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Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX (ODX) offer valuable genomic insights for HR+/HER2- patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-BCR-Auto, a deep learning-based computational pathology approach that predicts breast cancer recurrence risk from routine H&E-stained whole slide images (WSIs). Our methodology was validated on two independent cohorts: the TCGA-BRCA dataset and an in-house dataset from The Ohio State University (OSU). Deep-BCR-Auto demonstrated robust performance in stratifying patients into low- and high-recurrence risk categories. On the TCGA-BRCA dataset, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.827, significantly outperforming existing weakly supervised models (p=0.041). In the independent OSU dataset, Deep-BCR-Auto maintained strong generalizability, achieving an AUROC of 0.832, along with 82.0% accuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight the potential of computational pathology as a cost-effective alternative for recurrence risk assessment, broadening access to personalized treatment strategies. This study underscores the clinical utility of integrating deep learning-based computational pathology into routine pathological assessment for breast cancer prognosis across diverse clinical settings.

Nucleon structure functions, as measured in lepton-nucleon scattering, have historically provided a critical observable in the study of partonic dynamics within the nucleon. However, at very large parton momenta it is both experimentally and theoretically challenging to extract parton distributions due to the probable onset of non-perturbative contributions and the unavailability of high precision data at critical kinematics. Extraction of the neutron structure and the d-quark distribution have been further challenging due to the necessity of applying nuclear corrections when utilizing scattering data from a deuteron target to extract free neutron structure. However, a program of experiments has been carried out recently at the energy-upgraded Jefferson Lab electron accelerator aimed at significantly reducing the nuclear correction uncertainties on the d-quark distribution function at large partonic momentum. This allows leveraging the vast body of deuterium data covering a large kinematic range to be utilized for d-quark parton distribution function extraction. We present new data from experiment E12-10-002 carried out in Jefferson Lab Hall C on the deuteron to proton cross-section ratio at large BJorken-x. These results significantly improve the precision of existing data, and provide a first look at the expected impact on quark distributions extracted from global parton distribution function fits.

Biomolecular condensates are complex droplets comprising diverse molecules that interact using various mechanisms. Condensation is often driven by short-ranged attraction, but net charges can also mediate long-ranged repulsion. Using molecular dynamics simulations and an equilibrium field theory, we show that such opposing interactions can suppress coarsening so that many droplets of equal size coexist at equilibrium. This size control depends strongly on the charge asymmetry between constituents, while the strength of the short-ranged attractions has a weak influence. Essentially, droplets expel ions, so they cannot screen electrostatics effectively, implying droplets acquire a net charge and cannot grow indefinitely. Our work reveals how electrostatic effects control droplet size, which is relevant for understanding biomolecular condensates and creating synthetic patterns in chemical engineering., Comment: 4 figures and Appendix

The development of unbiased large language models is widely recognized as crucial, yet existing benchmarks fall short in detecting biases due to limited scope, contamination, and lack of a fairness baseline. SAGED(bias) is the first holistic benchmarking pipeline to address these problems. The pipeline encompasses five core stages: scraping materials, assembling benchmarks, generating responses, extracting numeric features, and diagnosing with disparity metrics. SAGED includes metrics for max disparity, such as impact ratio, and bias concentration, such as Max Z-scores. Noticing that metric tool bias and contextual bias in prompts can distort evaluation, SAGED implements counterfactual branching and baseline calibration for mitigation. For demonstration, we use SAGED on G20 Countries with popular 8b-level models including Gemma2, Llama3.1, Mistral, and Qwen2. With sentiment analysis, we find that while Mistral and Qwen2 show lower max disparity and higher bias concentration than Gemma2 and Llama3.1, all models are notably biased against countries like Russia and (except for Qwen2) China. With further experiments to have models role-playing U.S. presidents, we see bias amplifies and shifts in heterogeneous directions. Moreover, we see Qwen2 and Mistral not engage in role-playing, while Llama3.1 and Gemma2 role-play Trump notably more intensively than Biden and Harris, indicating role-playing performance bias in these models., Comment: COLING 2025 Main Conference Oral Presentation

For a symplectic 4-manifold $M$ equipped with a singular Lagrangian fibration with a section, the natural fiberwise addition given by the local Hamiltonian flow is well-defined on the regular points. We prove, in the case that the singularities are of focus-focus type, that the closure of the corresponding addition graph is the image of a Lagrangian immersion in $(M \times M)^- \times M$, and we study its geometry. Our main motivation for this result is the construction of a symmetric monoidal structure on the Fukaya category of such a manifold., Comment: 13 pages, 4 figures

