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This brief is a continuation of NWEA's research series examining the impact of the COVID-19 pandemic on student achievement and progress toward academic recovery. Initiated in the early phase of the pandemic, this series has leveraged NWEA's large national sample of longitudinal MAP® Growth™ data to track student performance and compare it to historical trends. The authors have tracked two critical aspects of student performance in the wake of the pandemic: achievement and growth. Achievement data reveals the extent of unfinished learning. The authors refer to the distance between current test scores and prepandemic trends as "achievement gaps." Growth data estimate how much test scores increase over time. They use these data to indicate whether students are making gains that keep pace with prepandemic trends. Understanding and tracking both achievement and growth is important. Achievement gaps quantify how much unfinished learning remains, while growth patterns help gauge the rate at which gaps will close. The system needs accelerated or above-average growth for students to catch up. The data provide insights into how effectively this is happening. The cumulative research shows that the harmful effects of the pandemic on student achievement steadily accumulated over the course of the 2020-21 school year (Lewis, Kuhfeld, Ruzek, & McEachin, 2021). Growth generally returned to, or slightly exceeded, prepandemic trends in the 2021-22 school year (Kuhfeld & Lewis, 2022). Progress stalled in 2022-23 when growth in nearly all grades fell short of prepandemic trends (Lewis & Kuhfeld, 2023). In this analysis, the authors use 2023-24 school year data to examine the current progress toward recovery. They examine test scores from approximately 7.7 million students currently in grades 3-8 in 22,400 public schools who have taken MAP Growth reading and math assessments since the onset of the pandemic. The "COVID sample" consists of six separate cohorts of students followed longitudinally across the last three school years. For instance, current fifth-graders are part of the grade 3-5 cohort; they measure this cohort's achievement across third grade in 2021-22, fourth grade in 2022-23, and fifth grade in 2023-24. The authors compared this COVID sample to a comparable group of 10 million students who tested in grades 3-8 in the pre-COVID school years of 2016-17 through 2018-19.

This study investigates the role of college major choices in labor market outcomes, with a focus on racial minorities and immigrants. Drawing upon research on school-to-work linkages, we examine two measures, linkage, the connection between college majors and specific occupations in the labor market, and match, the alignment of workers' occupations with their college majors. Analyzing data from the American Community Survey, 2013-2017, we show that linkage positively predicts earnings, particularly for workers in matched occupations, and negatively predicts unemployment. Notably, Black, Hispanic, and foreign-born workers in matched occupations benefit more from linkage strength than their White and U.S.-born counterparts. This advantage is more pronounced in states that are popular destinations for immigrants. Our findings suggest that earnings and unemployment disparities experienced among racial minorities and immigrants may diminish if they pursue majors closely tied to jobs in the labor market and secure jobs related to their college majors.

L 98-59 d is a Super-Earth planet orbiting an M-type star. We performed retrievals on the transmission spectrum of L 98-59 d obtained using NIRSpec G395H during a single transit, from JWST Cycle 1 GTO 1224. The wavelength range of this spectrum allows us to detect the presence of several atmospheric species. We found that the spectrum is consistent with a high mean molecular weight atmosphere. The atmospheric spectrum indicates the possible presence of the sulfur-bearing species H$_2$S and SO$_2$, which could hint at active volcanism on this planet if verified by future observations. We also tested for signs of stellar contamination in the spectrum, and found signs of unocculted faculae on the star. The tentative signs of an atmosphere on L 98-59 d presented in this work from just one transit bodes well for possible molecular detections in the future, particularly as it is one of the best targets among small exoplanets for atmospheric characterization using JWST., Comment: Accepted for publication to ApJL

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

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

This brief examines which students in the Houston Independent School District (HISD) were more likely to choose the STEM endorsement in ninth grade. The study examined STEM endorsement choice among students who began high school in the 2014-2015 and 2015-2016 school years. The analyses showed female students were significantly less likely to choose the STEM endorsement than male students. Furthermore, Asian and Pacific Islander students were more likely to select the STEM endorsement than Black, Hispanic, and White students. Finally, the analyses found a positive relationship between students' scores on the STAAR science test in eighth grade and STEM endorsement choice upon high school entry.

