We study a representation of a problem that appears in numerous transport systems: $N$ servers distributed over a given space (e.g., cars on an urban network), receive random requests from arriving users who get assigned to the closest server, after which this server is replaced by a new one at a random location. We show that this creates a negative feedback loop, which we call \textit{Increasing Gap Dynamics} (IGD): when a server is assigned a spatial gap forms, which is more likely to attract new users that further widen the gap. The simplest version of our model is a one-dimensional circle, for which we derive analytical results showing that the system converges to an inefficient equilibrium, worse than both balanced and fully random distributions of servers. We prove that an optimal assignment policy always matches the user to one of its two neighbouring servers so that long gaps tend to widen. Hence, the IGD persists even when assigning optimally rather than greedily. In two dimensions, the appearance of the IGD is illustrated through simulations on a square region. Finally, simulations of a proper ride-hailing system using real data from Manhattan confirms that the IGD arises and that it is responsible for the appearance of the well-known Wild Goose Chase.

Early mobilization is a structured protocol designed to facilitate motor recovery in intensive care unit (ICU) patients with ICU-acquired weakness. This process is typically implemented by an interdisciplinary team of nurses, physical therapists, and other healthcare professionals. However, its application is often constrained by the patients' critical conditions, limited mobility, and the challenges of coordinating care within resource-intensive ICU environments. In this study, we developed a patient-centered virtual reality (VR) exergame through an interdisciplinary design process involving clinicians and therapists, tailored to the constraints of critical care. The exergame incorporates progressive mobility levels that mirror early mobilization practices, and includes an embodied avatar to provide guidance and motivation. Using Meta Quest 3 body tracking, the system captures and visualizes patients' movements, thereby providing motivational engagement and quantifiable mobility metrics. We evaluated the exergame in two stages: a dual-user study involving healthy participants and healthcare professionals or students (N = 13), and a subsequent study with cardiothoracic ICU patients (N = 18) to assess feasibility, design validity, and clinical acceptance. Across both studies, participants reported high enjoyment and engagement without discomfort or stress. Furthermore, patients demonstrated increases in movement speed, range of motion, and workspace volume of the upper body across game levels. Physiological monitoring further indicated that the exergame elicited exertion without inducing excessive cardiovascular responses. These findings highlight the feasibility of VR exergames as a clinically acceptable and engaging adjunct to early mobilization in critical care, offering a novel pathway to improve rehabilitation outcomes for ICU patients.

We develop the computational framework for gravitational wave - black hole scattering in worldline quantum field theory (WQFT) without spin. Crucially, we prove on general grounds that, in the absence of dissipation, the exponential representation of the $S$-matrix maps -- through a partial-wave transformation -- directly onto the scattering phase shift from black hole perturbation theory (BHPT), indicating an exponentiation of the WQFT amplitude itself in partial-wave space. Computing explicitly, we reproduce the BHPT phase shift without spin up to $O(G^{3})$ from WQFT. While this result is expected, it lays the groundwork for higher-precision analyses involving non-minimal effects. Along the way, we outline our efficient diagram generation technique and include a pedagogical discussion on the computation of the required two-loop integrals.

