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Materials with on-demand control of thermal conductivity are the prerequisites to build thermal conductivity switches, where the thermal conductivity can be turned ON and OFF. However, the ideal switch, while required to develop novel approaches to solid-state refrigeration, energy harvesting, and even phononic circuits, is still missing. It should consist of an active material only, be environment friendly, and operate near room temperature with a reversible, fast, and large switching ratio. Here, we first predict by ab initio electronic structure calculations that ferroelectric domains in barium titanate exhibit anisotropic thermal conductivities. We confirm this prediction by combining frequency-domain thermoreflectance and scanning thermal microscopy measurements on a single crystal of barium titanate. We then use this gained knowledge to propose a lead-free thermal conductivity switch without inactive material, operating reversibly with an electric field. At room temperature, we find a switching ratio of 1.6 $\pm$ 0.3, exceeding the performances of state-of-the-art materials suggested for thermal conductivity switches.

Ferroelectrics under suitable electric boundary conditions can present a negative capacitance response, whereby the total voltage drop across the ferroelectric opposes the externally applied bias. When the ferroelectric is in a heterostructure, this behavior yields a voltage amplification in the other elements, an effect that could be leveraged in low-power electronic devices. Interestingly, the mentioned voltage amplification should have an accompanying elastic effect. Specifically, in the typical case that the materials in contact with the ferroelectric are non-polar dielectrics, those should present an enhanced electrostrictive response. Here we use atomistic simulations - of model PbTiO$_{3}$/SrTiO$_{3}$ ferroelectric/dielectric superlattices displaying negative capacitance - to show that this is indeed the case: we reveal the enhanced elastic response of the dielectric layer and show that it is clearly linked to the voltage amplification. We argue that this "elastic amplification" could serve as a convenient experimental fingerprint for negative capacitance. Further, we propose that it may have interest on its own, e.g. for the development of low-power electromechanical actuators.

Science is a collaborative endeavor in which "who collaborates with whom" profoundly influences scientists' career trajectories and success. Despite its relevance, little is known about how scholars facilitate new collaborations among their peers. In this study, we quantify brokerage in academia and study its effect on the careers of physicists worldwide. We find that early-career participation in brokerage increases later-stage involvement for all researchers, with increasing participation rates and greater career impact among more successful scientists. This cumulative advantage process suggests that brokerage contributes to the unequal distribution of success in academia. Surprisingly, this affects both women and men equally, despite women being more junior in all brokerage roles and lagging behind men's participation due to their late and slow arrival to physics. Because of its cumulative nature, promoting brokerage opportunities to early career scientists might help reduce the inequalities in academic success.

The properties of functional oxide heterostructures are strongly influenced by the physics governing their interfaces. Modern deposition techniques allow us to accurately engineer the interface physics through the growth of atomically precise heterostructures. This enables minute control over the electronic, magnetic, and structural characteristics. Here, we investigate the magnetic properties of tailor-made superlattices employing the ferromagnetic and insulating double perovskites RE$_2$NiMnO$_6$ (RE = La, Nd), featuring distinct Curie temperatures. Adjusting the superlattice periodicity at the unit cell level allows us to engineer their magnetic phase diagram. Large periodicity superlattices conserve the individual para- to ferromagnetic transitions of the La$_2$NiMnO$_6$ and Nd$_2$NiMnO$_6$ parent compounds. As the superlattice periodicity is reduced, the Curie temperatures of the superlattice constituents converge and, finally, collapse into one single transition for the lowest period samples. This is a consequence of the magnetic order parameter propagating across the superlattice interfaces, as supported by a minimal Landau theory model. Further, we find that the Nd-Ni/Mn exchange interaction can be enhanced by the superlattice interfaces. This leads to a field-induced reversal of the Nd magnetic moments, as confirmed by synchrotron X-ray magnetic circular dichroism measurements and supported by first-principles calculations. Our work demonstrates how superlattice engineering can be employed to fine-tune the magnetic properties in oxide heterostructures and broadens our understanding of magnetic interfacial effects.