This study examined whether there were gendered patterns in STEM endorsement choice in the Houston Independent School District (HISD) and how those patterns changed over time, in the wake of a policy change to guidance counseling. The first students required to choose high school endorsements were freshmen in 2014-2015 and graduated high school in spring 2018. The second cohort of students required to do so were freshmen in 2015-2016 and graduated in spring 2019. Students in this second cohort met with their guidance counselor during their senior year to check in on their endorsement progress; this meeting was called the Personal Graduation Plan (PGP) check-in. This was a new district policy implemented beginning in fall 2018, which means that the first cohort did not experience the PGP check-in, while the second cohort did. This study reveals gender gaps in STEM endorsement choice among ninth-grade students. For the high school graduating class of 2018, which did not experience the PGP check-in, female students were less likely than male students to choose the STEM endorsement at any point during high school. However, for the high school graduating class of 2019, which did experience the PGP check-in, female students were less likely than male students to choose the STEM endorsement upon high school entry. Importantly, however, this gender gap in STEM endorsement choice nearly disappeared by high school graduation, suggesting that individualized counseling efforts might be a strategy to improve female retention in STEM.

This brief examines which students in the Houston Independent School District (HISD) are more likely to earn the STEM endorsement and whether STEM endorsement completion predicts college enrollment. Analyses show that gender, STEM magnet program enrollment, and cumulative high school grade point average (GPA) predict STEM endorsement completion. While STEM endorsement completion is associated with college choice, the relationship appears to be explained by academic performance measures, particularly SAT scores and GPA. In fact, if STEM and non-STEM students had the same test scores and grades, college enrollment gaps at 2-year-or-less; 4-year, less-selective; and 4-year, more-selective institutions would reduce. Overall, the findings suggest that the STEM endorsement functions, by design or coincidence, as a funneling mechanism for high-performing students into more-selective colleges and universities.

This brief examines where STEM deserts were located within Houston Independent School District (HISD) boundaries and the students most likely to live in a STEM desert. STEM deserts are areas in which students have more limited access to STEM endorsement paths or STEM course offerings. The analyses showed STEM deserts were more common in the southern and eastern parts of HISD and less common in the northern and western parts of the district. Students in lower socioeconomic neighborhoods were more likely to live in a STEM desert than students in higher socioeconomic neighborhoods. Black students were also more likely than White students to live in a STEM desert. The findings suggest areas in which the district may target resources or interventions to increase STEM offerings.

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with their environment. These emergent information patterns can then be encoded within the organism's components, leading to self-modelling "tangled hierarchies". Our main conjecture is that when macro-scale patterns are encoded within micro-scale components, it creates fundamental tensions (computational inconsistencies) between what is encodable at a particular evolutionary stage and what is potentially realisable in the environment. A resolution of these tensions triggers an evolutionary transition which expands the problem-space, at the cost of generating new tensions in the expanded space, in a continual process. We argue that biological complexification can be interpreted computation-theoretically, within the G\"odel--Turing--Post recursion-theoretic framework, as open-ended generation of computational novelty. In general, this process can be viewed as a meta-simulation performed by higher-order systems that successively simulate the computation carried out by lower-order systems. This computation-theoretic argument provides a basis for hypothesising the biological arrow of time., Comment: 30 pages, 13 figures

The existence of bound doubly heavy tetraquark states was confirmed by the recent LHCb discovery of the doubly charmed $T_{cc}$, less than 1 MeV below the meson pair threshold. Others states with two heavy (bottom or charm) quarks could also be bound, perhaps more deeply. Here we discuss our previous work, and the improvements in our current, updated analysis of various heavy-heavy-light-light tetraquark candidates, including the light and heavy quark mass dependence of the binding., Comment: PoS contribution to LATTICE2024. 10 pages, 7 figures

