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The design and benchtop operation of a wireless miniature epiretinal stimulator implant is reported. The implant is optically powered and controlled using safe illumination at near-infrared wavelengths. An application-specific integrated circuit (ASIC) hosting a digital control unit is used to control the implant's electrodes. The ASIC is powered using an advanced photovoltaic (PV) cell and programmed using a single photodiode. Diamond packaging technology is utilized to achieve high-density integration of the implant optoelectronic circuitry, as well as individual connections between a stimulator chip and 256 electrodes, within a 4.6 mm x 3.7 mm x 0.9 mm implant package. An ultrahigh efficiency PV cell with a monochromatic power conversion efficiency of 55% is used to power the implant. On-board photodetection circuity with a bandwidth of 3.7 MHz is used for forward data telemetry of stimulation parameters. In comparison to implants which utilize inductively coupled coils, laser power delivery enables a high degree of miniaturization and lower surgical complexity. The device presented combines the benefits of implant miniaturization and a flexible stimulation strategy provided by a dedicated stimulator chip. This development provides a route to fully wireless miniaturized minimally invasive implants with sophisticated functionalities.

The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks. This study investigates the dual capabilities of LLMs as both facilitators and defenders against CSE threats. We develop a novel dataset, SEConvo, simulating CSE scenarios in academic and recruitment contexts, and designed to examine how LLMs can be exploited in these situations. Our findings reveal that, while off-the-shelf LLMs generate high-quality CSE content, their detection capabilities are suboptimal, leading to increased operational costs for defense. In response, we propose ConvoSentinel, a modular defense pipeline that improves detection at both the message and the conversation levels, offering enhanced adaptability and cost-effectiveness. The retrieval-augmented module in ConvoSentinel identifies malicious intent by comparing messages to a database of similar conversations, enhancing CSE detection at all stages. Our study highlights the need for advanced strategies to leverage LLMs in cybersecurity.

Star-galaxy separation is a crucial step in creating target catalogues for extragalactic spectroscopic surveys. A classifier biased towards inclusivity risks including spurious stars, wasting fibre hours, while a more conservative classifier might overlook galaxies, compromising completeness and hence survey objectives. To avoid bias introduced by a training set in supervised methods, we employ an unsupervised machine learning approach. Using photometry from the Wide Area VISTA Extragalactic Survey (WAVES)-Wide catalogue comprising 9-band $u-K_s$ data, we create a feature space with colours, fluxes, and apparent size information extracted by ${\rm P{\scriptsize RO} F{\scriptsize OUND}}$. We apply the non-linear dimensionality reduction method UMAP (Uniform Manifold Approximation and Projection) combined with the classifier ${\rm{\scriptsize HDBSCAN}}$ to classify stars and galaxies. Our method is verified against a baseline colour and morphological method using a truth catalogue from Gaia, SDSS, GAMA, and DESI. We correctly identify 99.72% of galaxies within the AB magnitude limit of $Z = 21.2$, with an F1 score of 0.9970 across the entire ground truth sample, compared to 0.9871 from the baseline method. Our method's higher purity (0.9966) compared to the baseline (0.9780) increases efficiency, identifying 11% fewer galaxy or ambiguous sources, saving approximately 70,000 fibre hours on the 4MOST instrument. We achieve reliable classification statistics for challenging sources including quasars, compact galaxies, and low surface brightness galaxies, retrieving 95.1%, 84.6%, and 99.5% of them respectively. Angular clustering analysis validates our classifications, showing consistency with expected galaxy clustering, regardless of the baseline classification., Comment: Submitted to MNRAS; 17 pages, 11 figures

We report the discovery of two new exoplanet systems around fully convective stars, found from the radial-velocity (RV) variations of their host stars measured with the nIR spectropolarimeter CFHT/SPIRou over multiple years. GJ 3378 b is a planet with minimum mass of $5.26^{+0.94}_{-0.97}$ Mearth in an eccentric 24.73-day orbit around an M4V star of 0.26 Msun. GJ 1289 b has a minimum mass of $6.27\pm1.25$ Mearth in a 111.74-day orbit, in a circular orbit around an M4.5V star of mass 0.21 Msun. Both stars are in the solar neighbourhood, at respectively 7.73 and 8.86 pc. The low-amplitude RV signals are detected after line-by-line post-processing treatment. These potential sub-Neptune class planets around cool stars may have temperate atmospheres and be interesting nearby systems for further studies. We also recovered the large-scale magnetic field of both stars, found to be mostly axisymmetric and dipolar, and with a polar strength of 20-30 G and 200-240 G for GJ 3378 (in 2019-21) and GJ 1289 (in 2022-23), respectively. The rotation periods measured with the magnetic field differ from the orbital periods, and in general, stellar activity is not seen in the studied nIR RV time series of both stars. GJ 3378 b detection is not confirmed by optical RVs and is therefore considered a candidate at this point., Comment: accepted in A&A

