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Body size drives the energetic demands of organisms, constraining trophic interactions between species and playing a significant role in shaping the feasibility of species' populations in a community. On macroevolutionary timescales, these demands feed back to shape the selective landscape driving the evolution of body size and diet. We develop a theoretical framework for a three-level trophic food chain -- typical for terrestrial mammalian ecosystems -- premised on bioenergetic trade-offs to explore mammalian population dynamics. Our results show that interactions between predators, prey, and external subsidies generate instabilities linked to body size extrema, corresponding to observed limits of predator size and diet. These instabilities generate size-dependent constraints on coexistence and highlight a feasibility range for carnivore size between 40 to 110 kg, encompassing the mean body size of terrestrial Cenozoic hypercarnivores. Finally, we show that predator dietary generalization confers a selective advantage to larger carnivores, which then declines at megapredator body sizes, aligning with diet breadth estimates for contemporary and Pleistocene species. Our framework underscores the importance of understanding macroevolutionary constraints through the lens of ecological pressures, where the selective forces shaping and reshaping the dynamics of communities can be explored., Comment: 14 pages, 3 figures, SI Appendices

As the signatories of this manifesto, we denounce the attention economy as inhumane and a threat to our sociopolitical and ecological well-being. We endorse policymakers' efforts to address the negative consequences of the attention economy's technology, but add that these approaches are often limited in their criticism of the systemic context of human attention. Starting from Buddhist philosophy, we advocate a broader approach: an ecology of attending, that centers on conceptualizing, designing, and using attention (1) in an embedded way and (2) focused on the alleviating of suffering. With 'embedded' we mean that attention is not a neutral, isolated mechanism but a meaning-engendering part of an 'ecology' of bodily, sociotechnical and moral frameworks. With 'focused on the alleviation of suffering' we explicitly move away from the (often implicit) conception of attention as a tool for gratifying desires., Comment: 21 pages, 1 figure

According to CMB measurements, baryonic matter constitutes about $5\%$ of the mass-energy density of the universe. A significant population of these baryons, for a long time referred to as `missing', resides in a low density, warm-hot intergalactic medium (WHIM) outside galaxy clusters, tracing the ``cosmic web'', a network of large scale dark matter filaments. Various studies have detected this inter-cluster gas, both by stacking and by observing individual filaments in compact, massive systems. In this paper, we study short filaments (< 10 Mpc) connecting massive clusters ($M_{500} \approx 3\times 10^{14} M_{\odot}$) detected by the Atacama Cosmology Telescope (ACT) using the scattering of CMB light off the ionised gas, a phenomenon known as the thermal Sunyaev-Zeldovich (tSZ) effect. The first part of this work is a search for suitable candidates for high resolution follow-up tSZ observations. We identify four cluster pairs with an intercluster signal above the noise floor (S/N $>$ 2), including two with a tentative $>2\sigma$ statistical significance for an intercluster bridge from the ACT data alone. In the second part of this work, starting from the same cluster sample, we directly stack on ${\sim}100$ cluster pairs and observe an excess SZ signal between the stacked clusters of $y=(7.2^{+2.3}_{-2.5})\times 10^{-7}$ with a significance of $3.3\sigma$. It is the first tSZ measurement of hot gas between clusters in this range of masses at moderate redshift ($\langle z\rangle\approx 0.5$). We compare this to the signal from simulated cluster pairs with similar redshifts and separations in the THE300 and MAGNETICUM Pathfinder cosmological simulations and find broad consistency. Additionally, we show that our measurement is consistent with scaling relations between filament parameters and mass of the embedded halos identified in simulations., Comment: 37 pages, 17 images

