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Competition for a limited resource is the hallmark of many complex systems, and often, that resource turns out to be the physical space itself. In this work, we study a novel model designed to elucidate the dynamics and emergence in complex adaptive systems in which agents compete for some spatially spread resource. Specifically, in the model, the dynamics result from the agents trying to position themselves in the quest to avoid physical crowding experienced locally. We characterize in detail the dependence of the emergent behavior of the model on the population density of the system and the individual-level agent traits such as the extent of space an agent considers as her neighborhood, the limit of occupation density one tolerates within that neighborhood, and the information accessibility of the agents about neighborhood occupancy. We show that inefficiency in utilizing physical space shows transition at a specific density and peaks at another distinct density. Furthermore, we demonstrate that the variation of inefficiency relative to the information accessible to the agents exhibits contrasting behavior above and below this second density. We also look into the inequality of resource sharing in the model and show that although inefficiency can be a non-monotonic function of information depending upon the parameters of the model, inequality, in general, decreases with information. Our study sheds light on the role of competition, spatial constraints, and agent traits within complex adaptive systems, offering insights into their emergent behaviors., Comment: 14 figures, 22 pages

Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine learning, but representing a grain boundary structure in a manner suitable for machine learning is not a trivial task. There are three key steps common to property prediction in grain boundaries and other variable-sized atom clustered structures. These are: (1) describe the atomic structure as a feature matrix, (2) transform the variable-sized feature matrices of different structures to a fixed length common to all structures, and (3) apply machine learning to predict properties from the transformed feature matrices. We examine these feature engineering steps to understand how they impact the accuracy of grain boundary energy predictions. A database of over 7000 grain boundaries serves to evaluate the different feature engineering combinations. We also examine how these combination of engineered features provide interpretability, or the ability to extract insightful physics from the obtained structure-property relationships., Comment: 18 pages, 8 figures

The construction of highly coherent x-ray sources has enabled new research opportunities across the scientific landscape. The maximum raw data rate per beamline now exceeds 40 GB/s, posing unprecedented challenges for the online processing and offline storage of the big data. Such challenge is particularly prominent for x-ray photon correlation spectroscopy (XPCS), where real time analyses require simultaneous calculation on all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The lossy compression reduces the computational time by a factor of 10,000, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

Electricity storage is crucial for a successful transition towards carbon-neutral energy production. Despite considerable research and a number of promising future alternatives such as hydrogen, battery storages currently remain the first choice. However, costs remain high and it remains to be shown whether an investment can be profitable. This article addresses this question by modelling a battery storage operating in the German power market. We consider two periods with very distinct price dynamics, namely a calm year (2020) and a turbulent year (2023). It shows that even for low battery costs a 2020 style price environment does not allow for profitable battery operation, whereas current market conditions allow for positive payoffs., Comment: Preprint submitted to IEEE I and CPS Asia Conference 2024

Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approaches have utilized self-supervised models pre-trained on human speech for feature extraction, capitalizing on their ability to learn a signal's intrinsic structure independently of its acoustic domain. However, the utility of such foundation models remains unclear for marmoset call analysis in terms of multi-class classification, bandwidth, and pre-training domain. This study assesses feature representations derived from speech and general audio domains, across pre-training bandwidths of 4, 8, and 16 kHz for marmoset call-type and caller classification tasks. Results show that models with higher bandwidth improve performance, and pre-training on speech or general audio yields comparable results, improving over a spectral baseline., Comment: Accepted at Interspeech 2024 satellite event (VIHAR 2024)

We propose a modified weighted Nadaraya-Watson estimator for the conditional distribution of a time series with heavy tails. We establish the asymptotic normality of the proposed estimator. Simulation study is carried out to assess the performance of the estimator. We illustrate our method using a dataset.

Violence-provoking speech -- speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech ($F_1 = 0.89$), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task ($F_1 = 0.69$). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises. The resources related to the study are available at https://claws-lab.github.io/violence-provoking-speech/., Comment: Accepted to ACL 2024 Main

The 2175 {\AA} bump shows considerable variations in its strength, width, and central wavelength when observed along different sightlines in the Milky Way and other galaxies. These variations offer valuable insights into the composition, size distribution, and processing of interstellar dust grains along different sightlines. This paper introduces a mission concept called UVESS (Ultraviolet Extinction Sky Survey) aimed at exploring the composition of the interstellar medium (ISM) within both the Milky Way and nearby Local Group Galaxies by mapping the variation of UV extinction curve slopes and the 2175 {\AA} feature across a majority of the sky to gain insights into the makeup of the ISM. Recent advancements in UV instrumentation and technologies pave the way for the development of high-throughput instruments in compact form factors. In this paper, we outline mission science goals and instrument concept tailored for a small satellite-based platform dedicated to the study of UV extinction.

