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The large apertures of the upcoming generation of Giant Segmented Mirror Telescopes will enable unprecedented angular resolutions that scale as $\propto$ $\lambda$/D and higher sensitivities that scale as $D^4$ for point sources corrected by adaptive optics. However, all will have pupil segmentation caused by mechanical struts holding up the secondary mirror [European Extremely Large Telescope and Thirty Meter Telescope] or intrinsically, by design, as in the Giant Magellan Telescope. These gaps will be separated by more than a typical atmospheric coherence length (Fried Parameter). The pupil fragmentation at scales larger than the typical atmospheric coherence length, combined with wavefront sensors with weak or ambiguous sensitivity to differential piston, can introduce differential piston areas of the wavefront known as "petal modes". Commonly used wavefront sensors, such as a pyramid WFS, also struggle with phase wrapping caused by >$\lambda$/2 differential piston WFE. We have developed the holographic dispersed fringe sensor, a single pupil-plane optic that employs holography to interfere the dispersed light from each segment onto different spatial locations in the focal plane to sense and correct differential piston between the segments. This allows for a very high and linear dynamic piston sensing range of approximately $\pm$10 $\mu$m. We have begun the initial attempts at phasing a segmented pupil utilizing the HDFS on the High Contrast Adaptive optics phasing Testbed and the Extreme Magellan Adaptive Optics instrument (MagAO-X) at the University of Arizona. Additionally, we have demonstrated use of the HDFS as a differential piston sensor on-sky for the first time. We were able to phase each segment to within $\pm\lambda$/11.3 residual piston WFE ($\lambda$ = 800 nm) of a reference segment and achieved ~50 nm RMS residual piston WFE across the aperture in poor seeing conditions., Comment: 34 pages, 26 figures

We say that a graph $G$ is anti-Ramsey for a graph $H$ if any proper edge-colouring of $G$ yields a rainbow copy of $H$, i.e. a copy of $H$ whose edges all receive different colours. In this work we determine the threshold at which the binomial random graph becomes anti-Ramsey for any fixed graph $H$, given that $H$ is sufficiently dense. Our proof employs a graph decomposition lemma in the style of the Nine Dragon Tree theorem that may be of independent interest.

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and versatility of such evaluations make the use of LLM-based judges a compelling solution for this challenge. Crucially, this approach requires first to validate the quality of the LLM judge itself. Previous work has focused on instance-based assessment of LLM judges, where a judge is evaluated over a set of responses, or response pairs, while being agnostic to their source systems. We argue that this setting overlooks critical factors affecting system-level ranking, such as a judge's positive or negative bias towards certain systems. To address this gap, we conduct the first large-scale study of LLM judges as system rankers. System scores are generated by aggregating judgment scores over multiple system outputs, and the judge's quality is assessed by comparing the resulting system ranking to a human-based ranking. Beyond overall judge assessment, our analysis provides a fine-grained characterization of judge behavior, including their decisiveness and bias.

We demonstrate electronic sensing of DNA nanostar (NS) condensate. Specifically, we use electrokinetic nanofluidics to observe and interpret how temperature-induced NS condensation affects nanochannel current. The increase in current upon filling a nanochannel with NS condensate indicates that its electrophoretic mobility is about half that of a single NS and its effective ionic strength is $\sim35$\% greater than that of 150mM NaCl in phosphate buffer. $\zeta$-potential measurements before and after exposure to NS show that condensate binds the silica walls of a nanochannel more strongly than individual NS do under identical conditions. This binding increases electroosmotic flow, possibly enough to completely balance, or even exceed, the electrophoretic velocity of NS condensate. Although the current through a flat nanochannel is erratic in the presence of NS condensate, tilting the nanochannel to accumulate NS condensate at one entrance (and away from the other) results in a robust electronic signature of the NS phase transition at temperatures $T_c$ = $f$([NaCl]) that agree with those obtained by other methods. Electrokinetic nanofluidic detection and measurement of NS condensate thus provides a foundation for novel biosensing technologies based on liquid-liquid phase separation.

Recent advances in language models opened new opportunities to address complex schema matching tasks. Schema matching approaches have been proposed that demonstrate the usefulness of language models, but they have also uncovered important limitations: Small language models (SLMs) require training data (which can be both expensive and challenging to obtain), and large language models (LLMs) often incur high computational costs and must deal with constraints imposed by context windows. We present Magneto, a cost-effective and accurate solution for schema matching that combines the advantages of SLMs and LLMs to address their limitations. By structuring the schema matching pipeline in two phases, retrieval and reranking, Magneto can use computationally efficient SLM-based strategies to derive candidate matches which can then be reranked by LLMs, thus making it possible to reduce runtime without compromising matching accuracy. We propose a self-supervised approach to fine-tune SLMs which uses LLMs to generate syntactically diverse training data, and prompting strategies that are effective for reranking. We also introduce a new benchmark, developed in collaboration with domain experts, which includes real biomedical datasets and presents new challenges to schema matching methods. Through a detailed experimental evaluation, using both our new and existing benchmarks, we show that Magneto is scalable and attains high accuracy for datasets from different domains.