One of the most innovative possibilities offered by oxides is the use of heat currents for computational purposes. Towards this goal, phase-change oxides, including ferroelectrics, ferromagnets and related materials, could reproduce sources, logic units and memories used in current and future computing schemes.

Solid-state cooling presents an energy-efficient and environmentally friendly alternative to traditional refrigeration technologies that rely on thermodynamic cycles involving greenhouse gases. However, conventional caloric effects face several challenges that impede their practical application in refrigeration devices. Firstly, operational temperature conditions must align closely with zero-field phase transition points; otherwise, the required driving fields become excessively large. But phase transitions occur infrequently near room temperature. Additionally, caloric effects typically exhibit strong temperature dependence and are sizeable only within relatively narrow temperature ranges. In this study, we employ first-principles simulation methods to demonstrate that light-driven phase transitions in polar oxide perovskites have the potential to overcome such limitations. Specifically, for the prototypical ferroelectric KNbO$_{3}$ we illustrate the existence of giant photocaloric effects induced by light absorption ($\Delta S_{\rm PC} \sim 100$~J~K$^{-1}$~kg$^{-1}$ and $\Delta T_{\rm PC} \sim 10$~K) across a vast temperature range of several hundred Kelvin, encompassing room temperature. These findings are expected to be generalizable to other materials exhibiting similar polar behavior., Comment: 5 pages, 5 figures

Antiferromagnets have attracted significant attention in the field of magnonics, as promising candidates for ultralow-energy carriers for information transfer for future computing. The role of crystalline orientation distribution on magnon transport has received very little attention. In multiferroics such as BiFeO$_3$ the coupling between antiferromagnetic and polar order imposes yet another boundary condition on spin transport. Thus, understanding the fundamentals of spin transport in such systems requires a single domain, a single crystal. We show that through Lanthanum(La) substitution, a single ferroelectric domain can be engineered with a stable, single-variant spin cycloid, controllable by an electric field. The spin transport in such a single domain displays a strong anisotropy, arising from the underlying spin cycloid lattice. Our work shows a pathway to understand the fundamental origins of spin transport in such a single domain multiferroic., Comment: 15 pages, 9 figure

This work presents a methodological approach to generate realistic $\gamma$-ray light curves of pulsars, resembling reasonably well the observational ones observed by the Fermi-Large Area Telescope instrument, fitting at the same time their high-energy spectra. The theoretical light curves are obtained from a spectral and geometrical model of the synchro-curvature emission. Despite our model relies on a few effective physical parameters, the synthetic light curves present the same main features observed in the observational $\gamma$-ray light curve zoo, such as the different shapes, variety in the number of peaks, and a diversity of peak widths. The morphological features of the light curves allows us to statistically compare the observed properties. In particular, we find that the proportion on the number of peaks found in our synthetic light curves is in agreement with the observational one provided by the third Fermi-LAT pulsar catalog. We also found that the detection probability due to beaming is much higher for orthogonal rotators (approaching 100%) than for small inclination angles (less than 20%).The small variation on the synthetic skymaps generated for different pulsars indicates that the geometry dominates over timing and spectral properties in shaping the gamma-ray light curves. This means that geometrical parameters like the inclination angle can be in principle constrained by gamma-ray data alone independently on the specific properties of a pulsar. At the same time, we find that $\gamma$-ray spectra seen by different observers can slightly differ, opening the door to constraining the viewing angle of a particular pulsar., Comment: 14 pages, 8 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Society

The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear -- one possibility is that people's traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual's personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages)., Comment: 25 pages, 10 figures

