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Frontier artificial intelligence (AI) systems present both benefits and risks to society. Safety cases - structured arguments supported by evidence - are one way to help ensure the safe development and deployment of these systems. Yet the evolving nature of AI capabilities, as well as changes in the operational environment and understanding of risk, necessitates mechanisms for continuously updating these safety cases. Typically, in other sectors, safety cases are produced pre-deployment and do not require frequent updates post-deployment, which can be a manual, costly process. This paper proposes a Dynamic Safety Case Management System (DSCMS) to support both the initial creation of a safety case and its systematic, semi-automated revision over time. Drawing on methods developed in the autonomous vehicles (AV) sector - state-of-the-art Checkable Safety Arguments (CSA) combined with Safety Performance Indicators (SPIs) recommended by UL 4600, a DSCMS helps developers maintain alignment between system safety claims and the latest system state. We demonstrate this approach on a safety case template for offensive cyber capabilities and suggest ways it can be integrated into governance structures for safety-critical decision-making. While the correctness of the initial safety argument remains paramount - particularly for high-severity risks - a DSCMS provides a framework for adapting to new insights and strengthening incident response. We outline challenges and further work towards development and implementation of this approach as part of continuous safety assurance of frontier AI systems., Comment: 75 pages, 41 tables/figures

Interaction of electric fields with biological cells is indispensable for many physiological processes. Thermal electrical noise in the cellular environment has long been considered as the minimum threshold for detection of electrical signals by cells. However, there is compelling experimental evidence that the minimum electric field sensed by certain cells and organisms is many orders of magnitude weaker than the thermal electrical noise limit estimated purely under equilibrium considerations. We resolve this discrepancy by proposing a non-equilibrium statistical mechanics model for active electromechanical membranes and hypothesize the role of activity in modulating the minimum electrical field that can be detected by a biological membrane. Active membranes contain proteins that use external energy sources to carry out specific functions and drive the membrane away from equilibrium. The central idea behind our model is that active mechanisms, attributed to different sources, endow the membrane with the ability to sense and respond to electric fields that are deemed undetectable based on equilibrium statistical mechanics. Our model for active membranes is capable of reproducing different experimental data available in the literature by varying the activity. Elucidating how active matter can modulate the sensitivity of cells to electric signals can open avenues for a deeper understanding of physiological and pathological processes.

Satellite to ground quantum communication typically operates at night to reduce background signals, however it remains susceptible to noise from light pollution of the night sky. In this study we compare several methodologies for determining whether a Quantum Ground Station (QGS) site is viable for exchanging quantum signals with the upcoming Quantum Encryption and Science Satellite (QEYSSat) mission. We conducted ground site characterization studies at three locations in Canada: Waterloo, Ontario, Calgary, Alberta, and Priddis, Alberta. Using different methods we estimate the background counts expected to leak into the satellite-ground quantum channel, and determined whether the noise levels could prevent a quantum key transfer. We also investigate how satellite data recorded from the Visible Infrared Imaging Radiometer Suite (VIIRS) can help estimate conditions of a particular site, and find reasonable agreement with the locally recorded data. Our results indicate that the Waterloo, Calgary, and Priddis QGS sites should allow both quantum uplinks and downlinks with QEYSSat, despite their proximity to urban centres. Furthermore, our approach allows the use of satellite borne instrument data (VIIRS) to remotely and efficiently determine the potential of a ground site., Comment: Submitted to EPJ: Prospects for Space Quantum Research, 20 pages, 12 figures

This report relates to a study group hosted by the EPSRC funded network, Integrating data-driven BIOphysical models into REspiratory MEdicine (BIOREME), and supported by SofTMech and Innovate UK, Business Connect. This report summarises the work undertaken on a challenge presented by two of the authors, Mathew Bulpett and Dr Emily Fraser. The aim was to identify approaches to analyse data collected using structured light plethysmography (SLP) from (n=31) healthy volunteers and (n=67) patients with Breathing Pattern Disorder (BPD) attributed to "long COVID", i.e. post-acute COVID-19 sequelae. This report explores several approaches including dimensionality reduction techniques on the available data and alternative indices extracted from variation in the time-series data for each measurement. Further proposals are also outlined such as different spatial indices that could be extracted from the SLP data, and the potential to couple to mechanical models of the lungs, chest and abdomen. However, running these latter analyses was beyond the scope of the limited study group timeframe. This exploratory analysis did not identify any clear SLP biomarkers of BPD in these cohorts, however recommendations are made for using SLP technologies in future BPD studies based on its findings., Comment: 16 pages (main text), 9 figures. Study group report