We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constraints and objectives on the final program. Advanced synthesis algorithms transform the model into a low-level quantum program that meets the user's specification and is directed at a stipulated hardware. This separation of description from implementation is essential for scale. The technology adapts electronic design automation methods to quantum computing, finding feasible implementations in a virtually unlimited functional space. The results show clear superiority over the compilation and transpilation methods used today. We expect that this technological approach will take over and prevail as quantum software become more demanding, complex, and essential.

Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these models are still prone to what are commonly known as hallucinations, causing them to emit unwanted and factually incorrect text. In this work, we propose a novel calibration method that can be used to combat hallucinations. We add a special [IDK] ("I don't know") token to the model's vocabulary and introduce an objective function that shifts probability mass to the [IDK] token for incorrect predictions. This approach allows the model to express uncertainty in its output explicitly. We evaluate our proposed method across multiple model architectures and factual downstream tasks. We find that models trained with our method are able to express uncertainty in places where they would previously make mistakes while suffering only a small loss of encoded knowledge. We further perform extensive ablation studies of multiple variations of our approach and provide a detailed analysis of the precision-recall tradeoff of our method., Comment: Published at NeurIPS 2024

Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitive workload in response to the task difficulty. Understanding and dynamically adjusting this balance is crucial to maximizing efficiency and user satisfaction. In this paper, we propose a novel benchmarking method for shared control systems based on Fitts' Law to formally parameterize the difficulty level of a target-reaching task. With this we systematically quantify and model the effect of task difficulty (i.e. size and distance of target) and robot autonomy on task performance and operators' cognitive load and trust levels. Our empirical results (N=24) not only show that both task difficulty and robot autonomy influence task performance, but also that the performance can be modelled using these parameters, which may allow for the generalization of this relationship across more diverse setups. We also found that the users' perceived cognitive load and trust were influenced by these factors. Given the challenges in directly measuring cognitive load in real-time, our adapted Fitts' model presents a potential alternative approach to estimate cognitive load through determining the difficulty level of the task, with the assumption that greater task difficulty results in higher cognitive load levels. We hope that these insights and our proposed framework inspire future works to further investigate the generalizability of the method, ultimately enabling the benchmarking and systematic assessment of shared control quality and user impact, which will aid in the development of more effective and adaptable systems., Comment: 9 pages, 5 figures. arXiv admin note: text overlap with arXiv:2402.02758

The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying biological data, especially omics data of various types. However, comparing the performance of these models consistently has proven to be a challenge due to differences in training data and downstream tasks. To tackle this problem, we developed an architecture-agnostic benchmarking approach that, instead of evaluating the models directly, leverages entity representation vectors from each model and trains simple predictive models for each benchmarking task. This ensures that all types of models are evaluated using the same input and output types. Here we focus on gene properties collected from professionally curated bioinformatics databases. These gene properties are categorized into five major groups: genomic properties, regulatory functions, localization, biological processes, and protein properties. Overall, we define hundreds of tasks based on these databases, which include binary, multi-label, and multi-class classification tasks. We apply these benchmark tasks to evaluate expression-based models, large language models, protein language models, DNA-based models, and traditional baselines. Our findings suggest that text-based models and protein language models generally outperform expression-based models in genomic properties and regulatory functions tasks, whereas expression-based models demonstrate superior performance in localization tasks. These results should aid in the development of more informed artificial intelligence strategies for biological understanding and therapeutic discovery. To ensure the reproducibility and transparency of our findings, we have made the source code and benchmark data publicly accessible for further investigation and expansion at github.com/BiomedSciAI/gene-benchmark.

Dynamic hand gestures play a crucial role in conveying nonverbal information for Human-Robot Interaction (HRI), eliminating the need for complex interfaces. Current models for dynamic gesture recognition suffer from limitations in effective recognition range, restricting their application to close proximity scenarios. In this letter, we present a novel approach to recognizing dynamic gestures in an ultra-range distance of up to 28 meters, enabling natural, directive communication for guiding robots in both indoor and outdoor environments. Our proposed SlowFast-Transformer (SFT) model effectively integrates the SlowFast architecture with Transformer layers to efficiently process and classify gesture sequences captured at ultra-range distances, overcoming challenges of low resolution and environmental noise. We further introduce a distance-weighted loss function shown to enhance learning and improve model robustness at varying distances. Our model demonstrates significant performance improvement over state-of-the-art gesture recognition frameworks, achieving a recognition accuracy of 95.1% on a diverse dataset with challenging ultra-range gestures. This enables robots to react appropriately to human commands from a far distance, providing an essential enhancement in HRI, especially in scenarios requiring seamless and natural interaction.

