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The use of small language models (SLMs), herein defined as models with less than three billion parameters, is increasing across various domains and applications. Due to their ability to run on more accessible hardware and preserve user privacy, SLMs possess the potential to democratize access to language models for individuals of different socioeconomic status and with different privacy preferences. This study assesses several state-of-the-art SLMs (e.g., Apple's OpenELM, Microsoft's Phi, Google's Gemma, and the Tinyllama project) for use in the financial domain to support the development of financial literacy LMs. Democratizing access to quality financial information for those who are financially under educated is greatly needed in society, particularly as new financial markets and products emerge and participation in financial markets increases due to ease of access. We are the first to examine the use of open-source SLMs to democratize access to financial question answering capabilities for individuals and students. To this end, we provide an analysis of the memory usage, inference time, similarity comparisons to ground-truth answers, and output readability of prominent SLMs to determine which models are most accessible and capable of supporting access to financial information. We analyze zero-shot and few-shot learning variants of the models. The results suggest that some off-the-shelf SLMs merit further exploration and fine-tuning to prepare them for individual use, while others may have limits to their democratization.

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot's actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, we are one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Additional videos are provided at https://sites.google.com/illinois.edu/comp-teleop., Comment: 8 pages, 4 figures, 3 tables

The CPT theorem states that a unitary and Lorentz-invariant theory must also be invariant under a discrete symmetry $\mathbf{CRT}$ which reverses charge, time, and one spatial direction. In this article, we study a $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry group, in which two of the nontrivial symmetries (``Reflection Reality'' and a 180 degree rotation) are implied by Unitarity and Lorentz Invariance respectively, while the third is $\mathbf{CRT}$. (In cosmology, Scale Invariance plays the role of Lorentz Invariance.) This naturally leads to converses of the CPT theorem, as any two of the discrete $\mathbb{Z}_2$ symmetries will imply the third one. Furthermore, in many field theories, the Reflection Reality $\mathbb{Z}_2$ symmetry is actually sufficient to imply the theory is fully unitary, over a generic range of couplings. Building upon previous work on the Cosmological Optical Theorem, we derive non-perturbative reality conditions associated with bulk Reflection Reality (in all flat FLRW models) and $\mathbf{CRT}$ (in de Sitter spacetime), in arbitrary dimensions. Remarkably, this $\mathbf{CRT}$ constraint suffices to fix the phase of all wavefunction coefficients at future infinity (up to a real sign) -- without requiring any analytic continuation, or comparison to past infinity -- although extra care is required in cases where the bulk theory has logarithmic UV or IR divergences. This result has significant implications for de Sitter holography, as it allows us to determine the phases of arbitrary $n$-point functions in the dual CFT., Comment: 58 pages, no figures

In this work, we present the elastic electron scattering longitudinal form factors of $A\le 10$ nuclei computed in a variational Monte Carlo approach. We employ the Norfolk family of local chiral interactions and a consistent electromagnetic charge operator. Our calculations are compared both to data and past theoretical evaluations. This work represents, to our knowledge, the first exact many-body calculation of longitudinal form factors in the $7\,\leq\,A\,\leq\,10$ mass range. Finally, we identify $^9$Be and $^{10}$B as candidate targets for renewed experimental interest, as they exhibit the potential to provide more stringent constraints on the theoretical models., Comment: 12 pages, 6 figures