Industry 4.0 introduced AI as a transformative solution for modernizing manufacturing processes. Its successor, Industry 5.0, envisions humans as collaborators and experts guiding these AI-driven manufacturing solutions. Developing these techniques necessitates algorithms capable of safe, real-time identification of human positions in a scene, particularly their hands, during collaborative assembly. Although substantial efforts have curated datasets for hand segmentation, most focus on residential or commercial domains. Existing datasets targeting industrial settings predominantly rely on synthetic data, which we demonstrate does not effectively transfer to real-world operations. Moreover, these datasets lack uncertainty estimations critical for safe collaboration. Addressing these gaps, we present HAGS: Hand and Glove Segmentation Dataset. This dataset provides challenging examples to build applications toward hand and glove segmentation in industrial human-robot collaboration scenarios as well as assess out-of-distribution images, constructed via green screen augmentations, to determine ML-classifier robustness. We study state-of-the-art, real-time segmentation models to evaluate existing methods. Our dataset and baselines are publicly available., Comment: draft paper to be submitted to IJRR

We investigate the behavior of large connected components in the Random Connection Model in the subcritical regime with any bounded connection function. We show that the asymptotic size of the largest component restricted to a window is the log of the volume of that window. We adopt methods from the discrete setting, including a sharpness result.

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increase productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, representing the physical process in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97\% and the overall standard deviation error is 5.54\% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time.

We infer the growth of large scale structure over the redshift range $0.4\lesssim z \lesssim 1$ from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latest Planck and ACT data. We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. We perform an extensive set of bandpower- and parameter-level systematics checks to ensure the robustness of our results and to characterize the uniformity of the LRG sample. We demonstrate that our results are stable to a wide range of modeling assumptions, finding excellent agreement with a linear theory analysis performed on a restricted range of scales. From a tomographic analysis of the four LRG photometric redshift bins we find that the rate of structure growth is consistent with $\Lambda$CDM with an overall amplitude that is $\simeq5-7\%$ lower than predicted by primary CMB measurements with modest $(\sim2\sigma)$ statistical significance. From the combined analysis of all four bins and their cross-correlations with Planck we obtain $S_8 = 0.765\pm0.023$, which is less discrepant with primary CMB measurements than previous DESI LRG cross Planck CMB lensing results. From the cross-correlation with ACT we obtain $S_8 = 0.790^{+0.024}_{-0.027}$, while when jointly analyzing Planck and ACT we find $S_8 = 0.775^{+0.019}_{-0.022}$ from our data alone and $\sigma_8 = 0.772^{+0.020}_{-0.023}$ with the addition of BAO data. These constraints are consistent with the latest Planck primary CMB analyses at the $\simeq 1.6-2.2\sigma$ level, and are in excellent agreement with galaxy lensing surveys., Comment: 60 pages, 26 figures, comments welcome

We present a high-significance cross-correlation of CMB lensing maps from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) with spectroscopically calibrated luminous red galaxies (LRGs) from the Dark Energy Spectroscopic Instrument (DESI). We detect this cross-correlation at a significance of 38$\sigma$; combining our measurement with the Planck Public Release 4 (PR4) lensing map, we detect the cross-correlation at 50$\sigma$. Fitting this jointly with the galaxy auto-correlation power spectrum to break the galaxy bias degeneracy with $\sigma_8$, we perform a tomographic analysis in four LRG redshift bins spanning $0.4 \le z \le 1.0$ to constrain the amplitude of matter density fluctuations through the parameter combination $S_8^\times = \sigma_8 \left(\Omega_m / 0.3\right)^{0.4}$. Prior to unblinding, we confirm with extragalactic simulations that foreground biases are negligible and carry out a comprehensive suite of null and consistency tests. Using a hybrid effective field theory (HEFT) model that allows scales as small as $k_{\rm max}=0.6$ $h/{\rm Mpc}$, we obtain a 3.3% constraint on $S_8^\times = \sigma_8 \left(\Omega_m / 0.3\right)^{0.4} = 0.792^{+0.024}_{-0.028}$ from ACT data, as well as constraints on $S_8^\times(z)$ that probe structure formation over cosmic time. Our result is consistent with the early-universe extrapolation from primary CMB anisotropies measured by Planck PR4 within 1.2$\sigma$. Jointly fitting ACT and Planck lensing cross-correlations we obtain a 2.7% constraint of $S_8^\times = 0.776^{+0.019}_{-0.021}$, which is consistent with the Planck early-universe extrapolation within 2.1$\sigma$, with the lowest redshift bin showing the largest difference in mean. The latter may motivate further CMB lensing tomography analyses at $z<0.6$ to assess the impact of potential systematics or the consistency of the $\Lambda$CDM model over cosmic time., Comment: Prepared for submission to JCAP (47 pages, 13 figures)