Sliding ferroelectricity is a unique type of polarity recently observed in a properly stacked van der Waals bilayer. However, electric-field control of sliding ferroelectricity is hard and could induce large coercive electric fields and serious leakage currents which corrode the ferroelectricity and electronic properties, which are essential for modern two-dimensional electronics and optoelectronics. Here, we proposed laser-pulse deterministic control of sliding ferroelectricity in bilayer h-BN by first principles and molecular dynamics simulation with machine-learned force fields. The laser pulses excite shear modes which exhibit certain directional movements of lateral sliding between bilayers. The vibration of excited modes under laser pulses is predicted to overcome the energy barrier and achieve the switching of sliding ferroelectricity. Furthermore, it is found that three possible sliding transitions - between AB (BA) and BA (AB) stacking - can lead to the occurrence of dynamical magnetic fields along three different directions. Remarkably, the magnetic fields are generated by the simple linear motion of nonmagnetic species, without any need for more exotic (circular, spiral) pathways. Such predictions of deterministic control of sliding ferroelectricity and multi-states of dynamical magnetic field thus expand the potential applications of sliding ferroelectricity in memory and electronic devices.

Spin waves in magnetic materials are promising information carriers for future computing technologies due to their ultra-low energy dissipation and long coherence length. Antiferromagnets are strong candidate materials due, in part, to their stability to external fields and larger group velocities. Multiferroic aniferromagnets, such as BiFeO$_3$ (BFO), have an additional degree of freedom stemming from magnetoelectric coupling, allowing for control of the magnetic structure, and thus spin waves, with electric field. Unfortunately, spin-wave propagation in BFO is not well understood due to the complexity of the magnetic structure. In this work, we explore long-range spin transport within an epitaxially engineered, electrically tunable, one-dimensional (1D) magnonic crystal. We discover a striking anisotropy in the spin transport parallel and perpendicular to the 1D crystal axis. Multiscale theory and simulation suggests that this preferential magnon conduction emerges from a combination of a population imbalance in its dispersion, as well as anisotropic structural scattering. This work provides a pathway to electrically-reconfigurable magnonic crystals in antiferromagnets.

Nanostructured ferroelectrics display exotic multidomain configurations resulting from the electrostatic and elastic boundary conditions they are subject to. While the ferroelectric domains appear frozen in experimental images, atomistic second-principles studies suggest that they may become spontaneously mobile upon heating, with the polar order {\sl melting} in a liquid-like fashion. Here we run molecular dynamics simulations of model systems (PbTiO$_3$/SrTiO$_3$ superlattices) to study the unique features of this transformation. Most notably, we find that the multidomain state looses its translational and orientational orders at different temperatures, resembling the behavior of liquid crystals and yielding an intermediate hexatic-like phase. Our simulations reveal the mechanism responsible for the melting and allow us to characterize the stochastic dynamics in the hexatic-like phase: we find evidence that it is thermally activated, with domain reorientation rates that grow from tens of gigahertzs to terahertzs in a narrow temperature window., Comment: 7 pages, 4 figures

Runway and taxiway pavements are exposed to high stress during their projected lifetime, which inevitably leads to a decrease in their condition over time. To make sure airport pavement condition ensure uninterrupted and resilient operations, it is of utmost importance to monitor their condition and conduct regular inspections. UAV-based inspection is recently gaining importance due to its wide range monitoring capabilities and reduced cost. In this work, we propose a vision-based approach to automatically identify pavement distress using images captured by UAVs. The proposed method is based on Deep Learning (DL) to segment defects in the image. The DL architecture leverages the low computational capacities of embedded systems in UAVs by using an optimised implementation of EfficientNet feature extraction and Feature Pyramid Network segmentation. To deal with the lack of annotated data for training we have developed a synthetic dataset generation methodology to extend available distress datasets. We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios., Comment: 12 pages, 6 figures, published in proceedings of 15th International Conference on Machine Vision (ICMV)

The metastable nature of the ferroelectric phase of HfO$_2$ is a significant impediment to its industrial application as a functional ferroelectric material. In fact, no polar phases exist in the bulk phase diagram of HfO$_2$, which shows a dominant non-polar monoclinic ground state. As a consequence, ferroelectric orthorhombic HfO$_2$ needs to be kinetically stabilized. Here, we propose an alternative approach, demonstrating the feasibility of thermodynamically stabilizing polar HfO$_2$ in superlattices with other simple oxides. Using the composition and stacking direction of the superlattice as design parameters, we obtain heterostructures that can be fully polar, fully antipolar or mixed, with improved thermodynamic stability compared to the orthorhombic polar HfO$_2$ in bulk form. Our results suggest that combining HfO$_2$ with an oxide that does not have a monoclinic ground state generally drives the superlattice away from this non-polar phase, favoring the stability of the ferroelectric structures that minimize the elastic and electrostatic penalties. As such, these diverse and tunable superlattices hold promise for various applications in thin-film ferroelectric devices.

Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation. The basic assumption of FL is that the devices communicate directly or indirectly with a parameter server that centrally coordinates the whole process, overcoming several challenges associated with it. However, in highly pervasive edge scenarios, the presence of a central controller that oversees the process cannot always be guaranteed, and the interactions (i.e., the connectivity graph) between devices might not be predetermined, resulting in a complex network structure. Moreover, the heterogeneity of data and devices further complicates the learning process. This poses new challenges from a learning standpoint that we address by proposing a communication-efficient Decentralised Federated Learning (DFL) algorithm able to cope with them. Our solution allows devices communicating only with their direct neighbours to train an accurate model, overcoming the heterogeneity induced by data and different training histories. Our results show that the resulting local models generalise better than those trained with competing approaches, and do so in a more communication-efficient way., Comment: Supported by the H2020 HumaneAI Net (#952026), H2020 INFRAIA-01-2018-2019 SoBigData++ (#871042), and by the CHIST-ERA-19-XAI010 SAI projects, FWF (grant No. I 5205). Also funded by PNRR MUR Partenariato Esteso PE00000013 FAIR, PNRR MUR Partenariato Esteso PE00000001 - "RESTART"

Enabling on-demand control of heat flow is key for the development of next-generation electronic devices, solid-state heat pumps, and thermal logic. However, precise and agile tuning of the relevant microscopic material parameters for adjusting thermal conductivities remains elusive. Here, we study several single crystals of lanthanum aluminate (LaAlO$_{3}$) with different domain structures and show that ferroelastic domain walls behave as boundaries that act like efficient controllers to govern thermal conductivity. At low temperature (3 K), we demonstrate a fivefold reduction in thermal conductivity induced by domain walls orthogonal to the heat flow and a twofold reduction when they are parallel to the heat flow. Atomistic calculations fully support this experimental observation. By breaking down phonon scattering mechanisms, we also analyze the temperature dependence of the thermal conductivity to derive a quantitative relation between thermal conductivity variations and domain wall organization and density.

Recent works on electric bubbles (including the experimental demonstration of electric skyrmions) constitute a breakthrough akin to the discovery of magnetic skyrmions some 15 years ago. So far research has focused on obtaining and visualizing these objects, which often appear to be immobile (pinned) in experiments. Thus, critical aspects of magnetic skyrmions - e.g., their quasiparticle nature, Brownian motion - remain unexplored (unproven) for electric bubbles. Here we use predictive atomistic simulations to investigate the basic dynamical properties of these objects in pinning-free model systems. We show that it is possible to find regimes where the electric bubbles can present long lifetimes ($\sim$ ns) despite being relatively small ($\varnothing <$ 2 nm). Additionally, we find that they can display stochastic dynamics with large and highly tunable diffusion constants. We thus establish the quasiparticle nature of electric bubbles and put them forward for the physical effects and applications (e.g., in token-based Probabilistic Computing) considered for magnetic skyrmions., Comment: 7 pages, 4 figures

In this work we use a phenomenological theory of ferroelectric switching in BiFeO$_3$ thin films to uncover the mechanism of the two-step process that leads to the reversal of the weak magnetization of these materials. First, we introduce a realistic model of a BiFeO$_3$ film, including the Landau energy of isolated domains as well as the constraints that account for the presence of the substrate and the multidomain configuration found experimentally. We use this model to obtain statistical information about the switching behavior - by running dynamical simulations based on the Landau-Khalatnikov time-evolution equation, including thermal fluctuations - and we thus identify the factors that drive the two-step polarization reversal observed in the experiments. Additionally, we apply our model to test potential strategies for optimizing the switching characteristics., Comment: 16 pages, 9 figures

