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Finite element method applications are a common approach to simulate a handful of phenomena but can take a lot of computing power, causing elevated waiting time to produce precise results. The radiofrequency ablation finite element method is an application to simulate the medical procedure of radiofrequency ablation, a minimally invasive liver cancer treatment. The application runs sequentially and can take up to 20 hours of execution to generate 15 minutes of simulation results. Most of this time arises from the need to solve a sparse system of linear equations. In this work, we accelerate this application by using three sparse solvers packages (MAGMA cuSOLVER, and QRMumps), including direct and iterative methods over different multicore and GPU architectures. We conducted a numerical result analysis to access the solution quality provided by the distinct solvers and their configurations, proposing the use of the peak signal-to-noise ratio metric. We were able to reduce the application execution time up to 40 times compared to the original sequential version while keeping a similar numerical quality for the results., Comment: 8 pages, 11 figures

Many cells face search problems, such as finding food, mates or shelter, where their success depends on their search strategy. In contrast to other unicellular organisms, the slime mold Physarum polycephalum forms a giant network-shaped plasmodium while foraging for food. What is the advantage of the giant cell on the verge of multicellularity? We experimentally study and quantify the migration behavior of P. polycephalum plasmodia on the time scale of days in the absence and presence of food. We develop a model which successfully describes its migration in terms of ten data-derived parameters. Using the mechanistic insights provided by our data-driven model, we find that regardless of the absence or presence of food, P. polycephalum achieves superdiffusive migration by performing a self-avoiding run-and-tumble movement. In the presence of food, the run duration statistics change, only controlling the short-term migration dynamics. However, varying organism size, we find that the long-term superdiffusion arises from self-avoidance determined by cell size, highlighting the potential evolutionary advantage that this macroscopically large cell may have., Comment: 10 pages, 4 figures

Teams of mobile [aerial, ground, or aquatic] robots have applications in resource delivery, patrolling, information-gathering, agriculture, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Robot teams traversing hazardous environments -- with e.g. rough terrain or seas, strong winds, or adversaries capable of attacking or capturing robots -- should plan and coordinate their trails in consideration of risks of disablement, destruction, or capture. Specifically, the robots should take the safest trails, coordinate their trails to cooperatively achieve the team-level objective with robustness to robot failures, and balance the reward from visiting locations against risks of robot losses. Herein, we consider bi-objective trail-planning for a mobile team of robots orienteering in a hazardous environment. The hazardous environment is abstracted as a directed graph whose arcs, when traversed by a robot, present known probabilities of survival. Each node of the graph offers a reward to the team if visited by a robot (which e.g. delivers a good to or images the node). We wish to search for the Pareto-optimal robot-team trail plans that maximize two [conflicting] team objectives: the expected (i) team reward and (ii) number of robots that survive the mission. A human decision-maker can then select trail plans that balance, according to their values, reward and robot survival. We implement ant colony optimization, guided by heuristics, to search for the Pareto-optimal set of robot team trail plans. As a case study, we illustrate with an information-gathering mission in an art museum., Comment: v0.0

In 2014, Vesti proposed the problem of determining the repetition threshold for infinite rich words, i.e., for infinite words in which all factors of length $n$ contain $n$ distinct nonempty palindromic factors. In 2020, Currie, Mol, and Rampersad proved a conjecture of Baranwal and Shallit that the repetition threshold for binary rich words is $2 + \sqrt{2}/2$. In this paper, we prove a structure theorem for $16/7$-power-free ternary rich words. Using the structure theorem, we deduce that the repetition threshold for ternary rich words is $1 + 1/(3 - \mu) \approx 2.25876324$, where $\mu$ is the unique real root of the polynomial $x^3 - 2x^2 - 1$., Comment: 60 pages

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability.

About seven years ago, Volvo Cars initiated a large-scale agile transformation. Midst this journey, a significant restructuring of the R&D department took place. Our study aims to illuminate how team maturity levels are impacted during such comprehensive reorganizations. We collected data from 63 teams to comprehend the effects of organizational changes on these agile teams. Additionally, qualitative data was gathered to validate our findings and explore underlying reasons. Contrary to what was expected, the reorganization did not significantly alter the distribution of team maturity. High turnover rates and frequent reorganizations were identified as key factors to why the less mature teams remained in the early stages of team development. Conversely, teams in the second category remained stable at a higher maturity stage, primarily because the teams themselves remained largely intact, with only management structures changing. In conclusion, while reorganizations may hinder some teams' development, others maintain stability at a higher level of maturity despite substantial managerial changes.

From 2019 to 2022, Volvo Cars successfully translated our research discoveries regarding group dynamics within agile teams into widespread industrial practice. We wish to illuminate the insights gained through the process of garnering support, providing training, executing implementation, and sustaining a tool embraced by approximately 700 teams and 9,000 employees. This tool was designed to empower agile teams and propel their internal development. Our experiences underscore the necessity of comprehensive team training, the cultivation of a cadre of trainers across the organization, and the creation of a novel software solution. In essence, we deduce that an automated concise survey tool, coupled with a repository of actionable strategies, holds remarkable potential in fostering the maturation of agile teams, but we also share many of the challenges we encountered during the implementation.

The theory of linear time invariant systems is well established and allows, among other things, to formulate and solve control problems in finite time. In this context the control laws are typically taken in a space of the form L^p(0,T;U). In this paper we consider the possibility of taking control laws in (H^1(0,T;U))* , which induces non-trivial issues. We overcome these difficulties by adapting the functional setting, notably by considering a generalized final state for the systems under consideration. In addition we collect time regularity properties and we pretend that in general it is not possible to consider control laws in H^{-1}(0,T;U). Then, we apply our results to propose an interpretation of the inifinite order of defect for an observability inequality, in terms of controllability properties.