The choice of lung protective ventilation settings for mechanical ventilation has a considerable impact on patient outcome, yet identifying optimal ventilatory settings for individual patients remains highly challenging due to the inherent inter- and intra-patient pathophysiological variability. In this validation study, we demonstrate that physics-based computational lung models tailored to individual patients can resolve this variability, allowing us to predict the otherwise unknown local state of the pathologically affected lung during mechanical ventilation. For seven ARDS patients undergoing invasive mechanical ventilation, physics-based, patient-specific lung models were created using chest CT scans and ventilatory data. By numerically resolving the interaction of the pathological lung with the airway pressure and flow imparted by the ventilator, we predict the time-dependent and heterogeneous local state of the lung for each patient and compare it against the regional ventilation obtained from bedside monitoring using Electrical Impedance Tomography. Excellent agreement between numerical simulations and experimental data was obtained, with the model-predicted anteroposterior ventilation profile achieving a Pearson correlation of 96% with the clinical reference data. Even when considering the regional ventilation within the entire transverse chest cross-section and across the entire dynamic ventilation range, an average correlation of more than 81% and an average root mean square error of less than 15% were achieved. The results of this first systematic validation study demonstrate the ability of computational models to provide clinically relevant information and thereby open the door for a truly patient-specific choice of ventilator settings on the basis of both individual anatomy and pathophysiology., Comment: 11 pages, 9 figures

Academic literature reviews have traditionally relied on techniques such as keyword searches and accumulation of relevant back-references, using databases like Google Scholar or IEEEXplore. However, both the precision and accuracy of these search techniques is limited by the presence or absence of specific keywords, making literature review akin to searching for needles in a haystack. We present vitaLITy 2, a solution that uses a Large Language Model or LLM-based approach to identify semantically relevant literature in a textual embedding space. We include a corpus of 66,692 papers from 1970-2023 which are searchable through text embeddings created by three language models. vitaLITy 2 contributes a novel Retrieval Augmented Generation (RAG) architecture and can be interacted with through an LLM with augmented prompts, including summarization of a collection of papers. vitaLITy 2 also provides a chat interface that allow users to perform complex queries without learning any new programming language. This also enables users to take advantage of the knowledge captured in the LLM from its enormous training corpus. Finally, we demonstrate the applicability of vitaLITy 2 through two usage scenarios. vitaLITy 2 is available as open-source software at https://vitality-vis.github.io., Comment: 7 pages, 4 figures, NLVIZ Workshop 2024 (IEEE VIS 2024)

Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalistic home videos captured via the mobile application GuessWhat. Through interactive games played between children and their guardians, GuessWhat has amassed over 3,000 structured videos from 382 children, both diagnosed with and without Autism Spectrum Disorder (ASD). This collection provides a robust dataset for training computer vision models to detect ASD-related phenotypic markers, including variations in emotional expression, eye contact, and head movements. We have developed a protocol to curate high-quality videos from this dataset, forming a comprehensive training set. Utilizing this set, we trained individual LSTM-based models using eye gaze, head positions, and facial landmarks as input features, achieving test AUCs of 86%, 67%, and 78%, respectively. To boost diagnostic accuracy, we applied late fusion techniques to create ensemble models, improving the overall AUC to 90%. This approach also yielded more equitable results across different genders and age groups. Our methodology offers a significant step forward in the early detection of ASD by potentially reducing the reliance on subjective assessments and making early identification more accessibly and equitable.

Return-Conditioned Transformer Decision Models (RCTDM) have demonstrated the potential to enhance transformer performance in offline reinforcement learning by replacing rewards in the input sequence with returns-to-go. However, to achieve the goal of learning an optimal policy from offline datasets composed of limited suboptimal trajectories, RCTDM required alternative methods. One prominent approach, trajectory stitching, was designed to enable the network to combine multiple trajectories to find the optimal path. To implement this using only transformers without auxiliary networks, it was necessary to shorten the input sequence length to better capture the Markov property in reinforcement learnings. This, however, introduced a trade-off, as it reduced the accuracy of action inference. Our study introduces a model named Decision MetaMamba to resolve these challenges. DMM employs an input token mixer to extract patterns from short sequences and uses a State Space Model (SSM) to selectively combine information from relatively distant sequences. Inspired by Metaformer, this structure was developed by transforming Mamba's input layer into various multi-modal layers. Fortunately, with the advent of Mamba, implemented using parallel selective scanning, we achieved a high-performance sequence model capable of replacing transformers. Based on these innovations, DMM demonstrated excellent performance across various datasets in offline RL, confirming that models using SSM can improve performance by domain-specific alterations of the input layer. Additionally, it maintained its performance even in lightweight models with fewer parameters. These results suggest that decision models based on SSM can pave the way for improved outcomes in future developments.

