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The frequency of the preferred order for a noun phrase formed by demonstrative, numeral, adjective and noun has received significant attention over the last two decades. We investigate the actual distribution of the preferred 24 possible orders. There is no consensus on whether it can be well-fitted by an exponential or a power law distribution. We find that an exponential distribution is a much better model. This finding and other circumstances where an exponential-like distribution is found challenge the view that power-law distributions, e.g., Zipf's law for word frequencies, are inevitable. We also investigate which of two exponential distributions gives a better fit: an exponential model where the 24 orders have non-zero probability or an exponential model where the number of orders that can have non-zero probability is variable. When parsimony and generalizability are prioritized, we find strong support for the exponential model where all 24 orders have non-zero probability. This finding suggests that there is no hard constraint on word order variation and then unattested orders merely result from undersampling, consistently with Cysouw's view.

Purpose: We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a biopsy sample taken from potential POE, taking into account the margin of error that arises from discrepancies between the orientation in the planning simulation and the actual orientation during the operation. Additionally, the study examines the impact of the characteristics of the lesion. Methods: The quality of the biopsy is given by a heatmap projected onto the skeleton of a patient-specific model of airways. The skeleton provides a 3D representation of airways structure, while the heatmap intensity represents the potential amount of tissue that it could be extracted from each POE. This amount of tissue is determined by the intersection of the lesion with a cone that represents the uncertainty area in the introduction of biopsy instruments. The cone, lesion, and skeleton are modelled as graphical objects that define a 3D scene of the intervention. Results: We have simulated different settings of the intervention scene from a single anatomy extracted from a CT scan and two lesions with regular and irregular shapes. The different scenarios are simulated by systematic rotation of each lesion placed at different distances from airways. Analysis of the heatmaps for the different settings show a strong impact of lesion orientation for irregular shape and the distance for both shapes. Conclusion: The proposed heatmaps help to visually assess the optimal POE and identify whether multiple optimal POEs exist in different zones of the bronchi. They also allow us to model the maximum allowable error in navigation systems and study which variables have the greatest influence on the success of the operation. Additionally, they help determine at what point this influence could potentially jeopardize the operation.

The syntactic structure of a sentence can be described as a tree that indicates the syntactic relationships between words. In spite of significant progress in unsupervised methods that retrieve the syntactic structure of sentences, guessing the right direction of edges is still a challenge. As in a syntactic dependency structure edges are oriented away from the root, the challenge of guessing the right direction can be reduced to finding an undirected tree and the root. The limited performance of current unsupervised methods demonstrates the lack of a proper understanding of what a root vertex is from first principles. We consider an ensemble of centrality scores, some that only take into account the free tree (non-spatial scores) and others that take into account the position of vertices (spatial scores). We test the hypothesis that the root vertex is an important or central vertex of the syntactic dependency structure. We confirm that hypothesis and find that the best performance in guessing the root is achieved by novel scores that only take into account the position of a vertex and that of its neighbours. We provide theoretical and empirical foundations towards a universal notion of rootness from a network science perspective.

Magnetic vector tomography allows for visualizing the 3D magnetization vector of magnetic nanostructures and multilayers with nanometric resolution. In this work, we present MARTApp (Magnetic Analysis and Reconstruction of Tomographies Application), a software designed to analyze the images obtained from a full-field or scanning transmission X-ray microscope and reconstruct the 3D magnetization of the sample. Here, its workflow and main features are described. Moreover, a synthetic test sample consisting of a hopfion is used to exemplify the workflow from raw images to the final 3D magnetization reconstruction., Comment: 21 pages, 8 figures, submitted to Journal of Synchrotron Radiation as Computer Program contribution

Many features have been proposed in on-line signature verification. Generally, these features rely on the position of the on-line signature samples and their dynamic properties, as recorded by a tablet. This paper proposes a novel feature space to describe efficiently on-line signatures. Since producing a signature requires a skeletal arm system and its associated muscles, the new feature space is based on characterizing the movement of the shoulder, the elbow and the wrist joints when signing. As this motion is not directly obtained from a digital tablet, the new features are calculated by means of a virtual skeletal arm (VSA) model, which simulates the architecture of a real arm and forearm. Specifically, the VSA motion is described by its 3D joint position and its joint angles. These anthropomorphic features are worked out from both pen position and orientation through the VSA forward and direct kinematic model. The anthropomorphic features' robustness is proved by achieving state-of-the-art performance with several verifiers and multiple benchmarks on third party signature databases, which were collected with different devices and in different languages and scripts.

