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We explore the distribution in space and time of a single-photon excitation shared by a network of dipole-dipole interacting atoms that are also coupled to a common photonic field mode. Time-averaged distributions reveal partial trapping of the excitation near the initially excited atom. This trapping is associated with resonances of the excitation at crossing points of the photon-dressed energy eigenvalues of the network. The predicted photon-induced many-atom trapped excitation (PIMATE) is sensitive to atomic position disorder which broadens the excitation resonances and transforms them to avoided crossings. PIMATE is shown to allow highly effective and accurate sensing of multi-atom networks and their disorder.

We investigate the task of deterministically condensing randomness from Online Non-Oblivious Symbol Fixing (oNOSF) sources, a natural model for which extraction is impossible [AORSV, EUROCRYPT'20]. A $(g,\ell)$-oNOSF source is a sequence of $\ell$ blocks where at least $g$ of the blocks are good (independent and have some min-entropy) and the remaining bad blocks are controlled by an online adversary where each bad block can be arbitrarily correlated with any block that appears before it. The existence of condensers was studied in [CGR, FOCS'24]. They proved condensing impossibility results for various values of $g, \ell$ and showed the existence of condensers matching the impossibility results in the case when $n$ is extremely large compared to $\ell$. In this work, we make significant progress on proving the existence of condensers with strong parameters in almost all parameter regimes, even when $n$ is a large enough constant and $\ell$ is growing. This almost resolves the question of the existence of condensers for oNOSF sources, except when $n$ is a small constant. We construct the first explicit condensers for oNOSF sources, achieve parameters that match the existential results of [CGR, FOCS'24], and obtain an improved construction for transforming low-entropy oNOSF sources into uniform ones. We find applications of our results to collective coin flipping and sampling, well-studied problems in fault-tolerant distributed computing. We use our condensers to provide simple protocols for these problems. To understand the case of small $n$, we focus on $n=1$ which corresponds to online non-oblivious bit-fixing (oNOBF) sources. We initiate a study of a new, natural notion of influence of Boolean functions which we call online influence. We establish tight bounds on the total online influence of Boolean functions, implying extraction lower bounds., Comment: 36 pages

Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions is hindered by data silos within financial institutions, limiting collaboration and overall efficacy. This research presents a novel privacy-preserving approach for collaborative AML machine learning, facilitating secure data sharing across institutions and borders while preserving privacy and regulatory compliance. Leveraging Fully Homomorphic Encryption (FHE), computations are directly performed on encrypted data, ensuring the confidentiality of financial data. Notably, FHE over the Torus (TFHE) was integrated with graph-based machine learning using Zama Concrete ML. The research contributes two key privacy-preserving pipelines. First, the development of a privacy-preserving Graph Neural Network (GNN) pipeline was explored. Optimization techniques like quantization and pruning were used to render the GNN FHE-compatible. Second, a privacy-preserving graph-based XGBoost pipeline leveraging Graph Feature Preprocessor (GFP) was successfully developed. Experiments demonstrated strong predictive performance, with the XGBoost model consistently achieving over 99% accuracy, F1-score, precision, and recall on the balanced AML dataset in both unencrypted and FHE-encrypted inference settings. On the imbalanced dataset, the incorporation of graph-based features improved the F1-score by 8%. The research highlights the need to balance the trade-off between privacy and computational efficiency., Comment: 14th International Conference on Security, Privacy, and Applied Cryptographic Engineering (SPACE) 2024

In this work we focus on the effect of the QCD coupling parameter ($\alpha_c$) on various physical properties of compact objects admitting the MIT bag model EoS in the framework of the Tolman IV potential in the presence of nonzero strange quark mass ($m_s$). The internal matter consisting of the deconfined phase of the $3$-flavour quark, is overall charge neutral due to the presence of electrons and is assumed to strongly interact. Interestingly, the coupling parameter $\alpha_c$ has an upper limit due to thermodynamic consistency, and affects the stability in terms of energy per baryon ($E_B$). Our model is suitable for stars with masses of approximately $\leq 2.00~M_{\odot}$. The predicted radius from our model is in agreement with the estimated values of the radius from the observations. Various stability checks of our model are carried out and it is found that the model is stable within the parameter space used here., Comment: 32 pages, 29 figures

We consider a chain of divalent atoms with two electronic orbitals per atom, whose length is periodically oscillating with time in response to an external oscillating pressure source. We introduce both inter-orbital and intra-orbital hopping between neighboring atoms which as a consequence of the oscillating lattice separation, are also periodically oscillating in time. Within the tight binding approximation and in the limit of very small inter-orbital hopping, we get two bands separated by an indirect band gap which itself oscillates due to the oscillating chain length. Under suitable choice of hopping parameters (about which it oscillates) and orbital energies, we show that there can be a periodic metal-insulator transition in the half-filled system. If the frequency of the metal-insulator transition resonates with an externally applied alternating electric field, it can give rise to the phenomena of half-wave rectification wherein in one half of the cycle the system is metallic and conducting, where as in the other half of the cycle it is insulating and non-conducting., Comment: 4 pages, 3 figures