The first-principles-based effective Hamiltonian scheme provides one of the most accurate modeling technique for large-scale structures, especially for ferroelectrics. However, the parameterization of the effective Hamiltonian is complicated and can be difficult for some complex systems such as high-entropy perovskites. Here, we propose a general form of effective Hamiltonian and develop an active machine learning approach to parameterize the effective Hamiltonian based on Bayesian linear regression. The parameterization is employed in molecular dynamics simulations with the prediction of energy, forces, stress and their uncertainties at each step, which decides whether first-principles calculations are executed to retrain the parameters. Structures of BaTiO$_3$, Pb(Zr$_{0.75}$Ti$_{0.25}$)O$_3$ and (Pb,Sr)TiO$_3$ system are taken as examples to show the accuracy of this approach, as compared with conventional parametrization method and experiments. This machine learning approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered complex systems with super-large-scale (more than $10^7$ atoms) atomic structures., Comment: 11 pages, 4 figures

Rejuvenation and memory, long considered the distinguishing features of spin glasses, have recently been proven to result from the growth of multiple length scales. This insight, enabled by simulations on the Janus~II supercomputer, has opened the door to a quantitative analysis. We combine numerical simulations with comparable experiments to introduce two coefficients that quantify memory. A third coefficient has been recently presented by Freedberg et al. We show that these coefficients are physically equivalent by studying their temperature and waiting-time dependence., Comment: 10 pages, 8 figures

We unveil the multifractal behavior of Ising spin glasses in their low-temperature phase. Using the Janus II custom-built supercomputer, the spin-glass correlation function is studied locally. Dramatic fluctuations are found when pairs of sites at the same distance are compared. The scaling of these fluctuations, as the spin-glass coherence length grows with time, is characterized through the computation of the singularity spectrum and its corresponding Legendre transform. A comparatively small number of site pairs controls the average correlation that governs the response to a magnetic field. We explain how this scenario of dramatic fluctuations (at length scales smaller than the coherence length) can be reconciled with the smooth, self-averaging behavior that has long been considered to describe spin-glass dynamics., Comment: 15 pages, 15 figures

A collective excitation of the spin structure in a magnetic insulator can transmit spin-angular momentum with negligible dissipation. This quantum of a spin wave, introduced more than nine decades ago, has always been manipulated through magnetic dipoles, (i.e., timereversal symmetry). Here, we report the experimental observation of chiral-spin transport in multiferroic BiFeO3, where the spin transport is controlled by reversing the ferroelectric polarization (i.e., spatial inversion symmetry). The ferroelectrically controlled magnons produce an unprecedented ratio of up to 18% rectification at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adja-cent magnets, with a spin-torque efficiency being comparable to the spin Hall effect in heavy metals. Utilizing such a controllable magnon generation and transmission in BiFeO3, an alloxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection, and magnetoelectric control. This observation opens a new chapter of multiferroic magnons and paves an alternative pathway towards low-dissipation nanoelectronics.

We consider the properties of an observable (such as a single spin component that squares to the identity) when expressed as a matrix in the basis of energy eigenstates, and then truncated to a microcanonical slice of energies of varying width. For a quantum chaotic system, we model the unitary or orthogonal matrix that relates the spin basis to the energy basis as a random matrix selected from the appropriate Haar measure. We find that the spectrum of eigenvalues is given by a centered Jacobi distribution that approaches the Wigner semicircle of a random hermitian matrix for small slices. For slices that contain more than half the states, there is a set of eigenvalues of exactly $\pm 1$. The transition to this qualitatively different behavior at half size is similar to that seen in other quantities such as entanglement entropy. Our results serve as a benchmark model for numerical calculations in realistic physical systems., Comment: 5 pages, 5 figures