We prove the $\Sigma^1$-conjecture for the family of balanced Artin groups, a family that generalizes the considered by Kochloukova in arXiv:2009.14269, the considered by Escart\'in-Martinez in arXiv:2309.03091 and the family of coherent Artin groups. We state a conjecture on the $\Sigma^2$-invariant for Artin groups and prove it for the families of $2$-dimensional and coherent Artin groups., Comment: 17 pages

The pressing demand for offshore wind energy has driven a significant increase in the size of wind turbines, as exemplified by the proposed 22MW turbine in the IEA Task 55 REFWIND. In addition, today it is common to see offshore farms with 100 or 150 turbines. The combination of turbines of increasing sizes in populated farms facilitates energy production but can lead to non-negligible aerodynamic noise emissions, which can be heard underwater. This paper quantifies the underwater aerodynamic noise footprint of three large offshore turbines (5MW, 10MW, and 22MW) and farms composed of these types of turbines. By comparing the underwater noise spectra with audiograms of marine animals, we confirm that the noise emissions of large offshore farms will soon pose an environmental problem. The proposed methodology allows wind turbine designers to predict and mitigate the impact of noise while promoting sustainable energy.

Fast-turn around methods to predict airfoil trailing-edge noise are crucial for incorporating noise limitations into design optimization loops of several applications. Among these aeroacoustic predictive models, Amiet's theory offers the best balance between accuracy and simplicity. The accuracy of the model relies heavily on precise wall-pressure spectrum predictions, which are often based on single-equation formulations with adjustable parameters. These parameters are calibrated for particular airfoils and flow conditions and consequently tend to fail when applied outside their calibration range. This paper introduces a new wall-pressure spectrum empirical model designed to enhance the robustness and accuracy of current state-of-the-art predictions while widening the range of applicability of the model to different airfoils and flow conditions. The model is developed using AI-based symbolic regression via a genetic-algorithm-based approach, and applied to a dataset of wall-pressure fluctuations measured on NACA 0008 and NACA 63018 airfoils at multiple angles of attack and inflow velocities, covering turbulent boundary layers with both adverse and favorable pressure gradients. Validation against experimental data (outside the training dataset) demonstrates the robustness of the model compared to well-accepted semi-empirical models. Finally, the model is integrated with Amiet's theory to predict the aeroacoustic noise of a full-scale wind turbine, showing good agreement with experimental measurements.

The standard of care for ovarian cancer comprises cytoreductive surgery, followed by adjuvant platinum-based chemotherapy plus taxane therapy and maintenance therapy with the antiangiogenic compound bevacizumab and/or a PARP inhibitor. Nevertheless, there is currently no clear clinical indication for the use of bevacizumab, highlighting the urgent need for biomarkers to assess the response to bevacizumab. In the present study, based on a novel RNA-seq dataset (n=181) and a previously published microarray-based dataset (n=377), we have identified an expression signature potentially associated with benefit from bevacizumab addition and assumed to reflect cancer stemness acquisition driven by activation of CTCFL. Patients with this signature demonstrated improved overall survival when bevacizumab was added to standard chemotherapy in both novel (HR=0.41(0.23-0.74), adj.p-value=7.70e-03) and previously published cohorts (HR=0.51(0.34-0.75), adj.p-value=3.25e-03), while no significant differences in survival explained by treatment were observed in patients negative for this signature. In addition to the CTCFL signature, we found several other reproducible expression signatures which may also represent biomarker candidates not related to established molecular subtypes of ovarian cancer and require further validation studies based on additional RNA-seq data.