Simulation models are nowadays indispensable to efficiently assess or optimize novel battery cell concepts during the development process. Electro-chemo-mechano models are widely used to investigate solid-state batteries during cycling and allow the prediction of the dependence of design parameters like material properties, geometric properties, or operating conditions on output quantities like the state of charge. One possibility of classification of these physics-based models is their level of geometric resolution, including three-dimensionally resolved models and geometrically homogenized models, known as Doyle-Fuller-Newman or pseudo two-dimensional models. Within this study, the advantages and drawbacks of these two types of models are identified within a wide range of the design parameter values. Therefore, the sensitivity of an output quantity of the models on one or a combination of parameters is compared. In particular, the global sensitivity, i.e., the sensitivity in a wide range of parameter values, is computed by using the Sobol indices as a measure. Furthermore, the local sensitivity of the difference in the output quantities of both models is evaluated to identify regions of parameter values in which they contain significant deviations. Finally, remarks on the potential interplay between both models to obtain fast and reliable results are given.

The National Technical Assistance Center on Transition: the Collaborative (NTACT:C) Collaborative Assessment Guide for Transition Planning (CAG) is intended to help students, family members, educators, vocational rehabilitation counselors, human services and health agency staff, and other partners develop a coordinated assessment approach for transition planning and service delivery. The set of resources which make up the CAG includes sections designed for students and families. It also includes a supplement to highlight the assessment requirements of the Individuals with Disabilities Education Act (IDEA, 2004) and the Rehabilitation Act of 1973 (Rehab Act), as amended by title IV of the Workforce Innovation and Opportunity Act (WIOA) as well as a supplement of Definitions referenced throughout. Finally, it includes a supplement of sample assessment tools. The assessment process for identifying post-school goals and relevant transition services for students and youth consists of six separate but interconnected processes: (1) Determining What to Assess; (2) Determining Stakeholders; (3) Selecting Appropriate Assessments; (4) Conducting Assessments; (5) Reviewing Assessment Results; and (6) Using Assessment Data.

We begin an investigation of a new holographic covariant entropy bound (HCEB) in gravity. This bound arises from Cauchy slice holography, a recently proposed duality between the bulk gravity theory and a `boundary' theory that lives on Cauchy slices. The HCEB is the logarithm of the maximum number of states of this theory that can pass through a given cut $\sigma$ of a Cauchy slice $\Sigma$ ($\sigma$ is thus a codimension-2 surface in the bulk). We show that the bound depends only on the codimension-2 data on $\sigma$, and is thus independent of the choice of slice $\Sigma$. For classical states, the HCEB upper bounds the entanglement between two subregions of the boundary of $\Sigma$. We calculate the bound explicitly in pure three-dimensional GR with negative cosmological constant, where the Cauchy slice theory is the $T \overline{T}$-deformation of the dual CFT. We find that the imaginary energy eigenstates in the spectrum of the deformed theory play a crucial role for obtaining a valid bound in Lorentzian signature. Our bound agrees with the area of a surface at certain marginal and extremal surfaces, but differs elsewhere. In particular, it exceeds the area by an arbitrarily large amount for (anti)trapped surfaces, such as those that lie inside a black hole. Finally, we discuss how these results can be used to write down tensor networks corresponding to arbitrary Cauchy slices., Comment: 43+14 pages, 16 figures

We address the problem of preserving non-interference across compiler transformations under speculative semantics. We develop a proof method that ensures the preservation uniformly across all source programs. The basis of our proof method is a new form of simulation relation. It operates over directives that model the attacker's control over the micro-architectural state, and it accounts for the fact that the compiler transformation may change the influence of the micro-architectural state on the execution (and hence the directives). Using our proof method, we show the correctness of dead code elimination. When we tried to prove register allocation correct, we identified a previously unknown weakness that introduces violations to non-interference. We have confirmed the weakness for a mainstream compiler on code from the libsodium cryptographic library. To reclaim security once more, we develop a novel static analysis that operates on a product of source program and register-allocated program. Using the analysis, we present an automated fix to existing register allocation implementations. We prove the correctness of the fixed register allocations with our proof method.

