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The Sachdev-Ye-Kitaev (SYK) model is a paradigm for extreme quantum chaos, non-Fermi-liquid behavior, and holographic matter. Yet, the dense random all-to-all interactions that characterize it are an extreme challenge for realistic laboratory realizations. Here, we propose a general scheme for densifying the coupling distribution of random disorder Hamiltonians, using a Trotterized cycling through sparse time-dependent disorder realizations. To diagnose the convergence of sparse to dense models, we introduce an information-theory inspired diagnostic. We illustrate how the scheme can come to bear in the realization of the complex SYK$_4$ model in cQED platforms with available experimental resources, using a single cavity mode together with a fast cycling through independent speckle patterns. The simulation scheme applies to the SYK class of models as well as spin glasses, spin liquids, and related disorder models, bringing them into reach of quantum simulation using single-mode cavity-QED setups and other platforms., Comment: the first two authors contributed equally to this work

The Crilin calorimeter is a semi-homogeneous calorimetric system based on Lead Fluoride (PbF$_2$) crystals with UV-extended Silicon Photomultipliers (SiPMs) proposed for the Muon Collider. This study investigates the radiation resistance of crystals and SiPMs, subjected to 10 kGy gamma irradiation, equivalent to a 10-year service life in the Muon Collider. Our findings indicate that while PbF$_2$ crystals exhibit a decrease in transmittance post-irradiation with partial recovery over time, the alternative PbWO$_4$-Ultra Fast (PWO-UF) demonstrates exceptional radiation hardness, maintaining stable transmittance. SiPMs showed an increase in dark current and breakdown voltage post-irradiation, with less degradation observed in the SiPM biased during the exposure to radiation compared to the unbiased component. These results underscore the viability of PbF$_2$ for radiation-tolerant calorimeters, though improvements in production homogeneity are needed. The superior performance of PWO-UF crystals suggests they are a promising alternative for high-radiation applications, but their higher cost must be carefully considered., Comment: 11 pages, 7 figures, 1 table

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes. We conceptualize these challenges through the lens of vulnerability, the potential for university assessments and learning outcomes to be impacted by student use of generative AI. We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses. Specifically, we compile a novel dataset of textual assessment questions from 50 courses at EPFL and evaluate whether two AI assistants, GPT-3.5 and GPT-4 can adequately answer these questions. We use eight prompting strategies to produce responses and find that GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions. When grouping courses in our dataset by degree program, these systems already pass non-project assessments of large numbers of core courses in various degree programs, posing risks to higher education accreditation that will be amplified as these models improve. Our results call for revising program-level assessment design in higher education in light of advances in generative AI., Comment: 20 pages, 8 figures

Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single resolution. Multi-resolution graph models leverage the spatial relationships of patches at multiple magnifications, learning the context for each patch. In this study, we conduct the most thorough validation of a graph model for ovarian cancer subtyping to date. Seven models were tuned and trained using five-fold cross-validation on a set of 1864 whole slide images (WSIs) from 434 patients treated at Leeds Teaching Hospitals NHS Trust. The cross-validation models were ensembled and evaluated using a balanced hold-out test set of 100 WSIs from 30 patients, and an external validation set of 80 WSIs from 80 patients in the Transcanadian Study. The best-performing model, a graph model using 10x+20x magnification data, gave balanced accuracies of 73%, 88%, and 99% in cross-validation, hold-out testing, and external validation, respectively. However, this only exceeded the performance of attention-based multiple instance learning in external validation, with a 93% balanced accuracy. Graph models benefitted greatly from using the UNI foundation model rather than an ImageNet-pretrained ResNet50 for feature extraction, with this having a much greater effect on performance than changing the subsequent classification approach. The accuracy of the combined foundation model and multi-resolution graph network offers a step towards the clinical applicability of these models, with a new highest-reported performance for this task, though further validations are still required to ensure the robustness and usability of the models., Comment: Initially submitted version of a paper which has been accepted in the GRAIL workshop at MICCAI 2024