The tensor-vector contraction (TVC) is the most memory-bound operation of its class and a core component of the higher order power method (HOPM). This paper brings distributed-memory parallelization to a native TVC algorithm for dense tensors that overall remains oblivious to contraction mode, tensor splitting and tensor order. Similarly, we propose a novel distributed HOPM, namely dHOPM3, that can save up to one order of magnitude of streamed memory and is about twice as costly in terms of data movement as a distributed TVC operation (dTVC) when using task-based parallelization. The numerical experiments carried out in this work on three different architectures featuring multi-core and accelerated systems confirm that the performance of dTVC and dHOPM3 remains relatively close to the peak system memory bandwidth (50%-80%, depending on the architecture) and on par with STREAM reference values. On strong scalability scenarios, our native multi-core implementations of these two algorithms can achieve similar and sometimes even greater performance figures than those based upon state-of-the-art CUDA batched kernels. Finally, we demonstrate that both computation and communication can benefit from mixed precision arithmetic also in cases where the hardware does not support low precision data types natively., Comment: 15 pages, 7 figures, preprint (accepted for publication at Journal of Future Generation Computer Systems)

Direct imaging of exoplanets is crucial for advancing our understanding of planetary systems beyond our solar system, but it faces significant challenges due to the high contrast between host stars and their planets. Wavefront aberrations introduce speckles in the telescope science images, which are patterns of diffracted starlight that can mimic the appearance of planets, complicating the detection of faint exoplanet signals. Traditional post-processing methods, operating primarily in the image intensity domain, do not integrate wavefront sensing data. These data, measured mainly for adaptive optics corrections, have been overlooked as a potential resource for post-processing, partly due to the challenge of the evolving nature of wavefront aberrations. In this paper, we present a differentiable rendering approach that leverages these wavefront sensing data to improve exoplanet detection. Our differentiable renderer models wave-based light propagation through a coronagraphic telescope system, allowing gradient-based optimization to significantly improve starlight subtraction and increase sensitivity to faint exoplanets. Simulation experiments based on the James Webb Space Telescope configuration demonstrate the effectiveness of our approach, achieving substantial improvements in contrast and planet detection limits. Our results showcase how the computational advancements enabled by differentiable rendering can revitalize previously underexploited wavefront data, opening new avenues for enhancing exoplanet imaging and characterization., Comment: Webpage: https://brandonyfeng.github.io/EDDO/

The hot interiors of massive stars in the later stages of their evolution provide an ideal place for the production of heavy axion-like particles (ALPs) with mass up to O(100 keV) range. We show that a fraction of these ALPs could stream out of the stellar photosphere and subsequently decay into two photons that can be potentially detected on or near the Earth. In particular, we estimate the photon flux originating from the spontaneous decay of heavy ALPs produced inside Horizontal Branch and Wolf-Rayet stars, and assess its detectability by current and future $X$-ray and gamma-ray telescopes. Our results indicate that current and future telescopes can probe axion-photon couplings down to $g_{a\gamma} \sim 4\times 10^{-11}$ GeV${}^{-1}$ for $m_a\sim 10-100$ keV, which covers new ground in the ALP parameter space., Comment: 18 pages, 10 figures, 3 tables

Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising solution to automate various procedures such as triaging, preliminary tests like visual acuity assessment, and report summaries. However, LLMs have demonstrated significantly varied performance across different languages in natural language question-answering tasks, potentially exacerbating healthcare disparities in Low and Middle-Income Countries (LMICs). This study introduces the first multilingual ophthalmological question-answering benchmark with manually curated questions parallel across languages, allowing for direct cross-lingual comparisons. Our evaluation of 6 popular LLMs across 7 different languages reveals substantial bias across different languages, highlighting risks for clinical deployment of LLMs in LMICs. Existing debiasing methods such as Translation Chain-of-Thought or Retrieval-augmented generation (RAG) by themselves fall short of closing this performance gap, often failing to improve performance across all languages and lacking specificity for the medical domain. To address this issue, We propose CLARA (Cross-Lingual Reflective Agentic system), a novel inference time de-biasing method leveraging retrieval augmented generation and self-verification. Our approach not only improves performance across all languages but also significantly reduces the multilingual bias gap, facilitating equitable LLM application across the globe., Comment: Accepted at the AAAI 2025 Artificial Intelligence for Social Impact Track (AAAI-AISI 2025)