Recent advances in types and extent of medical imaging technologies has led to proliferation of multimodal quantitative imaging data in cancer. Quantitative medical imaging data refer to numerical representations derived from medical imaging technologies, such as radiology and pathology imaging, that can be used to assess and quantify characteristics of diseases, especially cancer. The use of such data in both clinical and research setting enables precise quantifications and analyses of tumor characteristics that can facilitate objective evaluation of disease progression, response to therapy, and prognosis. The scale and size of these imaging biomarkers is vast and presents several analytical and computational challenges that range from high-dimensionality to complex structural correlation patterns. In this review article, we summarize some state-of-the-art statistical methods developed for quantitative medical imaging data ranging from topological, functional and shape data analyses to spatial process models. We delve into common imaging biomarkers with a focus on radiology and pathology imaging in cancer, address the analytical questions and challenges they present, and highlight the innovative statistical and machine learning models that have been developed to answer relevant scientific and clinical questions. We also outline some emerging and open problems in this area for future explorations.

Vision Transformers (ViTs) have emerged as the backbone of many segmentation models, consistently achieving state-of-the-art (SOTA) performance. However, their success comes at a significant computational cost. Image token pruning is one of the most effective strategies to address this complexity. However, previous approaches fall short when applied to more complex task-oriented segmentation (TOS), where the class of each image patch is not predefined but dependent on the specific input task. This work introduces the Vision Language Guided Token Pruning (VLTP), a novel token pruning mechanism that can accelerate ViT-based segmentation models, particularly for TOS guided by multi-modal large language model (MLLM). We argue that ViT does not need to process every image token through all of its layers -- only the tokens related to reasoning tasks are necessary. We design a new pruning decoder to take both image tokens and vision-language guidance as input to predict the relevance of each image token to the task. Only image tokens with high relevance are passed to deeper layers of the ViT. Experiments show that the VLTP framework reduces the computational costs of ViT by approximately 25% without performance degradation and by around 40% with only a 1% performance drop. The code associated with this study can be found at this URL., Comment: Accepted at WACV 2025

We study the theory of a scalar in the fundamental representation of the internal supergroup $SU(N|M)$. Remarkably, for $M=N+1$ its tree-level mass does not receive quantum corrections at one loop from either self-coupling or interactions with gauge bosons and fermions. This property comes at the price of introducing both degrees of freedom with wrong statistics and with wrong sign kinetic terms. We detail a method to break $SU(N|M)$ down to its bosonic subgroup through a Higgs-like mechanism, allowing for the partial decoupling of the dangerous modes, and study the associated vacuum structure up to one loop., Comment: 32 pages + appendices

Van der Waals (vdW) materials supporting phonon polaritons (PhPs) - light coupled to lattice vibrations - have gathered significant interest because of their intrinsic anisotropy and low losses. In particular, $\alpha$-MoO$_3$ supports PhPs with in-plane anisotropic propagation, which has been exploited to tune the optical response of twisted bilayers and trilayers. Additionally, various studies have explored the realization of polaritonic crystals (PCs) - lattices with periods comparable to the polariton wavelength -. PCs consisting of hole arrays etched in $\alpha$-MoO$_3$ slabs exhibit Bragg resonances dependent on the angle between the crystallographic axes and the lattice vectors. However, such PC concept, with a fixed orientation and size of its geometrical parameters, constrains practical applications and introduces additional scattering losses due to invasive fabrication processes. Here we demonstrate a novel PC concept that overcomes these limitations, enabling low-loss optical tuning. It comprises a rotatable pristine $\alpha$-MoO$_3$ layer located on a periodic hole array fabricated in a metallic layer. Our design prevents degradation of the $\alpha$-MoO$_3$ optical properties caused by fabrication, preserving its intrinsic low-loss and in-plane anisotropic propagation of PhPs. The resulting PC exhibits rotation of the Bloch modes, which is experimentally visualized by scanning near-field microscopy. In addition, we experimentally determine the polaritons momentum and reconstruct their band structure. These results pave the way for mechanically tunable nanooptical components based on polaritons for potential lasing, sensing, or energy harvesting applications.

The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful tool for nonparametric detection of dependence between random variables. It crucially depends, however, on the selection of reasonable kernels; commonly-used choices like the Gaussian kernel, or the kernel that yields the distance covariance, are sufficient only for amply sized samples from data distributions with relatively simple forms of dependence. We propose a scheme for selecting the kernels used in an HSIC-based independence test, based on maximizing an estimate of the asymptotic test power. We prove that maximizing this estimate indeed approximately maximizes the true power of the test, and demonstrate that our learned kernels can identify forms of structured dependence between random variables in various experiments.