In this article, we explore the properties of hybrid star composed of deconfined quarks and dark energy considering finite value of mass of strange quark ($m_s\neq0$). We have studied the various properties of such stars assuming a linear relation between dark energy pressure and density as $p^{de}=\omega\rho^{de}$, where $-1<\omega<-\frac{1}{3}$, within the framework of Finch-Skea ansatz of $g_{rr}$ component of line element by varying the dark energy coupling parameter ($\beta$). In this model, $\frac{\beta}{1+\beta}$ represents the percentage of dark energy. Following the relation $\rho^{de}=\beta\rho^Q$, we have noted some restriction on the coupling parameter $\beta$ as $0<\beta<-\frac{1}{3\omega}$. It is interesting to note that with the change of percentage composition of dark energy, there is a prominent change of phase within in such stars. Solving TOV equations, the maximum mass attainable in this model is $\approx2~M_{\odot}$ and radius $11.37~km$. Both mass and radius decrease with the increase of $m_s$ and $\beta$ for constant $\omega$. On the other hand, maximum mass increases with the decrease of $\omega$. Various stability conditions along with causality and energy conditions are studied and found to be in agreement with the conditions of a viable stellar model. We have predicted the radii of recently observed compact stars and lighter component of the GW event $170817$ and it is interesting to note that the predicted radius of the model is close to the estimated value of the radius from observations., Comment: 25 pages, 20 figures

Simulating quantum circuits is a computationally intensive task that relies heavily on tensor products and matrix multiplications, which can be inefficient. Recent advancements, eliminate the need for tensor products and matrix multiplications, offering significant improvements in efficiency and parallelization. Extending these optimizations, we adopt a block-simulation methodology applicable to qubit-qudit hybrid systems. This method interprets the statevector as a collection of blocks and applies gates without computing the entire circuit unitary. Our method, a spiritual successor of the simulator QuDiet \cite{Chatterjee_2023}, utilizes this block-simulation method, thereby gaining major improvements over the simulation methods used by its predecessor. We exhibit that the proposed method is approximately 10$\times$ to 1000$\times$ faster than the state-of-the-art simulator for simulating multi-level quantum systems with various benchmark circuits., Comment: 12 pages, 10 figures

Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which hinder accurate QoS predictions and limit the ability to capture diverse user preferences. Additionally, these methods often assume QoS data reliability, neglecting potential credibility issues such as outliers and the presence of greysheep users and services with atypical invocation patterns. Furthermore, traditional approaches fail to leverage diverse features, including domain-specific knowledge and complex higher-order patterns, essential for accurate QoS predictions. In this paper, we introduce a real-time, trust-aware framework for temporal QoS prediction to address the aforementioned challenges, featuring an end-to-end deep architecture called the Hypergraph Convoluted Transformer Network (HCTN). HCTN combines a hypergraph structure with graph convolution over hyper-edges to effectively address high-sparsity issues by capturing complex, high-order correlations. Complementing this, the transformer network utilizes multi-head attention along with parallel 1D convolutional layers and fully connected dense blocks to capture both fine-grained and coarse-grained dynamic patterns. Additionally, our approach includes a sparsity-resilient solution for detecting greysheep users and services, incorporating their unique characteristics to improve prediction accuracy. Trained with a robust loss function resistant to outliers, HCTN demonstrated state-of-the-art performance on the large-scale WSDREAM-2 datasets for response time and throughput., Comment: 16 pages, 12 figures

In recent decades, event studies have emerged as a central methodology in health and social research for evaluating the causal effects of staggered interventions. In this paper, we analyze event studies from the perspective of experimental design and focus on the use of information across units and time periods in the construction of effect estimators. As a particular case of this approach, we offer a novel decomposition of the classical dynamic two-way fixed effects (TWFE) regression estimator for event studies. Our decomposition is expressed in closed form and reveals in finite samples the hypothetical experiment that TWFE regression adjustments approximate. This decomposition offers insights into how standard regression estimators borrow information across different units and times, clarifying and supplementing the notion of forbidden comparison noted in the literature. We propose a robust weighting approach for estimation in event studies, which allows investigators to progressively build larger valid weighted contrasts by leveraging increasingly stronger assumptions on the treatment assignment and potential outcomes mechanisms. This weighting approach also allows for the generalization of treatment effect estimates to a target population. We provide diagnostics and visualization tools and illustrate these methods in a case study of the impact of divorce reforms on female suicide.

We investigate black hole-star binaries formed in $N$-body simulations of massive, dense star clusters. We simulate 32 clusters with varying initial masses ($10^{4}~\rm M_{\odot}$ to $10^{6}~\rm M_{\odot}$), densities ($1200~\rm M_{\odot}~pc^{-3}$ to $10^{5}~\rm M_{\odot}~pc^{-3}$), and metallicities $(Z = 0.01,~0.001,~0.0001)$. Our results reveal that star clusters produce a diverse range of BH-star binaries, with dynamical interactions leading to extreme systems characterised by large orbital separations and high black hole masses. Of the ejected BH-main sequence (BH-MS) binaries, $20\%$ form dynamically, while the rest originate from the primordial binary population initially present in the cluster. Ejected BH-MS binaries that are dynamically formed have more massive black holes, lower-mass stellar companions, and over half are in a hierarchical triple system. All unbound BH-giant star (BH-GS) binaries were ejected as BH-MS binaries and evolved into the BH-GS phase outside the cluster. Due to their lower-mass companions, most dynamically formed binaries do not evolve into BH-GS systems within a Hubble time. Consequently, only 2 of the 35 ejected BH-GS binaries are dynamically formed. We explore the formation pathways of Gaia-like systems, identifying two Gaia BH1-like binaries that formed through dynamical interactions, and two Gaia BH2-like systems with a primordial origin. We did not find any system resembling Gaia BH3, which may however be attributed to the limited sample size of our simulations.

