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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. We discuss why this general pattern is rare in practical scenarios, where a single clockwise loop is more typical. The paper discusses the impact of the road topology and demand parameters on the formation and intensity of hysteresis loops analytically. To substantiate these findings, we employ numerical simulations using the Cell Transmission Model (CTM). Our simulations 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, currently under review after having been submitted 20 July 2023

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 and Ka-band do 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.

We present a multi-wavelength study of the apparently non-repeating, heavily scattered fast radio burst, FRB 20221219A, detected by the Deep Synoptic Array 110 (DSA-110). The burst exhibits a moderate dispersion measure (DM) of $706.7^{+0.6}_{-0.6}$ $\mathrm{pc}~\mathrm{cm}^{-3}$ and an unusually high scattering timescale of $\tau_{\mathrm{obs}} = 19.2_{-2.7}^{+2.7}$ ms at 1.4 GHz. We associate the FRB with a Milky Way-like host galaxy at $z_{\mathrm{host}} = 0.554$ of stellar mass $\mathrm{log}_{10}(M_{\star, \mathrm{host}}) = 10.20^{+0.04}_{-0.03} ~M_\odot$. We identify two intervening galaxy halos at redshifts $z_{\mathrm{igh1}} = 0.492$ and $z_{\mathrm{igh2}} = 0.438$, with low impact parameters, $b_{\mathrm{igh1}} = 43.0_{-11.3}^{+11.3}$ kpc and $b_{\mathrm{igh2}} = 36.1_{-11.3}^{+11.3}$ kpc, and intermediate stellar masses, $\mathrm{log}_{10}(M_{\star, \mathrm{igh1}}) = 10.01^{+0.02}_{-0.02} ~M_\odot$ and $\mathrm{log}_{10}(M_{\star, \mathrm{igh2}}) = 10.60^{+0.02}_{-0.02} ~M_\odot$. The presence of two such galaxies suggests that the sightline is significantly overcrowded compared to the median sightline to this redshift, as inferred from the halo mass function. We perform a detailed analysis of the sightline toward FRB 20221219A, constructing both DM and scattering budgets. Our results suggest that, unlike most well-localized sources, the host galaxy does not dominate the observed scattering. Instead, we posit that an intersection with a single partially ionized cloudlet in the circumgalactic medium of an intervening galaxy could account for the substantial scattering in FRB 20221219A and remain in agreement with typical electron densities inferred for extra-planar dense cloud-like structures in the Galactic and extragalactic halos (e.g., high-velocity clouds)., Comment: 18 pages, 6 figures, submitted to ApJ, comments appreciated

In this study, we introduce a tensor-train (TT) finite difference WENO method for solving compressible Euler equations. In a step-by-step manner, the tensorization of the governing equations is demonstrated. We also introduce \emph{LF-cross} and \emph{WENO-cross} methods to compute numerical fluxes and the WENO reconstruction using the cross interpolation technique. A tensor-train approach is developed for boundary condition types commonly encountered in Computational Fluid Dynamics (CFD). The performance of the proposed WENO-TT solver is investigated in a rich set of numerical experiments. We demonstrate that the WENO-TT method achieves the theoretical $\text{5}^{\text{th}}$-order accuracy of the classical WENO scheme in smooth problems while successfully capturing complicated shock structures. In an effort to avoid the growth of TT ranks, we propose a dynamic method to estimate the TT approximation error that governs the ranks and overall truncation error of the WENO-TT scheme. Finally, we show that the traditional WENO scheme can be accelerated up to 1000 times in the TT format, and the memory requirements can be significantly decreased for low-rank problems, demonstrating the potential of tensor-train approach for future CFD application. This paper is the first study that develops a finite difference WENO scheme using the tensor-train approach for compressible flows. It is also the first comprehensive work that provides a detailed perspective into the relationship between rank, truncation error, and the TT approximation error for compressible WENO solvers.

