervention experiments. Our findings confirm the hypotheses on multiple datasets and tasks (natural language inference, sentiment analysis, and topic classification) and further suggest that the two functions can be localized in specific layers across various open-sourced models, including GEMMA-7B, MISTRAL-7B-V0.3, GEMMA-2-27B, and LLAMA-3.1-70B., Comment: EMNLP 2024 Findings

In many real-world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc). Another question is whether data subsets are similar enough so that it is beneficial to combine subsets during model training. We propose SOAK, Same/Other/All K-fold cross-validation, a new method which can be used to answer both questions. SOAK systematically compares models which are trained on different subsets of data, and then used for prediction on a fixed test subset, to estimate the similarity of learnable/predictable patterns in data subsets. We show results of using SOAK on six new real data sets (with geographic/temporal subsets, to check if predictions are accurate on new subsets), 3 image pair data sets (subsets are different image types, to check that we get smaller prediction error on similar images), and 11 benchmark data sets with predefined train/test splits (to check similarity of predefined splits).

We investigate the physical properties and redshift evolution of simulated galaxies residing in protoclusters at cosmic noon, to understand the influence of the environment on galaxy formation. This work is to build clear expectations for the ongoing ODIN survey, devoted to mapping large-scale structures at z=2.4, 3.1, and 4.5 using Ly$\alpha$-emitting galaxies (LAEs) as tracers. From the IllustrisTNG simulations, we define subregions centered on the most massive clusters ranked by total stellar mass at z=0 and study the properties of galaxies within, including LAEs. To model the LAE population, we take a semi-analytical approach that assigns Ly$\alpha$ luminosity and equivalent width based on the UV luminosities to galaxies in a probabilistic manner. We investigate stellar mass, star formation rate, major mergers, and specific star formation rate of the population of star-forming galaxies and LAEs in the field and protocluster environment and trace their evolution. We find that the overall shape of the UV luminosity function (LF) in simulated protocluster environments is characterized by a shallower faint-end slope and an excess on the bright end, signaling different formation histories for galaxies therein. The difference is milder for the Ly$\alpha$ LF. While protocluster galaxies follow the same SFR-$M_{\odot}$ scaling relation as average field galaxies, a larger fraction appears to have experienced major mergers in the last 200 Myr and as a result shows enhanced star formation at a ~60% level, leading to a flatter distribution in both SFR and $M_{\odot}$ relative to galaxies in the average field. We find that protocluster galaxies, including LAEs, begin to quench much earlier (z~0.8-1.6) than field galaxies (z~0.5-0.9); our result is in agreement with recent observational results and highlights the importance of large-scale environment on the overall formation history of galaxies., Comment: 18 pages, 13 figures, to be submitted to Astronomy & Astrophysics, the abstract was reduced to meet character requirements; edits include: updating coauthor list and affiliations, updating citations in Sec 2.1

The relationship between UV Bursts and solar surges is complex, with these events sometimes being observed together and sometimes being observed independently. Why this sporadic association exists is unknown, however, it likely relates to the physical conditions at the site of the energy release that drives these events. Here, we aim to better understand the relationship between UV Bursts and solar surges through a multi-instrument analysis of several associated events that occurred around the trailing sunspot in AR 12957. We use data from Solar Orbiter, the Solar Dynamics Observatory (SDO), and the Interface Region Imaging Spectrograph (IRIS) to achieve our aims. These data were sampled on 3rd March 2022 between 09:30:30 UT and 11:00:00 UT, during which time a coordinated observing campaign associated with the Slow Solar Wind Connection Solar Orbiter Observing Plan took place. Numerous small-scale negative polarity magnetic magnetic features (MMFs) are observed to move quickly (potentially up to 3.3 km/s) away from a sunspot until they collide with a more stable positive polarity plage region around 7 Mm away. Several UV Bursts are identified in IRIS slit-jaw imager (SJI) 1400 {\AA} data co-spatial to where these opposite polarity fields interact, with spatial scales (2 Mm<) and lifetimes (20< min) larger than typical values for such events. Two surges are also observed to occur at these locations, with one being short (5 Mm) and hot (bright in IRIS SJI images), whilst the other is a cooler (dark in coronal imaging channels), longer surge that appears to fill an active region loop. Magnetic reconnection between the negative polarity MMFs around the sunspot and the positive polarity plage region appears to be the driver of these events. Both the speed of the MMFs and the locally open magnetic topology of the plage region could possibly be important for forming the surges., Comment: 13 pages, 9 figures, accepted in A&A

Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling paradigm is to treat such data as realizations of a temporal point process, and in machine learning it is common to model temporal point processes in an autoregressive fashion using a neural network. While autoregressive models are successful in predicting the time of a single subsequent event, their performance can be unsatisfactory in forecasting longer horizons due to cascading errors. We propose EventFlow, a non-autoregressive generative model for temporal point processes. Our model builds on the flow matching framework in order to directly learn joint distributions over event times, side-stepping the autoregressive process. EventFlow is likelihood-free, easy to implement and sample from, and either matches or surpasses the performance of state-of-the-art models in both unconditional and conditional generation tasks on a set of standard benchmarks

Holographic models that consider classical vector fields in a 5-d background provide successful effective descriptions for heavy vector meson spectra. This holds both in the vacuum and in a thermal medium, like the quark gluon plasma. However, it is somehow mysterious the way that these phenomenological models work. In particular, what is the role of the fifth dimension and what is the relation between the holographic 5-d background and the physical (4-d) heavy mesons. Hadrons, in contrast to leptons, are composite particles with some internal structure, that depends on the energy at which they are observed. In this work, a static meson is represented by a heavy quark-antiquark pair with an interaction described by a Nambu Goto string living in the same 5-d background that provides field solutions leading to masses and decay constants of charmonium states. The interaction potential that shows up is linear for large distances with a string tension consistent with the effective Cornell potential. Introducing temperature $T$ in the background it is found, for the $J/\psi$ case, that there is a deconfining transition at some critical value of $T$. The results obtained indicate that the 5-d background is effectively representing the internal structure of the (static) charmonium (quasi) states., Comment: 27 pages, 13 figures

Mis/disinformation is a common and dangerous occurrence on social media. Misattribution is a form of mis/disinformation that deals with a false claim of authorship, which means a user is claiming someone said (posted) something they never did. We discuss the difference between misinformation and disinformation and how screenshots are used to spread author misattribution on social media platforms. It is important to be able to find the original post of a screenshot to determine if the screenshot is being correctly attributed. To do this we have built several tools to aid in automating this search process. The first is a Python script that aims to categorize Twitter posts based on their structure, extract the metadata from a screenshot, and use this data to group all the posts within a screenshot together. We tested this process on 75 Twitter posts containing screenshots collected by hand to determine how well the script extracted metadata and grouped the individual posts, F1 = 0.80. The second is a series of scrapers being used to collect a dataset that can train and test a model to differentiate between various social media platforms. We collected 16,620 screenshots have been collected from Facebook, Instagram, Truth Social, and Twitter. Screenshots were taken by the scrapers of the web version and mobile version of each platform in both light and dark mode.

Since the first detection of gravitational waves (GW) in 2015, there have been significant efforts to enhance the sensitivity of the ground-based detectors within the LIGO-Virgo-KAGRA collaboration. Despite these efforts, many gravitational wave signals remain below the detection threshold due to non-linear noise from the physical environment of the detectors, coupled with the main GW signal. \texttt{DeepClean}'s capability to handle a variety of noise sources, including linear, non-linear, and \emph{non-stationary} coupling mechanisms, is particularly important in fields like astrophysics and gravitational wave astronomy, where accurate data analysis is crucial for the detection and study of gravitational waves from astronomical events., Comment: This paper needs an internal review by LIGO-Virgo collaboration before proceeding

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures

Circumbinary planets - planets that orbit both stars in a binary system - offer the opportunity to study planet formation and orbital migration in a different environment compare to single stars. However, despite the fact that > 90% of binary systems in the solar neighbourhood are spectrally resolved double-lined binaries, there has been only one detection of a circumbinary planet orbitting a double-lined binary using the radial velocity method so far. Spectrally disentangling both components of a binary system is hard to do accurately. Weak spectral lines blend with one another in a time-varying way, and inaccuracy in spectral modelling can lead to an inaccurate estimation of the radial-velocity of each component. This inaccuracy adds scatter to the measurements that can hide the weak radial-velocity signature of circumbinary exoplanets. We have obtained new high signal-to-noise and high-resolution spectra with the SOPHIE spectrograph, mounted on the 193cm telescope at Observatoire de Haute-Provence (OHP) for six, bright, double-lined binaries for which a circumbinary exoplanet detection has been attempted in the past. To extract radial-velocities we use the DOLBY code, a recent method of spectral disentangling using Gaussian processes to model the time-varying components. We analyse the resulting radial-velocities with a diffusive nested sampler to seek planets, and compute sensitivity limits. We do not detect any new circumbinary planet. However, we show that the combination of new data, new radial-velocity extraction methods, and new statistical methods to determine a dataset's sensitivity to planets leads to an approximately one order of magnitude improvement compared to previous results. This improvement brings us into the range of known circumbinary exoplanets and paves the way for new campaigns of observations targeting double-lined binaries., Comment: 19 pages, 8 figures, 7 tables. Accepted for publication in MNRAS

