Scientific research increasingly depends on multi-author collaboration, yet the systems used to allocate authorship credit remain vulnerable to conflict, strategic behavior, and project breakdown. Although prior work has shown that authors may rationally issue ultimatums over authorship order within a single manuscript, much less is known about how such behavior unfolds over repeated collaborations embedded in evolving academic networks. In this study, we develop a repeated, networked game-theoretic model of co-authorship in which researchers form collaborations over time, accumulate reputation through an evolving friendship network, and, in a subset of cases, learn strategic behavior through deep reinforcement learning. Using large-scale agent-based simulations, we compare myopic and forward-looking authors across mixed populations. We find that strategic agents do not raise fewer ultimatums than greedy agents, but instead learn to avoid insisting after rejection, thereby eliminating destructive manuscript termination. As strategic prevalence increases, paper destruction falls from 0.120 to 0.000 per paper, completion rates rise from 0.853 to 0.970, and average completed papers per agent increase from 15.2 to 16.9. Strategic agents also obtain a substantial utility advantage, reaching 30.8\% when rare, while overall inequality remains stable. These results suggest that reputational feedback and long-term incentives can make academic collaboration more resilient, offering a computational testbed for designing fairer and more productive authorship policies.

In many systems, communication proceeds by broadcasting rather than single source-target routing, but network structures that maximize signal lifetime are not well understood. Degree correlations are known to influence robustness and spreading, yet their effect on signal persistence has remained unclear. Here we introduce Copy-Spread-Annihilate dynamics, a minimal synchronous broadcasting model with annihilation. We show that signal lifetimes vary non-monotonically with assortativity and are maximized near neutral assortativity, where hub-driven amplification is strong but annihilation via short cycles is still limited. Applying this framework to the mouse connectome suggests assortativity as a structural control parameter for broadcast signal persistence in brain-like and other complex networks.

We present a symmetry-resolved classification of two-dimensional spin-flip small-angle neutron scattering (SANS) patterns arising from dilute ensembles of spherical nanoparticles hosting magnetic vortex states. Based on a linear vortex ansatz with an axially symmetric distribution of vortex axes and the corresponding analytical expression for the orientationally averaged spin-flip SANS cross section, we show that the angular scattering patterns organize into four distinct symmetry regimes: a four-fold anisotropy corresponding to coherent field-aligned magnetization, vertical and horizontal two-fold anisotropies associated with aligned and isotropically distributed vortex ensembles, and an isotropic ring-like condition separating the two two-fold regimes. The corresponding symmetry boundaries are obtained analytically and define a compact symmetry landscape in the parameter space of vortex amplitude and vortex-axis distribution width. Comparison with a nonlinear vortex profile shows that these symmetry regions are robust with respect to the detailed radial structure of the vortex core. The angular anisotropies are therefore governed primarily by rotational symmetry and by the statistical distribution of vortex axes, providing a compact and model-transparent classification framework of experimental polarized SANS data.

The SCFT/VOA correspondence provides a powerful framework for studying 4d $\mathcal N=2$ superconformal field theories (SCFTs) through the mathematical machinery of 2d vertex operator algebras (VOAs). It captures the Schur operators of the underlying SCFT, whose spectrum is encoded by the Schur index and its refinement, the Macdonald index. While the Schur index is identified with the vacuum character of the associated VOA, a general VOA-based derivation of the Macdonald index has remained elusive. In this letter, we propose a novel and intrinsic method for recovering a special non-Schur limit of the Macdonald index directly from the VOA. The construction requires no additional assumptions and applies whenever the underlying 4d theory is unitary. We test the proposal in a variety of examples, and further extend it to the case with surface defects, suggesting a notion of graded unitarity in the presence of defects. Our method also introduces a new class of series for general VOAs, analogous to but distinct from the conventional character, and potentially useful in broader contexts.