Matter falling onto black holes, {also called} accretion discs, emit intense high-energy radiation. Accretion discs during {hard to hard intermediate} spectral states also emit bipolar outflows. Radiation drag was supposed to impose the upper limit on the terminal speed. It was later shown that a radiation field around an advective accretion disc imposes no upper limit on speed, about a few hundred of Schwarzschild radius from the disc surface. We {study radiatively driven electron-proton and electron-positron jets, for gemeotrically thick and slim transonic discs} by using numerical simulation. We show that pair-dominated jets can reach ultra-relativistic speeds by radiation driving. We also discuss at what limits radiative acceleration may fail., Comment: 10 pages, 6 figures. Refereed conference procedings of International Symposium on Recent Developments i Relativistic Astrophysics (ISRA 2023), held in SRM University Sikkim

Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather/climate models. An important open question is whether AI models can extrapolate from weaker weather events present in the training set to stronger, unseen weather extremes. To test this, we train independent versions of the AI model FourCastNet on the 1979-2015 ERA5 dataset with all data, or with Category 3-5 tropical cyclones (TCs) removed, either globally or only over the North Atlantic or Western Pacific basin. We then test these versions of FourCastNet on 2018-2023 Category 5 TCs (gray swans). All versions yield similar accuracy for global weather, but the one trained without Category 3-5 TCs cannot accurately forecast Category 5 TCs, indicating that these models cannot extrapolate from weaker storms. The versions trained without Category 3-5 TCs in one basin show some skill forecasting Category 5 TCs in that basin, suggesting that FourCastNet can generalize across tropical basins. This is encouraging and surprising because regional information is implicitly encoded in inputs. No version satisfies gradient-wind balance, implying that enforcing such physical constraints may not improve generalizability to gray swans. Given that current state-of-the-art AI weather/climate models have similar learning strategies, we expect our findings to apply to other models and extreme events. Our work demonstrates that novel learning strategies are needed for AI weather/climate models to provide early warning or estimated statistics for the rarest, most impactful weather extremes.

Automatic speech recognition (ASR) for low-resource languages remains a challenge due to the scarcity of labeled training data. Parameter-efficient fine-tuning and text-only adaptation are two popular methods that have been used to address such low-resource settings. In this work, we investigate how these techniques can be effectively combined using a multilingual multimodal model like SeamlessM4T. Multimodal models are able to leverage unlabeled text via text-only adaptation with further parameter-efficient ASR fine-tuning, thus boosting ASR performance. We also show cross-lingual transfer from a high-resource language, achieving up to a relative 17% WER reduction over a baseline in a zero-shot setting without any labeled speech.

Time series forecasts are often influenced by exogenous contextual features in addition to their corresponding history. For example, in financial settings, it is hard to accurately predict a stock price without considering public sentiments and policy decisions in the form of news articles, tweets, etc. Though this is common knowledge, the current state-of-the-art (SOTA) forecasting models fail to incorporate such contextual information, owing to its heterogeneity and multimodal nature. To address this, we introduce ContextFormer, a novel plug-and-play method to surgically integrate multimodal contextual information into existing pre-trained forecasting models. ContextFormer effectively distills forecast-specific information from rich multimodal contexts, including categorical, continuous, time-varying, and even textual information, to significantly enhance the performance of existing base forecasters. ContextFormer outperforms SOTA forecasting models by up to 30% on a range of real-world datasets spanning energy, traffic, environmental, and financial domains.

Movie posters are not just decorative; they are meticulously designed to capture the essence of a movie, such as its genre, storyline, and tone/vibe. For decades, movie posters have graced cinema walls, billboards, and now our digital screens as a form of digital posters. Movie genre classification plays a pivotal role in film marketing, audience engagement, and recommendation systems. Previous explorations into movie genre classification have been mostly examined in plot summaries, subtitles, trailers and movie scenes. Movie posters provide a pre-release tantalizing glimpse into a film's key aspects, which can ignite public interest. In this paper, we presented the framework that exploits movie posters from a visual and textual perspective to address the multilabel movie genre classification problem. Firstly, we extracted text from movie posters using an OCR and retrieved the relevant embedding. Next, we introduce a cross-attention-based fusion module to allocate attention weights to visual and textual embedding. In validating our framework, we utilized 13882 posters sourced from the Internet Movie Database (IMDb). The outcomes of the experiments indicate that our model exhibited promising performance and outperformed even some prominent contemporary architectures.

Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel framework that addresses these challenges by leveraging the recently proposed multi-step penalty (MP) optimization technique. Our approach extends the applicability of MP optimization to a wide range of deep learning architectures, including Fourier Neural Operators and UNETs. By introducing penalized local discontinuities in the forecast trajectory, we effectively handle the non-convexity of loss landscapes commonly encountered in training neural networks for chaotic systems. We demonstrate the effectiveness of our method through its application to two challenging use-cases: the prediction of flow velocity evolution in two-dimensional turbulence and ocean dynamics using reanalysis data. Our results highlight the potential of this approach for accurate and stable long-term prediction of chaotic dynamics, paving the way for new advancements in data-driven modeling of complex natural phenomena., Comment: 7 pages, 5 Figures, Submitted to CASML2024

Scalar Non-Standard Interactions (SNSI) in neutrinos can arise when a scalar mediator couples to both neutrinos and standard model fermions. This beyond the Standard Model (BSM) scenario is particularly interesting as the SNSI contribution appears as a density-dependent perturbation to the neutrino mass, rather than appearing as a matter-induced potential, and the neutrino oscillation probabilities uniquely depend on the absolute neutrino masses. In this work, we show the complex dependence of the SNSI contributions on the neutrino masses and discuss how the mass of the lightest neutrino would regulate any possible SNSI contribution in both mass ordering scenarios. We derive the analytic expressions for neutrino oscillation probabilities, employing the Cayley-Hamilton theorem, in the presence of diagonal elements of SNSI. The expressions are compact and shows explicit dependence on matter effects and the absolute neutrino masses. The analytic expressions calculated here allow us to obtain the dependence of the SNSI contribution on mass terms of the form $m_1 + m_2$, $m_2 - m_1$, $m_1c_{12}^2 + m_2s_{12}^2,$ $ m_1s_{12}^2 + m_2c_{12}^2$, and $m_3$. We then explore the non-trivial impact of neutrino mass ordering on the SNSI contribution. The dependence of the SNSI contribution on the 3$\nu$ parameters is then thoroughly explored using our analytic expressions., Comment: 40 pages, 11 figures

The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics. We find that fine-tuned PEGASUS-large outperforms the other models, including fine-tuned LLaMA-3-8B and GPT-3.5-turbo, across most metrics. We also demonstrate that ChatGPT can generate creative titles for papers. Our observations suggest that AI-generated paper titles are generally accurate and appropriate.

