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We prove that projectivised finite-dimensional linear random dynamical systems possess a unique finest weak Morse decomposition. Based on this result, we define the Morse spectrum and investigate its basic properties. In particular, we show that the Morse spectrum is given by a finite union of closed intervals. Furthermore we demonstrate that under a bounded growth condition, the Morse spectrum coincides with the non-uniform dichotomy spectrum.

We investigate automated in situ optimization of the potential landscape in a quantum point contact device, using a $3 \times 3$ gate array patterned atop the constriction. Optimization is performed using the covariance matrix adaptation evolutionary strategy, for which we introduce a metric for how "step-like" the conductance is as the channel becomes constricted. We first perform the optimization of the gate voltages in a tight-binding simulation and show how such in situ tuning can be used to mitigate a random disorder potential. The optimization is then performed in a physical device in experiment, where we also observe a marked improvement in the quantization of the conductance resulting from the optimization procedure., Comment: 8 pages, 4 figures (supplement: 6 pages, 3 figures)

Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and existing models are not easily comparable, limiting the development of architectures optimized specifically for volumetric data. To counteract this trend, we introduce MozzaVID - a large, clean, and versatile volumetric classification dataset. Our dataset contains X-ray computed tomography (CT) images of mozzarella microstructure and enables the classification of 25 cheese types and 149 cheese samples. We provide data in three different resolutions, resulting in three dataset instances containing from 591 to 37,824 images. While being general-purpose, the dataset also facilitates investigating mozzarella structure properties. The structure of food directly affects its functional properties and thus its consumption experience. Understanding food structure helps tune the production and mimicking it enables sustainable alternatives to animal-derived food products. The complex and disordered nature of food structures brings a unique challenge, where a choice of appropriate imaging method, scale, and sample size is not trivial. With this dataset we aim to address these complexities, contributing to more robust structural analysis models. The dataset can be downloaded from: https://archive.compute.dtu.dk/files/public/projects/MozzaVID/.

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance of retrieved documents, which is influenced by the semantic alignment of embeddings with the domain's specialized content. Although full fine-tuning can align language models to specific domains, it is computationally intensive and demands substantial data. This paper introduces Hierarchical Embedding Alignment Loss (HEAL), a novel method that leverages hierarchical fuzzy clustering with matrix factorization within contrastive learning to efficiently align LLM embeddings with domain-specific content. HEAL computes level/depth-wise contrastive losses and incorporates hierarchical penalties to align embeddings with the underlying relationships in label hierarchies. This approach enhances retrieval relevance and document classification, effectively reducing hallucinations in LLM outputs. In our experiments, we benchmark and evaluate HEAL across diverse domains, including Healthcare, Material Science, Cyber-security, and Applied Maths.

In recent years, the use of deep learning (DL) methods, including convolutional neural networks (CNNs) and vision transformers (ViTs), has significantly advanced computational pathology, enhancing both diagnostic accuracy and efficiency. Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) plays a crucial role by providing detailed tissue samples for the analysis and training of DL models. However, WSIs often contain regions with artifacts such as tissue folds, blurring, as well as non-tissue regions (background), which can negatively impact DL model performance. These artifacts are diagnostically irrelevant and can lead to inaccurate results. This paper proposes a fully automatic supervised DL pipeline for WSI Quality Assessment (WSI-QA) that uses a fused model combining CNNs and ViTs to detect and exclude WSI regions with artifacts, ensuring that only qualified WSI regions are used to build DL-based computational pathology applications. The proposed pipeline employs a pixel-based segmentation model to classify WSI regions as either qualified or non-qualified based on the presence of artifacts. The proposed model was trained on a large and diverse dataset and validated with internal and external data from various human organs, scanners, and H&E staining procedures. Quantitative and qualitative evaluations demonstrate the superiority of the proposed model, which outperforms state-of-the-art methods in WSI artifact detection. The proposed model consistently achieved over 95% accuracy, precision, recall, and F1 score across all artifact types. Furthermore, the WSI-QA pipeline shows strong generalization across different tissue types and scanning conditions., Comment: submitted to IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, November 25, 2024

This paper initiates the study of the Einstein equation on homogeneous supermanifolds. First, we produce explicit curvature formulas for graded Riemannian metrics on these spaces. Next, we present a construction of homogeneous supermanifolds by means of Dynkin diagrams, resembling the construction of generalised flag manifolds in classical (non-super) theory. We describe the Einstein metrics on several classes of spaces obtained through this approach. Our results provide examples of compact homogeneous supermanifolds on which the Einstein equation has no solutions, discrete families of solutions, and continuous families of Ricci-flat solutions among invariant metrics. These examples demonstrate that the finiteness conjecture from classical homogeneous geometry fails on supermanifolds, and challenge the intuition furnished by Bochner's vanishing theorem., Comment: 46 pages