Curl lensing, also known as lensing field-rotation or shear B-modes, is a distinct post-Born observable caused by two lensing deflections at different redshifts (lens-lens coupling). For the Cosmic Microwave Background (CMB), the field-rotation is approximately four orders of magnitude smaller than the CMB lensing convergence. Direct detection is therefore challenging for near-future CMB experiments such as the Simons Observatory (SO) or CMB `Stage-4' (CMB-S4). Instead, the curl can be probed in cross-correlation between a direct reconstruction and a template formed using pairs of large-scale structure (LSS) tracers to emulate the lens-lens coupling. In this paper, we derive a new estimator for the optimal curl template specifically adapted for curved-sky applications, and test it against non-Gaussian complications using N-body cosmology simulations. We find non-foreground biases to the curl cross-spectrum are purely Gaussian at the sensitivity of SO. However, higher-order curl contractions induce non-Gaussian bias at the order of $1\sigma$ for CMB-S4 using quadratic estimators (QE). Maximum a-Posteriori (MAP) lensing estimators significantly reduce biases for both SO and CMB-S4, in agreement with our analytic predictions. We also show that extragalactic foregrounds in the CMB can bias curl measurements at order of the signal, and evaluate a variety of mitigation strategies to control these biases for SO-like experiments. Near-future observations will be able to measure post-Born lensing curl B-modes., Comment: 19 pages, 17 figures, JCAP accepted version

We report and discuss phase-shift and phase travel time of low-frequency ({\nu} < 5.0 mHz) acoustic waves estimated within the photosphere and photosphere-chromosphere interface regions, utilizing multi-height velocities in the quiet Sun. The bisector method has been employed to estimate seven height velocities in the photosphere within the Fe I 6173 {\AA} line scan, while nine height velocities are estimated from the chromospheric Ca II 8542 {\AA} line scan observations obtained from the Narrow Band Imager instrument installed with the Multi-Application Solar Telescope operational at the Udaipur Solar Observatory, India. Utilizing fast Fourier transform at each pixel over the full field-of-view, phase shift and coherence have been estimated. The frequency and height-dependent phase shift integrated over the regions having an absolute line-of-sight magnetic field of less than 10 G indicates the non-evanescent nature of low-frequency acoustic waves within the photosphere and photosphere-chromosphere interface regions. Phase travel time estimated within the photosphere shows non-zero values, aligning with previous simulations and observations. Further, we report that the non-evanescent nature persists beyond the photosphere, encompassing the photospheric-chromospheric height range. We discuss possible factors contributing to the non-evanescent nature of low-frequency acoustic waves. Additionally, our observations reveal a downward propagation of high-frequency acoustic waves, indicating the refraction from higher layers in the solar atmosphere. This study contributes valuable insights into the understanding of the complex dynamics of acoustic waves within different lower solar atmospheric layers, shedding light on the non-evanescent nature and downward propagation of the acoustic waves., Comment: 12 pages, 10 figures, Accepted for Publication in the Astrophysical Journal

We study the wide-binary eccentricity ($e$) distribution in young star clusters and the role of turbulence in setting the form of the $e$ distribution using magnetohydrodynamical (MHD) simulations of star cluster formation. The simulations incorporate gravity, turbulence, magnetic fields, protostellar heating, and jets/outflows. We find that (1) simulations that employ purely compressive turbulence driving produce binaries with a superthermal $e$ distribution ($\alpha>1$ in $p(e) \propto e^\alpha$), while simulations with purely solenoidal driving or natural mixture of driving modes produce subthermal/thermal distributions ($\alpha \leq$ 1), (2) the $e$ distribution over the full range of binary separations in our simulations is set at the early stages of the star cluster formation process, (3) while binaries (separation of $r_{\mathrm{pair}} \leq 1000\, \mathrm{AU}$) have subthermal to thermal $e$ distributions ($\alpha \sim 0.8$), wide binaries ($r_{\mathrm{pair}} > 1000\, \mathrm{AU}$) have a superthermal distribution ($\alpha \sim 1.8$), and (4) low-mass binary systems (system masses of $M_{\mathrm{sys}} \leq 0.8\, \mathrm{M_\odot}$) have a highly superthermal distribution ($\alpha \sim 2.4$), whereas high-mass systems ($M_{\mathrm{sys}} > 0.8\, \mathrm{M_\odot}$) exhibit a subthermal/thermal distribution ($\alpha \sim 0.8$). The binary eccentricity distribution is often modelled as a thermal distribution. However, our results suggest that the $e$ distribution depends on the range of separation of the sampled binaries, which agrees with the findings from recent Gaia observations. We conclude that the dependence of the $e$ distribution on the binary separation and mass is linked to the binary formation mechanism governed by the turbulent properties of the parent cloud., Comment: 15 pages, 9 figures, 1 table (added additional citation, published in MNRAS)