We introduce the new cosmological simulation project cosmosTNG, a first-of-its-kind suite of constrained galaxy formation simulations for the universe at Cosmic Noon ($z\sim 2$). cosmosTNG simulates a $0.2$ deg$^2$ patch of the COSMOS field at $z \simeq 2.0-2.2$ using an initial density field inferred from galaxy redshift surveys and the CLAMATO Lyman-alpha forest tomography survey, reconstructed by the TARDIS algorithm. We evolve eight different realizations of this volume to capture small-scale variations. All runs use the IllustrisTNG galaxy formation model with a baryonic mass resolution of $10^6$ M$_\odot$, equal to TNG100-1. In this initial study, we demonstrate qualitative agreement between the evolved large-scale structure and the spatial distribution of observed galaxy populations in COSMOS, emphasizing the zFIRE protocluster region. We then compare the statistical properties and scaling relations of the galaxy population, covering stellar, gaseous, and supermassive black hole (SMBH) components, between cosmosTNG, observations in COSMOS, and $z \sim 2$ observational data in general. We find that galaxy quenching and environmental effects in COSMOS are modulated by its specific large-scale structure, particularly the collapsing protoclusters in the region. With respect to a random region of the universe, the abundance of high-mass galaxies is larger, and the quenched fraction of galaxies is significant lower at fixed mass. This suggests an accelerated growth of stellar mass, as reflected in a higher cosmic star formation rate density, due to the unique assembly histories of galaxies in the simulated COSMOS subvolume. The cosmosTNG suite will be a valuable tool for studying galaxy formation at cosmic noon, particularly when interpreting extragalactic observations with HST, JWST, and other large multi-wavelength survey programs of the COSMOS field.

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We use a simple model for cosmic ray (CR) production and transport to assess the impact of CRs on $z$\,$=$\,$0$ galaxy, circumgalactic medium (CGM), and halo properties. To do so, we run the first suite of large-volume cosmological magnetohydrodynamical simulations (25\,Mpc\,h$^{-1}$ boxes) with the IllustrisTNG galaxy formation model including CR physics. We select CR transport parameters that yield a reasonable trade off between realistic large-scale integrated properties, and galactic CR pressure profiles predicted by more complex models. The resulting simulations show that, at fixed halo mass, including CRs does not strongly impact the temperature, density, or (total) pressure structure of the CGM with respect to the fiducial TNG model. However, cosmic rays add significant non-thermal pressure support to the halo. This suppresses the star formation activity and thus stellar masses of galaxies, from dwarf to L$^\star$ halos. The cosmic star formation rate density, stellar mass function, and stellar mass to halo mass relation are all reshaped by CRs. Galaxy sizes and halo-scale gas fractions are more mildly affected, while lower gas densities in the interstellar medium inhibit supermassive black hole growth. Halo gas is also less magnetized, and less metal enriched. These differences are primarily driven by suppressed gas accretion onto halos and galaxies, as well as weaker galactic outflows in the presence of CRs. Our findings are in qualitative agreement with previous studies of the impact of CRs on galactic outflows, and motivate the inclusion of CR physics in future large-scale cosmological simulations., Comment: Submitted to A&A. 22 pages, 16 figures (including appendices)

We examine star-formation and dust properties for a sample of 660 galaxies at $1.37\leq z\leq 2.61$ in the MOSDEF survey by dividing them into groups with similarly-shaped spectral energy distributions (SEDs). For each group, we combine the galaxy photometry into a finely-sampled composite SED, and stack their spectra. This method enables the study of more complete galaxy samples, including galaxies with very faint emission lines. We fit these composite SEDs with Prospector to measure the stellar attenuation and SED-based star-formation rates (SFRs). We also derive emission-line properties from the spectral stacks, including Balmer decrements, dust-corrected SFRs, and metallicities. We find that stellar attenuation correlates most strongly with mass, while nebular attenuation correlates strongly with both mass and SFR. Furthermore, the excess of nebular compared to stellar attenuation correlates most strongly with SFR. The highest SFR group has 2 mag of excess nebular attenuation. Our results are consistent with a model in which star-forming regions become more dusty as galaxy mass increases. To explain the increasing excess nebular attenuation, we require a progressively larger fraction of star formation to occur in highly-obscured regions with increasing SFR. This highly-obscured star formation could occur in dusty clumps or central starbursts. Additionally, as each galaxy group represents a different evolutionary stage, we study their locations on the UVJ and SFR-mass diagrams. As mass increases, metallicity and dust attenuation increase, while sSFR decreases. However, the most massive group moves towards the quiescent region of the UVJ diagram, while showing less obscuration, potentially indicating removal of dust., Comment: 21 pages, 7 figures, accepted for publication in ApJ