Understanding and optimizing the design of helical micro-swimmers is crucial for advancing their application in various fields. This study presents an innovative approach combining Free-Form Deformation with Bayesian Optimization to enhance the shape of these swimmers. Our method facilitates the computation of generic swimmer shapes that achieve optimal average speed and efficiency. Applied to both monoflagellated and biflagellated swimmers, our optimization framework has led to the identification of new optimal shapes. These shapes are compared with biological counterparts, highlighting a diverse range of swimmers, including both pushers and pullers.

Leadership in agile teams is a collective responsibility where team members share leadership work based on expertise and skills. However, the understanding of leadership in this context is limited. This study explores the under-researched area of prototypical leadership, aiming to understand if and how leaders who are perceived as more representative of the team are more effective leaders. Qualitative interviews were conducted with eleven members of six agile software teams in five Swedish companies from various industries and sizes. In this study, the effectiveness of leadership was perceived as higher when it emerged from within the team or when leaders aligned with the group. In addition, leaders in managerial roles that align with the team's shared values and traits were perceived as more effective, contributing to overall team success.

Gaussian unitary transformations are generated by quadratic Hamiltonians, i.e., Hamiltonians containing quadratic terms in creations and annihilation operators, and are heavily used in many areas of quantum physics, ranging from quantum optics and condensed matter theory to quantum information and quantum field theory in curved spacetime. They are known to form a representation of the metaplectic and spin group for bosons and fermions, respectively. These groups are the double covers of the symplectic and special orthogonal group, respectively, and our goal is to analyze the behavior of the sign ambiguity that one needs to deal with when moving between these groups and their double cover. We relate this sign ambiguity to expectation values of the form $\langle 0|\exp{(-i\hat{H})}|0\rangle$, where $|0\rangle$ is a Gaussian state and $\hat{H}$ an arbitrary quadratic Hamiltonian. We provide closed formulas for $\langle 0|\exp{(-i\hat{H})}|0\rangle$ and show how we can efficiently describe group multiplications in the double cover without the need of going to a faithful representation on an exponentially large or even infinite-dimensional space. Our construction relies on an explicit parametrization of these two groups (metaplectic, spin) in terms of symplectic and orthogonal group elements together with a twisted U(1) group., Comment: 35+3 pages, 8 figures

We developed a jitted compiler for training Artificial Neural Networks using C++, LLVM and Cuda. It features object-oriented characteristics, strong typing, parallel workers for data pre-processing, pythonic syntax for expressions, PyTorch like model declaration and Automatic Differentiation. We implement the mechanisms of cache and pooling in order to manage VRAM, cuBLAS for high performance matrix multiplication and cuDNN for convolutional layers. Our experiments with Residual Convolutional Neural Networks on ImageNet, we reach similar speed but degraded performance. Also, the GRU network experiments show similar accuracy, but our compiler have degraded speed in that task. However, our compiler demonstrates promising results at the CIFAR-10 benchmark, in which we reach the same performance and about the same speed as PyTorch. We make the code publicly available at: https://github.com/NoSavedDATA/NoSavedKaleidoscope, Comment: 12 pages, 3 figures and 3 tables

The variety of today's multicore architectures motivates researchers to explore parallel scientific applications on different platforms. Load imbalance is one performance issue that can prejudice parallel applications from exploiting the computational power of these platforms. Ondes3D is a scientific application for seismic wave simulation used to assess the geological impact of earthquakes. Its parallelism relies on applying a regular domain decomposition in the geological domain provided and distributing each sub-domain to MPI ranks. Previous works investigate the significant spatial and temporal imbalance in Ondes3D and suggest new parallelization and load balancing techniques to minimize them. However, none explored its execution on different architectures. Our paper evaluates the performance of Ondes3D for two earthquake scenarios on eight different multicore architectures, including Intel, AMD, and ARM processors. We measure the load distribution per MPI rank, evaluate the temporal load imbalance, and compare the execution of the application's kernels. Our results show that the temporal load imbalance in Ondes3D depends on the architecture chosen, with some platforms minimizing such imbalance more effectively., Comment: The 2020 International Conference on High Performance Computing and Simulation (HPCS 2020)

Implementing visual and audio notifications on augmented reality devices is a crucial element of intuitive and easy-to-use interfaces. In this paper, we explored creating intuitive interfaces through visual and audio notifications. The study evaluated user performance and preference across three conditions: visual notifications in fixed positions, visual notifications above objects, and no visual notifications with monaural sounds. The users were tasked with cooking and serving customers in an open-source Augmented-Reality sandbox environment called ARtisan Bistro. The results indicated that visual notifications above objects combined with localized audio feedback were the most effective and preferred method by participants. The findings highlight the importance of strategic placement of visual and audio notifications in AR, providing insights for engineers and developers to design intuitive 3D user interfaces.