We derive an asymptotic expansion for off-diagonal coherent-state matrix elements of non-polynomial operators in gauge theories admitting holomorphic coherent-state representations. The derivation combines stationary-phase analysis with an operator-level treatment of the Taylor remainder, and yields explicit semiclassical error control under stated assumptions. As a primary application, we formulate the expansion for volume and flux related operators in Loop Quantum Gravity and compare it with the standard diagonal expansion proposed in arXiv:gr-qc/0607101. By organizing the expansion around the genuine off-diagonal Berezin symbol rather than a diagonal expectation value, the resulting formula preserves the full holomorphic structure of the geometric phase and reproduces benchmark matrix elements accurately in the numerical regimes tested here, particularly when the coherent-state labels are well separated.

Given an $H$-minor-free graph $G$ and an integer $k$, our main technical contribution is sampling in randomized polynomial time an induced subgraph $G'$ of $G$ and a tree decomposition of $G'$ of width $\widetilde{O}(k)$ such that for every $Z\subseteq V(G)$ of size $k$, with probability at least $\left(2^{\widetilde{O}(\sqrt{k})}|V(G)|^{O(1)}\right)^{-1}$, we have $Z \subseteq V(G')$ and every bag of the tree decomposition contains at most $\widetilde{O}(\sqrt{k})$ vertices of $Z$. Having such a tree decomposition allows us to solve a wide range of problems in (randomized) time $2^{\widetilde{O}(\sqrt{k})}n^{O(1)}$ where the solution is a pattern $Z$ of size $k$, e.g., Directed $k$-Path, $H$-Packing, etc. In particular, our result recovers all the algorithmic applications of the pattern-covering result of Fomin et al. [SIAM J. Computing 2022] (which requires the pattern to be connected) and the planar subgraph-finding algorithms of Nederlof [STOC 2020]. Furthermore, for $K_{h,3}$-free graphs (which include bounded-genus graphs) and for a fixed constant $d$, we signficantly strengthen the result by ensuring that not only $Z$ has intersection $\widetilde{O}(\sqrt{k})$ with each bag, but even the distance-$d$ neighborhood $N^d_{G}[Z]$ as well. This extension makes it possible to handle a wider range of problems where the neighborhood of the pattern also plays a role in the solution, such as partial domination problems and problems involving distance constraints.

We introduce a simple new method, based on the Caffarelli-Silvestre extension and a Duhamel-type formula, to derive exact pointwise identities for fractional commutators and nonlinear compositions associated with the fractional Laplacian on general Riemannian manifolds. As applications, we obtain a pointwise fractional Leibniz rule, a fractional Bochner's formula with an explicit Ricci curvature term, apparently the first of this kind, and exact remainders in the Córdoba-Córdoba and Kato inequalities for the fractional Laplacian. All these formulas are new even in the Euclidean space.

This paper studies an unmanned aerial vehicle (UAV) position and attitude sensing problem, where a base station equipped with an antenna array transmits signals to a predetermined potential flight region of a flying UAV, and exploits the reflected echoes for wireless imaging. The UAV is represented by an electromagnetic point cloud in this region that contains its spatial information and electromagnetic properties (EPs), enabling the unified extraction of UAV position, attitude, and shape from the reconstructed point cloud. To accomplish this task, we develop a generative UAV sensing approach. The position and signal-to-noise ratio embedding are adopted to assist the UAV features extraction from the estimated sensing channel under the measurement noise and channel variations. Guided by the obtained features, a conditional diffusion model is utilized to generate the point cloud. The simulation results demonstrate that the reconstructed point clouds via the proposed approach present higher fidelity compared to the competing schemes, thereby enabling a more accurate capture of the UAV attitude and shape information, as well as a more precise position estimation.