Sub-Neptune planets with radii smaller than Neptune (3.9 Re) are the most common type of planet known to exist in The Milky Way, even though they are absent in the Solar System. These planets can potentially have a large diversity of compositions as a result of different mixtures of rocky material, icy material and gas accreted from a protoplanetary disk. However, the bulk density of a sub-Neptune, informed by its mass and radius alone, cannot uniquely constrain its composition; atmospheric spectroscopy is necessary. GJ 1214 b, which hosts an atmosphere that is potentially the most favorable for spectroscopic detection of any sub-Neptune, is instead enshrouded in aerosols (thus showing no spectroscopic features), hiding its composition from view at previously observed wavelengths in its terminator. Here, we present a JWST NIRSpec transmission spectrum from 2.8 to 5.1 um that shows signatures of carbon dioxide and methane, expected at high metallicity. A model containing both these molecules is preferred by 3.3 and 3.6 sigma as compared to a featureless spectrum for two different data analysis pipelines, respectively. Given the low signal-to-noise of the features compared to the continuum, however, more observations are needed to confirm the carbon dioxide and methane signatures and better constrain other diagnostic features in the near-infrared. Further modeling of the planet's atmosphere, interior structure and origins will provide valuable insights about how sub-Neptunes like GJ 1214 b form and evolve., Comment: 22 pages, 11 figures, Accepted in ApJL, Please also see a companion paper Ohno et al. (2024)

Traditional anomaly detection techniques onboard satellites are based on reliable, yet limited, thresholding mechanisms which are designed to monitor univariate signals and trigger recovery actions according to specific European Cooperation for Space Standardization (ECSS) standards. However, Artificial Intelligence-based Fault Detection, Isolation and Recovery (FDIR) solutions have recently raised with the prospect to overcome the limitations of these standard methods, expanding the range of detectable failures and improving response times. This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft for the exploration of Small Solar System Bodies (SSSB), leveraging a multi-channel Convolutional Neural Network (CNN) to perform multi-target classification and independently detect faults in the sensors. Significant attention has been dedicated to ensuring the compatibility of the algorithm within the onboard FDIR system, representing a step forward to the in-orbit validation of a technology that remains experimental until its robustness is thoroughly proven. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level. The detection performances and the capability of the algorithm in reaction triggering are evaluated employing a set of custom-defined detection and system metrics, showing the outstanding performances of the algorithm in performing its FDIR task., Comment: submitted to Advances in Space Research

Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $\alpha$ of the total matter density in compact objects (which comprise stars, stellar remnants, small stellar groupings and primordial black holes) of mass $M > 0.03 M_{\odot}$ over cosmological distances. Using 1,532 SNe Ia from the Dark Energy Survey Year 5 sample (DES-SN5YR) combined with a Bayesian prior for the absolute magnitude $M$, we obtain $\alpha < 0.12$ at the 95\% confidence level after marginalisation over cosmological parameters, lensing due to large-scale structure, and intrinsic non-Gaussianity. Similar results are obtained using priors from the cosmic microwave background, baryon acoustic oscillations and galaxy weak lensing, indicating our results do not depend on the background cosmology. We argue our constraints are likely to be conservative (in the sense of the values we quote being higher than the truth), but discuss scenarios in which they could be weakened by systematics of the order of $\Delta \alpha \sim 0.04$, Comment: Submitted to MNRAS

Recent work has initiated the study of dense graph processing using graph sketching methods, which drastically reduce space costs by lossily compressing information about the input graph. In this paper, we explore the strange and surprising performance landscape of sketching algorithms. We highlight both their surprising advantages for processing dense graphs that were previously prohibitively expensive to study, as well as the current limitations of the technique. Most notably, we show how sketching can avoid bottlenecks that limit conventional graph processing methods. Single-machine streaming graph processing systems are typically bottlenecked by CPU performance, and distributed graph processing systems are typically bottlenecked by network latency. We present Landscape, a distributed graph-stream processing system that uses linear sketching to distribute the CPU work of computing graph properties to distributed workers with no need for worker-to-worker communication. As a result, it overcomes the CPU and network bottlenecks that limit other systems. In fact, for the connected components problem, Landscape achieves a stream ingestion rate one-fourth that of maximum sustained RAM bandwidth, and is four times faster than random access RAM bandwidth. Additionally, we prove that for any sequence of graph updates and queries Landscape consumes at most a constant factor more network bandwidth than is required to receive the input stream. We show that this system can ingest up to 332 million stream updates per second on a graph with $2^{17}$ vertices. We show that it scales well with more distributed compute power: given a cluster of 40 distributed worker machines, it can ingest updates 35 times as fast as with 1 distributed worker machine. Landscape uses heuristics to reduce its query latency by up to four orders of magnitude over the prior state of the art.

