Search

Spot patterns, in which vegetation patches form a hexagonal lattice, are frequent in nature and could serve as an early-warning indicator of abrupt vegetation collapses. Consequently, they have been intensively studied using both individual-based models and density-based field equations. Yet, the relationship between these two approaches remains unclear, particularly in scenarios where vegetation dynamics exhibit strong long-range spatial correlations and traditional mean-field approximations fail. To solve this issue, we develop a new method that refines mean-field approximations by describing both the dynamics of the biomass density field and its spatial correlations. This new approach harnesses the strengths of both individual and density-based mdoels, treating spatial correlations explicitly and allowing for the identification of spatial instabilities resulting in periodic patterns. Our results indicate that this new approximation predicts the parameter regimes where regular periodic patterns emerge more accurately than mean-field models, suggesting that it could provide a more robust framework to perform further nonlinear analysis.

Despite significant advancements in deep probabilistic models, learning low-dimensional discrete latent representations remains a challenging task. In this paper, we introduce a novel method that enhances variational inference in discrete latent variable models by leveraging Error Correcting Codes (ECCs) to introduce redundancy in the latent representations. This redundancy is then exploited by the variational posterior to yield more accurate estimates, thereby narrowing the variational gap. Inspired by ECCs commonly used in digital communications and data storage, we demonstrate proof-of-concept using a Discrete Variational Autoencoder (DVAE) with binary latent variables and block repetition codes. We further extend this idea to a hierarchical structure based on polar codes, where certain latent bits are more robustly protected. Our method improves generation quality, data reconstruction, and uncertainty calibration compared to the uncoded DVAE, even when trained with tighter bounds such as the Importance Weighted Autoencoder (IWAE) objective. In particular, we demonstrate superior performance on MNIST, FMNIST, CIFAR10, and Tiny ImageNet datasets. The general approach of integrating ECCs into variational inference is compatible with existing techniques to boost variational inference, such as importance sampling or Hamiltonian Monte Carlo. We also outline the key properties ECCs must have to effectively enhance discrete variational inference.

Asymptotically log Fano pairs were introduced by Cheltsov and Rubinstein, generalising a definition of Maeda. They have received attention in the last decade within the theory of K-stability, as they approximate log Calabi Yau pairs while staying in the log Fano setting. In this note, written for the ZAG Proceedings, we summarise our talk on 1st September 2020 (Day of Knowledge) given on Zoom during the 24-hour ZAG Marathon. In the talk, we reported on joint work with P. Cascini and Y. Rubinstein, on the classification of the two-dimensional case, known as asymptotically log del Pezzo pairs., Comment: For publication on the ZAG seminar proceedings. 3 pages

Frequent monitoring is necessary to stratify individuals based on their likelihood of developing gastrointestinal (GI) cancer precursors. In clinical practice, white-light imaging (WLI) and complementary modalities such as narrow-band imaging (NBI) and fluorescence imaging are used to assess risk areas. However, conventional deep learning (DL) models show degraded performance due to the domain gap when a model is trained on one modality and tested on a different one. In our earlier approach, we used a superpixel-based method referred to as "SUPRA" to effectively learn domain-invariant information using color and space distances to generate groups of pixels. One of the main limitations of this earlier work is that the aggregation does not exploit structural information, making it suboptimal for segmentation tasks, especially for polyps and heterogeneous color distributions. Therefore, in this work, we propose an approach for style-content disentanglement using instance normalization and instance selective whitening (ISW) for improved domain generalization when combined with SUPRA. We evaluate our approach on two datasets: EndoUDA Barrett's Esophagus and EndoUDA polyps, and compare its performance with three state-of-the-art (SOTA) methods. Our findings demonstrate a notable enhancement in performance compared to both baseline and SOTA methods across the target domain data. Specifically, our approach exhibited improvements of 14%, 10%, 8%, and 18% over the baseline and three SOTA methods on the polyp dataset. Additionally, it surpassed the second-best method (EndoUDA) on the Barrett's Esophagus dataset by nearly 2%.