For several years, the metastable helium triplet line has been successfully used as a tracer to probe atmospheric escape in transiting exoplanets. This absorption in the near-infrared (1083.3 nm) can be observed from the ground using high-resolution spectroscopy, providing new constraints on the mass-loss rate and the temperature characterizing the upper atmosphere of close-in exoplanets. The aim of this work is to search for the He triplet signature in 15 transiting exoplanets -- ranging from super-Earths to ultrahot Jupiters -- observed with SPIRou, a high-resolution (R~70 000) near-infrared spectropolarimeter at the CFHT, in order to bring new constraints or to improve existing ones regarding atmospheric escape through a homogeneous study. We developed a full data processing and analysis pipeline to correct for the residual telluric and stellar contributions. We then used two different 1D models based on the Parker-wind equations and nonlocal thermodynamic equilibrium (NLTE) radiative transfer to interpret the observational results. We confirm published He triplet detections for HAT-P-11 b, HD 189733 b, and WASP-69 b. We tentatively detect the signature of escaping He in HD 209458 b, GJ 3470 b, and WASP-76 b. We report new constraints on the mass-loss rate and temperature for our three detections and set upper limits for the tentative and nondetections. We notably report improved constraints on the mass-loss rate and temperature of the escaping gas for TOI-1807 b, and report a nondetection for the debated atmospheric escape in GJ 1214 b. We also conducted the first search for the He signature in GJ 486 b since its discovery and report a nondetection of the He triplet. Finally, we studied the impact of important model assumptions on our retrieved parameters, notably the limitations of 1D models and the influence of the H/He ratio on the derived constraints., Comment: 33 pages, 14 figures

Given the compact binary evolution problem of numerical relativity, in the finite-difference, block-based, adaptive mesh refinement context, choices must be made on how evolved fields are to be discretized. In GR-Athena++, the space-time solver was previously fixed to be vertex-centered. Here, our recent extensions to a cell-centered treatment, are described. Simplifications in the handling of variables during the treatment of general relativistic magneto-hydrodynamical (GRMHD) evolution are found. A novelty is that performance comparison for the two choices of grid sampling is made within a single code-base. In the case of a binary black hole inspiral-merger problem, by evolving geometric fields on vertex-centers, an average $\sim 20\%$ speed increase is observed, when compared against cell-centered sampling. The opposite occurs in the GRMHD setting. A binary neutron star inspiral-merger-collapse problem, representative of typical production simulations is considered. We find that cell-centered sampling for the space-time solver improves performance, by a similar factor.

The Near-InfraRed Planet Searcher or NIRPS is a precision radial velocity spectrograph developed through collaborative efforts among laboratories in Switzerland, Canada, Brazil, France, Portugal and Spain. NIRPS extends to the 0.98-1.8 $\mu$m domain of the pioneering HARPS instrument at the La Silla 3.6-m telescope in Chile and it has achieved unparalleled precision, measuring stellar radial velocities in the infrared with accuracy better than 1 m/s. NIRPS can be used either stand-alone or simultaneously with HARPS. Commissioned in late 2022 and early 2023, NIRPS embarked on a 5-year Guaranteed Time Observation (GTO) program in April 2023, spanning 720 observing nights. This program focuses on planetary systems around M dwarfs, encompassing both the immediate solar vicinity and transit follow-ups, alongside transit and emission spectroscopy observations. We highlight NIRPS's current performances and the insights gained during its deployment at the telescope. The lessons learned and successes achieved contribute to the ongoing advancement of precision radial velocity measurements and high spectral fidelity, further solidifying NIRPS' role in the forefront of the field of exoplanets., Comment: Proceeding at the SPIE Astronomical Telescopes + Instrumentation conference [Yokohama,Japan; June 2024]

`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, `scores` primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. `scores` not only includes common scores (e.g., Mean Absolute Error), it also includes novel scores not commonly found elsewhere (e.g., FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g., threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. `scores` supports `xarray` datatypes, allowing it to work with Earth system data in a range of formats including NetCDF4, HDF5, Zarr and GRIB among others. `scores` uses Dask for scaling and performance. Support for `pandas` is being introduced. The `scores` software repository can be found at https://github.com/nci/scores/, Comment: Minor revisions to text and table. Updated title. 6 pages, 1 table. Software repository at https://github.com/nci/scores/

We present a self-contained overview of GR-Athena++, a general-relativistic magnetohydrodynamics (GRMHD) code, that incorporates treatment of dynamical space-time, based on the recent work of (Daszuta+, 2021)[49] and (Cook+, 2023)[45]. General aspects of the Athena++ framework we build upon, such as oct-tree based, adaptive mesh refinement (AMR) and constrained transport, together with our modifications, incorporating the Z4c formulation of numerical relativity, judiciously coupled, enables GRMHD with dynamical space-times. Initial verification testing of GR-Athena++ is performed through benchmark problems that involve isolated and binary neutron star space-times. This leads to stable and convergent results. Gravitational collapse of a rapidly rotating star through black hole formation is shown to be correctly handled. In the case of non-rotating stars, magnetic field instabilities are demonstrated to be correctly captured with total relative violation of the divergence-free constraint remaining near machine precision. The use of AMR is show-cased through investigation of the Kelvin-Helmholtz instability which is resolved at the collisional interface in a merger of magnetised binary neutron stars. The underlying task-based computational model enables GR-Athena++ to achieve strong scaling efficiencies above $80\%$ in excess of $10^5$ CPU cores and excellent weak scaling up to $\sim 5 \times 10^5$ CPU cores in a realistic production setup. GR-Athena++ thus provides a viable path towards robust simulation of GRMHD flows in strong and dynamical gravity with exascale high performance computational infrastructure., Comment: Invited chapter for the edited book {\it New Frontiers in GRMHD Simulations} (Eds. C. Bambi, Y. Mizuno, S. Shashank and F. Yuan, Springer Singapore, expected in 2024)

Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual exploration with inter-generational information transmission. Despite its widespread success among humans, the capacity for artificial learning agents to accumulate culture remains under-explored. In particular, approaches to reinforcement learning typically strive for improvements over only a single lifetime. Generational algorithms that do exist fail to capture the open-ended, emergent nature of cultural accumulation, which allows individuals to trade-off innovation and imitation. Building on the previously demonstrated ability for reinforcement learning agents to perform social learning, we find that training setups which balance this with independent learning give rise to cultural accumulation. These accumulating agents outperform those trained for a single lifetime with the same cumulative experience. We explore this accumulation by constructing two models under two distinct notions of a generation: episodic generations, in which accumulation occurs via in-context learning and train-time generations, in which accumulation occurs via in-weights learning. In-context and in-weights cultural accumulation can be interpreted as analogous to knowledge and skill accumulation, respectively. To the best of our knowledge, this work is the first to present general models that achieve emergent cultural accumulation in reinforcement learning, opening up new avenues towards more open-ended learning systems, as well as presenting new opportunities for modelling human culture.

We study initially unbound systems of two black holes using numerical relativity (NR) simulations performed with GR-Athena++. We focus on regions of the parameter space close to the transition from scatterings to dynamical captures, considering equal mass and spin-aligned configurations, as well as unequal mass and nonspinning ones. The numerical results are then used to validate the effective-one-body (EOB) model TEOBResumS-Dal\'i for dynamical captures and scatterings. We find good agreement for the waveform phenomenologies, scattering angles, mismatches, and energetics in the low energy regime ($E_0\lesssim 1.02\,M$). In particular, mismatches are typically below or around the $1\%$ level, with only a few cases -- corresponding to spinning binaries -- slightly above the $3\%$ threshold. We also discuss dynamical captures in the test-mass limit by solving numerically the Zerilli equation with the time domain code RWZHyp. The latter analysis provides valuable insights into both the analytical noncircular corrections of the EOB waveform and the integration of NR Weyl scalars., Comment: 31 pages, 18 figures

Jupiter's moon Io hosts extensive volcanism driven by tidal heating. The isotopic composition of Io's inventory of volatile elements, including sulfur and chlorine, reflects its outgassing and mass loss history and provides an avenue for exploring its evolution. We used millimeter observations of Io's atmosphere to measure sulfur isotopes in gaseous SO2 and SO, and chlorine isotopes in gaseous NaCl and KCl. We find $^{34}$S/$^{32}$S=0.0595$\pm$0.0038 ($\delta^{34}$S=+347$\pm$86 per mille), which is highly enriched compared to average Solar System values and indicates that Io has lost 94 to 99% of its available sulfur. Our measurement of $^{37}$Cl/$^{35}$Cl=0.403$\pm$0.028 ($\delta^{37}$Cl=+263$\pm$88 per mille) shows chlorine is similarly enriched. These measurements indicate that Io has been volcanically active for most or all of its history, with potentially higher outgassing and mass-loss rates at earlier times., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on May 10, 2024, DOI: 10.1126/science.adj0625

Spatial point process models are widely applied to point pattern data from various fields in the social and environmental sciences. However, a serious hurdle in fitting point process models is the presence of duplicated points, wherein multiple observations share identical spatial coordinates. This often occurs because of decisions made in the geo-coding process, such as assigning representative locations (e.g., aggregate-level centroids) to observations when data producers lack exact location information. Because spatial point process models like the Log-Gaussian Cox Process (LGCP) assume unique locations, researchers often employ {\it ad hoc} solutions (e.g., jittering) to address duplicated data before analysis. As an alternative, this study proposes a Modified Minimum Contrast (MMC) method that adapts the inference procedure to account for the effect of duplicates without needing to alter the data. The proposed MMC method is applied to LGCP models, with simulation results demonstrating the gains of our method relative to existing approaches in terms of parameter estimation. Interestingly, simulation results also show the effect of the geo-coding process on parameter estimates, which can be utilized in the implementation of the MMC method. The MMC approach is then used to infer the spatial clustering characteristics of conflict events in Afghanistan (2008-2009).

Plutonium's phase diagram is host to complex structures and interactions that make the description of its ground state properties elusive. Using all-electron density functional theory, we study the thermodynamic properties of $\alpha$-Pu. To do this, we build on recent work in the literature by introducing a novel noncollinear magnetic ansatz for $\alpha$-Pu's ground state. The noncollinear ansatz accurately recovers the experimental phonon density of states, heat capacity, and thermal expansion. These new results on $\alpha$-Pu along with recent results on $\delta$-Pu demonstrate the efficacy of noncollinear ansatzes for the description of plutonium.