The ICH E9(R1) addendum provides guidelines on accounting for intercurrent events in clinical trials using the estimands framework. However, there has been limited attention on the estimands framework for meta-analysis. Using treatment switching, a well-known intercurrent event that occurs frequently in oncology, we conducted a simulation study to explore the bias introduced by pooling together estimates targeting different estimands in a meta-analysis of randomized clinical trials (RCTs) that allowed for treatment switching. We simulated overall survival data of a collection of RCTs that allowed patients in the control group to switch to the intervention treatment after disease progression under fixed-effects and random-effects models. For each RCT, we calculated effect estimates for a treatment policy estimand that ignored treatment switching, and a hypothetical estimand that accounted for treatment switching by censoring switchers at the time of switching. Then, we performed random-effects and fixed-effects meta-analyses to pool together RCT effect estimates while varying the proportions of treatment policy and hypothetical effect estimates. We compared the results of meta-analyses that pooled different types of effect estimates with those that pooled only treatment policy or hypothetical estimates. We found that pooling estimates targeting different estimands results in pooled estimators that reflect neither the treatment policy estimand nor the hypothetical estimand. This finding shows that pooling estimates of varying target estimands can generate misleading results, even under a random-effects model. Adopting the estimands framework for meta-analysis may improve alignment between meta-analytic results and the clinical research question of interest.

During electro-optical testing of the camera for the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, a unique low-signal pattern was found in differenced pairs of flat images used to create photon transfer curves, with peak-to-peak variations of a factor of 10^-3. A turbulent pattern of this amplitude was apparent in many differenced flat-fielded images. The pattern changes from image to image and shares similarities with atmospheric 'weather' turbulence patterns. We applied several strategies to determine the source of the turbulent pattern and found that it is representative of the mixing of the air and index of refraction variations caused by the internal camera purge system displacing air, which we are sensitive to due to our flat field project setup. Characterizing this changing environment with 2-D correlation functions of the 'weather' patterns provides evidence that the images reflect the changes in the camera environment due to the internal camera purge system. Simulations of the full optical system using the galsim and batoid codes show that the weather pattern affects the dispersion of the camera point-spread function at only the one part in 10^-4 level, Comment: 14 pages, 10 figures, accepted in JATISS

Resilience in social systems is crucial for mitigating the impacts of crises, such as climate change, which poses an existential threat to communities globally. As disasters become more frequent and severe, enhancing community resilience has become imperative. This study introduces a cutting-edge framework, quantitative network-based modeling called Complex Analysis for Socio-environmental Adaptation (CASA) to evaluate and strengthen social resilience. CASA transforms resilience models' linear and static structure into a complex network that integrates complexity and systems thinking, utilizing global scientific knowledge and complex network methodologies. The resulting resilience framework features rich interdependencies, and subsequent dimensionality reduction produces robust resilience indicators. This innovative application of network sciences is then demonstrated by quantitatively assessing what are known as "Sacrifice Zones," socio-environmentally sensitive areas. Results unveil the potential of this novel application of complex network methodologies as tools for systemic diagnostics, identifying vulnerabilities, and guiding policies and practices to enhance climate resilience and adaptation. The CASA framework represents a pioneering tool for assessing territorial resilience, leveraging network science applications, big data analytics, and artificial intelligence. CASA serves as a systemic diagnostic tool for urban resilience and a guide for policymakers, urban planners, and other professionals to promote sustainable, healthy cities in an era of climate change., Comment: 26 pages

We develop a powerful and general method to provide arbitrarily accurate rigorous upper and lower bounds for Lyapunov exponents of stochastic flows. Our approach is based on computer-assisted tools, the adjoint method and established results on the ergodicity of diffusion processes. We do not require any structural assumptions on the stochastic system and work under mild hypoellipticity conditions outside of perturbative regimes. Therefore, our method allows for the treatment of systems that were so far inaccessible from existing mathematical tools. We demonstrate our method to exhibit the chaotic nature of three non-Hamiltonian systems. Finally, we show that our approach is robust to continuation methods to produce bounds on Lyapunov exponents for large parameter regions.

This paper investigates the impact of noise in the quantum query model, a fundamental framework for quantum algorithms. We focus on the scenario where the oracle is subject to non-unitary (or irreversible) noise, specifically under the \textit{faulty oracle} model, where the oracle fails with a constant probability and acts as identity. Regev and Schiff (ICALP'08) showed that quantum advantage is lost for the search problem under this noise model. Our main result shows that every quantum query algorithm can be made robust in this noise model with a roughly quadratic blow-up in query complexity, thereby preserving quantum speedup for all problems where the quantum advantage is super-cubic. This is the first non-trivial robustification of quantum query algorithms against an oracle that is noisy.