Core-periphery is a key feature of large-scale networks underlying a wide range of social, biological, and transportation phenomena. Despite its prevalence in empirical data, it is unclear whether this property is a consequence of more fundamental network evolution processes. While preferential attachment can create degree heterogeneity indistinguishable from core-periphery, it doesn't explain why specific groups of nodes gain dominance and become cores. We show that even small amounts of assortative attachment, e.g. homophily in social networks, can break this symmetry, and that the interplay of the two mechanisms leads to one of the groups emerging as a prominent core. A systematic analysis of the phase space of the proposed model reveals the levels of assortative and preferential attachment necessary for a group to become either core or periphery, depending on initial conditions. We find that relative group size is significant, with minority groups typically having a disadvantage on becoming the core for similar assortative attachment levels among groups. We also find that growing networks are less prone to develop core-periphery than dynamically evolving networks, and that these two network evolution mechanisms lead to different types of core-periphery structures. Analyzing five empirical networks, our findings suggest that core nodes are highly assortative, illustrating the potential of our model as a tool for designing and analyzing interventions on evolving networks., Comment: 5 figures

Political polarization in online social platforms is a rapidly growing phenomenon worldwide. Despite their relevance to modern-day politics, the structure and dynamics of polarized states in digital spaces are still poorly understood. We analyze the community structure of a two-layer, interconnected network of French Twitter users, where one layer contains members of Parliament and the other one regular users. We obtain an optimal representation of the network in a four-dimensional political opinion space by combining network embedding methods and political survey data. We find structurally cohesive groups sharing common political attitudes and relate them to the political party landscape in France. The distribution of opinions of professional politicians is narrower than that of regular users, indicating the presence of more extreme attitudes in the general population. We find that politically extreme communities interact less with other groups as compared to more centrist groups. We apply an empirically tested social influence model to the two-layer network to pinpoint interaction mechanisms that can describe the political polarization seen in data, particularly for centrist groups. Our results shed light on the social behaviors that drive digital platforms towards polarization, and uncover an informative multidimensional space to assess political attitudes online.

The quantum Fisher information (QFI) associated with a particular process applied to a many-body quantum system has been suggested as a diagnostic for the nature of the system's quantum state, e.g., a thermal density matrix vs. a pure state in a system that obeys the eigenstate thermalization hypothesis (ETH). We compute the QFI for both an energy eigenstate and a thermal density matrix for a variety of processes in a system obeying ETH, including a change in the hamiltonian that is either sudden (a quench), slow (adiabatic), or followed by contact with a heat bath. We compare our results with earlier results for a local unitary transformation., Comment: 8 pages, no figures

A continuum approach to study magnetoelectric multiferroic $\mathrm{BiFeO}_3$ (BFO) is proposed. Our modeling effort marries the ferroelectric (FE) phase field method and micromagnetic simulations in order to describe the entire multiferroic order parameter sector (polarization, oxygen antiphase tilts, strain, and magnetism) self-consistently on the same time and length scale. In this paper, we discuss our choice of ferroelectric and magnetic energy terms and demonstrate benchmarks against known behavior. We parameterize the lowest order couplings of the structural distortions against previous predictions from density functional theory calculations giving access to simulations of the FE domain wall (DW) topology. This allows us to estimate the energetic hierarchy and thicknesses of the numerous structural DWs. We then extend the model to the canted antiferromagnetic order and demonstrate how the ferroelectric domain boundaries influence the resulting magnetic DWs. We also highlight some capabilities of this model by providing two examples relevant for applications. We demonstrate spin wave transmission through the multiferroic domain boundaries which identify rectification in qualitative agreement with recent experimental observations. As a second example of application, we model fully-dynamical magnetoelectric switching, where we find a sensitivity on the Gilbert damping with respect to switching pathways. We envision that this modeling effort will set the basis for further work on properties of arbitrary 3D nanostructures of BFO (and related multiferroics) at the mesoscale., Comment: 15 pages (double column), 8 figures