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 10 pages, 3 figures

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star-black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (FAR < 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg$^2$ within the skymaps of the high-significance GW events, reaching a median depth of g~20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag/day can peak at a magnitude brighter than -17.5 mag., Comment: submitted

A game's theme is an important part of its design -- it conveys narrative information, rhetorical messages, helps the player intuit strategies, aids in tutorialisation and more. Thematic elements of games are notoriously difficult for AI systems to understand and manipulate, however, and often rely on large amounts of hand-written interpretations and knowledge. In this paper we present a technique which connects game embeddings, a recent method for modelling game dynamics from log data, and word embeddings, which models semantic information about language. We explain two different approaches for using game embeddings in this way, and show evidence that game embeddings enhance the linguistic translations of game concepts from one theme to another, opening up exciting new possibilities for reasoning about the thematic elements of games in the future., Comment: 7 pages, PCG Workshop at FDG 2024

Effective communication, specifically through documentation, is the beating heart of collaboration among contributors in software development. Recent advancements in language models (LMs) have enabled the introduction of a new type of actor in that ecosystem: LM-powered assistants capable of code generation, optimization, and maintenance. Our study investigates the efficacy of small language models (SLMs) for generating high-quality docstrings by assessing accuracy, conciseness, and clarity, benchmarking performance quantitatively through mathematical formulas and qualitatively through human evaluation using Likert scale. Further, we introduce DocuMint, as a large-scale supervised fine-tuning dataset with 100,000 samples. In quantitative experiments, Llama 3 8B achieved the best performance across all metrics, with conciseness and clarity scores of 0.605 and 64.88, respectively. However, under human evaluation, CodeGemma 7B achieved the highest overall score with an average of 8.3 out of 10 across all metrics. Fine-tuning the CodeGemma 2B model using the DocuMint dataset led to significant improvements in performance across all metrics, with gains of up to 22.5% in conciseness. The fine-tuned model and the dataset can be found in HuggingFace and the code can be found in the repository., Comment: 12 pages, 4 figures

Massively multiplayer online (MMO) games boomed in the late 1990s to 2000s. In parallel, ethnographic studies of these communities emerged, generally involving participant observation and interviews. Several decades on, many MMOs have been reconfigured, remastered or are potentially no longer accessible at all, which presents challenges for their continued study and long-term preservation. In this paper we explore the "go-along" methodology, in which a researcher joins a participant on a walk through a familiar place and asks them questions, as a qualitative research method applicable for the study and preservation of games culture. Though the methodology has been introduced in digital media studies, to date it has had limited application in digital games, if at all. We report on a pilot study exploring applications of the go-along method to the sandbox MMO Wurm Online; a persistent, player-directed world with a rich history. We report on our motivations for the work, our analysis of the resulting interviews, and our reflections on both the use of go-alongs in digital games, as well as the unique and inspiring culture and community of this lesser-known game., Comment: 16 pages, 6 figures

Misinformation about climate change is a complex societal issue requiring holistic, interdisciplinary solutions at the intersection between technology and psychology. One proposed solution is a "technocognitive" approach, involving the synthesis of psychological and computer science research. Psychological research has identified that interventions in response to misinformation require both fact-based (e.g., factual explanations) and technique-based (e.g., explanations of misleading techniques) content. However, little progress has been made on documenting and detecting fallacies in climate misinformation. In this study, we apply a previously developed critical thinking methodology for deconstructing climate misinformation, in order to develop a dataset mapping different types of climate misinformation to reasoning fallacies. This dataset is used to train a model to detect fallacies in climate misinformation. Our study shows F1 scores that are 2.5 to 3.5 better than previous works. The fallacies that are easiest to detect include fake experts and anecdotal arguments, while fallacies that require background knowledge, such as oversimplification, misrepresentation, and slothful induction, are relatively more difficult to detect. This research lays the groundwork for development of solutions where automatically detected climate misinformation can be countered with generative technique-based corrections.

We present a model of radio continuum emission associated with star formation (SF) and active galactic nuclei (AGN) implemented in the Shark semi-analytic model of galaxy formation. SF emission includes free-free and synchrotron emission, which depend on the free-electron density and the rate of core-collapse supernovae with a minor contribution from supernova remnants, respectively. AGN emission is modelled based on the jet production rate, which depends on the black hole mass, accretion rate and spin, and includes synchrotron self-absorption. Shark reproduces radio luminosity functions (RLFs) at 1.4 GHz and 150 MHz for 0 $\leq$ z $\leq$ 4, and scaling relations between radio luminosity, star formation rate and infrared luminosity of galaxies in the local and distant universe in good agreement with observations. The model also reproduces observed number counts of radio sources from 150 MHz to 8.4 GHz to within a factor of two on average, though larger discrepancies are seen at the very bright fluxes at higher frequencies. We use this model to understand how the radio continuum emission from radio-quiet AGNs can affect the measured RLFs of galaxies. We find current methods to exclude AGNs from observational samples result in large fractions of radio-quiet AGNs contaminating the "star-forming galaxies" selection and a brighter end to the resulting RLFs. We investigate how this effects the infrared-radio correlation (IRRC) and show that AGN contamination can lead to evolution of the IRRC with redshift. Without this contamination our model predicts a redshift- and stellar mass-independent IRRC, except at the dwarf-galaxy regime., Comment: Accepted for publication in MNRAS. 17 pages, 7 figures