We show under certain constraints that $A$-valued semicircular systems give rise to C*-discrete inclusions, and thus are crossed products by an action of a tensor category. Along the way, we show the set of single algebraic generators of a dualizable bimodule forms an open subset. Furthermore, we obtain a Galois correspondence between the lattice of intermediate C*-discrete subalgebras intermediate to a given irreducible C*-discrete inclusion, and characterize these as targets of compatible expectations. Finally, we define ``freeness'' for actions of tensor categories on C*-algebras, and show simplicity is preserved under taking reduced crossed products., Comment: This is a split part of the article Discrete Inclusions of C*-algebras [2305.05072v1] by the same authors, which was partitioned and replaced by [2305.05072v2] to better lay out the results therein

By developing a theory of anticoarse spaces in the purely infinite setting and using 1-bounded entropy techniques along with recent strong convergence results in random matrix theory, we show that free Araki--Woods factors offer the first examples of type $\mathrm{III}$ factors satisfying vastly general degrees of indecomposability phenomena. Notably this includes strong solidity with respect to any weakening of the normalizer currently in the literature and the Peterson--Thom property., Comment: 33 pages. No figures. Added an appendix by Stefaan Vaes. Comments welcome!

The H{\alpha} nebular emission line is an optimal tracer for recent star formation in galaxies. With the advent of JWST, this line has recently become observable at z>3 for the first time. We present a catalog of 1013 H{\alpha} emitters at 3.73 obtained based purely on spectroscopic data, robustly tracing galaxy star formation rates (SFRs) beyond the peak of the cosmic star formation history. We compare our results with theoretical predictions from three different simulations and find good agreement at z~4-6. The UV LFs of this spectroscopically-confirmed sample are in good agreement with pre-JWST measurements obtained with photometrically-selected objects. Finally, we derive SFR functions and integrate these to compute the evolution of the cosmic star-formation rate densities across z~4-6, finding values in good agreement with recent UV estimates from Lyman-break galaxies, which imply a continuous decrease in SFR density by a factor of 3x over z~4 to z~6. Our work shows the power of NIRCam grism observations to efficiently provide new tests for early galaxy formation models based on emission line statistics., Comment: 17 pages, 14 figures

We report on the first deployment of a ytterbium (Yb) transportable optical lattice clock (TOLC), commercially shipping the clock 3,000 km from Boulder, Colorado to Washington DC. The system, composed of a rigidly mounted optical reference cavity, atomic physics package, and an optical frequency comb, fully realizes an independent frequency standard for comparisons in the optical and microwave domains. The shipped Yb TOLC was fully operational within 2 days of arrival, enabling frequency comparisons with rubidium (Rb) fountains at the United States Naval Observatory (USNO). To the best of our knowledge, this represents the first deployment of a fully independent TOLC, including the frequency comb, coherently uniting the optical stability of the Yb TOLC to the microwave output of the Rb fountain., Comment: 5 pages, 4 figures

Multiple approaches have already been proposed to mimic real driver behaviors in simulation. This article proposes a new one, based solely on the exploration of undisturbed observation of intersections. From them, the behavior profiles for each macro-maneuver will be discovered. Using the macro-maneuvers already identified in previous works, a comparison method between trajectories with different lengths using an Extended Kalman Filter (EKF) is proposed, which combined with an Expectation-Maximization (EM) inspired method, defines the different clusters that represent the behaviors observed. This is also paired with a Kullback-Liebler divergent (KL) criteria to define when the clusters need to be split or merged. Finally, the behaviors for each macro-maneuver are determined by each cluster discovered, without using any map information about the environment and being dynamically consistent with vehicle motion. By observation it becomes clear that the two main factors for driver's behavior are their assertiveness and interaction with other road users., Comment: Presented at IROS2024