Many natural phenomena are quantified by counts of observable events, from the annihilation of quasiparticles in a lattice to predator-prey encounters on a landscape to spikes in a neural network. These events are triggered at random intervals when an underlying dynamical system occupies a set of reactive states in its phase space. We derive a general expression for the distribution of times between events in such counting processes assuming the underlying triggering dynamics is a stochastic process that converges to a stationary distribution. Our results contribute to resolving a long-standing dichotomy in the study of reaction-diffusion processes, showing the inter-reaction point process interpolates between a reaction- and a diffusion-limited regime. At low reaction rates, the inter-reaction process is Poisson with a rate depending on stationary properties of the event-triggering stochastic process. At high reaction rates, inter-reaction times are dominated by the hitting times to the reactive states. To further illustrate the power of this approach we apply our framework to obtain the counting statistics of two counting processes appearing in several biophysical scenarios. First, we study the common situation of estimating an animal's activity level by how often it crosses a detector, showing that the mean number of crossing events can decrease monotonically with the hitting rate, a seemingly 'paradoxical' result that could possibly lead to misinterpretation of experimental count data. Second, we derive the ensemble statistics for the detection of many particles, recovering and generalizing known results in the biophysics of chemosensation. Overall, we develop a unifying theoretical framework to quantify inter-event time distributions in reaction-diffusion systems that clarifies existing debates in the literature and provide examples of application to real-world scenarios., Comment: 14 pages, 3 figures

We introduce a variational algorithm based on Matrix Product States that is trained by minimizing a generalized free energy defined using Tsallis entropy instead of the standard Gibbs entropy. As a result, our model can generate the probability distributions associated with generalized statistical mechanics. The resulting model can be efficiently trained, since the resulting free energy and its gradient can be calculated exactly through tensor network contractions, as opposed to standard methods which require estimating the Gibbs entropy by sampling. We devise a variational annealing scheme by ramping up the inverse temperature, which allows us to train the model while avoiding getting trapped in local minima. We show the validity of our approach in Ising spin-glass problems by comparing it to exact numerical results and quasi-exact analytical approximations. Our work opens up new possibilities for studying generalized statistical physics and solving combinatorial optimization problems with tensor networks., Comment: 6 pages, 6 figures + Supplementary material (5 pages, 5 figures)

Populations very often self-organize into regular spatial patterns with important ecological and evolutionary consequences. Yet, most existing models neglect the effect that external biophysical drivers might have both on pattern formation and the spatiotemporal population dynamics once patterns form. Here, we investigate the effect of environmental flows on pattern formation and population dynamics using a spatially nonlocal logistic model (or Fisher-Kolmogorov equation) coupled to a simple shear and a Rankine vortex flow. We find that, whereas population abundance generally decreases with increasing flow intensity, the effect of the flow on the pattern instability depends on the spatial structure of the flow velocity field. This result shows that the velocity field interacts with the spatial feedbacks responsible for pattern formation in non-trivial ways, leading to a variety of spatiotemporal population dynamics regimes in which the total population abundance can exhibit either regular oscillations with a characteristic frequency or more erratic dynamics without a well-defined period. More generally, the diversity of spatiotemporal population dynamics caused by the interplay between self-organizing feedbacks and environmental flows highlights the importance of incorporating environmental and biophysical processes when studying both ecological pattern formation and its consequences., Comment: 27 pages, 10 figures

We study the K-moduli of log del Pezzo pairs formed by a del Pezzo surface of degree $d$ and an anti-canonical divisor. These moduli spaces naturally depend on one parameter, providing a natural problem in variations of K-moduli spaces. For degrees 2, 3, 4, we establish an isomorphism between the K-moduli spaces and variations of Geometric Invariant Theory compactifications, which generalizes the isomorphisms in the absolute cases established by Odaka--Spotti--Sun and Mabuchi--Mukai., Comment: 41 pages, 6 tables. Comments are very welcome!