Recent mid-infrared observations with JWST/MIRI have resulted in the first direct detections of absorption features from silicate clouds in the transmission spectra of two transiting exoplanets, WASP-17 b and WASP-107 b. In this paper, we measure the mid-infrared ($5-12$ $\mu$m) dayside emission spectrum of the benchmark hot Jupiter HD 189733 b with MIRI LRS by combining data from two secondary eclipse observations. We confirm the previous detection of H$_2$O absorption at 6.5 $\mu$m from Spitzer/IRS and additionally detect H$_2$S as well as an absorption feature at 8.7 $\mu$m in both secondary eclipse observations. The excess absorption at 8.7 $\mu$m can be explained by the presence of small ($\sim$0.01 $\mu$m) grains of SiO$_2$[s] in the uppermost layers of HD 189733 b's dayside atmosphere. This is the first direct detection of silicate clouds in HD 189733 b's atmosphere, and the first detection of a distinct absorption feature from silicate clouds on the day side of any hot Jupiter. We find that models including SiO$_2$[s] are preferred by $6-7\sigma$ over clear models and those with other potential cloud species. The high altitude location of these silicate particles is best explained by formation in the hottest regions of HD 189733 b's dayside atmosphere near the substellar point. We additionally find that HD 189733 b's emission spectrum longward of 9 $\mu$m displays residual features not well captured by our current atmospheric models. When combined with other JWST observations of HD 189733 b's transmission and emission spectrum at shorter wavelengths, these observations will provide us with the most detailed picture to date of the atmospheric composition and cloud properties of this benchmark hot Jupiter., Comment: 21 pages, 7 Figures, 3 Tables, Accepted to ApJL

Efficient exploration of large-scale environments remains a critical challenge in robotics, with applications ranging from environmental monitoring to search and rescue operations. This article proposes a bio-mimetic multi-robot framework, \textit{Frontier Shepherding (FroShe)}, for large-scale exploration. The presented bio-inspired framework heuristically models frontier exploration similar to the shepherding behavior of herding dogs. This is achieved by modeling frontiers as a sheep swarm reacting to robots modeled as shepherding dogs. The framework is robust across varying environment sizes and obstacle densities and can be easily deployed across multiple agents. Simulation results showcase that the proposed method consistently performed irrespective of the simulated environment's varying sizes and obstacle densities. With the increase in the number of agents, the proposed method outperforms other state-of-the-art exploration methods, with an average improvement of $20\%$ with the next-best approach(for $3$ UAVs). The proposed technique was implemented and tested in a single and dual drone scenario in a real-world forest-like environment., Comment: 8 page article submitted to IEEE Robotics and Automation Letters (RA-L)

Quantum computing, with its vast potential, is fundamentally shaped by the intricacies of quantum mechanics, which both empower and constrain its capabilities. The development of a universal, robust quantum programming language has emerged as a key research focus in this rapidly evolving field. This paper explores Silq, a recent high-level quantum programming language, highlighting its strengths and unique features. We aim to share our insights on designing and implementing high-level quantum algorithms using Silq, demonstrating its practical applications and advantages for quantum programming., Comment: 14 pages

Articulated objects and their representations pose a difficult problem for robots. These objects require not only representations of geometry and texture, but also of the various connections and joint parameters that make up each articulation. We propose a method that learns a common Neural Radiance Field (NeRF) representation across a small number of collected scenes. This representation is combined with a parts-based image segmentation to produce an implicit space part localization, from which the connectivity and joint parameters of the articulated object can be estimated, thus enabling configuration-conditioned rendering., Comment: extended abstract as submitted to ICRA@40 anniversary conference

Machine learning models make mistakes, yet sometimes it is difficult to identify the systematic problems behind the mistakes. Practitioners engage in various activities, including error analysis, testing, auditing, and red-teaming, to form hypotheses of what can go (or has gone) wrong with their models. To validate these hypotheses, practitioners employ data slicing to identify relevant examples. However, traditional data slicing is limited by available features and programmatic slicing functions. In this work, we propose SemSlicer, a framework that supports semantic data slicing, which identifies a semantically coherent slice, without the need for existing features. SemSlicer uses Large Language Models to annotate datasets and generate slices from any user-defined slicing criteria. We show that SemSlicer generates accurate slices with low cost, allows flexible trade-offs between different design dimensions, reliably identifies under-performing data slices, and helps practitioners identify useful data slices that reflect systematic problems.

A database of abstract groups has been added to the L-functions and Modular Forms Database (LMFDB), available at https://www.lmfdb.org/Groups/Abstract/. We discuss the functionality of the database and what makes it distinct from other available databases of abstract groups. We describe solutions to mathematical problems we encountered while creating the database, as well as connections between the abstract groups database and other collections of objects in the LMFDB.