The benefit of exchange-only qubits compared to other spin qubit types is the universal control using only voltage controlled exchange interactions between neighboring spins. As a compromise, qubit operations have to be constructed from non-orthogonal rotation axes of the Bloch sphere and result in rather long pulsing sequences. This paper aims to develop a faster implementation of single-qubit and two-qubit gates using simultaneous exchange pulses. We introduce pulse sequences in which single-qubit gates could be executed faster and show that subsequences on three spins in two-qubit gates could be implemented in fewer steps. Our findings can particularly speed up gate sequences for realistic idle times between sequential pulses and we show that this advantage increases with more interconnectivity of the quantum dots. We further demonstrate how a phase operation can introduce a relative phase between the computational and some of the leakage states, which can be advantageous for the construction of two-qubit gates. In addition to our theoretical analysis, we experimentally demonstrate and characterize a simultaneous exchange implementation of $X$ rotations in a SiGe quantum dot device and compare to the state of the art with sequential exchange pulses.

We show that the mass of a scalar field transforming in the fundamental of an $SU(N|N+1)$ supergroup internal symmetry is protected against one-loop corrections from quartic, gauge, and yukawa interactions. Although the negative-norm states responsible for this protection are likely fatal to a unitary interpretation of the theory, such ultraviolet insensitivity may open a new avenue to understanding the lightness of the Higgs., Comment: 5 pages

The second author introduced 2-associahedra as a tool for investigating functoriality properties of Fukaya categories, and he conjectured that they could be realized as face posets of convex polytopes. We introduce a family of posets called categorical $n$-associahedra, which naturally extend the second author's 2-associahedra and the classical associahedra. Categorical $n$-associahedra give a combinatorial model for the poset of strata of a compactified real moduli space of a tree arrangement of affine coordinate subspaces. We construct a family of complete polyhedral fans, called velocity fans, whose coordinates encode the relative velocities of pairs of colliding coordinate subspaces, and whose face posets are the categorical $n$-associahedra. In particular, this gives the first fan realization of 2-associahedra. In the case of the classical associahedron, the velocity fan specializes to the normal fan of Loday's realization of the associahedron. For proving that the velocity fan is a fan, we first construct a cone complex of metric $n$-bracketings and then exhibit a piecewise-linear isomorphism from this complex to the velocity fan. We demonstrate that the velocity fan, which is not simplicial, admits a canonical smooth flag triangulation on the same set of rays, and we describe a second, finer triangulation which provides a new extension of the braid arrangement. We describe piecewise-unimodular maps on the velocity fan such that the image of each cone is a union of cones in the braid arrangement, and we highlight a connection to the theory of building sets and nestohedra. We explore the local iterated fiber product structure of categorical $n$-associahedra and the extent to which this structure is realized by the velocity fan. For the class of concentrated $n$-associahedra we exhibit generalized permutahedra having velocity fans as their normal fans., Comment: 143 pages, 39 figures

Hot, dense phases of Dirac fermions - predicted to resemble relativistic plasma - are uniquely accessible through photoexcitation of pristine, charge neutral graphene. We demonstrate a sensitive temperature probe of the photoexcited Dirac state, called interlayer optoelectronic thermometry, which measures out-of-plane transport of hot carriers in high-mobility, neutral graphene encapsulated within graphene-hBN-graphene heterostructures. At a critical intermediate sample temperature T = 50 K, the electronic temperature Te is quenched, exhibiting an intrinsic cooling rate that exceeds 10^14 Kelvin/s within the first picosecond after photoexcitation. Quenching is further enhanced by applying in-plane voltages within the stack-engineered heterostructure. Extreme sensitivity of Te to sample temperature and applied voltages reveals anomalously efficient hot-carrier quenching, which we identify as an essential feature of the strongly interacting hot Dirac excited state.

Recent large language model (LLM) defenses have greatly improved models' ability to refuse harmful queries, even when adversarially attacked. However, LLM defenses are primarily evaluated against automated adversarial attacks in a single turn of conversation, an insufficient threat model for real-world malicious use. We demonstrate that multi-turn human jailbreaks uncover significant vulnerabilities, exceeding 70% attack success rate (ASR) on HarmBench against defenses that report single-digit ASRs with automated single-turn attacks. Human jailbreaks also reveal vulnerabilities in machine unlearning defenses, successfully recovering dual-use biosecurity knowledge from unlearned models. We compile these results into Multi-Turn Human Jailbreaks (MHJ), a dataset of 2,912 prompts across 537 multi-turn jailbreaks. We publicly release MHJ alongside a compendium of jailbreak tactics developed across dozens of commercial red teaming engagements, supporting research towards stronger LLM defenses.