The 2175{\AA} bump is a prominent absorption feature at ultraviolet (UV) wavelengths in dust extinction and attenuation curves. Understanding the relative strength of this feature is important for accurate dust corrections at both low- and high-redshift. This feature is postulated to arise from polycyclic aromatic hydrocarbon (PAH) dust grains; however, the carrier has not been definitively established. We present results on the correlation between the 2175{\AA} feature and PAH abundances in a spatially-resolved manner for 15 local galaxies in the PHANGS-JWST survey that have NUV and mid-IR imaging data from Swift/UVOT and JWST/MIRI, respectively. We find a moderate positive correlation between the 2175{\AA} feature strength and PAH abundance, albeit with large intrinsic scatter. However, most of this trend can be attributed to a stronger negative correlation of both quantities with SFR surface density and specific-SFR (proxies of ionising radiation). The latter trends are consistent with previous findings that both the 2175{\AA} carrier and PAHs are small grains that are easily destroyed by UV photons, although the proxy for PAH abundance could also be influenced by dust heating. When controlling for SFR surface density, we find weaker correlations between the 2175{\AA} feature and PAH abundances, disfavouring a direct link. However, analyses based on spectroscopic measurements of the 2175{\AA} feature and PAH features are required to verify our findings. No significant trends with gas-phase metallicity are found for the 2175{\AA} feature and PAHs, however the metallicity range of our sample is limited. We provide prescriptions for the strength of the 2175{\AA} feature and PAHs in local massive (metal-rich) galaxies with SFR surface density and specific-SFR, however the former should be used with caution since bump strengths measured from Swift/UVOT are expected to be underestimated., Comment: 26 pages, 19 figures, 4 tables. Accepted for publication in PASA

Inspired by the embodied intelligence of biological creatures like the octopus, the soft robotic arm utilizes its highly flexible structure to perform various tasks in the complex environment. While the classic Cosserat rod theory investigates the bending, twisting, shearing, and stretching of the soft arm, it fails to capture the in-plane deformation that occurs during certain tasks, particularly those involving active lateral traction. This paper introduces an extended Cosserat rod theory addressing these limitations by incorporating an extra strain variable reflecting the in-plane inflation ratio. To accurately describe the viscoelasticity effect of the soft body in dynamics, the proposed model enhances the constitutive law by integrating the Saint-Venant Kirchhoff hyperelastic and Kelvin-Voigt viscous models. The active and environmental loads are accounted for the equations of motion, which are numerically solved by adapting the Geometric Variable Strain (GVS) approach to balance the accuracy and computational efficiency. Our contributions include the derivation of the extended Cosserat rod theory in dynamic context, and the development of a reduced-order numerical method that enables rapid and precise solutions. We demonstrate applications of the model in stiffness tuning of a soft robotic arm and the study of complex octopus' arm motions.

Human-machine teaming in medical AI requires us to understand to what degree a trained clinician should weigh AI predictions. While previous work has shown the potential of AI assistance at improving clinical predictions, existing clinical decision support systems either provide no explainability of their predictions or use techniques like saliency and Shapley values, which do not allow for physician-based verification. To address this gap, this study compares previously used explainable AI techniques with a newly proposed technique termed '2-factor retrieval (2FR)', which is a combination of interface design and search retrieval that returns similarly labeled data without processing this data. This results in a 2-factor security blanket where: (a) correct images need to be retrieved by the AI; and (b) humans should associate the retrieved images with the current pathology under test. We find that when tested on chest X-ray diagnoses, 2FR leads to increases in clinician accuracy, with particular improvements when clinicians are radiologists and have low confidence in their decision. Our results highlight the importance of understanding how different modes of human-AI decision making may impact clinician accuracy in clinical decision support systems.

Integrating multiple (sub-)systems is essential to create advanced Information Systems (ISs). Difficulties mainly arise when integrating dynamic environments across the IS lifecycle. A traditional approach is a registry that provides the API documentation of the systems' endpoints. Large Language Models (LLMs) have shown to be capable of automatically creating system integrations (e.g., as service composition) based on this documentation but require concise input due to input token limitations, especially regarding comprehensive API descriptions. Currently, it is unknown how best to preprocess these API descriptions. Within this work, we (i) analyze the usage of Retrieval Augmented Generation (RAG) for endpoint discovery and the chunking, i.e., preprocessing, of OpenAPIs to reduce the input token length while preserving the most relevant information. To further reduce the input token length for the composition prompt and improve endpoint retrieval, we propose (ii) a Discovery Agent that only receives a summary of the most relevant endpoints and retrieves details on demand. We evaluate RAG for endpoint discovery using the RestBench benchmark, first, for the different chunking possibilities and parameters measuring the endpoint retrieval recall, precision, and F1 score. Then, we assess the Discovery Agent using the same test set. With our prototype, we demonstrate how to successfully employ RAG for endpoint discovery to reduce the token count. While revealing high values for recall, precision, and F1, further research is necessary to retrieve all requisite endpoints. Our experiments show that for preprocessing, LLM-based and format-specific approaches outperform na\"ive chunking methods. Relying on an agent further enhances these results as the agent splits the tasks into multiple fine granular subtasks, improving the overall RAG performance in the token count, precision, and F1 score.

Studying the magnetic ground states of frustrated antiferromagnets provides unique insight into the stability of quantum spin liquids, even if the anticipated state is not realized towards T = 0. Particularly relevant are structural modifications setting in at temperatures where the magnetic correlations come into play. Here we explore the lattice dynamics of Y-kapellasite (Y3Cu9(OH)19Cl8) single crystals by infrared spectroscopy in combination with ab initio calculations. We observe significant changes in the phonon spectra at Ts = 32 K, that gradually evolve down to low temperatures. The increase in the number of phonon modes provides evidence for a lowering of symmetry and we discuss several possibilities of crystal structure modifications. Our analysis also reveals that the structural variation involves exclusively H and O atoms, while the other atoms remain rather unaffected. An 8% red shift of the lowest-lying phonon mode upon cooling indicates strong magneto-elastic effects upon decoupling Cu-6f hexagons through the lattice vibrations.

