Modern Earth Observation (EO) missions generate massive volumes of imagery that challenge existing downlink and ground-processing capabilities, particularly for time-critical applications. This work investigates how a low Earth orbit (LEO) satellite constellation equipped with heterogeneous edge computing resources can enable real-time semantic processing of data acquired by EO satellites. We introduce an energy-aware framework that optimizes the use of resources accounting for data acquisition, computing, and communication constraints. Although we focus on maritime surveillance, the formulation is task-agnostic and accommodates a broad class of semantic and goal-oriented inference problems. Specifically, we formulate two coupled optimization problems: (i) observation scheduling, which selects image acquisition opportunities while accounting for turbulence-induced image degradation and energy budget, and (ii) processing scheduling, which allocates semantic workloads across onboard and ground processors. We evaluate these mechanisms for the task of detection and localization of vessels, for which we quantify the benefits of turbulence-aware observation scheduling for preserving image quality and experimentally characterize the execution-time distribution of YOLOv8 on different computing platforms. Results demonstrate that task- and turbulence-aware observation scheduling can significantly improve the quality and quantity of observed targets. Furthermore, cooperative edge processing within the constellation substantially reduces power consumption compared to traditional downlink-centric architectures. These findings highlight the potential of distributed edge intelligence to enhance the responsiveness and autonomy of future satellite-based EO systems.

A key diagnostic in warm-dense matter (WDM) experiments is X-ray Thomson scattering (XRTS), but its interpretation is often limited by complex instrument effects and the high computationally expensive combinations of microscopic models with detector simulations. We present a proof-of-principle implementation of an event-driven approach to XRTS modelling, inspired by particle physics event-generators. Instead of computing the spectra via forward models, individual scattering events are sampled from the differential cross section and sent through a spectrometer simulation. This provides a statistically consistent representation that preserves full kinematic information and enables flexible and geometry-aware analysis. We demonstrate the feasibility and physical consistency of the method for non-resonant XRTS in a synthetic setup. By decoupling event generation from detector-level analysis, the framework allows efficient reuse of the sampled events and reduces computational overhead associated with repeated evaluations. The method is model-agnostic and establishes a new connection between particle-physics event generation techniques and WDM diagnostics, providing a scalable foundation for advanced XRTS analysis and inference.

We study the asymptotic bubbling behavior of sequences of weak genus-$p$ immersions with diverging conformal classes and limiting Willmore energy of $8π$. After applying suitable Möbius transformations, in a strong $W^{2,2}_{\mathrm{loc}}$-limit, we obtain two round spheres at the largest scale and $p+1$ catenoids at the smallest scales. Moreover, we apply this classification to sequences of isoperimetrically, conformally and normalized-total-mean-curvature constrained Willmore minimizers when the constraints approach the boundary of the domain where minimizers exist, respectively.

We investigate the quantum phases of a half-filled generalized interacting Su-Schrieffer-Heeger model with intracell, nearest-neighbor, and next-nearest-neighbor intercell hoppings, together with an on-site inter-sublattice interaction. In the noninteracting limit, the model hosts one topologically trivial phase and two symmetry-protected topological (SPT) phases, distinguished under periodic boundary conditions by different winding numbers and under open boundary conditions by two-fold and four-fold entanglement-spectrum degeneracies, respectively. When interactions are introduced, these free-fermion SPT phases evolve into distinct interacting topological phases that retain characteristic signatures such as entanglement-spectrum degeneracy structures, boundary modes, and nonzero string order parameters. For strong repulsive interactions, a symmetry-breaking phase with unequal but spatially uniform sublattice densities appears between the trivial and topological regimes. For strong attractive interactions, period-2 and period-4 charge-density-wave phases emerge from particle clustering. At intermediate attractive interactions, the competition between interaction-induced localization and hopping-induced delocalization gives rise to a Luttinger liquid phase, a paired Luttinger liquid phase, and a gapless symmetry-protected topological (gSPT) phase. The gSPT phase is characterized by a gapless charge mode together with symmetry-protected current-carrying edge states. We further characterize the gapless phases and the associated quantum phase transitions through central charges and critical exponents.

