Search

Summary: Mass spectrometry coupled to liquid chromatography (LC-MS/MS) is a powerful technique for the charac-terisation of proteomes. However, the diverse software platforms available for processing the raw proteomics data, each produce their own output format, making the extraction of meaningful and interpretable results a difficult task. We present TraianProt, a web-based, user-friendly proteomics data analysis platform, that enables the analysis of both label-free and labeled data from Data-Dependent or Data-Independent Acquisition mass spectrometry mode support-ing different computational platforms such as MaxQuant, MSFragger, DIA-NN, ProteoScape and Proteome Discoverer output formats. TraianProt provides a dynamic framework that includes several processing modules allowing the user to perform a complete downstream analysis covering the stages of data pre-processing, differential expression analy-sis, functional analysis and protein-protein interaction analysis. Data output includes a wide range of high-quality, cus-tomisable graphs such as heatmap, volcano plot, boxplot and barplot. This allows users to extract biological insights from proteomic data without any programming skills. Availability and implementation: TraianProt is implemented in R. Its code and documentation are available on GitHub at https://github.com/SamueldelaCamaraFuentes/TraianProt along with a step-by-step tutorial incorporated in the repository. Contact: sdelacam@ucm.es Supplementary information: Supplementary data are available at Bioinformatics online

Three-orifice baffles equally spaced along a circular tube are investigated as a means for heat transfer enhancement under net, oscillatory and compound flows. An unprecedented, systematic analysis of the relative orientation of consecutive baffles -- aligned or opposed -- is accomplished to assess the changes induced on the flow structure and their impact on the thermal-hydraulic performance. The results cover the Nusselt number, the net and oscillatory friction factors and the instantaneous velocity fields using PIV in an experimental campaign with a 32 mm tube diameter. The study is conducted in the range of net Reynolds numbers $50 < Re_n < 1000$ and oscillatory Reynolds numbers $0 < Re_{osc}< 750$, for a dimensionless amplitude $x_0/D = 0.5$ and $Pr=65$. In absence of oscillatory flow, opposed baffles advance the transition to turbulence from $Re_n = 100$ to $50$, increasing the net friction factor (40 %) for $Re_n > 50$ and the Nusselt number (maximum of 27 %) for $Re_n < 150$. When an oscillatory flow is applied, augmentations caused by opposed baffles are only observed for $Re_n < 150$ and $Re_{osc} < 150$. Above $Re_n$, $Re_{osc}>150$, opposed baffles are not recommended for the promotion of heat transfer, owing to friction penalties. However, the chaotic mixing and lack of short-circuiting between baffles observed with flow velocimetry over a wide range of operational conditions point out the interest of this configuration to achieve plug flow., Comment: 20 pages, 16 figures

In this work, we develop a symmetry-based classification of Chern phases in honeycomb photonic crystals, considering arbitrary nonreciprocal couplings compatible with energy conservation. Our analysis focuses on crystals formed through nonreciprocal perturbations of photonic graphene. These perturbations, which can have arbitrary spatial variations, are generally described by scalar and vector fields. Using a tight-binding model, we consider the most general nonreciprocal interactions, including gyromagnetic, pseudo-Tellegen, and moving medium responses, and examine how the corresponding nonreciprocal fields influence the crystal's topology. Our findings reveal that nonreciprocal interactions alone are insufficient to induce a topologically nontrivial phase. Instead, a nontrivial p6m component in the nonreciprocal fields is required to open a bandgap and achieve a non-zero Chern number. These results provide a symmetry-based roadmap for engineering photonic topological phases via nonreciprocal perturbations of photonic graphene, offering practical guidelines for designing topological phases in graphene-like photonic crystals., Comment: 47 pages, 8 figures

While deep learning models can learn human-like features at earlier levels, which suggests their utility in modeling human vision, few attempts exist to incorporate these features by design. Current approaches mostly optimize all parameters blindly, only constraining minor architectural aspects. This paper demonstrates how parametrizing neural network layers enables more biologically-plausible operations while reducing trainable parameters and improving interpretability. We constrain operations to functional forms present in human vision, optimizing only these functions' parameters rather than all convolutional tensor elements independently. We present two parametric model versions: one with hand-chosen biologically plausible parameters, and another fitted to human perception experimental data. We compare these with a non-parametric version. All models achieve comparable state-of-the-art results, with parametric versions showing orders of magnitude parameter reduction for minimal performance loss. The parametric models demonstrate improved interpretability and training behavior. Notably, the model fitted to human perception, despite biological initialization, converges to biologically incorrect results. This raises scientific questions and highlights the need for diverse evaluation methods to measure models' humanness, rather than assuming task performance correlates with human-like behavior.