Today, it would be difficult for us to live a full life without polymers, especially in medicine, where its applicability is constantly expanding, giving satisfactory results without any harm effects on health. This study focused on the formation of hexagonal domains doped with AgNPs using a KrF excimer laser ({\lambda}=248 nm) on the polyetheretherketone (PEEK) surface that acts as an unfailing source of the antibacterial agent - silver. The hexagonal structure was formed with a grid placed in front of the incident laser beam. Surfaces with immobilized silver nanoparticles (AgNPs) were observed by AFM and SEM. Changes in surface chemistry were studied by XPS. To determine the concentration of released Ag+ ions, ICP-MS analysis was used. The antibacterial tests proved the antibacterial efficacy of Ag-doped PEEK composites against Escherichia coli and Staphylococcus aureus as the most common pathogens. Because AgNPs are also known for their strong toxicity, we also included cytotoxicity tests in this study. The findings presented here contribute to the advancement of materials design in the biomedical field, offering a novel starting point for combating bacterial infections through the innovative integration of AgNPs into inert synthetic polymers.

The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images from a diverse set of 462 distinct individuals in two conditions: original and attractive after applying a beauty filter. Our study reveals that the same individuals receive statistically significantly higher ratings of attractiveness and other traits, such as intelligence and trustworthiness, in the attractive condition. We also study the impact of age, gender, and ethnicity and identify a weakening of the halo effect in the beautified condition, resolving conflicting findings from the literature and suggesting that filters could mitigate this cognitive bias. Finally, our findings raise ethical concerns regarding the use of beauty filters., Comment: 53 pages, 15 figures, 13 tables

The development and use of large-scale quantum computers relies on integrating quantum error-correcting (QEC) schemes into the quantum computing pipeline. A fundamental part of the QEC protocol is the decoding of the syndrome to identify a recovery operation with a high success rate. In this work, we implement a decoder that finds the recovery operation with the highest success probability by mapping the decoding problem to a spin system and using Population Annealing to estimate the free energy of the different error classes. We study the decoder performance on a 4.8.8 color code lattice under different noise models, including code capacity with bit-flip and depolarizing noise, and phenomenological noise, which considers noisy measurements, with performance reaching near-optimal thresholds. This decoding algorithm can be applied to a wide variety of stabilizer codes, including surface codes and quantum low-density parity-check (qLDPC) codes., Comment: 11 pages, 9 figures

Helical molecules have been identified as potential candidates for investigating electronic transport, spin filtering, or even piezoelectricity. However, the description of the transport mechanism is not straightforward in single molecular junctions. In this work, we study the electronic transport in break junctions of a series of three helical molecules: dithia[$n$]helicenes, with $n=7, 9, 11$ molecular units, and detail the synthesis of two kinds of dithia[11]helicenes, varying the location of the sulfur atoms. Our experimental study demonstrates low conductance values that remain similar across different biases and molecules. Additionally, we assess the length dependence of the conductance for each helicene, revealing an exponential decay characteristic of off-resonant transport. This behaviour is primarily attributed to the misalignment between the energy levels of the molecule-electrodes system. The length dependence trend described above is supported by \textit{ab initio} calculations, further confirming the off-resonant transport mechanism.

The emergence of a spatially-organized population distribution depends on the dynamics of the population and mediators of interaction (activators and inhibitors). Two broad classes of models have been used to investigate when and how self-organization is triggered, namely, reaction-diffusion and spatially nonlocal models. Nevertheless, these models implicitly assume smooth propagation scenarios, neglecting that individuals many times interact by exchanging short and abrupt pulses of the mediating substance. A recently proposed framework advances in the direction of properly accounting for these short-scale fluctuations by applying a coarse-graining procedure on the pulse dynamics. In this paper, we generalize the coarse-graining procedure and apply the extended formalism to new scenarios in which mediators influence individuals' reproductive success or their motility. We show that, in the slow- and fast-mediator limits, pulsed interactions recover, respectively, the reaction-diffusion and nonlocal models, providing a mechanistic connection between them. Furthermore, at each limit, the spatial stability condition is qualitatively different, leading to a timescale-induced transition where spatial patterns emerge as mediator dynamics becomes sufficiently fast.