Incorporating artificial intelligence (AI) into digital pathology offers promising prospects for automating and enhancing tasks such as image analysis and diagnostic processes. However, the diversity of tissue samples and the necessity for meticulous image labeling often result in biased datasets, constraining the applicability of algorithms trained on them. To harness synthetic histopathological images to cope with this challenge, it is essential not only to produce photorealistic images but also to be able to exert control over the cellular characteristics they depict. Previous studies used methods to generate, from random noise, semantic masks that captured the spatial distribution of the tissue. These masks were then used as a prior for conditional generative approaches to produce photorealistic histopathological images. However, as with many other generative models, this solution exhibits mode collapse as the model fails to capture the full diversity of the underlying data distribution. In this work, we present a pipeline, coined PriorPath, that generates detailed, realistic, semantic masks derived from coarse-grained images delineating tissue regions. This approach enables control over the spatial arrangement of the generated masks and, consequently, the resulting synthetic images. We demonstrated the efficacy of our method across three cancer types, skin, prostate, and lung, showcasing PriorPath's capability to cover the semantic mask space and to provide better similarity to real masks compared to previous methods. Our approach allows for specifying desired tissue distributions and obtaining both photorealistic masks and images within a single platform, thus providing a state-of-the-art, controllable solution for generating histopathological images to facilitate AI for computational pathology.

Neural networks, such as image classifiers, are frequently trained on proprietary and confidential datasets. It is generally assumed that once deployed, the training data remains secure, as adversaries are limited to query response interactions with the model, where at best, fragments of arbitrary data can be inferred without any guarantees on their authenticity. In this paper, we propose the memory backdoor attack, where a model is covertly trained to memorize specific training samples and later selectively output them when triggered with an index pattern. What makes this attack unique is that it (1) works even when the tasks conflict (making a classifier output images), (2) enables the systematic extraction of training samples from deployed models and (3) offers guarantees on the extracted authenticity of the data. We demonstrate the attack on image classifiers, segmentation models, and a large language model (LLM). We demonstrate the attack on image classifiers, segmentation models, and a large language model (LLM). With this attack, it is possible to hide thousands of images and texts in modern vision architectures and LLMs respectively, all while maintaining model performance. The memory back door attack poses a significant threat not only to conventional model deployments but also to federated learning paradigms and other modern frameworks. Therefore, we suggest an efficient and effective countermeasure that can be immediately applied and advocate for further work on the topic.

We present and discuss two catalogues of UV-selected (NUV $< 22.8$ mag) galaxies that lie within a 200 deg$^2$ area of sky covered by the ASKAP FLASH survey and have an impact parameter of less than 20 arcsec to a FLASH radio continuum source. These catalogues are designed to enable a future search for 21 cm HI absorption in and around star-forming galaxies at redshift $0.480 percent) star-forming and resemble the overall WiggleZ population. The ($g - i$) colour evolution with redshift is consistent with a history of active star-formation, but the radio-associated objects are typically redder and have colours similar to high-excitation radio galaxies. The redshift distribution of the two catalogues matches the overall distribution for WiggleZ galaxies, despite their otherwise rare radio properties. These catalogues can be expanded in future as new radio data become available, and a forthcoming paper will present the HI absorption results., Comment: 22 pages, 18 figures, accepted for publication in MNRAS

The Atacama Large Millimeter/submillimeter Array Survey of Orion Planck Galactic Cold Clumps (ALMASOP) reveals complex nested morphological and kinematic features of molecular outflows through the CO (J = 2 - 1) and SiO (J = 5 - 4) emission. We characterize the jet and outflow kinematics of the ALMASOP sample in four representative sources (HOPS 10, 315, 358, and G203.21-11.20W2) through channel maps and position-velocity diagrams (PVDs) parallel and transverse to the outflow axes. The combined CO and SiO emission exhibits the coexistence of the conventional extremely-high-velocity (EHV) jets and shell-like low-velocity (LV) cavity walls and new features. More complex, nested bubble-like and filamentary structures in the images and channel maps, triangle-shaped regions near the base of the parallel PVDs, and regions composed of rhombus/oval shapes in the transverse PVDs, are also evident. Such features find natural explanations within the bubble structure of the unified model of jet, wind, and ambient medium. The reverse shock cavity is revealed on the PVD base regions, and other features naturally arise within the dynamic postshock region of magnetic interaction. The finer nested shells observed within the compressed wind region reveal previously unnoticed shocked emission between the jet and the conventional large cavity walls. These pseudopulse-produced filamentary features connect to the jet-like knotty blobs, creating an impression of episodicity in mass ejection. SiO emission is enhanced downstream of the reverse shock boundary, with jet-like excitation conditions. Combined, these observed features reveal the extended structures induced by the magnetic interplay between a jet-bearing magnetized wide-angle wind and its ambient magnetized surrounding medium., Comment: 27 pages, 11 figures, accepted by ApJ

Higher-point functions in N = 4 super Yang-Mills theory can be constructed using integrability by triangulating the surfaces on which Feynman graphs would be drawn. It remains hard to analytically compute the necessary re-gluing of the tiles by virtual particles. We propose a new approach to study a series of residues encountered in the two-particle gluing of the planar one-loop five-point function of stress tensor multiplets. After exposing the twisted period nature of the integral functions, we employ intersection theory to derive canonical differential equations and present a solution., Comment: 11 pages, 1 figure, LaTeX

