We present a novel methodology for mapping dust extinction in nearby galaxies at parsec-scale resolution. We apply it to HST 68 fields within the Small and Large Magellanic Clouds (23 fields in the SMC and 45 fields in the LMC) using multi-band HST photometry from the Scylla and METAL surveys. Our technique leverages \textit{kriging}, a geostatistical interpolation method built on the principles of Gaussian Process regression, combined with Gaussian mixture modeling to statistically isolate background stellar sources and account for line-of-sight depth effects. 3D dust simulations demonstrate the method's capability to recover column densities to an accuracy of $A_V \approx 0.1$ mag in fields with at least 1000 sources. The resulting $4^{\prime\prime}$ resolution ($\sim1$-pc) dust maps reveal detailed structure and strong spatial correlation with ancillary ISM tracers, especially in star-forming regions like 30 Doradus. Global extinction of total column densities follows log-normal profiles in both galaxies, with the SMC exhibiting slightly higher mean extinction ($e^μ=0.47$ mag) than the broader LMC ($e^μ=0.43$ mag), likely due to significant line-of-sight depths. We find systematic offsets between dust mass surface densities ($Σ_{D}$) derived from extinction versus FIR emission in both galaxies, with $Σ_{D, FIR}/Σ_{D, A_V}$ ratios ranging from $0.6-1.8$. This work provides the highest-resolution dust extinction maps in SMC and LMC to date, which offer a vital independent benchmark for constraining dust emissivity, $\text{CO}$-dark gas fractions, and the multi-scale structure of the ISM in low-metallicity environments.

We investigate the emergence of nontrivial topology in a twisted cuprate bilayer described by the Hubbard model in the weak-interaction regime. Our results show that the topological character depends sensitively on the doping level. For $U/t=3.85$, the Chern number assumes a value of $\pm 8$ in the electron-doped case, whereas it vanishes (0) in the hole-doped regime. The presence of nontrivial topology is further supported by an analysis the associated edge states and their chirality in a finite-width geometry, while keeping full correlations.

Given a quasi-split connected reductive $\mathbb{R}$-group $G$ and a finite group $A$ acting on $G$ by $\mathbb{R}$-automorphisms that preserve an $\mathbb{R}$-pinning, we construct for each discrete $L$-parameter for $G$ a corresponding $L$-packet of irreducible discrete series representations on each inner forms $\tilde G_z(\mathbb{R})$ of the disconnected group $\tilde G = G \rtimes A$. We prove that these $L$-packets satisfy the endoscopic character identities with respect to normalized transfer factors. This proves the conjectural refined local Langlands correspondence for inner forms of quasi-split disconnected real reductive groups, as recently formulated by the first author.

Recent studies suggest that gradient-based methods applied to relaxed box-constrained Quadratic Unconstrained Binary Optimization (QUBO) formulations can outperform classical heuristics in some large-scale regimes, often relying on heavy parallelization. However, these methods still underperform heuristics in other settings. In this work, we clarify this apparent discrepancy through a detailed analysis of the relaxed non-convex QUBO local maxima for both the Maximum Independent Set (MIS) and Maximum Cut (MaxCut) problems, and by introducing a new quadratic objective for MaxCut. Motivated by this analysis, we propose a mutation-based differentiable global reset algorithm, combined with local search to escape local maxima. We term our approach mQO, standing for mutation-based Quadratic combinatorial Optimization. The proposed strategy dramatically improves the performance of gradient-based solvers without heavy reliance on GPU parallelized initializations, indicating that stalling, rather than model capacity or compute, is the dominant bottleneck. As a result, on large-scale graphs, mQO achieves superior performance against state-of-the-art heuristics, commercial integer programming solvers, and recent GPU methods.