The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and interval-valued functional data are considered as inputs in an ordinal classification problem. Six ordinal classifiers for interval data and interval-valued functional data are proposed. Three of them are parametric, one of them is based on ordinal binary decompositions and the other two are based on ordered logistic regression. The other three methods are based on the use of distances between interval data and kernels on interval data. One of the methods uses the weighted $k$-nearest-neighbor technique for ordinal classification. Another method considers kernel principal component analysis plus an ordinal classifier. And the sixth method, which is the method that performs best, uses a kernel-induced ordinal random forest. They are compared with na\"ive approaches in an extensive experimental study with synthetic and original real data sets, about human global development, and weather data. The results show that considering ordering and interval-valued information improves the accuracy. The source code and data sets are available at https://github.com/aleixalcacer/OCFIVD.

We introduce a new subclass of Fano varieties (Casagrande-Druel varieties), that are $n$-dimensional varieties constructed from Fano double covers of dimension $n-1$. We conjecture that a Casagrande-Druel variety is K-polystable if the double cover and its base space are K-polystable. We prove this for smoothable Casagrande-Druel threefolds, and for Casagrande-Druel varieties constructed from double covers of $\mathbb{P}^{n-1}$ ramified over smooth hypersurfaces of degree $2d$ with $n>d>\frac{n}{2}>1$. As an application, we describe the connected components of the K-moduli space parametrizing smoothable K-polystable Fano threefolds in the families 3.9 and 4.2 in the Mori-Mukai classification., Comment: 58 pages

By identifying K-polystable limits in 4 specific deformations families of smooth Fano 3-folds, we complete the classification of one-dimensional components in the K-moduli space of smoothable Fano 3-folds., Comment: 30 pages

We describe three algorithms to determine the stable, semistable, and torus-polystable loci of the GIT quotient of a projective variety by a reductive group. The algorithms are efficient when the group is semisimple. By using an implementation of our algorithms for simple groups, we provide several applications to the moduli theory of algebraic varieties, including the K-moduli of algebraic varieties, the moduli of algebraic curves and the Mukai models of the moduli space of curves for low genus. We also discuss a number of potential improvements and some natural open problems arising from this work., Comment: 32 pages, 3 figures. 1 table

In this work we identify coherent electron-vibron interactions between near-resonant and non-resonant electronic levels that contribute beyond standard optomechanical models for off-resonant or resonance SERS. By developing an open-system quantum model using first molecular interaction principles, we show how the Raman interference of both resonant and non-resonant contributions can provide several orders of magnitude modifications of the SERS peaks with respect to former optomechanical models and over the fluorescence backgrounds. This cooperative optomechanical mechanism allows for generating an enhancement of nonclassical photon pair correlations between Stokes and anti-Stokes photons, which can be detected by photon-counting measurements. Our results demonstrate Raman enhancements and suppressions of coherent nature that significantly impact the standard estimations of the optomechanical contribution from SERS spectra and their quantum mechanical observable effects.

We present a kinematic study of a thousand of dwarf satellites of MW/M31-like hosts from the IllustrisTNG50 simulation. Internal kinematics were derived for all the snapshots to obtain a historical record of their rotation velocity in the plane of the sky ($|V_T|$) and the amplitude of their velocity gradients along the line of sight ($A_{\rm grad}^{v_z}$) measured from the host. For the majority of the satellites we initially detected rotation in the plane of the sky (65%) or velocity gradients (80%), and this was progressively reduced to 45% and 68% at $z = 0$ respectively. We find that the evolution of the rotation in the plane of the sky and the velocity gradients differs according to type of dwarfs, which could be explained in terms of their different masses and orbital histories. We observe that interaction with the host has an impact on the evolution of the internal kinematics of the satellites. The rotation signal of the satellites is progressively reduced during pericentric passages, the first pericentre being especially disruptive for the initial kinematics. We observe temporary increases in $A_{\rm grad}^{v_z}$ during pericentric passage caused by tidal interaction with the host, $A_{\rm grad}^{v_z}$ increasing as the satellites approach their pericentre and dropping as they move away. In summary, we conclude that the presence of detectable rotation in dwarf satellites is not uncommon, and that the evolution of their internal kinematics is clearly affected by their interaction with the host., Comment: 12 pages, 9 figures, 2 tables. Accepted for publication in MNRAS