We present our numerical simulation approach for the End-to-End (E2E) model applied to various astronomical spectrographs, such as SOXS (ESO-NTT), CUBES (ESO-VLT), and ANDES (ESO-ELT), covering multiple wavelength regions. The E2E model aim at simulating the expected astronomical observations starting from the radiation of the scientific sources (or calibration sources) up to the raw-frame data produced by the detectors. The comprehensive description includes E2E architecture, computational models, and tools for rendering the simulated frames. Collaboration with Data Reduction Software (DRS) teams is discussed, along with efforts to meet instrument requirements. The contribution to the cross-correlation algorithm for the Active Flexure Compensation (AFC) system of CUBES is detailed., Comment: 19 pages, 17 figures, SPIE Astronomical Telescopes + Instrumentation, Yokohama 2024. arXiv admin note: text overlap with arXiv:2209.07185, arXiv:2012.12684

SOXS (Son Of X-Shooter) will be the new medium-resolution (R 4500 for 1 slit), high-efficiency, wide-band spectrograph for the ESO NTT at La Silla Observatory, Chile. It will be dedicated to the follow-up of any kind of transient events, ensuring fast time, high efficiency, and availability. It consists of a central structure (common path) that supports two spectrographs optimized for the UV-Visible and a Near-Infrared range. Attached to the common path is the Acquisition and Guiding Camera system (AC), equipped with a filter wheel that can provide science-grade imaging and moderate high-speed photometry. The AC Unit was integrated and aligned during the summer months of 2022 and has since been mounted in the NTTs telescope simulator. This work gives an update on the Acquisition Camera Unit status, describes the Image Quality Tests that were performed, and discusses the AC Optical Performance., Comment: SPIE Proceedings Volume 13096, Ground-based and Airborne Instrumentation for Astronomy X; 1309672 (2024)

SOXS (Son Of X-Shooter) is the new single object spectrograph for the ESO New Technology Telescope (NTT) at the La Silla Observatory, able to cover simultaneously both the UV-VIS and NIR bands (350-2000 nm). The instrument is currently in the integration and test phase, approaching the Preliminary Acceptance in Europe (PAE) before shipment to Chile for commissioning. After the assembly and preliminary test of the control electronics at INAF - Astronomical Observatory of Capodimonte (Napoli), the two main control cabinets of SOXS are now hosted in Padova, connected to the real hardware. This contribution describes the final electronic cabinets layout, the control strategy and the different integration phases, waiting for the Preliminary Acceptance in Europe and the installation of the instrument in Chile.

The Son Of X-Shooter (SOXS) will be the specialized facility to observe any transient event with a flexible scheduler at the ESO New Technology Telescope (NTT) at La Silla, Chile. SOXS is a single object spectrograph offering simultaneous spectral coverage in UV-VIS (350-850 nm) and NIR (800-2000 nm) wavelength regimes with an average of R~4500 for a 1arcsec slit. SOXS also has imaging capabilities in the visible wavelength regime. Currently, SOXS is being integrated at the INAF-Astronomical Observatory of Padova. Subsystem- and system-level tests and verification are ongoing to ensure and confirm that every requirement and performance are met. In this paper, we report on the integration and verification of SOXS as the team and the instrument prepare for the Preliminary Acceptance Europe (PAE).

The Son Of X-Shooter (SOXS) is a single object spectrograph, built by an international consortium for the 3.58-m ESO New Technology Telescope at the La Silla Observatory [1]. It offers a simultaneous spectral coverage over 350-2000 nm, with two separate spectrographs. In this paper we present the status of the Near InfraRed (NIR) cryogenic echelle cross-dispersed spectrograph [1], in the range 0.80-2.00 {\mu}m with 15 orders, equipped with an 2k x 2k Hawaii H2RG IR array from Teledyne, working at 40K, that is currently assembled and tested on the SOXS instrument, in the premises of INAF in Padova. We describe the different tests and results of the cryo, vacuum, opto-mechanics and detector subsystems that finally will be part of the PAE by ESO., Comment: 8 Pages, 7 Figures, Astronomical Telescopes and Instrumentation, SPIE Proceedings 2024

