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This is a case study produced by Andrea Pacheco, Protection Officer at Oxfam in Venezuela, and Yordy Bermúdez, Monitoring, Evaluation, Accountability and Learning (MEAL) Officer at Azul Positivo, as part of the Resource Pack on Community-Based Protection. The document discusses access to services, community identification, COVID-19, negative coping strategies, referrals and service mapping.

This paper presents a computational model, based on the Finite Element Method (FEM), that simulates the thermal response of laser-irradiated tissue. This model addresses a gap in the current ecosystem of surgical robot simulators, which generally lack support for lasers and other energy-based end effectors. In the proposed model, the thermal dynamics of the tissue are calculated as the solution to a heat conduction problem with appropriate boundary conditions. The FEM formulation allows the model to capture complex phenomena, such as convection, which is crucial for creating realistic simulations. The accuracy of the model was verified via benchtop laser-tissue interaction experiments using agar tissue phantoms and ex-vivo chicken muscle. The results revealed an average root-mean-square error (RMSE) of less than 2 degrees Celsius across most experimental conditions., Comment: Submitted to the International Symposium on Medical Robotics 2025

Automated definition generation systems have been proposed to support vocabulary expansion for language learners. The main barrier to the success of these systems is that learners often struggle to understand definitions due to the presence of potentially unfamiliar words and grammar, particularly when non-standard language is involved. To address these challenges, we propose CLIX, the task of Cross-Lingual explanations of Idiomatic eXpressions. We explore the capabilities of current NLP models for this task, and observe that while it remains challenging, large language models show promise. Finally, we perform a detailed error analysis to highlight the key challenges that need to be addressed before we can reliably incorporate these systems into educational tools.

Due to the Covid-19 pandemic, employees from around the world were compelled to work remotely from home and, in many cases, without much preparation. A substantial body of international research has been conducted on the experiences and attitudes of remote workers as well as the implications of this phenomenon for organisations. While New Zealand research evidence is growing, most existing inquiry is qualitative. This paper provides a quantitative snapshot of remote working using survey data from participants whose jobs can be done from home (n=415). Data collection took place when the country was facing Covid-related measures. Based on descriptive and inferential statistics, it was found that, not only was remote working common, but that hybrid working arrangements were also more prevalent. While half of the participants wanted to work from home more frequently, age, but not gender, was significantly associated with this preference. Another relevant finding is that perceived change in the workplace culture due to flexible work arrangements was significantly associated with preference for working remotely more often. Finally, the most common perceived barriers to working from home were slow internet speed, the need to attend face-to-face meetings, and limited space at home to work. The implications of the results are discussed and some directions for future research are proposed., Comment: 20 pages, 6 tables, 1 figure

This article proposes a framework for estimating temperature fields in food-freezing applications that significantly reduces computational load while ensuring accurate temperature monitoring, representing a promising technological tool for optimizing and controlling food engineering processes. The strategy is based on (i) a mathematical model of a convection-dominated problem coupling thermal convection and turbulence and (ii) a least-squares approach for solving the inverse data assimilation problem, regularized by projecting the governing dynamics onto a reduced-order model (ROM). The unsteady freezing process considers an idealized salmon slice in a freezer cabinet, modeled with temperature-dependent thermophysical properties. The forward problem is approximated using a third-order WENO finite volume solver, including an optimized second-order backward scheme for time discretization. We employ our data assimilation framework to reconstruct the temperature field from a limited number of sensor data and to estimate temperature distributions within frozen food. Sensor placement is optimized using a new greedy algorithm, relying on maximizing the observability of the reduced-order dynamics for a fixed set of sensors. The proposed approach allows efficient extrapolation from external sensor measurements to the internal temperature of the food, which is crucial for maintaining food quality.

Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on threat modeling frameworks, including STRIDE, DREAD, and MITRE ATT\&CK, to systematically identify and mitigate potential risks. The survey examines key components of AV architectures, such as sensors, communication modules, and electronic control units (ECUs), and explores common attack vectors like wireless communication exploits, sensor spoofing, and firmware vulnerabilities. Through case studies of real-world incidents, such as the Jeep Cherokee and Tesla Model S exploits, the paper highlights the critical need for robust security measures. Emerging technologies, including blockchain for secure Vehicle-to-Everything (V2X) communication, AI-driven threat detection, and secure Over-The-Air (OTA) updates, are discussed as potential solutions to mitigate evolving threats. The paper also addresses legal and ethical considerations, emphasizing data privacy, user safety, and regulatory compliance. By combining threat modeling frameworks, multi-layered security strategies, and proactive defenses, this survey offers insights and recommendations for enhancing the cybersecurity of autonomous vehicles.