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

In his book "Metric structures for Riemannian and non-Riemannian spaces", Gromov defined two properties of Riemannian manifolds, ellipticity and quasiregular ellipticity, and suggested that there may be a connection between the two. Since then, groups of researchers working independently have proved strikingly similar results about these two concepts. We obtain new topological obstructions to the two properties: most notably, we show that closed manifolds of both types must have virtually abelian fundamental group. We also give the first examples of open manifolds which are elliptic but not quasireguarly elliptic and vice versa. Whether there is a direct connection between these properties -- and, in particular, whether they are equivalent for closed manifolds -- remains elusive., Comment: 25 pages, 2 figures. Minor additions and corrections in v2

Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. Theoretically explaining the benefits of complex parameterizations for SSMs is an open problem. The current paper takes a step towards its resolution, by establishing formal gaps between real and complex diagonal SSMs. Firstly, we prove that while a moderate dimension suffices in order for a complex SSM to express all mappings of a real SSM, a much higher dimension is needed for a real SSM to express mappings of a complex SSM. Secondly, we prove that even if the dimension of a real SSM is high enough to express a given mapping, typically, doing so requires the parameters of the real SSM to hold exponentially large values, which cannot be learned in practice. In contrast, a complex SSM can express any given mapping with moderate parameter values. Experiments corroborate our theory, and suggest a potential extension of the theory that accounts for selectivity, a new architectural feature yielding state of the art performance., Comment: 12 pages. Accepted to NeurIPS 2024

To manipulate objects or dance together, humans and robots exchange energy and haptic information. While the exchange of energy in human-robot interaction has been extensively investigated, the underlying exchange of haptic information is not well understood. Here, we develop a computational model of the mechanical and sensory interactions between agents that can tune their viscoelasticity while considering their sensory and motor noise. The resulting stochastic-optimal-information-and-effort (SOIE) controller predicts how the exchange of haptic information and the performance can be improved by adjusting viscoelasticity. This controller was first implemented on a robot-robot experiment with a tracking task which showed its superior performance when compared to either stiff or compliant control. Importantly, the optimal controller also predicts how connected humans alter their muscle activation to improve haptic communication, with differentiated viscoelasticity adjustment to their own sensing noise and haptic perturbations. A human-robot experiment then illustrated the applicability of this optimal control strategy for robots, yielding improved tracking performance and effective haptic communication as the robot adjusted its viscoelasticity according to its own and the user's noise characteristics. The proposed SOIE controller may thus be used to improve haptic communication and collaboration of humans and robots.

Type Ia supernovae (SNe Ia) are likely the thermonuclear explosions of carbon-oxygen (CO) white-dwarf (WD) stars, but the exact nature of their progenitor systems remains uncertain. Recent studies have suggested that a propagating detonation within a thin helium shell surrounding a sub-Chandrasekhar mass CO core can subsequently trigger a detonation within the core (the double-detonation model, DDM). The resulting explosion resembles a central ignition of a sub-Chandrasekhar mass CO WD (SCD), which is known to be in tension with the observed $t_0$-$M_{\rm{Ni}56}$ relation, where $t_0$ (the $\gamma$-rays' escape time from the ejecta) is positively correlated with $M_{\rm{Ni}56}$ (the synthesized $^{56}$Ni mass). SCD predicts an anticorrelation between $t_0$ and $M_{\rm{Ni}56}$, with $t_0\mathord{\approx}30\,\textrm{day}$ for luminous ($M_{\rm{Ni}56}\gtrsim0.5\,M_{\odot}$) SNe Ia, while the observed $t_0$ is in the range of 35-45 day. In this study, we apply our recently developed numerical scheme to calculate in 2D the impact of off-centre ignition in sub-Chandrasekhar mass CO WD, aiming to better emulate the behaviour expected in the DDM scenario. Our calculations of the $t_0$-$M_{\rm{Ni}56}$ relation, which do not require radiation transfer calculations, achieve convergence to within a few per cent with a numerical resolution of $\mathord{\sim}1\,\rm{km}$. We find that the results only slightly depend on the ignition location, mirroring the SCD model, and consequently, the discrepancy with the observed $t_0$-$M_{\rm{Ni}56}$ relation remains unresolved., Comment: 12 pages, 8 figures. Published on MNRAS

HOPS 87 is a Class 0 protostellar core known to harbor an extremely young bipolar outflow and a hot corino. We report the discovery of localized, chemically rich regions near the bases of the two-lobe bipolar molecular outflow in HOPS 87 containing molecules such as H$_2$CO, $^{13}$CS, H$_2$S, OCS, and CH$_3$OH, the simplest complex organic molecule (COM). The locations and kinematics suggest that these localized features are due to jet-driven shocks rather than being part of the hot corino region encasing the protostar. The COM compositions of the molecular gas in these jet-localized regions are relatively simpler than those in the hot corino zone. We speculate that this simplicity is due to either the liberation of ice with a less complex chemical history or the effects of shock chemistry. Our study highlights the dynamic interplay between the protostellar bipolar outflow, disk, inner core environment, and the surrounding medium, contributing to our understanding of molecular complexity in solar-like young stellar objects., Comment: 16 pages, 6+2 figures, accepted by ApJ