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results., Comment: 24 pages, 8 figures, 6 tables, Scientia Horticulturae

In his seminal work in 1923, Neyman studied the variance estimation problem for the difference-in-means estimator of the average treatment effect in completely randomized experiments. He proposed a variance estimator that is conservative in general and unbiased when treatment effects are homogeneous. While widely used under complete randomization, there is no unique or natural way to extend this estimator to more complex designs. To this end, we show that Neyman's estimator can be alternatively derived in two ways, leading to two novel variance estimation approaches: the imputation approach and the contrast approach. While both approaches recover Neyman's estimator under complete randomization, they yield fundamentally different variance estimators for more general designs. In the imputation approach, the variance is expressed as a function of observed and missing potential outcomes and then estimated by imputing the missing potential outcomes, akin to Fisherian inference. In the contrast approach, the variance is expressed as a function of several unobservable contrasts of potential outcomes and then estimated by exchanging each unobservable contrast with an observable contrast. Unlike the imputation approach, the contrast approach does not require separately estimating the missing potential outcome for each unit. We examine the theoretical properties of both approaches, showing that for a large class of designs, each produces conservative variance estimators that are unbiased in finite samples or asymptotically under homogeneous treatment effects.

The third-order anomalous Hall effect (TOAHE) driven by Berry connection polarizability in Dirac materials offers a promising avenue for exploring quantum geometric phenomena. We investigate the role of impurity scattering on TOAHE using the semiclassical Boltzmann framework, via a comparison of the intrinsic contributions (stemming from the Berry connection polarizability effect) with the extrinsic contributions caused by the disorder. To validate our theoretical findings, we employ a generalized two-dimensional low-energy Dirac model to analytically assess the intrinsic and extrinsic contributions to the TOAHE. Our analysis reveals distinct disorder-mediated effects, including skew scattering and side jump contributions. We also elucidate their intriguing dependencies on Fermi surface anisotropy and discuss opportunities for experimental exploration., Comment: 6 Pages, 1 Figure

The maritime industry stands at a critical juncture, where the imperative for technological advancement intersects with the pressing need for robust cybersecurity measures. Maritime cybersecurity refers to the protection of computer systems and digital assests within the maritime industry, as well as the broader network of interconnected components that make up the maritime ecosystem. In this survey, we aim to identify the significant domains of maritime cybersecurity and measure their effectiveness. We have provided an in-depth analysis of threats in key maritime systems, including AIS, GNSS, ECDIS, VDR, RADAR, VSAT, and GMDSS, while exploring real-world cyber incidents that have impacted the sector. A multi-dimensional taxonomy of maritime cyber attacks is presented, offering insights into threat actors, motivations, and impacts. We have also evaluated various security solutions, from integrated solutions to component specific solutions. Finally, we have shared open challenges and future solutions. In the supplementary section, we have presented definitions and vulnerabilities of vessel components that have discussed in this survey. By addressing all these critical issues with key interconnected aspects, this review aims to foster a more resilient maritime ecosystem., Comment: 23 pages, long survey paper, submitted to IEEE journals because ACM computing survey is not a good fit in terms of their dedicated scope

We present the first explicit construction of two-sided lossless expanders in the unbalanced setting (bipartite graphs that have many more nodes on the left than on the right). Prior to our work, all known explicit constructions in the unbalanced setting achieved only one-sided lossless expansion. Specifically, we show that the one-sided lossless expanders constructed by Kalev and Ta-Shma (RANDOM'22)--that are based on multiplicity codes introduced by Kopparty, Saraf, and Yekhanin (STOC'11)--are, in fact, two-sided lossless expanders. Using our unbalanced bipartite expander, we easily obtain lossless (non-bipartite) expander graphs on $N$ vertices with polynomial degree and a free group action. As far as we know, this is the first explicit construction of lossless (non-bipartite) expanders with $N$ vertices and degree $\ll N$.

Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-grained categorization. Moreover, foundational models trained on one domain do not generalize well on a different domain without fine-tuning. We attribute these to the limitations of the VLM's semantic representations and attempt to improve their fine-grained visual awareness using generative modeling. Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe). Utilizing text-to-image diffusion models, GCPL significantly improves the visio-linguistic synergy in class embeddings by conditioning on few-shot exemplars with learnable class prompts. CoMPLe builds on this foundation by introducing a contrastive learning component that encourages inter-class separation during the generative optimization process. Our empirical results demonstrate that such a generative class prompt learning approach substantially outperform existing methods, offering a better alternative to few shot image recognition challenges. The source code will be made available at: https://github.com/soumitri2001/GCPL., Comment: Accepted in BMVC 2024

The distribution of entanglement in a multiparty system can be described through the principles of monogamy or polygamy. Monogamy is a fundamental characteristic of entanglement that restricts its distribution among several number of parties(more than two). In this work, our aim is to explore how quantum entanglement can be distributed in accordance with monogamy relations by utilizing both the genuine multipartite entanglement measures and bipartite entanglement measures. Specifically, we treat source entanglement as the genuine multipartite entanglement measure and use the entanglement of formation specifically for bipartite cases. For GHZ class states, we analytically demonstrate that the square of the source entanglement serves as an upper bound for the sum of the squares of the entanglement of formation of the reduced subsystems, with some exceptions for specific non-generic GHZ states. We also present numerical evidence supporting this result for W class states. Additionally, we explore the monogamy relation by using accessible entanglement as an upper bound., Comment: 17 pages, 22 figures, revtex, comments welcome