Parallel input performance issues are often neglected in large scale parallel applications in Computational Science and Engineering. Traditionally, there has been less focus on input performance because either input sizes are small (as in biomolecular simulations) or the time doing input is insignificant compared with the simulation with many timesteps. But newer applications, such as graph algorithms add a premium to file input performance. Additionally, over-decomposed systems, such as Charm++/AMPI, present new challenges in this context in comparison to MPI applications. In the over-decomposition model, naive parallel I/O in which every task makes its own I/O request is impractical. Furthermore, load balancing supported by models such as Charm++/AMPI precludes assumption of data contiguity on individual nodes. We develop a new I/O abstraction to address these issues by separating the decomposition of consumers of input data from that of file-reader tasks that interact with the file system. This enables applications to scale the number of consumers of data without impacting I/O behavior or performance. These ideas are implemented in a new input library, CkIO, that is built on Charm++, which is a well-known task-based and overdecomposed-partitions system. CkIO is configurable via multiple parameters (such as the number of file readers and/or their placement) that can be tuned depending on characteristics of the application, such as file size and number of application objects. Additionally, CkIO input allows for capabilities such as effective overlap of input and application-level computation, as well as load balancing and migration. We describe the relevant challenges in understanding file system behavior and architecture, the design alternatives being explored, and preliminary performance data.

Accurate diagnosis and prognosis assisted by pathology images are essential for cancer treatment selection and planning. Despite the recent trend of adopting deep-learning approaches for analyzing complex pathology images, they fall short as they often overlook the domain-expert understanding of tissue structure and cell composition. In this work, we focus on a challenging Open-ended Pathology VQA (PathVQA-Open) task and propose a novel framework named Path-RAG, which leverages HistoCartography to retrieve relevant domain knowledge from pathology images and significantly improves performance on PathVQA-Open. Admitting the complexity of pathology image analysis, Path-RAG adopts a human-centered AI approach by retrieving domain knowledge using HistoCartography to select the relevant patches from pathology images. Our experiments suggest that domain guidance can significantly boost the accuracy of LLaVA-Med from 38% to 47%, with a notable gain of 28% for H&E-stained pathology images in the PathVQA-Open dataset. For longer-form question and answer pairs, our model consistently achieves significant improvements of 32.5% in ARCH-Open PubMed and 30.6% in ARCH-Open Books on H\&E images. Our code and dataset is available here (https://github.com/embedded-robotics/path-rag).

Counting distinct permutations with replacement, especially when involving multiple subwords, is a longstanding challenge in combinatorial analysis, with critical applications in cryptography, bioinformatics, and statistical modeling. This paper introduces a novel framework that presents closed-form formulas for calculating distinct permutations with replacement, fundamentally reducing the time complexity from exponential to linear relative to the sequence length for single-subword calculations. We then extend our foundational formula to handle multiple subwords through the development of an additional formula. Unlike traditional methods relying on brute-force enumeration or recursive algorithms, our approach leverages novel combinatorial constructs and advanced mathematical techniques to achieve unprecedented efficiency. This comprehensive advancement in reducing computational complexity not only simplifies permutation counting but also establishes a new benchmark for scalability and versatility. We also demonstrate the practical utility of our formulas through diverse applications, including the simultaneous identification of multiple genetic motifs in DNA sequences and complex pattern analysis in cryptographic systems, using a computer program that runs the proposed formulae., Comment: 15 pages, 1 figure

Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by cheaper initial IR systems. This is because, though expensive, rerankers are assumed to be more effective. We challenge this assumption by measuring reranker performance for full retrieval, not just re-scoring first-stage retrieval. Our experiments reveal a surprising trend: the best existing rerankers provide diminishing returns when scoring progressively more documents and actually degrade quality beyond a certain limit. In fact, in this setting, rerankers can frequently assign high scores to documents with no lexical or semantic overlap with the query. We hope that our findings will spur future research to improve reranking.

Over many decades fully nonlinear PDEs, and the complex Monge-Amp\`ere equation in particular played a central role in the study of complex manifolds. Most previous works focused on problems that can be expressed through equations involving real $(1, 1)$ forms. As many important questions, especially those linked to higher cohomology classes in complex geometry involve real $(p, p)$ forms for $p > 1$, there is a strong need to develop PDE techniques to study them. In this paper we consider a fully nonlinear equation for $(p, p)$ forms on compact Hermitian manifolds. We establish the existence of classical solutions for a large class of these equations by a parabolic approach, proving the long-time existence and convergence of solutions to the elliptic case.