V1674 Her (Nova Her 2021) is the fastest classical nova ever recorded, with an optical decline time of $t_2 \sim 1$ day, typically interpreted as evidence for a white dwarf mass close to the Chandrasekhar limit. We present a broadband X-ray study of V1674 Her combining contemporaneous XMM-Newton and NuSTAR observations in quiescence to directly constrain the white dwarf mass and magnetic field strength. The hard X-ray emission is modeled using a physically motivated post-shock accretion column model that accounts for the temperature gradient in the flow and reflection from the white dwarf surface. Under the assumption that the accretion disk is truncated at the co-rotation radius, we obtain a white dwarf mass of $M = 1.09^{+0.07}_{-0.06}\,M_\odot$. An independent constraint derived from timing analysis of the X-ray power spectrum yields a consistent value of $M = 1.12 \pm 0.06\,M_\odot$. These values are significantly lower than those inferred from empirical decline-time relations, suggesting that such relations may overestimate white dwarf masses in extreme fast novae. From the inferred accretion rate and magnetospheric radius, we estimate a surface magnetic field strength of $B = 21.3^{+6.6}_{-5.7}\,(\mathrm{stat})^{+12.9}_{-8.1}\,(\mathrm{sys})\,\mathrm{MG}$, placing V1674 Her at the high end of the magnetic field distribution for intermediate polars. Our results demonstrate that even the fastest novae do not necessarily host near-Chandrasekhar white dwarfs, highlighting the importance of direct X-ray constraints and suggesting that additional parameters beyond white dwarf mass play a key role in setting nova timescales.

Software obfuscation and encryption present persistent challenges for program comprehension and security analysis, particularly when adversaries conceal Indicators of Compromise (IoCs) such as IP addresses within source code. While Large Language Models (LLMs) have recently demonstrated remarkable progress in code reasoning and transformation, their resilience against adversarial concealment techniques remains largely uncharted. This paper introduces a systematic benchmark for secret detection under adversarial code transformations, designed to evaluate the capacity of LLMs to recover IoCs embedded in obfuscated and encrypted JavaScript programs. We construct a dataset of 336 programs, progressively transformed through 12 levels of obfuscation and cryptographic concealment (including XOR and AES-256), to emulate realistic threat scenarios. An automated evaluation framework standardizes LLM queries and responses, enabling reproducible, large-scale testing across diverse models. Our results reveal a dichotomy: while LLMs exhibit high success against lightweight transformations such as variable renaming and Base64 encoding, encryption-based concealment severely degrades detection performance. These findings establish encryption as a critical frontier for LLM-driven code analysis and highlight both current limitations and avenues for advancing automated threat intelligence.

Advance in technology offer the potential for future adoption of a combination of virtual reality (VR) and real-time adaptivity to enhance training and education. Providing a valid neuro-ergonomic measure of cognitive load can enable an adaptive training regime to continuously adjust tas difficulty to an optimal level as training progresses. The current study validated the functional near-infrared spectroscopy (fNIRS) measure of cognitive load to reflect the demands of two different forms of lad within Cognitive Load Theory: extraneous and intrinsic to he task to be mastered. Thirty-six participants completed a VR shape assembly training task followed by a test of their skill retention They wore near-full head coverage fNIRS and provided subjective ratings of ther workload. The fNIRS findings largely corroborate intrinsic workload literature with significant activation in cortical regions (dorsolateral and rostral prefrontal cortex and left angular gyrus) associated with working memory, short term memory buffers, multisensory integration, and attention. These fNIRS results were tracked closely by NASA TLS measures of mental workload. The results also revealed far less brain activity associated with extraneous load, namely just the right angular gyrus, deemed irrelevant to the mastery of the task.

Scattering processes in high-energy physics are inherently quantum mechanical, yet are typically analyzed at the level of final states, where entanglement appears as a property of the outcome rather than a consequence of the underlying dynamics. We reformulate scattering at the level of the process itself by representing helicity transition matrices as quantum circuits. Once the kinematic configuration and scattering channel are fixed, the problem reduces to a finite-dimensional quantum map, making a circuit description natural. Within this framework, an example of the process $e^+e^-\to μ^+μ^-$ is shown, which decomposes into unitary and non-unitary components, corresponding to coherent mixing and postselection effects. This representation reorganizes the amplitude into distinct operational elements, providing a perspective in which collider processes can be viewed as constrained quantum circuits and their entanglement structure can be understood in terms of the underlying circuit dynamics, opening the door to analyzing their properties using the language of quantum information.