In the framework of Failure Detection, Isolation and Recovery (FDIR) on spacecraft, new AI-based approaches are emerging in the state of the art to overcome the limitations commonly imposed by traditional threshold checking. The present research aims at characterizing two different approaches to the problem of stuck values detection in multivariate time series coming from spacecraft attitude sensors. The analysis reveals the performance differences in the two approaches, while commenting on their interpretability and generalization to different scenarios., Comment: Accepted for the ESA SPAICE Conference 2024

Large transformer-based models have significant potential for speech transcription and translation. Their self-attention mechanisms and parallel processing enable them to capture complex patterns and dependencies in audio sequences. However, this potential comes with challenges, as these large and computationally intensive models lead to slow inference speeds. Various optimization strategies have been proposed to improve performance, including efficient hardware utilization and algorithmic enhancements. In this paper, we introduce Whisper-Medusa, a novel approach designed to enhance processing speed with minimal impact on Word Error Rate (WER). The proposed model extends the OpenAI's Whisper architecture by predicting multiple tokens per iteration, resulting in a 50% reduction in latency. We showcase the effectiveness of Whisper-Medusa across different learning setups and datasets., Comment: Under Review

Perception still remains a challenging problem for autonomous navigation in unknown environment, especially for aerial vehicles. Most mapping algorithms for autonomous navigation are specifically designed for their very intended task, which hinders extended usage or cooperative task. In this paper, we propose a voxel mapping system that can build an adaptable map for multiple tasks. The system employs hash table-based map structure and manages each voxel with spatial and temporal priorities without explicit map boundary. We also introduce an efficient map-sharing feature with minimal bandwidth to enable multi-agent applications. We tested the system in real world and simulation environment by applying it for various tasks including local mapping, global mapping, cooperative multi-agent navigation, and high-speed navigation. Our system proved its capability to build customizable map with high resolution, wide coverage, and real-time performance regardless of sensor and environment. The system can build a full-resolution map using the map-sharing feature, with over 95 % of bandwidth reduction from raw sensor data., Comment: 8 pages, 6 figures, 3 tables

Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performance across species and habitats, especially in complex soundscapes. In this study, we explore the effectiveness of transfer learning in large-scale bird sound classification across various conditions, including single- and multi-label scenarios, and across different model architectures such as CNNs and Transformers. Our experiments demonstrate that both fine-tuning and knowledge distillation yield strong performance, with cross-distillation proving particularly effective in improving in-domain performance on Xeno-canto data. However, when generalizing to soundscapes, shallow fine-tuning exhibits superior performance compared to knowledge distillation, highlighting its robustness and constrained nature. Our study further investigates how to use multi-species labels, in cases where these are present but incomplete. We advocate for more comprehensive labeling practices within the animal sound community, including annotating background species and providing temporal details, to enhance the training of robust bird sound classifiers. These findings provide insights into the optimal reuse of pretrained models for advancing automatic bioacoustic recognition., Comment: 25 pages

Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. We experimentally demonstrate this new approach, reaching an improvement in the relevant figure of merit by a factor of up to 5. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies., Comment: 12 pages including supplementary, 6 figures

The study of coherence dynamics in open quantum systems, specifically addressing various physical realizations of quantum systems and environments, is a long-standing and central pillar of quantum science and technology. As such, a large body of work establishes a firm theoretical understanding of these processes. Nevertheless, a fundamental aspect of decoherence dynamics, namely the central limit theorem of qubit dephasing in the central spin model, which leads to a Gaussian approximation, lacks formal proof in realistically relevant scenarios. Here we prove this approximation for a bath depicted by an Ising spin system, in the presence of disorder and several (most relevant) functional forms of qubit-bath coupling. Importantly, we show that in certain cases, namely for short-range (exponentially decaying) coupling, this approximation breaks. These results provide a theoretical framework for studying decoherence dynamics in various systems and lead to insights into dephasing behavior with implications for applications in quantum information, quantum computing, and other quantum technologies.