Real-time control (RTC) is pivotal for any Adaptive Optics (AO) system, including high-contrast imaging of exoplanets and circumstellar environments. It is the brain of the AO system, and what wavefront sensing and control (WFS\&C) techniques need to work with to achieve unprecedented image quality and contrast, ultimately advancing our understanding of exoplanetary systems in the context of high contrast imaging (HCI). Developing WFS\&C algorithms first happens in simulation or a lab before deployment on-sky. The transition to on-sky testing is often challenging due to the different RTCs used. Sharing common RTC standards across labs and telescope instruments would considerably simplify this process. A data architecture based on the interprocess communication method known as shared memory is ideally suited for this purpose. The CACAO package, an example of RTC based on shared memory, was initially developed for the Subaru-SCExAO instrument and now deployed on several benches and instruments. This proceeding discusses the challenges, requirements, implementation strategies, and performance evaluations associated with integrating a shared memory-based RTC. The Santa Cruz Extreme AO Laboratory (SEAL) bench is a platform for WFS\&C development for large ground-based segmented telescopes. Currently, SEAL offers the user a non-real-time version of CACAO, a shared-memory based RTC package initially developed for the Subaru-SCExAO instrument, and now deployed on several benches and instruments. We show here the example of the SEAL RTC upgrade as a precursor to both RTC upgrade at the 3-m Shane telescopes at Lick Observatory (Shane-AO) and a future development platform for the Keck II AO. This paper is aimed at specialists in AO, astronomers, and WFS\&C scientists seeking a deeper introduction to the world of RTCs., Comment: SPIE Astronomical Telescope and Intrumentation 2024

Two prominent objectives in social choice are utilitarian - maximizing the sum of agents' utilities, and leximin - maximizing the smallest agent's utility, then the second-smallest, etc. Utilitarianism is typically computationally easier to attain but is generally viewed as less fair. This paper presents a general reduction scheme that, given a utilitarian solver, produces a distribution over states (deterministic outcomes) that is leximin in expectation. Importantly, the scheme is robust in the sense that, given an approximate utilitarian solver, it produces a lottery that is approximately-leximin (in expectation) - with the same approximation factor. We apply our scheme to several social choice problems: stochastic allocations of indivisible goods, giveaway lotteries, and fair lotteries for participatory budgeting., Comment: Accepted to AAAI-2025

We analyze $\textit{JWST}$ CEERS NIRCam images to present {the first estimate} of the observed fraction and properties of bars out to $z \sim 4$. We analyze a sample of 1770 galaxies with stellar mass $M_\star > 10^{10} M_\odot$ at $0.5 \leq z \leq 4$ and identify barred galaxies via ellipse fits and visual classification of both F200W and F444W images. Our results apply mainly to bars with projected semi-major axis $a_{\rm bar}$ $> 1.5 $ kpc ($\sim$ 2 $\times$ PSF in F200W images) that can be robustly traced by ellipse fits. For such bars, the {observed} bar fraction at $z\sim$ 2-4 is low ($\lesssim 10\%$), and they appear to be emerging at least as early as $z\sim 4$ when the Universe was $\sim$ 13\% of its present age. At $z\sim$ 2-4, compared to our results, TNG50 simulations {predict} a significantly larger bar fraction due to a large population of small bars with $a_{\rm bar}$ $< 1.5$ kpc {that we cannot robustly detect}. If such a population exists, the true bar fraction may be significantly higher than our results. At $z \ge 1.5$, many barred galaxies show nearby neighbors, suggesting bars may be tidally triggered. {From $z \sim 4$ to $z \sim 0.5$, the observed bar fraction, average projected bar length, and projected bar strength rise.} Our results highlight the early emergence and evolution of barred galaxies and the rising importance of bar-driven secular evolution from $z \sim$4 to today., Comment: 25 pages, 16 figures, submitted to ApJ, Comments are welcome

Filaments are believed to play a key role in high-mass star formation. We present a systematic study of the filaments and their hosting clumps in the G35 molecular complex using JCMT SCUBA-2 850 $\micron$ continuum data. We identified five clouds in the complex and 91 filaments within them, some of which form 10 hub-filament systems (HFSs), each with at least 3 hub-composing filaments. We also compiled a catalogue of 350 dense clumps, 183 of which are associated with the filaments. We investigated the physical properties of the filaments and clumps, such as mass, density, and size, and their relation to star formation. We find that the global mass-length trend of the filaments is consistent with a turbulent origin, while the hub-composing filaments of high line masses ($m_{\rm l}\,>$\,230\,$\mathrm{M_{\odot}~pc^{-1}}$) in HFSs deviate from this relation, possibly due to feedback from massive star formation. We also find that the most massive and densest clumps (R\,$>$\,0.2\,pc, M\,$>35\,\mathrm{M_{\odot}}$, $\mathrm{\Sigma}>\,0.05\,\mathrm{g~cm^{-2}}$) are located in the filaments and in the hubs of HFS with the latter bearing a higher probability of occurrence of high-mass star-forming signatures, highlighting the preferential sites of HFSs for high-mass star formation. We do not find significant variation in the clump mass surface density across different evolutionary environments of the clouds, which may reflect the balance between mass accretion and stellar feedback., Comment: 34 pages, 17 figures. Accepted for publication in ApJ