There is growing evidence of the effectiveness of Shampoo, a higher-order preconditioning method, over Adam in deep learning optimization tasks. However, Shampoo's drawbacks include additional hyperparameters and computational overhead when compared to Adam, which only updates running averages of first- and second-moment quantities. This work establishes a formal connection between Shampoo (implemented with the 1/2 power) and Adafactor -- a memory-efficient approximation of Adam -- showing that Shampoo is equivalent to running Adafactor in the eigenbasis of Shampoo's preconditioner. This insight leads to the design of a simpler and computationally efficient algorithm: $\textbf{S}$hampo$\textbf{O}$ with $\textbf{A}$dam in the $\textbf{P}$reconditioner's eigenbasis (SOAP). With regards to improving Shampoo's computational efficiency, the most straightforward approach would be to simply compute Shampoo's eigendecomposition less frequently. Unfortunately, as our empirical results show, this leads to performance degradation that worsens with this frequency. SOAP mitigates this degradation by continually updating the running average of the second moment, just as Adam does, but in the current (slowly changing) coordinate basis. Furthermore, since SOAP is equivalent to running Adam in a rotated space, it introduces only one additional hyperparameter (the preconditioning frequency) compared to Adam. We empirically evaluate SOAP on language model pre-training with 360m and 660m sized models. In the large batch regime, SOAP reduces the number of iterations by over 40% and wall clock time by over 35% compared to AdamW, with approximately 20% improvements in both metrics compared to Shampoo. An implementation of SOAP is available at https://github.com/nikhilvyas/SOAP.

According to Boltzmann-Gibbs (BG) statistical mechanics, the thermodynamic response, such as the isothermal susceptibility, at critical points (CPs) presents a divergent-like behavior. An appropriate parameter to probe both classical and quantum CPs is the so-called Gr\"uneisen ratio $\Gamma$. Motivated by the results reported in Phys. Rev. B $\textbf{108}$, L140403 (2023), we extend the quantum version of $\Gamma$ to the non-additive $q$-entropy $S_q$. Our findings indicate that using $S_q$ at the unique value of $q$ restoring the extensivity of the entropy, $\Gamma$ is universally non-diverging at CPs. We unprecedentedly introduce $\Gamma$ in terms of $S_q$, being BG recovered for $q \rightarrow 1$. We thus solve a long-standing problem related to the $\textit{illusory}$ diverging susceptibilities at CPs., Comment: 5 pages, 3 figs, comments are welcome

k-Contact geometry appeared as a generalisation of contact geometry to analyse field theories. This work provides a new insightful approach to k-contact geometry by devising a theory of k-contact forms and proving that the kernel of a k-contact form is locally equivalent to a distribution of corank k that is distributionally maximally non-integrable and admits k commuting Lie symmetries: a so-called k-contact distribution. Compact manifolds admitting a global k-contact form are analysed, we give necessary topological conditions for their existence, k-contact Lie groups are defined and studied, we extend the Weinstein conjecture for the existence of closed orbits of Reeb vector fields in compact manifolds to the k-contact setting after studying compact low-dimensional manifolds endowed with a global k-contact form, and we provide some physical applications of some of our results. Polarisations for k-contact distributions are introduced and it is shown that a polarised k-contact distribution is locally diffeomorphic to the Cartan distribution of the first-order jet bundle over a fibre bundle of order k, which is a globally defined polarised k-contact distribution. Then, we relate k-contact manifolds to presymplectic and k-symplectic manifolds on fibre bundles of larger dimension and define for the first time types of submanifolds in k-contact geometry. We also review the theory of Hamiltonian k-vector fields, studying Hamilton-De Donder-Weyl equations in general and in Lie groups, which are here studied in an unprecedented manner. A theory of k-contact Hamiltonian vector fields is developed, which describes the theory of characteristics for Lie symmetries for first-order partial differential equations in a k-contact Hamiltonian manner. Our new Hamiltonian k-contact techniques are illustrated by analysing Hamilton-Jacobi and Dirac equations., Comment: 59 pp

Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic forecasts, work on calibration for multivariate forecasts is much more limited. This paper introduces a novel post-hoc recalibration approach that addresses multivariate calibration for potentially misspecified models. Our method involves constructing local mappings between vectors of marginal probability integral transform values and the space of observations, providing a flexible and model free solution applicable to continuous, discrete, and mixed responses. We present two versions of our approach: one uses K-nearest neighbors, and the other uses normalizing flows. Each method has its own strengths in different situations. We demonstrate the effectiveness of our approach on two real data applications: recalibrating a deep neural network's currency exchange rate forecast and improving a regression model for childhood malnutrition in India for which the multivariate response has both discrete and continuous components.

Current automotive E/E architectures are subject to significant transformations: Computing-power-intensive advanced driver-assistance systems, bandwidth-hungry infotainment systems, the connection of the vehicle with the internet and the consequential need for cyber-security drives the centralization of E/E architectures. A centralized architecture is often seen as a key enabler to master those challenges. Available research focuses mostly on the different types of E/E architectures and contrasts their advantages and disadvantages. There is a research gap on guidelines for system designers and function developers to analyze the potential of their systems for centralization. The present paper aims to quantify centralization potential reviewing relevant literature and conducting qualitative interviews with industry practitioners. In literature, we identified seven key automotive system properties reaching limitations in current automotive architectures: busload, functional safety, computing power, feature dependencies, development and maintenance costs, error rate, modularity and flexibility. These properties serve as quantitative evaluation criteria to estimate whether centralization would enhance overall system performance. In the interviews, we have validated centralization and its fundament - the conceptual systems engineering - as capabilities to mitigate these limitations. By focusing on practical insights and lessons learned, this research provides system designers with actionable guidance to optimize their systems, addressing the outlined challenges while avoiding monolithic architecture. This paper bridges the gap between theoretical research and practical application, offering valuable takeaways for practitioners., Comment: In Practice track paper submitted as preprint to 'The Journal of Systems & Software'. 1 page for highlights, 28 pages for content and 3 pages for references. 5 figures, 10 tables