This study investigates the spatial confinement of topological $π$-modes in one-dimensional chiral-symmetric systems. In conventional periodic and quasiperiodic structures, edge-mode wave functions inevitably penetrate the bulk. To suppress this, inverse design of a potential sequence is performed using a generative model under a global topological constraint. The generated sequence reveals a characteristic structure consisting of a topological boundary layer and a macroscopic S-dense domain, leading to enhanced confinement ($ξ=0.85$) while preserving topology. Based on the physical principle extracted from this result, a minimal heterostructure composed of only two S-blocks is manually constructed, which further reduces the localization length to $ξ=0.75$. These results provide a compact design principle for strongly localized topological states.

ErgoAI is a high level, multi-paradigm logic programming language and system developed by Coherent Knowledge Systems as an enhancement of and a successor to the popular Flora-2 system. ErgoAI is oriented towards scalable knowledge representation and reasoning, and can exploit both structured knowledge as well as knowledge derived from external sources such as vector embeddings. From the start, ErgoAI (and Flora-2 before it) were designed to exploit the well-founded semantics for reasoning in a multi-paradigm environment, including object-based logic (F-logic) with non-monotonic inheritance; higher order syntax in the style of HiLog; defeasibility of rules; semantically clean transactional updates; extensive use of subgoal delay for handling unsafe queries and for better performance; and optional support for bounded rationality at a module level. Although Flora-2 programs are compiled into XSB and adopt many Prolog features, ErgoAI is altogether a different language and system. Under consideration in Theory and Practice of Logic Programming (TPLP).

Precise homogeneous stellar characterisation is crucial for our understanding of the physical properties of exoplanets, their demographics and the environment from which they are formed. We present a homogeneous catalogue of 717,807 TESS FGK dwarfs and early subgiants, making use of isochrones along with Gaia DR3 inputs of photometry, parallax and spectroscopic temperature and metallicity, thus providing one of the largest homogeneous catalogues of stellar ages for TESS stars to date. We determine values for distance, $\log g$, $[\mathrm{M/H}]$, $T_{\mathrm{eff}}$, radius, mass and age. For our best fit values, we calculate absolute median errors of $0.06\,R_\odot$, $0.05\,M_\odot$,$104\,K$ and $2.1\,\mathrm{Gyr}$ on radius, mass, temperature and age respectively. We compare and validate our catalogue values to various literature sources which employ other isochrone grids and asteroseismology. In addition, we identify 278 TESS exoplanet hosts and 915 candidates and recalculate the planet radii for such systems. These homogeneous parameters provide a state-of-the art sample to probe the effect of physical stellar parameters on exoplanet characteristics and architectures.

We discuss how the stochastic approach for supersymmetric theories leads to new ways of characterizing anomalies in how supersymmetry can be broken.

In this paper, we construct several new quantum Floquet codes on compact, orientable, as well as non-orientable surfaces. In order to obtain such codes, we identify these surfaces with hyperbolic polygons and examine hyperbolic semi-regular tessellations on such surfaces. The method of construction presented here generalizes similar constructions concerning hyperbolic Floquet codes on connected and compact surfaces with genus $g \geq 2$. A performance analysis and an investigation of the asymptotic behavior of these codes are also presented.

In this paper, we establish an $\varepsilon$-regularity theorem for minimizers of an Alt-Phillips type functional subject to constraint maps. We prove that under sufficiently small energy, the minimizers exhibit regularity, and hence proving the smoothness of these maps. From here, we bootstrap to optimal regularity.

In this work, we study the problems of certifying and learning quantum $k$-local Hamiltonians, for a constant $k$. Our main contributions are as follows: - Certification of Hamiltonians. We show that certifying a local Hamiltonian in normalized Frobenius norm via access to its time-evolution operator can be achieved with only $O(1/\varepsilon)$ evolution time. This is optimal, as it matches the Heisenberg-scaling lower bound of $Ω(1/\varepsilon)$. To our knowledge, this is the first optimal algorithm for testing a Hamiltonian property. A key ingredient in our analysis is the Bonami Hypercontractivity Lemma from Fourier analysis. - Learning Gibbs states. We design an algorithm for learning Gibbs states of local Hamiltonians in trace norm that is sample-efficient in all relevant parameters. In contrast, previous approaches learned the underlying Hamiltonian (which implies learning the Gibbs state), and thus inevitably suffered from exponential sample complexity scaling in the inverse temperature. - Certification of Gibbs states. We give an algorithm for certifying Gibbs states of local Hamiltonians in trace norm that is both sample and time-efficient in all relevant parameters, thereby solving a question posed by Anshu (Harvard Data Science Review, 2022).