Integrating named entity recognition (NER) with automatic speech recognition (ASR) can significantly enhance transcription accuracy and informativeness. In this paper, we introduce WhisperNER, a novel model that allows joint speech transcription and entity recognition. WhisperNER supports open-type NER, enabling recognition of diverse and evolving entities at inference. Building on recent advancements in open NER research, we augment a large synthetic dataset with synthetic speech samples. This allows us to train WhisperNER on a large number of examples with diverse NER tags. During training, the model is prompted with NER labels and optimized to output the transcribed utterance along with the corresponding tagged entities. To evaluate WhisperNER, we generate synthetic speech for commonly used NER benchmarks and annotate existing ASR datasets with open NER tags. Our experiments demonstrate that WhisperNER outperforms natural baselines on both out-of-domain open type NER and supervised finetuning.

The TESS mission searches for transiting exoplanets by monitoring the brightness of hundreds of thousands of stars across the entire sky. M-type planet hosts are ideal targets for this mission due to their smaller size and cooler temperatures, which makes it easier to detect smaller planets near or within their habitable zones. Additionally, M~dwarfs have a smaller contrast ratio between the planet and the star, making it easier to measure the planet's properties accurately. Here, we report the validation analysis of 13 TESS exoplanet candidates orbiting around M dwarfs. We studied the nature of these candidates through a multi-colour transit photometry transit analysis using several ground-based instruments (MuSCAT2, MuSCAT3, and LCO-SINISTRO), high-spatial resolution observations, and TESS light curves. We present the validation of five new planetary systems: TOI-1883b, TOI-2274b, TOI2768b, TOI-4438b, and TOI-5319b, along with compelling evidence of a planetary nature for TOIs 2781b and 5486b. We also present an empirical definition for the Neptune desert boundaries. The remaining six systems could not be validated due to large true radius values overlapping with the brown dwarf regime or, alternatively, the presence of chromaticity in the MuSCAT2 light curves.

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to injury risk. Given human motion and ground reaction forces, we present a computational framework that enables the design of personalized braces by optimizing the distribution of microstructures and elasticity. As varied brace designs yield different reaction forces that influence kinematics and kinetics analysis outcomes, the optimization process is formulated as a differentiable end-to-end pipeline in which the design domain of microstructure distribution is parameterized onto a neural network. The optimized distribution of microstructures is obtained via a self-learning process to determine the network coefficients according to a carefully designed set of losses and the integrated biomechanical and physical analyses. Since knees and ankles are the most commonly injured joints, we demonstrate the effectiveness of our pipeline by designing, fabricating, and testing prophylactic braces for the knee and ankle to prevent potentially harmful joint movements.

In order to preserve the leading role of the action principle in formulating all field theories one needs quantum field theory, with the associated BRST symmetry, and Feynman-DeWitt-Faddeev-Popov ghost fields. Such fields result from the fibre-bundle structure of the space of histories, but the physics-oriented literature used them formally because a rigorous theory of measure and integration was lacking. Motivated by this framework, this paper exploits previous work of Gill and Zachary, where the use of Banach spaces for the Feynman integral was proposed. The Henstock-Kurzweil integral is first introduced, because it makes it possible to integrate functions like the exponential of (i times x squared). The Lebesgue measure on R(infinity) is then built and used to define the measure on every separable Hilbert space. The subsequent step is the construction of a new Hilbert space KS2, which contains square-integrable functions on R**n as a continuous dense embedding, and contains both the test functions and their dual, the Schwartz space of distributions, as continuous embeddings. This space allows us to construct the Feynman path integral in a manner that maintains its intuitive and computational advantages. We also extend this space to KS2[H], where H is any separable Banach space. Last, the existence of a unique universal definition of time, tau(h), that we call historical time, is proved. We use tau(h) as the order parameter for our construction of Feynman's time ordered operator calculus, which in turn is used to extend the path integral in order to include all time dependent groups and semigroups with a kernel., Comment: 65 pages, Latex

A weaving knot is an alternating knot whose minimal diagram is a closed braid of a lattice-like pattern. In this paper, the warping degree of a braid diagram is defined, and upper bounds of the unknotting number and the region unknotting number for some families of weaving knots are given by diagrammatical and combinatorial examination of the warping degree of weaving knot diagrams., Comment: 21 pages, 14 figures