Phonon resistance from dislocation scattering is often divided into short-range core interactions and long-range strain field interactions. Using electron energy-loss spectroscopy on a GaN dislocation, we report observations of vibrational modes localized at specific core atoms (short-range) and strain-driven phonon energy shifts around the dislocation (long-range). Ab initio calculations support these findings and draw out additional details. This study reveals atomically resolved vibrational spectra of dislocations, thus offering insights for engineering improved material functionalities.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants with known toxicity and bioaccumulation issues. Their widespread industrial use and resistance to degradation have led to global environmental contamination and significant health concerns. While a minority of PFAS have been extensively studied, the toxicity of many PFAS remains poorly understood due to limited direct toxicological data. This study advances the predictive modeling of PFAS toxicity by combining semi-supervised graph convolutional networks (GCNs) with molecular descriptors and fingerprints. We propose a novel approach to enhance the prediction of PFAS binding affinities by isolating molecular fingerprints to construct graphs where then descriptors are set as the node features. This approach specifically captures the structural, physicochemical, and topological features of PFAS without overfitting due to an abundance of features. Unsupervised clustering then identifies representative compounds for detailed binding studies. Our results provide a more accurate ability to estimate PFAS hepatotoxicity to provide guidance in chemical discovery of new PFAS and the development of new safety regulations., Comment: 8 pages, 9 figures, submitted to IEEE BIBM 2024

In multivariate extreme value analysis, the estimation of the dependence structure in extremes is a challenging task, especially in the context of high-dimensional data. Therefore, a common approach is to reduce the model dimension by considering only the directions in which extreme values occur. In this paper, we use the concept of sparse regular variation recently introduced by Meyer and Wintenberger (2021) to derive information criteria for the number of directions in which extreme events occur, such as a Bayesian information criterion (BIC), a mean-squared error-based information criterion (MSEIC), and a quasi-Akaike information criterion (QAIC) based on the Gaussian likelihood function. As is typical in extreme value analysis, a challenging task is the choice of the number $k_n$ of observations used for the estimation. Therefore, for all information criteria, we present a two-step procedure to estimate both the number of directions of extremes and an optimal choice of $k_n$. We prove that the AIC of Meyer and Wintenberger (2023) and the MSEIC are inconsistent information criteria for the number of extreme directions whereas the BIC and the QAIC are consistent information criteria. Finally, the performance of the different information criteria is compared in a simulation study and applied on wind speed data.

Interpretability studies often involve tracing the flow of information through machine learning models to identify specific model components that perform relevant computations for tasks of interest. Prior work quantifies the importance of a model component on a particular task by measuring the impact of performing ablation on that component, or simulating model inference with the component disabled. We propose a new method, optimal ablation (OA), and show that OA-based component importance has theoretical and empirical advantages over measuring importance via other ablation methods. We also show that OA-based component importance can benefit several downstream interpretability tasks, including circuit discovery, localization of factual recall, and latent prediction.

Large Language Models (LLMs) have increasingly demonstrated the ability to facilitate the development of multi-agent systems that allow the interpretation of thoughts and actions generated by each individual. Promising advancements have also been made in LLM-based interaction with existing worlds, particularly in interacting with simulated environments. This paper aims to integrate both aforementioned topics (agents & world interaction) into a single simulation where multiple agents can work together to solve a problem, modeling how groups of humans can often solve problems better than individuals. By showing whether LLMs demonstrate the synergy of human collaboration, it could lead to advancements in the applications of LLMs. We implemented two simulations: a physical studio apartment with two roommates, and another where agents collaborate to complete a programming task. We provide a multi-agent framework, discuss the performance of the agents in each simulation, and discuss potential future additions., Comment: 15 pages, 5 figures, published in the MICS 2024 conference

To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multi-stage neural spline flow-based networks to learn this mapping at redshift z=0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution N-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors. The code was developed as part of the Simons Collaboration on Learning the Universe and is publicly available at \url{https://github.com/shivampcosmo/CHARM}., Comment: 12 pages and 8 figures. This is a Learning the Universe Publication

Solid-state quantum light sources based on semiconductor quantum dots (QDs) are increasingly employed in photonic quantum information applications. Especially when moving towards real-world scenarios outside shielded lab environments, the efficient and robust coupling of nanophotonic devices to single-mode optical fibers offers substantial advantage by enabling "plug-and-play" operation. In this work we present a fiber-pigtailed cavity-enhanced source of flying qubits emitting single indistinguishable photons at clock-rates exceeding 1 GHz. This is achieved by employing a fully deterministic technique for fiber-pigtailing optimized QD-devices based on hybrid circular Bragg grating (hCBG) micro-cavities. The fabricated fiber-pigtailed hCBGs feature radiative emission lifetimes of $<$80 ps, corresponding to a Purcell factor of $\sim$9, a suppression of multiphoton emission events with $g^{(2)}$(0) $<$1%, a photon-indistinguishability >80% and a measured single-photon coupling efficiency of 53% in a high numerical aperture single-mode fiber, corresponding to 1.2 Megaclicks per second at the single-photon detectors. Our results show that fiber-pigtailed quantum light sources based on hCBG cavities are a prime candidate for applications of quantum information science., Comment: 13 pages, 5 figures. The current v1 version of the manuscript referes to a Supplementary Information (S.I.) for further experimental details. This S.I. will be provided attached to a v2-Version of the manuscript that incoorperates potential revisions for a related journal submission

