This article investigates a coupled viscoelastic Navier--Stokes--Biot system describing the interaction between an incompressible viscous fluid and a poro--viscoelastic medium in three spatial dimensions. The coupling between the fluid and the porous medium is realized through Beavers--Joseph--Saffman type interface conditions. Using spectral analysis, it is proved that the coupled system admits a unique, strong, global solution for small initial data. In addition, a Serrin--type blow-up criterion is established.

The fragmentation of plastic debris and the direct release of nanoplastics have emerged as a pressing ecological concern. Once dispersed, these enduring particles infiltrate food webs, accumulate within organisms, and bind toxic co-contaminants, posing long-term risks to ecosystems and human health. Despite growing awareness, the detection and characterization of nanoplastics remain highly challenging due to their minute size. Moreover, obtaining additional critical information, such as the particle shape or material composition, further exacerbates these detection hurdles. Conventional analytical techniques capable of providing more detailed information often demand substantial experimental and lab-bound effort, costly instrumentation, and lengthy measurement times. Here, we introduce a novel photonic sensing platform based on nanoscale voids that enables the simultaneous material- and morphology-sensitive detection of particles below 500 nm. Arrays of voids embedded in a high-refractive-index material act in parallel as both sorting elements and direct color reporters. Spherical and elongated particles are selectively trapped in circular and elliptical voids, while polymer types such as PS, PMMA, and PET are distinguished via the specific color signatures arising from their refractive index contrasts. This approach offers a cheap and scalable route toward rapid optical identification of nanoplastics in complex environmental and biological settings. Its compatibility with quick, high-throughput analysis positions it as a promising tool for real-time monitoring and comparative studies of heterogeneous nanoplastic populations.

Quantum batteries modeled as two-qubit systems coupled to Markovian thermal reservoirs have been shown to benefit from measurement-assisted charging, where projective measurements enhance the charging rate at an infinite thermodynamic resource cost. In this work, we consider weak, continuous measurements implemented via quantum point contact detectors (QPC), which enhance the charging rate at a definite and quantifiable resource cost. We analyze three measurement configurations namely, a single QPC, two independent QPCs, and a series-coupled (coherent) two-QPC scheme, and study their effect on the steady-state charging rate, defined as the rate of energy flow from the charger qubit to the battery qubit, relative to the unmeasured baseline. We find that the charging rate enhancement is non-monotonic as a function of the temperature gradient and potential gradient required to drive the QPCs, exhibiting a plateau of near-optimal enhancement. Comparing the three configurations, the plateau of optimal enhancement contracts toward lower temperature and chemical potential for both the cases with two QPCs compared to the single QPC case. The coherent measurement further shows a lowering of the resource requirement relative to the two independent QPC case for achieving the same enhancement. The hierarchy is coherent greater than two independent QPCs which is greater than single QPC with respect to both the magnitude of the charging rate enhancement and the minimization of measurement resources.

Multi-stakeholder platforms (MSPs) coordinate diverse stakeholder groups, often with competing or conflicting requirements. As these platforms increasingly take digital form, engineers building them make architectural decisions about data visibility, service decomposition, and algorithm design that directly determine which stakeholder requirements are prioritized when conflicts arise. Software architecture literature provides patterns for data isolation and access control among tenants but does not address how architectural decisions resolve conflicts among stakeholders with structurally divergent interests. MSP governance literature identifies the principles at stake but treats technology as neutral infrastructure. Neither addresses the translation between governance principles and architectural decision spaces. This paper proposes a governance-architecture correspondence framework that surfaces implicit governance decisions, making them explicit and debatable before deployment. The framework maps five MSP governance principles to the architectural decision spaces where they must be addressed, identifying for each the governance-aware design choice and the technically convenient default it overrides. We illustrate the framework in a constructed knowledge platform for pig farming in Rwanda, where five stakeholder types present structurally conflicting requirements. As work in progress, the framework is proposed but not yet empirically validated; a planned pre/post judgment study with platform users across all stakeholder types will test falsifiable predictions about governance outcomes.