Critical experimental design issues connecting energy transduction and inheritable information within a protocell are explored and elucidated. The protocell design utilizes a photo-driven energy transducer (a ruthenium complex) to turn resource molecules into building blocks, in a manner that is modulated by a combinatorial DNA-based co-factor. This co-factor molecule serves as part of an electron relay for the energy transduction mechanism, where the charge-transport rates depend on the sequence that contains an oxo-guanine. The co-factor also acts as a store of inheritable information due to its ability to replicate non-enzymatically through template-directed ligation. Together, the energy transducer and the co-factor act as a metabolic catalyst that produces co-factor DNA building blocks as well as fatty acids (from picolinium ester and modified DNA oligomers), where the fatty acids self-assemble into vesicles on which exterior surface both the co-factor (DNA) and the energy transducer are anchored with hydrophobic tails. Here we use simulations to study how the co-factor sequence determines its fitness as reflected by charge transfer and replication rates. To estimate the impact on the protocell, we compare these rates with previously measured metabolic rates from a similar system where the charge transfer is directly between the ruthenium complex and the oxo-guanine (without DNA replication and charge transport). Replication and charge transport turn out to have different and often opposing sequence requirements. Functional information of the co-factor molecules is used to probe the feasibility of randomly picking co-factor sequences from a limited population of co-factors molecules, where a good co-factor can enhance both metabolic biomass production and its own replication rate.

Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification in whole slide images (WSIs) is laborious and subject to variability, potentially undermining patient outcomes. Our study introduces an automated pipeline that utilizes semi-stochastic patch sampling, patch classification to retain prognostically relevant patches, and cell quantification using the HoVer-Net model to streamline the TIL evaluation process. This pipeline efficiently excludes approximately 70% of areas not relevant for prognosis and requires only 5% of the remaining patches to maintain prognostic accuracy (c-index 0.65 +- 0.01). The computational efficiency achieved does not sacrifice prognostic accuracy, as demonstrated by the TILs score's strong correlation with patient survival, which surpasses traditional CD8 IHC scoring methods. While the pipeline demonstrates potential for enhancing NSCLC prognostication and personalization of treatment, comprehensive clinical validation is still required. Future research should focus on verifying its broader clinical utility and investigating additional biomarkers to improve NSCLC prognosis., Comment: 18 pages, 7 figures, 6 appendix pages

Widely available measurement equipment in electrical distribution grids, such as power-quality measurement devices, substation meters, or customer smart meters do not provide phasor measurements due to the lack of high resolution time synchronisation. Instead such measurement devices allow to obtain magnitudes of voltages and currents and the local phase angle between those. In addition, these measurements are subject to measurement errors of up to few percent of the measurand. In order to utilize such measurements for grid monitoring, this paper presents and assesses a stochastic grid calculation approach that allows to derive confidence regions for the resulting current and voltage phasors. Two different metering models are introduced: a PMU model, which is used to validate theoretical properties of the estimator, and an Electric Meter model for which a Gaussian approximation is introduced. The estimator results are compared for the two meter models and case study results for a real Danish distribution grid are presented.