We develop a diffeomorphism-invariant formulation of differential entropy for Riemannian spaces, addressing the lack of a fine-grained, coordinate-independent notion of information for continuous variables in physical space. We extend this formulation to the quantum domain by generalizing Wigner's quasiprobability density function to arbitrary Riemannian spaces and analytically continuing Shannon's differential entropy to include contributions from intermediate virtual quantum states. To demonstrate this framework, we compute the quantum phase space entropy of the harmonic oscillator energy eigenstates in Minkowski and anti-de Sitter geometries. Additionally, we derive a generalized quantum entropic uncertainty relation, extending the Bialynicki-Birula and Mycielski inequality to curved backgrounds. This work bridges concepts from information theory, geometry, and quantum physics to provide a systematic approach to studying quantum information in continuous and curved sample spaces., Comment: 13 pages, 4 figures, 1 appendix. v3: Various improvements in the abstract, introduction, and discussion

We consider the canonical Wiener-Hopf factorisation of $2 \times 2$ symmetric matrices $\mathcal M$ with respect to a contour $\Gamma$. For the case that the quotient $q$ of the two diagonal elements of $\mathcal M$ is a rational function, we show that due to the symmetric nature of the matrix $\mathcal M$, the second column in each of the two matrix factors that arise in the factorisation is determined in terms of the first column in each of these matrix factors, by multiplication by a rational matrix, and we give a method for determining the second columns of these factors. We illustrate our method with two examples in the context of a Riemann-Hilbert approach to obtaining solutions to the Einstein field equations., Comment: 20 pages

Light-matter interactions are of fundamental scientific and technological interest. Ultrafast electron microscopy and diffraction with combined femtosecond-nanometer resolution elucidate the laser-induced dynamics in structurally heterogeneous systems. These measurements, however, remain challenging due to the brightness limitation of pulsed electron sources, leading to an experimental trade-off between resolution and contrast. Higher signals without compromising the electron beam coherence can be achieved by enhanced duty cycles, thus far limited to few-kHz repetition rates by the cooling times in thin-film specimens. Here, we combine nanometric electron-beam probing with sample support structures tailored to accommodate rapid thermal relaxation. A charge-density wave (CDW) phase transformation allows us to quantify the mean temperature increase induced by pulsed laser illumination. Varying the excitation fluence and repetition rate, we gauge the impact of excitation confinement and efficient dissipation on the thermal relaxation of different sample designs. In particular, a thermally optimized support can dissipate average laser intensities of up to \SI{200}{\micro\watt\per\um^2} within a few nanoseconds, allowing for reversible driving and probing of the CDW transition at a repetition rate of \SI{2}{\MHz}. Sample designs tailored to ultrafast measurement schemes will thus extend the capabilities of electron diffraction and microscopy, enabling high-resolution investigations of structural dynamics.

Social-media platforms are one of the most prevalent communication media today. In such systems, a large amount of content is generated and available to the platform. However, not all content can be transmitted to every possible user at all times. At the other end are the users, who have their own preferences about which content they enjoy, which is often unknown ex ante to the platform. We model the interaction between the platform and the users as a signaling game with asymmetric information, where each user optimizes its preference disclosure policy, and the platform optimizes its information disclosure policy. We provide structural as well as existence of policies that constitute Bayesian Nash Equilibria, and necessary optimality conditions used to explicitly compute the optimal policies., Comment: Submitted to American Control Conference 2025