The tension between direct measurements of the Hubble constant and those stemming from Cosmic Microwave Background probes has triggered a multitude of studies. The connection between cosmology and particle physics has shown to be a valuable approach to addressing the Hubble tension. In particular, increasing the number of relativistic degrees of freedom in the early universe helps alleviate the problem. In this work, we write down effective field theory describing relativistic dark matter production in association with neutrinos leading to a larger $H_0$. We derive limits on the effective energy scale that governs this relativistic production of dark matter as a function of the dark matter mass for fermion, vector, and scalar dark matter fields. In particular, scalar dark matter particles are more effective in increasing the effective number of relativistic species. Also, if they have weak scale masses, then the relativistic production of dark matter should be governed by Planck scale effective operators in order to alleviate the Hubble tension., Comment: 9 pages, 8 figures

Protoplanetary disks exhibit a rich variety of substructure in millimeter continuum emission, often attributed to unseen planets. As these planets carve gaps in the gas, dust particles can accumulate in the resulting pressure bumps, forming bright features in the dust continuum. We investigate the role of dust dynamics in the gap-opening process with 2D radiation hydrodynamics simulations of planet--disk interaction and a two-population dust component modeled as a pressureless fluid. We consider the opacity feedback and backreaction due to drag forces as mm grains accumulate in pressure bumps at different stages of dust growth. We find that dust dynamics can significantly affect the resulting substructure driven by the quasi-thermal-mass planet with $M_p/M_\star=10^{-4}$. Opacity feedback causes nonaxisymmetric features to become more compact in azimuth, whereas the drag-induced backreaction tends to dissolve nonaxisymmetries. For our fiducial model, this results in multiple concentric rings of dust rather than the expected vortices and corotating dust clumps found in models without dust feedback. A higher coagulation fraction disproportionately enhances the effect of dust opacity feedback, favoring the formation of crescents rather than rings. Our results suggest that turbulent diffusion is not always necessary to explain the rarity of observed nonaxisymmetric features, and that incorporating dust dynamics is vital for interpreting the observed substructure in protoplanetary disks. We also describe and test the implementation of the publicly-available dust fluid module in the PLUTO code., Comment: 16 pages, 17 figures, 2 tables; accepted in MNRAS

Magnetic colloids can be driven with time-varying fields to form clusters and voids that re-organize over vastly different timescales. However, the driving force behind these non-equilibrium dynamics is not well-understood. Here, we introduce a topological framework that predicts protected edge flows despite strong thermal motion. Notably, these edge flows produce shear stress that creates global rotation of clusters but not of voids. We verify this theory experimentally using micron-sized super-paramagnetic colloids to demonstrate these emergent physical predictions and show how they drive system re-organization differentially at long timescales. Our results elucidate fundamental principles that shape and control non-equilibrium colloidal aggregates., Comment: 7 pages, 4 figures in main text; the first two authors contributed equally to this work. In v2: added details about experimental setup and minor clarifications; in SM, added new sections about theoretical model and data analysis

Covariate measurement error in regression analysis is an important issue that has been studied extensively under the classical additive and the Berkson error models. Here, we consider cases where covariates are derived from tumor tissue histology, and in particular tissue microarrays. In such settings, biomarkers are evaluated from tissue cores that are subsampled from a larger tissue section so that these biomarkers are only estimates of the true cell densities. The resulting measurement error is non-negligible but is seldom accounted for in the analysis of cancer studies involving tissue microarrays. To adjust for this type of measurement error, we assume that these discrete-valued biomarkers are conditionally Poisson distributed, based on a Poisson process model governing the spatial locations of marker-positive cells. Existing methods for addressing conditional Poisson surrogates, particularly in the absence of internal validation data, are limited. We extend the simulation extrapolation (SIMEX) algorithm to accommodate the conditional Poisson case (POI-SIMEX), where measurement errors are non-Gaussian with heteroscedastic variance. The proposed estimator is shown to be strongly consistent in a linear regression model under the assumption of a conditional Poisson distribution for the observed biomarker. Simulation studies evaluate the performance of POI-SIMEX, comparing it with the naive method and an alternative corrected likelihood approach in linear regression and survival analysis contexts. POI-SIMEX is then applied to a study of high-grade serous cancer, examining the association between survival and the presence of triple-positive biomarker (CD3+CD8+FOXP3+ cells), Comment: 18 pages, 2 figures

Instruction tuning commonly means finetuning a language model on instruction-response pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction tuning, yet still yield instruction following; we call this implicit instruction tuning. We first find that instruction-response pairs are not necessary: training solely on responses, without any corresponding instructions, yields instruction following. This suggests pretrained models have an instruction-response mapping which is revealed by teaching the model the desired distribution of responses. However, we then find it's not necessary to teach the desired distribution of responses: instruction-response training on narrow-domain data like poetry still leads to broad instruction-following behavior like recipe generation. In particular, when instructions are very different from those in the narrow finetuning domain, models' responses do not adhere to the style of the finetuning domain. To begin to explain implicit instruction tuning, we hypothesize that very simple changes to a language model's distribution yield instruction following. We support this by hand-writing a rule-based language model which yields instruction following in a product-of-experts with a pretrained model. The rules are to slowly increase the probability of ending the sequence, penalize repetition, and uniformly change 15 words' probabilities. In summary, adaptations made without being designed to yield instruction following can do so implicitly.