In this paper, we introduce a novel method for active learning of deterministic real-time one-counter automata (DROCA). The existing techniques for learning DROCA rely on observing the behaviour of the DROCA up to exponentially large counter-values. Our algorithm eliminates this need and requires only a polynomial number of queries. Additionally, our method differs from existing techniques as we learn a minimal counter-synchronous DROCA, resulting in much smaller counter-examples on equivalence queries. Learning a minimal counter-synchronous DROCA cannot be done in polynomial time unless P = NP, even in the case of visibly one-counter automata. We use a SAT solver to overcome this difficulty. The solver is used to compute a minimal separating DFA from a given set of positive and negative samples. We prove that the equivalence of two counter-synchronous DROCAs can be checked significantly faster than that of general DROCAs. For visibly one-counter automata, we have discovered an even faster algorithm for equivalence checking. We implemented the proposed learning algorithm and tested it on randomly generated DROCAs. Our evaluations show that the proposed method outperforms the existing techniques on the test set.

The natural van der Waals heterostructure 4H$_b$-TaS$_2$ composed of alternating 1T- and 1H-TaS$_2$ layers serves as a platform for investigating the electronic correlations and layer-dependent properties of novel quantum materials. The temperature evolution of the conductivity spectra $\sigma(\omega)$ obtained through infrared spectroscopy elucidates the influence of band modifications associated with the charge-density-wave (CDW) superlattice on the 1T layer, resulting in a room-temperature energy gap, $\Delta_{\rm CDW}\approx$ 0.35 eV. However, there is no gap associated to the 1H layer. Supported by density functional theory calculations, we attribute the behavior of interband transitions to the convergence of the layers, which amplifies the charge transfer from the 1T to the 1H layers, progressing as the temperature decreases. This phenomenon leads to an enhanced low-energy spectral weight and carrier density. The presence of an energy gap and the temperature-tunable charge transfer within the bulk of 4H$_b$-TaS$_2$ driven by layer-dependent CDW states contribute to a more comprehensive understanding of other complex compounds of transition-metal dichalcogenides., Comment: 11 pages including SM

The cosmic velocity field is an unbiased probe of the total matter distribution but is challenging to measure directly at intermediate and high redshifts. The large-scale velocity field imprints a signal in the cosmic microwave background (CMB) through the kinetic Sunyaev-Zeldovich (kSZ) effect. We perform the first 3d reconstruction of the large-scale velocity field from the kSZ effect by applying a quadratic estimator to CMB temperature maps and the 3d positions of galaxies. We do so by combining CMB data from the fifth data release of the Atacama Cosmology Telescope (in combination with Planck) and a spectroscopic galaxy sample from the Sloan Digital Sky Survey. We then measure the galaxy-velocity cross-power spectrum and detect the presence of the kSZ signal at a signal-to-noise ratio of 7.2$\sigma$. Using this galaxy-velocity cross-correlation alone, we constrain the amplitude of local primordial non-Gaussianity finding $f_{\rm NL}=-90^{+210}_{-350}$. This pathfinder measurement sets the stage for joint galaxy-CMB kSZ constraints to significantly enhance the $f_{\rm NL}$ information obtained from galaxy surveys through sample variance cancellation.

The standard $\Lambda$CDM cosmological model informed by cosmic microwave background (CMB) anisotropies makes a precise prediction for the growth of matter density fluctuations over cosmic time on linear scales. A variety of cosmological observables offer independent and complementary ways of testing this prediction, but results have been mixed, with many constraints on the amplitude of structure $S_8$ being 2-3$\sigma$ lower than the expectation from Planck primary CMB anisotropies. It is currently unclear whether these discrepancies are due to observational systematics, non-linearities and baryonic effects or new physics. We review how gravitational lensing of the CMB has and will continue to provide insights into this problem, including through tomographic cross-correlations with galaxy surveys over cosmic time., Comment: This is a review based on a talk I was invited to give at the Royal Society Meeting "Challenging the standard cosmological model" (London, April 2024). This matches the version accepted for publication in "Philosophical Transactions of the Royal Society A" (Accepted Oct 2024). 35 pages, 4 figures

Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers and text aligned to timestamps, and includes data from two different domains: climate science and healthcare. Our data is a significant contribution to the rare selection of available multimodal datasets. We also propose the Hybrid Multi-Modal Forecaster (Hybrid-MMF), a multimodal LLM that jointly forecasts both text and time series data using shared embeddings. However, contrary to our expectations, our Hybrid-MMF model does not outperform existing baselines in our experiments. This negative result highlights the challenges inherent in multimodal forecasting. Our code and data are available at https://github.com/Rose-STL-Lab/Multimodal_ Forecasting., Comment: 21 pages, 4 tables, 2 figures

A complex Monge-Amp\`ere equation for differential $(p,p)$ forms is introduced on compact K\"ahler manifolds. For any $1 \leq p < n$, we show the existence of smooth solutions unique up to adding constants. For $p=1$, this corresponds to the Calabi-Yau theorem proved by S. T. Yau, and for $p=n-1$, this gives the Monge-Amp\`ere equation for $(n-1)$ plurisubharmonic functions solved by Tosatti-Weinkove. For other $p$ values, this defines a non-linear PDE that falls outside of the general framework of Caffarelli-Nirenberg-Spruck. Further, we define a geometric flow for higher-order forms that preserves their cohomology classes, and extends the K\"ahler-Ricci flow naturally to $(p,p)$ forms. As a consequence of our main theorem, we show that this flow exists in a maximal time interval and can be shown to converge under some assumptions. A modified flow is introduced in order to study the evolution of Hodge classes of $M$ with respect to the rational span of Chern classes of holomoprhic vector bundles. The convergence of the associated normalized flow is shown and some potential applications are discussed.

Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.

Algorithms that use derivatives of governing equations have accelerated rigid robot simulations and improved their accuracy, enabling the modeling of complex, real-world capabilities. However, extending these methods to soft and hybrid soft-rigid robots is significantly more challenging due to the complexities in modeling continuous deformations inherent in soft bodies. A considerable number of soft robots and the deformable links of hybrid robots can be effectively modeled as slender rods. The Geometric Variable Strain (GVS) model, which employs the screw theory and the strain parameterization of the Cosserat rod, extends the rod theory to model hybrid soft-rigid robots within the same mathematical framework. Using the Recursive Newton-Euler Algorithm, we developed the analytical derivatives of the governing equations of the GVS model. These derivatives facilitate the implicit integration of dynamics and provide the analytical Jacobian of the statics residue, ensuring fast and accurate computations. We applied these derivatives to the mechanical simulations of six common robotic systems: a soft cable-driven manipulator, a hybrid serial robot, a fin-ray finger, a hybrid parallel robot, a contact scenario, and an underwater hybrid mobile robot. Simulation results demonstrate substantial improvements in computational efficiency, with speed-ups of up to three orders of magnitude. We validate the model by comparing simulations done with and without analytical derivatives. Beyond static and dynamic simulations, the techniques discussed in this paper hold the potential to revolutionize the analysis, control, and optimization of hybrid robotic systems for real-world applications., Comment: 27 pages including appendix, 17 figures

In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7% while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution.

This paper introduces deterministic weighted real-time one-counter automaton (DWROCA). A DWROCA is a deterministic real-time one-counter automaton whose transitions are assigned a weight from a field. Two DWROCAs are equivalent if every word accepted by one is accepted by the other with the same weight. DWROCA is a sub-class of weighted one-counter automata with counter-determinacy. It is known that the equivalence problem for this model is in P. This paper gives a simpler proof and a better polynomial-time algorithm for checking the equivalence of two DWROCAs., Comment: 14 pages, 4 figures

Rapidly-exploring Random Tree (RRT) algorithms have been applied successfully to challenging robot motion planning and under-actuated nonlinear control problems. However a fundamental limitation of the RRT approach is the slow convergence in configuration spaces with narrow channels because of the small probability of generating test points inside narrow channels. This paper presents an improved RRT algorithm that takes advantage of narrow channels between the initial and goal states to find shorter paths by improving the exploration of narrow regions in the configuration space. The proposed algorithm detects the presence of narrow channel by checking for collision of neighborhood points with the infeasible set and attempts to add points within narrow channels with a predetermined bias. This approach is compared with the classical RRT and its variants on a variety of benchmark planning problems. Simulation results indicate that the algorithm presented in this paper computes a significantly shorter path in spaces with narrow channels.

AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have yet to be explored. Here, we demonstrate how 10 - 1000+ AI agents behave and progress within agent societies. We first introduce the PIANO (Parallel Information Aggregation via Neural Orchestration) architecture, which enables agents to interact with humans and other agents in real-time while maintaining coherence across multiple output streams. We then evaluate agent performance in agent simulations using civilizational benchmarks inspired by human history. These simulations, set within a Minecraft environment, reveal that agents are capable of meaningful progress -- autonomously developing specialized roles, adhering to and changing collective rules, and engaging in cultural and religious transmission. These preliminary results show that agents can achieve significant milestones towards AI civilizations, opening new avenues for large simulations, agentic organizational intelligence, and integrating AI into human civilizations., Comment: 35 pages, 14 figures

In this paper, we study the nonlinear wave modulation of arbitrary amplitude periodic traveling wave solutions of the Camassa-Holm (CH) equation. Slow modulations of wave trains is often described through Whitham's theory of modulations, which at leading order models the slow evolution of the fundamental wave characteristics (such as the wave's frequency, mass and momentum) through a disperionless system of quasi-linear partial differential equations. The modulational stability or instability of such a slowly modulated wave is considered to be determined by the hyperbolicity or ellipticity of this Whitham modulation system of equations. In work by Abenda \& Grava, the Whitham modulation system for the CH equation was derived through averaged Lagrangian methods and was further shown to always be hyperbolic (although strict hyperbolicity may fail). In this work, we provide an independent derivation of the Whitham modulation system for the CH equation through nonlinear WKB / multiple scales expansions. We further provide a rigorous connection between the Whitham modulation equations for the CH equation and the spectral stability of the underlying periodic wave train to localized (i.e. integrable on the line) perturbations. In particular, we prove that the strict hyperbolicity of the Whitham system implies spectral stability in a neighborhood of the origin in the spectral plane, i.e. spectral modulational stability. As an illustration of our theory, we examine the Whitham modulation system for wave trains with asymptotically small oscillations about their total mass., Comment: 35 pages, 2 figures