We are interested in the long-time behaviour of the kinetic Vicsek equation, rigorously derived as the mean-field limit~\cite{bolley2012meanfield} of a coupled system of~$N$ stochastic differential equations describing particles moving at unit velocity and aligning with their neighbours. We focus on the local-in-space version (that may for instance appear as a moderate interaction limit instead of mean-field), which is not a priori globally well-posed and could explode in finite time. Despite its simple expression, little is rigorously established about the behaviour of its solutions. We use hypocoercivity methods to show that finite time explosion does not occur in the vicinity of uniform and homogeneous equilibria in space below the critical threshold. We recast the now-classic~\cite{villani2009hypocoercivity} approach of modifying Sobolev-type norms by adding cross-terms, linked to commutators between the different operators appearing in the kinetic equation. However, the fact that the velocity space is the sphere adds significant subtleties and requires to develop an adapted algebraic framework of operators. Taking advantage of this new framework, we manage to perform an approach \textit{à la} Hérau~\cite{herau2007short} to show the nonlinear stability. Our main results are a quantitative decay estimate in the case of the whole space, despite the absence of control of the $L^1$ norm of the perturbation, and a gain in regularity at the nonlinear level which allows to have well-posedness and stability in the space~$H^{s,0}(\mathbb{R}^d\times§)$ for some~$s<\frac{d}2$ (that is to say without a priori uniform bound in space on the~$L^2$ norm in velocity, that would come with Sobolev injection in the case~$s>\frac{d}2$).

Emotion recognition from physiological signals has substantial potential for applications in mental health and emotion-aware systems. However, the lack of standardized, large-scale evaluations across heterogeneous datasets limits progress and model generalization. We introduce FEEL, the first large-scale benchmarking study of emotion recognition using electrodermal activity (EDA) and photoplethysmography (PPG) signals across 19 publicly available datasets. We evaluate 16 architectures spanning traditional machine learning, deep learning, and self-supervised pretraining approaches, structured into four representative modeling paradigms. Our study includes both within-dataset and cross-dataset evaluations, analyzing generalization across variations in experimental settings, device types, and labeling strategies. Our results showed that fine-tuned contrastive signal-language pretraining (CLSP) models (71/114) achieve the highest F1 across arousal and valence classification tasks, while simpler models like Random Forests, LDA, and MLP remain competitive (36/114). Models leveraging handcrafted features (107/114) consistently outperform those trained on raw signal segments, underscoring the value of domain knowledge in low-resource, noisy settings. Further cross-dataset analyses reveal that models trained on real-life setting data generalize well to lab (F1 = 0.79) and constraint-based settings (F1 = 0.78). Similarly, models trained on expert-annotated data transfer effectively to stimulus-labeled (F1 = 0.72) and self-reported datasets (F1 = 0.76). Moreover, models trained on lab-based devices also demonstrated high transferability to both custom wearable devices (F1 = 0.81) and the Empatica E4 (F1 = 0.73), underscoring the influence of heterogeneity. More information about FEEL can be found on our website https://alchemy18.github.io/FEEL_Benchmark/.

We show that propagating switching fronts mediate directional state transmission and polarity selection in a passive many-body suspension. In confined trains of slipper-shaped deformable particles in Poiseuille flow, this behavior originates from directionally biased switching between neighboring particles: owing to the fore-aft asymmetry of the slipper, an upstream particle drives switching of its downstream neighbor more effectively than in the reverse direction. A local transition from an opposite-sign pair to a same-sign pair therefore launches a streamwise front that relays the inclination sign from particle to particle. A minimal coarse-grained model with local bistability and directional coupling captures front propagation and arrest. In periodic trains, the fronts coarsen into a uniformly polarized state, whereas in long open trains they arrest and leave persistent polarized domains. Our results point to local bistability and directional coupling as a route to collective polarization in passive many-body systems.