We have measured, at various wavelengths, the spiral arm pitch angles of a sample of distant spiral galaxies from the Hubble Space Telescope eXtreme Deep Field (XDF). According to density wave theory, we should detect colour jumps from red-to-blue across the spiral arms. Colour jumps are a consequence of large-scale shocks, which also generate the classic blue-to-red age/colour gradients, and have only been detected until now in nearby spiral galaxies. Our results indicate that colour jumps and gradients have been occurring in distant galaxies for at least the last 8 Gyr, in agreement with density wave theory., Comment: Accepted for publication in MNRAS

Ecologists have long investigated how demographic and movement parameters determine the spatial distribution and critical habitat size of a population. However, most models oversimplify movement behavior, neglecting how landscape heterogeneity influences individual movement. We relax this assumption and introduce a reaction-advection-diffusion equation that describes population dynamics when individuals exhibit space-dependent movement bias toward preferred regions. Our model incorporates two types of these preferred regions: a high-quality habitat patch, termed `habitat', which is included to model avoidance of degraded habitats like deforested regions; and a preferred location, such as a chemoattractant source or a watering hole, that we allow to be asymmetrically located with respect to habitat edges. In this scenario, the critical habitat size depends on both the relative position of the preferred location and the movement bias intensities. When preferred locations are near habitat edges, the critical habitat size can decrease when diffusion increases, a phenomenon called the drift paradox. Also, ecological traps arise when the habitat overcrowds due to excessive attractiveness or the preferred location is near a low-quality region. Our results highlight the importance of species-specific movement behavior and habitat preference as drivers of population dynamics in fragmented landscapes and, therefore, in the design of protected areas., Comment: 21 pages, 8 figures

Intraspecific interactions are key drivers of population dynamics because they establish relations between individual fitness and population density. The component Allee effect is defined as a positive correlation between any fitness component of a focal organism and population density, and it can lead to positive density dependence in the population per capita growth rate. The spatial structure is key to determining whether and to which extent a component Allee effect will manifest at the demographic level because it determines how individuals interact with one another. However, existing spatial models to study the Allee effect impose a fixed spatial structure, which limits our understanding of how a component Allee effect and the spatial dynamics jointly determine the existence of demographic Allee effects. To fill this gap, we introduce a spatially-explicit theoretical framework where spatial structure and population dynamics are emergent properties of the individual-level demographic rates. Depending on the intensity of the individual processes the population exhibits a variety of spatial patterns that determine the demographic-level by-products of an existing individual-level component Allee effect. We find that aggregation increases population abundance and allows populations to survive in harsher environments and at lower global population densities when compared with uniformly distributed organisms. Moreover, aggregation can prevent the component Allee effect from manifesting at the population level or restrict it to the level of each independent group. These results provide a mechanistic understanding of how component Allee effects operate for different spatial population structures and show at the population level. Our results contribute to better understanding population dynamics in the presence of Allee effects and can potentially inform population management strategies.