This Letter reports new results from the HAYSTAC experiment's search for dark matter axions in our galactic halo. It represents the widest search to date that utilizes squeezing to realize sub-quantum limited noise. The new results cover 1.71 $\mu$eV of newly scanned parameter space in the mass ranges 17.28--18.44 $\mu$eV and 18.71--19.46 $\mu$eV. No statistically significant evidence of an axion signal was observed, excluding couplings $|g_\gamma|\geq$ 2.75$\times$$|g_{\gamma}^{\text{KSVZ}}|$ and $|g_\gamma|\geq$ 2.96$\times$$|g_{\gamma}^{\text{KSVZ}}|$ at the 90$\%$ confidence level over the respective region. By combining this data with previously published results using HAYSTAC's squeezed state receiver, a total of 2.27 $\mu$eV of parameter space has now been scanned between 16.96--19.46 $\mu$eV, excluding $|g_\gamma|\geq$ 2.86$\times$$|g_{\gamma}^{\text{KSVZ}}|$ at the 90$\%$ confidence level. These results demonstrate the squeezed state receiver's ability to probe axion models over a significant mass range while achieving a scan rate enhancement relative to a quantum-limited experiment., Comment: 6 pages, 3 figures

As part of a broader look at the impact of generative AI, this study investigated the emotional responses of journalists to the release of ChatGPT at the time of its launch. By analyzing nearly 1 million Tweets from journalists at major U.S. news outlets, we tracked changes in emotional tone and sentiment before and after the introduction of ChatGPT in November 2022. Using various computational and natural language processing techniques to measure emotional shifts in response to ChatGPT's release, we found an increase in positive emotion and a more favorable tone post-launch, suggesting initial optimism toward AI's potential. This research underscores the pivotal role of journalists as interpreters of technological innovation and disruption, highlighting how their emotional reactions may shape public narratives around emerging technologies. The study contributes to understanding the intersection of journalism, emotion, and AI, offering insights into the broader societal impact of generative AI tools.

Ionising radiation interactions in matter can trigger a cascade of processes that underpin long-lived damage in the medium. To date, however, a lack of suitable methodologies has precluded our ability to understand the role that material nanostructure plays in this cascade. Here, we use transient photoabsorption to track the lifetime of free electrons (t_c) in bulk and nanostructured SiO2 (aerogel) irradiated by picosecond-scale (10^-12 s) bursts of X-rays and protons from a laser-driven accelerator. Optical streaking reveals a sharp increase in t_c from < 1 ps to > 50 ps over a narrow average density (p_av) range spanning the expected phonon-fracton crossover in aerogels. Numerical modelling suggests that this discontinuity can be understood by a quenching of rapid, phonon-assisted recovery in irradiated nanostructured SiO_2. This is shown to lead to an extended period of enhanced energy density in the excited electron population. Overall, these results open a direct route to tracking how low-level processes in complex systems can underpin macroscopically observed phenomena and, importantly, the conditions that permit them to emerge.

Recent observations by the James Webb Space Telescope confirm the existence of massive black holes ($>10^6$ $\rm{M_{\odot}}$) beyond the redshift of $z=10$. However, their formation mechanism(s) still remain an open question. Light seed black holes are one such formation pathway, forming as the end stage of metalfree (Population III) stars. Light seed black holes can grow into massive black holes as long as they accrete near the Eddington limit for substantial periods or undergo several bursts of super-Eddington accretion. In this work, our aim is to ascertain if light seeds can grow in gas rich galaxies - similar to those expected at high redshift (z $\gtrsim 10$). Using the Arepo code, we follow self-consistently the formation of Population III stars and black holes in galaxies with total masses in the range $10^8$ $\rm{M_{\odot}}$. We find that in the absence of feedback, black holes can grow to $10^5$ $\rm{M_{\odot}}$ in just $10^4$ years. These black holes do not decouple from the gas clumps in which they are born and are able to accrete at hyper-Eddington rates. In the presence of supernova feedback, the number of actively growing black holes diminishes by an order of magnitude. However, we still observe hyper-Eddington accretion in approximately 1 % of the black hole population despite supernova feedback. This (idealised) work lays the foundation for future works, where we will test our models in a cosmological framework.