Correlation Clustering is a classic clustering objective arising in numerous machine learning and data mining applications. Given a graph $G=(V,E)$, the goal is to partition the vertex set into clusters so as to minimize the number of edges between clusters plus the number of edges missing within clusters. The problem is APX-hard and the best known polynomial time approximation factor is 1.73 by Cohen-Addad, Lee, Li, and Newman [FOCS'23]. They use an LP with $|V|^{1/\epsilon^{\Theta(1)}}$ variables for some small $\epsilon$. However, due to the practical relevance of correlation clustering, there has also been great interest in getting more efficient sequential and parallel algorithms. The classic combinatorial \emph{pivot} algorithm of Ailon, Charikar and Newman [JACM'08] provides a 3-approximation in linear time. Like most other algorithms discussed here, this uses randomization. Recently, Behnezhad, Charikar, Ma and Tan [FOCS'22] presented a $3+\epsilon$-approximate solution for solving problem in a constant number of rounds in the Massively Parallel Computation (MPC) setting. Very recently, Cao, Huang, Su [SODA'24] provided a 2.4-approximation in a polylogarithmic number of rounds in the MPC model and in $\tilde{O} (|E|^{1.5})$ time in the classic sequential setting. They asked whether it is possible to get a better than 3-approximation in near-linear time? We resolve this problem with an efficient combinatorial algorithm providing a drastically better approximation factor. It achieves a $\sim 2-2/13 < 1.847$-approximation in sub-linear ($\tilde O(|V|)$) sequential time or in sub-linear ($\tilde O(|V|)$) space in the streaming setting. In the MPC model, we give an algorithm using only a constant number of rounds that achieves a $\sim 2-1/8 < 1.876$-approximation.

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG., Comment: Accepted at IEEE ISDFS

K\"ulshammer, K\"onig and Ovsienko proved that for any quasi-hereditary algebra $(A,\leq_A)$ there exists a Morita equivalent quasi-hereditary algebra $(R, \leq_R)$ containing a basic exact Borel subalgebra $B$. The obtained Borel subalgebra is in fact a regular exact Borel subalgebra. Later, Conde showed that given a quasi-hereditary algebra $(R,\leq_R)$ with a basic regular exact Borel subalgebra $B$ and a Morita equivalent quasi-hereditary algebra $(R',\leq_{R'})$ with a basic regular exact Borel subalgebra $B'$, the algebras $R$ and $R'$ are isomorphic, and K\"ulshammer and Miemietz showed that there is even an isomorphism $\varphi:R\rightarrow R'$ such that $\varphi(B)=B'$. In this article, we show that if $R=R'$, then $\varphi$ can be chosen to be an inner automorphism. Moreover, instead of just proving this for regular exact Borel subalgebras of quasi-hereditary algebras, we generalize this to an appropriate class of subalgebras of arbitrary finite-dimensional algebras. As an application, we show that if $(A, \leq_A)$ is a finite-dimensional algebra and $G$ is a finite group acting on $A$ via automorphisms, then under some natural compatibility conditions, there is a Morita equivalent quasi-hereditary algebra $(R, \leq_R)$ with a basic regular exact Borel subalgebra $B$ such that $g(B)=B$ for every $g\in G$., Comment: 53 pages

National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods for analysis. However, shortcomings in existing ML approaches hinder their mass adoption. These challenges include detection of novel malware and the ability to perform malware classification in the face of class imbalance: a situation where malware families are not equally represented in the data. Our work addresses these shortcomings with MalwareDNA: an advanced dimensionality reduction and feature extraction framework. We demonstrate stable task performance under class imbalance for the following tasks: malware family classification and novel malware detection with a trade-off in increased abstention or reject-option rate., Comment: Accepted at IEEE 12th International Symposium on Digital Forensics and Security (ISDFS), 2024. arXiv admin note: text overlap with arXiv:2309.01350

We study a skew product transformation associated to an irrational rotation of the circle [0,1]/~. This skew product keeps track of the number of times an orbit of the rotation lands in the two complementary intervals of {0,1/2} in the circle. We show that under certain conditions on the continued fraction expansion of the irrational number defining the rotation, the skew product transformation has certain dense orbits. This is in spite of the presence of numerous non-dense orbits. We use this to construct laminations on infinite type surfaces with exotic properties. In particular, we show that for every infinite type surface with an isolated planar end, there is an infinite clique of 2-filling rays based at that end. These 2-filling rays are relevant to Bavard--Walker's loop graphs., Comment: v2: Added funding information. 21 pages, 9 figures