The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON., Comment: 10 pages, 10 figures, 2 algorithms, accepted by the 4th International Conference on Electrical, Computer and Energy Technologies (ICECET) to be presented in July 2024

In this paper we report near-infrared observations of the classical T Tauri star TW Hya with the SPIRou high-resolution spectropolarimeter and velocimeter at the 3.6-m Canada-France-Hawaii Telescope in 2019, 2020, 2021 and 2022. By applying Least-Squares Deconvolution (LSD) to our circularly polarized spectra, we derived longitudinal fields that vary from year to year from -200 to +100 G, and exhibit low-level modulation on the 3.6 d rotation period of TW Hya, despite the star being viewed almost pole-on. We then used Zeeman-Doppler Imaging to invert our sets of unpolarized and circularly-polarized LSD profiles into brightness and magnetic maps of TW Hya in all 4 seasons, and obtain that the large-scale field of this T Tauri star mainly consists of a 1.0-1.2 kG dipole tilted at about 20{\deg} to the rotation axis, whereas the small-scale field reaches strengths of up to 3-4 kG. We find that the large-scale field is strong enough to allow TW Hya to accrete material from the disc on the polar regions at the stellar surface in a more or less geometrically stable accretion pattern, but not to succeed in spinning down the star. We also report the discovery of a radial velocity signal of semi-amplitude $11.1^{+3.3}_{-2.6}$ m/s (detected at 4.3$\sigma$ at a period of 8.3 d in the spectrum of TW Hya, whose origin may be attributed to either a non-axisymmetric density structure in the inner accretion disc, or to a $0.55^{+0.17}_{-0.13}$ Jupiter mass candidate close-in planet (if orbiting in the disc plane), at an orbital distance of $0.075\pm0.001$ au., Comment: MNRAS, in press (23 pages, 16 figures, 6 tables)

Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep learning classifier to automatically classify the images and route them to appropriate segmentation models, we hope that our workflow can segment the images with different pathology accurately. The data we used in this study are 350 CT images from patients affected by polycystic liver disease and 350 CT images from patients presenting with liver metastases from colorectal cancer. All images had the liver manually segmented by trained imaging analysts. Our proposed adaptive segmentation workflow achieved a statistically significant improvement for the task of total liver segmentation compared to the generic single segmentation model (non-parametric Wilcoxon signed rank test, n=100, p-value << 0.001). This approach is applicable in a wide range of scenarios and should prove useful in clinical implementations of segmentation pipelines., Comment: J Digit Imaging. Inform. med. (2024)

The tight relationship between infrared luminosity (L$_\mathrm{TIR}$) and 1.4 GHz radio continuum luminosity (L$_\mathrm{1.4GHz}$) has proven useful for understanding star formation free from dust obscuration. Infrared emission in star-forming galaxies typically arises from recently formed, dust-enshrouded stars, whereas radio synchrotron emission is expected from subsequent supernovae. By leveraging the wealth of ancillary far-ultraviolet - far-infrared photometry from the Deep Extragalactic VIsible Legacy Survey (DEVILS) and Galaxy and Mass Assembly (GAMA) surveys, combined with 1.4 GHz observations from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey and Deep Investigation of Neutral Gas Origins (DINGO) projects, we investigate the impact of timescale differences between far-ultraviolet - far-infrared and radio-derived star formation rate (SFR) tracers. We examine how the SED-derived star formation histories (SFH) of galaxies can be used to explain discrepancies in these SFR tracers, which are sensitive to different timescales. Galaxies exhibiting an increasing SFH have systematically higher L$_\mathrm{TIR}$ and SED-derived SFRs than predicted from their 1.4 GHz radio luminosity. This indicates that insufficient time has passed for subsequent supernovae-driven radio emission to accumulate. We show that backtracking the SFR(t) of galaxies along their SED-derived SFHs to a time several hundred megayears prior to their observed epoch will both linearise the SFR-L$_\mathrm{1.4GHz}$ relation and reduce the overall scatter. The minimum scatter in the SFR(t)-L$_\mathrm{1.4GHz}$ is reached at 200 - 300 Myr prior, consistent with theoretical predictions for the timescales required to disperse the cosmic ray electrons responsible for the synchrotron emission., Comment: 23 pages, 13 figures, 2 tables. Accepted for publication in MNRAS