We present the advancements in the development of the scheduler for the Son Of X-shooter instrument at the ESO-NTT 3.58-m telescope in La Silla, Chile. SOXS is designed as a single-object spectroscopic facility and features a high-efficiency spectrograph with two arms covering the spectral range of 350-2000 nm and a mean resolving power of approximately R=4500. It will conduct UV-visible and near-infrared follow-up observations of astrophysical transients, drawing from a broad pool of targets accessible through the streaming services of wide-field telescopes, both current and future, as well as high-energy satellites. The instrument will cater to various scientific objectives within the astrophysical community, each entailing specific requirements for observation planning. SOXS will operate at the European Southern Observatory (ESO) in La Silla, without the presence of astronomers on the mountain. This poses a unique challenge for the scheduling process, demanding a fully automated algorithm that is autonomously interacting with the appropriate databases and the La Silla Weather API, and is capable of presenting the operator not only with an ordered list of optimal targets (in terms of observing constraints) but also with optimal backups in the event of changing weather conditions. This imposes the necessity for a scheduler with rapid-response capabilities without compromising the optimization process, ensuring the high quality of observations and best use of the time at the telescope. We thus developed a new highly available and scalable architecture, implementing API Restful applications like Docker Containers, API Gateway, and Python-based Flask frameworks. We provide an overview of the current state of the scheduler, which is now ready for the approaching on-site testing during Commissioning phase, along with insights into its web interface and preliminary performance tests.

The SOXS spectrograph, designed for the ESO NTT telescope, operates in both the optical (UV-VIS: 350-850 nm) and NIR (800-2000 nm) bands. This article provides an overview of the final tests conducted on the UV-VIS camera system using a telescope simulator. It details the system's performance evaluation, including key metrics such as gain, readout noise, and linearity, and highlights the advancements made in the upgraded acquisition system. The testing process, conducted in the Padua laboratory, involved comprehensive simulations of the telescope environment to ensure the results closely resemble those expected at the ESO-NTT telescope. The successful completion of these tests confirms the system's readiness for deployment to Chile, where it will be installed on the NTT telescope, marking a significant milestone in the SOXS project., Comment: SPIE Proceedings Volume 13096, Ground-based and Airborne Instrumentation for Astronomy X; 130962U (2024)

This paper presents the opto-mechanical integration and alignment, functional and optical performance verification of the NIR arm of Son Of X-Shooter (SOXS) instrument. SOXS will be a single object spectroscopic facility for the ESO-NTT 3.6-m telescope, made by two arms high efficiency spectrographs, able to cover the spectral range 350 2050 nm with a mean resolving power R~4500. In particular the NIR arm is a cryogenic echelle cross-dispersed spectrograph spanning the 780-2050 nm range. We describe the integration and alignment method performed to assemble the different opto-mechanical elements and their installation on the NIR vacuum vessel, which mostly relies on mechanical characterization. The tests done to assess the image quality, linear dispersion and orders trace in laboratory conditions are summarized. The full optical performance verification, namely echellogram format, image quality and resulting spectral resolving power in the whole NIR arm (optical path and science detector) is detailed. Such verification is one of the most relevant prerequisites for the subsequent full instrument assembly and provisional acceptance in Europe milestone, foreseen in 2024., Comment: SPIE Proceedings Volume 13096, Ground-based and Airborne Instrumentation for Astronomy X; 130962T (2024)

SOXS (Son Of X-Shooter) is the new ESO instrument that is going to be installed on the 3.58-m New Technology Telescope at the La Silla Observatory. SOXS is a single object spectrograph offering a wide simultaneous spectral coverage from U- to H-band. Although such an instrument may have potentially a large variety of applications, the consortium designed it with a clear science case: it is going to provide the spectroscopic counterparts to the ongoing and upcoming imaging surveys, becoming one of the main follow-up instruments in the Southern hemisphere for the classification and characterization of transients. The NTT+SOXS system is specialized to observe all transients and variable sources discovered by imaging surveys with a flexible schedule maintained by the consortium, based on a remote scheduler which will interface with the observatory software infrastructure. SOXS is realized timely to be highly synergic with transients discovery machines like the Vera C. Rubin Observatory. The instrument has been integrated and tested in Italy, collecting and assembling subsystems coming from all partners spread over six countries in three continents. The first preparatory activities in Chile have been completed at the telescope. This article gives an updated status of the project before the shipping of the instrument to Chile., Comment: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