We present a minimal cosmological solution to the hierarchy problem. Our model consists of a light pseudoscalar and an extra Higgs doublet in addition to the field content of the Standard Model. We consider a landscape of vacua with varying values of the electroweak vacuum expectation value (VEV). The vacuum energy in our model peaks in a region of the landscape where the electroweak VEV is non-zero and much smaller than the cutoff. During inflation, due to exponential expansion, such regions of the landscape with maximal vacuum energy, dominate the universe in volume, thus explaining the smallness of the electroweak scale with respect to the cutoff. The pseudoscalar potential in our model is that of a completely generic pseudogoldstone boson$-$not requiring the clockwork mechanism$-$and its field value never exceeds its decay constant or the Planck scale. Our mechanism is robust to the variation of other model parameters in the landscape along with the electroweak VEV. It also predicts a precise and falsifiable relationship between the masses and couplings of the different Higgs boson mass-eigenstates. Moreover, the pseudoscalar in our model can account for the observed dark matter relic density., Comment: 28 pages, 4 figures

For a Markov and stationary stochastic process described by the well-known classical master equation, we introduce complex transition rates instead of real transition rates to study the pre-thermal oscillatory behaviour in complex probabilities. Further, for purely imaginary transition rates we obtain persistent infinitely long lived oscillations in complex probability whose nature depends on the dimensionality of the state space. We also take a peek into cases where we perturb the relaxation matrix for a dichotomous process with an oscillatory drive where the relative sign of the angular frequency of the drive decides whether there will be dissipation in the complex probability or not., Comment: 6 pages, 4 figures

Optimizing tail latency while efficiently managing computational resources is crucial for delivering high-performance, latency-sensitive services in edge computing. Emerging applications, such as augmented reality, require low-latency computing services with high reliability on user devices, which often have limited computational capabilities. Consequently, these devices depend on nearby edge servers for processing. However, inherent uncertainties in network and computation latencies stemming from variability in wireless networks and fluctuating server loads make service delivery on time challenging. Existing approaches often focus on optimizing median latency but fall short of addressing the specific challenges of tail latency in edge environments, particularly under uncertain network and computational conditions. Although some methods do address tail latency, they typically rely on fixed or excessive redundancy and lack adaptability to dynamic network conditions, often being designed for cloud environments rather than the unique demands of edge computing. In this paper, we introduce SafeTail, a framework that meets both median and tail response time targets, with tail latency defined as latency beyond the 90^th percentile threshold. SafeTail addresses this challenge by selectively replicating services across multiple edge servers to meet target latencies. SafeTail employs a reward-based deep learning framework to learn optimal placement strategies, balancing the need to achieve target latencies with minimizing additional resource usage. Through trace-driven simulations, SafeTail demonstrated near-optimal performance and outperformed most baseline strategies across three diverse services., Comment: This work has been submitted to the IEEE for possible publication

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

We study small non-trivial solutions of quadratic congruences of the form $x_1^2+\alpha_2x_2^2+\alpha_3x_3^2\equiv 0 \bmod{q}$, with $q$ being an odd natural number, in an average sense. This extends previous work of the authors in which they considered the case of prime power moduli $q$. Above, $\alpha_2$ is arbitrary but fixed and $\alpha_3$ is variable, and we assume that $(\alpha_2\alpha_3,q)=1$. We show that for all $\alpha_3$ modulo $q$ which are coprime to $q$ except for a small number of $\alpha_3$'s, an asymptotic formula for the number of solutions $(x_1,x_2,x_3)$ to the congruence $x_1^2+\alpha_2x_2^2+\alpha_3x_3^2\equiv 0 \bmod{q}$ with $\max\{|x_1|,|x_2|,|x_3|\}\le N$ and $(x_3,q)=1$ holds if $N\ge q^{11/24+\varepsilon}$ and $q$ is large enough. It is of significance that we break the barrier 1/2 in the above exponent. Key tools in our work are Burgess's estimate for character sums over short intervals and Heath-Brown's estimate for character sums with binary quadratic forms over small regions whose proofs depend on the Riemann hypothesis for curves over finite fields. We also formulate a refined conjecture about the size of the smallest solution of a ternary quadratic congruence, using information about the Diophantine properties of its coefficients., Comment: 14 Pages

This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable students to practice their skills. In cybersecurity, hands-on exercises are often complex and require knowledge of many topics. Therefore, students may miss solutions due to gaps in their knowledge and become frustrated, which impedes their learning. Targeted aid by the instructor helps, but since the instructor's time is limited, efficient ways to detect struggling students are needed. This paper develops automated tools to predict when a student is having difficulty. We formed a dataset with the actions of 313 students from two countries and two learning environments: KYPO CRP and EDURange. These data are used in machine learning algorithms to predict the success of students in exercises deployed in these environments. After extracting features from the data, we trained and cross-validated eight classifiers for predicting the exercise outcome and evaluated their predictive power. The contribution of this paper is comparing two approaches to feature engineering, modeling, and classification performance on data from two learning environments. Using the features from either learning environment, we were able to detect and distinguish between successful and struggling students. A decision tree classifier achieved the highest balanced accuracy and sensitivity with data from both learning environments. The results show that activity data from cybersecurity exercises are suitable for predicting student success. In a potential application, such models can aid instructors in detecting struggling students and providing targeted help. We publish data and code for building these models so that others can adopt or adapt them., Comment: To appear for publication in the FIE 2024 conference proceedings