Osteoarthritis (OA) is a prevalent degenerative joint disease, with the knee being the most commonly affected joint. Modern studies of knee joint injury and OA often measure biomechanical variables, particularly forces exerted during walking. However, the relationship among gait patterns, clinical profiles, and OA disease remains poorly understood. These biomechanical forces are typically represented as curves over time, but until recently, studies have relied on discrete values (or landmarks) to summarize these curves. This work aims to demonstrate the added value of analyzing full movement curves over conventional discrete summaries. Using data from the Intensive Diet and Exercise for Arthritis (IDEA) study (Messier et al., 2009, 2013), we developed a shape-based representation of variation in the full biomechanical curves. Compared to conventional discrete summaries, our approach yields more powerful predictors of disease progression and relevant clinical traits, as demonstrated by a nested model comparison. Notably, our work is among the first to use movement curves to predict disease progression and to quantitatively evaluate the added value of analyzing full movement curves over conventional discrete summaries

Gravitational wave detectors like the Einstein Telescope and LISA generate long multivariate time series, which pose significant challenges in spectral density estimation due to a number of overlapping signals as well as the presence of correlated noise. Addressing both issues is crucial for accurately interpreting the signals detected by these instruments. This paper presents an application of a variational inference spectral density estimation method specifically tailored for dealing with correlated noise in the data. It is flexible in that it does not rely on any specific parametric form for the multivariate spectral density. The method employs a blocked Whittle likelihood approximation for stationary time series and utilizes the Cholesky decomposition of the inverse spectral density matrix to ensure a positive definite estimator. A discounted regularized horseshoe prior is applied to the spline coefficients of each Cholesky factor, and the posterior distribution is computed using a stochastic gradient variational Bayes approach. This method is particularly effective in addressing correlated noise, a significant challenge in the analysis of multivariate data from co-located detectors. The method is demonstrated by analyzing 2000 seconds of simulated Einstein Telescope noise, which shows its ability to produce accurate spectral density estimates and quantify coherence between time series components. This makes it a powerful tool for analyzing correlated noise in gravitational wave data.

We present the Deciphering the Interplay between the Interstellar medium, Stars, and the Circumgalactic medium (DIISC) Survey. This survey is designed to investigate the correlations in properties between the circumgalactic medium (CGM), the interstellar medium (ISM), stellar distributions, and young star-forming regions. The galaxies were chosen to have a QSO sightline within 3.5 times the HI radii probing the disk-CGM interface. The sample contains 34 low-redshift galaxies with a median stellar mass of 10$^{10.45}~\rm M_{\odot}$ probed at a median impact parameter of $\rho=55~kpc$. The survey combines ultraviolet spectroscopic data from the Cosmic Origins Spectrograph aboard the Hubble Space Telescope with HI 21 cm hyperfine transition imaging with the Very Large Array (VLA), ultraviolet imaging from Galaxy Evolution Explorer (GALEX), and optical imaging and spectroscopy with the MMT and Vatican Advanced Technology Telescope. We describe the specific goals of the survey, data reduction, high-level data products, and some early results. We present the discovery of a strong inverse correlation, at a confidence level of 99.99%, between Lyman $\alpha$ equivalent width, $\rm W_{Ly\alpha}$, and impact parameter normalized by the HI radius ($\rho/R_{HI}$). We find $\rho/R_{HI}$ to be a better empirical predictor of Lyman $\alpha$ equivalent width than virial radius normalized impact parameter ($\rho/R_{vir}$) or parameterizations combining $\rho,~R_{vir}$, stellar mass, and star formation rate. We conclude that the strong anticorrelation between the Lyman $\alpha$ equivalent width and $\rho/R_{HI}$ indicates that the neutral gas distribution of the CGM is more closely connected to the galaxy's gas disk rather than its stellar and dark matter content., Comment: 29 pages, 12 figures, 4 tables Submitted to AAS Journals

A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and microwave radiometric measurements from the GOES and DMSP satellites and is trained with merged ground radar and gauge data over southeastern United States. The generated precipitation maps reproduce the spatial autocovariance and other multiscale statistical properties of the gauge-radar reference fields on average. Conditioning the generation on the satellite measurements allows us to constrain the magnitude and location of each generated precipitation feature. The mean of the 128- member ensemble shows high spatial coherence with the reference fields with 0.82 linear correlation between the two. On average, the coherence between any two ensemble members is approximately the same as the coherence between any ensemble member and the ground reference, attesting that the ensemble dispersion is a proper measure of the estimation uncertainty. From the generated ensembles we can easily derive the probability of the precipitation intensity exceeding any given intensity threshold, at the 5-km resolution of the generation or at any desired aggregated resolution.