We investigate in detail the discovery potential of the doubly charged Higgs boson at the Compact Linear Collider in $e^-e^-$, $e^-γ$, $γγ$, and $e^+e^-$ collision modes, within the Higgs triplet model at two extreme benchmark points as representatives of the Yukawa-like and gauge-like regions. In the Yukawa-like region, the most promising production mechanism is the single production via $e^-e^-$ and $e^-γ$ collisions. Given the subsequent decay of the doubly charged Higgs into a same-sign lepton pair, CLIC can achieve statistical significance well beyond the discovery threshold, within the parameter space permitted by experimental constraints. In the gauge-like region, with the $\ell^{\pm}\ell^{\pm} + \geq 3j$ final state, CLIC exhibits robust discovery potential for the doubly charged Higgs boson, up to a mass of approximately $1.2~\mathrm{TeV}$. We also investigate the search for doubly charged Higgs at the HL-LHC. Our results demonstrate that CLIC possesses greater advantages and offers superior discovery potential for the doubly charged Higgs boson, compared to the HL-LHC.

Random hypergraphs extend the classical notion of random graphs by allowing hyperedges to join more than two vertices, making them well-suited for modeling higher-order interactions in complex systems. Despite their broad applicability, many structural properties of random hypergraphs remain less understood than in the graph setting. One such property is simplicity: the absence of self-loops, multi-hyperedges, and, in the hypergraph context, degenerate hyperedges where hyperedges contain a copy of the same vertex at least twice. While the behaviour of the number of such self-loops and multi-hyperedges is well understood for random graphs through the configuration model, analogous results for hypergraphs are comparatively sparse. In this work, we study both undirected and directed hypergraphs generated by the configuration model with prescribed vertex and hyperedge degrees. We derive exact, explicit expressions for the expected number of self-loops, multi-hyperedges and degenerate hyperedges, extending classical results from the graph setting. In addition, an asymptotical analysis shows that, under mild moment conditions on the degree distribution, the expected fraction of self-loops, multi-hyperedges and degenerate hyperedges vanishes as the number of vertices grows. Our results provide a systematic understanding of simplicity in directed and undirected hypergraph models.

Deep-lying core electrons carry highly localized, site-specific information that forms the basis of X-ray photoelectron spectroscopy. Accurately predicting their associated core ionization potentials (IPs) is a demanding theoretical task, requiring a balanced treatment of strong orbital relaxation, electron correlation, and relativistic effects. Over the years, a variety of approaches have been developed, ranging from state-specific wave function methods to linear-response formalisms and Green's function techniques. However, their assessment has often relied on comparisons with experiment, where multiple sources of error (basis set incompleteness, relativistic corrections, and vibrational effects) are entangled, making it difficult to isolate the performance of correlation treatments. In the present work, we establish a consistent, theory-based benchmark for core IPs by computing 84 non-relativistic values (73 second-row and 11 third-row IPs) at the full configuration interaction level within the core-valence separation approximation, using large correlation-consistent basis sets augmented with tight-core and diffuse functions (aug-cc-pCVXZ). These results define theoretical best estimates within a fixed finite basis set, providing a chemically accurate reference for method development and validation. Importantly, our dataset allows for systematic, theory-versus-theory comparisons that disentangle correlation and relaxation effects from other physical contributions. On this basis, we assess the performance of widely used approximate methods, including equation-of-motion coupled-cluster approaches up to the inclusion of quadruple excitations, the one-shot $G_0W_0$ scheme, as well as state-specific methods.

We calculate the resummed perturbative free energy of ${\cal N} = 4$ supersymmetric Yang-Mills in four spacetime dimensions (SYM$_{44}$) to order $λ^{5/2}$ in the 't Hooft coupling at finite temperature and zero chemical potential. All infrared divergences cancel when we include contributions from SYM$_{44}$ ring diagrams and the final result is both ultraviolet and infrared finite. Our result has special significance since order $λ^{5/2}$ is the highest order calculation that can be done with perturbation theory, because there are nonperturbative effects associated with the magnetic mass scale that come into play at order $λ^3$. We compare results obtained with regularization by dimensional reduction (RDR), which preserves supersymmetry, and canonical dimensional regularization (DR). We also compare with a generalized Padé approximant constructed by matching the weak coupling result at order $λ^2$ and the large $N_c$ strong coupling result at order $λ^{-3/2}$. Finally we make a comparison between our result and the QCD free energy and show that SYM$_{44}$ has better convergence properties.