Natural climate solutions (NCS) are critical for mitigating climate change through ecosystem-based carbon removal and emissions reductions. NCS implementation can also generate biodiversity and human well-being co-benefits and trade-offs ("NCS co-impacts"), but the volume of evidence on NCS co-impacts has grown rapidly across disciplines, is poorly understood, and remains to be systematically collated and synthesized. A global evidence map of NCS co-impacts would overcome key barriers to NCS implementation by providing relevant information on co-benefits and trade-offs where carbon mitigation potential alone does not justify NCS projects. We employ large language models to assess over two million articles, finding 257,266 relevant articles on NCS co-impacts. We analyze this large and dispersed body of literature using innovative machine learning methods to extract relevant data (e.g., study location, species, and other key variables), and create a global evidence map on NCS co-impacts. Evidence on NCS co-impacts has grown approximately ten-fold in three decades, although some of the most abundant evidence is associated with pathways that have less mitigation potential. We find that studies often examine multiple NCS pathways, indicating natural NCS pathway complements, and each NCS is often associated with two or more coimpacts. Finally, NCS co-impacts evidence and priority areas for NCS are often mismatched--some countries with high mitigation potential from NCS have few published studies on the broader co-impacts of NCS implementation. Our work advances and makes available novel methods and systematic and representative data of NCS co-impacts studies, thus providing timely insights to inform NCS research and action globally., Comment: 28 pages, 5 figures

The evaluation of procedural content generation (PCG) systems for generating video game levels is a complex and contested topic. Ideally, the field would have access to robust, generalisable and widely accepted evaluation approaches that can be used to compare novel PCG systems to prior work, but consensus on how to evaluate novel systems is currently limited. We argue that the field can benefit from a structured analysis of how procedural level generation systems can be evaluated, and how these techniques are currently used by researchers. This analysis can then be used to both inform on the current state of affairs, and to provide data to justify changes to this practice. This work aims to provide this by first developing a novel taxonomy of PCG evaluation approaches, and then presenting the results of a survey of recent work in the field through the lens of this taxonomy. The results of this survey highlight several important weaknesses in current practice which we argue could be substantially mitigated by 1) promoting use of evaluation free system descriptions where appropriate, 2) promoting the development of diverse research frameworks, 3) promoting reuse of code and methodology wherever possible., Comment: To be published in the proceedings of the Foundations of Digital Games Conference 2024. 10 Pages, 4 Figures

Misinformation about climate change poses a significant threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model, the Augmented CARDS model, specifically designed for detecting contrarian climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors or conspiracy theories. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.

This paper is based on two insights: (1) that general relativity alone does not specify how much of the matter density contributes to the source term in Friedmann's equation, and how much contributes as the source of the gravitational potential of condensed objects; and (2) the source of the gravitational potential in the currently accepted cosmological model, for the small scales of condensed objects, is not what one would expect from Newtonian theory. Here insight (1) is used to restore the source of the gravitational potential to its Newtonian value, giving a new cosmological model. This model uses only conventional general relativity (no speculative physics) and the usual cosmological sources of gravitation, and there is no Hubble tension in this model if the total gravitational potential of condensed objects falls in a certain range. The deceleration parameter in the new model differs from that in the currently favored onel, suggesting a test to distinguish between the two models., Comment: 14 pages, 3 figures

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75 micron and 5 micron with spectral resolving power ~40 between 0.75 and 3.8 micron and ~120 between 3.8 and 5 micron At the end of its two-year mission, SPHEREx will provide 0.75-to-5 micron spectra of each 6.2"x6.2" pixel on the sky - 14 billion spectra in all. This paper updates an earlier description of SPHEREx presenting changes made during the mission's Preliminary Design Phase, including a discussion of instrument integration and test and a summary of the data processing, analysis, and distribution plans.

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/

F\"orster resonance energy transfer (FRET) is a quantum mechanical phenomenon involving the non-radiative transfer of energy between coupled electric dipoles. Due to the strong dependence of FRET on the distance between the dipoles, it is frequently used as a ``molecular ruler" in biology, chemistry, and physics. This is done by placing dipolar molecules called dyes on molecules of interest. In time-resolved confocal single-molecule FRET (smFRET) experiments, the joint distribution of the FRET efficiency and the donor fluorescence lifetime can reveal underlying molecular conformational dynamics via deviation from their theoretical F\"orster relationship. This deviation is referred to as a dynamic shift. Quantifying the dynamic shift caused by the motion of the fluorescent dyes is essential to decoupling the dynamics of the studied molecules and the dyes. We develop novel Langevin models for the dye linker dynamics, including rotational dynamics, based on first physics principles and proper dye linker chemistry to match accessible volumes predicted by molecular dynamics simulations. By simulating the dyes' stochastic translational and rotational dynamics, we show that the observed dynamic shift can largely be attributed to the mutual orientational dynamics of the electric dipole moments associated with the dyes, not their accessible volume., Comment: 13 Pages, 7 Figures

We use the Canadian Hydrogen Intensity Mapping Experiment (CHIME) Fast Radio Burst (FRB) Project to search for FRBs that are temporally and spatially coincident with gamma-ray bursts (GRBs) occurring between 2018 July 7 and 2023 August 3. We do not find any temporal (within 1 week) and spatial (within overlapping 3 sigma localization regions) coincidences between any CHIME/FRB candidates and all GRBs with 1 sigma localization uncertainties <1 deg. As such, we use CHIME/FRB to constrain the possible FRB-like radio emission for 27 short gamma-ray bursts (SGRBs) that were within 17 deg. of CHIME/FRB's meridian at a point either 6 hrs prior up to 12 hrs after the high-energy emission. Two SGRBs, GRB 210909A and GRB 230208A, were above the horizon at CHIME at the time of their high-energy emission and we place some of the first constraints on simultaneous FRB-like radio emission from SGRBs. While neither of these two SGRBs have known redshifts, we construct a redshift range for each GRB based on their high-energy fluence and a derived SGRB energy distribution. For GRB 210909A, this redshift range corresponds to z = [0.009, 1.64] with a mean of z=0.13. Thus, for GRB 210909A, we constrain the radio luminosity at the time of the high-energy emission to L <2 x 10e46 erg s-1, L < 5 x 10e44 erg s-1, and L < 3 x 10e42 erg s-1 assuming redshifts of z=0.85, z=0.16, and z=0.013, respectively. We compare these constraints with the predicted simultaneous radio luminosities from different compact object merger models., Comment: 24 pages, 5 figures, 7 tables

Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10\,s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that is critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new DAQ modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit PMTs during a supernova burst and the second, the Veto module, prescales the high rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead time less than 1\,ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800~pc will trigger Veto module resulting in a prescaling of the observed neutrino data., Comment: 28 pages, 18 figures. Submitted to PTEP

Rapid growth in the number of small sensor devices known as the Internet of Things (IoT) has seen the development of lightweight encryption algorithms. Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. These lightweight encryption algorithms are based on the efficient Feistel block structure which is known to exhibit vulnerabilities to differential cryptanalysis. Consequently, it is necessary to test these algorithms for resilience against such attacks. While existing state-of-the-art research has demonstrated novel heuristic methods of differential cryptanalysis that improve time efficiency on previous techniques, the large state sizes of these encryption algorithms inhibit cryptanalysis time efficiency. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis (VISTA-CRYPT) - a time-efficient enhancement of differential cryptanalysis of lightweight encryption algorithms. The proposed technique introduces a simple framework of quota sampling that produces state-of-the-art results with time reductions of up to $76\%$ over existing techniques. Further, we present a preliminary graph-based analysis of the output differentials for the identification of relationships within the data and future research opportunities to further enhance the performance of differential cryptanalysis. The code designed for this work and associated datasets will be available at https://github.com/johncook1979/simon-cryptanalysis., Comment: 24 pages, 21 figures, 7 tables

We present the first of two papers dedicated to verifying the Australian Epoch of Reionisation pipeline (AusEoRPipe) through simulation. The AusEoRPipe aims to disentangle 21-cm radiation emitted by gas surrounding the very first stars from contaminating foreground astrophysical sources, and has been in development for close to a decade. In this paper, we build an accurate 21-cm sky model that can be used by the WODEN simulation software to create visibilities containing a predictable 21-cm signal. We verify that the power spectrum estimator CHIPS can recover this signal in the absence of foregrounds. We also investigate how measurements in Fourier-space are correlated, and how their gridded density affects the power spectrum. We measure and fit for this effect using Gaussian-noise simulations of the MWA phase I layout. We find a gridding density correction factor of 2.651 appropriate for integrations equal to or greater than 30 minutes of data, which contain observations with multiple primary beam pointings and LSTs. Paper II of this series will use the results of this paper to test the AusEoRPipe in the presence of foregrounds and instrumental effects., Comment: 13 pages, 11 Figures, 1 table, accepted in the Publications of the Astronomical Society of Australia (PASA)

This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug achieves this by allowing each arm and drive base to act as an individual agent, significantly reducing planning complexity. Stickbug uses a compact holonomic Kiwi drive to navigate narrow greenhouse rows, a tall mast to support multiple manipulators and reach plant heights, a detection model and classifier to identify Bramble flowers, and a felt-tipped end-effector for contact-based pollination. Initial experimental validation demonstrates that Stickbug can attempt over 1.5 pollinations per minute with a 50% success rate. Additionally, a Bramble flower perception dataset was created and is publicly available alongside Stickbug's software and design files., Comment: 7 pages, 7 figures

The Understanding Children's Transitions from Head Start to Kindergarten (HS2K) Project was a multi-year project funded by the United States Department of Health and Human Services, Administration for Children and Families, Office of Planning, Research and Evaluation. The purpose of this project was to better understand how to improve children's transitions from Head Start programs to elementary schools. The transition to kindergarten occurs within and between two distinct systems (early care and education [ECE] and K-12 education) that each work under their own governance structures, philosophies, and accountability metrics. Specifically, this project aimed to explore the definition of "successful transitions" from a multi-level and systemic perspective. Within the context of this project, the focus is on one type of ECE program--Head Start. Head Start is embedded within a broader "ECE system" that includes many other types of programs, including state-funded pre-k, child care, home-based care, friend and family care, and privately-funded centers. This report details findings from one component of the HS2K Project -- a comparative multi-case study of five Head Start and Local Education Agency (LEA) partnerships, including their families, Head Start teachers and leaders, kindergarten teachers, elementary school leaders, LEA leaders, and community partners. The case-specific descriptions should be read as in-depth illustrations of the inner workings of kindergarten transition processes as they existed and were implemented in context at the time of data collection.