In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent convection-diffusion-reaction (CDR) equation. We reformulate the assembly process of the spectral element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the spectral element operators. Recognizing the banded structure inherent in the spectral element framework's discrete operators, we further exploit the QTT format of the CDR to achieve greater speed and compression. Additionally, we present a comprehensive approach for integrating variable coefficients of CDR into the global discrete operators within the TT/QTT framework. The effectiveness of the proposed method, in terms of memory efficiency and computational complexity, is demonstrated through a series of numerical experiments, including a semi-linear example.

We study the asymptotic behaviour of stationary densities of one-dimensional random diffeomorphisms, at the boundaries of their support, which correspond to deterministic fixed points of extremal diffeomorphisms. In particular, we show how this stationary density at a boundary depends on the underlying noise distribution, as well as the linearisation of the extremal diffeomorphism at the boundary point (in case the corresponding fixed point is hyperbolic), or the leading nonlinear term of the extremal diffeomorphism (in case the corresponding fixed point is not hyperbolic).

Motivated by a long-standing question of Ihara, we investigate heavenly abelian varieties -- abelian varieties defined over a number field $K$ that exhibit constrained $\ell$-adic Galois representations at some rational prime $\ell$. We demonstrate a finiteness result for heavenly elliptic curves where $\ell$ is fixed and the field of definition varies. Introducing the notion of a balanced heavenly abelian variety, characterized by the structure of its $\ell$-torsion as a group scheme, we show that all heavenly abelian varieties are balanced for sufficiently large $\ell$, with this result holding uniformly once the degree of the number field and the dimension of the abelian variety are fixed. We study the Frobenius traces on balanced heavenly elliptic curves, showing that they satisfy certain congruences modulo $\ell$ akin to those of elliptic curves with complex multiplication. Conjecturally, we propose that balanced elliptic curves over quadratic number fields must possess complex multiplication. Finally, we produce an explicit list of elliptic curves with irrational $j$-invariants, which contains all heavenly elliptic curves with complex multiplication defined over quadratic fields, supported by computational evidence that every curve on the list is heavenly., Comment: 30 pages, 1 figure, 2 tables

With Signal's position as one of the most popular secure messaging protocols in use today, the threat of government coercion and mass surveillance, i.e., active Man-in-the-Middle (MitM) attacks, are more relevant than ever. On the other hand, studies [29, 33, 37, 38] have shown that user awareness is very poor when it comes to authenticating keys in instant messaging applications, e.g., comparing key fingerprints out-of-band. The ideal solution to this problem should not require the active participation of the users. Our solution to active MitM attacks builds directly on Signal. We automate the process of key confirmation without relying on the intervention of users, and without using an out-of-band communication channel, at the cost of slightly altered trust assumptions on the server. We consider a powerful active MitM that not only controls the communication channel, but also has (one time) access to all secrets on one of the clients, i.e., can perform a key compromise attack. Our solution utilises the server to keep track of the changes in the clients key fingerprint as ratcheting is performed. Given that the server can keep a message log already, we find that any impact on deniability is minimal in practice. We present our detailed modifications to Signal, and document the new security guarantees while preserving the existing security guarantees of Signal. Our proof-of-concept implementation, which is based on the open-source Signal library used in real-world instant messaging applications, shows that our solution is practical and integrates well with the library. Our experimental results further show that our solution only has a tiny performance overhead when compared to Signal.

Radiation therapy (RT) is a vital part of treatment for head and neck cancer, where accurate segmentation of gross tumor volume (GTV) is essential for effective treatment planning. This study investigates the use of pre-RT tumor regions and local gradient maps to enhance mid-RT tumor segmentation for head and neck cancer in MRI-guided adaptive radiotherapy. By leveraging pre-RT images and their segmentations as prior knowledge, we address the challenge of tumor localization in mid-RT segmentation. A gradient map of the tumor region from the pre-RT image is computed and applied to mid-RT images to improve tumor boundary delineation. Our approach demonstrated improved segmentation accuracy for both primary GTV (GTVp) and nodal GTV (GTVn), though performance was limited by data constraints. The final DSCagg scores from the challenge's test set evaluation were 0.534 for GTVp, 0.867 for GTVn, and a mean score of 0.70. This method shows potential for enhancing segmentation and treatment planning in adaptive radiotherapy. Team: DCPT-Stine's group.