We report second-principles simulations on the structural and energetic properties of domains in (PbTiO$_{3}$)$_{n}$/(SrTiO$_{3}$)$_{n}$ superlattices. For the explored layer thickness ($n$ ranging between 8 and 16 unit cells) and lateral sizes of the domains, the most stable configuration corresponds to polar domains separated by a sequence of counter-rotating vortices (clockwise/counterclockwise) perpendicular to the stacking direction and acting as domain walls. The balance between the domain wall energy and the electrostatic energy yields to an optimal domain period $\omega$ that is proportional to the square-root of the thickness of the PbTiO$_{3}$ layer, following the Kittel law. For a given lateral size of the simulation box, suboptimal domain structures (with a width larger than the one predicted by the Kittel law) can be obtained in a metastable form. However, at finite temperature, molecular dynamics simulations show the spontaneous change of periodicity, which implies the formation of new domains whose generation is initiated by the nucleation of vortices and antivortices at the interface between the SrTiO$_{3}$ and the PbTiO$_{3}$ layers. The vortices progressively elongate and eventually annihilate with the antivortices yielding the formation of new domains to comply the Kittel law via a topological phase transition., Comment: 7 pages, 6 Figures

When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace's equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively., Comment: Accepted at the 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023)

It was recently noted that the entanglement entropy for a subsystem of a chaotic eigenstate exhibits an enhanced correction when the subsystem approaches a phase transition at half the total system size. This enhanced correction was derived for general subsystems by Dong and Wang by summing over noncrossing permutations, which can be thought of as ``saddles'' either in a sum emerging from averaging over Wick contractions or in an analogous gravitational calculation. We extend these results to the case of entanglement negativity, an entanglement measure defined on a bipartite density matrix. We focus on a particular transition previously studied in a toy model of JT gravity, one for which the sum over permutations was found to give similar (or even stronger) enhanced corrections. We derive and resum the relevant permutations to give a form for the averaged negativity spectrum, reproducing the gravitational answer for some quantities and finding tension with other quantities, namely the partially transposed entropy. Along the way, we extend the results of Dong and Wang to the case of $n < 1$ R\'enyi entropy, showing that it always receives volume law corrections., Comment: 29+5 pages, 6 figures

Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at both the network and the individual level. Egocentric networks, networks of relationships around a focal individual, exhibit a small number of strong ties and a larger number of weaker ties, a pattern that is evident in electronic communication records, such as mobile phone calls. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and the driving mechanisms of this tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets containing records of interactions between millions of people over time periods ranging from months to years. Our findings reveal a remarkable universality in the distribution of tie strengths and their individual-level variation across different modes of communication, even in channels that may not reflect offline social relationships. With the help of an analytically tractable model of egocentric network evolution, we show that the observed universality can be attributed to the competition between cumulative advantage and random choice, two general mechanisms of tie reinforcement whose balance determines the amount of heterogeneity in tie strengths. Our results provide new insights into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior., Comment: 13 pages, 3 figures. SI: 21 pages, 10 figures

Hafnia ferroelectrics combine technological promise and unprecedented behaviors. Their peculiarity stems from the many active extrinsic mechanisms that contribute to their properties and from a continuously growing number of novel intrinsic features. Partly because of their unconventional nature, basic questions about these materials remain open and one may wonder about the pertinence of some frequent theoretical assumptions. Aided by first-principles simulations, here we show that, by adopting an original high-symmetry reference phase as the starting point of the analysis, we can develop a mathematically simple and physically transparent treatment of the ferroelectric state of hafnia. The proposed approach describes hafnia as a uniaxial ferroic, as suggested by recent studies of (woken-up) samples with well developed polarization. Also, it is compatible with the occurrence of polar soft modes and proper ferroelectric order. Further, our theory provides a straightforward and unified description of all low-energy polymorphs, shedding light into old questions (e.g., the prevalence of the monoclinic ground state), pointing at exciting possibilities (e.g., an antiferroelastic behavior) and facilitating the future development of perturbative theories (from Landau to second-principles potentials). Our work thus yields a deeper understanding of hafnia ferroelectrics, improving our ability to optimize their properties and induce new ones., Comment: 8 pages, 4 figures. Improved discussion