The high electron temperature in galaxy clusters ($>1\,$keV or $>10^7\,$K) leads to corrections at the level of a few percent in their thermal Sunyaev-Zeldovich effect signatures. Both the size and frequency dependence of these corrections, which are known as relativistic temperature corrections, depend upon the temperature of the objects. In this work we exploit this effect to measure the average temperature of a stack of Compton-$y$ selected clusters. Specifically, we apply the "spectroscopic method" and search for the temperature that best fits the clusters' signal measured at frequencies from 30 to 545 GHz by the Atacama Cosmology Telescope and Planck satellite. We measure the average temperature of clusters detected in the ACT maps to be $8.5\pm 2.4\,$keV, with an additional systematic error of comparable amplitude dominated by passband uncertainty. Upcoming surveys, such as the Simons Observatory and CMB-S4, have the potential to dramatically improve upon these measurements and thereby enable precision studies of cluster temperatures with millimeter observations. The key challenge for future observations will be mitigating instrumental systematic effects, which already limit this analysis., Comment: 21 pages with 17 figures

In this paper, we revisit the stability of power-law models, focusing on an alternative approach that differs significantly from the standard approaches used in studying power-law models. In the standard approach, stability is studied by reducing the system of background FRW equations to a one-dimensional system for a new background variable $X$ in terms of the number of e-foldings. However, we rewrote the equations, incorporating $H$ into the system and went on to do the calculations up to the second order. We demonstrate by computing the deviations from the power-law exact solution to second-order in time and show that power-law contraction is never an attractor in time, regardless of parameter values. Our analysis shows that while first-order corrections align with existing interpretations, second-order corrections introduce significant deviations that cannot be explained by a simple time shift that explains the first-order diverging terms. With importance, we note that in the number of e-folds, the system remains an attractor, while in cosmic time, it is unstable. We also support our claim with numerical results. This new insight has broader implications for the study of attractor behaviour of differential equation solutions and raises questions about the stability of scenarios like the ekpyrotic bounce driven by an exponential potential. Our work also hints that the different temporal variables we use might not be equivalent., Comment: 9 pages, 1 figure, 1 table

Low-mass emission-line stars belong to various evolutionary stages, from pre-main-sequence young stars to evolved stars. In this work, we present a catalog of late-type (F0 to M9) emission-line stars from the LAMOST Data Release 6. Using the scipy package, we created a Python code that finds the emission peak at H-alpha in all late-type stellar spectra. A dataset of 38,152 late-type emission-line stars was obtained after a rigorous examination of the photometric quality flags and the signal-to-noise ratio of the spectra. Adopting well-known photometric and spectroscopic methods, we classified our sample into 438 infrared excess sources, 4,669 post-main-sequence candidates, 9,718 Fe/Ge/Ke sources, and 23,264 dMe sources. From a cross-match with known databases, we found that 29,222 sources, comprising 65 IR excess sources, 7,899 Fe/Ge/Ke stars, 17,533 dMe stars, and 3,725 PtMS candidates, are new detections. We measured the equivalent width of the major emission lines observed in the spectra of our sample of emission-line stars. Furthermore, the trend observed in the line strengths of major emission lines over the entire late-type spectral range is analyzed. We further classified the sample into 4 groups based on the presence of Hydrogen and Calcium emission lines. This work presents a large dataset of late-type emission-line stars, which can be used to study active phenomena in late-type stars., Comment: Accepted for publication in RAA, 24 pages, 12 figures, 2 tables

Weak-line T Tauri stars (WTTS) exhibit X-ray flares, likely resulting from magnetic reconnection that heats the stellar plasma to very high temperatures. These flares are difficult to identify through targeted observations. Here, we report the serendipitous detection of the brightest X-ray flaring state of KM Ori in the eROSITA DR1 survey. Observations from SRG/eROSITA, Chandra X-ray Observatory, and XMM-Newton are analysed to assess the X-ray properties of KM Ori, thereby establishing its flaring state at the eROSITA epoch. The long-term (1999-2020) X-ray light curve generated for the Chandra observations confirmed that eROSITA captured the source at its highest X-ray flaring state recorded to date. Multi-instrument observations support the X-ray flaring state of the source, with time-averaged X-ray luminosity ($L_{0.2-5keV}$) reaching $\sim 1.9\times10^{32}{erg~s^{-1}}$ at the eROSITA epoch, marking it the brightest and possibly the longest flare observed to date. Such intense X-ray flares have been detected only in a few WTTS. The X-ray spectral analysis unveils the presence of multiple thermal plasma components at all epochs. The notably high luminosity ($L_{0.5-8~keV}\sim10^{32}{erg~s}^{-1}$), energy ($E_{0.5-8~keV}\sim10^{37}$erg), and the elevated emission measures of the thermal components in the eROSITA epoch indicate a superflare/megaflare state of KM Ori. Additionally, the H$\alpha$ line equivalent width of $\sim$-5\AA from our optical spectral analysis, combined with the lack of infrared excess in the spectral energy distribution, were used to re-confirm the WTTS (thin disk/disk-less) classification of the source. The long-duration flare of KM Ori observed by eROSITA indicates the possibility of a slow-rise top-flat flare. The detection demonstrates the potential of eROSITA to uncover such rare, transient events, thereby providing new insights into the X-ray activity of WTTS., Comment: 11 pages, 6 figures, 2 tables. This paper has been accepted for publication in PASA