Liebeck, Nikolov, and Shalev conjectured that for every subset A of a finite simple group S with |A|>1, there exist O( log|S| / log|A| ) conjugates of A whose product is S. This paper is a companion to [Lifshitz: Completing the proof of the Liebeck-Nikolov-Shalev conjecture] and together they prove the conjecture. In this paper we prove the conjecture in the regime where $|A|>|S|^c$ for an absolute constant c>0. We also prove that the following Skew Product Theorem holds for all finite simple groups. Namely we show that either the product of two conjugates of A has size at least $|A|^{1.49}$, or S is the product of boundedly many conjugates of A., Comment: Citation added. Supporting grant numbers corrected

We investigate the relationship between Krylov complexity and operator quantum speed limits (OQSLs) of the complexity operator and level repulsion in random/integrable matrices and many-body systems. An enhanced level-repulsion corresponds to increased OQSLs in random/integrable matrices. However, in many-body systems, the dynamics is more intricate due to the tensor product structure of the models. Initially, as the integrability-breaking parameter increases, the OQSL also increases, suggesting that breaking integrability allows for faster evolution of the complexity operator. At larger values of integrability-breaking, the OQSL decreases, suggesting a slowdown in the operator's evolution speed. Information-theoretic properties, such as scrambling, coherence and entanglement, of Krylov basis operators in many-body systems, are also investigated. The scrambling behaviour of these operators exhibits distinct patterns in integrable and chaotic cases. For systems exhibiting chaotic dynamics, the Krylov basis operators remain a reliable measure of these properties of the time-evolved operator at late times. However, in integrable systems, the Krylov operator's ability to capture the entanglement dynamics is less effective, especially during late times., Comment: 10 Pages, 13 Figures

We report the discovery of the most eccentric transiting brown dwarf in the brown dwarf desert, TOI02490b. The brown dwarf desert is the lack of brown dwarfs around main sequence stars within $\sim3$~AU and is thought to be caused by differences in formation mechanisms between a star and planet. To date, only $\sim40$ transiting brown dwarfs have been confirmed. \systemt is a $73.6\pm2.4$ \mjupnospace, $1.00\pm0.02$ \rjup brown dwarf orbiting a $1.004_{-0.022}^{+0.031}$ \msunnospace, $1.105_{-0.012}^{+0.012}$ \rsun sun-like star on a 60.33~d orbit with an eccentricity of $0.77989\pm0.00049$. The discovery was detected within \tess sectors 5 (30 minute cadence) and 32 (2 minute and 20 second cadence). It was then confirmed with 31 radial velocity measurements with \feros by the WINE collaboration and photometric observations with the Next Generation Transit Survey. Stellar modelling of the host star estimates an age of $\sim8$~Gyr, which is supported by estimations from kinematics likely placing the object within the thin disc. However, this is not consistent with model brown dwarf isochrones for the system age suggesting an inflated radius. Only one other transiting brown dwarf with an eccentricity higher than 0.6 is currently known in the brown dwarf desert. Demographic studies of brown dwarfs have suggested such high eccentricity is indicative of stellar formation mechanisms., Comment: Accepted for publication in MNRAS, 18 pages, 14 figures

The Robot Operating System 2 (ROS~2) is a widely used middleware that provides software libraries and tools for developing robotic systems. In these systems, tasks are scheduled by ROS~2 executors. Since the scheduling behavior of the default ROS~2 executor is inherently different from classical real-time scheduling theory, dedicated analyses or alternative executors, requiring substantial changes to ROS~2, have been required. In 2023, the events executor, which features an events queue and allows the possibility to make scheduling decisions immediately after a job completes, was introduced into ROS~2. In this paper, we show that, with only minor modifications of the events executor, a large body of research results from classical real-time scheduling theory becomes applicable. Hence, this enables analytical bounds on the worst-case response time and the end-to-end latency, outperforming bounds for the default ROS 2 executor in many scenarios. Our solution is easy to integrate into existing ROS 2 systems since it requires only minor backend modifications of the events executor, which is natively included in ROS 2. The evaluation results show that our ROS~2 events executor with minor modifications can have significant improvement in terms of dropped jobs, worst-case response time, end-to-end latency, and performance compared to the default ROS~2 executor.