This study investigates the iterative refinement method applied to the solution of linear discrete inverse problems by considering its application to the Tikhonov problem in mixed precision. Previous works on mixed precision iterative refinement methods for the solution of symmetric positive definite linear systems and least-squares problems have shown regularization to be a key requirement when computing low precision factorizations. For problems that are naturally severely ill-posed, we formulate the iterates of iterative refinement in mixed precision as a filtered solution using the preconditioned Landweber method with a Tikhonov-type preconditioner. Through numerical examples simulating various mixed precision choices, we showcase the filtering properties of the method and the achievement of comparable or superior accuracy compared to results computed in double precision as well as another approximate method., Comment: 22 pages

We use Bayesian statistics to infer the breakdown scale of pionless effective field theory in its standard power counting and with renormalization of observables carried out using the power-divergence subtraction scheme and cutoff regularization. We condition our inference on predictions of the total neutron-proton scattering cross section up next-to-next-to leading order. We quantify a median breakdown scale of approximately 1.4$m_\pi$. The 68% degree of belief interval is $[0.96,1.69]m_\pi$. This result confirms the canonical expectation that the pion mass is a relevant scale in low-energy nuclear physics., Comment: 6 pages,5 figures and 2 pages of supplemental material

Thermodynamics is based on a coarse-grained approach, from which its fundamental variables emerge, effectively erasing the complicate details of the microscopic dynamics within a macroscopic system. The strength of Thermodynamics lies in the universality provided by this paradigm. In contrast, quantum mechanics focuses on describing the dynamics of microscopic systems, aiming to make predictions about experiments we perform, a goal shared by all fundamental physical theories, which are often framed as gauge theories in modern physics. Recently, a gauge theory for quantum thermodynamics was introduced, defining gauge invariant work and heat, and exploring their connections to quantum phenomena. In this work, we extend that theory in two significant ways. First, we incorporate energy spectrum degeneracies, which were previously overlooked. Additionally, we define gauge-invariant entropy, exploring its properties and connections to other physical and informational quantities. This results in a complete framework for quantum thermodynamics grounded in the principle of gauge invariance. To demonstrate some implications of this theory, we apply it to well-known critical systems.

Gas and moisture sensing devices leveraging the resistive switching effect in transition metal oxide memristors promise to revolutionize next-generation, nano-scaled, cost-effective, and environmentally sustainable sensor solutions. These sensors encode readouts in resistance state changes based on gas concentration, yet their nonlinear current-voltage characteristics offer richer dynamics, capturing detailed information about REDOX reactions and surface kinetics. Traditional vertical devices fail to fully exploit this complexity. This study demonstrates planar resistive switching devices, moving beyond the Butler-Volmer model. A systematic investigation of the electrochemical processes in Na-doped ZnO with lateral planar contacts reveals intricate patterns resulting from REDOX reactions on the device surface. When combined with advanced algorithms for pattern recognition, allow the analysis of complex switching patterns, including crossings, loop directions, and resistance values, providing unprecedented insights for next-generation complex sensors.

Robots too small to see by eye have rapidly evolved in recent years thanks to the incorporation of on-board microelectronics. Semiconductor circuits have been used in microrobots capable of executing controlled wireless steering, prescribed legged gait patterns, and user-triggered transitions between digital states. Yet these promising new capabilities have come at the steep price of complicated fabrication. Even though circuit components can be reliably built by semiconductor foundries, currently available actuators for electronically integrated microrobots are built with intricate multi-step cleanroom protocols and use mechanisms like articulated legs or bubble generators that are hard to design and control. Here, we present a propulsion system for electronically integrated microrobots that can be built with a single step of lithographic processing, readily integrates with microelectronics thanks to low current/low voltage operation (1V, 10nA), and yields robots that swim at speeds over one body length per second. Inspired by work on micromotors, these robots generate electric fields in a surrounding fluid, and by extension propulsive electrokinetic flows. The underlying physics is captured by a model in which robot speed is proportional to applied current, making design and control straightforward. As proof, we build basic robots that use on-board circuits and a closed-loop optical control scheme to navigate waypoints and move in coordinated swarms. Broadly, solid-state propulsion clears the way for robust, easy to manufacture, electronically controlled microrobots that operate reliably over months to years.

Photonic lanterns are waveguide devices enabling high throughput single mode spectroscopy and high angular resolution. We aim to present the first on-sky demonstration of a photonic lantern (PL) operating in visible light, to measure its throughput and assess its potential for high-resolution spectroscopy of compact objects. We used the SCExAO instrument (a double stage extreme AO system installed at the Subaru telescope) and FIRST mid-resolution spectrograph (R 3000) to test the visible capabilities of the PL on internal source and on-sky observations. The best averaged coupling efficiency over the PL field of view was measured at 51% +/- 10% with a peak at 80%. We also investigate the relationship between coupling efficiency and the Strehl ratio for a PL, comparing them with those of a single-mode fiber (SMF). Findings show that in the AO regime, a PL offers better coupling efficiency performance than a SMF, especially in the presence of low spatial frequency aberrations. We observed Ikiiki (alpha Leo - mR = 1.37) and `Aua (alpha Ori - mR = -1.17) at a frame rate of 200 Hz. Under median seeing conditions (about 1 arcsec measured in H band) and large tip/tilt residuals (over 20 mas), we estimated an average light coupling efficiency of 14.5% +/- 7.4%, with a maximum of 42.8% at 680 nm. We were able to reconstruct both star's spectra, containing various absorption lines. The successful demonstration of this device opens new possibilities in terms of high throughput single-mode fiber-fed spectroscopy in the Visible. The demonstrated on-sky coupling efficiency performance would not have been achievable with a single SMF injection setup under similar conditions, partly because the residual tip/tilt alone exceeded the field of view of a visible SMF (18 mas at 700 nm). Thus emphasizing the enhanced resilience of PL technology to such atmospheric disturbances. The additional, Comment: Accepted in Astronomy and Astrophysics journal on 9/11/2024