The dynamical and physical properties of asteroid family members are widely used to reconstruct the collisional evolution of the main belt and of individual objects. Families offer insights into the properties of the parent bodies and the fragmentation processes responsible for their formation. We investigate a poorly constrained phase of early collisional evolution among members of the same family. Our goal is to determine an intrinsic collision probability associated with intrafamily collisions and to assess their relevance compared to collisions with the background asteroid population. We performed numerical simulations of the early dynamical evolution of families, up to the randomization age of the true anomalies, recording mutual impacts between family members and converting them into an intrinsic collision probability. This probability was used to study intrafamily collisions for generic size distributions. We identified an intense phase of low-velocity intrafamily collisions occurring in the first few years after family formation. The collision probability can reach values up to $10^{-10}$ yr$^{-1}$km$^{-2}$ shortly after breakup and then decreases exponentially, following the same temporal trend predicted by previous statistical models. Variations among the orbital elements of the parent body and the properties of the ejection velocity field can change the collision probability by up to one or two orders of magnitude, without affecting its temporal evolution. Depending on the assumed size distribution, the number of impacts on the largest remnant ranges from fewer than ten to several million. Intrafamily collisions represent a physical mechanism whose importance must be assessed on a case-by-case basis. Although they are not expected to produce further fragmentation, they might contribute to early surface and structural evolution in some cases, while being negligible in others.

We study the modular curves defined by Weber functions, and associated modular polynomials, action of $\mathrm{SL}_2(\mathbb{Z})$, and parametrizations of elliptic curves with a view to the study of the isogeny graphs that they determine, particularly for supersingular elliptic curves. In addition to applications to efficient isogeny computation in cryptographic applications, we present an application to explicit Galois representations.

Speculative execution enhances processor performance by predicting intermediate results and executing instructions based on these predictions. However, incorrect predictions can lead to security vulnerabilities, as speculative instructions leave traces in microarchitectural components that attackers can exploit. This is demonstrated by the family of Spectre attacks. Unfortunately, existing countermeasures to these attacks lack a formal security characterization, making it difficult to verify their effectiveness. In this paper, we propose a novel framework for detecting information flows introduced by speculative execution and reasoning about software defenses. The theoretical foundation of our approach is speculative non-interference (SNI), a novel semantic notion of security against speculative execution attacks. SNI relates information leakage observed under a standard non-speculative semantics to leakage arising under semantics that explicitly model speculative execution. To capture their combined effects, we extend our framework with a mechanism to safely compose multiple speculative semantics, each focussing on a single aspect of speculation. This allows us to analyze the complex interactions and resulting leaks that can arise when multiple speculative mechanisms operate together. On the practical side, we develop Spectector, a symbolic analysis tool that uses our compositional framework and leverages SMT solvers to detect vulnerabilities and verify program security with respect to multiple speculation mechanisms. We demonstrate the effectiveness of Spectector through evaluations on standard security benchmarks and new vulnerability scenarios.