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope., Comment: 17 pages, 25 pages, published in the Astronomical Journal

Increasing the density of superconducting circuits requires compact components, however, superconductor-based capacitors typically perform worse as dimensions are reduced due to loss at surfaces and interfaces. Here, parallel plate capacitors composed of aluminum-contacted, crystalline silicon fins are shown to be a promising technology for use in superconducting circuits by evaluating the performance of lumped element resonators and transmon qubits. High aspect ratio Si-fin capacitors having widths below $300nm$ with an approximate total height of 3$\mu$m are fabricated using anisotropic wet etching of Si(110) substrates followed by aluminum metallization. The single-crystal Si capacitors are incorporated in lumped element resonators and transmons by shunting them with lithographically patterned aluminum inductors and conventional $Al/AlO_x/Al$ Josephson junctions respectively. Microwave characterization of these devices suggests state-of-the-art performance for superconducting parallel plate capacitors with low power internal quality factor of lumped element resonators greater than 500k and qubit $T_1$ times greater than 25$\mu$s. These results suggest that Si-Fins are a promising technology for applications that require low loss, compact, superconductor-based capacitors with minimal stray capacitance.

This report looks at the provision of GCSE subjects in England in 2021. Provision in a GCSE subject is defined as the number or percentage of schools with at least one student taking the subject. This may not cover all of the actual 'provision' since schools might offer to provide a subject, but none of their students wants to study it. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service. The dataset consisted of Key Stage 4 (KS4) entry numbers for all GCSE subjects in each school in England. Other school information, such as schools' sex composition, school type, and admissions policy were also available. However, some data (available in previous years) was not available in the 2021 dataset, including GCSE grades achieved and the percentage of disadvantaged students in each school. The decision was made to use data (for these tables only) from the 2019 data set. The report presents only the provision of GCSE subjects in schools with students taking GCSEs in 2021. Only GCSEs (9-1) full course (both single and double award) were counted for this report. Schools with at least 10 students taking GCSEs were included in the analysis. In this report, the level of provision is presented by the following school classifications: school type, school attainment, school deprivation level, school sex composition, and school size. However, it should be noted that the analysis is at the simplest level and takes no account of interactions between these classifications.

This report is focused on the uptake of GCSE subjects in England in 2021. Uptake in a GCSE subject is defined as the number or percentage of students at the end of Key Stage 4 (KS4) taking the subject. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service. The data set consisted of KS4 entry numbers in all GCSE subjects in England. Other school information, such as schools' sex composition, school type, and admissions policy, was also available. However, some data (available in previous years) was not available in the 2021 dataset, including GCSE grades achieved and the percentage of disadvantaged students in each school. This data was not available because the DfE did not want schools to be held to account for their students' performance in 2021 due to the disruption in learning caused by the COVID-19 pandemic. This meant that the authors were not able to produce the tables in this report with breakdowns by school level attainment or deprivation using 2021 data. Instead, the decision was made to use data (for these tables only) from the 2019 data set. It was assumed that for most schools, the average attainment and the percentage of disadvantaged students would not change significantly in two years and so the 2019 data would be a reasonable proxy for 2021. Only GCSEs (9-1) full course (both single and double award) were counted for this report. GCSE uptake is presented by the following school classifications: school type, school attainment, school deprivation level, and school sex composition.

This report is focused on the uptake of GCSE subjects in England in 2022. Uptake in a GCSE subject is defined as the number or percentage of students at the end of Key Stage 4 (KS4) taking the subject. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service. The dataset consisted of KS4 results in all subjects provided by each school in England. Other school information, such as schools' sex composition, admissions policy, and the number of disadvantaged pupils at the end of KS4 were also available. Only GCSEs (9-1) full course (both single and double award) were counted for this report. In this report, GCSE uptake is presented by the following school classifications: school type, school attainment, school deprivation level, and school sex composition.

This report looks at the provision of A level subjects in England in 2021. Provision in a subject is defined as the number or percentage of schools with at least one student taking the subject. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service and the dataset consisted of Key Stage 5 (KS5) entry numbers for all subjects provided in each school and college in England. Provision of other qualifications (e.g., Pre-U, International Baccalaureate, Cambridge Technicals, BTECs, etc.) was not investigated. The level of provision is presented by the following school classifications: school type, school attainment, school deprivation level, school sex composition, and school size.