We present a geometric design rule for size-controlled clustering of self-propelled particles. Active particles that tend to rotate under an external force have an intrinsic signed-parameter with units of curvature, which we term curvity, derivable from first principles. Robot experiments and numerical simulations show that the properties of the individual robot alone -- radius and curvity -- control pair-cohesion in a binary system as well as the stability of flocking and clustering in a swarm. Our results have applications in meta-materials and embodied decentralized control., Comment: 7 pages, 4 figures

Attaching enzymes to nanostructures has proven useful to the study of enzyme functionality under controlled conditions and has led to new technologies. Often, the utility and interest of enzyme-tethered nanostructures lie in how the enzymatic activity is affected by how the enzymes are arranged in space. Therefore, being able to conjugate enzymes to nanostructures while preserving the enzymatic activity is essential. In this paper, we present a method to conjugate single-stranded DNA to the enzyme urease while maintaining enzymatic activity. We show evidence of successful conjugation and quantify the variables that affect the conjugation yield. We also show that the enzymatic activity is unchanged after conjugation compared to the enzyme in its native state. Finally, we demonstrate the tethering of urease to nanostructures made using DNA origami with high site-specificity. Decorating nanostructures with enzymatically-active urease may prove to be useful in studying, or even utilizing, the functionality of urease in disciplines ranging from biotechnology to soft-matter physics. The techniques we present in this paper will enable researchers across these fields to modify enzymes without disrupting their functionality, thus allowing for more insightful studies into their behavior and utility., Comment: 17 pages, 5 figures, SI is 2 pages

The external environments surrounding molecular clouds vary widely across galaxies such as the Milky Way, and statistical samples of clouds from surveys are required to understand them. We present the Perseus Arm Molecular Survey (PAMS), a James Clerk Maxwell Telescope (JCMT) survey of $^{13}$CO and C$^{18}$O ($J$=3$-$2) of several molecular cloud complexes including W5 and NGC 7538 in the outer Perseus spiral arm situated at $\ell \approx 110^{\circ}$ and $\ell \approx 135^{\circ}$, with a total survey area of $\sim$6 deg$^2$. The PAMS data have an effective resolution of 17.2 arcsec, and rms sensitivity of $T_\rm{mb} = 0.7$ K in 0.3 km/s channels. We present a first look at the data, and compare the PAMS regions in the Outer Galaxy with Inner Galaxy regions from the CO Heterodyne Inner Milky Way Plane Survey (CHIMPS), incorporating archival $^{12}$CO (3$-$2) data. By comparing the various CO data with maps of H$_2$ column density from $\textit{Herschel}$, we find that the CO-to-H$_2$ column density $X$-factors do not vary significantly between Galactocentric radii of 4 and 10 kpc, and present representative values of $X_{^{12}\rm{CO} 3-2}$ and $X_{^{13}\rm{CO} 3-2}$. We find that the emission profiles, size-linewidth and mass-radius relationships of $^{13}$CO-traced structures are similar between the Inner and Outer Galaxy. Although PAMS sources are more massive than their Inner Galaxy counterparts for a given size scale, the discrepancy can be accounted for by the Galactic gradient in gas-to-dust mass ratio, uncertainties in the $X$-factors, and selection biases. We have made the PAMS data publicly available, complementing other CO surveys targeting different regions of the Galaxy in different isotopologues and transitions., Comment: Currently under review at MNRAS

Accurate classification of weather conditions in images is essential for enhancing the performance of object detection and classification models under varying weather conditions. This paper presents a comprehensive study on classifying weather conditions in images into four categories: rainy, low light, haze, and clear. The motivation for this work stems from the need to improve the reliability and efficiency of automated systems, such as autonomous vehicles and surveillance, which must operate under diverse weather conditions. Misclassification of weather conditions can lead to significant performance degradation in these systems, making robust weather classification crucial. Utilizing the Support Vector Machine (SVM) algorithm, our approach leverages a robust set of features, including brightness, saturation, noise level, blur metric, edge strength, motion blur, Local Binary Patterns (LBP) mean and variance for radii 1, 2, and 3, edges mean and variance, and color histogram mean and variance for blue, green, and red channels. Our SVM-based method achieved a notable accuracy of 92.8%, surpassing typical benchmarks in the literature, which range from 80% to 90% for classical machine learning methods. While deep learning methods can achieve up to 94% accuracy, our approach offers a competitive advantage in terms of computational efficiency and real-time classification capabilities. Detailed analysis of each feature's contribution highlights the effectiveness of texture, color, and edge-related features in capturing the unique characteristics of different weather conditions. This research advances the state-of-the-art in weather image classification and provides insights into the critical features necessary for accurate weather condition differentiation, underscoring the potential of SVMs in practical applications where accuracy is paramount.