Let $G$ be a simple connected graph with vertex set $V(G)$ and edge set $E(G)$. A $k$-matching of a graph $G$ is a function $f:E(G)\rightarrow \{0,1,\ldots, k\}$ satisfying $\sum_{e \in E_G(v)} f(e) \leq k$ for every vertex $v \in V(G)$, where $E_G(v)$ is the set of edges incident with $v$ in $G$. A $k$-matching of a graph $G$ is perfect if $ \sum_{e \in E_G(v) } f(e) = k $ for any vertex $v \in V(G)$. The $k$-Berge-Tutte-formula of a graph $G$ is defined as: \[ \defk(G) = \max_{S \subseteq V(G)} \begin{cases} k \cdot i(G - S) - k|S|, & k \text{ is even;} \\[6pt] \odd(G - S) + k \cdot i(G - S) - k|S|, & k \text{ is odd.} \end{cases} \] A $k$-barrier of the graph $G$ is the subset $S \subseteq V(G)$ that reaches the maximum value in $k$-Berge-Tutte-formula. A connected graph \( G \) of odd (even) order is a {generalized factor-critical (generalized bicritical) graph about integer \( k \)-matching}, abbreviated as a \( \mathrm{GFC}_k (\mathrm{GBC}_k)\) graph, if $\emptyset$ is a unique $k$-barrier. When $k$ is odd, let \( 1 \leq d \leq k \) and \( |V(G)| \equiv d \pmod{2} \). If for any \( v \in V(G) \), there exists a \( k \)-matching \( h \) such that $\sum_{e \in E_G(v)} h(e) = k - d$ {and} $\sum_{e \in E_G(u)} h(e) = k$ for any \( u \in V(G) - \{v\} \), then \( G \) is said to be \( k \)-\( d \)-critical. In this paper, we provide sufficient conditions in terms of distance spectral radius to ensure that a graph has a perfect $k$-matching and a graph is \( k \)-\( d \)-critical, $\mathrm{GFC}_k$ or $\mathrm{GBC}_k$, respectively.

We compute the critical exponents of the zero-temperature spin glass transition in a field on a one-dimensional long-range model, a proxy for higher-dimensional systems. Our approach is based on a novel loop expansion within the Bethe $M$-layer formalism, whose adaptation to this specific case is detailed here. The resulting estimates provide crucial benchmarks for numerical simulations that can access larger system sizes in one dimension, thus offering a key test of the theory of spin glasses in a field.

Millions of people now use non-clinical Large Language Model (LLM) tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool's "active ingredients" for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool's pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies - LLM tools as supplements, drugs, yoga instructors, and primary care providers - can scaffold further conversations about their responsible design.

The Pay-as-Clear (PaC) mechanism currently used in the European electricity market can generate significant submarginal profits for renewable sources when the clearing price is determined by the marginal offers of gas-fired generation units and the cost of natural gas exceeds certain levels. This exposes consumers to high price volatility related to the cost of natural gas. This report analyzes the recently proposed Segmented Pay-as-Clear (SPaC) mechanism as a market alternative, evaluating its system cost-effectiveness through simulations based on Reinforcement Learning (Q-Learning) to model the strategic behavior of operators. Three market models are compared, the two classic Pay-as-Clear (PaC) and Pay-as-Bid (PaB) along with SPaC, under two scenarios: a simplified one based on the 2030 NECP objectives and one built on the portfolios of ten operators obtained from the GME's 2024 public offers. The results show that the SPaC market clearing mechanism reduces intramarginal profits and price volatility compared to PaC, while maintaining fair participation incentives for all operators, and is more robust than PaB to the exercise of market power in oligopolistic contexts. The developed framework can serve as a support tool for regulators and policymakers in the evaluation of proposals for market design reforms.

We study the formation, detection and coarse-graining of black holes in AdS/CFT, with an emphasis on the tension between boundary unitarity and the production of mixed state Hawking radiation in the bulk. We construct CFT states dual to black hole formation and evaporation by colliding bulk particle wavepackets at trans-Planckian energy. We propose boundary probes which are able to distinguish small AdS black holes from other states within the microcanonical ensemble. We investigate different coarse-graining prescriptions acting on the evolving CFT state, including averaging over CFT data, Hamiltonians and time windows, and compare their purities to those expected from the bulk semiclassical description. Our results clarify how semiclassical black hole behaviour can arise from an ensemble-averaging of the exact unitary dynamics, and take a step towards a better understanding of coarse-graining in the single-sided black hole information problem.