Obstructions to the existence of spacelike solitons depending on the growth of the mean curvature $H$ are proved for Lorentzian products $(M\times \mathbb{R}, \bar g=g_M-dt^2)$ with lowerly bounded curvature. The role of these bounds for both the completeness of the soliton $\Sigma$ and the applicability of the Omori-Yau principle for the drift Laplacian, is underlined. The differences between bounds in terms of the intrinsic distance $g$ of the soliton and the distance $g_M$ in the ambiance are analyzed, and lead to a revision of classic results on completeness for spacelike submanifolds. In particular, primary bounds, including affine $g$-bounds and logarithmic $g_M$-bounds, become enough to ensure both completeness and Omori-Yau's. Therefore, they become an obstruction to the existence of solitons when the ambiance Ricci is non-negative. These results, illustrated with a detailed example, deepen the uniqueness of solutions of elliptic equations which are not uniformly elliptic, providing insights into the interplay between mean curvature growth and the global properties of this geometric flow., Comment: 37 pages, 2 figures

Many development decisions affect the results obtained from ML experiments: training data, features, model architecture, hyperparameters, test data, etc. Among these aspects, arguably the most important design decisions are those that involve the evaluation procedure. This procedure is what determines whether the conclusions drawn from the experiments will or will not generalize to unseen data and whether they will be relevant to the application of interest. If the data is incorrectly selected, the wrong metric is chosen for evaluation or the significance of the comparisons between models is overestimated, conclusions may be misleading or result in suboptimal development decisions. To avoid such problems, the evaluation protocol should be very carefully designed before experimentation starts. In this work we discuss the main aspects involved in the design of the evaluation protocol: data selection, metric selection, and statistical significance. This document is not meant to be an exhaustive tutorial on each of these aspects. Instead, the goal is to explain the main guidelines that should be followed in each case. We include examples taken from the speech processing field, and provide a list of common mistakes related to each aspect., Comment: v1.0

Tangible User Interfaces (TUI) for human--computer interaction (HCI) provide the user with physical representations of digital information with the aim to overcome the limitations of screen-based interfaces. Although many compelling demonstrations of TUIs exist in the literature, there is a lack of research on TUIs intended for daily two-handed tasks and processes, such as cooking. In response to this gap, we propose SPICE (Smart Projection Interface for Cooking Enhancement). SPICE investigates TUIs in a kitchen setting, aiming to transform the recipe following experience from simply text-based to tangibly interactive. SPICE includes a tracking system, an agent-based software, and vision large language models to create and interpret a kitchen environment where recipe information is projected directly onto the cooking surface. We conducted a comparative usability study of SPICE and text-based recipe following with 30 participants, assessing the task difficulty, total duration, and efficiency, as well as user confidence and taste perception. The results indicate that SPICE allowed participants to perform the recipe with less stops and in shorter time while also improving self-reported efficiency, confidence, and taste. Despite this, participants self-reported no change in overall difficulty, which is a direction for future research. Overall, the SPICE project demonstrates the potential of using TUIs to improve everyday activities, paving the way for future research in HCI and new computing interfaces., Comment: Article submitted to IUI 2025

Online signature parameters, which are based on human characteristics, broaden the applicability of an automatic signature verifier. Although kinematic and dynamic features have previously been suggested, accurately measuring features such as arm and forearm torques remains challenging. We present two approaches for estimating angular velocities, angular positions, and force torques. The first approach involves using a physical UR5e robotic arm to reproduce a signature while capturing those parameters over time. The second method, a cost effective approach, uses a neural network to estimate the same parameters. Our findings demonstrate that a simple neural network model can extract effective parameters for signature verification. Training the neural network with the MCYT300 dataset and cross validating with other databases, namely, BiosecurID, Visual, Blind, OnOffSigDevanagari 75 and OnOffSigBengali 75 confirm the models generalization capability.