Urban vibrancy is an important measure of the energetic nature of a city that is related to why and how people use urban spaces, and it is inherently connected with our social behaviour. Increasingly, people use a wide range of mobile phone apps in their daily lives to connect socially, search for information, make decisions, and arrange travel, amongst many other reasons. However, the relationship between online app usage and urban vibrancy remains unclear, particularly regarding how sociospatial behaviours interact with urban features. Here, we use app-usage data as a digital signature to investigate this question. To do this, we use a high-resolution data source of mobile service-level traffic volumes across eighteen cities in France. We investigate the social component of cities using socially relevant urban features constructed from OpenStreetMap 'Points of Interest'. We developed a methodology for identifying and classifying multidimensional app usage time series based on similarity. We used these in predictive models to interpret the results for each city and across France. Across cities, there were spatial behavioural archetypes, characterised by multidimensional properties. We found patterns between the week and the weekend, and across cities, and the country. These archetypes correspond to changes in socially relevant urban features that impact urban vibrancy. Our results add further evidence for the importance of using computational approaches to understand urban environments, the use of sociological concepts in computational science, and urban vibrancy in cities., Comment: 40 pages, 10 figures. Submitted to Computers, Environment and Urban Systems

Magnetic skyrmions are chiral spin textures with rich physics and great potential for unconventional computing. Typically, skyrmions form in bulk crystals with reduced symmetry or ultrathin film multilayers involving heavy metals. Here, we demonstrate the formation of fractional Bloch skyrmion tubes at room temperature by 3D printing ferromagnetic double-helix nanowires with two regions of opposite chirality. Using X-ray microscopy and micromagnetic simulations, we show that the coexistence of vortex and anti-parallel spin states induces the formation of fractional skyrmion tubes at zero magnetic fields, minimising the energy cost of breaking the coupling between geometric and magnetic chirality. We also demonstrate control over zero-field states, including pure vortex, or mixed skyrmion-vortex states, highlighting the magnetic reconfigurability of these 3D nanowires. This work shows how interfacing chiral geometries at the nanoscale can enable advanced forms of topological spintronics.

Obstructed atomic phases, with their realizations in systems of diverse dimensionality, have recently arisen as one of the topological states with greatest potential to show higher-order phenomena. In this work we report a special type of obstruction, known as orbital-mediated atomic obstruction, in monolayers of materials with spatial symmetry described by the space group P-3m1. By means of a minimal tight-binding model and first-principles calculations, we show that this obstructed phase is related to the mismatch of the charge centers coming from the atomic limit with respect to the centers that are obtained from the reciprocal space description. Although we find atomic limits that correspond with occupied atomic sites, orbital-mediated atomic obstruction requires the presence of orbitals that have no support in real space. In order to demonstrate the nontrivial character of the obstruction, we confirm the presence of a filling anomaly for finite geometries that is directly associated with the bulk configuration, and discuss the role of the boundary states and their underlying mechanism. Several material examples are presented to illustrate the ubiquity of these nontrivial responses and, in turn, to discuss the differences related to the particular ground state configuration. In addition, we perform a survey of materials and elaborate a list of candidate systems which will host this obstructed phase in monolayer form., Comment: Supplementary Material in preparation

Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast real-time trajectory generation approach for soft robot manipulators, which creates dynamically-feasible motions for arbitrary kinematically-feasible paths of the robot's end effector. Our insight is that piecewise constant curvature (PCC) dynamics models of soft robots can be differentially flat, therefore control inputs can be calculated algebraically rather than through a nonlinear differential equation. We prove this flatness under certain conditions, with the curvatures of the robot as the flat outputs. Our two-step trajectory generation approach uses an inverse kinematics procedure to calculate a motion plan of robot curvatures per end-effector position, then, our flatness diffeomorphism generates corresponding control inputs that respect velocity. We validate our approach through simulations of our representative soft robot manipulator along three different trajectories, demonstrating a margin of 23x faster than real-time at a frequency of 100 Hz. This approach could allow fast verifiable replanning of soft robots' motions in safety-critical physical environments, crucial for deployment in the real world.