Ab initio Born-Oppenheimer molecular dynamics (AIMD) is a valuable method for simulating physico-chemical processes of complex systems, including reactive systems, and for training machine learning models and force fields. Speed and stability issues on traditional hardware preclude routine AIMD simulations for larger systems and longer timescales. We postulate that any practically useful AIMD simulation must generate a trajectory of a minimum 1000 MD steps a day on a moderate cloud resource. In this work, we implement a computing workflow that enables routine calculations at this throughput and demonstrate results for several non-trivial atomistic dynamical systems. In particular, we have employed the GPU implementation of the Quantum ESPRESSO code which we will show increases AIMD productivity compared to the CPU version. In order to take advantage of transient servers (which are more cost and energy effective compared to the stable servers), we have implemented automatic restart/continuation of the AIMD runs within the Schr\"odinger Materials Science Suite. Finally, to reduce simulation size and thus reduce compute time when modeling surfaces, we have implemented a wall potential constraint. Our benchmarks using several reactive systems (lithium anode surface/solvent interface, hydrogen diffusion in an iron grain boundary) show a significant speed up when running on a GPU-enabled transient server using our updated implementation.

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices was directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that were previously inaccessible. Their reconfiguration and competition were quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics were quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity were drastically altered by light, holding promise for light-programmable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for remote control of adaptive nanoscale domain networks.

This paper lays the groundwork for a unifying approach to stochastic dominance testing under survey nonresponse that integrates the partial identification approach to incomplete data and design-based inference for complex survey data. We propose a novel inference procedure for restricted $s$th-order stochastic dominance, tailored to accommodate a broad spectrum of nonresponse assumptions. The method uses pseudo-empirical likelihood to formulate the test statistic and compares it to a critical value from the chi-squared distribution with one degree of freedom. We detail the procedure's asymptotic properties under both null and alternative hypotheses, establishing its uniform validity under the null and consistency against various alternatives. Using the Household, Income and Labour Dynamics in Australia survey, we demonstrate the procedure's utility in a sensitivity analysis of temporal poverty comparisons among Australian households.

We developed a modular silicon photomultiplier camera to detect Earth-skimming PeV to EeV tau neutrinos with the imaging atmospheric Cherenkov technique. We built two cameras, a 256-pixel camera with S14161-6050HS SiPMs for the Trinity Demonstrator located on Frisco Peak, Utah, and a 512-pixel camera with S14521-6050AN SiPMs for the EUSO-SPB2 Cherenkov Telescope. The front-end electronics are based on the eMUSIC ASIC, and the camera signals are sampled and digitized with the 100MS/s and 12-bit AGET system. Both cameras are liquid-cooled. We detail the camera concept and the results from characterizing the SiPMs, bench testing, and calibrating the two cameras., Comment: Submitted to Nuclear Instruments and Methods in Physics Research A

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor - corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series however traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.

Model performance evaluation is a critical and expensive task in machine learning and computer vision. Without clear guidelines, practitioners often estimate model accuracy using a one-time completely random selection of the data. However, by employing tailored sampling and estimation strategies, one can obtain more precise estimates and reduce annotation costs. In this paper, we propose a statistical framework for model evaluation that includes stratification, sampling, and estimation components. We examine the statistical properties of each component and evaluate their efficiency (precision). One key result of our work is that stratification via k-means clustering based on accurate predictions of model performance yields efficient estimators. Our experiments on computer vision datasets show that this method consistently provides more precise accuracy estimates than the traditional simple random sampling, even with substantial efficiency gains of 10x. We also find that model-assisted estimators, which leverage predictions of model accuracy on the unlabeled portion of the dataset, are generally more efficient than the traditional estimates based solely on the labeled data., Comment: To appear at ECCV 2024