Perpendicular magnetic anisotropy (PMA) and Dzyaloshinskii-Moriya interactions are key interactions in modern spintronics. These interactions are thought to be dominated by the oxidation of the Co|Al interface in the archetypal Platinum-Cobalt-Aluminum oxide system. Here, we observe a double sign change in the anisotropy and about threefold variation in interfacial chiral interaction, influenced not only by the oxidation, but also by the metallic Al thickness. Contrary to previous assumptions about negligible spin-orbit effects at light metal interfaces, we not only observe strong PMA with fully oxidized Al, decreasing and turning negative (in-plane) with less oxygen at the Co|Al interface, we also observe that the magnetic anisotropy reverts to positive (out-of-plane) values at fully metallic Co|Al interface. These findings suggest modification in Co d band via Co|Al orbital hybridization, an effect supported by X-ray absorption spectroscopy and ab initio theory calculations, highlighting the key impact of strain on interfacial mechanisms at fully metallic Co|Al interface., Comment: 6 pages, 5 figures

The use of nanostructures to enhance the emission of single-photon sources has withdrawn some attention in last decade due to the development of quantum technologies. In particular, the use of metallic and high refractive index dielectric materials has been proposed. However, the utility of moderate refractive index dielectric nanostructures to achieve more efficient single-photon sources remains unexplored. Here, a systematic comparison of various metallic, high refractive index and moderate refractive index dielectric nanostructures has been performed to optimize the excitation and emission of a CdSe/ZnS single quantum dot at visible spectral region. Several geometries have been evaluated in terms of electric field enhancement and Purcell factor, considering the combination of metallic, high refractive index and moderate refractive index dielectric materials conforming homogeneous and hybrid nanocylinder dimers. Our results demonstrate that moderate refractive index dielectric nanoparticles can enhance the photoluminescence signal of quantum emitters due to their broader electric and magnetic dipolar resonances compared to high refractive index dielectric nanoparticles. However, hybrid combinations of metallic and high refractive index dielectric nanostructures offer the largest intensity enhancement and Purcell factors at the excitation and emission wavelengths of the quantum emitter, respectively. The results of this work may find applications in the development of single-photon sources

The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at the International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts., Comment: Scholarly Hybrid Question answering challenge from the International Semantic Web Conference of 2024(ISWC), 7 pages, 8 figures

This study presents a novel methodology for stratifying post-myocardial infarction patients at risk of ventricular arrhythmias using patient-specific 3D cardiac models derived from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images. The method integrates imaging and computational simulation with a simplified cellular automaton model, Arrhythmic3D, enabling rapid and accurate VA risk assessment in clinical timeframes. Applied to 51 patients, the model generated thousands of personalized simulations to evaluate arrhythmia inducibility and predict VA risk. Key findings include the identification of slow conduction channels (SCCs) within scar tissue as critical to reentrant arrhythmias and the localization of high-risk zones for potential intervention. The Arrhythmic Risk Score (ARRISK), developed from simulation results, demonstrated strong concordance with clinical outcomes and outperformed traditional imaging-based risk stratification. The methodology is fully automated, requiring minimal user intervention, and offers a promising tool for improving precision medicine in cardiac care by enhancing patient-specific arrhythmia risk assessment and guiding treatment strategies.

We present an assignment for a full Parallel Computing course. Since 2017/2018, we have proposed a different problem each academic year to illustrate various methodologies for approaching the same computational problem using different parallel programming models. They are designed to be parallelized using shared-memory programming with OpenMP, distributed-memory programming with MPI, and GPU programming with CUDA or OpenCL. The problem chosen for this year implements a brute-force solution for exact DNA sequence alignment of multiple patterns. The program searches for exact coincidences of multiple nucleotide strings in a long DNA sequence. The sequential implementation is designed to be clear and understandable to students while offering many opportunities for parallelization and optimization. This assignment addresses key concepts many students find difficult to apply in practical scenarios: race conditions, reductions, collective operations, and point-to-point communications. It also covers the problem of parallel generation of pseudo-random sequences and strategies to notify and stop speculative computations when matches are found. This assignment serves as an exercise that reinforces basic knowledge and prepares students for more complex parallel computing concepts and structures. It has been successfully implemented as a practical assignment in a Parallel Computing course in the third year of a Computer Engineering degree program. Supporting materials for this and previous assignments in this series are publicly available., Comment: 3 pages, 1 figure, 1 artifact and reproducibility appendix. Accepted for presentation at EduHPC-24: Workshop on Education for High-Performance Computing, to be held during Supercomputing 2024 conference