Coronary angioplasty with stent implantation is the most frequently used interventional treatment for coronary artery disease. However, reocclusion within the stent, referred to as in-stent restenosis, occurs in up to 10% of lesions. It is widely accepted that mechanical loads on the vessel wall strongly affect adaptive and maladaptive mechanisms. Yet, the role of procedural and lesion-specific influence on restenosis risk remains understudied. Computational modeling of the stenting procedure can provide new mechanistic insights, such as local stresses, that play a significant role in tissue growth and remodeling. Previous simulation studies often featured simplified artery and stent geometries and cannot be applied to real-world examples. Realistic simulations were computationally expensive since they featured fully resolved stenting device models. The aim of this work is to develop and present a mixed-dimensional formulation to simulate the patient-specific stenting procedure with a reduced-dimensional beam model for the stent and 3D models for the artery. In addition to presenting the numerical approach, we apply it to realistic cases to study the intervention's mechanical effect on the artery and correlate the findings with potential high-risk locations for in-stent restenosis. We found that high artery wall stresses develop during the coronary intervention in severely stenosed areas and at the stent boundaries. Herewith, we lay the groundwork for further studies towards preventing in-stent restenosis after coronary angioplasty.

In this paper, we prove interior a priori estimates for singularities of the Lagrangian mean curvature flow assuming the Lagrangian phase is supercritical. We prove a Jacobi inequality that holds good when the Lagrangian phase is critical and supercritical. We further extend our results to a broader class of Lagrangian mean curvature type equations., Comment: arXiv admin note: text overlap with arXiv:2403.07235

The spatial organization of a passive scalar plume originating from a point source in a boundary layer is studied to understand its meandering characteristics. We focus shortly downstream of the isokinetic injection ($1.5\le x/\delta \le 3$, $\delta$ being boundary layer thickness) where the scalar concentration is highly intermittent, the plume rapidly meanders, and breaks-up into concentrated scalar pockets due to the action of turbulent structures. Two injection locations were considered: the center of logarithmic-region and the wake-region of the boundary layer. Simultaneous quantitative planar laser-induced fluorescence (PLIF) and particle-image velocimetry (PIV) were performed in a wind-tunnel, using acetone vapors to measure scalar mixture fraction and velocity fields. Single- and multi-point statistics were compared to established works to validate the diagnostic novelties. Additionally, the spatial characteristics of the plume intermittency were quantified using `blob' size, shape, orientation and mean concentration. It was observed that straining and break up were the primary plume-evolution modes in this region, with little small-scale homogenization. Further, the dominant role of coherent vortex motions in meandering and break-up of the plume was evident. Their action is found to be the primary mechanism by which the scalar injected within the log-region is transported away from the wall (`large meander events'). Strong spatial correlation was observed in both instantaneous and conditional fields between the high scalar concentration regions and the individual vortex heads. This coherent transport was weaker for wake-injection case, where the plume only interacts with outer vortex motions. A coherent-structure based mechanism is suggested to explain these transport mechanisms.