In the rapidly evolving field of cybersecurity, the integration of flow-level and packet-level information for real-time intrusion detection remains a largely untapped area of research. This paper introduces "XG-NID," a novel framework that, to the best of our knowledge, is the first to fuse flow-level and packet-level data within a heterogeneous graph structure, offering a comprehensive analysis of network traffic. Leveraging a heterogeneous graph neural network (GNN) with graph-level classification, XG-NID uniquely enables real-time inference while effectively capturing the intricate relationships between flow and packet payload data. Unlike traditional GNN-based methodologies that predominantly analyze historical data, XG-NID is designed to accommodate the heterogeneous nature of network traffic, providing a robust and real-time defense mechanism. Our framework extends beyond mere classification; it integrates Large Language Models (LLMs) to generate detailed, human-readable explanations and suggest potential remedial actions, ensuring that the insights produced are both actionable and comprehensible. Additionally, we introduce a new set of flow features based on temporal information, further enhancing the contextual and explainable inferences provided by our model. To facilitate practical application and accessibility, we developed "GNN4ID," an open-source tool that enables the extraction and transformation of raw network traffic into the proposed heterogeneous graph structure, seamlessly integrating flow and packet-level data. Our comprehensive quantitative comparative analysis demonstrates that XG-NID achieves an F1 score of 97\% in multi-class classification, outperforming existing baseline and state-of-the-art methods. This sets a new standard in Network Intrusion Detection Systems by combining innovative data fusion with enhanced interpretability and real-time capabilities., Comment: 19 pages, 6 figures

Molecular property prediction and generative design via deep learning models has been the subject of intense research given its potential to accelerate development of new, high-performance materials. More recently, these workflows have been significantly augmented with the advent of large language models (LLMs) and systems of autonomous agents capable of utilizing pre-trained models to make predictions in the context of more complex research tasks. While effective, there is still room for substantial improvement within agentic systems on the retrieval of salient information for material design tasks. Within this context, alternative uses of predictive deep learning models, such as leveraging their latent representations to facilitate cross-modal retrieval augmented generation within agentic systems for task-specific materials design, has remained unexplored. Herein, we demonstrate that large, pre-trained chemistry foundation models can serve as a basis for enabling structure-focused, semantic chemistry information retrieval for both small-molecules, complex polymeric materials, and reactions. Additionally, we show the use of chemistry foundation models in conjunction with multi-modal models such as OpenCLIP facilitate unprecedented queries and information retrieval across multiple characterization data domains. Finally, we demonstrate the integration of these models within multi-agent systems to facilitate structure and topological-based natural language queries and information retrieval for different research tasks.

The application of large-language models (LLMs) to digital hardware code generation is an emerging field. Most LLMs are primarily trained on natural language and software code. Hardware code, such as Verilog, represents only a small portion of the training data and few hardware benchmarks exist. To address this gap, the open-source VerilogEval benchmark was released in 2023, providing a consistent evaluation framework for LLMs on code completion tasks. It was tested on state-of-the-art models at the time including GPT-4. However, VerilogEval and other Verilog generation benchmarks lack failure analysis and, in present form, are not conducive to exploring prompting techniques. Also, since VerilogEval's release, both commercial and open-source models have seen continued development. In this work, we evaluate new commercial and open-source models of varying sizes against an improved VerilogEval benchmark suite. We enhance VerilogEval's infrastructure and dataset by automatically classifying failures, introduce new prompts for supporting in-context learning (ICL) examples, and extend the supported tasks to specification-to-RTL translation. We find a measurable improvement in commercial state-of-the-art models, with GPT-4 Turbo achieving a 59% pass rate on spec-to-RTL tasks. We also study the performance of open-source and domain-specific models that have emerged, and demonstrate that models can benefit substantially from ICL. We find that recently-released Llama 3.1 405B achieves a pass rate of 58%, effectively matching that of GPT-4 Turbo, and that the much smaller domain-specific RTL-Coder 6.7B models achieve an impressive 37% pass rate. However, prompt engineering is key to achieving good pass rates, and varies widely with model and task. A benchmark infrastructure that allows for prompt engineering and failure analysis is key to continued model development and deployment., Comment: This paper revisits and improves the benchmark first presented in arXiv:2309.07544. Seven pages, three figures