The Instrument Control Software of SOXS (Son Of X-Shooter), the forthcoming spectrograph for the ESO New Technology Telescope at the La Silla Observatory, has reached a mature state of development and is approaching the crucial Preliminary Acceptance in Europe phase. Now that all the subsystems have been integrated in the laboratories of the Padova Astronomical Observatory, the team operates for testing purposes with the whole instrument at both engineering and scientific level. These activities will make use of a set of software peculiarities that will be discussed in this contribution. In particular, we focus on the synoptic panel, the co-rotator system special device, on the Active Flexure Compensation system which controls two separate piezo tip-tilt devices., Comment: 6 pages, 3 figures, SPIE conference

We revisit the input perturbations framework for differential privacy where noise is added to the input $A\in \mathcal{S}$ and the result is then projected back to the space of admissible datasets $\mathcal{S}$. Through this framework, we first design novel efficient algorithms to privately release pair-wise cosine similarities. Second, we derive a novel algorithm to compute $k$-way marginal queries over $n$ features. Prior work could achieve comparable guarantees only for $k$ even. Furthermore, we extend our results to $t$-sparse datasets, where our efficient algorithms yields novel, stronger guarantees whenever $t\le n^{5/6}/\log n\,.$ Finally, we provide a theoretical perspective on why \textit{fast} input perturbation algorithms works well in practice. The key technical ingredients behind our results are tight sum-of-squares certificates upper bounding the Gaussian complexity of sets of solutions., Comment: 21 ppages, ICML 2024

Graph clustering is a central topic in unsupervised learning with a multitude of practical applications. In recent years, multi-view graph clustering has gained a lot of attention for its applicability to real-world instances where one has access to multiple data sources. In this paper we formalize a new family of models, called \textit{multi-view stochastic block models} that captures this setting. For this model, we first study efficient algorithms that naively work on the union of multiple graphs. Then, we introduce a new efficient algorithm that provably outperforms previous approaches by analyzing the structure of each graph separately. Furthermore, we complement our results with an information-theoretic lower bound studying the limits of what can be done in this model. Finally, we corroborate our results with experimental evaluations., Comment: 31 pages, ICML 2024

We consider the semi-random graph model of [Makarychev, Makarychev and Vijayaraghavan, STOC'12], where, given a random bipartite graph with $\alpha$ edges and an unknown bipartition $(A, B)$ of the vertex set, an adversary can add arbitrary edges inside each community and remove arbitrary edges from the cut $(A, B)$ (i.e. all adversarial changes are \textit{monotone} with respect to the bipartition). For this model, a polynomial time algorithm is known to approximate the Balanced Cut problem up to value $O(\alpha)$ [MMV'12] as long as the cut $(A, B)$ has size $\Omega(\alpha)$. However, it consists of slow subroutines requiring optimal solutions for logarithmically many semidefinite programs. We study the fine-grained complexity of the problem and present the first near-linear time algorithm that achieves similar performances to that of [MMV'12]. Our algorithm runs in time $O(|V(G)|^{1+o(1)} + |E(G)|^{1+o(1)})$ and finds a balanced cut of value $O(\alpha)$. Our approach appears easily extendible to related problem, such as Sparsest Cut, and also yields an near-linear time $O(1)$-approximation to Dagupta's objective function for hierarchical clustering [Dasgupta, STOC'16] for the semi-random hierarchical stochastic block model inputs of [Cohen-Addad, Kanade, Mallmann-Trenn, Mathieu, JACM'19]., Comment: 24 pages, ICML 2024