The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Observing exoplanets through transmission spectroscopy supplies detailed information on their atmospheric composition, physics, and chemistry. Prior to JWST, these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we present a combined analysis of JWST transmission spectroscopy across four different instrumental modes spanning 0.5-5.2 micron using Early Release Science observations of the Saturn-mass exoplanet WASP-39b. Our uniform analysis constrains the orbital and stellar parameters within sub-percent precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to-date, and further confirms the presence of Na, K, H$_2$O, CO, CO$_2$, and SO$_2$ atmospheric absorbers. Through this process, we also improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. This work provides strong evidence that uniform light curve analysis is an important aspect to ensuring reliability when comparing the high-precision transmission spectra provided by JWST., Comment: 34 pages, 11 figures, 3 tables. Nat Astron (2024)

The Internet and computer commercialization have transformed the computing systems area over the past sixty years, affecting society. Computer systems have evolved to meet diverse social needs thanks to technological advances. The Internet of Things (IoT), cloud computing, fog computing, edge computing, and other emerging paradigms provide new economic and creative potential. Therefore, this article explores and evaluates the elements impacting the advancement of computing platforms, including both long standing systems and frameworks and more recent innovations like cloud computing, quantum technology, and edge AI. In this article, we examine computing paradigms, domains, and next generation computing systems to better understand the past, present, and future of computing technologies. This paper provides readers with a comprehensive overview of developments in computing technologies and highlights promising research gaps for the advancement of future computing systems., Comment: Preprint Version Accepted for Publication in Proceedings of the 21st International Conference on Smart Business Technologies, ICSBT 2024, Dijon, France, July 9 11, 2024

SARS-CoV-2 usually evolves at a relatively constant rate over time. Occasionally, however, lineages arise with higher-than-expected numbers of mutations given the date of sampling. Such lineages can arise for a variety of reasons, including selection pressures imposed by evolution during a chronic infection or exposure to mutation-inducing drugs like molnupiravir. We have developed an open-source web-based application (SMDP: SARS-CoV-2 Mutation Distribution Profiler; https://eringill.shinyapps.io/covid_mutation_distributions) that compares a list of user-submitted lineage-defining mutations with established mutation distributions including those observed during (1) the first nine months of the pandemic, (2) during the global transmission of Omicron, (3) during the chronic infection of immunocompromised patients, and (4) during zoonotic spillover from humans to deer. The application calculates the most likely distribution for the user's mutation list and displays log likelihoods for all distributions. In addition, the transition:transversion ratio of the user's list is calculated to determine whether there is evidence of exposure to a mutation-inducing drug such as molnupiravir and indicates whether the list contains mutations in the proofreading domain of nsp14 which could lead to a higher-than-expected mutation rate in the lineage. This tool will be useful for public health and researchers seeking to rapidly infer evolutionary histories of SARS-CoV-2 variants, which can aid risk assessment and public health responses.

Microservice architecture has transformed traditional monolithic applications into lightweight components. Scaling these lightweight microservices is more efficient than scaling servers. However, scaling microservices still faces the challenges resulted from the unexpected spikes or bursts of requests, which are difficult to detect and can degrade performance instantaneously. To address this challenge and ensure the performance of microservice-based applications, we propose a status-aware and elastic scaling framework called StatuScale, which is based on load status detector that can select appropriate elastic scaling strategies for differentiated resource scheduling in vertical scaling. Additionally, StatuScale employs a horizontal scaling controller that utilizes comprehensive evaluation and resource reduction to manage the number of replicas for each microservice. We also present a novel metric named correlation factor to evaluate the resource usage efficiency. Finally, we use Kubernetes, an open-source container orchestration and management platform, and realistic traces from Alibaba to validate our approach. The experimental results have demonstrated that the proposed framework can reduce the average response time in the Sock-Shop application by 8.59% to 12.34%, and in the Hotel-Reservation application by 7.30% to 11.97%, decrease service level objective violations, and offer better performance in resource usage compared to baselines., Comment: 26 pages