In this Roadmap, we present a vision for the future of submillimetre and millimetre astronomy in the United Kingdom over the next decade and beyond. This Roadmap has been developed in response to the recommendation of the Astronomy Advisory Panel (AAP) of the STFC in the AAP Astronomy Roadmap 2022. In order to develop our stragetic priorities and recommendations, we surveyed the UK submillimetre and millimetre community to determine their key priorities for both the near-term and long-term future of the field. We further performed detailed reviews of UK leadership in submillimetre/millimetre science and instrumentation. Our key strategic priorities are as follows: 1. The UK must be a key partner in the forthcoming AtLAST telescope, for which it is essential that the UK remains a key partner in the JCMT in the intermediate term. 2. The UK must maintain, and if possible enhance, access to ALMA and aim to lead parts of instrument development for ALMA2040. Our strategic priorities complement one another: AtLAST (a 50m single-dish telescope) and an upgraded ALMA (a large configurable interferometric array) would be in synergy, not competition, with one another. Both have identified and are working towards the same overarching science goals, and both are required in order to fully address these goals., Comment: 91 pages plus cover, 38 figures. Submitted to the Science and Technology Facilities Council, August 2024. One figure corrected (v2); new appendix with STFC Q&A; corrected SMA access statement; updated references, acronyms & author list (v3)

We present the initial data for the ($J = 3 \to 2$) transition of $^{13}$CO obtained from the Central Molecular Zone (CMZ) of the Milky Way as part of the CO Heterodyne Inner Milky Way Plane Survey 2 (CHIMPS2). Covering $359^\circ \leq l \leq 1^\circ$ and $|b| \leq 0.5^\circ$ with an angular resolution of 19 arcsec, velocity resolution of 1 km s$^{-1}$, and rms $T_A^* = 0.59$ K at these resolutions, our observations unveil the complex structure of the CMZ molecular gas in improved detail. Complemented by the $^{12}$CO CHIMPS2 data, we estimate a median optical depth of $\tau_{13} = 0.087$. The preliminary analysis yields a median $^{13}$CO column density range equal to $N(^{13}\text{CO})= 2$--$5 \times 10^{18}$ cm$^{-2}$, median H$_2$ column density equal to $N(\text{H}_2)= 4 \times 10^{22}$ cm$^{-2}$ to $1 \times 10^{23}$ cm$^{-2}$. We derive $N(\text{H}_2)$-based total mass estimates of $M(\text{H}_2)= 2$--$6 \times 10^7\, M_{\odot}$, in agreement with previous studies. We analyze the relationship between the integrated intensity of $^{13}$CO and the surface density of compact sources identified by Herschel Hi-GAL, and find that younger Hi-GAL sources detected at 500 $\mu$m but not at 70 $\mu$m follow the dense gas of the CMZ more closely than those that are bright at 70 $\mu$m. The latter, actively star-forming sources, appear to be more associated with material in the foreground spiral arms., Comment: 13 pages, 9 figures

First-order logic has been established as an important tool for modeling and verifying intricate systems such as distributed protocols and concurrent systems. These systems are parametric in the number of nodes in the network or the number of threads, which is finite in any system instance, but unbounded. One disadvantage of first-order logic is that it cannot distinguish between finite and infinite structures, leading to spurious counterexamples. To mitigate this, we offer a verification approach that captures only finite system instances. Our approach is an adaptation of the cutoff method to systems modeled in first-order logic. The idea is to show that any safety violation in a system instance of size larger than some bound can be simulated by a safety violation in a system of a smaller size. The simulation provides an inductive argument for correctness in finite instances, reducing the problem to showing safety of instances with bounded size. To this end, we develop a framework to (i) encode such simulation relations in first-order logic and to (ii) validate the simulation relation by a set of verification conditions given to an SMT solver. We apply our approach to verify safety of a set of examples, some of which cannot be proven by a first-order inductive invariant., Comment: 28 pages, 6 figures, 1 table

This study proposes and explores a linear hydrodynamic thermo-elasticity system within mixture models, comprising fluid and solid phases, with a focus on biological tissues, particularly tumor-related phenomena. Although tumor growth is not yet incorporated, this work aims to comprehend the interaction between thermal effects and hydrodynamics on short-time scales where the tumor size typically remains stable. We establish the existence of a unique weak solution within the framework of implicit evolution equations, overcoming challenges posed by intricate coupling mechanisms within the system. To further investigate the model, we then study the one-dimensional model and explore in detail the complex interplay between fluid flow, solid deformation, and heat transfer. This complex coupled system of equations is reduced for the short time scale to obtain the semi-analytical solution.