In this work, a systematic study of electromagnetic instabilities driven by the temperature gradient in magnetically confined fusion plasmas with reversed magnetic shear is conducted using gyrokinetic particle-in-cell simulations. An electromagnetic instability arising in the low-beta regime is investigated, where beta=8*pi*nT/B^2 denotes the ratio of plasma pressure to magnetic pressure. Within a reversed shear safety factor (q) profile, when a mode rational surface coincides with the position of zero shear, an instability dominated by only one poloidal harmonic emerges, rather than the conventional ion-temperature-gradient (ITG) mode. Simulation results demonstrate that the instability exhibits pronounced electromagnetic polarization even in the low-beta regime, with a real frequency significantly higher than that of ITG modes, and show that it is destabilized by the temperature gradient and not by the density gradient. This instability can be observed even for a monotonic q profile with weak magnetic shear. Based on a systematic comparison with other typical electrostatic and electromagnetic instabilities, this instability is identified as a weak shear Alfvenic-ion-temperature-gradient (WSAITG) mode, which may provide an explanation for the low-frequency Alfven modes (LFAM) observed in experiments. Wave-particle resonance analysis in phase space reveals that, in contrast to the ITG mode, well-passing particles provide an additional resonant population that drives the WSAITG mode.

This paper studies model-free optimal control design and its convergence properties for linear time-invariant systems subject to probabilistic risk or chance constraints. In particular, we study a natural policy gradient (NPG)-based actor-critic (AC) algorithm with two timescales, using a Lagrangian primal-dual framework to enforce the constraint. Furthermore, the risk is defined as the probability that a function of the one-step-ahead state exceeds a user-specified threshold. To our knowledge, this is the first work to study the analytical convergence properties for NPG-based AC in a chance-constrained linear-quadratic Gaussian (LQG) regulator setting without model knowledge. We establish the coercivity and gradient dominance properties of the Lagrangian function, which ensure linear convergence and closed-loop stability during training for the actor. On the other hand, we analyse the convergence properties of the temporal difference (TD(0)) learning for the critic, applying stochastic approximation theory. Also, we demonstrate no duality gap in the constrained optimisation problem. Additionally, we have performed numerical analysis of the convergence properties and accuracy of the proposed method, comparing it with model-based chance-constrained LQR and scenario-based MPC. Results show that our approach effectively limits risk while maintaining near-optimal performance, without requiring full model knowledge or real-time optimisation.

Achieving sub-millikelvin electron temperatures in nanoelectronic devices could unveil new transport phenomena, extend quantum coherence times, and enhance the precision of quantum metrology. However, maintaining such low temperatures continuously remains a long-standing challenge. Here, we propose and simulate an on-chip cooling cycle that harnesses the entropy difference between an electron liquid (EL) and a Wigner crystal (WC) in flavor-degenerate flat-band materials. Cooling is driven by a current through a device with a locally gated region. Within this region, the charge carrier density is tuned such that a WC forms beneath the gate. As carriers transition from an EL to WC phase, their entropy increases, extracting heat and the sliding WC advects this heat along the device. The heat is then released when carriers transition back to the EL phase, which establishes distinct hot and cold regions and a steady temperature gradient over the device. Simulations show net cooling for sufficiently low current densities, typically below $1~\mathrm{nA}/μ\mathrm{m}$, whereas Joule heating dominates at higher currents. Within the gated region, we estimate cooling powers of up to $8.4~\mathrm{aW}/μ\mathrm{m}$ at a bath temperature of $4~\mathrm{mK}$. Our approach can achieve electron temperatures well below $1~\mathrm{mK}$ under suitable conditions, promising a route towards continuous on-chip cooling in this temperature regime. Our approach applies to any flat-band material with low-energy flavor degeneracy (valley and/or orbital) and low disorder, including gapped Bernal-stacked bilayer graphene, rhombohedral-stacked multilayer graphene, and magic-angle twisted bilayer graphene.

We introduce the notion of worst-case posterior and worst-case likelihood sensitivity. These measure, respectively, the sensitivity of the expected cost to worst-case perturbations of the posterior distribution and worst-case perturbations of the likelihood of a Bayesian model. Each defines a quantitative measure of robustness. A decision maker concerned about the sensitivity of the out-of-sample expected cost to deviations from her assumptions will want a decision for which both sensitivities are small. We derive posterior and likelihood sensitivities for uncertainty sets defined in terms of deviation measures. Posterior sensitivity vanishes when the posterior variance shrinks to zero, which occurs when parameter uncertainty is eliminated from learning. Parameter learning does not eliminate likelihood sensitivity. A distributionally robust formulation of a Bayesian optimization problem makes a near-Pareto-optimal tradeoff between performance (expected cost) and robustness (posterior and likelihood sensitivity).