This paper considers paired operators in the context of the Lebesgue Hilbert space $L^2$ on the unit circle and its subspace, the Hardy space $H^2$. The kernels of such operators, together with their analytic projections, which are generalizations of Toeplitz kernels, are studied. Inclusion relations between such kernels are considered in detail, and the results are applied to describing the kernels of finite-rank asymmetric truncated Toeplitz operators., Comment: 22 pages

We review the basic properties of paired operators and their adjoints, the transposed paired operators, with particular reference to commutation relations, and we study the properties of their kernels, bringing out their similarities and also, somewhat surprisingly, their stark differences. Various notions expressing different invariance properties are also reviewed and we extend to paired operators some known invariance results., Comment: 25 pages

Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of failures. Previous work suggested that a biologically motivated computation, the so-called Divisive Normalization, could be useful to deal with image variability, but its effects have not been systematically studied over different data sources and environmental factors. Here we put segmentation U-nets augmented with Divisive Normalization to work far from training conditions to find where this adaptation is more critical. We categorize the scenes according to their radiance level and dynamic range (day/night), and according to their achromatic/chromatic contrasts. We also consider video game (synthetic) images to broaden the range of environments. We check the performance in the extreme percentiles of such categorization. Then, we push the limits further by artificially modifying the images in perceptually/environmentally relevant dimensions: luminance, contrasts and spectral radiance. Results show that neural networks with Divisive Normalization get better results in all the scenarios and their performance remains more stable with regard to the considered environmental factors and nature of the source. Finally, we explain the improvements in segmentation performance in two ways: (1) by quantifying the invariance of the responses that incorporate Divisive Normalization, and (2) by illustrating the adaptive nonlinearity of the different layers that depends on the local activity.

The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the individual chambers. Changes in the size, shape and movement of the chambers can be important markers for cardiac disease and modeling this in relation to clinical demography or disease is therefore of interest. Existing methods for spatio-temporal modeling of the human heart require shape correspondence over time or suffer from large memory requirements, making it difficult to use for complex anatomies. We introduce a novel conditional generative model, where the shape and movement is modeled implicitly in the form of a spatio-temporal neural distance field and conditioned on clinical demography. The model is based on an auto-decoder architecture and aims to disentangle the individual variations from that related to the clinical demography. It is tested on the left atrium (including the left atrial appendage), where it outperforms current state-of-the-art methods for anatomical sequence completion and generates synthetic sequences that realistically mimics the shape and motion of the real left atrium. In practice, this means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies., Comment: Accepted for MICCAI2024

In this paper, we address one of the most basic and fundamental problems in the theory of foliations and ODEs, the topological invariance of the algebraic multiplicity of a holomorphic foliation. For instance, we prove an adapted version of A'Campo-L\^e's Theorem for foliations, i.e., the algebraic multiplicity equal to one is a topological invariant in dimension two. This result is further generalized to higher dimensions under mild conditions; as a consequence, we prove that saddle-nodes are topologically invariant. We prove that the algebraic multiplicity is a topological invariant in several classes of foliations that contain, for instance, the generalized curves and the foliations of second type. Additionally, we address a fundamental result by Rosas-Bazan, which states that the existence of a homeomorphism extending through a neighborhood of the exceptional divisor of the first blow-up implies the topological invariance of the algebraic multiplicity. We show that the result holds if the homeomorphism extends locally near a singularity, even if it does not extend over the entire divisor., Comment: The title was changed, the paper was rewritten and Section 4 and Subsection 5.1 are new; 20 pages