Polylogarithmic functions (polylogs) in $n$ variables can be viewed as elements of $(U\mathfrak{p}_{m})^*$, the dual of the universal enveloping algebra of the Lie algebra $\mathfrak{p}_{m}$ of infinitesimal spherical pure braids with $m=n+3$ strands. Polylogs with $m=4,5$ are used in the theory relating double shuffle relations and Drinfeld associators \cite{furusho_double_2011}. We give explicit formulas for elements of $(U\mathfrak{p}_{m})^*$ representing polylogs, and compute the left ideal $J_{m} \subset U\mathfrak{p}_{m}$ given by their joint kernel. We introduce Lie subalgebras $\mathfrak{k}_{m}=\mathfrak{p}_{m} \cap J_{m}$, and we compute them for $m=4, 5$.

Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the application of first-principles simulations to interfaces is hampered by the computational cost for evaluating the electronic structure for the large number of atoms typically involved. We hereby present MAD-SURF, a machine learning interatomic potential specifically tailored for molecular adsorption on coinage metal surfaces. Trained on a broad dataset spanning diverse molecules, adsorption motifs, surfaces, molecular dynamics trajectories and non-covalent aggregates, MAD-SURF achieves accuracy comparable to the underlying DFT reference while enabling simulations orders of magnitude faster than density functional theory. The model reliably reproduces energies, forces and adsorption geometries across the three coinage metal substrates. We demonstrate its capabilities on experimentally characterized systems, including organic monolayers, polycyclic aggregates, flexible biomolecules and the long-range herringbone reconstruction of gold. By merging accuracy, speed, and generalizability, MAD-SURF offers a practical framework for accelerating atomistic simulations and advancing data-driven workflows in surface science.

The Great Oxidation Event (GOE), which marked the transition from an anoxic to an oxygenated atmosphere, occurred 2.4 billion years ago on Earth, several hundreds of millions of years after the emergence of oxygenic photosynthesis. This long delay implies that specific conditions in terms of biomass productivity and burial were necessary to trigger the GOE. It could be a limiting factor for the development of oxygenated atmospheres on inhabited exoplanets. In this study, we explore the specificities of a terrestrial planet in the habitable zone of an M dwarf for a GOE. Using a 1D coupled photochemical-climate model, we simulate the atmospheric evolution of TRAPPIST-1 e, an Earth-like exoplanet, exploring the effect of oxygen sources (biotic or abiotic). Our results show that the stellar energy distribution promotes O3 production at lower O2 concentrations compared to Earth, and the ozone layer on TRAPPIST-1 e forms more efficiently. This lowers the threshold for atmospheric oxidation, suggesting that the GOE on TRAPPIST-1 e would occur quickly after the rise of oxygenic photosynthesis, up to 1Gyrs earlier than on Earth, and would reach O2 enabling oxygenic respiration and thus the development of animals. We may question whether this is a general behavior around several M-stars. Furthermore, we discuss how the overproduction of ozone could make O3 detection possible using the James Webb Space Telescope, providing a potential method to observe oxygenation signatures on exoplanets in the near future. Previous studies predicted that for an Earth-like atmosphere O3 would require over 150 transits for detection, but our results show that significantly fewer transits could be needed.

This demonstration paper presents CMind, an artificial intelligence agent for localizing C memory bugs. The novel aspect to CMind is that it follows steps that we observed human programmers perform during empirical study of those programmers finding memory bugs in C programs. The input to the tool is a C program's source code and a bug report describing the problem. The output is the tool's hypothesis about the reason for the bug and its location. CMind reads the bug report to find potential entry points to the program, then navigates the program's source code, analyzes that source code, and generates a hypothesis location and rationale that fit a template. The tool combines large language model reasoning with guided decision making we encoded to mimic human behavior. The video demonstration is available at https://youtu.be/_vVd0LRvVHI.