The settling of dust particles plays a critical role in the growth and dynamics of dust grains. We performed a detailed modeling of the ALMA continuum substructures for six highly inclined protoplanetary discs using radiative transfer simulations, to constrain the vertical height of millimetre dust grains and the settling strength. Our modeling results are a very thin millimetre dust disc in T Cha ($\text{h}_{\text{dust}}<$ 0.1 au throughout the disc), a vertically extended dust disc in DoAr 25 ($\text{h}_{\text{dust}}$ of $\sim$ 4.7 au at 140 au) and tentatively a thin disc in MY Lup ($\text{h}_{\text{dust}}<$ 0.5 au at 70 au). From lower resolution observations we found a very thin disc for PDS 111 ($\text{h}_{\text{dust}}<$ 0.1 au throughout the disc) and a more vertically extended millimetre dust disc in V409 Tau ($\text{h}_{\text{dust}}$ of $\sim$ 1.3 au at 35 au). We could not measure the vertical height in the asymmetric disc of RY Lup. We also found that the input dust opacities are a source of degeneracy in our models. Our tentative results, assuming the Ricci dust opacities, point to a diverse settling strength in our sample and possible radial variations. We also compared the models that best fit the ALMA data with the SPHERE data to test if they can reproduce the vertical distribution of small dust grains. This comparison suggests that models that reproduce the dust density distribution in the midplane cannot reproduce the distribution of small dust grains in the upper layers, reinforcing the need for more complex models.

We study problems related to connecting multi-interface networks of wireless devices. These problems are modeled using graphs, where vertices represent the devices and edges represent potential communication links. Each vertex can activate multiple interfaces, and a connection between two vertices is established if they share at least one common active interface. We consider two problems arising in multi-interface networks: Coverage and Connectivity. In the Coverage problem, every connection defined in the network must be established, while in the Connectivity problem, groups of terminals specified in the input should be connected. The solution should minimize the maximum cost incurred by a vertex or the total cost incurred by all vertices. In this work we are interested in approximating the former of the two cost criterions. We model both problems using ILPs and we design approximation algorithms based on a randomized rounding of the solution of the linear programming relaxation. For the Coverage problem, this yields an $O(\log m)$-approximation algorithm, which is tight, since the problem generalizes Set-Cover. This improves upon the $O(b\cdot\log n)$-approximation algorithm, where $b$ is a certain graph parameter which can be as large as $Ω(n)$ [Algorithmica '12]. The same relaxation can also be used to get an $k$-approximation algorithm, where $k$ is the number of different interfaces. This generalizes a similar result for the uniform cost case. For the Connectivity problem, we obtain an $O(\log^2 m)$-approximation algorithm, which is the first non-trivial approximation algorithm for this problem. The algorithm is based on a similar LP relaxation with additional cut constraints to ensure connectivity. The rounding procedure is similar to the one for the Coverage problem but requires a more careful analysis to ensure that the connectivity constraints are satisfied.

We present the analysis of high-resolution synthetic dust polarization maps derived from large-scale simulations of magnetized multiphase interstellar turbulence carried out with the AthenaK code on the $Frontier$ exascale supercomputer at the Oak Ridge National Laboratory. Our turbulence model accurately captures spectral properties of the $E$- and $B$-modes measured by $Planck$ at 353 GHz. The simulations provide new insights into the physical origins of the observed $E/B$ asymmetry and positive $TE$ signal, facilitating the development of advanced models of Galactic foreground emission for current and future CMB experiments.