We present Quantum Monte Carlo calculations of magnetic moments, form factors, and densities of $A\le 10$ nuclei within a chiral effective field theory approach. We use the Norfolk two- and three-body chiral potentials and their consistent electromagnetic one- and two-nucleon current operators. We find that two-body contributions to the magnetic moment can be large (up to $\sim33\%$ in $A=9$ systems). We study the model dependence of these observables and place particular emphasis on investigating their sensitivity to using different cutoffs to regulate the many-nucleon operators. Calculations of elastic magnetic form factors for $A\leq 10$ nuclei show excellent agreement with the data out to momentum transfers $q\approx 3$ fm$^{-1}$., Comment: 25 pages, 14 figures, 6 tables

Understanding how light modifies long-range order is key to improve our ability to control material functionality on an ultrafast timescale. Transient spatial heterogeneity has been proposed in many materials, but isolating the dynamics of different regions experimentally has been challenging. Here we address this issue and measure the dynamics of orbital order melting in the layered manganite, La0.5Sr1.5MnO4, and isolate the surface dynamics from the bulk for the first time. Bulk measurements show orbital order is rapidly suppressed, but the correlation length surprisingly increases. However, the surface dynamics, show a stronger suppression and a significant decrease in correlation length. By isolating the surface changes, we find that light preferentially melts a less ordered surface and the loss of long-range order is likely driven by the formation of local and disordered polarons. Melting the disordered surface effectively increases the average correlation of the bulk probed volume, resolving the contradictory response. These results show that surface scattering methods are necessary to understand both surface and bulk dynamics in heterogeneous materials., Comment: 22 pages, 8 figures

We present Quantum Monte Carlo calculations of magnetic form factors in $A=6-10$ nuclei, based on Norfolk two- and three-nucleon interactions, and associated one- and two-body electromagnetic currents. Agreement with the available experimental data for $^6$Li, $^7$Li, $^9$Be and $^{10}$B up to values of momentum transfer $q\sim 3$ fm$^{-1}$ is achieved when two-nucleon currents are accounted for. We present a set of predictions for the magnetic form factors of $^7$Be, $^8$Li, $^9$Li, and $^9$C. In these systems, two-body currents account for $\sim40-60\%$ of the total magnetic strength. Measurements in any of these radioactive systems would provide valuable insights on the nuclear magnetic structure emerging from the underlying many-nucleon dynamics. A particularly interesting case is that of $^7$Be, as it would enable investigations of the magnetic structure of mirror nuclei., Comment: 5 pages, 3 figures, 1 table

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Changes to motion patterns in these cells are one of the important features to be characterized to understand how an introduced drug or disease may alter the human heart beat. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently lacking. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

This study uses the 2021 Census to describe the educational attainment and earnings of the Canadian-born Black population, focusing on three groups: (1) those with at least one African-born parent (African-origin); (2) those with at least one Caribbean-born parent (Caribbean-origin) and (3) those whose parents were both born in Canada (Canadian-origin). Comparisons are drawn with the non-racialized, non-Indigenous population, both second generation and third generation or more. The study provides a descriptive analysis of the demographic and educational characteristics of the three Canadian-born Black populations, followed by a regression analysis examining factors affecting earnings, including educational attainment, job characteristics, and other factors.

Electrostatically stabilized nanocrystals (NCs) and, in particular, quantum dots (QDs) hold promise for forming strongly coupled superlattices due to their compact and electronically conductive surface ligands. However, studies on the colloidal dispersion and interparticle interactions of electrostatically stabilized sub-10 nm NCs have been limited, hindering the optimization of colloidal stability and self-assembly. In this study, we employed small-angle X ray scattering (SAXS) experiments to investigate the interparticle interactions and arrangement of PbS QDs with thiostannate ligands (PbS-Sn2S64-) in polar solvents. The study reveals significant deviations from ideal solution behavior in electrostatically stabilized QD dispersions. Our results demonstrate that PbS-Sn2S64- QDs exhibit long-range interactions within the solvent, in contrast to the short-range steric repulsion characteristic of PbS QDs with oleate ligands (PbS-OA). Introducing highly charged multivalent electrolytes screens electrostatic interactions between charged QDs, reducing the length scale of the repulsive interactions. Furthermore, we calculate the second virial (B2) coefficients from SAXS data, providing insights into how surface chemistry, solvent, and size influence pair potentials. Finally, we explore the influence of long-range interparticle interactions of PbS-Sn2S64- QDs on the morphology of films produced by drying or spin-coating colloidal solutions. The long-range repulsive term of PbS-Sn2S64- QDs promotes the formation of amorphous films, and screening the electrostatic repulsion by addition of an electrolyte enables the formation of a crystalline film. These findings highlight the critical role of NC-NC interactions in tailoring the properties of functional nanomaterials.