Locally repairable codes (LRCs) were originally introduced to enable efficient recovery from erasures in distributed storage systems by accessing only a small number of other symbols. While their structural properties-such as bounds and constructions-have been extensively studied, the performance of LRCs under random erasures and errors has remained largely unexplored. In this work, we study the error- and erasure-correction performance of binary linear LRCs under majority-logic decoding (MLD). Focusing on LRCs with fixed locality and varying availability, we derive explicit upper bounds on the probability of decoding failure over the memoryless Binary Erasure Channel (BEC) and Binary Symmetric Channel (BSC). Our analysis characterizes the behavior of the bit-error rate (BER) and block-error rate (BLER) as functions of the locality and availability parameters. We show that, under mild growth conditions on the availability, the block decoding failure probability vanishes asymptotically, and that majority-logic decoding can successfully correct virtually all of error and erasure patterns of weight linear in the blocklength. The results reveal a substantial gap between worst-case guarantees and typical performance under stochastic channel models.

Field-angle-resolved specific-heat measurements were performed on a clean single crystal of a spin-triplet superconductor UTe$_2$ with $T_{\rm c}=2.1$ K and a low residual electronic specific heat. At low temperatures, the specific heat exhibits a linear dependence on the magnetic field when the field is applied precisely along the $b$ axis, in stark contrast to its rapid increase at low fields for other orientations. This pronounced anisotropy suggests the presence of nodal quasiparticle excitations with the Fermi velocity predominantly aligned along the $b$ axis. Considering the characteristic field-angle dependences of both the specific heat and the upper critical field, these observations are broadly compatible with theoretical models that assume a superconducting gap structure featuring either point nodes consistent with $B_{\rm 2u}$ symmetry, allowed in the infinitely strong spin-orbit coupling scheme, or line nodes confined to flat regions of the quasi-two-dimensional Fermi surface, consistent with $^3B_{\rm 3u}$ symmetry in the finite spin-orbit classification scheme. These results yield crucial hints for resolving the pairing symmetry of UTe$_2$, paving the way for a deeper understanding of its spin-triplet superconductivity.

Recently, a new method was introduced for computing $V_{g,1}(b)$, the Weil-Petersson volumes of the moduli space of Riemann surfaces of genus $g$ with one geodesic boundary of length $b$, various supersymmetric generalizations of them, as well as analogous quantities in intersection theory. The physical setting is the computation of a certain one-point function in a variety of models of 2D gravity for which there is a double-scaled random matrix model (RMM) description. The method combines perturbative solutions of two ordinary differential equations (ODEs), the Gel'fand-Dikii resolvent equation, and the RMM's string equation. In this paper, we extend the method to extract non-perturbative information about the $V_{g,1}(b)$ (and their analogues) that is naturally contained in the full ODEs, providing an efficient prescription for computing the transseries coefficients of the one-point correlation function, fully incorporating ZZ-brane and FZZT-brane effects, and for the first time, mixed ZZ-FZZT-effects. We use as a case study the (2,3) minimal string, computing perturbative and non-perturbative quantities, comparing them to perturbative results from topological recursion, and to results from the recent non-perturbative topological recursion framework. As a particularly powerful further application we provide general predictions for the large order in $g$ growth of $V_{g,1}(b)$, and apply them to JT gravity, finding agreement with known results, and for analogous quantities in ${N} {=} 1$ JT supergravity, proving a conjecture of Stanford and Witten. Our predictions yield new growth formulae for the cases of ${N} {=} 2$ and ${N}{=}4$ JT supergravity.

We study traces of Hecke operators on spaces of elliptic cusp forms and Drinfeld cusp forms and show that, modulo any prime power, these traces are periodic in the weight.

We investigate a unitary matrix model with a complex potential with Fisher-Hartwig singularities. We show that the model exhibits finite-$N$ phase transitions. The order of the phase transition is coupling-dependent. At large-$N$, these transitions are replaced by third-order Gross-Witten-Wadia transitions between multiple ungapped phases and a single gapped phase, with transitions between ungapped phases forbidden. At both finite and large $N$, the phases are characterized by the locations of the Fisher-Hartwig singularities in the complex plane.