Modern random access mechanisms combine packet repetitions with multi-user detection mechanisms at the receiver to maximize the throughput and reliability in massive Internet of Things (IoT) scenarios. However, optimizing the access policy, which selects the number of repetitions, is a complicated problem, and failing to do so can lead to an inefficient use of resources and, potentially, to an increased congestion. In this paper, we follow a game-theoretic approach for optimizing the access policies of selfish users in modern random access mechanisms. Our goal is to find adequate values for the rewards given after a success to achieve a Nash equilibrium (NE) that optimizes the throughput of the system while considering the cost of transmission. Our results show that a mixed strategy, where repetitions are selected according to the irregular repetition slotted ALOHA (IRSA) protocol, attains a NE that maximizes the throughput in the special case with two users. In this scenario, our method increases the throughput by 30% when compared to framed ALOHA. Furthermore, we present three methods to attain a NE with near-optimal throughput for general modern random access scenarios, which exceed the throughput of framed ALOHA by up to 34%., Comment: Submitted for publication to IEEE WCNC 2025

Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, when it comes to domain-specific and knowledge-intensive tasks, LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. Additionally, fine tuning LLMs' intrinsic knowledge to highly specific domains is an expensive and time consuming process. The retrieval-augmented generation (RAG) process has recently emerged as a method capable of optimization of LLM responses, by referencing them to a predetermined ontology. It was shown that using a Knowledge Graph (KG) ontology for RAG improves the QA accuracy, by taking into account relevant sub-graphs that preserve the information in a structured manner. In this paper, we introduce SMART-SLIC, a highly domain-specific LLM framework, that integrates RAG with KG and a vector store (VS) that store factual domain specific information. Importantly, to avoid hallucinations in the KG, we build these highly domain-specific KGs and VSs without the use of LLMs, but via NLP, data mining, and nonnegative tensor factorization with automatic model selection. Pairing our RAG with a domain-specific: (i) KG (containing structured information), and (ii) VS (containing unstructured information) enables the development of domain-specific chat-bots that attribute the source of information, mitigate hallucinations, lessen the need for fine-tuning, and excel in highly domain-specific question answering tasks. We pair SMART-SLIC with chain-of-thought prompting agents. The framework is designed to be generalizable to adapt to any specific or specialized domain. In this paper, we demonstrate the question answering capabilities of our framework on a corpus of scientific publications on malware analysis and anomaly detection., Comment: 9 pages 7 figures, 1 table, 1 cypher code Accepted to ICMLA 2024

Fast Radio Bursts (FRBs) are millisecond-duration events detected from beyond the Milky Way. FRB emission characteristics favor highly magnetized neutron stars, or magnetars, as the sources, as evidenced by FRB-like bursts from a galactic magnetar, and the star-forming nature of FRB host galaxies. However, the processes that produce FRB sources remain unknown. Although galactic magnetars are often linked to core-collapse supernovae (CCSNe), it's uncertain what determines which supernovae result in magnetars. The galactic environments of FRB sources can be harnessed to probe their progenitors. Here, we present the stellar population properties of 30 FRB host galaxies discovered by the Deep Synoptic Array. Our analysis shows a significant deficit of low-mass FRB hosts compared to the occurrence of star-formation in the universe, implying that FRBs are a biased tracer of star-formation, preferentially selecting massive star-forming galaxies. This bias may be driven by galaxy metallicity, which is positively correlated with stellar mass. Metal-rich environments may favor the formation of magnetar progenitors through stellar mergers, as higher metallicity stars are less compact and more likely to fill their Roche lobes, leading to unstable mass transfer. Although massive stars do not have convective interiors to generate strong magnetic fields by dynamo, merger remnants are thought to have the requisite internal magnetic-field strengths to result in magnetars. The preferential occurrence of FRBs in massive star-forming galaxies suggests that CCSN of merger remnants preferentially forms magnetars., Comment: Accepted for publication in Nature. The final version will be published by the journal