Estimating viral substitution rates is central to evolutionary epidemiology, and recent interest in within-host evolution has sharpened the question of what such rates measure. I distinguish two classes of evolutionary rate estimand that are rarely separated in phylogenetic analysis: the virion-level substitution rate (VLSR), a molecular quantity counting mutational events along lineages, and consensus-level substitution rates (CLSRs), population-summary quantities counting changes in the consensus sequences. CLSRs are indexed by the consensus-generation rule. The VLSR and CLSRs are both biologically meaningful, but not interchangeable. Because the consensus-generation rule defines a given CLSR, it should be a routine reporting requirement. This reflection should help analysts make more informed methodological choices when working with sets of virus sequences.

Assessing the dynamical stability of computationally predicted metal--organic frameworks (MOFs) is essential to distinguish synthetically feasible structures from dynamically unstable ones. However, reliable first-principles phonon calculations on these systems remain challenging: their large, flexible unit cells and soft collective modes make the vibrational spectrum highly sensitive to the numerical settings. Using MOF-5 as a representative case study, we establish a finite-displacement workflow to identify and isolate the origins of imaginary phonon modes. We demonstrate how numerical force convergence thresholds, real-space grid resolutions, symmetry-standardization protocols, and alternative unit-cell representations can qualitatively and spuriously alter the predicted lattice stability. Once numerical noise is confidently excluded, the remaining imaginary modes can be analyzed through mode mapping or stochastic Monte Carlo symmetry-breaking distortions to locate lower-energy local minima. This protocol provides a robust, transferable strategy for the reliable assessment of dynamical stability and lattice vibrations in flexible porous frameworks.

Processing astronomical data can take up a significant amount of researchers' time. The 80cm telescope at Xizang University is currently in its trial operation phase; however, it lacks a data processing program, which makes efficient handling of the data it generates an urgent concern. To address this issue, we have developed an automatic pipeline for processing photometric data and extracting light curves using Python 3. This pipeline has several advantages, including high speed, ease of use, and modularity. The differential photometric accuracy of this pipeline is comparable to that of data processing programs used by other similar telescopes. This development effectively overcomes the limitations of manually processing data, providing efficient and reliable support for future studies of variable stars. The pipeline has already been integrated into the telescope's operational system.

We define cone-additive functions for random walks on free products of countable graphs. These functions satisfy a limit theorem under mild assumptions. In fact, cone-additivity is present in several well-studied notions, like entropy, asymptotic range and drift. Cone-additivity can be seen as a separation property by space -- a quite different perspective than the well-studied concept of sub-additivity in the context of free products of groups, which is a separation by time. In our inhomogeneous setting of free products of graphs, this separation by space allows us to deduce new limit theorems for travelling salesman problems (that is, distance functions of lamplighter random walks on free products), for weight functions on edges and the range of the $r$-th visit.

Spiking neural networks (SNNs) provide event-driven and low-power computation inspired by biological neural systems, but current implementations rely on von Neumann graphics processing units (GPUs) and central processing units (CPUs) platforms, where memory and computation bottlenecks limit energy efficiency. To address this challenge, this paper proposes an analog memristor-based spiking neural network (SNN) accelerator that integrates in-memory synaptic computation with analog integrate-and-fire (IF) neurons, eliminating multi-transistor CMOS synapse circuits and enabling asynchronous event-driven operation at the 45nm technology node. Additionally, a digital SNN accelerator is designed and optimized at the 5 nm technology node for comparison. The proposed architecture is evaluated using a predator-prey tracking task that emulates pursuit behavior. In this task, the analog SNN accelerator's inference closely matches the ideal software inference with a mean squared error (MSE) of 0.004. HSPICE simulation results show that the proposed analog SNN accelerator achieves 12.7 times lower energy consumption and 1.26 times lower delay compared to the digital baseline, demonstrating the potential of memristor-based neuromorphic circuits for energy-efficient real-time edge intelligence.