Optical spin textures with nontrivial topology hold promise for structured light and photonic information processing, yet their generation typically relies heavily on externally structured light with care. This raises questions about their universal existence and true robustness. Here, we uncover and experimentally verify a meron-like spin texture that emerges intrinsically in focused fields, without any wavefront engineering. This intrinsic meron spin texture, unlike their externally engineered counterparts, exhibits exceptional robustness against a wide range of inputs, including partially polarized and spatially disordered pupils corrupted by decoherence and depolarization. We attribute its resilience to topological protection from phase vortices in the focal field. Our findings reveal a naturally occurring spin structure that is intrinsic to the focused field with exceptional robustness against noise, which complements the existing externally engineered ones. It offers new ingredients into topological spin textures in optics and enriches their potentials for disorder-resilient photonic applications.

We study identification of differentiated product demand from market-level data when product characteristics can be endogenous. Past work suggests nonparametric identification may be impossible: that is, in addition to standard price instruments, exogenous characteristic-based instruments are essentially necessary to identify sufficiently flexible demand models with standard index restrictions. We show, however, that price counterfactuals are nonparametrically identified using recentered instruments -- which combine exogenous price instruments with possibly endogenous product characteristics -- under a weaker index restriction and a new condition we term faithfulness. We argue that faithfulness, like the usual completeness condition for nonparametric instrumental variable identification, is best viewed as a technical requirement on the strength of identifying variation rather than a substantive economic or statistical restriction. We show the two conditions are closely related, though generally distinct. We conclude with several practical implications for the parametric estimation of demand counterfactuals.

We study energy dissipation and propagation of information encoded by Josephson vortices in two types of circular shift register: a) a uniform register composed of sections of discrete Josephson transmission lines (JTL) forming a closed loop with a flux pump allowing to change the number of moving fluxon; b) a nonuniform register composed of sections of the regular JTL and sections of JTLs utilizing nSQUIDs - dc-SQUIDs with negative inductance between their arms - instead of single Josephson junctions. nSQUIDs are parametric devices with a flexible double-well potential that were proposed as components for reversible computing. For the uniform register, we demonstrate the energy dissipation per bit-shift operation below the Landauer's thermodynamic limit $E_T=k_BTln2$ up to propagation delays of ~0.7 ns, corresponding to the circular information motion with frequencies up to ~1.4 GHz. This does not contradict Landauer's minimum energy requirement for computations since information is not destroyed. For the nonuniform register, we find the minimum energy dissipation per bit-shift of about 16$E_T$ and attribute this to a nonuniform movement of vortices and energy barriers between the regular JTL and nSQUID sections. Differences of Josephson vortex propagation in both types of circular registers are discussed based on the measured current-voltage characteristics, extracted effective resistance and the terminal speed of Josephson vortices, and their dependences on the number of moving vorticies. nSQUID inductance connecting JJs to the ground leads to an unusual type of lossless discrete transmission line with frequency-dependent impedance and propagation speed, both different from the regular JTLs.

We generalize the information lattice, originally defined for one-dimensional open-boundary chains, to characterize quantum many-body states in higher-dimensional geometries. In one dimension, the information lattice provides a position- and scale-resolved decomposition of von Neumann information. Its generalization is nontrivial because overlapping subsystems can form loops, allowing multiple regions to encode the same information. This prevents information from being assigned uniquely to any one of them. We address this by introducing a higher-dimensional information lattice in which local information is defined through an inclusion-exclusion principle. The inclusion-exclusion local information is assigned to the lattice vertices, each labeled by subsystem position and scale. We implement this construction explicitly in two dimensions and apply it to a range of many-body ground states with distinct entanglement structures. Within this position- and scale-resolved framework, we extract information-based localization lengths, direction-dependent critical exponents, characteristic edge mode information, long-range information patterns due to topological order, and signatures of non-Abelian fusion channels. Our work establishes a general information-theoretic framework for isolating the universal scale-resolved features of quantum many-body states in higher-dimensional geometries.