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

This study aims to explore the impact of expansion strategies and specific attributes of hotel establishments on the performance of international hotel chains, focusing on four key performance indicators: RevPAR, efficiency, occupancy, and asset turnover. Data were collected from 255 hotels across various international hotel chains, providing a comprehensive assessment of how different expansion strategies and hotel attributes influence performance. The research employs compositional data analysis (CoDA) to address the methodological limitations of traditional financial ratios in statistical analysis. The findings indicate that ownership-based expansion strategies result in higher operational performance, as measured by revenue per available room, but yield lower economic performance due to the high capital investment required. Non-ownership strategies, such as management contracts and franchising, show superior economic efficiency, offering more flexibility and reduced financial risk. This study contributes to the hospitality management literature by applying CoDA, a novel methodological approach in this field, to examine the performance of different hotel expansion strategies with a sound and more appropriate method. The insights provided can guide hotel managers and investors in making informed decisions to optimize both operational and economic performance., Comment: 21 pages

Field work is an essential component not just for organismal biology, but also for the expanding umbrella of disciplines which have turned their attention towards living systems. Observing organisms in naturalistic contexts is a critical component of discovery; however, conducting field research can be a massive barrier for scientists who do not have experience working with organisms in a naturalistic context under challenging field conditions. Here we propose 8 critical steps for organizing and executing interdisciplinary curiosity-driven field research, drawing on the insights from The in Situ Jungle Biomechanics Lab (JBL). The JBL program is a field research course that helps early-career scientists gain experience in organizing and conducting interdisciplinary field research. JBL uses a curiosity-driven approach to field science education by encouraging early-career researchers to explore scientific questions in the Peruvian Amazon with a non-prescriptive approach to research output. We achieve an inclusive research space by bringing scientists from across disciplines together, with local communities to collaborate and spark new questions and ideas. To stoke curiosity, the JBL imparts a naturalist tradition set forth by organismal biologists of the 20th century who have extolled the merits of observing the natural world as a form of scientific exploration.

The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude, we have identified future trends and challenges such as graph-based models for digital twins or model transferability and generalization., Comment: Paper accepted to Journal of Network and Computer Applications

Twisting two-dimensional van der Waals magnets allows the formation and control of different spin-textures, as skyrmions or magnetic domains. Beyond the rotation angle, different spin reversal processes can be engineered by increasing the number of magnetic layers forming the twisted van der Waals heterostructure. Here, we consider pristine monolayers and bilayers of the A-type antiferromagnet CrSBr as building blocks. By rotating 90 degrees these units, we fabricate symmetric (monolayer/monolayer and bilayer/bilayer) and asymmetric (monolayer/bilayer) heterostructures. The magneto-transport properties reveal the appearance of magnetic hysteresis, which is highly dependent upon the magnitude and direction of the applied magnetic field and is determined not only by the twist-angle but also by the number of layers forming the stack. This high tunability allows switching between volatile and non-volatile magnetic memory at zero-field and controlling the appearance of abrupt magnetic reversal processes at either negative or positive field values on demand. The phenomenology is rationalized based on the different spin-switching processes occurring in the layers, as supported by micromagnetic simulations. Our results highlight the combination between twist-angle and number of layers as key elements for engineering spin-switching reversals in twisted magnets, of interest towards the miniaturization of spintronic devices and realizing novel spin textures., Comment: 18 pages, 5 figures

A current trend in HPC systems is the utilization of architectures with SIMD or vector extensions to exploit data parallelism. There are several ways to take advantage of such modern vector architectures, each with a different impact on the code and its portability. For example, the use of intrinsics, guided vectorization via pragmas, or compiler autovectorization. Our objectives are to maximize vectorization efficiency and minimize code specialization. To achieve these objectives, we rely on compiler autovectorization. We leverage a set of hardware and software tools that allow us to analyze in detail where autovectorization is suboptimal. Thus, we apply an iterative methodology that allows us to incrementally improve the efficient use of the underlying hardware. In this paper, we apply this methodology to a CFD production code. We evaluate the performance on an innovative configurable platform powered by a RISC-V core coupled with a wide vector unit capable of operating with up to 256 double precision elements. Following the vectorization process, we demonstrate a single-core speedup of 7.6$\times$ compared to its scalar implementation. Furthermore, we show that code portability is not compromised, as our solution continues to exhibit performance benefits, or at the very least, no drawbacks, on other HPC architectures such as Intel x86 and NEC SX-Aurora., Comment: Main track paper, presented at IPDPS 2024