Millimeter-sized particles trapped at the surface of a liquid bath attract each other through the deformation of the liquid-air interface, a phenomenon known as "the Cheerios effect". We consider here a situation similar at first sight: the interaction between two millimeter-sized particles trapped in an horizontal soap film. In this geometry, the deformation of the film due to the weight of one particle extends over the entire system size, which induces an extremely long-ranged attraction. Combined with the low viscous friction in the film, this leads to intricate particle orbits, lasting up to ten seconds before the two particles eventually collide. By tracking the particles dynamics, we measure the force exerted by each particle on the other, and we develop a theoretical model. Because the interface deformation induced by a particle depends on its position in the soap film, the attractive force has two features that fundamentally depart from classical interaction forces. The force exerted by one particle on the other differs both in direction and magnitude from the reverse interaction, with an asymmetry reaching 150% when one particle is close to the center and the other one close to the frame. Reciprocity is recovered when both particles are close to the film center. These results are a original example of non-reciprocal effective interactions due to boundary conditions.

This paper studies a dominance relation among scoring rules with respect to avoiding the selection of the Condorcet loser. In a voting model with three or more alternatives, we say that a scoring rule $f$ Condorcet-loser-dominates (CL-dominates) another scoring rule $g$ if the set of profiles where $f$ selects a Condorcet loser is a proper subset of the set where $g$ does. We show that the Borda rule not only CL-dominates all other scoring rules, but also is the only scoring rule that CL-dominates some scoring rule.

Quantum advantage, broadly understood as the ability of quantum systems to significantly outperform their classical counterparts, underpins current interest to quantum technologies and is a topic of active investigation. In many situations, its existence is subject to debate, and the areas of supremacy of large-scale quantum systems are not well defined. Here, we uncover a novel niche where quantum advantage can be clearly defined and proven. We study a time-optimal transfer of excitations in the lattice involving both nearest-neighbor and longer-range couplings. We prove that the quantum-mechanical property of a particle to propagate along several trajectories simultaneously speeds up the transfer process, which takes a shorter time compared to any particular trajectory and thus provides a clear example of quantum advantage.

We demonstrate that graph neural network (GNN) embeddings of injector configurations provide a practical operational coordinate system for the Continuous Electron Beam Accelerator Facility (CEBAF) injector at Jefferson Lab. Using 137,389 snapshots spanning January 2022 through March 2023, we show that injector operation occupies a small number of persistent, well-separated neighborhoods in a 16-dimensional learned state space rather than a featureless continuum. Density-based clustering identifies ten recurring operating regimes with strong operational run alignment, and regime persistence statistics confirm that these regimes are stable over timescales of hours to weeks. Large relocations between neighborhoods are rare and episodic; 99.6% of one-hour operating windows fall within an empirically derived jitter baseline. Geometric outlier screening narrows a year-long dataset to a small set of intervals warranting operational review, and nearest-neighbor retrieval enables case-based reasoning over the historical archive. A controlled beam study validates that deliberate injector reconfiguration traces coherent, interpretable trajectories in embedding space. Together these capabilities demonstrate that machine-state embeddings support holistic operational monitoring in ways that single-channel inspection cannot.

Electroencephalography (EEG) source imaging aims to reconstruct the spatial distribution of neural activity within the brain from non-invasive scalp measurements. This inverse problem is severely ill-posed due to the low spatial resolution of EEG and the presence of measurement noise, necessitating robust regularization techniques. Bayesian approaches provide a principled framework for incorporating prior knowledge into the solution, where regularization naturally arises through prior distributions and their associated hyperparameters. In this work, we provide an overview of key Bayesian methods for EEG source imaging based on Gaussian, Laplace, and group Laplace priors, with particular emphasis on hierarchical models that promote sparsity. We analyze the connections between these hierarchical formulations and classical optimization techniques, and provide an analytical description of their implementation using expectation -maximization and alternating optimization algorithms. To address the issue of depth bias where deeper sources are systematically underestimated or mislocalized - we extend a statistical signal-to-noise ratio (SNR) framework to derive depth-weighted priors that account for differences in how strongly sources at different depths are reflected in the measurements. Finally, we illustrate the behaviour of the considered models through simulation studies involving sources at varying depths. The results highlight the impact of prior selection and depth weighting on reconstruction accuracy and demonstrate the importance of informed model design for depth-sensitive EEG source localization.