We present a sample of 50 H-alpha detected broad-line active galactic nuclei (BLAGN) at redshifts 3.50), independent of the contributions of emission lines to the broadband photometry. We construct the black hole (BH) mass function at 3.5

We describe the motivation, design, and early results for our 42-night, 125 star Subaru/SCExAO direct imaging survey for planets around accelerating stars. Unlike prior large surveys, ours focuses only on stars showing evidence for an astrometric acceleration plausibly due to the dynamical pull of an unseen planet or brown dwarf. Our program is motivated by results from a recent pilot program that found the first planet jointly discovered from direct imaging and astrometry and resulted in a planet and brown dwarf discovery rate substantially higher than previous unbiased surveys like GPIES. The first preliminary results from our program reveal multiple new companions; discovered planets and brown dwarfs can be further characterized with follow-up data, including higher-resolution spectra. Finally, we describe the critical role this program plays in supporting the Roman Space Telescope Coronagraphic Instrument, providing a currently-missing list of targets suitable for the CGI technological demonstration without which the CGI tech demo risks failure., Comment: 13 pages, 7 figures; Proc. SPIE in press

In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithms. However, the robustness of these models is often severely compromised in the presence of data shifts (e.g., different stains, organs, centers, etc.). Alternatively, continual learning (CL) techniques aim to reduce the forgetting of past data when learning new data with distributional shift conditions. Specifically, rehearsal-based CL techniques, which store some past data in a buffer and then replay it with new data, have proven effective in medical image analysis tasks. However, privacy concerns arise as these approaches store past data, prompting the development of our novel Generative Latent Replay-based CL (GLRCL) approach. GLRCL captures the previous distribution through Gaussian Mixture Models instead of storing past samples, which are then utilized to generate features and perform latent replay with new data. We systematically evaluate our proposed framework under different shift conditions in histopathology data, including stain and organ shift. Our approach significantly outperforms popular buffer-free CL approaches and performs similarly to rehearsal-based CL approaches that require large buffers causing serious privacy violations., Comment: Accepted in MICCAI 2024

Searches of new signals in particle physics are usually done by training a supervised classifier to separate a signal model from the known Standard Model physics (also called the background model). However, even when the signal model is correct, systematic errors in the background model can influence supervised classifiers and might adversely affect the signal detection procedure. To tackle this problem, one approach is to use the (possibly misspecified) classifier only to perform a preliminary signal-enrichment step and then to carry out a bump hunt on the signal-rich sample using only the real experimental data. For this procedure to work, we need a classifier constrained to be decorrelated with one or more protected variables used for the signal detection step. We do this by considering an optimal transport map of the classifier output that makes it independent of the protected variable(s) for the background. We then fit a semi-parametric mixture model to the distribution of the protected variable after making cuts on the transformed classifier to detect the presence of a signal. We compare and contrast this decorrelation method with previous approaches, show that the decorrelation procedure is robust to moderate background misspecification, and analyse the power of the signal detection test as a function of the cut on the classifier., Comment: 67 pages, 21 figures

We analyze $\textit{JWST}$ CEERS NIRCam images to present {the first estimate} of the observed fraction and properties of bars out to $z \sim 4$. We analyze a sample of 1770 galaxies with stellar mass $M_\star > 10^{10} M_\odot$ at $0.5 \leq z \leq 4$ and identify barred galaxies via ellipse fits and visual classification of both F200W and F444W images. Our results apply mainly to bars with projected semi-major axis $a_{\rm bar}$ $> 1.5 $ kpc ($\sim$ 2 $\times$ PSF in F200W images) that can be robustly traced by ellipse fits. For such bars, the {observed} bar fraction at $z\sim$ 2-4 is low ($\lesssim 10\%$), and they appear to be emerging at least as early as $z\sim 4$ when the Universe was $\sim$ 13\% of its present age. At $z\sim$ 2-4, compared to our results, TNG50 simulations {predict} a significantly larger bar fraction due to a large population of small bars with $a_{\rm bar}$ $< 1.5$ kpc {that we cannot robustly detect}. If such a population exists, the true bar fraction may be significantly higher than our results. At $z \ge 1.5$, many barred galaxies show nearby neighbors, suggesting bars may be tidally triggered. {From $z \sim 4$ to $z \sim 0.5$, the observed bar fraction, average projected bar length, and projected bar strength rise.} Our results highlight the early emergence and evolution of barred galaxies and the rising importance of bar-driven secular evolution from $z \sim$4 to today., Comment: 25 pages, 16 figures, submitted to ApJ, Comments are welcome

This paper presents a comprehensive overview of monotone priority queues, focusing on their evolution and application in shortest path algorithms. Monotone priority queues are characterized by the property that their minimum key does not decrease over time, making them particularly effective for label-setting algorithms like Dijkstra's. Some key data structures within this category are explored, emphasizing those derived directly from Dial's algorithm, including variations of multi-level bucket structures and radix heaps. Theoretical complexities and practical considerations of these structures are discussed, with insights into their development and refinement provided through a historical timeline., Comment: 21 pages, 5 figures, 1 table