For $k\geq 1$ and $n\geq 2$, we introduce the multi-welded twin group $M_kWT_n$, a natural welded analogue of the multi-virtual twin group. We show that $M_kWT_n$ arises naturally as a quotient of the universal welded braid group $UW_n(k)$, placing it within the unified framework of universal virtual and welded braid-type groups. We establish natural quotient maps relating $M_kWT_n$ to the multi-virtual twin group $M_kVT_n$, the welded twin group $WT_n$, and the corresponding virtual and welded braid-type groups. Several structural properties of $M_kWT_n$ are obtained. In particular, we compute its abelianization, prove that its commutator subgroup is perfect for $n\ge5$, and show that the symmetric group $S_n$ is its smallest non-abelian finite quotient. We also investigate the representation theory of $M_kWT_n$. In fact, we classify all non-trivial complex homogeneous $2$-local representations of $M_kWT_n$, showing that only one family survives under the additional twin and welded relations. Furthermore, we classify all non-trivial complex homogeneous $3$-local representations of $M_2WT_n$. We further investigate the reducibility and faithfulness properties of both the $2$-local and $3$-local representations.

The generalized Langevin equation (GLE) is widely used to model complex soft-matter systems, including biomolecular dynamics, by incorporating memory effects and colored noise into coarse-grained descriptions. However, recent results suggest that combining memory with non-linear forces, ubiquitous in soft matter, introduces fundamental analytical inconsistencies. Here, using a simplified model, we investigate the practical numerical consequences of these analytical results. We show that non-linear forces generate cross-correlations with the noise, modifying the fluctuation-dissipation theorem and rendering the noise position-dependent and irreversible. This implies that the commonly assumed reversible Gaussian noise in GLE simulations fails to capture essential features of the microscopic fluctuations. For weak non-linearities, these issues can be partially resolved either by using an iterative optimization of memory or by using microscopically consistent noise, which unexpectedly synchronizes GLE trajectories with the underlying microscopic dynamics. For stronger non-linearities like high barriers or shoulders in the external potential, however, iterative reconstruction fails and we observe desynchronization, indicating that the non-linear GLE no longer correctly reproduces the microscopic dynamics. Our results show in which situations non-linear GLEs can be accurately applied and when they fail, thus providing practical guidance for their application to coarse-grain soft-matter systems.

Mass transfer is arguably the most defining interaction in binary stellar systems, yet many aspects of its physics remain poorly understood, from stability to endpoints and observable products. Comparing theory and observations is challenging because post-mass-transfer systems are studied across largely independent communities with different methods, nomenclature, and evolutionary frameworks. % We present a unified review and catalog of ongoing and post-mass-transfer binaries spanning the full stellar mass range, but restricted to systems likely to have experienced only a single episode of mass transfer (i.e., only one component is evolved). We review 16 observational classes of binary interaction products and compile a curated sample of 5,452 systems into a publicly available, community-driven catalog at https://binary-observations.github.io/post_mt_catalog/. % Using this catalog, we investigate global trends in orbital periods, eccentricities, masses, and mass ratios across post-mass-transfer binaries. We find I) non-zero eccentricities are common at all periods and system classes, with both median values and scatter increasing with period, II) the $e(\log P)$ relation depends on donor progenitor mass, with neutron-star and black-hole binaries showing the highest median eccentricities, likely reflecting effects of natal kicks, III) period distributions are broad and overlapping across evolutionary channels, and IV) the Gaia BH and NS systems are extreme in mass ratio but otherwise consistent with the general post-mass-transfer population. Together, these results support a unified empirical view of post-mass-transfer binaries that highlights several tensions between theory and observations.

Parameter-dependent dynamical systems that exhibit bifurcations pose significant computational challenges, as traditional continuation methods require repeated, costly simulations across large ranges of parameter values to capture sudden qualitative changes in the solution. In this work, we propose a systematic approach to reconstruct the branches of the entire bifurcation diagram in a single numerical solver leveraging generalized Polynomial Chaos (PC) expansion. By treating the parameter as a random variable, we cast the deterministic parameter-dependent model in a weak stochastic form, and then use a Galerkin projection to recover bifurcation branches globally across the parameter domain without iterative pointwise continuation. We show that the resulting Galerkin system, in the non-uniqueness regime, produces many discrete algebraic roots that naturally split into two classes: highly oscillatory solutions and branch-approximating ones. We develop a rigorous theoretical framework that establishes consistency, proves convergence of the branch-approximating solutions to the true steady states, and guarantees uniqueness of the Galerkin solution under suitable assumptions. Finally, we confirm these theoretical results with numerical experiments on several parameter-dependent ordinary differential equations (ODEs), demonstrating the accuracy and computational efficiency of our single-run framework in capturing complex bifurcation diagrams for both scalar and vector-valued systems.