Large pretrained transformers are increasingly being developed as generalised foundation models which can underpin powerful task-specific artificial intelligence models. Histopathology foundation models show great promise across many tasks, but analyses have typically been limited by arbitrary hyperparameters that were not tuned to the specific task. We report the most rigorous single-task validation of histopathology foundation models to date, specifically in ovarian cancer morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained feature extractors and fourteen histopathology foundation models. The training set consisted of 1864 whole slide images from 434 ovarian carcinoma cases at Leeds Teaching Hospitals NHS Trust. Five-class classification performance was evaluated through five-fold cross-validation, and these cross-validation models were ensembled for hold-out testing and external validation on the Transcanadian Study and OCEAN Challenge datasets. The best-performing model used the H-optimus-0 foundation model, with five-class balanced accuracies of 89%, 97%, and 74% in the test sets. Normalisations and augmentations aided the performance of the ImageNet-pretrained ResNets, but these were still outperformed by 13 of the 14 foundation models. Hyperparameter tuning the downstream classifiers improved performance by a median 1.9% balanced accuracy, with many improvements being statistically significant. Histopathology foundation models offer a clear benefit to ovarian cancer subtyping, improving classification performance to a degree where clinical utility is tangible, albeit with an increased computational burden. Such models could provide a second opinion to histopathologists diagnosing challenging cases and may improve the accuracy, objectivity, and efficiency of pathological diagnoses overall.

Cavity quantum electrodynamics offers the possibility to observe and control the motion of few or individual atoms, enabling the realization of various quantum technological tasks such as quantum-enhanced metrology or quantum simulation of strongly-correlated matter. A core limitation of these experiments lies in the mode structure of the cavity field, which is hard-coded in the shape and geometry of the mirrors. As a result, most applications of cavity QED trade spatial resolution for enhanced sensitivity. Here, we propose and demonstrate a cavity-microscope device capable of controlling in space and time the coupling between atoms and light in a single-mode high-finesse cavity, reaching a spatial resolution an order-of-magnitude lower than the cavity mode waist. This is achieved through local Floquet engineering of the atomic level structure, imprinting a corresponding atom-field coupling. We illustrate this capability by engineering micrometer-scale coupling, using cavity-assisted atomic measurements and optimization. Our system forms an optical device with a single optical axis and has the same footprint and complexity as a standard Fabry-Perot cavity or confocal lens pair, and can be used for any atomic species. This technique opens a wide range of perspectives from ultra-fast, cavity-enhanced mid-circuit readout to the quantum simulation of fully connected models of quantum matter such as the Sachdev-Ye-Kitaev model.

We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant number of blocks. The statistical utility guarantees match those of the previous best information-theoretic (exponential-time) node-private mechanisms for these problems. The algorithm is based on an exponential mechanism for a score function defined in terms of a sum-of-squares relaxation whose level depends on the number of blocks. The key ingredients of our results are (1) a characterization of the distance between the block graphons in terms of a quadratic optimization over the polytope of doubly stochastic matrices, (2) a general sum-of-squares convergence result for polynomial optimization over arbitrary polytopes, and (3) a general approach to perform Lipschitz extensions of score functions as part of the sum-of-squares algorithmic paradigm., Comment: 71 pages, accepted to STOC 2024

Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conventional ABM, enabling automated model calibration and reducing the computational resources needed for scaling up the model, we have developed a tensorized and differentiable agent-based model by coupling Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network. The model was employed to investigate the 2019 measles outbreak occurred in New Zealand, demonstrating a promising ability to accurately simulate the outbreak dynamics, particularly during the peak period of repeated cases. This paper shows that by leveraging the latest Artificial Intelligence (AI) technology and the capabilities of traditional ABMs, we gain deeper insights into the dynamics of infectious disease outbreaks. This, in turn, helps us make more informed decision when developing effective strategies that strike a balance between managing outbreaks and minimizing disruptions to everyday life., Comment: 11 pages, 9 figures

We study the approximability of the MaxCut problem in the presence of predictions. Specifically, we consider two models: in the noisy predictions model, for each vertex we are given its correct label in $\{-1,+1\}$ with some unknown probability $1/2 + \epsilon$, and the other (incorrect) label otherwise. In the more-informative partial predictions model, for each vertex we are given its correct label with probability $\epsilon$ and no label otherwise. We assume only pairwise independence between vertices in both models. We show how these predictions can be used to improve on the worst-case approximation ratios for this problem. Specifically, we give an algorithm that achieves an $\alpha + \widetilde{\Omega}(\epsilon^4)$-approximation for the noisy predictions model, where $\alpha \approx 0.878$ is the MaxCut threshold. While this result also holds for the partial predictions model, we can also give a $\beta + \Omega(\epsilon)$-approximation, where $\beta \approx 0.858$ is the approximation ratio for MaxBisection given by Raghavendra and Tan. This answers a question posed by Ola Svensson in his plenary session talk at SODA'23., Comment: 18 pages