Topological boundary modes utilizing valley mode waveguides have opened opportunities in, for instance, the design of high transmission waveguides with tolerance to geometrical defects and sharp bends. Applications of these waveguides include linear computational processes and the emulation of logic gates using linear structures, among other scenarios. Here we present the design of a 6-port junction that exhibits equal power splitting to three other ports when excited at single port with no reflections. In studying this structure, a scattering matrix is extracted at telecom wavelengths (around 1550 nm). The linearity of the system along with the scattering matrix are exploited to produce linear operations such as routing of information considering two incident signals or multiple signals applied from different ports. Our work may be exploited to analytically design larger networks without the need of computationally expensive trial and error numerical methods., Comment: 27 pages, 5 Figures, 83 References

Understanding the relationship between network features and misinformation propagation is crucial for mitigating the spread of false information. Here, we investigate how network density and segregation affect the dissemination of misinformation using a susceptible-infectious-recovered framework. We find that a higher density consistently increases the proportion of misinformation believers. In segregated networks, our results reveal that minorities affect the majority: denser minority groups increase the number of believers in the majority, demonstrating how the structure of a segregated minority can influence misinformation dynamics within the majority group., Comment: 9 pages, 3 figures, Social Simulation Conference 2024

The introduction of time as an additional degree of freedom to control wave-matter interactions have opened new avenues to fully control wave propagation in four dimensions (x,y,z,t). Time interfaces (rapid changes of the constitutive relations of the medium where a wave propagates) have recently become popular as they are the temporal analogue of spatial interfaces. While recent groundbreaking experimental work has demonstrated time interfaces from water waves, microwaves and the optical regime, rapidly changing, for instance, the permittivity of the medium requires carefully engineered structures. Here, we study the possibility of implementing smooth temporal transitions of the refractive index of the medium in order to mimic the response of time interfaces via adiabatic modulations. It is shown that indeed, as long as the signal is inside the structure during the modulation time, there are some values of rising/falling time of the adiabatic modulation that enables a full approximate emulation of a time interface. These results may open further avenues to explore by relaxing the speed at which the time-modulation should be introduced when designing four-dimensional media., Comment: 24 pages. 5 figures, 79 references

In-operando techniques enable real-time measurement of intricate physical properties at the micro- and nano-scale under external stimuli, allowing the study of a wide range of materials and functionalities. In nanomagnetism, in-operando techniques greatly benefit from precise three-dimensional (3D) magnetic field control, enabling access to complex magnetic states forming in systems where multiple energies are set to compete with each other. However, achieving such precision is challenging and uncommon, as specific applications impose constraints on the type and geometry of magnetic field sources, limiting their capabilities. Here, we introduce an approach that leverages machine learning algorithms to achieve precise 3D magnetic field control using a hexapole electromagnet that is composed of three independent, non-collinear dipole electromagnets. In our experimental setup, magnetic field sensors are placed at a distance from the sample position due to inherent constraints, leading to indirect field measurements that differ from the magnetic field experienced by the sample. We find that the existing relationship between the remote and sample frames of reference is non-linear, thus requiring a more complex calibration method. To address this, we employ a multi-layer perceptron neural network that processes multiple inputs from a dynamic magnetic field sequence, effectively capturing the time-dependent non-linear field response. The network achieves high calibration accuracy and demonstrates exceptional generalization to unseen magnetic field sequences. This study highlights the significant potential of machine learning in achieving high-precision control and calibration, crucial for in-operando experiments where direct measurement at the point of interest is not possible.