The extra-dimensional axion solves the strong CP problem while largely circumventing the quality problem that plagues its four-dimensional counterparts. Such high quality can be clearly understood in terms of the generalized global symmetries of the higher-dimensional theory. We emphasize that an electric one-form symmetry is entirely responsible for protecting the potential of axions arising from 5D gauge theories and use this to systematically characterize the extra-dimensional axion quality problem. We identify three ways of breaking this one-form symmetry to generate an axion potential: adding electrically charged matter, gauging a magnetic higher-form symmetry, and turning on an ABJ anomaly. In the latter case, we identify new ways of generating an axion potential via extra-dimensional magnetic monopoles. We also discuss how the axion is modified if the protective electric one-form symmetry is itself gauged. Finally, we relate these effects to gravitational expectations for the quality problem via generalized weak gravity conjectures. The clarity that generalized symmetries bring to the extra-dimensional axion quality problem highlights their relevance to particle phenomenology., Comment: 11 pages, 1 table; v2: Matches published version

Plasma is capable of mediating the conversion of two pump photons into two different photons through a relativistic four-wave mixing nonlinearity. Spontaneously created photon pairs are emitted at symmetric angles with respect to the colinear pump direction, and the emission rate is largest if they have identical frequency. Thus, two orthogonally polarized pumps can produce polarization-entangled photon pairs through a mm-long homogeneous plasma. The noise from Raman scattering can be avoided if the pump detuning differs from twice the plasma frequency. On the other hand, pump detuning exactly equal to twice the plasma frequency can significantly enhance the interaction rate, which allows for the production of strong two-mode squeezed states. Remarkably, the amplified noise from Raman scattering are correlated and hence can be suppressed in one of the output quadratures, thereby maintaining the squeezing magnitude., Comment: 11 pages, 4 figures

Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computational goal. Task-based execution frameworks abstract the parallel execution of an application's tasks on arbitrary hardware. Research into these task executors has accelerated as computational sciences increasingly need to take advantage of parallel compute and/or heterogeneous hardware. However, the lack of evaluation standards makes it challenging to compare and contrast novel systems against existing implementations. Here, we introduce TaPS, the Task Performance Suite, to support continued research in parallel task executor frameworks. TaPS provides (1) a unified, modular interface for writing and evaluating applications using arbitrary execution frameworks and data management systems and (2) an initial set of reference synthetic and real-world science applications. We discuss how the design of TaPS supports the reliable evaluation of frameworks and demonstrate TaPS through a survey of benchmarks using the provided reference applications., Comment: To appear in the Proceedings of 20th IEEE International Conference on e-Science

Recovering natural language prompts for image generation models, solely based on the generated images is a difficult discrete optimization problem. In this work, we present the first head-to-head comparison of recent discrete optimization techniques for the problem of prompt inversion. We evaluate Greedy Coordinate Gradients (GCG), PEZ , Random Search, AutoDAN and BLIP2's image captioner across various evaluation metrics related to the quality of inverted prompts and the quality of the images generated by the inverted prompts. We find that focusing on the CLIP similarity between the inverted prompts and the ground truth image acts as a poor proxy for the similarity between ground truth image and the image generated by the inverted prompts. While the discrete optimizers effectively minimize their objectives, simply using responses from a well-trained captioner often leads to generated images that more closely resemble those produced by the original prompts., Comment: 9 Pages, 4 Figures

The electrical properties and performance characteristics of niobium dioxide (NbO$_\mathrm{2}$)-based memristive devices are examined at cryogenic temperatures. Sub-stoichiometric Nb$_\mathrm{2}$O$_\mathrm{5}$ was deposited via magnetron sputtering and patterned in microscale (2$\times$2 - 15$\times$15 $\mu$m$^2$) cross-bar Au/Ru/NbO$_\mathrm{x}$/Pt devices and electroformed at 3-5 V to make NbO$_\mathrm{2}$ filaments. At cryogenic temperatures, the threshold voltage ($V_\mathrm{th}$) increased by more than a factor of 3. The hold voltage ($V_\mathrm{h}$) was significantly lower than the threshold voltage for fast voltage sweeps (200 ms per measurement). If the sample is allowed to cool between voltage measurements, the hold voltage increases, but never reaches the threshold voltage, indicating the presence of non-volatile Nb$_\mathrm{2}$O$_\mathrm{5}$ in the filament. The devices have an activation energy of $E_a \approx 1.4$ eV, lower than other NbO$_\mathrm{2}$ devices reported. Our works shows that even nominally ``bad" memristive devices can be improved by reducing the leakage current and increases the sample resistance at cryogenic temperatures., Comment: 16 pages, 5 figures

Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial to driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating biological systems and revealing biochemical regulatory mechanisms. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks and uncover biochemical mechanisms in healthy and diseased states. The rapid development of high-throughput sequencing techniques and computational tools has recently enabled models that span multiple scales, often integrating signaling, gene regulatory, and metabolic networks. These multiscale models enable comprehensive investigations of cellular networks and thus reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from classical mechanistic models to larger, multiscale models that integrate multiple layers of cellular networks. We introduce several examples of models of hypertrophic cardiomyopathy, exercise, and cancer cell proliferation. Additionally, we discuss methods that increase the certainty and accuracy of model predictions. Integrating multiscale models has become a powerful tool for understanding disease and inspiring drug discoveries by incorporating omics data within the cell and across tissues and organisms., Comment: 32 pages, 4 figures

The widespread use of large language models has resulted in a multitude of tokenizers and embedding spaces, making knowledge transfer in prompt discovery tasks difficult. In this work, we propose FUSE (Flexible Unification of Semantic Embeddings), an inexpensive approach to approximating an adapter layer that maps from one model's textual embedding space to another, even across different tokenizers. We introduce a third-order tensor-based representation of a model's embedding space that aligns semantic embeddings that have been split apart by different tokenizers, and use this representation to derive an approximation of the gradient of one model's outputs with respect to another model's embedding space. We show the efficacy of our approach via multi-objective optimization over vision-language and causal language models for image captioning and sentiment-based image captioning., Comment: Published as a Conference Paper at COLM 2024; 10 Pages; https://github.com/jnwilliams/FUSE_prompt_inversion.git

We describe Qonductor, a cloud orchestrator for hybrid quantum-classical applications that run on heterogeneous hybrid resources. Qonductor exposes the $Qonductor~API$, a high-level and hardware-agnostic API for customizable hybrid application development and execution, that abstracts away the complexity of hybrid resource management. To guide hybrid resource management, the $resource~estimator$ accurately estimates execution fidelity and runtime to generate and offer resource plans. The $hybrid~scheduler$ leverages the resource plans to automate job scheduling on hybrid resources and balance the tradeoff between users' objectives of high fidelity and low runtimes and the cloud operator's objective of resource efficiency. We implement an open-source prototype of Qonductor by building on top of Kubernetes and evaluate it using more than 7000 real quantum runs on the IBM quantum cloud to simulate real cloud workloads. Qonductor achieves up to 54\% lower job completion times (JCTs) while sacrificing 6\% fidelity, balances the load across QPU which increases quantum resource utilization by up to 66\%, and scales with increasing system sizes and loads.

Epsilon-near-zero (ENZ) materials have shown strong refractive nonlinearities that can be fast in an absolute sense. While continuing to advance fundamental science, such as time varying interactions, the community is still searching for an application that can effectively make use of the strong index modulation offered. Here we combine the effect of strong space-time index modulation in ENZ materials with the beam deflection technique to introduce a new approach to optical pulse characterization that we term a space-time knife edge. We show that in this approach, we are able to extract temporal and spatial information of a Gaussian beam with only two time resolved measurements. The approach achieves this without phase-matching requirements (<1 micron thick film) and can achieve a high signal to noise ratio by combining the system with lock-in detection, facilitating the measurement of weak refractive index changes (delta_n ~ 10^-5) for low intensity beams. Thus, the space-time knife edge can offer a new avenue for ultrafast light measurement and demonstrates a use cases of ENZ materials. In support of this, we outline temporal dynamics for refractive index changes in non-colinear experiments opening avenues for better theoretical understanding of both the spatial and temporal dynamics of emerging ENZ films.

Receiver operating characteristic (ROC) analysis is a tool to evaluate the capacity of a numeric measure to distinguish between groups, often employed in the evaluation of diagnostic tests. Overall classification ability is sometimes crudely summarized by a single numeric measure such as the area under the empirical ROC curve. However, it may also be of interest to estimate the full ROC curve while leveraging assumptions regarding the nature of the data (parametric) or about the ROC curve directly (semiparametric). Although there has been recent interest in methods to conduct comparisons by way of stochastic ordering, nuances surrounding ROC geometry and estimation are not widely known in the broader scientific and statistical community. The overarching goals of this manuscript are to (1) provide an overview of existing frameworks for ROC curve estimation with examples, (2) offer intuition for and considerations regarding methodological trade-offs, and (3) supply sample R code to guide implementation. We utilize simulations to demonstrate the bias-variance trade-off across various methods. As an illustrative example, we analyze data from a recent cohort study in order to compare responses to SARS-CoV-2 vaccination between solid organ transplant recipients and healthy controls., Comment: 23 pages, 6 figures