We establish a general matched filter principle for order flow normalization: optimal normalization must match the scaling behaviour of the signal-generating process. For capacity-constrained institutional investors, market capitalization normalization ($S^{MC}$) is the matched filter; for volume-targeting traders (e.g., VWAP/TWAP algorithms), trading value normalization ($S^{TV}$) is optimal. Monte Carlo simulations confirm this principle works bidirectionally, with matched filters achieving up to $1.99\times$ higher signal correlation. Empirical validation using 2.7 million stock-day observations from the Korean market (2020--2024) reveals symmetric normalization dominance across investor types: domestic institutional flows predict next-day returns significantly under $S^{MC}$ ($t = 9.65$), while foreign flows exhibit stronger predictability under $S^{TV}$ ($t = 16.35$) -- with no sign reversal at longer horizons, indicating durable private information rather than temporary price impact. These findings motivate the ``Informed Executor'' hypothesis: sophisticated foreign investors possess genuine private information but employ volume-targeting algorithms for stealth execution -- volume-scaling reflects execution methodology, not absence of information. Information-theoretic validation using KL divergence independently corroborates these results. The matched filter principle generalises to any market where signal scaling varies across trader types, with implications for trading algorithms, factor construction, and market microstructure methodology.

Throughout history, a prevailing paradigm in mental healthcare has been one in which distressed people may receive treatment with little understanding around how their experience is perceived by their care provider, and in turn, the decisions made by their provider around how treatment will progress. Paralleling this offline model of care, people who seek mental health support from artificial intelligence (AI)-based chatbots are similarly provided little context for how their expressions of distress are processed by the model, and subsequently, any reasoning or theoretical grounding that may underlie model responses. People in severe distress who turn to AI chatbots for support thus find themselves caught between black boxes, contending with unique forms of agony that arise from these intersecting opacities. In this paper, we argue that the distinct psychological state of individuals experiencing severe mental distress uniquely necessitates a higher standard of end-user interpretability in comparison to general AI chatbot use. We propose a reflective interpretability approach to AI-mediated mental health support, which nudges users to engage in an agency-preserving and iterative process of reflection and interpretation of model outputs, towards creating meaning from interactions (rather than accepting outputs as directive instructions). Drawing on interpretability practices from four mental health fields (psychotherapy, crisis intervention, psychiatry, and care authorization), we describe concrete design approaches for reflective interpretability in AI-mediated mental health support, including role induction, prosocial advance directives, intervention titration, and well-defined mechanisms for recourse, alongside a discussion of potential risks and mitigation measures.

All-optical switching in a ferromagnetic spin valve is studied here using atomistic spin drift-diffusion dynamics, which includes contributions from spin pumping and superdiffusive transport. We find the switching is governed principally by spin-polarized currents due to non-equilibrium hot electrons excited by the laser pulse and re-equilibration currents. In particular, an initial superdiffusive forward flow of electrons, polarized by the free layer, is generated. This drives parallel to antiparallel switching of the free layer through accumulation of minority spins at the reference layer. A diffusive backward flow of electrons, repolarized by the reference layer, follows the initial superdiffusive flow as the charge distribution re-equilibrates. Due to the pulse width-dependent asymmetric amplitudes of the forward and backward transients, the latter can drive antiparallel to parallel switching, and create multi-domain structures at higher laser fluences and longer pulses. The results obtained here are in agreement with experimental observations, providing a framework for self-consistent modelling of all-optical switching in metallic heterostructures.

In the context of linear control systems, a commonly-held intuition is that negative and positive feedback cannot both be stability enhancing. The canonical linear prototype is the scalar system $\dot x=u$ which, under negative linear feedback $u=-kx$ ($k >0$) is exponentially stable for all $k >0 $, whereas the lack of exponential instability of the (marginally stable) uncontrolled system is amplified by positive feedback $u=kx$ ($k >0)$. By contrast, for nonlinear systems it is shown, by example, that this intuitive dichotomy may fail to hold.