This paper introduces SO(2)-Equivariant Gaussian Sculpting Networks (GSNs) as an approach for SO(2)-Equivariant 3D object reconstruction from single-view image observations. GSNs take a single observation as input to generate a Gaussian splat representation describing the observed object's geometry and texture. By using a shared feature extractor before decoding Gaussian colors, covariances, positions, and opacities, GSNs achieve extremely high throughput (>150FPS). Experiments demonstrate that GSNs can be trained efficiently using a multi-view rendering loss and are competitive, in quality, with expensive diffusion-based reconstruction algorithms. The GSN model is validated on multiple benchmark experiments. Moreover, we demonstrate the potential for GSNs to be used within a robotic manipulation pipeline for object-centric grasping., Comment: Accepted to RSS 2024 Workshop on Geometric and Algebraic Structure in Robot Learning

We present the Red Unknowns: Bright Infrared Extragalactic Survey (RUBIES), providing JWST/NIRSpec spectroscopy of red sources selected across ~150 arcmin$^2$ from public JWST/NIRCam imaging in the UDS and EGS fields. RUBIES novel observing strategy offers a well-quantified selection function: the survey is optimised to reach high (>70%) completeness for bright and red (F150W-F444W>2) sources that are very rare. To place these rare sources in context, we simultaneously observe a reference sample of the 23$ using only the G395M disperser. The RUBIES data reveal a highly diverse population of red sources that span a broad redshift range ($z_{spec}\sim1-9$), with photometric redshift scatter and outlier fraction that are 3 times higher than for similarly bright sources that are less red. This diversity is not apparent from the photometric SEDs. Only spectroscopy reveals that the SEDs encompass a mixture of galaxies with dust-obscured star formation, extreme line emission, a lack of star formation indicating early quenching, and luminous active galactic nuclei. As a first demonstration of our broader selection function we compare the stellar masses and rest-frame U-V colours of the red sources and our reference sample: red sources are typically more massive ($M_*\sim10^{10-11.5} M_\odot$) across all redshifts. However, we find that the most massive systems span a wide range in U-V colour. We describe our data reduction procedure and data quality, and publicly release the reduced RUBIES data and vetted spectroscopic redshifts of the first half of the survey through the DJA., Comment: 21 pages, 16 figures; submitted to A&A

We investigate the spatial offsets between dust and ultraviolet (UV) emission in high-redshift galaxies using the Cosmic Dawn III (CoDa III) simulation, a state-of-the-art fully coupled radiation-hydrodynamics cosmological simulation. Recent observations have revealed puzzling spatial disparities between ALMA dust continuum and UV emission as seen by HST and JWST in galaxies at z=5-7, compelling us to propose a physical interpretation of such offsets. Our simulation, which incorporates a dynamical dust model, naturally reproduces these offsets in massive, UV-bright galaxies (log$_{10}$(M$_{\rm{DM}}$/M$_{\odot}$)>11.5, M$_{\rm{AB1500}}$<-20). We find that dust-UV offsets increase with halo mass and UV brightness, reaching up to $\sim 2$ pkpc for the most massive systems, in good agreement with observational data from the ALPINE and REBELS surveys. Our analysis reveals that these offsets primarily result from severe dust extinction in galactic centers rather than a misalignment between dust and stellar mass distributions. The dust remains well-aligned with the bulk stellar component, and we predict the dust continuum should therefore align well with the stellar rest-frame NIR component, less affected by dust attenuation. This study provides crucial insights into the complex interplay between star formation, dust distribution, and observed galaxy morphologies during the epoch of reionization, highlighting the importance of dust in shaping the appearance of early galaxies at UV wavelengths., Comment: 9 pages, 5 figures, submitted to A&A

The mountain pine beetle (MPB), a destructive pest native to Western North America, has recently extended its range into Alberta, Canada. Predicting the dispersal of MPB is challenging due to their small size and complex dispersal behavior. Because of these challenges, estimates of MPB's typical dispersal distances have varied widely, ranging from 10 meters to 18 kilometers. Here, we use high-quality data from helicopter and field-crew surveys to parameterize a large number of dispersal kernels. We find that fat-tailed kernels -- those which allow for a small number of long-distance dispersal events -- consistently provide the best fit to the data. Specifically, the radially-symmetric Student's t-distribution with parameters {\nu} = 0.012 and {\rho} = 1.45 stands out as parsimonious and user-friendly; this model predicts a median dispersal distance of 60 meters, but with the 95th percentile of dispersers travelling nearly 5 kilometers. The best-fitting mathematical models have biological interpretations. The Student's t-distribution, derivable as a mixture of diffusive processes with varying settling times, is consistent with observations that most beetles fly short distances while few travel far; early-emerging beetles fly farther; and larger beetles from larger trees exhibit greater variance in flight distance. Finally, we explain why other studies have found such a wide variation in the length scale in MPB dispersal, and we demonstrate that long-distance dispersal events are critical for modelling MPB range expansion., Comment: 32 pages, 11 figures, 8 tables