The onset and growth of rigid clusters in a two-dimensional (2D) suspension in shear flow are studied by numerical simulations. The suspension exhibits the lubricated-to-frictional rheology transition, but the key results here are for stresses above the levels that cause extreme shear-thickening. At large solid fraction, $\phi$, but below the stress-dependent jamming fraction, we find a critical $\phi_{c}\left(\sigma,\mu\right)$ where $\sigma$ is a dimensionless shear stress and $\mu$ is the interparticle friction coefficient. For $\phi>\phi_c$, the proportion of particles in rigid clusters grows sharply, as $f_{\rm rig} \sim \left\vert\phi-\phi_{c}\right\vert^{\beta}$ with $\beta=1/8$, as do the fluctuations in the fraction of rigid particles, with a susceptibility measure $\chi_{\rm rig} \sim \left\vert\phi-\phi_{c}\right\vert^{-\gamma}$ with $\gamma = 7/4$. The system is thus found to exhibit criticality. The results are shown to depend on an effective field $h\left(\mu\right)$, which provides data collapse near $\phi_c$ for both $f_{\rm rig}$ and $\chi_{\rm rig}$. This behavior occurs over a range of stresses, with $\phi_c\left(\sigma,\mu\right)$ increasing as the stress decreases.

Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear. Higher magnifications offer abundant cytological information, whereas lower magnifications give a broader histoarchitectural overview. Using attention-based multiple instance learning, we performed the most extensive analysis of ovarian cancer tissue magnifications to date, with data at six magnifications subjected to the same preprocessing, hyperparameter tuning, cross-validation and hold-out testing procedures. The lowest magnifications (1.25x and 2.5x) performed best in cross-validation, and intermediate magnifications (5x and 10x) performed best in hold-out testing (62% and 61% accuracy, respectively). Lower magnification models were also significantly faster, with the 5x model taking 5% as long to train and 31% as long to evaluate slides compared to 40x. This indicates that the standard usage of high magnifications for computational ovarian cancer subtyping may be unnecessary, with lower magnifications giving faster, more accurate alternatives., Comment: This work has been submitted to the IEEE for possible publication

For many patients, current ovarian cancer treatments offer limited clinical benefit. For some therapies, it is not possible to predict patients' responses, potentially exposing them to the adverse effects of treatment without any therapeutic benefit. As part of the automated prediction of treatment effectiveness in ovarian cancer using histopathological images (ATEC23) challenge, we evaluated the effectiveness of deep learning to predict whether a course of treatment including the antiangiogenic drug bevacizumab could contribute to remission or prevent disease progression for at least 6 months in a set of 282 histopathology whole slide images (WSIs) from 78 ovarian cancer patients. Our approach used a pretrained Hierarchical Image Pyramid Transformer (HIPT) to extract region-level features and an attention-based multiple instance learning (ABMIL) model to aggregate features and classify whole slides. The optimal HIPT-ABMIL model had an internal balanced accuracy of 60.2% +- 2.9% and an AUC of 0.646 +- 0.033. Histopathology-specific model pretraining was found to be beneficial to classification performance, though hierarchical transformers were not, with a ResNet feature extractor achieving similar performance. Due to the dataset being small and highly heterogeneous, performance was variable across 5-fold cross-validation folds, and there were some extreme differences between validation and test set performance within folds. The model did not generalise well to tissue microarrays, with accuracy worse than random chance. It is not yet clear whether ovarian cancer WSIs contain information that can be used to accurately predict treatment response, with further validation using larger, higher-quality datasets required., Comment: Submission to ATEC23 challenge at MICCAI 2023 conference