Challenges in the automated evaluation of Retrieval-Augmented Generation (RAG) Question-Answering (QA) systems include hallucination problems in domain-specific knowledge and the lack of gold standard benchmarks for company internal tasks. This results in difficulties in evaluating RAG variations, like RAG-Fusion (RAGF), in the context of a product QA task at Infineon Technologies. To solve these problems, we propose a comprehensive evaluation framework, which leverages Large Language Models (LLMs) to generate large datasets of synthetic queries based on real user queries and in-domain documents, uses LLM-as-a-judge to rate retrieved documents and answers, evaluates the quality of answers, and ranks different variants of Retrieval-Augmented Generation (RAG) agents with RAGElo's automated Elo-based competition. LLM-as-a-judge rating of a random sample of synthetic queries shows a moderate, positive correlation with domain expert scoring in relevance, accuracy, completeness, and precision. While RAGF outperformed RAG in Elo score, a significance analysis against expert annotations also shows that RAGF significantly outperforms RAG in completeness, but underperforms in precision. In addition, Infineon's RAGF assistant demonstrated slightly higher performance in document relevance based on MRR@5 scores. We find that RAGElo positively aligns with the preferences of human annotators, though due caution is still required. Finally, RAGF's approach leads to more complete answers based on expert annotations and better answers overall based on RAGElo's evaluation criteria., Comment: Accepted to LLM4Eval @ SIGIR24

We present a novel neural encoder system for acoustic-to-articulatory inversion. We leverage the Pink Trombone voice synthesizer that reveals articulatory parameters (e.g tongue position and vocal cord configuration). Our system is designed to identify the articulatory features responsible for producing specific acoustic characteristics contained in a neural latent representation. To generate the necessary latent embeddings, we employed two main methodologies. The first was a self-supervised variational autoencoder trained from scratch to reconstruct the input signal at the decoder stage. We conditioned its bottleneck layer with a subnetwork called the "projector," which decodes the voice synthesizer's parameters. The second methodology utilized two pretrained models: EnCodec and Wav2Vec. They eliminate the need to train the encoding process from scratch, allowing us to focus on training the projector network. This approach aimed to explore the potential of these existing models in the context of acoustic-to-articulatory inversion. By reusing the pretrained models, we significantly simplified the data processing pipeline, increasing efficiency and reducing computational overhead. The primary goal of our project was to demonstrate that these neural architectures can effectively encapsulate both acoustic and articulatory features. This prediction-based approach is much faster than traditional methods focused on acoustic feature-based parameter optimization. We validated our models by predicting six different parameters and evaluating them with objective and ViSQOL subjective-equivalent metric using both synthesizer- and human-generated sounds. The results show that the predicted parameters can generate human-like vowel sounds when input into the synthesizer. We provide the dataset, code, and detailed findings to support future research in this field., Comment: Presented at AES Europe 2024 in Madrid

Nuclear magnetic resonance spectroscopy is a prominent analytical tool, with applications throughout chemistry, medicine and solid-state physics. While conventional NMR spectrometers require large magnetic fields to interrogate a sample, recent advances in atomic magnetometry have enabled this spectroscopy far below geomagnetic field strengths. This zero-to-ultralow (ZULF) field regime can be advantageous since it mitigates relaxation and reveals spin couplings that are otherwise obscured, all while using compact and lower-overhead instrumentation. The resulting spectra are nonetheless difficult to interpret without computation, which can be taxing due to the presence of vector couplings and long-range spin networks. Following recent proposals, we demonstrate how fault-tolerant quantum computation could be used to simulate these spectra. Our analysis spans from input selection to the construction of explicit circuits based on qubitized quantum dynamics. By maintaining parity with experimental requirements, we demonstrate how NMR spectral prediction might be an early application for fault-tolerant quantum computers.

In the context of the 2HDM, and assuming that neutrinos acquire masses via the Weinberg operator, we perform a systematic analysis to determine the minimal quark and lepton flavour patterns, compatible with masses, mixing and CP violation data, realisable by Abelian symmetries. We determine four minimal models for quarks, where the number of independent parameters matches the number of observables. For the lepton sector, three minimal predictive models are identified. Namely, we find scenarios with a preference for the upper/lower octant of the $\theta_{23}$ atmospheric mixing angle, that exhibit lower bounds on the lightest neutrino masses currently probed by cosmology and testable at future neutrinoless double beta decay experiments, even for a normally-ordered neutrino masses. We investigate the phenomenology of each model taking into account all relevant theoretical, electroweak precision observables, scalar sector constraints, as well as stringent quark flavour processes such as $\overline{B} \rightarrow X_s \gamma$, $B_s \rightarrow \mu^- \mu^+$ and meson oscillations, and the charged lepton flavour-violating decays $e_\alpha^{-} \rightarrow e_\beta^{-} e_\gamma^{+} e_\delta^{-}$ and $e_\alpha \rightarrow e_\beta \gamma$. We show that, in some cases, Abelian flavour symmetries provide a natural framework to suppress flavour-changing neutral couplings and lead to scenarios featuring heavy neutral/charged scalar masses below the TeV scale within the reach of current experiments., Comment: 32 LaTeX pages; 7 figures