Fishing is a valuable recreational activity in our society. To assess future fishing activity, identifying variables related to differences in fishing activity, such as gender or Covid-19, is helpful. We conducted a Canada-wide email survey of users of an online fishing platform and analyzed responses with a focus on gender, the impact of Covid-19, and variables directly related to fisher effort. Genders (90.1% male and 9.9% female respondents) significantly differed in demographics, socioeconomic status, and fishing skills but were similar in fishing preferences, fisher effort in terms of trip frequency, and travel distance. For almost half of the fishers, Covid-19 caused a change in trip frequency, determined by the activity level and gender of the fisher. A Bayesian network revealed that travel distance was the main determinant of trip frequency and negatively impacted the fishing activity of 61% of the fishers. Fisher effort was also directly related to fishing expertise. The study shows how online surveys and Bayesian networks can help understand the relationship between fishers' characteristics and activity and predict future fishing trends., Comment: 50 pages

In the early stages of drug discovery, decisions regarding which experiments to pursue can be influenced by computational models. These decisions are critical due to the time-consuming and expensive nature of the experiments. Therefore, it is becoming essential to accurately quantify the uncertainty in machine learning predictions, such that resources can be used optimally and trust in the models improves. While computational methods for drug discovery often suffer from limited data and sparse experimental observations, additional information can exist in the form of censored labels that provide thresholds rather than precise values of observations. However, the standard approaches that quantify uncertainty in machine learning cannot fully utilize censored labels. In this work, we adapt ensemble-based, Bayesian, and Gaussian models with tools to learn from censored labels by using the Tobit model from survival analysis. Our results demonstrate that despite the partial information available in censored labels, they are essential to accurately and reliably model the real pharmaceutical setting.

Directed graphs provide more subtle and precise modelling tools for optimization in road networks than simple graphs. In particular, they are more suitable in the context of alternative fuel vehicles and new automotive technologies, like electric vehicles. In this paper, we introduce the new general concept of a reachability digraph associated with a road network to model the placement of refuelling facilities in road networks as k-dominating sets in the reachability digraph. Two new greedy heuristics are designed and experimentally tested to search for small k-dominating sets in two types of digraphs, including the reachability digraphs. Refined greedy strategies are shown to be efficient, capable of finding good quality solutions, and suitable for application in very large digraphs and road networks. Also, a probabilistic method is used to prove a new upper bound on the k-domination number of a digraph, which informs the development of a new randomized heuristic to search for k-dominating sets in the digraph. Generalizing the randomized heuristic ideas, making the heuristic more flexible, tuning and combining it with the greedy strategies allows us to obtain even better results for the reachability digraphs. Computational experiments are conducted for a case study of road networks in the West Midlands (UK)., Comment: 27 pages, 1 figure, 7 tables; preliminary results are presented in a short conference paper and at the International Network Optimization Conference INOC 2024, University College Dublin (Ireland) https://inoc2024.sciencesconf.org/

In the last decade, the first law of binary black hole mechanics played an important unifying role in the gravitational two-body problem. More recently, binary black hole scattering and the application of high-energy physics methods have provided a new avenue into this classical problem. In this Letter, we connect these two themes by extending the first law to the case of scattering orbits. We present derivations based on classical S-matrix, Hamiltonian, and pseudo-Hamiltonian methods, the last of which allows us to include dissipative effects for the first time. Finally, a "boundary to bound" map links this first law to the traditional bound-orbit version. Through this map a little-known observable for scatter orbits, the elapsed proper time, is mapped to the Detweiler redshift for bound orbits., Comment: 5 pages + supplemental material

In recent years there has been increasing interest in the field of Open-Set Recognition, which allows a classification model to identify inputs as "unknown" when it encounters an object or class not in the training set. This ability to flag unknown inputs is of vital importance to many real world classification applications. As almost all modern training methods for neural networks use extensive amounts of regularization for generalization, it is therefore important to examine how regularization techniques impact the ability of a model to perform Open-Set Recognition. In this work, we examine the relationship between common regularization techniques and Open-Set Recognition performance. Our experiments are agnostic to the specific open-set detection algorithm and examine the effects across a wide range of datasets. We show empirically that regularization methods can provide significant improvements to Open-Set Recognition performance, and we provide new insights into the relationship between accuracy and Open-Set performance.