Classical nonlinear dynamical systems are often characterized by their steady-state probability distribution functions (PDFs). Typically, PDFs are accumulated from numerical simulations that involve solving the underlying dynamical equations of motion using integration techniques. An alternative procedure, direct statistical simulation (DSS), solves for the statistics directly. One approach to DSS is the Fokker-Planck Equation (FPE), which can be used to find the PDF of classical dynamical systems. Here, we investigate the utility of Noisy Intermediate-Scale Quantum (NISQ) computers to find steady-state solutions to the FPE. We employ the Quantum Phase Estimation (QPE) and the Variational Quantum Eigensolver (VQE) algorithms to find the zero-mode of the FPE for one-dimensional Ornstein-Uhlenbeck problems enabling comparison with exact solutions. The quantum computed steady-state probability distribution functions (PDFs) are demonstrated to be in reasonable agreement with the classically computed PDFs. We conclude with a discussion of potential extensions to higher-dimensional dynamical systems., Comment: 14 pages, 6 figures

Schwarzschild dynamical models are now regularly employed in large surveys of galaxies in the local and distant Universe to derive information on galaxies' intrinsic properties such as their orbital structure and their (dark matter and stellar) mass distribution. Comparing the internal orbital structures and mass distributions of galaxies in the distant Universe with simulations is key to understanding what physical processes are responsible for shaping galaxy properties. However it is first crucial to understand whether observationally derived properties are directly comparable with intrinsic ones in simulations. To assess this, we build Schwarzschild dynamical models for MUSE-like IFS cubes (constructed to be like those obtained by the MAGPI survey) of 75 galaxies at z ~ 0.3 from the Eagle simulations. We compare the true particle-derived properties with the galaxies' model-derived properties. In general, we find that the models can recover the true galaxy properties qualitatively well, with the exception of the enclosed dark matter, where we find a median offset of 48%, which is due to the assumed NFW profile not being able to reproduce the dark matter distribution in the inner region of the galaxies. We then compare our model-derived properties with Schwarzschild models-derived properties of observed MAGPI galaxies and find good agreement between MAGPI and Eagle: the majority of our galaxies (57%) have non-oblate shapes within 1 effective radius. More triaxial galaxies show higher fractions of hot orbits in their inner regions and tend to be more radially anisotropic., Comment: 22 pages. Accepted for publication in MNRAS

We present NLO QCD results for Higgs boson production in association with one jet, including anomalous Higgs-top and effective Higgs-gluon couplings within non-linear Effective Field Theory (HEFT), as well as the full top quark mass dependence. We provide differential results for the Higgs boson transverse momentum spectrum at $\sqrt{s}=13.6$ TeV and discuss the effects of the anomalous couplings., Comment: 25 pages, 6 figures

The petaling effect, induced by pupil fragmentation from the telescope spider, drastically affects the performance of high contrast instruments by inducing core splitting on the PSF. Differential piston/tip/tilt aberrations within each optically separated fragment of the pupil are poorly measured by commonly used Adaptive Optics (AO) systems. We here pursue a design of dedicated low-order wavefront sensor -- or petalometers -- to complement the main AO. Interferometric devices sense differential aberrations between fragments with optimal sensitivity; their weakness though is their limitation to wrapped phase measurements. We show that by combining multiple spectral channels, we increase the capture range for petaling aberrations beyond several microns, enough to disambiguate one-wave wrapping errors made by the main AO system. We propose here to implement a petalometer from the multi-wavelength imaging mode of the VAMPIRES visible-light instrument, deployed on SCExAO at the Subaru Telescope. The interferometric measurements obtained in four spectral channels through a 7 hole non-redundant mask allow us to effiiently reconstruct diffierential piston between pupil petals., Comment: Paper 13097-89 from SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse information when analyzing pairs of imperfectly aligned images. Two main alignment challenges in this pancreas segmentation study are 1) the pancreas is deformable and 2) breathing deforms the abdomen. Even after image registration, relevant deformations are often not corrected. We examine how early through late fusion impacts pancreas segmentation. We used 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas labels. We used image registration (deeds) to align the image pairs. We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images. We assessed generalization of fusion points on nnUNet. The single-modality T2w baseline using a basic UNet model had a Dice score of 0.73, while the same baseline on the nnUNet model achieved 0.80. For the basic UNet, the best fusion approach occurred in the middle of the encoder (early/mid fusion), which led to a statistically significant improvement of 0.0125 on Dice score compared to the baseline. For the nnUNet, the best fusion approach was na\"ive image concatenation before the model (early fusion), which resulted in a statistically significant Dice score increase of 0.0021 compared to baseline. Fusion in specific blocks can improve performance, but the best blocks for fusion are model specific, and the gains are small. In imperfectly registered datasets, fusion is a nuanced problem, with the art of design remaining vital for uncovering potential insights. Future innovation is needed to better address fusion in cases of imperfect alignment of abdominal image pairs., Comment: 13.5 pages of manuscript content