Exoplanets hosting M dwarfs are the best targets to characterize Earth-like or super-Earth planetary atmospheres with the James Webb Space Telescope (JWST). We determine detailed stellar parameters ($T_{\rm eff}$, log$g$, and $\xi$) and individual abundances of twelve elements for four cool M dwarfs hosting exoplanets TOI-1685, GJ 436, GJ 3470, and TOI-2445, scheduled for future observations by the JWST. The analysis utilizes high-resolution near-infrared spectra from the SDSS-IV APOGEE survey between 1.51-1.69$\mu$. Based on 1D-LTE plane-parallel models, we find that TOI-2445 is slightly metal-poor ([Fe/H] = -0.16$\pm$0.09), while TOI-1685, GJ 436 and GJ 3470 are more metal-rich ([Fe/H] = 0.06$\pm$0.18, 0.10$\pm$0.20 dex, 0.25$\pm$0.15). The derived C/O ratios for TOI-2445, TOI-1685, GJ 436, and GJ 3470 are 0.526$\pm$0.027, 0.558$\pm$0.097, 0.561$\pm$0.029, and 0.638$\pm$0.015, respectively. From results for 28 M dwarfs analyzed homogeneously from APOGEE spectra, we find exoplanet-hosting M dwarfs exhibit a C/O abundance ratio approximately 0.01 to 0.05 higher than those with non-detected exoplanets, at limits of a statistically significant offset. A linear regression of [Fe/H] \textit{vs.} C/O distribution reveals a noticeable difference in the angular coefficient between FGK dwarfs (0.27) and M dwarfs (0.13). Assuming our abundance ratios of Ca/Mg, Si/Mg, Al/Mg, and Fe/Mg, we determine a mass of 3.276$^{+0.448}_{-0.419}$$M_{\oplus}$ for TOI-2445 b, having density (6.793$^{+0.005}_{-0.099}$ g.cm$^{-3}$) and core mass fraction (0.329$_{-0.049}^{+0.028}$) very similar to Earth's. We also present an atlas of 113 well-defined spectral lines to analyze M dwarfs in the $H$-band and a comprehensive evaluation of uncertainties from variations in the atmospheric parameters, signal-to-noise, and pseudo-continuum., Comment: 32 pages, 3 figures. Accepted to the Astrophysical Journal (ApJ)

Assume interest is in sampling from a probability distribution $\mu$ defined on $(\mathsf{Z},\mathscr{Z})$. We develop a framework to construct sampling algorithms taking full advantage of numerical integrators of ODEs, say $\psi\colon\mathsf{Z}\rightarrow\mathsf{Z}$ for one integration step, to explore $\mu$ efficiently and robustly. The popular Hybrid/Hamiltonian Monte Carlo (HMC) algorithm [Duane, 1987], [Neal, 2011] and its derivatives are example of such a use of numerical integrators. However we show how the potential of integrators can be exploited beyond current ideas and HMC sampling in order to take into account aspects of the geometry of the target distribution. A key idea is the notion of integrator snippet, a fragment of the orbit of an ODE numerical integrator $\psi$, and its associate probability distribution $\bar{\mu}$, which takes the form of a mixture of distributions derived from $\mu$ and $\psi$. Exploiting properties of mixtures we show how samples from $\bar{\mu}$ can be used to estimate expectations with respect to $\mu$. We focus here primarily on Sequential Monte Carlo (SMC) algorithms, but the approach can be used in the context of Markov chain Monte Carlo algorithms as discussed at the end of the manuscript. We illustrate performance of these new algorithms through numerical experimentation and provide preliminary theoretical results supporting observed performance.

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not a social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments with patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions.