We classify non-factorial nodal Fano threefolds with $1$ node and class group of rank $2$., Comment: 14 pages. Minor changes; improved exposition

In the field of molecular electronics, particularly in quantum transport studies, the orientation of molecules plays a crucial role. This orientation, with respect to the electrodes, can be defined through the cavity of ring-shaped monocyclic hydrocarbon molecules. In this manuscript, we unveil the geometrical conformation of these molecules when they are trapped between two atomically sharp electrodes through a combination of dynamic simulations, electronic transport calculations based on density functional theory, and break junction experiments under room conditions. Moreover, we present a novel criterion for determining the molecular orientation of benzene, toluene, (aromatic) and cyclohexane (aliphatic) solvents. Our findings for the identification of the molecular orientations on gold metal nanocontacts and their associated transport properties, can improve the understanding of molecular electronics using more complex cyclic hydrocarbons., Comment: 8 pages,9 figures, suplemental material included

Combinatorial optimization problems are ubiquitous in industry. In addition to finding a solution with minimum cost, problems of high relevance involve a number of constraints that the solution must satisfy. Variational quantum algorithms have emerged as promising candidates for solving these problems in the noisy intermediate-scale quantum stage. However, the constraints are often complex enough to make their efficient mapping to quantum hardware difficult or even infeasible. An alternative standard approach is to transform the optimization problem to include these constraints as penalty terms, but this method involves additional hyperparameters and does not ensure that the constraints are satisfied due to the existence of local minima. In this paper, we introduce a new method for solving combinatorial optimization problems with challenging constraints using variational quantum algorithms. We propose the Multi-Objective Variational Constrained Optimizer (MOVCO) to classically update the variational parameters by a multiobjective optimization performed by a genetic algorithm. This optimization allows the algorithm to progressively sample only states within the in-constraints space, while optimizing the energy of these states. We test our proposal on a real-world problem with great relevance in finance: the Cash Management problem. We introduce a novel mathematical formulation for this problem, and compare the performance of MOVCO versus a penalty based optimization. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima that do not satisfy any of the mandatory constraints., Comment: 19 pages, 9 figures + appendix (4 pages, 2 figures)

Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several mental and metabolic disorders, constituting an essential marker for preventing, evaluating, and treating different health conditions. Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. In this work, we propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner, in contrast to traditional sleep assessment methods that usually rely on intrusive and subjective procedures. A Heterogeneous Hidden Markov Model is used to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by tested wearables, proving the effectiveness of the proposed approach and advocating its use to assess sleep without more reliable sources., Comment: v1.0

Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes. Frequently, sleep disturbances and mental health deterioration are closely related, as mental health condition worsening regularly entails shifts in the patients' circadian rhythms. Therefore, Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles and to detect behavioural changes among them. Moreover, mobile passively sensed data captured from smartphones, thanks to these devices' ubiquity, constitute an excellent alternative to profile patients' biorhythm. In this work, we aim to identify major sleep episodes based on passively sensed data. To do so, a Heterogeneous Hidden Markov Model is proposed to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by clinically tested wearables, proving the effectiveness of the proposed approach., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 10 pages (6 pages + 4 pages of references and appendices)

Domain shift is a well-known problem in the medical imaging community. In particular, for endoscopic image analysis where the data can have different modalities the performance of deep learning (DL) methods gets adversely affected. In other words, methods developed on one modality cannot be used for a different modality. However, in real clinical settings, endoscopists switch between modalities for better mucosal visualisation. In this paper, we explore the domain generalisation technique to enable DL methods to be used in such scenarios. To this extend, we propose to use super pixels generated with Simple Linear Iterative Clustering (SLIC) which we refer to as "SUPRA" for SUPeRpixel Augmented method. SUPRA first generates a preliminary segmentation mask making use of our new loss "SLICLoss" that encourages both an accurate and color-consistent segmentation. We demonstrate that SLICLoss when combined with Binary Cross Entropy loss (BCE) can improve the model's generalisability with data that presents significant domain shift. We validate this novel compound loss on a vanilla U-Net using the EndoUDA dataset, which contains images for Barret's Esophagus and polyps from two modalities. We show that our method yields an improvement of nearly 20% in the target domain set compared to the baseline., Comment: This work has been accepted at the LatinX in Computer Vision Research Workshop at CVPR 2023