We prove the exponential convergence to a unique invariant measure for locally damped nonlinear Schrödinger equations, perturbed by bounded noise acting on only two Fourier modes. To tackle the lack of smoothing effect, we introduce asymptotic compactness of linearized system to enhance the coupling method. Inspired by [14,33,39], we establish a new criterion for exponential mixing. Elements from global stability, nonlinear smoothing, and geometric control are combined when applying this criterion.

We study a two-dimensional incompressible vorticity equation on the torus driven by transport-type fractional Brownian noise with Hurst parameter $H \in (1/2,1)$. The model captures persistent, long-range correlated forcing consistent with inertial-range scaling laws and fractional Brownian approximations of turbulent fluctuations. A central ingredient of our approach is a version of the sewing lemma adapted to a class of integrands that includes, but is not limited to, transport-type structures. This result provides a flexible tool for constructing the Young integral and serves as a basis for analysing a wider class of stochastic partial differential equations. Using this approach, we establish existence and uniqueness of solutions via a fixed point argument and investigate statistical properties of the flow. In particular, we study quadratic functionals of the solution and derive an estimator for the Hurst parameter $H$.

We present a new set of 35 transit light curves of the hot Jupiter WASP-43~b, obtained through the SPEARNET network. These datasets were analyzed together with previously published ground-based observations, as well as space-based data from \emph{TESS}, \emph{HST}, and \emph{JWST}, to refine the planetary parameters of WASP-43~b. A total of 188 mid-transit times, measured with \texttt{TransitFit}, were analyzed for potential timing variations. The transit timing variations do not show any significant evidence of orbital decay. Atmospheric retrievals using \emph{HST}/WFC3 G141 transmission spectra suggest that higher-temperature solutions are associated with higher water abundances. However, when these data are combined with observations from ground-based telescopes, \emph{TESS}, and \emph{JWST}, the increased modeling complexity across the broad wavelength baseline presents significant challenges for atmospheric characterization. These results highlight that high-precision, multi-instrument datasets will be necessary to break existing degeneracies in the atmospheric modeling of this target in the future.

PG~1553+113 is the blazar with the most-significantly detected periodic patter in its multiwavelength (MWL) emission, making it one of the most promising candidates for hosting a supermassive black hole binary. However, the presence of this periodic behavior in the X-ray band remains under debate, largely due to the lack of continuous monitoring. This has led to differing conclusions in previous studies. In addition, we aim to examine whether the recently identified linear long-term trends in the gamma-ray and optical bands also exist in the X-ray regime. Here, we evaluate the 2.1-year period in the X-ray light curve of PG 1553+113 using two dedicated monitoring campaigns with Swift-XRT and UVOT, guided by predictions of future oscillation phases. We also examine whether the long-term trend is present in X-rays, the potential periodic behavior of the X-ray power-law photon index, and its potential correlation to the X-ray flux. As a result, we find tentative evidence for a correlation between the predicted high-emission states in the gamma-ray band and those observed in the X-ray and UV bands. Therefore, we do not find a strong evidence of the same periodic pattern in X-ray. In addition, we find that the X-ray light curve is consistent with the presence of a long-term linear trend, in agreement with those previously reported in gamma-ray, optical, and radio. Overall, these results indicate that the X-ray emission is likely to share the same long-term behavior observed in the gamma-ray and optical bands. Nevertheless, the pronounced stochastic variability that characterizes the X-ray light curve limits our ability to draw firm conclusions regarding the presence of the periodic behavior.

Many natural and industrial systems involve particle-laden interfaces. Because interfacial particles prevent the coalescence and coarsening of drops, they hold promise for various applications requiring stable emulsions. Despite their remarkable ability to stabilize emulsions, it remains challenging to characterize how particles influence the interfacial transport of dissolved solutes. Here, we quantify the diffusion from a single particle-coated drop by confining it to a two-dimensional configuration. Using fluorescence microscopy, we extract the intensity profiles of the fluorescent dye as it diffuses from the drop, yielding spatio-temporal measurements of the concentration field. Over a range of particle sizes, the particles impose minimal resistance to diffusion. We rationalize this counterintuitive result with a mathematical model that couples interfacial mass transfer to a particle-coated interface. We show that the particle monolayer controls the temporal dynamics of the flux across the interface, hindering transport only at extreme coverage fractions beyond the close-packing limit. This framework reveals why particles often fail to hinder diffusion, offering new pathways to harness mass transfer in particle-stabilized emulsions.