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability at large scales. Here, we present an integration of PIMD methods, including thermostatted ring-polymer molecular dynamics (TRPMD), into the open-source GPUMD package, combined with highly accurate and efficient machine-learned neuroevolution potential (NEP) models. This approach achieves almost the accuracy of first-principles calculations with the computational efficiency of empirical potentials, enabling large-scale atomistic simulations that incorporate nuclear quantum effects. We demonstrate the efficacy of the combined NEP-PIMD approach by examining various thermal properties of diverse materials, including lithium hydride (LiH), three porous metal-organic frameworks (MOFs), and elemental aluminum. For LiH, our NEP-PIMD simulations successfully capture the isotope effect, reproducing the experimentally observed dependence of the lattice parameter on the reduced mass. For MOFs, our results reveal that achieving good agreement with experimental data requires consideration of both nuclear quantum effects and dispersive interactions. For aluminum, the TRPMD method effectively captures thermal expansion and phonon properties, aligning well with quantum mechanical predictions. This efficient NEP-PIMD approach opens new avenues for exploring complex material properties influenced by nuclear quantum effects, with potential applications across a broad range of materials., Comment: 14 pages, 6 figures in the main text; 1 table and 5 figures in the SI

Full coherent control and generation of superpositions of the quantum harmonic oscillator are not only of fundamental interest but are crucial for applications in quantum simulations, quantum-enhanced metrology and continuous-variable quantum computation. The extension of such superpositions to nonclassical states increases their power as a resource for such applications. Here, we create arbitrary superpositions of nonclassical and non-Gaussian states of a quantum harmonic oscillator using the motion of a trapped ion coupled to its internal spin states. We interleave spin-dependent nonlinear bosonic interactions and mid-circuit measurements of the spin that preserve the coherence of the oscillator. These techniques enable the creation of superpositions between squeezed, trisqueezed, and quadsqueezed states, which have never been demonstrated before, with independent control over the complex-valued squeezing parameter and the probability amplitude of each constituent, as well as their spatial separation. We directly observe the nonclassical nature of these states in the form of Wigner negativity following a full state reconstruction. Our methods apply to any system where a quantum harmonic oscillator is coupled to a spin.

Modeling of the collimator-detector response (CDR) in SPECT reconstruction enables improved resolution and more accurate quantitation, especially for higher energy imaging (e.g.Lu-177 and Ac-225). Such modeling, however, can pose a significant computational bottleneck when there are substantial components of septal penetration and scatter in the acquired data, since a direct convolution-based approach requires large 2D kernels. The present work presents an alternative method for fast and accurate CDR compensation using a linear operator built from 1D convolutions and rotations (1D-R). To enable open-source development and use of these models in image reconstruction, we release a SPECTPSFToolbox repository for the PyTomography project on GitHub.

The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of 181 scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.

This paper introduces the combination of Laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy (RS) and diffuse reflectance spectroscopy (DRS) in the field of biomedical research. Thereby, the results from RS and LIBS are combined with previous DRS results. These noninvasive optical methods, used together, offer thorough analysis of the absorptive and scattering behaviour, elemental composition, and molecular bindings of tissue, without resulting in considerable harm or changes to the sample or the requirement of markers. The study centres on applying this triad of techniques to tissues affected by Femoroacetabular Impingement (FAI), a condition characterised by reduced hip mobility due to developmental deformities of the hip joint. The research results in a biochemical pathway model of the condition causing the red staining caused by FAI which origin was unknown until now. This proves that this approach may have significant implications for numerous medical applications and support in exploring complex chemical and biochemical pathways.

Although fundamental to the advancement of Machine Learning, the classic evaluation metrics extracted from the confusion matrix, such as precision and F1, are limited. Such metrics only offer a quantitative view of the models' performance, without considering the complexity of the data or the quality of the hit. To overcome these limitations, recent research has introduced the use of psychometric metrics such as Item Response Theory (IRT), which allows an assessment at the level of latent characteristics of instances. This work investigates how IRT concepts can enrich a confusion matrix in order to identify which model is the most appropriate among options with similar performance. In the study carried out, IRT does not replace, but complements classical metrics by offering a new layer of evaluation and observation of the fine behavior of models in specific instances. It was also observed that there is 97% confidence that the score from the IRT has different contributions from 66% of the classical metrics analyzed., Comment: 15 pages, 8 figures, 3 tables, submitted for the BRACIS'24 conference

Uranus and Neptune have atmospheres dominated by molecular hydrogen and helium. In the upper troposphere, methane is the third main molecule and condenses, yielding a vertical gradient in CH4. This condensable species being heavier than H2 and He, the resulting change in mean molecular weight due to condensation comes as a factor countering dry and moist convection. As observations also show latitudinal variations in methane abundance, one can expect different vertical gradients from one latitude to another. In this paper, we investigate the impact of this methane vertical gradient on the atmospheric regimes, especially on the formation and inhibition of moist convective storms in the troposphere of ice giants. We develop a 3D cloud-resolving model to simulate convective processes. Using our simulations, we conclude that typical velocities of dry convection in the deep atmosphere are rather low (of the order of 1 m/s) but sufficient to sustain upward methane transport, and that moist convection at methane condensation level is strongly inhibited. Previous studies derived an analytical criterion on the methane vapor amount above which moist convection should be inhibited. We first validate this analytical criterion numerically. We then show that the critical methane abundance governs the inhibition and formation of moist convective storms, and we conclude that the intensity and intermittency of these storms should depend on the methane abundance and saturation. In ice giants, dry convection is weak, and moist convection is strongly inhibited. However, when enough methane is transported upwards, through dry convection and turbulent diffusion, sporadic moist convective storms can form. These storms should be more frequent on Neptune than on Uranus, because of Neptune's internal heat flow. Our results can explain the observed sporadicity of clouds in ice giants.