The widespread use of Large Language Models (LLMs) in society creates new information security challenges for developers, organizations, and end-users alike. LLMs are trained on large volumes of data, and their susceptibility to reveal the exact contents of the source training datasets poses security and safety risks. Although current alignment procedures restrict common risky behaviors, they do not completely prevent LLMs from leaking data. Prior work demonstrated that LLMs may be tricked into divulging training data by using out-of-distribution queries or adversarial techniques. In this paper, we demonstrate a simple, query-based decompositional method to extract news articles from two frontier LLMs. We use instruction decomposition techniques to incrementally extract fragments of training data. Out of 3723 New York Times articles, we extract at least one verbatim sentence from 73 articles, and over 20% of verbatim sentences from 6 articles. Our analysis demonstrates that this method successfully induces the LLM to generate texts that are reliable reproductions of news articles, meaning that they likely originate from the source training dataset. This method is simple, generalizable, and does not fine-tune or change the production model. If replicable at scale, this training data extraction methodology could expose new LLM security and safety vulnerabilities, including privacy risks and unauthorized data leaks. These implications require careful consideration from model development to its end-use.

JWST has revealed diverse new populations of high-redshift ($z\sim4-11$) AGN and extreme star-forming galaxies that challenge current models. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. From a parent sample of 205 $\mathrm{z_{spec}}>3$ UNCOVER galaxies with NIRSpec/PRISM follow-up, we identify 12 C IV, He II, and C III] emitters. Leveraging the combined rest-optical and UV coverage of PRISM, we limit the emission-line model space using the sample's [O III]/H$\beta$ distribution, significantly decreasing the overlap between AGN and star-formation models in the UV diagnostics. We then find that the five He II emitters are the strongest AGN candidates, with further support from two [Ne V] detections and one X-ray detection from Chandra. We cannot robustly quantify the AGN fraction in this sample, but we note that close to 20% of $\mathrm{M_{*}>2\times10^{9}\,M_{\odot}}$ parent sample galaxies are AGN candidates. The lower-mass line emitters, which are consistent with both AGN and star-forming photoionization models, have more compact sizes and higher specific star formation rates than the parent sample. Higher-resolution and deeper data on these UV line emitters should provide much stronger constraints on the obscured AGN fraction at $z > 3$., Comment: Submitted to ApJ

Purpose: Demonstrate quantitative characterization of 3D patient-specific absorbed dose distributions using Haralick texture analysis and interpret measures in terms of underlying physics and radiation dosimetry. Methods: Retrospective analysis is performed for 137 patients who underwent permanent implant prostate brachytherapy using two simulation conditions: ``TG186'' (realistic tissues including 0-3.8% intraprostatic calcifications; interseed attenuation) and ``TG43'' (water-model; no interseed attenuation). Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are calculated using the original Haralick formalism, and a modified approach designed to reduce grey-level quantization sensitivity. Trends in textural features are compared to clinical dosimetric measures (D90; minimum absorbed dose to the hottest 90% of a volume) and changes in patient target volume % intraprostatic calcifications by volume (%IC). Results: Both original and modified measures quantify the spatial differences in absorbed dose distributions. Strong correlations between differences in textural measures calculated under TG43 and TG186 conditions and %IC are observed for all measures. For example, differences between measures of contrast and correlation increase and decrease respectively as patients with higher levels of %IC are evaluated, reflecting the large differences across adjacent voxels (higher dose in voxels with calcification) when calculated under TG186 conditions. Conversely, the D90 metric is relatively weakly correlated with textural measures, as it generally does not characterize the spatial distribution of absorbed dose. Conclusion: patient-specific 3D dose distributions may be quantified using Haralick analysis, and trends may be interpreted in terms of fundamental physics.