The wealth of complex polar topologies recently found in nanoscale ferroelectrics result from a delicate balance between the materials intrinsic tendency to develop a homogeneous polarization and the electric and mechanic boundary conditions imposed upon them. Ferroelectric dielectric interfaces are model systems where polarization curling originates from open circuit like electric boundary conditions, to avoid the build-up of polarization charges through the formation of flux closure domains that evolve into vortex like structures at the nanoscale. Interestingly, while ferroelectricity is known to couple strongly to strain (both homogeneous and inhomogeneous), the effect of mechanical constraints on thin film nanoscale ferroelectrics has been comparatively less explored because of the relative paucity of strain patterns that can be implemented experimentally. Here we show that the stacking of freestanding ferroelectric perovskite layers with controlled twist angles opens an unprecedented opportunity to tailor these topological nanostructures in a way determined by the lateral strain modulation associated to the twisting. Interestingly, we find that a peculiar pattern of polarization vortices and antivortices emerges from the flexoelectric coupling of polarization to strain gradients. This finding opens exciting opportunities to create two-dimensional high density vortex crystals that would allow us to explore novel physical effects and functionalities.

Understanding various aspects of ferroelectricity in hafnia-based nanomaterials is of vital importance for the development of future non-volatile memory and logic devices. Here, the unconventional and weak electromechanical response of epitaxial $La_{0.67}Sr_{0.33}MnO_{3}$/$Hf_{0.5}Zr_{0.5}O_{2}$/$La_{0.67}Sr_{0.33}MnO_{3}$ ferroelectric capacitors is investigated, via the sensitivity offered by nanobeam X-ray diffraction experiments during application of electrical bias. It is shown that the pristine rhombohedral phase exhibits a negative linear piezoelectric effect with piezoelectric coefficient ($d_{33}$) ~ -0.5 to -0.8 pm$V^{-1}$. First-principles calculations support an intrinsic negative piezoresponse. In addition, it is found that the piezoelectric response is suppressed above the coercive voltage. For higher voltages, and with the onset of DC conductivity throughout the capacitor, a second-order effect is observed. The electromechanical response observed in this work is clearly different from that of normal ferroelectrics, again underlining the unconventional nature of polarization switching in the samples.

The Observatorio Astrof\'isico de Javalambre (OAJ{\dag}1) in Spain is a young astronomical facility, conceived and developed from the beginning as a fully automated observatory with the main goal of optimizing the processes in the scientific and general operation of the Observatory. The OAJ has been particularly conceived for carrying out large sky surveys with two unprecedented telescopes of unusually large fields of view (FoV): the JST/T250, a 2.55m telescope of 3deg field of view, and the JAST/T80, an 83cm telescope of 2deg field of view. The most immediate objective of the two telescopes for the next years is carrying out two unique photometric surveys of several thousands square degrees, J-PAS{\dag}2 and J-PLUS{\dag}3, each of them with a wide range of scientific applications, like e.g. large structure cosmology and Dark Energy, galaxy evolution, supernovae, Milky Way structure, exoplanets, among many others. To do that, JST and JAST are equipped with panoramic cameras under development within the J-PAS collaboration, JPCam and T80Cam respectively, which make use of large format (~ 10k x 10k) CCDs covering the entire focal plane. This paper describes in detail, from operations point of view, a comparison between the detailed cost of the global automation of the Observatory and the standard automation cost for astronomical facilities, in reference to the total investment and highlighting all benefits obtained from this approach and difficulties encountered. The paper also describes the engineering development of the overall facilities and infrastructures for the fully automated observatory and a global overview of current status, pinpointing lessons learned in order to boost observatory operations performance, achieving scientific targets, maintaining quality requirements, but also minimizing operation cost and human resources., Comment: Global Observatory Control System GOCS