Although mass transfer from bubbles to liquid is essential for the prediction of the efficiency of reactors, the mass transfer from bubbles is not fully understood. To determine the effect of the local velocity profile on the mass transfer for a wobbling bubble with an Eötvös number of 2 and a Morton number of 10-11, 15 simulations were performed with a Front Tracking method using a sub-grid scale model for the mass transfer in the vicinity of the interface. The vortical structures created by the bubble are influenced by the exact physical properties chosen for the liquid and gas. These changes in the vortical structures also resulted in changes in mass transfer. In addition, the vortical structures created transport barriers between the wake and the bulk of liquid, which were identified by the high-value Finite Time Lyapunov Exponents. These barriers prevent convective mass transfer from the bubble wake to the bulk of the liquid. Therefore, mass transfer from the gas phase to the bulk liquid should take into account both the mass transfer from the gas to the liquid and the transfer from the wake to the bulk of the liquid.

GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitation, the analysis and treatment of biases derived from it have been largely overlooked in existing epidemic modeling studies, raising concerns about the robustness of downstream inferences. We introduce a principled framework to quantify the impact of trajectory sparsity on key epidemic modeling outcomes across different levels of missingness. Our approach leverages a highly-complete dataset that exhibits both near-complete and sparse GPS trajectories. Near-complete trajectories provide baseline epidemic outcomes, while sparse trajectories provide realistic missingness patterns that we impose on the baseline to measure bias. In this way, we show how missing records can result in substantial underestimation of key measures of epidemic intensity, explained not only by the amount of missing data, but by more complex features of data missingness that should be taken into account when designing correction methods. Finally, we propose and evaluate a correction based on inverse probability weighting of network edges before epidemic model calibration, which is shown to reduce bias and parameter misspecification. We also demonstrate this correction on a separate anonymized sample from a commercial GPS mobility dataset and report on its effect. Together, our findings provide a first rigorous quantification of trajectory-sparsity bias in epidemic modeling, offering initial guidance on the treatment of this issue.

We study the thermal behavior of the $ρ$ meson using the isospectral family of the softwall AdS/QCD model. By computing spectral functions at finite temperature and chemical potential for different members of this family, we isolate the effect of the ground-state electromagnetic decay constant $f_1$ on the melting temperature $T_m$ of the $ρ(770)$ meson. A clear monotonic increase of $T_m$ with $f_1$ is found, supporting the interpretation of $f_1$ as a key scale controlling quarkonium dissociation. For excited states, the same qualitative trend appears but is strongly suppressed as the radial quantum number increases. Using the isospectral parameter to fix $f_1$ to its experimental value ($226$ MeV) yields a holographic model whose spectral function gives a melting temperature $T_m = 157$ MeV and a smooth crossover from confinement to deconfinement. The thermal mass shows a mild decrease near the critical point, while the width grows monotonically. Our results demonstrate that the isospectral transformation provides a controlled way to adjust ground-state decay constants without altering the mass spectrum, enabling precise studies of medium effects on vector mesons.

In this paper, we propose an integro-differential model for the spatio-temporal evolution of infectious diseases with asymptomatic transmission. The model consists of a reaction-diffusion system with an integral memory term accounting for the distribution of the incubation period. We first analyze the asymptotic behavior and the properties of the integro-differential model. Then, we prove the local existence of a weak solution of the system by means of the Faedo-Galerkin method and a compactness argument. The model is applied to simulate the geographical evolution of a disease in Lebanon.

This paper studies the control of discrete-time linear fractional-order networks, a flexible modeling framework for systems with long-range memory such as power grids, biological networks, and neuronal circuits. In contrast to the common view that fractional exponents (time-scales) are fixed parameters, we show that they can be systematically steered, together with the network coupling matrix, by appropriately designed input sequences. We first derive algebraic conditions under which the coupling matrix and the vector of fractional exponents of a given network can be reconfigured to desired values, and we characterize how truncating the infinite-memory term impacts the resulting dynamics. Building on these results, we construct an equivalent linear representation that isolates the contribution of memory, and we introduce a fractional reachability matrix that provides explicit conditions for jointly steering both network parameters and state in a finite number of steps. To address practical implementations, we further formulate an energy-constrained steering problem that incorporates actuator bounds and finite-memory approximations as a quadratic program. The framework is illustrated on low-dimensional toy examples, on larger networks with Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz topologies, and on a brain network model inferred from electrocorticography recordings of an epilepsy patient, where we showcase transitions between pre-seizure and seizure configurations.