Galaxy formation scenarios can be interpreted through galaxy morphology and the level of rotational versus pressure support, quantified through the ratio of a galaxy's rotation speed to its velocity dispersion: $V/σ$. Observational studies of dwarf galaxies find that $V/σ$ does not strongly depend on environment, and may weakly depend on galaxy mass, which could shift our understanding of how dwarf galaxies form. We utilize the Marvelous Massive Dwarfs suite to examine whether $V/σ$ depends on mass in simulations, and understand how this varies for different baryonic components of the galaxy: HI gas, young stars ($<$ 1 Gyr) and old stars ($>$ 1 Gyr). We use a simulation sample of 67 isolated dwarf galaxies with M$_\star=10^6-10^9$ M$_\odot$ and produce line-of-sight maps for rotation speed and dispersion for different viewing angles of each galaxy. We find that $V/σ$ increases with mass, and that HI gas and young stars are more rotation-supported ($V/σ\approx 1-13$) while old stars are more dispersion-supported ($V/σ\approx 0.2-5$). This result is consistent with the scenario where young stars are born from dynamically cold gas in the interstellar medium and undergo dynamical heating over time. We quantify the effects of spatial resolution in observational determinations of $V/σ$ and find that existing observations using old stars may underestimate the intrinsic $V/σ$. We find a correlation between $V/σ_\mathrm{HI,global}$ and HI line profile shape that is qualitatively similar to previous simulation results, but we find higher $V/σ_\mathrm{HI,global}$ compared to prior work which found values $\lesssim 2$ for most galaxies in this mass range. Our results motivate future work to examine $V/σ$ and dwarf galaxy formation with different kinematic tracers of the galaxy.

Dengue transmission is rapidly expanding beyond its historical tropical range, raising concerns about how climate change may alter the collective dynamics of epidemics. While most studies focus on transmission risk, much less is known about how climate affects the synchronization of outbreaks. In this work, we investigate dengue synchronization using epidemiological and climate data from 74 municipalities in the state of Paraná (southern Brazil) between 2010 and 2024. We quantify outbreak coherence using the Event Synchronization (ES) method. Our results reveal a transition from a low-transmission regime to a high-transmission regime accompanied by a marked increase in synchronization across cities. We also show that climate anomalies increase the number of permissive days for dengue transmission. Our results suggest that such days are significantly associated with outbreak synchronization. We identify a two-stage climate mechanism: conducive climatic conditions first reduce the probability of asynchronous states and coincide with the emergence of synchronized outbreaks, and subsequently sustain higher synchronization levels. Extending the analysis through comparative analyses in Ceará and Minas Gerais, we uncover that climate consistently amplifies synchronization, although its role in the onset of synchronization depends on regional climatic regimes. These findings highlight climate-driven synchronization as an emerging feature shaping dengue dynamics.

We consider activated random walk (ARW), an interacting particle system and prototypical model of self-organized criticality in a setting which combines mean-field behavior with the geometry of an arbitrary graph, which we call the village model of ARW, or VARW for short. VARW is obtained from a fixed graph by replacing each vertex with a 'village' that consists of n replicas of that vertex. We focus on VARW where particles walk according to a strictly sub-stochastic transition kernel on a finite underlying graph, so mass is sometimes lost (which guarantees that the system eventually stabilizes almost surely). Under a subcriticality assumption on the initial state we prove a law of large numbers as n goes to infinity for the resulting stable configuration of particles and the odometer of the process, to a limit which is uniquely characterized by a system of non-linear equations.

6G networks will require quantum-secure cryptography deployed across core infrastructure, edge nodes, resource-constrained IoT devices. Although post-quantum cryptographic (PQC) algorithms have been standardized by NIST, their practical deployability in bandwidth and latency limited wireless systems remains unclear. This paper presents a practical evaluation of NIST selected PQC schemes, including ML-KEM (Kyber), ML-DSA (Dilithium), and Falcon. Benchmarks conducted with OpenSSL and the OQS provider on heterogeneous platforms show that while computational performance is acceptable, ciphertext and signature size expansion significantly impact handshake reliability and bandwidth efficiency, particularly at the network edge. The results highlight key system-level trade-offs and motivate the need for PQC optimization and deployment-aware design for future quantum-secure 6G networks.