Approximately half of the Universe's dark matter resides in collapsed halos; significantly less than half of the baryonic matter (protons and neutrons) remains confined to halos. A small fraction of baryons are in stars and the interstellar medium within galaxies. The lion's share are diffuse (less than $10^{-3}$ cm$^{-3}$) and ionized (neutral fraction less than $10^{-4}$), located in the intergalactic medium (IGM) and in the halos of galaxy clusters, groups, and galaxies. The quantity and spatial distribution of this diffuse ionized gas is notoriously difficult to measure, but has wide implications for galaxy formation, astrophysical feedback, and precision cosmology. Recently, the dispersion of extragalactic Fast Radio Bursts (FRBs) has been used to measure the total content of cosmic baryons. However, past efforts had modest samples and methods that cannot discriminate between IGM and halo gas, which is critical for studying feedback and for observational cosmology. Here, we present a large cosmological sample of FRB sources localized to their host galaxies. We have robustly partitioned the missing baryons into the IGM, galaxy clusters, and galaxies, providing a late-Universe measurement of the total baryon density of $\Omega_b h_{70}$=0.049$\pm$0.003. Our results indicate efficient feedback processes that can expel gas from galaxy halos and into the intergalactic medium, agreeing with the enriched cosmic web scenario seen in cosmological simulations. The large diffuse baryon fraction that we have measured disfavours bottom-heavy stellar initial mass functions, which predict a large total stellar density, $\Omega_*$.

We theoretically and computationally investigate the cooling of antihydrogen, $\bar{H}$, using optical molasses cooling. This updates the results in Ref. [1] to the current capabilities of the ALPHA experiment. Through Monte Carlo simulation, we show that $\bar{H}$s do not give the standard cooling even in an ideal optical molasses because of their small mass and large transition frequency. For optical molasses cooling in the ALPHA trap, the photons are constrained to travel in one direction only. It is only through the phase space mixing in the trap that cooling in all directions can be achieved. We explore the nontrivial role that laser intensity plays in the cooling. We also investigate the possibility for simultaneously cooling atoms in either of the trapped ground states.

Macroscopic fundamental diagrams (MFDs) and related network traffic dynamics models have received both theoretical support and empirical validation with the emergence of new data collection technologies. However, the existence of well-defined MFD curves can only be expected for traffic networks with specific topologies and is subject to various disturbances, most importantly hysteresis phenomena. This study aims to improve the understanding of hysteresis in Macroscopic Fundamental Diagrams and Network Exit Functions (NEFs) during rush hour conditions. We apply the LWR theory to a highway corridor featuring a location-dependent downstream bottleneck to identify a figure-eight hysteresis pattern, clockwise on the top and counter-clockwise on the bottom. Our empirical observations confirm the occurrence of counter-clockwise loops in real conditions, an effect which we can attribute to demand asymmetries through theoretical analysis. The paper discusses the impact of the road topology and demand patterns on the formation and intensity of hysteresis loops analytically. To substantiate these findings, we analyze empirical MFD data from two bottlenecks and present statistical evidence that, under otherwise identical conditions, a continuous bottleneck causes less hysteresis than a discontinuous one. We conduct numerical experiments using the Cell Transmission Model (CTM) to show that even a slight reduction in the capacity of the homogeneous section can significantly decrease MFD hysteresis while maintaining outflow at the corridor's downstream end. These reductions can be achieved with minimal intervention through standard traffic control measures, such as dynamic speed limits or ramp metering.

This paper considers substitution patterns in the perturbed utility route choice model. We provide a general result that determines the marginal change in link flows following a marginal change in link costs across the network. We give a general condition on the network structure under which all paths are necessarily substitutes and an example in which some paths are complements. The presence of complementarity contradicts a result in a previous paper in this journal; we point out and correct the error.

We consider the $A$ series and exceptional $E_6$ Restricted Solid-On-Solid lattice models as prototypical examples of the critical Yang-Baxter integrable two-dimensional $A$-$D$-$E$ lattice models. We focus on type I theories which are characterized by the existence of an extended chiral symmetry in the continuum scaling limit. Starting with the commuting family of column transfer matrices on the torus, we build matrix representations of the Ocneanu graph fusion algebra as integrable seams for arbitrary finite-size lattices with the structure constants specified by Petkova and Zuber. This commutative seam algebra contains the Verlinde, fused adjacency and graph fusion algebras as subalgebras. Our matrix representation of the Ocneanu algebra encapsulates the quantum symmetry of the commuting family of transfer matrices. In the continuum scaling limit, the integrable seams realize the topological defects of the associated conformal field theory and the known toric matrices encode the twisted conformal partition functions., Comment: 16 pages

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development., Comment: 16 pages, 5 figures (+4 supplementary figures), 4 tables

The SPARC tokamak is a compact high-field device that will operate at high plasma density with the aim to demonstrate net fusion energy. The experimentally unexplored plasma conditions in SPARC will require a carefully selected set of diagnostics for plasma monitoring and control. Here we explore conceptual design options and potential measurement capabilities of a collective Thomson scattering diagnostic at SPARC. We show that a 140 GHz X-mode CTS system is the most attractive option in terms of optimizing the signal-to-noise ratio and limiting sensitivity to refraction, as well as from a technological readiness perspective. Such a setup can provide core-localized measurements of the fusion -born alpha distribution function, main-ion temperature and toroidal rotation, fuel-ion ratio, and 3He content with relevant spatio-temporal resolution. Our proposed diagnostic layout can in principle be integrated into SPARC and could provide a valuable addition to its diagnostic suite at limited development costs and time., Comment: 26 pages, 10 figures, submitted to Nuclear Fusion