Hubble Space Telescope imaging shows that most star-forming galaxies at cosmic noon -- the peak of cosmic star formation history -- appear disk-dominated, leaving the origin of the dense cores in their quiescent descendants unclear. With the James Webb Space Telescope's (JWST) high-resolution imaging to 5 {\mu}m, we can now map the rest-frame near-infrared emission, a much closer proxy for stellar mass distribution, in these massive galaxies. We selected 70 star-forming galaxies with 10$<$log(M)$<$12 and 1.5$<$z$<$3 in the CEERS survey and compare their morphologies in the rest-frame optical to those in the rest-frame near-IR. While the bulk of these galaxies are disk-dominated in 1.5 {\mu}m (rest-frame optical) imaging, they appear more bulge-dominated at 4.4 {\mu}m (rest-frame near-infrared). Our analysis reveals that in massive star-forming galaxies at z$\sim$2, the radial surface brightness profiles steepen significantly, from a slope of $\sim$0.3/dex at 1.5 {\mu}m to $\sim$1.4/dex at 4.4 {\mu}m within radii $<$ 1 kpc. Additionally, we find their total flux contained within the central 1 kpc is approximately 7 times higher in F444W than in F150W. In rest-optical emission, a galaxy's central surface density appears to be the strongest indicator of whether it is quenched or star-forming. Our most significant finding is that at redder wavelengths, the central surface density ratio between quiescent and star-forming galaxies dramatically decreases from $\sim$10 to $\sim$1. This suggests the high central densities associated with galaxy quenching are already in place during the star-forming phase, imposing new constraints on the transition from star formation to quiescence., Comment: 9 pages, 5 figures, published in ApJL

Stars mostly form in clusters where neighboring stars can influence proto-planetary disc evolution. Besides gravitational interactions, external photoevaporation can shape these discs. Depending on the strength of photoevaporation, discs can be destroyed within 1-2 Myrs or more gradually. We use the chemcomp code, incorporating a viscous disc evolution model with pebble drift and evaporation, to calculate the chemical composition of protoplanetary discs. This code is extended to include external photoevaporation based on the FRIED grid. Initially, the disc evolves purely viscously, with the inner disc's C/O ratio decreasing due to inward drifting and evaporating water ice pebbles. Over time, the C/O ratio increases as water vapor accretes onto the star and carbon-rich gas migrates inward. Once external photoevaporation starts, the outer disc disperses, but the inner disc's chemical evolution follows that of a purely viscous disc, as most pebbles have already drifted inward within 1 Myr. At low viscosity, the inner disc's C/O ratio remains sub-solar until dispersion by photoevaporation. At high viscosity, the C/O ratio can reach super-solar values, due to faster accretion of water vapor and inward migration of carbon-rich gas, provided the disc survives a few Myrs. In both cases, there is no significant difference in the inner disc's chemical composition compared to a purely viscous model due to the rapid inward drift of pebbles. Our model predicts that inner disc chemistry should be similar for discs subject to external photoevaporation and isolated discs, consistent with JWST observations., Comment: 15 pages, 7 figures, accepted for publication in Astronomy and Astrophysics Journal

We study experimentally contests in which players make investment decisions sequentially, and information on prior investments is revealed between stages. Using a between-subject design, we consider all possible sequences in contests of three players and test two major comparative statics of the subgame-perfect Nash equilibrium: The positive effect of the number of stages on aggregate investment and earlier mover advantage. The former prediction is decidedly rejected, as we observe a reduction in aggregate investment when more sequential information disclosure stages are added to the contest. The evidence on earlier mover advantage is mixed but mostly does not support theory as well. Both predictions rely critically on large preemptive investment by first movers and accommodation by later movers, which does not materialize. Instead, later movers respond aggressively, and reciprocally, to first movers' investments, while first movers learn to accommodate those responses.

Convolutional Neural Networks (CNNs) have been utilised in many image and video processing applications. The convolution operator, also known as a spatial filter, is usually a linear operation, but this linearity compromises essential features and details inherent in the non-linearity present in many applications. However, due to its slow processing, the use of a nonlinear spatial filter is a significant bottleneck in many software applications. Further, due to their complexity, they are difficult to accelerate in FPGA or VLSI architectures. This paper presents novel FPGA implementations of linear and nonlinear spatial filters. More specifically, the arithmetic computations are carried out in custom floating-point, enabling a tradeoff of precision and hardware compactness, reducing algorithm development time. Further, we show that it is possible to process video at a resolution of 1080p with a frame rate of 60 frames per second, using a low-cost FPGA board. Finally, we show that using a domain-specific language will allow the rapid prototyping of image processing algorithms in custom floating-point arithmetic, allowing non-experts to quickly develop real-time video processing applications.