Marine plankton play a crucial role in carbon storage, oxygen production, global climate, and ecosystem function. Planktonic ecosystems are embedded in a Lagrangian patches of water that are continuously moving, stretching, and diluting. These processes drive inhomegeneities on a range of scales, with implications for the integrated ecosystem properties, but are hard to characterize. We present a theoretical framework which accounts for all these aspects; tracking the water patch hosting a drifting ecosystem along with its physical, environmental, and biochemical features. The model resolves patch dilution and internal physical mixing as a function of oceanic strain and diffusion. Ecological dynamics are parameterized by an idealized nutrient and phytoplankton population and we specifically capture the propagation of the biochemical spatial variances to represent within-patch heterogeneity. We find that, depending only on the physical processes to which the water patch is subjected, the plankton biomass response to a resource perturbation can vary several fold. This work indicates that we must account for these processes when interpreting and modelling marine ecosystems and provides a framework with which to do so.

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery., Comment: This paper is to be submitted to International journal of computer vision

We present a kinematic study of six dwarf spheroidal galaxies (dSph) satellites of the Milky Way (MW), namely Carina, Draco, Fornax, Sculptor, Sextans, and Ursa Minor. We combine proper motions (PMs) from the $Gaia$ Data Release 3 (DR3) and line-of-sight velocities ($v_{\mathrm{los}}$) from the literature to derive their 3D internal kinematics and to study the presence of internal velocity gradients. We find velocity gradients along the line-of-sight for Carina, Draco, Fornax, and Ursa Minor, at $\geq 1\sigma$ level of significance . The value of such gradients appears to be related to the orbital history of the dwarfs, indicating that the interaction with the MW is causing them. Dwarfs that are close to the MW and moving towards their orbits pericentres show, on average, larger velocity gradients. On the other hand, dwarfs that have recently left their orbits pericentres show no significant gradients. Lastly, dwarfs located at large Galactocentric distances show gradients with an intermediate intensity. Our results would indicate that the torque caused by the strong tidal forces exerted by the MW induces a strong velocity gradient when the dwarfs approach their orbits pericentres. During the pericentre passage, the rapid change in the forces direction would disrupt such gradient, which may steadily recover as the galaxies recede. We assess our findings by analysing dwarfs satellites from the TNG50 simulation. We find a significant increase in the intensity of the detected gradients as the satellites approach their pericentre, followed by a sharp drop as they abandon it, supporting our results for the dSphs of the MW., Comment: 13 pages, 9 figures, 1 table. Accepted for publication in MNRAS

We report a comparative theoretical study of the \textit{Pnma} and \textit{R-3m} phases of Sb$_2$S$_3$, Bi$_2$S$_3$, and Sb$_2$Se$_3$ close to ambient pressure. Our enthalpy calculations at 0 K show that at ambient pressure the \textit{R-3m} (tetradymite-like) phase of Sb$_2$Se$_3$ is energetically more stable than the \textit{Pnma} phase, contrary to what is observed for Sb$_2$S$_3$ and Bi$_2$S$_3$, and irrespective of the exchange-correlation functional employed in the calculations. The result for Sb$_2$Se$_3$ is in contradiction to experiments where all three compounds are usually grown in the \textit{Pnma} phase. This result is further confirmed by free-energy calculations taking into account the temperature dependence of the unit-cell volumes and phonon frequencies. Lattice dynamics and elastic tensor calculations further show that both \textit{Pnma} and \textit{R-3m} phases of Sb$_2$Se$_3$ are dynamically and mechanically stable at zero applied pressure. Since these results suggest that the formation of the \textit{R-3m} phase for Sb$_2$Se$_3$ should be feasible at close to ambient conditions, we provide a theoretical crystal structure and simulated Raman and infrared spectra to help in its identification. We also discuss the results of the two published works that have claimed to have synthesized tetradymite-like Sb$_2$Se$_3$. Finally, the stability of the \textit{R-3m} phase across the three group-15 \textit{A$_2$X$_3$} sesquichalcogenides is analysed based on their van der Waals gap and X-X in-plane geometry., Comment: 17 pages, 9 figures