There has been a transition from broad to more specific research questions in the practice of network meta-analysis (NMA). Such convergence is also taking place in the context of individual registrational trials, following the recent introduction of the estimand framework, which is impacting the design, data collection strategy, analysis and interpretation of clinical trials. The language of estimands has much to offer to NMA, particularly given the "narrow" perspective of treatments and target populations taken in health technology assessment., Comment: 6 pages, discussion paper based on Ades et al. (2024), accepted for publication by Research Synthesis Methods

Quantum computing (QC) provides a promising avenue toward enabling quantum chemistry calculations, which are classically impossible due to a computational complexity that increases exponentially with system size. As fully fault-tolerant algorithms and hardware, for which an exponential speedup is predicted, are currently out of reach, recent research efforts are dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices to showcase the practical utility of such machines. To demonstrate the utility of NISQ devices in the field of chemistry, we apply our recently developed FAST-VQE algorithm and a novel quantum gate reduction strategy based on propositional satisfiability together with standard optimization tools for the simulation of the rate-determining proton transfer step for CO2 hydration catalysed by carbonic anhydrase resulting in the first application of a quantum computing device for the simulation of an enzymatic reaction. To this end, we have combined classical force field simulations with quantum mechanical methods on classical and quantum computers in a hybrid calculation approach. The presented technique significantly enhances the accuracy and capabilities of QC-based molecular modeling and finally pushes it into compelling and realistic applications. The framework is general and can be applied beyond the case of computational enzymology.

As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its resilience to such attacks is Non-negative Matrix Factorization (NMF), an algorithm that decomposes input data into lower-dimensional latent features. However, the introduction of powerful computational tools such as Pytorch enables the computation of gradients of the latent features with respect to the original data, raising concerns about NMF's reliability. Interestingly, naively deriving the adversarial loss for NMF as in the case of ML would result in the reconstruction loss, which can be shown theoretically to be an ineffective attacking objective. In this work, we introduce a novel class of attacks in NMF termed Latent Feature Attacks (LaFA), which aim to manipulate the latent features produced by the NMF process. Our method utilizes the Feature Error (FE) loss directly on the latent features. By employing FE loss, we generate perturbations in the original data that significantly affect the extracted latent features, revealing vulnerabilities akin to those found in other ML techniques. To handle large peak-memory overhead from gradient back-propagation in FE attacks, we develop a method based on implicit differentiation which enables their scaling to larger datasets. We validate NMF vulnerabilities and FE attacks effectiveness through extensive experiments on synthetic and real-world data., Comment: LA-UR-24-26951

In this paper, we introduce a high-order tensor-train (TT) finite volume method for the Shallow Water Equations (SWEs). We present the implementation of the $3^{rd}$ order Upwind and the $5^{th}$ order Upwind and WENO reconstruction schemes in the TT format. It is shown in detail that the linear upwind schemes can be implemented by directly manipulating the TT cores while the WENO scheme requires the use of TT cross interpolation for the nonlinear reconstruction. In the development of numerical fluxes, we directly compute the flux for the linear SWEs without using TT rounding or cross interpolation. For the nonlinear SWEs where the TT reciprocal of the shallow water layer thickness is needed for fluxes, we develop an approximation algorithm using Taylor series to compute the TT reciprocal. The performance of the TT finite volume solver with linear and nonlinear reconstruction options is investigated under a physically relevant set of validation problems. In all test cases, the TT finite volume method maintains the formal high-order accuracy of the corresponding traditional finite volume method. In terms of speed, the TT solver achieves up to 124x acceleration of the traditional full-tensor scheme.