We compute the algebraic Picard group of the category of $K(n)$-local spectra, for all heights $n$ and all primes $p$. In particular, we show that it is always finitely generated over $\mathbb{Z}_p$ and, whenever $n \geq 2$, is of rank $2$, thereby confirming a prediction made by Hopkins in the early 1990s. In fact, with the exception of the anomalous case $n=p=2$, we provide a full set of topological generators for these groups. Our arguments rely on recent advances in $p$-adic geometry to translate the problem to a computation on Drinfeld's symmetric space, which can then be solved using results of Colmez--Dospinescu--Niziol., Comment: 46 pages; all comments welcome

An infinite family of association schemes obtained from the general unitary groups acting transitively on the sets of isotropic vectors in the finite unitary spaces are investigated. We compute the parameters and determine the character tables for all nontrivial commutative association schemes belonging to this family.

Every known communication problem whose randomized communication cost is constant (independent of the input size) can be reduced to $k$-Hamming Distance, that is, solved with a constant number of deterministic queries to some $k$-Hamming Distance oracle. We exhibit the first examples of constant-cost problems which cannot be reduced to $k$-Hamming Distance. To prove this separation, we relate it to a natural coding-theoretic question. For $f : \{2, 4, 6\} \to \mathbb{N}$, we say an encoding function $E : \{0, 1\}^n \to \{0, 1\}^m$ is an $f$-code if it transforms Hamming distances according to $\mathrm{dist}(E(x), E(y)) = f(\mathrm{dist}(x, y))$ whenever $f$ is defined. We prove that, if there exist $f$-codes for infinitely many $n$, then $f$ must be affine: $f(4) = (f(2) + f(6))/2$.

Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical risk. How the demographics of the reference class influence clinical interpretation of deviations is unknown. Using normative modeling, a method for building reference classes, we evaluate the fairness (racial bias) in reference models of structural brain images that are widely used in psychiatry and neurology. We test whether including race in the model creates fairer models. We predict self-reported race using the deviation scores from three different reference class normative models, to better understand bias in an integrated, multivariate sense. Across all of these tasks, we uncover racial disparities that are not easily addressed with existing data or commonly used modeling techniques. Our work suggests that deviations from the norm could be due to demographic mismatch with the reference class, and assigning clinical meaning to these deviations should be done with caution. Our approach also suggests that acquiring more representative samples is an urgent research priority.

Proteins' fuzziness are features for communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. Binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit, but it is unclear whether the limited experimental data available can be used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. With our designs, we provided a framework of explainable machine learning model to annotate atomic charges of calcium ions in calcium-binding proteins with domain knowledge in response to the chemical changes in an environment based on the limited size of scientific data in a genome space., Comment: submitted

Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms -- which include tree-based models, deep neural networks, and graphical models -- differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species -- fly, mouse, and human -- we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets., Comment: 33 pages, 15 figures

Casualty insurance-linked securities (ILS) are appealing to investors because the underlying insurance claims, which are directly related to resulting security performance, are uncorrelated with most other asset classes. Conversely, casualty ILS are appealing to insurers as an efficient capital managment tool. However, securitizing casualty insurance risk is non-trivial, as it requires forecasting loss ratios for pools of insurance policies that have not yet been written, in addition to estimating how the underlying losses will develop over time within future accident years. In this paper, we lay out a Bayesian workflow that tackles these complexities by using: (1) theoretically informed time-series and state-space models to capture how loss ratios develop and change over time; (2) historic industry data to inform prior distributions of models fit to individual programs; (3) stacking to combine loss ratio predictions from candidate models, and (4) both prior predictive simulations and simulation-based calibration to aid model specification. Using historic Schedule P filings, we then show how our proposed Bayesian workflow can be used to assess and compare models across a variety of key model performance metrics evaluated on future accident year losses., Comment: 31 pages, 7 figures

Stability is one of the most fundamental aspects regarding planetary systems. It plays an important role in our understanding on the formation channel of the planetary systems, as well as their habitability. Many approaches have been adopted to determine the stability of these systems, including brute-force N-body simulations, semi-analytical calculations, and more recently machine learning methods. This allows significant advances in our understanding of planetary system dynamics, as well as providing tools to constrain unknown parameters of exoplanetary systems (assuming these systems are stable). In the following, we focus on planets around single star hosts, and we provide an overview of the studies of planetary system stability for compact multi-planet systems and hierarchical multi-planet systems., Comment: Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Dimitri Veras) to be published by Elsevier as a Reference Module. The number of references was capped