The mechanism of anomalous superlinear temperature-dependent resistivity, $ρ(T)$, in the metallic unconventional clathrate BaNi$_2$P$_4$ was studied by examining its evolution with artificial disorder induced by low-temperature ($\sim$ 20 K) 2.5 MeV electron irradiation. We find a dominant effect of the tetragonal-orthorhombic transition at $T_s$ ($ \sim$373 to 378 K, depending on heat cycle rate and direction) on $ρ(T)$, with standard metallic $T-$linear resistivity above the transition and anomalous behavior in the orthorhombic phase below. The transition is accompanied by the formation of structural domains and a notable (about 4~K) hysteresis in the magnetization and resistivity measurements, clearly showing its first order character. Matthiessen rule is obeyed both above and below the transition, suggesting negligible changes in the electronic structure. This conclusion is supported by the smooth evolution of the Hall effect through the transition. The Hall number is in good agreement with band structure calculations both above and below the transition. The transition temperature is notably suppressed with electron irradiation. Raman scattering at temperatures above room temperature find softening of local Ba vibration mode in the orthorhombic phase on approaching the transition. $^{31}P$ NMR line splits in the orthorhombic phase, suggesting a partial shift of the Ba atom from the central position in the cage. We suggest that local Ba rattling leads to enhanced residual contribution to resistivity in the high temperature tetragonal phase, the decay of which is responsible for the anomalous temperature-dependent resistivity in the orthorhombic phase.

Future wireless systems increasingly require predictive and transferable representations that can support multiple physical-layer (PHY) tasks under dynamic environments. However, most existing supervised learning-based methods are designed for a single task, which leads to high adaptation cost. To address this issue, we propose a joint-embedding predictive architecture for multimodal sensing-assisted communications (JEPA-MSAC), a self-supervised multimodal predictive representation learning framework for wireless environments. The proposed framework first maps multimodal sensing and communication measurements into a unified token space, and then pretrains a shared backbone using temporal block-masked JEPA to learn a predictive latent space that captures environment dynamics and cross-modal dependencies. After pretraining, the backbone is frozen and reused as a general future-feature generator, on top of which lightweight task heads are trained for localization, beam prediction, and received signal strength indicator (RSSI) prediction. Extensive experiments show the latent state supports accurate multi-task prediction with low adaptation cost. Additionally, ablation studies reveal its scaling behavior and the impact of key pretraining setups.

We show that any topological, closed, oriented, non-spin $4$-manifold with fundamental group $\mathbb{Z}_{4k}$ and $\min(b_2^+, b_2^-)\geq 15$, has either none or infinitely many distinct smooth structures. Furthermore, we construct infinitely many non-diffeomorphic, irreducible, smooth structures on manifolds with signature zero, $b_2^+$ even and fundamental group $\mathbb{Z}_2\times G$, for any finite group $G$. This extends the results of Baykur-Stipsicz-Szabó.

Light primordial black holes (PBHs) that fully evaporate before Big Bang Nucleosynthesis (BBN) produce dark radiation (DR) via Hawking radiation of gravitons, contributing to the effective number of relativistic species $ΔN_{\rm eff}$. If the particle spectrum contains a beyond-the-Standard-Model (BSM) boson with Compton wavelength comparable to the black hole (BH) gravitational radius, superradiant instability extracts angular momentum from the BH into a bosonic cloud, whose gravitational wave (GW) emission contributes an additional source of DR. By simultaneously evolving the BH mass and spin, superradiant mode occupation numbers, comoving entropy and cosmological energy densities in an expanding early-universe background, we find that superradiance generically suppresses $ΔN_{\rm eff}$: by extracting angular momentum before Hawking radiation can convert it into gravitons, superradiance starves the dominant dark-radiation channel. The GWs emitted by the superradiant cloud can partially compensate this loss, but only when the superradiant and BH evaporation timescales are comparable; otherwise the cloud GWs are emitted too early and diluted by cosmological expansion. The results imply that existing $ΔN_{\rm eff}$ bounds on PBH mass and spin derived without superradiance must be revisited if BSM bosons are present in the particle spectrum.