The thermal conductivity of glasses is well-known to be significantly harder to theoretically describe compared to crystalline materials. Because of this fact, the fundamental understanding of thermal conductivity in glasses remain extremely poor when moving beyond the case of simple glasses, e.g., glassy SiO$_2$, and into so-called 'modified' oxide glasses, that is, glasses where other oxides (e.g. alkali oxides) have been added to break up the network and alter e.g. elastic and thermal properties. This lack of knowledge is apparent despite how modified glasses comprise the far majority of known glasses. In the present work we study an archetypical series of sodium silicate ($x\text{Na}_2\text{O}\text{-}(100\text{-}x)\text{SiO}_2$) glasses. Analyses of modal contributions reveal how increasing Na$_2$O content induces increasing vibrational localization with a change of vibrations to be less ordered, and a related general decrease in modal contributions to thermal conductivity. We find the vibrational phases (acoustic vs. optical) of sodium vibrations to be relatively disordered compared to the network-forming silicon and oxygen species, explaining how increasing Na$_2$O content decreases thermal conductivity. Our work sheds new light on the fundamentals of glassy heat transfer as well as the interplay between thermal conduction and modal characteristics in glasses., Comment: The following article has been submitted to The Journal of Chemical Physics

Semiconductor quantum dots (QD) are a promising platform for multiple different qubit implementations, all of which are voltage-controlled by programmable gate electrodes. However, as the QD arrays grow in size and complexity, tuning procedures that can fully autonomously handle the increasing number of control parameters are becoming essential for enabling scalability. We propose a bootstrapping algorithm for initializing a depletion mode QD device in preparation for subsequent phases of tuning. During bootstrapping, the QD device functionality is validated, all gates are characterized, and the QD charge sensor is made operational. We demonstrate the bootstrapping protocol in conjunction with a coarse tuning module, showing that the combined algorithm can efficiently and reliably take a cooled-down QD device to a desired global state configuration in under 8 minutes with a success rate of 96 %. Importantly, by following heuristic approaches to QD device initialization and combining the efficient ray-based measurement with the rapid radio-frequency reflectometry measurements, the proposed algorithm establishes a reference in terms of performance, reliability, and efficiency against which alternative algorithms can be benchmarked., Comment: 9 pages, 3 figures, 1 table

Topic modeling is a technique for organizing and extracting themes from large collections of unstructured text. Non-negative matrix factorization (NMF) is a common unsupervised approach that decomposes a term frequency-inverse document frequency (TF-IDF) matrix to uncover latent topics and segment the dataset accordingly. While useful for highlighting patterns and clustering documents, NMF does not provide explicit topic labels, necessitating subject matter experts (SMEs) to assign labels manually. We present a methodology for automating topic labeling in documents clustered via NMF with automatic model determination (NMFk). By leveraging the output of NMFk and employing prompt engineering, we utilize large language models (LLMs) to generate accurate topic labels. Our case study on over 34,000 scientific abstracts on Knowledge Graphs demonstrates the effectiveness of our method in enhancing knowledge management and document organization., Comment: Accepted to ACM Symposium on Document Engineering 2024 (DocEng 24), 2024

Dusty circumnuclear disks (CNDs) in luminous early-type galaxies (ETGs) show regular, dynamically cold molecular gas kinematics. For a growing number of ETGs, Atacama Large Millimeter/sub-millimeter Array (ALMA) CO imaging and detailed gas-dynamical modeling facilitate moderate-to-high precision black hole (BH) mass ($M_{BH}$) determinations. From the ALMA archive, we identified a subset of 26 ETGs with estimated $M_{BH}/M_{\odot} \gtrsim 10^8$ to a few $\times$10$^9$ and clean CO kinematics but that previously did not have sufficiently high angular resolution near-IR observations to mitigate dust obscuration when constructing stellar luminosity models. We present new optical and near-IR Hubble Space Telescope (HST) images of this sample to supplement the archival HST data, detailing the sample properties and data analysis techniques. After masking the most apparent dust features, we measure stellar surface brightness profiles and model the luminosities using the multi-Gaussian expansion (MGE) formalism. Some of these MGEs have already been used in CO dynamical modeling efforts to secure quality \mbh\ determinations, and the remaining ETG targets here are expected to significantly improve the high-mass end of the current BH census, facilitating new scrutiny of local BH mass-host galaxy scaling relationships. We also explore stellar isophotal behavior and general dust properties, finding these CNDs generally become optically thick in the near-IR ($A_H \gtrsim 1$ mag). These CNDs are typically well-aligned with the larger-scale stellar photometric axes with a few notable exceptions. Uncertain dust impact on the MGE often dominates the BH mass error budget, so extensions of this work will focus on constraining CND dust attenuation., Comment: 47 pages, 37 Figures, Accepted for publication in ApJ