The aim of this study is to investigate the interaction of Earth-mass planets with a planetesimal disk. It is shown that an Earth-mass planet, initially located near the inner boundary of the planetesimal disk, migrates into the disk. The depth of penetration of the planet into the disk is a random quantity determined by the angular momentum distribution of planetesimals approaching the planet. However, at a certain stage, the direction of the planet's migration changes, and the planet returns to the inner boundary of the disk. During such reversible migration, the planet perturbs the orbits of planetesimals and increases their relative velocities in the region of the disk traversed during its migration. The relative velocities of planetesimals increase to values sufficient for their fragmentation in collisions. Our estimates show that, after the passage of an Earth-mass planet through the outer planetesimal disk, the mean relative velocities in the main part of the disk increase to values sufficient to disrupt monolithic basaltic planetesimals with sizes of 40 km. Thus, the interaction of even a relatively low-mass planet (of order an Earth mass) with a planetesimal disk can lead to the formation of dust particles observed in outer debris disks.

Laser-driven high-Q Kerr-nonlinear optical microresonators enable parametric oscillation with low-power continuous-wave lasers and host a variety of coherent dissipative structures, including dissipative Kerr solitons and switching waves. These time-periodic structures constitute coherent optical frequency combs, and photonic-chip integration has miniaturized them to the chip scale. Such photonic-integrated, microresonator-based frequency combs - often termed 'microcombs' or 'Kerr combs' - have been demonstrated in various system-level and scientific applications. They complement femtosecond-laser-based frequency combs when high repetition rates, broad bandwidths, or high power per comb line are needed. This review introduces the field of microcombs and outlines the fundamental physical principles governing the generation of coherent frequency combs in microresonators.

This article derives full asymptotic expansions for integrals of the form \[ \int_{0}^{1}f(u)(1+q\cdot u^{n})^{w/n}du \] as $n\rightarrow\infty$, with parameters real $w\neq 0$ and $q\in(-1,1]$, or positive $w$ for $q=-1$. We relate the coefficients of the asymptotic expansions to Nielsen's generalized polylogarithms. For $q=-1$, we obtain an expansion in terms of multiple zeta values, which in this setting, reduce to ordinary zeta values. A key point is that for $q=1$, the integrals typically produce alternating multiple zeta values; we formulate a precise symmetry constraint on the relevant coefficient sequence under which all coefficients reduce to polynomials in ordinary zeta values. We also translate this symmetry into a statement about a binomial transform, and we verify the condition for several classical Appell-type families, like Euler, Bernoulli, Genocchi, and Hermite. Finally, we obtain precise results about the convergence of norms of random variables.

A parametric simulation tool for pixel sensors is presented. A realistic pixel response is simulated purely based on measurement input, without requiring detailed knowledge of the underlying manufacturing process. As such, it provides an efficient alternative to the use of Technology Computer-Aided Design simulations, which typically depend on proprietary process information. Due to its parametric approach, the package is fast and thus particularly useful for larger detector systems and high hit rate environments. This work presents measurements, simulation and its validation for the MALTA2 sensor. It is a small collection electrode monolithic active pixel sensor produced in the Tower 180nm Complementary Metal-Oxide-Semiconductor imaging process. Modifications to the sensor's periphery, mainly in the hit merger, are studied in order to optimize the performance for tracking and calorimetry. This optimization is of special interest as part of the MALTA3 sensor redesign in the 65nm Tower Partners Semiconductor Co. process.

For dilute solutions of polymers, chaotic flow states can occur at lower Reynolds numbers than required for inertial turbulence in Newtonian fluids, offering the potential for increased mixing efficiency. These states may be promoted by the flow geometry, and in recent years, porous media have gained attention as a promising setting in which viscoelastic instabilities may be exploited, although studies have primarily been in the creeping flow regime. Cylinder arrays serve as a prototypical porous media, giving a controlled setting in which to investigate flow dynamics. Here we explore the transition to elasto-inertial turbulence (EIT) in cylinder arrays via detailed numerical simulations. With increasing elasticity, EIT is reached via an initial sub-critical saddle-node bifurcation from the Newtonian state and then follows a series of supercritical bifurcations, in a Ruelle-Takens-Newhouse route to chaos. This transition is driven by the interaction between vortex shedding in cylinder wakes, and the bulk flow between cylinders. Within the EIT regime, we observe an interaction between slow dynamics in cylinder wakes, and fast dynamics in channels between cylinders, leading to two distinct slopes in the energy spectra. At low Reynolds numbers arrowhead structures are present, but these are suppressed at higher inertia. In the present configuration, we find no direct connection between EIT and purely elastic instabilities.