Dependence logic extends first-order logic with dependence atoms asserting that the value of a variable is determined by the values of certain other variables. The semantics of dependence logic has a second-order character and involves sets of assignments, called teams, instead of individual assignments as in the classical Tarski semantics. Since the model-checking problem is known to be NP-complete even for quantifier-free dependence logic (DQF) formulas, researchers have pursued conditions on formulas that make this problem tractable. In 2010, Jarmo Kontinen introduced the notion of k-coherence for dependence logic formulas, where k is a positive integer. This notion asserts that if the formula is satisfied in a structure by all k-element subteams of a given team, then the given team itself satisfies the formula. It has been proved that k-coherent DQF-formulas have a tame model-checking problem, because such formulas admit a first-order rewriting. In this paper, we investigate the structural and algorithmic aspects of coherence. We show that if a DQF-formula is first-order ewritable, then it is k-coherent for some positive integer k. Thus, for DQF-formulas, coherence is equivalent to first-order rewritability. Furthermore, we show that an analogous result holds for universally quantified dependence logic formulas under a stronger notion of coherence. After this, we focus on the complexity of deciding if a given dependence logic formula is k-coherent. We establish that this decision problem is highly undecidable for arbitrary dependence logic formulas, while for DQF-formulas this problem is co-recursively enumerable. Furthermore, we pinpoint the computational complexity of the coherence problem for propositional dependence logic formulas by showing that this problem is complete for the second level of the exponential hierarchy.

The experimental validation of diffuse scattering models has long been limited by the inability to spatially separate specular and diffuse contributions in measured channels. This paper overcomes this limitation by combining super-resolution multipath component (MPC) extraction, which resolves individual propagation paths including the specular component, with digital-twin-assisted geometry, enabling the spatial separation of specular and diffuse contributions from bistatic measurements at 28~GHz. Using this framework, we provide the first measurement-driven validation of the Effective Roughness (ER) model with independent characterization of diffuse scattering across ten common building materials, each measured over 266 angular configurations and all polarization combinations (HH, HV, VH, VV). Furthermore, we extend the ER framework by proposing a novel angle-dependent cross-polarization discrimination (XPD) model, capturing the geometry-dependent nature of depolarization that is neglected in existing approaches. The proposed method reproduces the measured diffuse power trends, achieving RMSE values as low as 3 dB across the tested materials, and improves XPD prediction over the baseline constant-XPD model for nearly all material-polarization cases. These results establish a physically consistent and practically viable approach for high-fidelity channel modeling in mmWave systems.

Intertemporal choice data are usually summarized through scalar discount-rate parameters or fitted by predetermined parametric discount functions, although relevant information may lie in the shape of the whole discounting trajectory. This paper proposes a Functional Data Analysis framework for reconstructing and analyzing implicit subjective-time trajectories from discrete intertemporal equivalence judgments. Monetary equivalence responses from a multilingual questionnaire are transformed into individual discount curves, regularized by monotone smoothing, and used to recover normalized implicit subjective-time trajectories. The trajectories are examined through derivative summaries, Functional Principal Component Analysis, and clustering on standardized component scores. The empirical application, based on 107 participants, shows that heterogeneity in intertemporal choice is not fully captured by scalar discount-rate variation. The first two functional principal components explain 97.44% of the variability, indicating a low-dimensional structure. Functional clustering identifies three stable profiles of temporal deformation, supported by bootstrap stability analysis and sensitivity checks on components, algorithms, distances, smoothing specifications, and outlier treatment. Parametric benchmarks based on exponential, Weber-Fechner, and Stevens specifications provide accurate fits for many individuals, but do not fully recover the functional clustering structure. The comparison with explicit subjective-time perception measures reveals only partial alignment between implicit trajectories reconstructed from choices and directly reported temporal perception. Functional Data Analysis provides an applied statistical framework for representing intertemporal choice heterogeneity as variation in functional shape, complementing scalar discount-rate and parametric subjective-time models.