Expansion of the universe is caused by the departure from the holographic equipartition. This principle, the law of emergence, first postulated in the context of Einstein's gravity has been extended successfully to more general gravity theories like Gauss-Bonnet and Lovelock gravity. We derive the law of emergence for braneworld models of gravity, starting from the more fundamental and well established principle, the first law of thermodynamics. More specifically, we derive the law of emergence in the context of RS II braneworld, Warped DGP model and Gauss-Bonnet braneworld and compare the derived law with the one proposed by Sheykhi for the braneworld models. We further show that the law of emergence leads to the maximization of horizon entropy in all these braneworld models. While the law of emergence effectively implies the maximization of horizon entropy, it could be derived from the first law of thermodynamics. Our results suggest that the horizon thermodynamics is the backbone of the law of emergence in the braneworld scenarios., Comment: 17 pages

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

Crucial for star formation is the interplay between gravity and turbulence. The observed cloud virial parameter, $\alpha_{\mathrm{vir}}$, which is the ratio of twice the turbulent kinetic energy to the gravitational energy, is found to vary significantly in different environments, where the scatter among individual star-forming clouds can exceed an order of magnitude. Therefore, a strong dependence of the initial mass function (IMF) on $\alpha_{\mathrm{vir}}$ may challenge the notion of a universal IMF. To determine the role of $\alpha_{\mathrm{vir}}$ on the IMF, we compare the star-particle mass functions obtained in high-resolution magnetohydrodynamical simulations including jet and heating feedback, with $\alpha_{\mathrm{vir}}=0.0625$, $0.125$, and $0.5$. We find that varying $\alpha_{\mathrm{vir}}$ from $\alpha_{\mathrm{vir}}\sim0.5$ to $\alpha_{\mathrm{vir}}<0.1$ shifts the peak of the IMF to lower masses by a factor of $\sim2$ and increases the star formation rate by a similar factor. The dependence of the IMF and star formation rate on $\alpha_{\mathrm{vir}}$ is non-linear, with the dependence subsiding at $\alpha_{\mathrm{vir}}<0.1$. Our study shows a systematic dependence of the IMF on $\alpha_{\mathrm{vir}}$. Yet, it may not be measurable easily in observations, considering the uncertainties, and the relatively weak dependence found in this study., Comment: 13 pages, 12 figures, 2 tables (MNRAS submitted)

The Duke Robotics Club is proud to present our robot for the 2024 RoboSub Competition: Oogway. Now in its second year, Oogway has been dramatically upgraded in both its capabilities and reliability. Oogway was built on the principle of independent, well-integrated, and reliable subsystems. Individual components and subsystems were tested and designed separately. Oogway's most advanced capabilities are a result of the tight integration between these subsystems. Such examples include a re-envisioned controls system, an entirely new electrical stack, advanced sonar integration, additional cameras and system monitoring, a new marker dropper, and a watertight capsule mechanism. These additions enabled Oogway to prequalify for Robosub 2024.

The elementary action of symplectic and orthogonal groups on unimodular rows of length $2n$ is transitive for $2n \geq \max(4, d+2)$ in the symplectic case, and $2n \geq \max(6, 2d+4)$ in the orthogonal case, over monoid rings $R[M]$, where $R$ is a commutative noetherian ring of dimension $d$, and $M$ is commutative cancellative torsion free monoid. As a consequence, one gets the surjective stabilization bound for the $K_1$ for classical groups. This is an extension of J. Gubeladze's results for linear groups., Comment: Transformation Groups (2024)

Let $S$ be an independent set of a simple undirected graph $G$. Suppose that each vertex of $S$ has a token placed on it. The tokens are allowed to be moved, one at a time, by sliding along the edges of $G$, so that after each move, the vertices having tokens always form an independent set of $G$. We would like to determine whether the tokens can be eventually brought to stay on the vertices of another independent set $S'$ of $G$ in this manner. In other words, we would like to decide if we can transform $S$ into $S'$ through a sequence of steps, each of which involves substituting a vertex in the current independent set with one of its neighbours to obtain another independent set. This problem of determining if one independent set of a graph ``is reachable'' from another independent set of it is known to be PSPACE-hard even for split graphs, planar graphs, and graphs of bounded treewidth. Polynomial time algorithms have been obtained for certain graph classes like trees, interval graphs, claw-free graphs, and bipartite permutation graphs. We present a polynomial time algorithm for the problem on block graphs, which are the graphs in which every maximal 2-connected subgraph is a clique. Our algorithm is the first generalization of the known polynomial time algorithm for trees to a larger class of graphs (note that trees form a proper subclass of block graphs)., Comment: 36 pages, 5 figures