The performance of fiber-fed astronomical spectrographs is highly influenced by the properties of fibers. The near-field and far-field scrambling characteristics have a profound impact on the line spread function (LSF) of the spectra. Focal ratio degradation (FRD) influences the output beam size, thereby affecting the throughput, as well as the size of the collimator and dispersion elements. While previous research has indicated that these properties depend on the shape of the fiber core and showed that non-circular core fibers can yield uniform near-field scrambling, the result remains inconclusive for far-field. In this study, we investigate the near-field and far-field scrambling properties, along with the FRD, of 50-micron core fibers with different core geometries. We find that in addition to excellent near-field scrambling, octagonal-core fibers can also produce more uniform far-field output when compared to circular-core fibers. They also have less FRD effect when being fed with a f/3 beam., Comment: 13 pages, 11 figures, SPIE proceedings, Ground-based and Airborne Instrumentation for Astronomy X

Sensory stimuli in animals are encoded into spike trains by neurons, offering advantages such as sparsity, energy efficiency, and high temporal resolution. This paper presents a signal processing framework that deterministically encodes continuous-time signals into biologically feasible spike trains, and addresses the questions about representable signal classes and reconstruction bounds. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons using a convolve-then-threshold mechanism with various convolution kernels. A closed-form solution to the inverse problem, from spike trains to signal reconstruction, is derived in the Hilbert space of shifted kernel functions, ensuring sparse representation of a generalized Finite Rate of Innovation (FRI) class of signals. Additionally, inspired by real-time processing in biological systems, an efficient iterative version of the optimal reconstruction is formulated that considers only a finite window of past spikes, ensuring robustness of the technique to ill-conditioned encoding; convergence guarantees of the windowed reconstruction to the optimal solution are then provided. Experiments on a large audio dataset demonstrate excellent reconstruction accuracy at spike rates as low as one-fifth of the Nyquist rate, while showing clear competitive advantage in comparison to state-of-the-art sparse coding techniques in the low spike rate regime., Comment: 22 pages, including a 9-page appendix, 8 figures. A GitHub link to the project implementation is embedded in the paper

For an algebraic number field $K$, the P\'{o}lya group of $K$, denoted by $Po(K),$ is the subgroup of the ideal class group $Cl_{K}$ generated by the ideal classes of the products of prime ideals of same norm. The number field $K$ is said to be P\'{o}lya if $Po(K)$ is trivial. Motivated by several recent studies on the group $Po(K)$ when $K$ is a totally real bi-quadratic field, we investigate the same with five distinct odd primes ramifying in $K/\mathbb{Q}$. This extends the previous results on this problem, where the number of distinct ramified primes was at most four., Comment: 7 - 2epsilon pages, comments are welcome

The current work reports a study on bounce cosmology with a highly generalized holographic dark fluid inspired by S. Nojiri and S. D. Odintsov, 2017, European Physical Journal C, 77, pp.1-8. The holographic dark fluid that is mostly used for late-time acceleration has been implemented to reconstruct towards realisation of cosmological bounce. We first used the most generalized Nojiri-Odintsov(NO) cutoff to implement the holographic dark fluid. Accordingly, we have reconstructed this dark fluid via some solutions of scale factors. With those solutions, we have explored the evolution of different cosmological parameters. We have examined the effects of each reconstructed parameter in the context of the realization of the cosmic bounce. Next, we use the analytical inferences of the scalar spectral index, tensor-to-scalar ratio, and slow-roll characteristics of the model to study a bounce inflationary scenario. Since inflation is usually associated with the existence of scalar fields, we looked at a possible relationship between NO generalized holographic dark energy and scalar field models. Plotting the evolution of the potential that results from the scalar fields against time. Finally, we investigated the GSL of thermodynamics in the pre-and post-bounce scenarios., Comment: 31 pages, 16 figures

We consider the problem of source sampling and transmission scheduling for age-of-information minimization in a system consisting of multiple energy harvesting (EH) sources and a sink node. At each time, one of the sources is selected by the scheduler and the quality of its channel to the sink is measured. This probed channel quality is then used to decide whether a source will sample an observation and transmit the packet to the sink in that time slot. We formulate this problem as a constrained Markov decision process (CMDP) assuming i.i.d. energy arrival and channel fading processes, and relax it using a Lagrange multiplier. We apply a near optimal Whittle's index policy to decide the node to be probed. Next, for the probed node, we derive an optimal threshold policy, which recommends source sampling and observation transmission from the probed source only when the measured channel quality is above a threshold. Our proposed policy is called Whittle's index and threshold based source scheduling and sampling (WITS3) policy. However, in order to calculate Whittle's indices, one must be aware of the underlying processes' transition matrices, which are occasionally concealed from the scheduler. Therefore, we further propose a variant Q-WITS3 of WITS3 based on Q-learning assisted by two timescale asynchronous stochastic approximation, which seeks to learn Whittle's indices and optimal policies for the case with unknown channel states and EH characteristics. Numerical results demonstrate the efficacy of our algorithms over two baseline policies., Comment: 10 pages, 7 figures

Cybercrime and the market for cyber-related compromises are becoming attractive revenue sources for state-sponsored actors, cybercriminals and technical individuals affected by financial hardships. Due to burgeoning cybercrime on new technological frontiers, efforts have been made to assist digital forensic investigators (DFI) and law enforcement agencies (LEA) in their investigative efforts. Forensic tool innovations and ontology developments, such as the Unified Cyber Ontology (UCO) and Cyber-investigation Analysis Standard Expression (CASE), have been proposed to assist DFI and LEA. Although these tools and ontologies are useful, they lack extensive information sharing and tool interoperability features, and the ontologies lack the latest Smart City Infrastructure (SCI) context that was proposed. To mitigate the weaknesses in both solutions and to ensure a safer cyber-physical environment for all, we propose the Smart City Ontological Paradigm Expression (SCOPE), an expansion profile of the UCO and CASE ontology that implements SCI threat models, SCI digital forensic evidence, attack techniques, patterns and classifications from MITRE. We showcase how SCOPE could present complex data such as SCI-specific threats, cybercrime, investigation data and incident handling workflows via an incident scenario modelled after publicly reported real-world incidents attributed to Advanced Persistent Threat (APT) groups. We also make SCOPE available to the community so that threats, digital evidence and cybercrime in emerging trends such as SCI can be identified, represented, and shared collaboratively.