We propose a novel extension to the recently developed non-commutative Riemannian foliated branch-cut quantum gravity (BCQG). Based on an extended Faddeev-Jackiw symplectic deformation of the conventional Poisson algebra, we investigate non-commutativity effects on a symplectic topological manifold that provides a natural isomorphic setting composed by a triad of canonically conjugate scalar complex fields which comprise quantum complementary dualities. Based on a complementary analytically continued Friedmann-type equation, combined with a quantum approach based on the Ho\v{r}awa-Lifshitz quantum gravity, we describe the dynamic evolution of the universe's wave function, unfolding unprecedented predictions for the cosmic evolution and inflation. The non-commutative foliated quantum gravity approach offers a new perspective on explaining the accelerated cosmic expansion of the universe, strongly suggesting that non-commutative algebra induces the late accelerated growth of both the universe's wave function and the corresponding scale factor, along with their quantum counterparts. In contrast to the conventional inflationary model, where inflation requires a remarkably fine-tuned set of initial conditions in a patch of the universe, non-commutative foliated quantum gravity, analytically continued to the complex plane, captures short and long scales of spacetime, leading to an evolutionary cosmic dynamic through a topological reconfiguration of the primordial cosmic matter and energy content. This result introduces new speculative framework elements regarding the reconfiguration of matter and energy due to an underlying non-commutative spatio-temporal structure as a driver of spacetime cosmic acceleration.

The determination of the $DD^{*}$ scattering amplitude from lattice QCD is complicated by long-range interactions. In particular, the L\"uscher method is no longer applicable in the kinematical region close to the left-hand cut. We tackle this problem by adopting plane-wave and effective-field-theoretic methods, which also address partial wave mixing. In addition, we incorporate a diquark-antidiquark interpolator in the operator basis (along with the relevant scattering operators) in order to achieve a better resolution of the energy spectrum. Results show that inclusion of it already has some impact at physical charm quark mass, although it is more significant for larger heavy quark masses, in line with expectations., Comment: 9 pages. Proceedings of the 41st International Symposium on Lattice Field Theory, LATTICE2024, 28th July -3rd August, 2024, University of Liverpool, UK

A new model to follow the complete evolution of a drop in Leidenfrost state is presented in this work. The main ingredients of the phenomenon were considered, including: 1) the shape and weight of a sessile drop, according to its size, compared to the capillary length, using the Young-Laplace equation; 2) the evaporation at the entire surface of the drop, due to the heat transfer across the vapor film, to the proximitiy of a hot plate and to the diffusion in air; 3) the velocity, pressure and temperature fields at the vapor film, between the drop and the hot plate, which are recovered by means of a Hankel transform method, being valid for any size of drops and any thickness of vapor films (below the vapor film stability threshold); 4) an estimation of the thermo-capillary Marangoni convection flow, without simulating numerically the flow within the drop. The aforementioned features were addressed and calculated, in order to include their effect within a single non-linear ODE, describing the temporal evolution of the size of the drop, through the Bond number. Three dimensionless parameters, relating the thermophysical properties of the drop fluid and the surrounding air, control the development of the phenomenon. All those properties were calculated according to the ideal gas approximation and to widely used empirical correlations, without any fitting parameter. The model predictions were compared against experimental results, using different organic and inorganic compounds, for which a good agreement has been found, when no bounce or rotation of the drop spontaneously occurs., Comment: 15 pages, 9 figures

Experimentally, the doubly charm tetraquark channel $cc\bar q\bar q$ with $q\!=\!u,d$ features an exotic hadron, $T_{cc}$, with isospin $I\!=\!0$ near the $DD^*$ threshold, while no peak was observed for $I\!=\!1$. We present a lattice QCD study of this channel with $I\!=\!1$, $J^P\!=\!1^+$ and $m_\pi\simeq 280~$MeV. Finite-volume energies calculated across five charm quark masses consistently indicate a repulsive interaction in this channel. These energies are used to compute the $DD^*$ scattering amplitude using both the standard L\"uscher method and the recently proposed effective-field-theory-based approach in the plane-wave basis, which incorporates the long-range interactions and the left-hand cut. Both analyses reaffirm the repulsive interaction resulting in a scattering amplitude that does not feature any poles in the energy region near the $DD^*$ threshold, in line with LHCb results. We identify that the Wick contraction resembling $t$-channel isovector-vector meson exchanges between $D$ and $D^*$ plays a key role in distinguishing between the $I=0$ and $I=1$ channels, leading to repulsion in the $I=1$ and attraction in the $I=0$ channel., Comment: 14 pages and 11 figures

Variable viscosity arises in many flow scenarios, often imposing numerical challenges. Yet, discretisation methods designed specifically for non-constant viscosity are few, and their analysis is even scarcer. In finite element methods for incompressible flows, the most popular approach to allow equal-order velocity-pressure interpolation are residual-based stabilisations. For low-order elements, however, the viscous part of that residual cannot be approximated, often compromising accuracy. Assuming slightly more regularity on the viscosity field, we can construct stabilisation methods that fully approximate the residual, regardless of the polynomial order of the finite element spaces. This work analyses two variants of this fully consistent approach, with the generalised Stokes system as a model problem. We prove unique solvability and derive expressions for the stabilisation parameter, generalising some classical results for constant viscosity. Numerical results illustrate how our method completely eliminates the spurious pressure boundary layers typically induced by low-order PSPG-like stabilisations.