The kinematic Sunyaev--Zel'dovich (kSZ) effect induces a non-zero density-density-temperature bispectrum, which we can use to reconstruct the large-scale velocity field from a combination of cosmic microwave background (CMB) and galaxy density measurements, in a procedure known as ``kSZ velocity reconstruction''. This method has been forecast to constrain large-scale modes with future galaxy and CMB surveys, improving their measurement beyond what is possible with the galaxy surveys alone. Such measurements will enable tighter constraints on large-scale signals such as primordial non-Gaussianity, deviations from homogeneity, and modified gravity. In this work, we demonstrate a statistically significant measurement of kSZ velocity reconstruction for the first time, by applying quadratic estimators to the combination of the ACT DR6 CMB+kSZ map and the DESI LRG galaxies (with photometric redshifts) in order to reconstruct the velocity field. We do so using a formalism appropriate for the 2-dimensional projected galaxy fields that we use, which naturally incorporates the curved-sky effects important on the largest scales. We find evidence for the signal by cross-correlating with an external estimate of the velocity field from the spectroscopic BOSS survey and rejecting the null (no-kSZ) hypothesis at $3.8\sigma$. Our work presents a first step towards the use of this observable for cosmological analyses., Comment: 16 pages (main)+5 pages (Appendix); 13 figures (main) + 8 figures (appendix)

How can we precisely estimate a large language model's (LLM) accuracy on questions belonging to a specific topic within a larger question-answering dataset? The standard direct estimator, which averages the model's accuracy on the questions in each subgroup, may exhibit high variance for subgroups (topics) with small sample sizes. Synthetic regression modeling, which leverages the model's accuracy on questions about other topics, may yield biased estimates that are too unreliable for large subgroups. We prescribe a simple yet effective solution: an empirical Bayes (EB) estimator that balances direct and regression estimates for each subgroup separately, improving the precision of subgroup-level estimates of model performance. Our experiments on multiple datasets show that this approach consistently provides more precise estimates of the LLM performance compared to the direct and regression approaches, achieving substantial reductions in the mean squared error. Confidence intervals for EB estimates also have near-nominal coverage and are narrower compared to those for the direct estimator. Additional experiments on tabular and vision data validate the benefits of this EB approach., Comment: To appear at EMNLP 2024

The morphology of quantum dots plays an important role in governing their photophysics. Here, we explore the photoluminescence of spheroidal CsPbBr3 quantum dots synthesized via the room-temperature trioctlyphosphine oxide/PbBr2 method. Despite photoluminescence quantum yields nearing 100%, these spheroidal quantum dots exhibit an elongated red photoluminescence tail not observed in typical cubic quantum dots synthesized via hot injection. We explore this elongated red tail through structural and optical characterization including small-angle x-ray scattering, transmission electron microscopy and time-resolved, steady-state, and single quantum dot photoluminescence. From these measurements we conclude that the red tail originates from emissive surface traps. We hypothesize that these emissive surface traps are located on the (111) surfaces and show that the traps can be passivated by adding phenethyl ammonium bromide, resulting in a more symmetric line shape

Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question answering for clinical decision support. However, our study shows that incorporating non decisive sociodemographic factors such as race, sex, income level, LGBT+ status, homelessness, illiteracy, disability, and unemployment into the input of LLMs can lead to incorrect and harmful outputs for these populations. These discrepancies risk exacerbating existing health disparities if LLMs are widely adopted in healthcare. To address this issue, we introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM based medical applications. Our evaluation demonstrates its efficacy in promoting equitable outcomes across diverse populations.

The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to the disadvantage of others has been identified as a central form of undesirable agent cooperation. The use of information hiding (steganography) in agent communications could render collusion practically undetectable. This underscores the need for evaluation frameworks to monitor and mitigate steganographic collusion capabilities. We address a crucial gap in the literature by demonstrating, for the first time, that robust steganographic collusion in LLMs can arise indirectly from optimization pressure. To investigate this problem we design two approaches -- a gradient-based reinforcement learning (GBRL) method and an in-context reinforcement learning (ICRL) method -- for reliably eliciting sophisticated LLM-generated linguistic text steganography. Importantly, we find that emergent steganographic collusion can be robust to both passive steganalytic oversight of model outputs and active mitigation through communication paraphrasing. We contribute a novel model evaluation framework and discuss limitations and future work. Our findings imply that effective risk mitigation from steganographic collusion post-deployment requires innovation in passive and active oversight techniques.

In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1 -- like previous LLMs -- is sensitive to the probability of examples and tasks, performing better and requiring fewer "thinking tokens" in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model's probability sensitivity., Comment: 6 pages; updated to fix typo in Fig 4 caption

Fix two words over the binary alphabet $\{0,1\}$, and generate iid Bernoulli$(p)$ bits until one of the words occurs in sequence. This setup, commonly known as Penney's ante, was popularized by Conway, who found (in unpublished work) a simple formula for the probability that a given word occurs first. We study win probabilities in Penney's ante from an analytic and combinatorial perspective, building on previous results for the case $p = \frac{1}{2}$ and words of the same length. For words of arbitrary lengths, our results bound how large the win probability can be for the longer word. When $p = \frac{1}{2}$ we characterize when a longer word can be statistically favorable, and for $p \neq \frac{1}{2}$ we present a conjecture describing the optimal pairs, which is supported by computer computations. Additionally, we find that Penney's ante often exhibits symmetry under the transformation $p \to 1-p$. We construct new explicit bijections that account for these symmetries, under conditions that can be easily verified by examining auto- and cross-correlations of the words., Comment: 20 pages, 2 figures