We study truthful mechanisms for approximating the Maximin-Share (MMS) allocation of agents with additive valuations for indivisible goods. Algorithmically, constant factor approximations exist for the problem for any number of agents. When adding incentives to the mix, a jarring result by Amanatidis, Birmpas, Christodoulou, and Markakis [EC 2017] shows that the best possible approximation for two agents and $m$ items is $\lfloor \frac{m}{2} \rfloor$. We adopt a learning-augmented framework to investigate what is possible when some prediction on the input is given. For two agents, we give a truthful mechanism that takes agents' ordering over items as prediction. When the prediction is accurate, we give a $2$-approximation to the MMS (consistency), and when the prediction is off, we still get an $\lceil \frac{m}{2} \rceil$-approximation to the MMS (robustness). We further show that the mechanism's performance degrades gracefully in the number of ``mistakes" in the prediction; i.e., we interpolate (up to constant factors) between the two extremes: when there are no mistakes, and when there is a maximum number of mistakes. We also show an impossibility result on the obtainable consistency for mechanisms with finite robustness. For the general case of $n\ge 2$ agents, we give a 2-approximation mechanism for accurate predictions, with relaxed fallback guarantees. Finally, we give experimental results which illustrate when different components of our framework, made to insure consistency and robustness, come into play.

This paper presents a novel approach for ultra-range gesture recognition, addressing Human-Robot Interaction (HRI) challenges over extended distances. By leveraging human gestures in video data, we propose the Temporal-Spatiotemporal Fusion Network (TSFN) model that surpasses the limitations of current methods, enabling robots to understand gestures from long distances. With applications in service robots, search and rescue operations, and drone-based interactions, our approach enhances HRI in expansive environments. Experimental validation demonstrates significant advancements in gesture recognition accuracy, particularly in prolonged gesture sequences.

In this study, we employ analytical and numerical techniques to examine a phase transition model with moving boundaries. The model displays two relevant spatial scales pointing out to a macroscopic phase and a microscopic phase, interacting on disjoint inclusions. The shrinkage or the growth of the inclusions is governed by a modified Gibbs-Thomson law depending on the macroscopic temperature, but without accessing curvature information. We use the Hanzawa transformation to transform the problem onto a fixed reference domain. Then a fixed-point argument is employed to demonstrate the well-posedness of the system for a finite time interval. Due to the model's nonlinearities and the macroscopic parameters, which are given by differential equations that depend on the size of the inclusions, the problem is computationally expensive to solve numerically. We introduce a precomputing approach that solves multiple cell problems in an offline phase and uses an interpolation scheme afterward to determine the needed parameters. Additionally, we propose a semi-implicit time-stepping method to resolve the nonlinearity of the problem. We investigate the errors of both the precomputing and time-stepping procedures and verify the theoretical results via numerical simulations., Comment: 40 pages

The high contrast and spatial resolution requirements for directly imaging exoplanets requires effective coordination of wavefront control, coronagraphy, observation techniques, and post-processing algorithms. However, even with this suite of tools, identifying and retrieving exoplanet signals embedded in resolved scattered light regions can be extremely challenging due to the increased noise from scattered light off the circumstellar disk and the potential misinterpretation of the true nature of the detected signal. This issue pertains not only to imaging terrestrial planets in habitable zones within zodiacal and exozodiacal emission but also to young planets embedded in circumstellar, transitional, and debris disks. This is particularly true for H{\alpha} detection of exoplanets in transitional disks. This work delves into recent H{\alpha} observations of three transitional disks systems with MagAO-X, an extreme adaptive optics system for the 6.5-meter Magellan Clay telescope. We employed angular differential imaging (ADI) and simultaneous spectral differential imaging (SSDI) in combination with KLIP, a PCA algorithm in post-processing, for optimal starlight suppression and quasi-static noise removal. We discuss the challenges in protoplanet identification with MagAO-X in environments rich with scattered and reflected light from disk structures and explore a potential solution for removing noise contributions from real astronomical objects with current observation and post-processing techniques.

The next generation of extreme adaptive optics (AO) must be calibrated exceptionally well to achieve the desired contrast for ground-based direct imaging exoplanet targets. Current wavefront sensing and control system responses deviate from lab calibration throughout the night due to non linearities in the wavefront sensor (WFS) and signal loss. One cause of these changes is the optical gain (OG) effect, which shows that the difference between actual and reconstructed wavefronts is sensitive to residual wavefront errors from partially corrected turbulence. This work details on-sky measurement of optical gain on MagAO-X, an extreme AO system on the Magellan Clay 6.5m. We ultimately plan on using a method of high-temporal frequency probes on our deformable mirror to track optical gain on the Pyramid WFS. The high-temporal frequency probes, used to create PSF copies at 10-22 lambda /D, are already routinely used by our system for coronagraph centering and post-observation calibration. This method is supported by the OG measurements from the modal response, measured simultaneously by sequenced pokes of each mode. When tracked with DIMM measurements, optical gain calibrations show a clear dependence on Strehl Ratio, and this relationship is discussed. This more accurate method of calibration is a crucial next step in enabling higher fidelity correction and post processing techniques for direct imaging ground based systems., Comment: 7 pages, 6 figures, proceedings of Adaptive Optics Systems IX at SPIE Astronomical Telescopes + Instrumentation 2024