This paper examines the interdependence between green financial instruments, represented by green bonds and green stocks, and a set of major conventional assets, such as Treasury, investment-grade and high-yield corporate bonds, general stocks, crude oil, and gold. To that end, a novel wavelet-based network approach that allows for assessing the degree of interconnection between green financial products and traditional asset classes across different investment horizons is applied. The~empirical results show that green bonds are tightly linked to Treasury and investment-grade corporate bonds, while green stocks are strongly tied to general stocks, regardless of the specific time period and investment horizon considered. However, despite their common climate-friendly nature, there is no a remarkable association between green bonds and green stocks. This means that these green investments constitute basically two independent asset classes, with a distinct risk-return profile and aimed at a different type of investor. Furthermore, green financial products have a weak connection with high-yield corporate bonds and crude oil. These findings can have important implications for investors and policy makers in terms of investment decision, hedging strategies, and sustainability and energy policies., Comment: 24 pages, 4 figures

Thermoelectric materials, enabling direct waste-heat to electricity conversion, need to be highly electrically conducting while simultaneously thermally insulating. This is fundamentally challenging since electrical and thermal conduction are usually coupled. Here, we discover that quasi-2D conjugated coordination polymer films exhibit this ideal mix of antithetical properties due to coexistence of defect-tolerant charge transport and defect-sensitive heat propagation. The former is highlighted by the highest conductivities > 2000 S cm-1 with metallic temperature dependence observed in disordered films with paracrystallinity > 10%, while the latter manifests in low, temperature-activated lattice thermal conductivities (< 0.38 W m-1 K-1) originating from small-amplitude, quasi-harmonic lattice dynamics with disorder-limited lifetimes and vibrational scattering length on the order of interatomic spacing. Based on temperature-dependent thermoelectric and magnetotransport experiments we identify a two-carrier (hole-electron), ambipolar metallic transport regime as the origin of relatively small Seebeck coefficients in these materials. Our findings identify conjugated coordination polymers as attractive materials for applications in thermoelectric energy harvesting, (bio)electronics and energy storage., Comment: 28 pages, 5 figures

We present GSLoc: a new visual localization method that performs dense camera alignment using 3D Gaussian Splatting as a map representation of the scene. GSLoc backpropagates pose gradients over the rendering pipeline to align the rendered and target images, while it adopts a coarse-to-fine strategy by utilizing blurring kernels to mitigate the non-convexity of the problem and improve the convergence. The results show that our approach succeeds at visual localization in challenging conditions of relatively small overlap between initial and target frames inside textureless environments when state-of-the-art neural sparse methods provide inferior results. Using the byproduct of realistic rendering from the 3DGS map representation, we show how to enhance localization results by mixing a set of observed and virtual reference keyframes when solving the image retrieval problem. We evaluate our method both on synthetic and real-world data, discussing its advantages and application potential.

Higher idempotent completion gives a formal inductive construction of the $n$-category of finite dimensional $n$-vector spaces starting with the complex numbers. We propose a manifestly unitary construction of low dimensional higher Hilbert spaces, formally constructing the $\mathrm{C}^*$-3-category of 3-Hilbert spaces from Baez's 2-Hilbert spaces, which itself forms a 3-Hilbert space. We prove that the forgetful functor from 3-Hilbert spaces to 3-vector spaces is fully faithful., Comment: 71 pages, 5 figures, many tikz diagrams. Comments welcome!

Red teaming assesses how large language models (LLMs) can produce content that violates norms, policies, and rules set during their safety training. However, most existing automated methods in the literature are not representative of the way humans tend to interact with AI models. Common users of AI models may not have advanced knowledge of adversarial machine learning methods or access to model internals, and they do not spend a lot of time crafting a single highly effective adversarial prompt. Instead, they are likely to make use of techniques commonly shared online and exploit the multiturn conversational nature of LLMs. While manual testing addresses this gap, it is an inefficient and often expensive process. To address these limitations, we introduce the Generative Offensive Agent Tester (GOAT), an automated agentic red teaming system that simulates plain language adversarial conversations while leveraging multiple adversarial prompting techniques to identify vulnerabilities in LLMs. We instantiate GOAT with 7 red teaming attacks by prompting a general-purpose model in a way that encourages reasoning through the choices of methods available, the current target model's response, and the next steps. Our approach is designed to be extensible and efficient, allowing human testers to focus on exploring new areas of risk while automation covers the scaled adversarial stress-testing of known risk territory. We present the design and evaluation of GOAT, demonstrating its effectiveness in identifying vulnerabilities in state-of-the-art LLMs, with an ASR@10 of 97% against Llama 3.1 and 88% against GPT-4 on the JailbreakBench dataset.