The Riemann-Hilbert approach, in conjunction with the canonical Wiener-Hopf factorisation of certain matrix functions called monodromy matrices, enables one to obtain explicit solutions to the non-linear field equations of some gravitational theories. These solutions are encoded in the elements of a matrix $M$ depending on the Weyl coordinates $\rho$ and $v$, determined by that factorisation. We address here, for the first time, the underlying question of what happens when a canonical Wiener-Hopf factorisation does not exist, using the close connection of Wiener-Hopf factorisation with Toeplitz operators to study this question. For the case of rational monodromy matrices, we prove that the non-existence of a canonical Wiener-Hopf factorisation determines curves in the $(\rho,v)$ plane on which some elements of $M(\rho,v)$ tend to infinity, but where the space-time metric may still be well behaved. In the case of uncharged rotating black holes in four space-time dimensions and, for certain choices of coordinates, in five space-time dimensions, we show that these curves correspond to their ergosurfaces., Comment: 23 pages; v2: matches published version

The merger of two magnetized compact objects, such as neutron stars, forms a compact object which may launch a relativistic and collimated jet. Numerical simulations of the process show that a dense and highly magnetized medium surrounds the system. This study presents a semi-analytical model that models the effects that a static magnetized medium with a tangled field produces in relativistic, collimated, and non-magnetized jets. The model is a first approximation that addresses the magnetic field present in the medium and is based on pressure equilibrium principles between the jet, cocoon, and external medium. A fraction of the ambient medium field is allowed to be entrained in the cocoon. We find that the jet and cocoon properties may be affected by high magnetic fields ($\gtrsim 10^{15}$~G) and mixing. The evolution of the system may vary up to $\sim10\%$ (compared to the non-magnetized case). Low-mixing may produce a slower-broader jet with a broader and more energetic cocoon would be produced. On the other hand, high-mixing could produce a faster-narrower jet with a narrow and less-energetic cocoon. Two-dimensional hydrodynamical simulations are used to validate the model and to constrain the mixing parameter. Although the magnetic field and mixing have a limited effect, our semi-analytic model captures the general trend consistent with numerical results. For high magnetization, the results were found to be more consistent with the low mixing case in our semi-analytic model., Comment: 10 pages, 6 figures, 1 table, submitted to MNRAS

Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is commonly done by maximising the Bayesian Active Learning by Disagreement (BALD) acquisitions function. However, we highlight that it is challenging to estimate BALD when the new data points are subject to censorship, where only clipped values of the targets are observed. To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censored regression ($\mathcal{C}$-BALD). We propose a novel modelling approach to estimate the $\mathcal{C}$-BALD objective and use it for active learning in the censored setting. Across a wide range of datasets and models, we demonstrate that $\mathcal{C}$-BALD outperforms other Bayesian active learning methods in censored regression.

Patients with atrial fibrillation have a 5-7 fold increased risk of having an ischemic stroke. In these cases, the most common site of thrombus localization is inside the left atrial appendage (LAA) and studies have shown a correlation between the LAA shape and the risk of ischemic stroke. These studies make use of manual measurement and qualitative assessment of shape and are therefore prone to large inter-observer discrepancies, which may explain the contradictions between the conclusions in different studies. We argue that quantitative shape descriptors are necessary to robustly characterize LAA morphology and relate to other functional parameters and stroke risk. Deep Learning methods are becoming standardly available for segmenting cardiovascular structures from high resolution images such as computed tomography (CT), but only few have been tested for LAA segmentation. Furthermore, the majority of segmentation algorithms produces non-smooth 3D models that are not ideal for further processing, such as statistical shape analysis or computational fluid modelling. In this paper we present a fully automatic pipeline for image segmentation, mesh model creation and statistical shape modelling of the LAA. The LAA anatomy is implicitly represented as a signed distance field (SDF), which is directly regressed from the CT image using Deep Learning. The SDF is further used for registering the LAA shapes to a common template and build a statistical shape model (SSM). Based on 106 automatically segmented LAAs, the built SSM reveals that the LAA shape can be quantified using approximately 5 PCA modes and allows the identification of two distinct shape clusters corresponding to the so-called chicken-wing and non-chicken-wing morphologies., Comment: Onsubmitted paper from 2019