An (artificial cardiac) pacemaker is an implantable electronic device that sends electrical impulses to the heart to regulate the heartbeat. As the number of pacemaker users continues to rise, so does the demand for features with additional sensors, adaptability, and improved battery performance. Reinforcement learning (RL) has recently been proposed as a performant algorithm for creative design space exploration, adaptation, and statistical verification of cardiac pacemakers. The design of correct reward functions, expressed as a reward machine, is a key programming activity in this process. In 2007, Boston Scientific published a detailed description of their pacemaker specifications. This document has since formed the basis for several formal characterizations of pacemaker specifications using real-time automata and logic. However, because these translations are done manually, they are challenging to verify. Moreover, capturing requirements in automata or logic is notoriously difficult. We posit that it is significantly easier for domain experts, such as electrophysiologists, to observe and identify abnormalities in electrocardiograms that correspond to patient-pacemaker interactions. Therefore, we explore the possibility of learning correctness specifications from such labeled demonstrations in the form of a reward machine and training an RL agent to synthesize a cardiac pacemaker based on the resulting reward machine. We leverage advances in machine learning to extract signals from labeled demonstrations as reward machines using recurrent neural networks and transformer architectures. These reward machines are then used to design a simple pacemaker with RL. Finally, we validate the resulting pacemaker using properties extracted from the Boston Scientific document., Comment: 8 pages, 3 page appendix

We prove what might have been expected: The Williams Conjecture in symbolic dynamics and Graded Morita Equivalence Conjecture for Leavitt/$C^*$-graph algebras hold for ``small graphs'', i.e., connected graphs with three vertices, no parallel edges, no sinks with no trivial hereditary and saturated subsets. Namely, two small graphs are shift equivalent if and only if they are strong shift equivalent if and only if their Leavitt/$C^*$-graph algebras are graded/equivariant Morita equivalent., Comment: 33 pages

Interplanetary shocks are fundamental constituents of the heliosphere, where they form as a result of solar activity. We use previously unavailable measurements of interplanetary shocks in the inner heliosphere provided by Solar Orbiter, and present a survey of the first 100 shocks observed in situ at different heliocentric distances during the rising phase of solar cycle 25. The fundamental shock parameters (shock normals, shock normal angles, shock speeds, compression ratios, Mach numbers) have been estimated and studied as a function of heliocentric distance, revealing a rich scenario of configurations. Comparison with large surveys of shocks at 1~au show that shocks in the quasi-parallel regime and with high speed are more commonly observed in the inner heliosphere. The wave environment of the shocks has also been addressed, with about 50\% of the events exhibiting clear shock-induced upstream fluctuations. We characterize energetic particle responses to the passage of IP shocks at different energies, often revealing complex features arising from the interaction between IP shocks and pre-existing fluctuations, including solar wind structures being processed upon shock crossing. Finally, we give details and guidance on the access use of the present survey, available on the EU-project ``solar energetic particle analysis platform for the inner heliosphere'' (SERPENTINE) website. The algorithm used to identify shocks in large datasets, now publicly available, is also described.

We introduce the Cuntz-Thomsen picture of $\mathcal{C}$-equivariant Kasparov theory, denoted $\mathrm{KK}^\mathcal{C}$, for a unitary tensor category $\mathcal{C}$ with countably many isomorphism classes of simple objects. We use this description of $\mathrm{KK}^\mathcal{C}$ to prove the stable uniqueness theorem in this setting.