The Super-pressure Balloon-borne Imaging Telescope (SuperBIT) is a near-diffraction-limited 0.5m telescope that launched via NASA's super-pressure balloon technology on April 16, 2023. SuperBIT achieved precise pointing control through the use of three nested frames in conjunction with an optical Fine Guidance System (FGS), resulting in an average image stability of 0.055" over 300-second exposures. The SuperBIT FGS includes a tip-tilt fast-steering mirror that corrects for jitter on a pair of focal plane star cameras. In this paper, we leverage the empirical data from SuperBIT's successful 45-night stratospheric mission to inform the FGS design for the next-generation balloon-borne telescope. The Gigapixel Balloon-borne Imaging Telescope (GigaBIT) is designed to be a 1.35m wide-field, high resolution imaging telescope, with specifications to extend the scale and capabilities beyond those of its predecessor SuperBIT. A description and analysis of the SuperBIT FGS will be presented along with methodologies for extrapolating this data to enhance GigaBIT's FGS design and fine pointing control algorithm. We employ a systems engineering approach to outline and formalize the design constraints and specifications for GigaBIT's FGS. GigaBIT, building on the SuperBIT legacy, is set to enhance high-resolution astronomical imaging, marking a significant advancement in the field of balloon-borne telescopes., Comment: 13 pages, 7 figures, SPIE Astronomical Telescopes + Instrumentation 2024

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyze data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. Edge AI aims to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research from 2014 to the present, it has shown significant and rapid development over the last five years. This article presents a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions., Comment: Preprint Version Accepted for Publication in Springer Cluster Computing, 2024

Is explainability a false promise? This debate has emerged from the insufficient evidence that explanations aid people in situations they are introduced for. More human-centered, application-grounded evaluations of explanations are needed to settle this. Yet, with no established guidelines for such studies in NLP, researchers accustomed to standardized proxy evaluations must discover appropriate measurements, tasks, datasets, and sensible models for human-AI teams in their studies. To help with this, we first review fitting existing metrics. We then establish requirements for datasets to be suitable for application-grounded evaluations. Among over 50 datasets available for explainability research in NLP, we find that 4 meet our criteria. By finetuning Flan-T5-3B, we demonstrate the importance of reassessing the state of the art to form and study human-AI teams. Finally, we present the exemplar studies of human-AI decision-making for one of the identified suitable tasks -- verifying the correctness of a legal claim given a contract., Comment: 9 pages main, 7 pages references, 32 pages appendix

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

In this paper we present results from a qualitative field study on explainable AI (XAI) for lay users (n = 18) who were subjected to AI cyberattacks. The study was based on a custom-built smart heating application called Squid and was conducted over seven weeks in early 2023. Squid combined a smart radiator valve installed in participant homes with a web application that implemented an AI feature known as setpoint learning, which is commonly available in consumer smart thermostats. Development of Squid followed the XAI principle of interpretability-by-design where the AI feature was implemented using a simple glass-box machine learning model with the model subsequently exposed to users via the web interface (e.g. as interactive visualisations). AI attacks on users were simulated by injecting malicious training data and by manipulating data used for model predictions. Research data consisted of semi-structured interviews, researcher field notes, participant diaries, and application logs. In our analysis we reflect on the impact of XAI on user satisfaction and user comprehension as well as its use as a tool for diagnosing AI attacks. Our results show only limited engagement with XAI features and suggest that, for Squid users, common assumptions found in the XAI literature were not aligned to reality. On the positive side, users appear to have developed better mental models of the AI feature compared to previous work, and there is evidence that users did make some use of XAI as a diagnostic tool.

This paper studies permutation statistics that count occurrences of patterns. Their expected values on a product of $t$ permutations chosen randomly from $\Gamma \subseteq S_{n}$, where $\Gamma$ is a union of conjugacy classes, are considered. Hultman has described a method for computing such an expected value, denoted $\mathbb{E}_{\Gamma}(s,t)$, of a statistic $s$, when $\Gamma$ is a union of conjugacy classes of $S_{n}$. The only prerequisite is that the mean of $s$ over the conjugacy classes is written as a linear combination of irreducible characters of $S_{n}$. Therefore, the main focus of this article is to express the means of pattern-counting statistics as such linear combinations. A procedure for calculating such expressions for statistics counting occurrences of classical and vincular patterns of length 3 is developed, and is then used to calculate all these expressions. The results can be used to compute $\mathbb{E}_{\Gamma}(s,t)$ for all the above statistics, and for all functions on $S_{n}$ that are linear combinations of them., Comment: This paper is a part of my PhD Thesis which was written 2013