K-Origins is a neural network layer designed to improve image-based network performances when learning colour, or intensities, is beneficial. Over 250 encoder-decoder convolutional networks are trained and tested on 16-bit synthetic data, demonstrating that K-Origins improves semantic segmentation accuracy in two scenarios: object detection with low signal-to-noise ratios, and segmenting multiple objects that are identical in shape but vary in colour. K-Origins generates output features from the input features, $\textbf{X}$, by the equation $\textbf{Y}_k = \textbf{X}-\textbf{J}\cdot w_k$ for each trainable parameter $w_k$, where $\textbf{J}$ is a matrix of ones. Additionally, networks with varying receptive fields were trained to determine optimal network depths based on the dimensions of target classes, suggesting that receptive field lengths should exceed object sizes. By ensuring a sufficient receptive field length and incorporating K-Origins, we can achieve better semantic network performance., Comment: 16 pages, 13 figures, 1 table

Several different proof translations exist between classical and intuitionistic logic (negative translations), and intuitionistic and linear logic (Girard translations). Our aims in this paper are (1) to show that all these systems can be expressed as extensions of a basic logical system (essentially intuitionistic linear logic), and that (2) with this common logical basis, a common approach to devising and simplifying such proof translations can be formalised. Via this process of ``simplification'' we get the most well-known translations in the literature.

The possibility of observing spectral features in exoplanet atmospheres with space missions like JWST and ARIEL necessitates the accurate modelling of cloud particle opacities. In exoplanet atmospheres, cloud particles can be made from multiple materials and be considerably chemically heterogeneous. Therefore, assumptions on the morphology of cloud particles are required to calculate their opacities. The aim of this work is to analyse how different approaches to calculate the opacities of heterogeneous cloud particles affect cloud particle optical properties. We calculate cloud particle optical properties using seven different mixing treatments: four effective medium theories (EMTs: Bruggeman, Landau-Lifshitz-Looyenga (LLL), Maxwell-Garnett, and Linear), core-shell, and two homogeneous cloud particle approximations. We study the mixing behaviour of 21 commonly considered cloud particle materials for exoplanets. To analyse the impact on observations, we study the transmission spectra of HATS-6b, WASP-39b, WASP-76b, and WASP-107b.Materials with large refractive indices, like iron-bearing species or carbon, can change the optical properties of cloud particles when they comprise less than 1\% of the total particle volume. The mixing treatment of heterogeneous cloud particles also has an observable effect on transmission spectroscopy. Assuming core-shell or homogeneous cloud particles results in less muting of molecular features and retains the cloud spectral features of the individual cloud particle materials. The predicted transit depth for core-shell and homogeneous cloud particle materials are similar for all planets used in this work. If EMTs are used, cloud spectral features are broader and cloud spectral features of the individual cloud particle materials are not retained. Using LLL leads to less molecular features in transmission spectra compared to Bruggeman., Comment: 21 pages, 15 figures, Accepted by A&A

Short-period gas giant planets have been shown to be significantly rarer for host stars less massive than the Sun. We report the discovery of two transiting giant planets - TOI-2379 b and TOI-2384 b - with low-mass (early M) host stars. Both planets were detected using TESS photometry and for both the transit signal was validated using ground based photometric facilities. We confirm the planetary nature of these companions and measure their masses using radial velocity observations. We find that TOI-2379 b has an orbital period of 5.469 d and a mass and radius of $5.76\pm0.20$ M$_{J}$ and $1.046\pm0.023$ R$_{J}$ and TOI-2384 b has an orbital period of 2.136 d and a mass and radius of $1.966\pm0.059$ M$_{J}$ and $1.025\pm0.021$ R$_{J}$. TOI-2379 b and TOI-2384 b have the highest and third highest planet-to-star mass ratios respectively out of all transiting exoplanets with a low-mass host star, placing them uniquely among the population of known exoplanets and making them highly important pieces of the puzzle for understanding the extremes of giant planet formation., Comment: Accepted for publication in MNRAS. 15 pages, 12 figures

This article investigates neighborhoods' sizes in the enhanced power graph (as known as the cyclic graph) associated with a finite group. In particular, we characterize finite $p$-groups with the smallest maximum size for neighborhoods of nontrivial element in its enhanced power graph., Comment: 6 pages