Data visualizations help extract insights from datasets, but reaching these insights requires decomposing high level goals into low-level analytic tasks that can be complex due to varying degrees of data literacy and visualization experience. Recent advancements in large language models (LLMs) have shown promise for lowering barriers for users to achieve tasks such as writing code and may likewise facilitate visualization insight. Scalable Vector Graphics (SVG), a text-based image format common in data visualizations, matches well with the text sequence processing of transformer-based LLMs. In this paper, we explore the capability of LLMs to perform 10 low-level visual analytic tasks defined by Amar, Eagan, and Stasko directly on SVG-based visualizations. Using zero-shot prompts, we instruct the models to provide responses or modify the SVG code based on given visualizations. Our findings demonstrate that LLMs can effectively modify existing SVG visualizations for some tasks like Cluster but perform poorly on tasks requiring mathematical operations like Compute Derived Value. We also discovered that LLM performance can vary based on factors such as the number of data points, the presence of value labels, and the chart type. Our findings contribute to gauging the general capabilities of LLMs and highlight the need for further exploration and development to fully harness their potential in supporting visual analytic tasks.

The ability to understand and ultimately control the transformations and properties of various nanoscale systems, from proteins to synthetic nanomaterial assemblies, hinges on the ability to directly elucidate their dynamics on their characteristic length and time scales. Here, we use MHz X-ray photon correlation spectroscopy (XPCS) to directly elucidate the characteristic microsecond-dynamics of density fluctuations of semiconductor nanocrystals (NCs), not only in a colloidal dispersion but also in a liquid phase consisting of densely packed, yet mobile, NCs with no long-range order. By carefully disentangling X-ray induced effects, we find the wavevector-dependent fluctuation rates in the liquid phase are suppressed relative to those in the colloidal phase and to those in experiments and hydrodynamic theories of densely packed repulsive particles. We show that the suppressed rates are due to a substantial decrease in the self-diffusion of NCs in the liquid phase, which we attribute to explicit attractive interactions. Via comparison with simulations, we find that the extracted strength of the attractions explains the stability of the liquid phase, in contrast to the gelation observed via XPCS in many other charged colloidal systems. This work opens the door to elucidating fast, condensed phase dynamics in a variety of complex fluids and other nanoscale soft matter systems, such as densely packed proteins and non-equilibrium self-assembly processes., Comment: 15 pages, 4 figures

Bottom-up assembly of nanocrystals (NCs) into ordered arrays, or superlattices (SLs), is a promising route to design materials with new functionalities, but the degree of control over assembly into functional structures remains challenging. Using electrostatics, rather than density, to tune the interactions between semiconductor NCs, we watch self-assembly proceeding through a metastable liquid phase. We systematically investigate the phase behavior as a function of quench conditions in situ and in real time using small angle X-ray scattering (SAXS). Through quantitative fitting to colloid, liquid, and SL models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the phase diagram's predictive power, we establish control of the self-assembly rate over three orders of magnitude, and identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. Importantly, the presence of the metastable liquid increases SL formation rates relative to the equivalent one-step pathway, and SL order counterintuitively increases with the rate, revealing a highly desirable and generalizable kinetic strategy to promote and enhance ordered assembly., Comment: 16 pages, 4 figures

Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibration approaches have successfully quantified the uncertainties inherent in identified parameters. Yet, routinely estimating the posterior distribution for all BC parameters in 3D simulations has been unattainable due to the infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors using results from a single high-fidelity three-dimensional (3D) model evaluation. We only evaluate the 3D model once for an initial choice of BCs and use the result to create a highly accurate zero-dimensional (0D) surrogate. We then perform Sequential Monte Carlo (SMC) using the optimized 0D model to derive the high-dimensional Windkessel BC posterior distribution. We validate this approach in a publicly available dataset of N=72 subject-specific vascular models. We found that optimizing 0D models to match 3D data a priori lowered their median approximation error by nearly one order of magnitude. In a subset of models, we confirm that the optimized 0D models still generalize to a wide range of BCs. Finally, we present the high-dimensional Windkessel parameter posterior for different measured signal-to-noise ratios in a vascular model using SMC. We further validate that the 0D-derived posterior is a good approximation of the 3D posterior. The minimal computational demand of our method using a single 3D simulation, combined with the open-source nature of all software and data used in this work, will increase access and efficiency of Bayesian Windkessel calibration in cardiovascular fluid dynamics simulations.

Pair-breaking edges of $d$-wave superconductors feature Andreev bound states at the Fermi energy. Since these states are energetically highly unfavorable they are susceptible to effects that shift them to finite energy. We investigate the free energy of two different mechanisms: spontaneous phase gradients in the superconducting order parameter and surface ferromagnetism caused by Fermi liquid interaction effects. We find that the surface magnetization appears at lower temperatures than the spontaneous current flow of the phase-crystal state. The magnetic state can, however, be energetically favorable at lower temperatures for sufficiently strong Fermi liquid effects. As a result, first-order transitions between the two states are possible, suggesting a rich low-temperature phase diagram in $d$-wave superconductors., Comment: 10 pages, 6 figures

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Unfitted finite element methods, like CutFEM, have traditionally been implemented in a matrix-based fashion, where a sparse matrix is assembled and later applied to vectors while solving the resulting linear system. With the goal of increasing performance and enabling algorithms with polynomial spaces of higher degrees, this contribution chooses a more abstract approach by matrix-free evaluation of the operator action on vectors instead. The proposed method loops over cells and locally evaluates the cell, face, and interface integrals, including the contributions from cut cells and the different means of stabilization. The main challenge is the efficient numerical evaluation of terms in the weak form with unstructured quadrature points arising from the unfitted discretization in cells cut by the interface. We present design choices and performance optimizations for tensor-product elements and demonstrate the performance by means of benchmarks and application examples. We demonstrate a speedup of more than one order of magnitude for the operator evaluation of a discontinuous Galerkin discretization with polynomial degree three compared to a sparse matrix-vector product and develop performance models to quantify the performance properties over a wide range of polynomial degrees.

In this paper, we prove interior Hessian estimates for shrinkers, expanders, translators, and rotators of the Lagrangian mean curvature flow under the assumption that the Lagrangian phase is hypercritical. We further extend our results to a broader class of Lagrangian mean curvature type equations., Comment: 28 pages

Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers, Transcription Factor Binding Site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and analyze their pros and cons. We also point out obstacles of computational prediction of 3D interactions and suggest future research directions., Comment: Systematic review, one figure, three tables, 29 pages

Surface ion traps with two-dimensional layouts of trapping regions are natural architectures for storing large numbers of ions and supporting the connectivity needed to implement quantum algorithms. Many of the components and operations needed to fully exploit this architecture have already been demonstrated, including operation at cryogenic temperatures with low heating, low excitation transport, and ion control and detection with integrated photonics. Here we demonstrate a trap that addresses the scaling challenge of increasing power dissipation as the RF electrode increases in size. By raising the RF electrode and removing most of the insulating dielectric layer below it we reduce both ohmic and dielectric power dissipation. We also measure heating rates across a range of motional frequencies and for different voltage sources in a trap with a raised RF electrode but solid dielectric., Comment: 7 pages, 4 figures