There is an abundance of research on the college admissions process and the practices selective schools employ to evaluate and select students. Most findings are complex, nuanced, or conflicting. Less than a month after the Supreme Court of the United States (SCOTUS) ruled in "Students for Fair Admissions v. Harvard" and "Students for Fair Admissions v. University of North Carolina" that colleges and universities could no longer consider an applicant's race or ethnicity when making admissions decisions, the Urban Institute hosted a convening of academic researchers, officials from the US Department of Education and the Biden-Harris administration, and representatives of philanthropic foundations and advocacy organizations for a wide-ranging discussion about the future of college admissions from a research perspective. Informed largely by the discussions from that convening, this report identifies key themes from the relevant research on the recruitment and admissions practices that will play a significant role in the future of college student diversity. Additionally, this report highlights areas for more research and the data needed to support additional areas of future research.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on ELA achievement, as measured by SBA scores. We compared "striving learner" students who were assigned to use i-Ready Personalized Instruction (Treatment students) and "striving learner" students assigned to only receive i-Ready Diagnostic assessments (Comparison students). The study sample consisted of 8,722 "striving learner" students in Grades 3-6 from four school districts in California. All four districts used i-Ready Diagnostic assessments, but schools in one district assigned some students to receive the Personalized Instruction product in addition to the Diagnostic product. Striving learner students in this district assigned to both the Personalized Instruction and Diagnostic products were compared to striving learner students in the other districts who were only assigned to the Diagnostic product. Results of this study did not show any statistically significant associations between i-Ready Personalized Instruction usage and SBA ELA achievement. Notably, less than 20% of treatment students met i-Ready's recommended usage guidelines of at least 30 minutes per week of Personalized Instruction use for at least 18 weeks. Students that met usage guidelines directionally outperformed comparison students, although this impact was not significant. Overall, impacts were directionally positive for students who met usage guidelines, but the large percentage of students who did meet recommended guidelines may have impacted overall patterns of results. Taken with the results of similar mathematics achievement analyses, additional implementation focus may be warranted on ensuring that as many students as possible reach Personalized Instruction usage guidelines.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on math achievement, as measured by SBA scores. We compared "striving learner" students who were assigned to use i-Ready Personalized Instruction (Treatment students) and "striving learner" students assigned to only receive i-Ready Diagnostic assessments (Comparison students). The study sample consisted of 11,034 "striving learner" students in Grades 3-6 from four school districts in California. All four districts used i-Ready Diagnostic assessments, but schools in one district assigned some students to receive the Personalized Instruction product in addition to the Diagnostic product. Striving learner students in this district assigned to both the Personalized Instruction and Diagnostic products were compared to striving learner students in the other districts who were only assigned to the Diagnostic product. Overall, this analysis presents promising evidence of i-Ready Personalized Instruction on student math achievement. This relationship between i-Ready Personalized Instruction and math achievement was strongest for students in Grades 4 and 5 and students identified as Black. Usage was also related to achievement and future studies should seek to further examine the reasons behind this variation in usage.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on math achievement, as measured by SBA scores. We compared students in schools using i-Ready Personalized Instruction school-wide (Treatment students) and students who only received i-Ready Diagnostic assessments and who were in schools only partially using i-Ready Personalized Instruction (Comparison students). The study sample consisted of 5,330 students in 22 schools from Grades 3-6 from one school district in southern California. All schools used i-Ready Diagnostic assessments, but some schools were considered "full instruction" and assigned all students to receive both the Diagnostic and Personalized Instruction products. Students in these schools were compared to "diagnostic-only" students in other "partial instruction" schools where only some students received the Instruction product in addition to the Diagnostic product. (These Diagnostic and Instruction students in partial instruction schools are not included in this study.) Overall, this analysis presents promising evidence of i-Ready Personalized Instruction on student math achievement. This relationship between i-Ready Personalized Instruction and math achievement was strongest for students in Title I eligible schools, Grade 4, and among students who met the recommended usage guidelines. Future studies should seek to further examine the reasons behind this variation in usage.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on reading achievement, as measured by SBA scores. We compared students in schools using i-Ready Personalized Instruction school-wide (Treatment students) to students who only received i-Ready Diagnostic assessments and who were in schools only partially using i-Ready Personalized Instruction (Comparison students). The study sample consisted of about 5,632 students in 22 schools from Grades 3-6 from one school district in southern California. All schools used i-Ready Diagnostic assessments, but some schools were considered "full instruction" and assigned all students to receive both the Diagnostic and Personalized Instruction products. Students in these schools were compared to "Diagnostic-only" students in other "partial Instruction" schools where only some students received the Personalized Instruction product in addition to the Diagnostic product. (These Diagnostic and Personalized Instruction students in partial instruction schools are not included in this study.) We did not find that treatment students had significantly higher SBA ELA scores, although directionally, treatment students averaged higher SBA ELA scores than did comparison students. Supplementary analysis of students achieving proficiency in reading on their SBA score showed that treatment did have a statistically significant impact on this outcome. Specifically, students in the treatment group had 1.6 times higher odds of scoring proficient (versus not scoring at least proficient) than their peers in the comparison group. No significant patterns were found in the impact of treatment on specific student subgroups. Overall, this analysis presents promising evidence of i-Ready Personalized Instruction on student reading proficiency. This relationship between i-Ready Personalized Instruction and reading achievement was strongest for students with higher usage of the program. Future studies should seek to further examine the reasons behind this variation in usage.