Defects in semiconductor crystals can have significant detrimental effects on their performance as radiation detectors. Defects cause charge trapping and recombination, leading to lower signal amplitudes and poor energy resolution. We have designed and built a modular 3D scanner for analyzing these defects in semiconductor samples using commercial off-the-shelf components. Previous solutions offer great spatial resolution, but have limited sample holding capacity and use continuum light sources which can cause difficulty differentiating between different materials within samples. Our design also includes a modular sample holder allowing for easy changing of samples. In this paper, we showcase first results achieved with this custom built scanner as well as planned developments., Comment: Submitted to Journal of Instrumentation for inclusion in the International Workshop on Radiation Imaging Detectors 2024 proceedings

Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm manufactures are aimed to their robots could increasingly perform tasks collaborating with humans. One of the ways to improve this collaboration is by making the movement of robots more humanlike. This way, it would be easier for a human to foresee the movement of the robot and approach it without fear of contact. The main difference between the movement of a human and of a robotic arm is that the former has a bell-shaped speed profile while the latter has a uniform speed one. To generate this speed profile, the kinematic theory of rapid human movements and its Sigma-Lognormal model has been used. This model is widely used to explain most of the basic phenomena related to the control of human movements. Both human-like and robotic-like movements are transferred to the UR3 robot. In this paper we detail the how the UR3 robot was programmed to produce both kinds of movement. The dissimilarities result between the input motion and output motion to the robot confirm the possibility to develop human-like velocities in the UR3 robot., Comment: 6 pages, 5 figures

This paper investigates a critical aspect of wind energy research - the development of wind turbine wake and its significant impact on wind farm efficiency. The study focuses on the exploration and comparison of two mesh refinement strategies, h- and p-refinement, in their ability to accurately compute the development of wind turbine wake. The h-refinement method refines the mesh by reducing the size of the elements, while the p-refinement method increases the polynomial degree of the elements, potentially reducing the error exponentially for smooth flows. A comprehensive comparison of these methods is presented that evaluates their effectiveness, computational efficiency, and suitability for various scenarios in wind energy. The findings of this research could potentially guide future studies and applications in wind turbine wake modeling, thus contributing to the optimization of wind farms using high-order h/p methods. This study fills a gap in the literature by thoroughly investigating the application of these methods in the context of wind turbine wake development., Comment: 35 pages, 24 figures

Simulators are crucial during the development of a chip, like the RISC-V accelerator designed in the European Processor Initiative project. In this paper, we showcase the limitations of the current simulation solutions in the project and propose using QEMU with RAVE, a plugin we implement and describe in this document. This methodology can rapidly simulate and analyze applications running on the v1.0 and v0.7.1 RISC-V V-extension. Our plugin reports the vector and scalar instructions alongside useful information such as the vector-length being used, the single-element-width, and the register usage, among other vectorization metrics. We provide an API used from the simulated Application to control the RAVE plugin and the capability to generate vectorization traces that can be analyzed using Paraver. Finally, we demonstrate the efficiency of our solution between different evaluated machines and against other simulation methods used in the European Processor Accelerator (EPAC) project.

In this paper the neutral 2SC phase of color superconductivity is investigated in the presence of a magnetic field and for diquark coupling constants and baryonic densities that are expected to characterize neutron stars. Specifically, the behavior of the charged gluons Meissner masses is investigated in the parameter region of interest taking into account, in addition, the contribution of a rotated magnetic field. It is found that up to moderately-high diquark coupling constants the mentioned Meissner masses become tachyonic independently of the applied magnetic-field amplitude, hence signalizing the chromomagnetic instability of this phase. To remove the instability, the restructuring of the system ground state is proposed, which now will be formed by vortices of the rotated charged gluons. These vortices boost the applied magnetic field having the most significant increase for relatively low applied magnetic fields. Finally, considering that with the stellar rotational frequency observed for magnetars a field of the order of $10^8$ G can be generated by dynamo effect, we show that by the boosting effect just described the field can be amplified to $10^{17}$ G that is in the range of inner core fields expected for magnetars. Thus, we conclude that the described mechanism could be the one responsible for the large fields characterizing magnetars if the cores of these compact objects are formed by neutral 2SC matter., Comment: 14 pages, 7 figures; Version appeared in PRD

orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

Automated speech analysis is a thriving approach to detect early markers of Alzheimer's disease (AD). Yet, recording conditions in most AD datasets are heterogeneous, with patients and controls often evaluated in different acoustic settings. While this is not a problem for analyses based on speech transcription or features obtained from manual alignment, it does cast serious doubts on the validity of acoustic features, which are strongly influenced by acquisition conditions. We examined this issue in the ADreSSo dataset, derived from the widely used Pitt corpus. We show that systems based on two acoustic features, MFCCs and Wav2vec 2.0 embeddings, can discriminate AD patients from controls with above-chance performance when using only the non-speech part of the audio signals. We replicated this finding in a separate dataset of Spanish speakers. Thus, in these datasets, the class can be partly predicted by recording conditions. Our results are a warning against the use of acoustic systems for identifying patients based on non-standardized recordings. We propose that acoustically heterogeneous datasets for dementia studies should be either (a) analyzed using only transcripts or other features derived from manual annotations, or (b) replaced by datasets collected with strictly controlled acoustic conditions., Comment: 5 pages, 1 figure, 1 table

We experimentally study the statistics of the transition path time taken by a submicron bead to successfully traverse an energy barrier created by two optical tweezers in two prototypical viscoelastic fluids, namely, aqueous polymer and micellar solutions. We find a very good agreement between our experimental distributions and a theoretical expression derived from the generalized Langevin equation for the particle motion. Our results reveal that the mean transition path time measured in such viscoelastic fluids have a non-trivial dependence on the barrier curvature and they can be significantly reduced when compared with those determined in Newtonian fluids of the same zero-shear viscosity. We verify that the decrease of the mean transition path time can be described in terms of an effective viscosity that quantitatively coincides with that measured by linear microrheology at a frequency determined by the reactive mode that gives rise to the unstable motion over the barrier. Therefore, our results uncover the linear response of the particle during its thermally activated escape from a metastable state even when taking place in a non-Markovian bath., Comment: 4 figures

This work extends our previous study from S. Shrestha et al. (2024) by introducing a new abstract framework for Variational Multiscale (VMS) methods at the discrete level. We introduce the concept of what we define as the optimal projector and present an approach where the infinite-dimensional unresolved scales are approximated in a finite-dimensional subspace using the numerically computed Fine-Scale Greens' function of the underlying symmetric problem. The proposed approach involves solving the VMS problem on two separate meshes: a coarse mesh for the full PDE and a fine mesh for the symmetric part of the continuous differential operator. We consider the 1D and 2D steady advection-diffusion problems in both direct and mixed formulations as the test cases in this paper. Moreover, we demonstrate the working of this method using the Mimetic Spectral Element Method (MSEM), however, it may be applied to other Finite/Spectral Element or Isogeometric frameworks. Furthermore, we propose that VMS should not be viewed as a stabilisation technique; instead, the base scheme should be inherently stable, with VMS enhancing the solution quality by supplementing the base scheme.

Single photons emitters (SPEs) are key components in quantum information applications and are commonly generated in 2D materials by inhomogeneous strain engineering. Here, we report an alternative approach that involves a 2D semiconductor/2D magnet heterostructure. The optical study of the WSe2/CrSBr heterostructures reveals several new emission lines at lower energies compared to characteristic WSe2 emissions, that are assigned to localized excitons. Further investigation demonstrates that one of these emergent lines is an SPE with a strong valley polarization response and large energy shift with the field-induced metamagnetic transition in CrSBr, linking it to the magnetic proximity effect of the adjacent CrSBr layer. In contrast to previous reports on WSe2 that only allow tuning of the SPEs by out-of-plane magnetic field, our emitter is sensitive to both in- and out-of-plane fields. Our findings demonstrate the potential of this approach for improved control and polarization of SPEs in 2D materials., Comment: 17 pages, 4 figures, plus suporting information