To analyze the flow of information online, experts often rely on platform-provided data from social media companies, which typically attribute all resharing actions to an original poster. This obscures the true dynamics of how information spreads online, as users can be exposed to content in various ways. While most researchers analyze data as it is provided by the platform and overlook this issue, some attempt to infer the structure of these information cascades. However, the absence of ground truth about actual diffusion cascades makes verifying the efficacy of these efforts impossible. This study investigates the implications of the common practice of ignoring reconstruction all together. Two case studies involving data from Twitter and Bluesky reveal that reconstructing cascades significantly alters the identification of influential users, therefore affecting downstream analyses in general. We also propose a novel reconstruction approach that allows us to evaluate the effects of different assumptions made during the cascade inference procedure. Analysis of the diffusion of over 40,000 true and false news stories on Twitter reveals that the assumptions made during the reconstruction procedure drastically distort both microscopic and macroscopic properties of cascade networks. This work highlights the challenges of studying information spreading processes on complex networks and has significant implications for the broader study of digital platforms.

Trust and interpretability are crucial for the use of Artificial Intelligence (AI) in scientific research, but current models often operate as black boxes offering limited transparency and justifications for their outputs. We introduce AiSciVision, a framework that specializes Large Multimodal Models (LMMs) into interactive research partners and classification models for image classification tasks in niche scientific domains. Our framework uses two key components: (1) Visual Retrieval-Augmented Generation (VisRAG) and (2) domain-specific tools utilized in an agentic workflow. To classify a target image, AiSciVision first retrieves the most similar positive and negative labeled images as context for the LMM. Then the LMM agent actively selects and applies tools to manipulate and inspect the target image over multiple rounds, refining its analysis before making a final prediction. These VisRAG and tooling components are designed to mirror the processes of domain experts, as humans often compare new data to similar examples and use specialized tools to manipulate and inspect images before arriving at a conclusion. Each inference produces both a prediction and a natural language transcript detailing the reasoning and tool usage that led to the prediction. We evaluate AiSciVision on three real-world scientific image classification datasets: detecting the presence of aquaculture ponds, diseased eelgrass, and solar panels. Across these datasets, our method outperforms fully supervised models in low and full-labeled data settings. AiSciVision is actively deployed in real-world use, specifically for aquaculture research, through a dedicated web application that displays and allows the expert users to converse with the transcripts. This work represents a crucial step toward AI systems that are both interpretable and effective, advancing their use in scientific research and scientific discovery.

In this study, we employ the combined charged particle measurements from Integrated Science Investigation of the Sun (\ISOIS) onboard the Parker Solar Probe (PSP) and Energetic Particle Detector (EPD) onboard the Solar Orbiter (SolO) to study the composition variation of the solar energetic particle (SEP) event occurring on May 16, 2023. During the event, SolO and PSP were located at a similar radial distance of ~0.7 au and were separated by $\sim$60$^\circ$ in longitude. The footpoints of both PSP and SolO were west of the flare region but the former was much closer (18$^\circ$ vs 80$^\circ$). Such a distribution of observers is ideal for studying the longitudinal dependence of the ion composition with the minimum transport effects of particles along the radial direction. We focus on H, He, O, and Fe measured by both spacecraft in sunward and anti-sunward directions. Their spectra are in a double power-law shape, which is fitted best by the Band function. Notably, the event was Fe-rich at PSP, where the mean Fe/O ratio at energies of 0.1 - 10 Mev/nuc was 0.48, higher than the average Fe/O ratio in previous large SEP events. In contrast, the mean Fe/O ratio at SolO over the same energy range was considerable lower at 0.08. The Fe/O ratio between 0.5 and 10 MeV/nuc at both spacecraft is nearly constant. Although the He/H ratio shows energy dependence, decreasing with increasing energy, the He/H ratio at PSP is still about twice as high as that at SolO. Such a strong longitudinal dependence of element abundances and the Fe-rich component in the PSP data could be attributed to the direct flare contribution. Moreover, the temporal profiles indicate that differences in the Fe/O and He/H ratios between PSP and SolO persisted throughout the entire event rather than only at the start., Comment: 11 pages, 5 figures

Pre-trained language models (PLMs) are trained on large amounts of data, which helps capture world knowledge alongside linguistic competence. Due to this, they are extensively used for ultra-fine entity typing tasks, where they provide the entity knowledge held in its parameter space. Given that PLMs learn from co-occurrence patterns, they likely contain more knowledge or less knowledge about entities depending on their how frequent they are in the pre-training data. In this work, we probe PLMs to elicit encoded entity probabilities and demonstrate that they highly correlate with their frequency in large-scale internet data. Then, we demonstrate that entity-typing approaches that rely on PLMs struggle with entities at the long tail on the distribution. Our findings suggests that we need to go beyond PLMs to produce solutions that perform well for rare, new or infrequent entities.