Detecting atmospheres around planets with a radius below 1.6 R$_{\oplus}$, commonly referred to as rocky planets (Rogers_2015, Rogers_2021), has proven to be challenging. However, rocky planets orbiting M-dwarfs are ideal candidates due to their favorable planet-to-star radius ratio. Here, we present one transit observation of the Super-Earth L98-59d (1.58 R$_{\oplus}$, 2.31 M$_{\oplus}$), at the limit of rocky/gas-rich, using the JWST NIRSpec G395H mode covering the 2.8 to 5.1 microns wavelength range. The extracted transit spectrum from a single transit observation deviates from a flat line by 2.6 to 5.6$\sigma$, depending on the data reduction and retrieval setup. The hints of an atmospheric detection are driven by a large absorption feature between 3.3 to 4.8 microns. A stellar contamination retrieval analysis rejected the source of this feature as being due to stellar inhomogeneities, making the best fit an atmospheric model including sulfur-bearing species, suggesting that the atmosphere of L98-59d may not be at equilibrium. This result will need to be confirmed by the analysis of the second NIRSpec G395H visit in addition to the NIRISS SOSS transit observation., Comment: Accepted for publication in the Astrophysical Journal Letters (ApJL), August 25, 2024

Although exoplanetary science was not initially projected to be a substantial part of the Spitzer mission, its exoplanet observations set the stage for current and future surveys with JWST and Ariel. We present a comprehensive reduction and analysis of Spitzer's 4.5 micron phase curves of 29 hot Jupiters on low-eccentricity orbits. The analysis, performed with the Spitzer Phase Curve Analysis (SPCA) pipeline, confirms that BLISS mapping is the best detrending scheme for most, but not all, observations. Visual inspection remains necessary to ensure consistency across detrending methods due to the diversity of phase curve data and systematics. Regardless of the model selection scheme - whether using the lowest-BIC or a uniform detrending approach - we observe the same trends, or lack thereof. We explore phase curve trends as a function of irradiation temperature, orbital period, planetary radius, mass, and stellar effective temperature. We discuss the trends that are robustly detected and provide potential explanations for those that are not observed. While it is almost tautological that planets receiving greater instellation are hotter, we are still far from confirming dynamical theories of heat transport in hot Jupiter atmospheres due to the sample's diversity. Even among planets with similar temperatures, other factors like rotation and metallicity vary significantly. Larger, curated sample sizes and higher-fidelity phase curve measurements from JWST and Ariel are needed to firmly establish the parameters governing day-night heat transport on synchronously rotating planets., Comment: 16 pages, 6 figures, submitted to AAS journal

Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don't fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets.

We use an ensemble of laser-cooled strontium atoms in a high-finesse cavity to cleanly emulate the technique of rf spectroscopy employed in studies of BEC-BCS physics in fermionic superfluids of degenerate cold gases. Here, we leverage the multilevel internal structure of the atoms to study the physics of Cooper pair breaking in this system. In doing so, we observe and distinguish the properties of two distinct many-body gaps, the BCS pairing gap and the spectral gap, using nondestructive readout techniques. The latter is found to depend on the populations of the internal atomic states, reflecting the chemical potential dependence predicted in fermionic superfluids. This work opens the path for more fully exploiting the rich internal structure of atoms in cavity QED emulators to study both analogous systems and also more exotic states yet to be realized., Comment: Main Text with Supplementary Material, 15 pages, 5 figures

The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU's AI Act will now shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance, for which the Act relies on Harmonized Standards. Though a high-level mapping to the Act's requirements will be part of such harmonization, determining the degree to which standards conformity delivers regulatory compliance with the AI Act remains a complex challenge. Variance and gaps in the definitions of concepts and how they are used in requirements between the Act and harmonized standards may impact the consistency of compliance claims across organizations, sectors, and applications. This may present regulatory uncertainty, especially for SMEs and public sector bodies relying on standards conformance rather than proprietary equivalents for developing and deploying compliant high-risk AI systems. To address this challenge, this paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards, e.g., AI Act and ISO management system standards, texts into open knowledge graphs. This representation is used to assess the adequacy of standards conformance to regulatory compliance and thereby provide a basis for identifying areas where further technical consensus development in trustworthy AI value chains is required to achieve regulatory compliance., Comment: This work was presented at the 9th International Symposium on Language & Knowledge Engineering (LKE 2024) Dublin, Ireland, 4 - 6 June, 2024