In this paper, we consider the parameterization of the spectra of the three-state critical Potts quantum chain with integrable twisted boundary conditions in terms of Bethe ansatz type equations. The Bethe equations are found by investigating the structure of the eigenvalues of the respective twisted transfer matrices, and with the help of certain identities satisfied by the product of transfer matrix operators. We have studied the completeness of the spectrum in terms of the Bethe roots for small lattice sizes and have computed the eigenstate momenta. We found that the spins of the low-lying excitations can have fractional values in accordance with predictions of the underlying conformal field theory. We argue that our framework can be used to build integrable Hamiltonians whose spectra are determined by mixing different toroidal boundary conditions.

We study the topological obstructions of dynamical convexity on contact manifolds focusing on fillability by cotangent bundles and subcritical surgeries. Using links to algebraic geometry, we motivate and define a stronger version of dynamical convexity, and investigate the topology of these manifolds. More precisely, we show that strongly dynamically convex contact manifolds cannot arise as a unit cotangent bundle $(ST^*M,λ_{std})$ of a closed manifold $M$ and in particular that simply connected dynamically convex contact manifolds cannot be filled by cotangent bundles. We demonstrate that dynamical convexity can be used to recover homotopy groups of topologically simple fillings with vanishing symplectic homology. We also show obstructions to dynamical convexity that come from studying different kinds of subcritical surgeries.

We investigate whether the structural connectivity of urban road networks helps explain dengue incidence in Recife, Brazil (2015--2024). For each neighborhood, we compute the average \emph{communicability curvature}, a graph-theoretic measure capturing the ability of a locality to influence others through multiple network paths. We integrate this metric into Negative Binomial models, fixed-effects regressions, SAR/SAC spatial models, and a hierarchical INLA/BYM2 specification. Across all frameworks, curvature is the strongest and most stable predictor of dengue risk. In the BYM2 model, the structured spatial component collapses ($ϕ\approx 0$), indicating that functional network connectivity explains nearly all spatial dependence typically attributed to adjacency-based CAR terms. The results show that dengue spread in Recife is driven less by geographic contiguity and more by network-mediated structural flows.

The response of a MoRe-based superconducting resonator operating near 5 K to pulsed infrared irradiation is investigated, and the underlying physical mechanisms are analyzed. The device exhibits a pronounced nonlinear response dominated by nonequilibrium quasiparticle dynamics rather than uniform thermal heating. Infrared pulses produce strong distortions of the resonance curve and a transient decrease in the resonance frequency, consistent with increased kinetic inductance caused by quasiparticle generation. The frequency shift scales approximately linearly with absorbed power, whereas the dissipation response saturates at higher powers, indicating the formation of a nonequilibrium steady-state quasiparticle population. These observations demonstrate a transition from a linear pair-breaking regime to a saturated dissipation regime, likely associated with a quasiparticle relaxation bottleneck or partial suppression of the smaller superconducting gap in MoRe. The results highlight the relevance of nonequilibrium processes in MoRe and confirm its potential for microwave kinetic-inductance detector applications.

We develop a practical approach to electrically tuning the nonlinear photoresponse of two-dimensional semiconductors by explicitly incorporating a static out-of-plane electric field into the electronic ground state prior to optical excitation, as a gate bias. The method is implemented by dressing a Wannier-interpolated Hamiltonian with the field through its position matrix elements, which allows the gate bias to modify orbital hybridization and band dispersion beyond perturbative treatments. Within the independent-particle approximation, the resulting second-order (shift) conductivity is evaluated for both centrosymmetric and non-centrosymmetric layered systems. Applied to MoS$_2$, the approach captures the emergence of a finite shift current in centrosymmetric bilayers and the tunability of intrinsic responses in polar structures. The shift conductivity rises linearly at small fields and saturates at higher intensities, reflecting the competition between the growing shift vector and the weakening interband coupling as resonant transitions move away from high-symmetry valleys. A Taylor expansion of the field-dressed conductivity connects this behavior to the third-order optical response, revealing a unified picture of field-induced nonlinearities. These results establish field dressing of Wannier Hamiltonians as a practical route to model and predict nonlinear photocurrents in layered materials.