In recent years, nanosatellites have revolutionized the space sector due to their significant economic and time-saving advantages. As a result, they have fostered the testing of advanced instruments intended for larger space science missions. The case of MELISA is presented in this work. MELISA is a magnetic measurement instrument which aims at demonstrating the in-orbit performance of AMR sensors featuring dedicated noise reduction techniques at sub-millihertz frequencies. Such low frequency ranges are relevant for future space-borne gravitational wave detectors, where the local magnetic environment of the satellite might yield a significant contribution to the overall noise budget of the observatory. The demanding magnetic noise levels required for this bandwidth, down to 0.1 mHz, make measurements arduous. To explore sensing solutions within the H2020 European Commission Programme with the involvement of ESA, the functional performance of MELISA-III will be validated in-orbit. During operations, there is the possibility to measure the low-frequency magnetic contribution stemming from orbiting the Earth's magnetic field, impeding the characterization of the intrinsic performance of the sensor. With the objective of minimizing excess noise during the in-flight operations, the present research aims to simulate the environmental magnetic conditions in LEO to identify and subtract undesired contributions to the measurements. The in-orbit long-term magnetic fluctuations are replicated using a Helmholtz coil system. A fluxgate magnetometer allows the correlation of the generated field with the payload measurements, leading to the subsequent subtraction. Proving the effect of this approach will facilitate the noise characterization of magnetic sensors in LEO, paving the way for the in-orbit validation of MELISA-III for use in magnetically demanding missions with long integration times.

For the incompressible Navier--Stokes system with variable density and viscosity, we propose and analyse an IMEX framework treating the convective and diffusive terms semi-implicitly. This extends to variable density and second order in time some methods previously analysed for variable viscosity and constant density. We present three new schemes, both monolithic and fractional-step. All of them share the methodological novelty that the viscous term is treated in an implicit-explicit (IMEX) fashion, which allows decoupling the velocity components. Unconditional temporal stability is proved for all three variants. Furthermore, the system to solve at each time step is linear, thus avoiding the costly solution of nonlinear problems even if the viscosity follows a non-Newtonian rheological law. Our presentation is restricted to the semi-discrete case, only considering the time discretisation. In this way, the results herein can be applied to any spatial discretisation. We validate our theory through numerical experiments considering finite element methods in space. The tests range from simple manufactured solutions to complex two-phase viscoplastic flows., Comment: 23 pages

We investigate the stability of weak symmetry-protected topological phases (SPTs) in the presence of short-range interactions, focusing on the tenfold way classification. Using Atiyah's Real $\mathit{KR}$-theory and Anderson-dualized bordism, we classify free and interacting weak phases across all Altland-Zirnbauer symmetry classes in low dimensions. Extending the free-to-interacting map of Freed-Hopkins, we mathematically compute how the behavior of free weak SPTs changes when interactions are introduced as well as predict intrinsically-interacting weak phases in certain classes. Our mathematical techniques involve T-duality and the James splitting of the torus. Our results provide a mathematical framework for understanding the persistence of weak SPTs under interactions, with potential implications for experimental and theoretical studies of these phases., Comment: 48 pages. Comments welcome!

We present a study of two-body decay widths of heavy baryons into another heavy baryon and a light pseudoscalar meson in the framework of the non-relativistic quark model in combination with the elementary emission model for the strong couplings. The present study includes the decays of 1S- and 1P-wave baryons with one, two or three heavy quarks, either charm (c) or bottom (b). The relative partial widths are given by the appropriate flavor SU(3) isoscalar factors. The flavor, spin and orbital contributions are discussed explicitly as well as selection rules for forbidden decays. The total widths are compared with the available experimental data as well as with other theoretical studies. The calculated widths of singly-heavy baryons are found to be in reasonable agreement with the observed widths. The decay widths of doubly-heavy baryons are suppressed with respect to those of the singly-heavy baryons by the ratios of the quark masses appearing in the orbital contributions., Comment: 16 pages, 4 figures, 8 tables, submitted to Physical Review D