Sign languages are multi-channel languages that communicate information through not just the hands (manual signals) but also facial expressions and upper body movements (non-manual signals). However, since automatic sign language translation is usually performed by generating a single sequence of glosses, researchers eschew non-manual and co-occurring manual signals in favor of a simplified list of manual glosses. This can lead to significant information loss and ambiguity. In this paper, we introduce a new task named multi-channel sign language translation (MCSLT) and present a novel metric, SignBLEU, designed to capture multiple signal channels. We validated SignBLEU on a system-level task using three sign language corpora with varied linguistic structures and transcription methodologies and examined its correlation with human judgment through two segment-level tasks. We found that SignBLEU consistently correlates better with human judgment than competing metrics. To facilitate further MCSLT research, we report benchmark scores for the three sign language corpora and release the source code for SignBLEU at https://github.com/eq4all-projects/SignBLEU., Comment: Published in LREC-Coling 2024

Accurately predicting heart activity and other biological signals is crucial for diagnosis and monitoring. Given that speech is an outcome of multiple physiological systems, a significant body of work studied the acoustic correlates of heart activity. Recently, self-supervised models have excelled in speech-related tasks compared to traditional acoustic methods. However, the robustness of data-driven representations in predicting heart activity remained unexplored. In this study, we demonstrate that self-supervised speech models outperform acoustic features in predicting heart activity parameters. We also emphasize the impact of individual variability on model generalizability. These findings underscore the value of data-driven representations in such tasks and the need for more speech-based physiological data to mitigate speaker-related challenges., Comment: Accepted at Interspeech 2024

As emerging software vulnerabilities continuously threaten enterprises and Internet services, there is a critical need for improved security research capabilities. This paper introduces the Security Exploit Telemetry Collection (SETC) framework - an automated framework to generate reproducible vulnerability exploit data at scale for robust defensive security research. SETC deploys configurable environments to execute and record rich telemetry of vulnerability exploits within isolated containers. Exploits, vulnerable services, monitoring tools, and logging pipelines are defined via modular JSON configurations and deployed on demand. Compared to current manual processes, SETC enables automated, customizable, and repeatable vulnerability testing to produce diverse security telemetry. This research enables scalable exploit data generation to drive innovations in threat modeling, detection methods, analysis techniques, and remediation strategies. The capabilities of the framework are demonstrated through an example scenario. By addressing key barriers in security data generation, SETC represents a valuable platform to support impactful vulnerability and defensive security research., Comment: 7 pages, 2 figures

We introduce the space of mixed-volume forms endowed with an $L^2$ metric on a balanced manifold. A geodesic equation can be derived in this space that has an interesting structure and extends the equation of Donaldson \cite{Donaldson10} and Chen-He \cite{CH11} in the space of volume forms on a Riemannian manifold. This nonlinear PDE is studied in detail and the existence of weak solution is shown for the Dirichlet problem, under a positivity assumption. Later we study the Calabi-Yau equation for balanced metrics and introduce a geometric criteria for prescribing volume forms that is closely related to the positivity assumption above. We call this assumption the sub-Astheno-K\"ahler condition. By deriving $C^0$ a priori estimates, we show that the existence of solutions can be established on all sub-Astheno-K\"ahler manifolds., Comment: Corrected minor errors, and added more details in the introduction

Consider a random uniform sample of $n$ points in a compact region $A$ of Euclidean $d$-space, $d \geq 2$, with a smooth or (when $d=2$) polygonal boundary. Fix $k \in {\bf N}$. Let $T_{n,k}$ be the threshold $r$ at which the geometric graph on these $n$ vertices with distance parameter $r$ becomes $k$-connected. We show that if $d=2$ then $n (\pi/|A|) T_{n,1}^2 - \log n$ is asymptotically standard Gumbel. For $(d,k) \neq (2,1)$, it is $n (\theta_d/|A|) T_{n,k}^d - (2-2/d) \log n - (4-2k-2/d) \log \log n$ that converges in distribution to a nondegenerate limit, where $\theta_d$ is the volume of the unit ball. The limit is Gumbel with scale parameter 2 except when $(d,k)=(2,2)$ where the limit is two component extreme value distributed. The different cases reflect the fact that boundary effects are more more important in some cases than others. We also give similar results for the largest $k$-nearest neighbour link $U_{n,k}$ in the sample, and show $T_{n,k}=U_{n,k}$ with high probability. We provide estimates on rates of convergence and give similar results for Poisson samples in $A$. Finally, we give similar results even for non-uniform samples, with a less explicit sequence of centring constants., Comment: 39 pages, 3 figures. Various minor corrections and clarifications of v1