Fast and efficient simulations of metal additive manufacturing (AM) processes are highly relevant to exploring the full potential of this promising manufacturing technique. The microstructure composition plays an important role in characterizing the part quality and deriving mechanical properties. When complete parts are simulated, one often needs to resort to strong simplifications such as layer-wise heating due to the large number of simulated time steps compared to the small time step sizes. This article proposes a scan-resolved approach to the coupled thermo-microstructural problem. Building on a highly efficient thermal model, we discuss the implementation of a phenomenological microstructure model for the evolution of the three main constituents of Ti-6Al-4V: stable $\alpha_s$-phase, martensite $\alpha_m$-phase and $\beta$-phase. The implementation is tailored to modern hardware features using vectorization and fast approximations of transcendental functions. A performance model and numerical examples verify the high degree of optimization. We demonstrate the applicability and predictive power of the approach and the influence of scan strategy and geometry. Depending on the specific example, results can be obtained with moderate computational resources in a few hours to days. The numerical examples include a prediction of the microstructure on the full NIST AM Benchmark cantilever specimen.

In the prevalent picture of ultrafast structural phase transitions, the atomic motion occurs in a slowly varying potential energy surface determined adiabatically by the fast electrons. However, this ignores non-conservative forces caused by electron-lattice collisions, which can significantly influence atomic motion. Most ultrafast techniques only probe the average structure and are less sensitive to random displacements, and therefore do not detect the role played by non-conservative forces in phase transitions. Here we show that the lattice dynamics of the prototypical insulator-to-metal transition of VO2 cannot be described by a potential energy alone. We use the sample temperature to control the preexisting lattice disorder before ultrafast photoexcitation across the phase transition and our ultrafast diffuse scattering experiments show that the fluctuations characteristic of the rutile metal develop equally fast (120 fs) at initial temperatures of 100 K and 300 K. This indicates that additional non-conservative forces are responsible for the increased lattice disorder. These results highlight the need for more sophisticated descriptions of ultrafast phenomena beyond the Born-Oppenheimer approximation as well as ultrafast probes of spatial fluctuations beyond the average unit cell measured by diffraction.

The increasing variety and quantity of tagged multimedia content on a variety of online platforms offer a unique opportunity to advance the field of human action recognition. In this study, we utilize 283,582 unique, unlabeled TikTok video clips, categorized into 386 hashtags, to train a domain-specific foundation model for action recognition. We employ VideoMAE V2, an advanced model integrating Masked Autoencoders (MAE) with Vision Transformers (ViT), pre-trained on this diverse collection of unstructured videos. Our model, fine-tuned on established action recognition benchmarks such as UCF101 and HMDB51, achieves state-of-the-art results: 99.05% on UCF101, 86.08% on HMDB51, 85.51% on Kinetics-400, and 74.27% on Something-Something V2 using the ViT-giant backbone. These results highlight the potential of using unstructured and unlabeled videos as a valuable source of diverse and dynamic content for training foundation models. Our investigation confirms that while initial increases in pre-training data volume significantly enhance model performance, the gains diminish as the dataset size continues to expand. Our findings emphasize two critical axioms in self-supervised learning for computer vision: (1) additional pre-training data can yield diminishing benefits for some datasets and (2) quality is more important than quantity in self-supervised learning, especially when building foundation models., Comment: 10 pages

Expanding the work of arXiv:1504.08040, we show that black holes obey a second law for linear perturbations to bifurcate Killing horizons, in any covariant higher curvature gravity coupled to scalar and vector fields. The vector fields do not need to be gauged, and (like the scalars) can have arbitrary non-minimal couplings to the metric. The increasing entropy has a natural expression in covariant phase space language, which makes it manifestly invariant under JKM ambiguities. An explicit entropy formula is given for f(Riemann) gravity coupled to vectors, where at most one derivative acts on each vector. Besides the previously known curvature terms, there are three extra terms involving differentiating the Lagrangian by the symmetric vector derivative (which therefore vanish for gauge fields)., Comment: 38 pages, 2 figures, 2 tables; references and comments added in v2