DNS integrations leverage the discovery, trust, and uniqueness of the global Domain Name System with a linkage to another naming ecosystem, so the DNS name can help identify resources such as a cryptocurrency wallet or software component. While DNS ownership is verified at linkage creation, many ecosystems do not track subsequent DNS changes. The result is zombie linkages, where the DNS ownership has expired or changed, but the mapping to the linked resource persists. We define a threat model for DNS integrations, identifying five classes of attacks that leverage or exploit zombie linkages. We measure zombie occurrence across three DNS integrations -- Web PKI; ENS, a blockchain naming system; and Maven Central, a Java software repository. We show that zombies exist in every ecosystem, but at very different fractions -- zombies make up roughly 3% of TLS certificates for new domains, 24% of ENS on-chain imports, and 15% of Maven Central namespaces. We evaluate how integration design choices affect outcomes, with validate-once integrations (ENS on-chain, Maven Central) accumulating long-lasting zombies, linkages with expiration (Web PKI) limiting damage, while integrations that validate on every use (ENS gasless) are zombie-free by design. We look for specific attacks, finding attacks actively available for exploitation in both Web PKI and Maven Central. Finally, we recommend steps to reduce zombie occurrence.

We establish sharp regularity for the value function, the pressure, and the free boundary in one-dimensional first-order mean field games with power coupling and compactly supported density. Under a standard nondegeneracy assumption on the initial datum, the pressure \(p=m^θ\) is Lipschitz continuous, the value function \(u\) is \(C^{1,1/2}\), and the two free boundary curves are smooth in time. If the initial pressure is smooth, then both \(p\) and \(u\) are smooth up to the free boundary from inside the positive phase. The proof works in Lagrangian coordinates and, through a singular change of variables, recasts the boundary degeneracy as a removable radial axis in effective dimension \(N=4+2/θ\), allowing the application of recent estimates for even solutions to elliptic problems with degenerate weights.

The variational model for a ferroelectric nematic bears close resemblance to the well-known energy model for micromagnetics. Despite this similarity, the two models operate in fundamentally distinct parameter regimes describing different physics. In this paper we establish that the ferroelectric nematic energy functional $Γ$-converges to the energy of a nematic with high elastic anisotropy.

We consider the problem of estimating the underlying edge probabilities of a time-varying network observed at multiple time points. The probability structure is represented by a time-varying graphon that satisfies temporal Hölder smoothness and piecewise Lipschitz conditions in the latent variables. We propose a multi-stage smoothing estimator that first applies temporal local smoothing to each edge and then performs node-domain smoothing using a data-driven neighborhood construction adapted from the method. An additional temporal smoothing step is introduced as an optional refinement when uniform accuracy over the entire time domain is required. Simulation studies demonstrate the benefits of combining temporal and node-domain smoothing under different generative models. We also apply the method to a real time-varying network dataset and show that it captures both smooth temporal evolution and structural patterns in the connectivity.

In this work, we derive the propagator of a massive charged vector boson in the presence of a homogeneous and constant magnetic field of arbitrary strength, working in the unitary gauge and in the mostly minus metric. The propagator is constructed using the Ritus eigenfunction method, which allows for an explicit treatment of Landau-level quantization and spin degrees of freedom. We present a detailed analysis of the polarization vectors for all Landau levels. Using the Ritus representation, we formulate and derive the LSZ reduction formula for massive charged vector bosons in a magnetic field background, providing a useful tool for the calculation of self-energies and radiative corrections with external charged states. Furthermore, we establish a systematic connection between the Ritus' eigenfunctions and Schwinger proper-time representations of the propagator, identifying Schwinger's phase and exhibiting a slight discrepancy with previous results in the literature that arise in the unitary gauge, highlighting the importance of a careful treatment of spin and gauge structures in an external magnetic field.

We introduce a counterdiabatic (CD) extension of the Quantum Approximate Optimization Algorithm (QAOA) for constrained portfolio optimization. By incorporating approximate adiabatic gauge potentials generated from nested commutators of the Ising-type portfolio problem Hamiltonian and the Hamming weight-preserving XY mixer Hamiltonian into our variational ansatz, the resulting Constrained Counterdiabatic QAOA (CCD-QAOA) achieves improved optimization performance under realistic budget and risk constraints. Benchmarking against standard XY-mixer QAOA, Grover-mixer QAOA, and penalty-based QAOA formulations, our numerical simulations demonstrate that, for a fixed QAOA depth, our CCD-QAOA approach consistently results in better approximation ratios.