The mainstream AI community has seen a rise in large-scale open-source classifiers, often pre-trained on vast datasets and tested on standard benchmarks; however, users facing diverse needs and limited, expensive test data may be overwhelmed by available choices. Deep Neural Network (DNN) classifiers undergo training, validation, and testing phases using example dataset, with the testing phase focused on determining the classification accuracy of test examples without delving into the inner working of the classifier. In this work, we evaluate a DNN classifier's training quality without any example dataset. It is assumed that a DNN is a composition of a feature extractor and a classifier which is the penultimate completely connected layer. The quality of a classifier is estimated using its weight vectors. The feature extractor is characterized using two metrics that utilize feature vectors it produces when synthetic data is fed as input. These synthetic input vectors are produced by backpropagating desired outputs of the classifier. Our empirical study of the proposed method for ResNet18, trained with CAFIR10 and CAFIR100 datasets, confirms that data-less evaluation of DNN classifiers is indeed possible.

Thermally bistable fluid tends to self-organize into clouds of hot and cold material, which are internally uniform and separated by thin conduction fronts. The evolution of these clouds has been studied for isobaric systems, but when pressure is instead treated as a dynamical quantity and allowed to evolve self-consistently, fundamentally different dynamics appear. Such a treatment is necessary in some laboratory plasmas, whose volume is constrained but whose pressure can vary. Solutions are derived for the evolution of clouds, accounting for pressure variation and interactions between conduction fronts. Additional stable configurations and secondary instabilities are derived, which may be relevant to fusion plasmas and to the study of photoionized plasma in the laboratory., Comment: 17 pages, 6 figures

We present the Code Documentation and Analysis Tool (CoDAT). CoDAT is a tool designed to maintain consistency between the various levels of code documentation, e.g. if a line in a code sketch is changed, the comment that documents the corresponding code is also changed. That is, comments are linked and updated so as to remain internally consistent and also consistent with the code. By flagging "out of date" comments, CoDAT alerts the developer to maintain up-to-date documentation. We use a large language model to check the semantic consistency between a fragment of code and the comments that describe it. Thus we also flag semantic inconsistency as well as out of date comments. This helps programers write code that correctly implements a code sketch, and so provides machine support for a step-wise refinement approach, starting with a code sketch and proceeding down to code through one or more refinement iterations. CoDAT is implemented in the Intellij IDEA IDE where we use the Code Insight daemon package alongside a custom regular expression algorithm to mark tagged comments whose corresponding code blocks have changed. CoDAT's backend is structurally decentralized to allow a distributed ledger framework for code consistency and architectural compilation tracking., Comment: 31 pages

Context. Common envelope evolution of a massive star and a neutron star companion has two possible outcomes: formation of a short-period binary (a potential gravitational wave source progenitor) or a merger of the massive star with the neutron star. If the binary merges, a structure with a neutron star core surrounded by a large diffuse envelope, a so-called Thorne-\.Zytkow object (T\.ZO), may form. The predicted appearance of this hypothetical class of star is very similar to red supergiants, making observational identification difficult. Aims. Our objective is to understand the properties of systems that are potential T\.ZO progenitors, e.g., binary systems that enter a common envelope phase with a neutron star companion. We also aim to distinguish those that have been through a previous stable mass transfer phase, which can rejuvenate the accretor. We estimate the number of T\.ZOs in the Milky Way and assess the impact of uncertainties in their formation. Methods. We use the rapid population synthesis code COMPAS at Solar metallicity and with common envelope efficiency parameter set to unity to determine the population demographics of T\.ZOs. We use one-dimensional evolutionary T\.ZO models from the literature to determine a fit for T\.ZO lifetime in order to estimate the current number of T\.ZOs in the Galaxy as well as to assess core disruption during the merger. Results. We explore the progenitors in the Hertzsprung-Russell diagram, calculate formation rates, and investigate kinematics of the progenitor stars. We find that the vast majority ($\approx 92\%$) of T\.ZO progenitors in our population have experienced mass transfer and become rejuvenated before their formation event. Using a constant star formation rate we estimate $\approx 2\times 10 ^{-4}$ T\.ZOs per $M_\odot$ in our Galaxy, corresponding to $\approx 5\pm 1$ T\.ZOs in the Milky Way at present., Comment: 12 pages, 6 figures. Accepted in A&A