Emerging tensor network techniques for solutions of Partial Differential Equations (PDEs), known for their ability to break the curse of dimensionality, deliver new mathematical methods for ultrafast numerical solutions of high-dimensional problems. Here, we introduce a Tensor Train (TT) Chebyshev spectral collocation method, in both space and time, for solution of the time dependent convection-diffusion-reaction (CDR) equation with inhomogeneous boundary conditions, in Cartesian geometry. Previous methods for numerical solution of time dependent PDEs often use finite difference for time, and a spectral scheme for the spatial dimensions, which leads to slow linear convergence. Spectral collocation space-time methods show exponential convergence, however, for realistic problems they need to solve large four-dimensional systems. We overcome this difficulty by using a TT approach as its complexity only grows linearly with the number of dimensions. We show that our TT space-time Chebyshev spectral collocation method converges exponentially, when the solution of the CDR is smooth, and demonstrate that it leads to very high compression of linear operators from terabytes to kilobytes in TT-format, and tens of thousands times speedup when compared to full grid space-time spectral method. These advantages allow us to obtain the solutions at much higher resolutions.

Deep learning presents novel opportunities for the auto-segmentation of gross tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods usually necessitate significant manual refinement. This study investigates the Segment Anything Model (SAM), recognized for requiring minimal human prompting and its zero-shot generalization ability across natural images. We specifically examine MedSAM, a version of SAM fine-tuned with large-scale public medical images. Despite its progress, the integration of multi-modality images (CT, PET, MRI) for effective GTV delineation remains a challenge. Focusing on SAM's application in HNC GTV segmentation, we assess its performance in both zero-shot and fine-tuned scenarios using single (CT-only) and fused multi-modality images. Our study demonstrates that fine-tuning SAM significantly enhances its segmentation accuracy, building upon the already effective zero-shot results achieved with bounding box prompts. These findings open a promising avenue for semi-automatic HNC GTV segmentation.

NorthWind, a collaborative research initiative supported by the Research Council of Norway, industry stakeholders, and research partners, aims to advance cutting-edge research and innovation in wind energy. The core mission is to reduce wind power costs and foster sustainable growth, with a key focus on the development of digital twins. A digital twin is a virtual representation of physical assets or processes that uses data and simulators to enable real-time forecasting, optimization, monitoring, control and informed decision-making. Recently, a hierarchical scale ranging from 0 to 5 (0 - Standalone, 1 - Descriptive, 2 - Diagnostic, 3 - Predictive, 4 - Prescriptive, 5 - Autonomous has been introduced within the NorthWind project to assess the capabilities of digital twins. This paper elaborates on our progress in constructing digital twins for wind farms and their components across various capability levels.

We investigate the dynamic properties of elastic lattices defined by tessellations of a curved hyperbolic space. The lattices are obtained by projecting nodes of a regular hyperbolic tessellation onto a flat disk and then connecting those sites with simple linkages. Numerical and experimental investigations illustrate how their vibrational spectral properties are characterized by a high density of modes that are localized at the domain boundaries. Such properties govern the propagation of waves induced by broadband inputs. This suggests the potential for applications seeking the protection of bulk media from boundary-incident perturbations. We uncover the boundary-dominated nature of an exemplary hyperbolic lattice through the evaluation and analysis of its integrated density of states and vibrational spectrum. The dynamics of the lattice are also contextualized by comparing them with those of continuous disks characterized by Euclidean and hyperbolic property distributions, which confirms that the lattice retains the boundary-dominated spectrum observed in the hyperbolic plane. We then numerically investigate the response of the lattice to transient pulses incident on the boundary and find that edge-confined wave propagation occurs. The modal and transient pulse propagation behavior of the lattice is experimentally validated in a milled aluminum sample. By leveraging hyperbolic geometry, our mechanical lattice ushers in a novel class of mechanical metamaterials with boundary-dominated wave phenomena reminiscent of topologically protected systems suitable for applications in advanced wave control., Comment: 27 pages, 8 figures