We propose a bridge between oriented supersingular elliptic curves and the arithmetic of modular curves. To an $\mathcal{O}$-oriented supersingular curve, we attach a class in the relative homology group $H(X_0(N),C,\mathbb{Z})$, i.e. modular symbols, compatible with the Hecke action. We then compute vectors of $\ell$-adic periods by pairing with weight $2$ cusp forms via Coleman integration. This yields an explicit, computable map from short combinatorial homology representatives to truncated vectors in $(\mathbb{Z}/\ell^m\mathbb{Z})^d$. Motivated by this encoding, we formulate the Modular Symbol Inversion (MSI) problem -- recovering a short homology representative from its truncated $\ell$-adic period data -- and discuss its arithmetic structure, its relation to path problems on isogeny graphs and Bruhat-Tits trees, and potential applications to cryptographic constructions.

The study of Feynman integrals through the lens of intersection theory offers a unifying framework for their analysis, capturing both the linear and quadratic relations that arise among integrals. In doing so, it provides a powerful method for systematically reducing them to the so called master integrals, a necessary strategy for multiloop contributions, whose huge number make direct calculation unfeasible. The Twisted de Rham cohomology offers a powerful tool for describing integrals with multivalued integrands, arising in dimensional regularization. However, it fails whenever the underlying geometry shows richer structures, as singularities and intricate monodromies. In this thesis we propose a systematic approach to identify and construct the appropriate homology and cohomology that allows to interpret Feynman integrals in parameter representation as exponential periods. This reformulation, together with the analytic continuation of the dimensional regularizator, provides a perfect framework to properly analyze the wall crossing structure and to correctly take into account Stokes phenomena for a sharp counting of the number of Master integrals. This framework allows to embed within the same formalism not only perturbative integrals, coming both from quantum field theories and string theory, but also wide class of physically relevant integrals, from Fourier calculus to statistical mechanics partition functions, from quantum mechanics expectation values to conformal field theory correlators.

For events $A$ and $B$, we have \[ \mathbb{P}(A\mid B) > \mathbb{P}(A\mid \neg B) \qquad\Longleftrightarrow\qquad \mathbb{P}(B\mid A) > \mathbb{P}(B\mid \neg A) \] whenever all four quantities are defined. In other words, $B$ is evidence for $A$ if and only if $A$ is evidence for $B$. This note gives seven different proofs of this fact -- by cross-multiplication, covariance, coupling parameters, odds ratios, pointwise mutual information, combinatorial double counting, and mixed discrete derivatives -- and develops a surrounding web of interpretations. Once the marginals $\mathbb{P}(A)$ and $\mathbb{P}(B)$ are fixed, a $2\times 2$ table has only one degree of freedom, so every scalar notion of positive association must be governed by the same signed parameter.

We present Q-Transform Amplitude Modulation (QTAM), a novel, fully invertible implementation of the Constant-Q Transform algorithm, designed to enable robust signal denoising and the disentanglement of overlapping transient events in current and next generation gravitational wave (GW) observatories. Time-frequency (TF) analysis faces a fundamental dichotomy: critically sampled transforms are computationally efficient but lack time-shift invariance, limiting their efficacy for robust pattern recognition and Deep Learning applications. While alternative approaches such as the Dual-Tree Complex Wavelet Transform provide efficient approximate shift-invariance, their wavelet constructions remain tied to dyadic scale frequency tilings that are poorly matched to the simultaneous representation of GW chirps and instrumental glitches. Conversely, overcomplete transforms provide the necessary shift-invariance and tunable frequency resolution, but their implementations generate highly redundant data volumes that are prohibitive for low-latency (LL) processing. Furthermore, standard attempts to compress these dense representations rely on lossy interpolation, destroying the phase coherence required to reconstruct the signal. QTAM bridges this gap by employing a methodology inspired by Amplitude Modulation radio broadcasting. By modeling the Q-transform output as a slowly varying complex envelope carried by a deterministic high-frequency term, we achieve lossless data decimation via spectral shifting to baseband. We demonstrate that QTAM is linear and fully invertible, allowing exact reconstruction of the original signal with machine precision while retaining the shift-invariance of dense spectrograms. Leveraging native GPU acceleration, QTAM enables TF pipelines to operate within LL O(1s) bounds. We validate the method's potential for denoising and disentanglement on GW data and signal injections.