Determining the chronology of ancient handwritten manuscripts is essential for reconstructing the evolution of ideas. For the Dead Sea Scrolls, this is particularly important. However, there is an almost complete lack of date-bearing manuscripts evenly distributed across the timeline and written in similar scripts available for palaeographic comparison. Here, we present Enoch, a state-of-the-art AI-based date-prediction model, trained on the basis of new radiocarbon-dated samples of the scrolls. Enoch uses established handwriting-style descriptors and applies Bayesian ridge regression. The challenge of this study is that the number of radiocarbon-dated manuscripts is small, while current machine learning requires an abundance of training data. We show that by using combined angular and allographic writing style feature vectors and applying Bayesian ridge regression, Enoch could predict the radiocarbon-based dates from style, supported by leave-one-out validation, with varied MAEs of 27.9 to 30.7 years relative to the radiocarbon dating. Enoch was then used to estimate the dates of 135 unseen manuscripts, revealing that 79 per cent of the samples were considered 'realistic' upon palaeographic post-hoc evaluation. We present a new chronology of the scrolls. The radiocarbon ranges and Enoch's style-based predictions are often older than the traditionally assumed palaeographic estimates. In the range of 300-50 BCE, Enoch's date prediction provides an improved granularity. The study is in line with current developments in multimodal machine-learning techniques, and the methods can be used for date prediction in other partially-dated manuscript collections. This research shows how Enoch's quantitative, probability-based approach can be a tool for palaeographers and historians, re-dating ancient Jewish key texts and contributing to current debates on Jewish and Christian origins., Comment: 16 pages of main article, 103 pages of supplementary materials; the first version of this article is originally prepared in July 2023 after the completion of all the experiments

Global numerical weather models are starting to resolve atmospheric moist convection which comes with a critical need for observational constraints. One avenue for such constraints is spaceborne radar which tend to operate at three wavelengths, Ku-, Ka- and W-band. Many studies of deep convection in the past have primarily leveraged Ku-band because it is less affected by attenuation and multiple scattering. However, future spaceborne radar missions might not contain a Ku-band radar and thus considering the view of convection from Ka-band or W-band compared to the Ku-band would be useful. This study examines a coincident dataset between the Global Precipitation Measurement (GPM) Mission and CloudSat as well as the entire GPM record to compare convective characteristics across various wavelengths within deep convection. We find that W-band reflectivity (Z) tends to maximize near the Ku-band defined echo-top while Ka-band often maximizes 4-5 km below. The height of the maximum Z above the melting level for W-band does not linearly relate to the Ku-band maximum. However, using the full GPM record the Ka-band 30 dBZ echo-tops can be linearly related to the Ku-band 40 dBZ echo-top with an $R^2$ of 0.62 and a root mean squared error of about 1 km. The spatial distribution of echo-tops from Ka-band corresponds well to the Ku-band echo-tops, highlighting regions of relatively large ice water path. This paper suggests that Ka-band only missions, like NASA's Investigation for Convective Updrafts, should be able to characterize global convection in a similar manner to a Ku-band system.

Data fusion models based on Coupled Matrix and Tensor Factorizations (CMTF) have been effective tools for joint analysis of data from multiple sources. While the vast majority of CMTF models are based on the strictly multilinear CANDECOMP/PARAFAC (CP) tensor model, recently also the more flexible PARAFAC2 model has been integrated into CMTF models. PARAFAC2 tensor models can handle irregular/ragged tensors and have shown to be especially useful for modelling dynamic data with unaligned or irregular time profiles. However, existing PARAFAC2-based CMTF models have limitations in terms of possible regularizations on the factors and/or types of coupling between datasets. To address these limitations, in this paper we introduce a flexible algorithmic framework that fits PARAFAC2-based CMTF models using Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). The proposed framework allows to impose various constraints on all modes and linear couplings to other matrix-, CP- or PARAFAC2-models. Experiments on various simulated and a real dataset demonstrate the utility and versatility of the proposed framework as well as its benefits in terms of accuracy and efficiency in comparison with state-of-the-art methods., Comment: 15 pages, 15 figures,1 table

Spectral methods provide highly accurate numerical solutions for partial differential equations, exhibiting exponential convergence with the number of spectral nodes. Traditionally, in addressing time-dependent nonlinear problems, attention has been on low-order finite difference schemes for time discretization and spectral element schemes for spatial variables. However, our recent developments have resulted in the application of spectral methods to both space and time variables, preserving spectral convergence in both domains. Leveraging Tensor Train techniques, our approach tackles the curse of dimensionality inherent in space-time methods. Here, we extend this methodology to the nonlinear time-dependent convection-diffusion equation. Our discretization scheme exhibits a low-rank structure, facilitating translation to tensor-train (TT) format. Nevertheless, controlling the TT-rank across Newton's iterations, needed to deal with the nonlinearity, poses a challenge, leading us to devise the "Step Truncation TT-Newton" method. We demonstrate the exponential convergence of our methods through various benchmark examples. Importantly, our scheme offers significantly reduced memory requirement compared to the full-grid scheme.