Based on the conviction of switching information from locally accessible to locally hidden environs, the concept of hidden nonlocality activation has recently been highlighted by Bandyopadhyay et al. in [Phys. Rev. A 104, L050201 (2021)]. They have demonstrated that a certain locally distinguishable set of pure quantum states can be transformed into a locally indistinguishable set with certainty through orthogonality preserving local measurements (OPLMs). This transformation makes the set locally inaccessible, despite being locally accessible before. This phenomenon is defined as the activation of hidden nonlocality. In this paper, we present two classes of locally distinguishable sets within $(2m+1) \otimes 2 \otimes (2m+1)$ systems. One class reveals nonlocality through local operations, whereas the other requires joint measurements for it. As the later class depends on nonlocal operations to exhibit nonlocality, it arguably has a lower degree of nonlocality, and accordingly, can be considered as more local compared to the first class. This analysis exhibits a stronger manifestation of locality by elucidating the nuanced interplay between these distinct local phenomena within the framework of quantum state discrimination. Furthermore, we also explore their significant applications in the context of data hiding. Additionally, we introduce the concept of \emph{``strong local"} set and compare it with various activatable sets, highlighting differences in terms of locality., Comment: 15 pages, 7 figures, latex2e, revtex, updated version, comments welcome

This article presents a new model for anisotropic compact stars that are confined to physical dark matter in the background of $f(T)$ teleparallel gravity. The model is based on the equation of state (EoS) of the bag model type and the Bose-Einstein dark matter density profile. The derived solutions meet the energy conditions, the causality conditions, and the required conditions on the stability factor and adiabatic index, indicating that they are physically well-behaved and represent the physical and stable matter configuration. We also determine the maximum mass, surface redshift, and compactness parameter at the surface. Interestingly, all of these numbers fall within the specified range, supporting the physical viability of our proposal. Additionally, the various masses that are derived for varying the model parameter $k$ correspond to five compact, realistic compact objects, including LMC X-4, Her X-1, 4U 1538-52, SAX J1808.4-3658, and Cen X-3. We have also illustrated the radially symmetric profiles of energy density and the moment of inertia for non-rotating stars., Comment: 23 pages, 28 figures

Background: Understanding the nuclear reactions between light charged nuclei at sub-coulomb energy region holds significant importance in several astrophysical processes. Determination of the precise reaction cross-section within the astrophysically important Gamow range is difficult because of electron screening. Various polynomial fits, R-Matrix and Indirect Trojan horse method estimate much higher electron screening energies as compared to the adiabatic limit. Purpose: Obtain the bare astrophysical S-factor of 6 Li(p,{\alpha})3 He using Multi-Layer Perceptron based Artificial Neural Network based analysis and extract the electron screening energies. Methods: Experimental S-factor of 6 Li(p,{\alpha})3 He, available in literature, are reanalyzed using the Multi-LayerPerceptron based Artificial Neural Network based algorithm to obtain the energy dependent astrophysical S-factor. Bare astrophysical S-factor is also calculated using the same Feed-forward Artificial Neural Network from the data range above 60 keV where the electron screening effect is expected to be negligible. Electron screening potential is then obtained by taking the ratio of total shielded S-factor with the bare S-factor. Results and Conclusions: The electron screening potential obtained from the Present work through the Artificial Neural network based algorithm is found to be 220 eV. The extracted electron screening potential through the present analysis indicates that the Artificial Neural Network might be an alternative tools for estimation the electron screening potential involving light nuclei., Comment: arXiv admin note: substantial text overlap with arXiv:2407.20049

In recent decades, there has been a significant increase in the measurement of complete fusion cross-sections for various reactions, with particular emphasis on the reactions involving weakly bound projectile. It has been well established that the complete fusion cross-section involving weakly bound nuclei is suppressed at above barrier energies due to the breakup effect. Accurate determination of suppression factor is essential to understand the effect of breakup on complete fusion suppression. In this study, Feedforward Artificial Neural Network (ANN) methods based on Multilayer Perceptron is utilized to estimate the complete fusion suppression factor for reactions involving 6,7 Li projectile from the comparison of ANN predicted reduced fusion functions (F (x)) with the Universal Fusion Function(F0 (x)). Average suppression factor has been estimated as 0.68 and 0.74 for 6 Li and 7 Li induced reactions respectively. Normalized Mean Squared Error(NMSE) has been calculated as 1.85% for training and 1.92% for testing data of ANN for 6 Li case, while for 7 Li cases, the same is for 3.73% and 6.48% respectively. Present results have been compared with three other alternative methods Support Vector Regression, Random Forest Regression and Gaussian Process Regression. Relevant performance matrices has been estimated. It has been observed that, ANN algorithm is giving the better accuracy as compared with other. This results indicate that ANN might be an alternative tool for estimation of complete fusion suppression factor for weakly bound projectile.