We address a noncooperative game problem in multi-controller system under delayed and asymmetric information structure. Under these conditions, the classical separation principle fails as estimation and control design become strongly coupled, complicating the derivation of an explicit Nash equilibrium. To resolve this, we employ a common-private information decomposition approach, effectively decoupling control inputs and state estimation to obtain a closed-form Nash equilibrium. By applying a forward iterative method, we establish the convergence of the coupled Riccati and estimation error covariance recursions, yielding both the steady-state Kalman filter and the Nash equilibrium. Furthermore, we quantify the impact of asymmetric information, proving that a richer information set reduces the costs for the corresponding player. Finally, numerical examples are provided to demonstrate the effectiveness of the results.

Urban deprivation is traditionally measured using static, residence-based indicators, capturing the socioeconomic, demographic, and spatial conditions of neighborhoods. However, this approach overlooks how daily movement allows residents to navigate the city, potentially exposing them to opportunities that differ significantly from their residential environments. To bridge this gap, we quantify the extent of bubble breaking - travel to less deprived areas - by analyzing mobile phone mobility networks combined with satellite-derived deprivation indices across 64 cities in India and Mexico. We find that residents of deprived areas systematically travel to better-off locations to meet daily needs, exhibiting a compensatory mobility pattern that significantly exceeds expectations derived from gravity models based on population and road networks. This residual bubble breaking (the part gravity models can not explain) is associated with a tension in the built environment: while high local amenity diversity allows residents to satisfy needs locally, high amenity density and positive spillovers from neighboring areas is associated with movement across socioeconomic boundaries. Overall, residual bubble breaking reflects the extent to which residents rely on cross-neighborhood mobility to overcome local amenity deficits, a dimension of spatial inequality that residence-based measures leave unobserved.

We investigate brane inflation, focusing on warm inflation realizations within a warped throat geometry. While the standard scenario relies on a single mobile $D3$-brane moving radially toward an anti-$D3$-brane at the tip of the throat, we propose two distinct inflationary pictures. In our approach, the radial and angular coordinates of a $D3$-brane on a warped deformed conifold act as two independent inflaton fields. We address moduli stabilization by incorporating a supersymmetrically embedded $D7$-brane, which generates the necessary radial and angular scalar potentials. Evaluating these radial and angular brane inflation setups within the warm inflation paradigm, we demonstrate that dissipation effects allow the models to satisfy recent observational constraints more naturally than their cold inflation counterparts for a given parameter space.

We investigate the flat-band properties and topological phase transitions in a non-Hermitian twisted bilayer $α-T_3$ lattice. Here, non-Hermiticity is introduced via Hatano-Nelson-type asymmetric hopping, while an aligned hexagonal boron nitride substrate provides a staggered sublattice mass to the system. We find that the introduction of non-reciprocal hopping splits the conventional single magic angle into three distinct non-Hermitian magic angles (NHMAs). Unlike the exceptional magic angles driven by spectral singularities, these NHMAs host perfectly isolated flat bands where the real and imaginary parts of the bandwidth simultaneously vanish. By mapping the complex eigenspectrum across the moiré Brillouin zone, we show that the scattered energy eigenvalues coalesce into well-defined, closed loop-like structures as the non-Hermitian parameter strength increases, indicating emergence of a nontrivial point-gap topology and hence the non-Hermitian skin effect. Furthermore, we characterize the topological phases by computing the direct band gap and the biorthogonal Chern number. While the system exhibits a transition to a higher topological phase at weak non-Hermiticity, we demonstrate that stronger non-Hermiticity drives the gap-closing boundaries to merge and their topological charges to mutually annihilate. This convergence results in a trivial gap closing and a complete suppression of the intermediate topological phase, confirming that non-Hermiticity fundamentally plays a crucial role with regard to destabilizing the robust topological features of this moiré system.