We theoretically investigate the ultrafast generation of spin and orbital magnetism in a two-dimensional gapped Dirac system with spin-orbit coupling. This system is representative of two-dimensional hexagonal semiconductors, such as transition-metal dichalcogenides that exhibit valley-selective optical selection rules arising from the valley-contrasting magnetic texture of their band structure. Using a time-dependent density-matrix formalism, we demonstrate that circularly polarized laser pulses generate nonequilibrium magnetization under both resonant and multiphoton resonant conditions. We show that the induced spin and orbital magnetic moments can be distinctly controlled via the photon energy and polarization of the driving field. Furthermore, spin and orbital dynamics originate from fundamentally different light-matter coupling mechanisms, leading to qualitatively dissimilar temporal behaviors. The orbital magnetic moment couples directly to the external electric field, resulting in faster dynamics and pronounced Rabi-like oscillations, whereas the spin response develops gradually through spin-orbit coupling. Consequently, orbital dynamics is significantly more sensitive to electron-hole dephasing than the spin response. Our results highlight the importance of properly accounting for orbital contributions in future technologies that utilize femtosecond control of magnetism.

We experimentally demonstrate a non-linear dependence of the spin noise variance on atomic density in a warm alkali vapor. Implementing high-bandwidth spin noise spectroscopy (SNS) near the D2 transition of rubidium, a quadratic spin noise contribution is shown to arise at high densities, in contrast with the linear dependence valid in non-interacting ensembles. This non-linear scaling is shown to crucially depend on the residual optical excitation of the vapor by the probe beam, suggesting it stems from atomic cross-correlations due to resonant dipole-dipole interaction (DDI) in the vapor. We support this claim by introducing an additional experimental protocol to quench the ddi, resulting in a suppression of both the quadratic scaling of the spin variance and the distortions of the spin noise spectrum induced by the interaction. These results extend the applications of SNS to the characterization of many-body correlations in complex quantum systems.

We survey the definitions and main properties of first-order (FO) definable unary operations on relational structures, called FO-transductions, and of FO-definable binary operations based on disjoint union and Cartesian product. We focus our study on Backwards Translation Theorems and Splitting Theorems that permit to express FO properties of output structures in terms of finitely many FO properties of the corresponding input ones. In the particular cases where the operations are defined by quantifier-free (QF) formulas, the quantifier-heights of the obtained sentences are no larger than those of the input ones. It follows that the class of finite models of a FO sentence is recognizable with respect to the considered QF operations. Recognizability has interesting algorithmic properties based on finite automata on terms, for structures having bounded tree-width or clique-width. We extend our results to FO sentences constructed with modulo counting existential quantifiers.

Elasto-capillarity of a droplet wetting an elastic sheet provides an interesting system, both for fundamental and applied research. The droplet sinks into the sheet and assumes the shape of a lens. To determine the equilibrium shape in simulations, we formulate a boundary element method (BEM) extending our earlier approaches, and apply the BEM to three specific protocols for the boundary conditions of the sheet. For a clamped elastic sheet, we use various morphological metrics to demonstrate that the lens shape crucially depends on the sheet thickness. Stretching the sheet isotropically, allows for an additional control parameter to influence the droplet shape and the tension in the sheet, which we quantify by radial profiles of the azimuthal and radial elastic stresses. We further demonstrate how the focal length of a liquid lens can be tuned by varying the applied tension. Finally, stretching the sheet along one direction, elongates the droplet, and the sheet shows folds and dimples.

With strong evidence in the literature showing that fairness and truthfulness are incompatible, there is a recent line of work focusing on the fairness properties of equilibria of simple fair division mechanisms, especially Round-Robin. We consider the Envy Cycle Elimination (E-C-E) procedure of Lipton et al. [23], one of the most versatile tools in fair division. While this simple and intuitive algorithm achieves allocations that are envy-free up to one item (EF1) for any number of agents and general monotone valuation functions, surprisingly little is known about its behavior when agents act strategically. We demonstrate how the presence of incentives, although highly natural and relevant for the majority of applications, completely removes the intuitive clarity in the algorithm's execution, even for a few agents and very simple valuation functions. Additionally, while in the standard algorithmic setting there is great flexibility in how the details of E-C-E are implemented, here additional specifications are needed before the procedure is clearly defined, the choice of which has a potentially huge impact on the agents' behavior. Despite these obstacles, for various natural versions of E-C-E, we give the first results on the existence of Pure Nash Equilibria of the resulting mechanisms, and show there exist versions where fairness guarantees are approximately preserved for agents who play best responses.