Sulfide chalcopyrite, Cu(In,Ga)S2, having wide bandgap (larger than 1.5 eV), favorable optoelectronic properties, and high stability, is a promising top-cell absorber for tandem applications. Adapting device structures optimized for 1.0 - 1.2 eV absorbers to wide bandgap absorbers requires modification of the buffer layer. This work investigates atomic layer deposition of ZnSnO as an alternative buffer layer to conventional CdS. A critical parameter for bufferperformance is the conduction band offsets on both sides of the buffer. To investigate these buffers we electrically characterize solar cells utilizing different compositions of ZnSnO. The Sn/(Sn+Zn) atomic ratio is controlled by the ratio of ZnO to SnO cycles during atomic layer deposition. Solar cells were fabricated utilizing CuInSe2, Cu(In,Ga)Se2, and Cu(In,Ga)S2 absorbers, allowing cross-comparison with a variety of conduction band minimum energies. Buffer variation has two primary effects on cell performance: 1. Low tin buffers decrease the activation energy of interface recombination, reducing open circuit voltage. These observations indicates a cliff, a decrease of the conduction band minimum from absorber to buffer. 2. High tin buffers reduce the fill factor for all measured cells, and reduce the short circuit current under certain conditions. This observation indicates an electron transport barrier, conduction band offsets which limit the transport of electrons across the buffer, in either direction. We conclude that tin content correlates positively with the conduction band minimum of these buffers. Comparing different absorbers, cliffs occurs at lower Sn contents and the effects of barriers are more dramatic for absorbers with lower conduction band minima.

In this paper mathematical models for the evolutionary conserved Notch-Delta pathway are developed and analyzed in order to better understand how two neighboring biological cells can become different. We pursue a structure-based stoichiometric type of approach, such that no specific reaction kinetics have to be defined. Only their dependencies on the relevant species participating in the model network are taken into account. Reaction networks and their related systems of ODEs are presented and analyzed with respect to their capacity for symmetry-induced bifurcations. The possibility to obtain a singular Jacobian is analyzed symbolically. This approach is valid for parameter-rich kinetics, where the parametrization of the steady-state fluxes and of the first derivatives of the reaction rates evaluated at the steady state are independent. In this context, also with the help of abstract minimal models, we could mathematically identify some of the Notch pathway's features being more relevant than others.

This note develops some fundamental properties of resultants and related notions. It represents my own personal exploration of this domain, which I found more instructive than seeking answers in the standard literature. Consequently, notation and terminology may be quite idiosyncratic, and the approach is very algebraic. Read at your own risk.

Algorithms based on spatial tree traversal are widely regarded as among the most efficient and flexible approaches for many problems in CPU-based high-performance computing (HPC). However, directly transferring these algorithms to GPU architectures often yields substantially smaller performance gains than expected in light of the high computational throughput of modern GPUs. The branching nature of tree algorithms leads to thread divergence and irregular memory access patterns -- both of which may severely limit GPU performance. To address these challenges, we propose a Morton (z-order) 'plane-based tree hierarchy' that is specifically designed for GPU architectures. The resulting flattened data layout enables efficient dual-tree traversal with collaborative execution across thread groups, leading to highly coalesced memory access patterns. Based on this framework we present implementations of two important spatial algorithms -- exact $k$-nearest neighbour search and friends-of-friends (FoF) clustering. For both cases, we observe more than an order-of-magnitude performance improvement over the closest competing GPU libraries for large problem sizes ($N \gtrsim 10^7$), together with strong scaling to distributed multi-GPU systems. We provide an open-source implementation, 'JZ-Tree' (JAX z-order tree), which serves as a foundation for efficient GPU implementations of a broad class of tree-based algorithms.