We undertake extensive analysis of English Premier League data over the period 2009/10 to 2017/18 to identify and rank key factors affecting the economic and footballing performances of the teams. Alternative end-of-season league tables are generated by re-ranking the teams based on five different descriptors - total expenditure, total funds spent on players, total funds spent on foreign players, the ratio of foreign to British players and the overall profit. The unequal distribution of resources and expenditure between the clubs is analyzed through Lorenz curves. A comparative analysis of the differences between the alternative tables and the conventional end-of-season league table establishes the most likely factors to influence the performances of the teams that we also rank using Principal Component Analysis. We find that the top teams in the league are also those that tend to have the highest expenditure overall, for all players, including foreign players; they also have the highest ratios of foreign to British players. Our statistical and machine learning study also indicates that successful performance on the field may not guarantee healthy profits at the end of the season., Comment: Graphical Abstract & 8 figures in the main text, 3 Appendices with additional figures and tables

The study explores Artificial Intelligence (AI) powered modeling to predict the evolution of cancer tumor cells in mice under different forms of treatment. The AI models are analyzed against varying ambient and systemic parameters, e.g. drug dosage, volume of the cancer cell mass, and time taken to destroy the cancer cell mass. The data required for the analysis have been synthetically extracted from plots available in both published and unpublished literature (primarily using a Matlab architecture called "Grabit"), that are then statistically standardized around the same baseline for comparison. Three forms of treatment are considered - saline (multiple concentrations used), magnetic nanoparticles (mNPs) and fluorodeoxyglycose iron oxide magnetic nanoparticles (mNP-FDGs) - analyzed using three Machine Learning (ML) algorithms, Decision Tree (DT), Random Forest (RF), Multilinear Regression (MLR), and a Deep Learning (DL) module, the Adaptive Neural Network (ANN). The AI models are trained on 60-80% data, the rest used for validation. Assessed over all three forms of treatment, ANN consistently outperforms other predictive models. Our models predict mNP-FDG as the most potent treatment regime that kills the cancerous tumor completely in ca 13 days from the start of treatment. The models can be generalized to other forms of cancer treatment regimens., Comment: 7 figures, 24 pages

Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3x3 or 5x5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers >40% relative improvement over traditional filtering in background rejection in standard 0.3-10keV energy range, at the expense of only a small (<2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user's specific scientific needs., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Future strategic X-ray satellite telescopes, such as the probe-class Advanced X-ray Imaging Satellite (AXIS), will require excellent soft energy response in their imaging detectors to enable maximum discovery potential. In order to characterize Charge-Coupled Device (CCD) and Single Electron Sensitive Read Output (SiSeRO) detectors in the soft X-ray region, the X-ray Astronomy and Observational Cosmology (XOC) group at Stanford has developed, assembled, and commissioned a 2.5-meter-long X-ray beamline test system. The beamline is designed to efficiently produce monoenergetic X-ray fluorescence lines in the 0.3-10 keV energy range and achieve detector temperatures as low as 173 K. We present design and simulation details of the beamline, and discuss the vacuum, cooling, and X-ray fluorescence performance achieved. As a workhorse for future detector characterization at Stanford, the XOC beamline will support detector development for a broad range of X-ray astronomy instruments., Comment: To appear in SPIE Astronomical Telescopes and Instrumentation 2024 proceedings. 17 pages, 13 figures

The Advanced X-ray Imaging Satellite (AXIS) is a NASA probe class mission concept designed to deliver arcsecond resolution with an effective area ten times that of Chandra (at launch). The AXIS focal plane features an MIT Lincoln Laboratory (MIT-LL) X-ray charge-coupled device (CCD) detector working in conjunction with an application specific integrated circuit (ASIC), denoted the Multi-Channel Readout Chip (MCRC). While this readout ASIC targets the AXIS mission, it is applicable to a range of potential X-ray missions with comparable readout requirements. Designed by the X-ray astronomy and Observational Cosmology (XOC) group at Stanford University, the MCRC ASIC prototype (MCRC-V1.0) uses a 350 nm technology node and provides 8 channels of high speed, low noise, low power consumption readout electronics. Each channel implements a current source to bias the detector output driver, a preamplifier to provide gain, and an output buffer to interface directly to an analog-to-digital (ADC) converter. The MCRC-V1 ASIC exhibits comparable performance to our best discrete electronics implementations, but with ten times less power consumption and a fraction of the footprint area. In a total ionizing dose (TID) test, the chip demonstrated a radiation hardness equal or greater to 25 krad, confirming the suitability of the process technology and layout techniques used in its design. The next iteration of the ASIC (MCRC-V2) will expand the channel count and extend the interfaces to external circuits, advancing its readiness as a readout-on-a-chip solution for next generation X-ray CCD-like detectors. This paper summarizes our most recent characterization efforts, including the TID radiation campaign and results from the first operation of the MCRC ASIC in combination with a representative MIT-LL CCD., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Future strategic X-ray astronomy missions will require unprecedentedly sensitive wide-field imagers providing high frame rates, low readout noise and excellent soft energy response. To meet these needs, our team is employing a multi-pronged approach to advance several key areas of technology. Our first focus is on advanced readout electronics, specifically integrated electronics, where we are collaborating on the VERITAS readout chip for the Athena Wide Field Imager, and have developed the Multi-Channel Readout Chip (MCRC), which enables fast readout and high frame rates for MIT-LL JFET (junction field effect transistor) CCDs. Second, we are contributing to novel detector development, specifically the SiSeRO (Single electron Sensitive Read Out) devices fabricated at MIT Lincoln Laboratory, and their advanced readout, to achieve sub-electron noise performance. Hardware components set the stage for performance, but their efficient utilization relies on software and algorithms for signal and event processing. Our group is developing digital waveform filtering and AI methods to augment detector performance, including enhanced particle background screening and improved event characterization. All of these efforts make use of an efficient, new X-ray beamline facility at Stanford, where components and concepts can be tested and characterized., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024