Quantum annealing is a computational paradigm in which optimisation problems are mapped onto the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming a simple initial Hamiltonian into one whose ground state encodes the solution, the system traverses a complex landscape via a combination of quantum fluctuations, tunnelling processes, and dissipative dynamics. Unlike gate-based quantum computing, quantum annealing is a specialised and near-term approach aimed primarily at discrete optimisation and sampling tasks. While it is not expected to provide polynomial-time solutions to NP-hard problems in the worst case, it offers a physically motivated heuristic for navigating rugged energy landscapes that arise across science and engineering. Modern quantum annealers realise programmable spin systems with thousands of qubits, placing them among the largest controllable quantum devices currently available. As a result, their significance extends beyond optimisation: they also function as experimental platforms for studying non-equilibrium many-body quantum dynamics in regimes that are difficult to access using classical simulation. In this review we present an accessible introduction to the principles of quantum annealing, describe the main hardware platforms and algorithmic techniques, and analyse how tunnelling, spectral gaps, and open-system effects shape computational performance. We survey applications ranging from optimisation and machine learning to quantum simulation and many-body physics, and discuss the central challenges in benchmarking, scaling, and control. These perspectives position quantum annealing as a distinctive framework at the interface of optimisation, stochastic sampling, and programmable quantum dynamics, with a role that is complementary to both classical algorithms and gate-based quantum computing.

Passive 3D sensing is increasingly critical for early detection and tracking of small aerial vehicles (UAVs), where traditional active ranging can be tactically undesirable. We present SCHeimpflug for Optical Ranging TechnologY (SCHORTY), a single-aperture passive and active ranging architecture that exploits the Scheimpflug principle to encode range along a tilted object space plane by tilting the sensor relative to the imaging optics. SCHORTY requires only a one-time geometric calibration to map pixel coordinates to range and is inherently sensor and waveband agnostic. We implement SCHORTY using both a visible frame-based camera and an event-based camera (EBC) with closely matched pixel sizes for comparable horizontal resolutions and range binning. Controlled flights of an octocopter and a fixed-wing UAV equipped with GPS provide ground truth distances out to 1.1 km. Experimental results show that SCHORTY achieves deterministic range assignment limited primarily by the projected pixel size, which grows squared distance, while avoiding computationally intensive inverse reconstructions common in coded aperture and PSF engineered systems. In the EBC configuration, EBC-SCHORTY inherently suppresses static background and emphasizes motion, improving UAV detectability in cluttered natural scenes and under turbulence and motion blur. Additionally, we observe an asymmetric defocus blur about the object plane that depends on UAV trajectory, suggesting an extra cue for localization and trajectory inference. These results demonstrate SCHORTY as a practical and Size, Weight, and Power (SWaP) efficient passive ranging solution for medium-range UAV observation and motivate future integration with 2.5D/3D PSF engineering and event-based deconvolution to enhance 3D sensing performance.

We study the facial geometry of the homogeneous pseudo-moment cone \(Σ_{n,2d}^*\) and its implications for atomic decomposition of moment matrices. For fixed \(d \ge 2\), we show that if a moment matrix is formed by \(O(n^d)\) generically chosen weighted atoms, then its minimal face in the matrix realization of the pseudo-moment cone is \emph{simplicial} and generated by the planted rank-one atoms. Based on this geometric result, we develop a Carathéodory-type extreme-ray decomposition algorithm for spectrahedral cones and show that, when specialized to the pseudo-moment cone, it yields an efficient atomic decomposition method for generically generated moment matrices in the same regime. A stabilized numerical implementation demonstrates strong recovery performance and suggests that, outside the guaranteed regime, the algorithm may serve as a practical sampler of high-rank extreme rays.

The transition to post-quantum cryptography in blockchain systems such as Bitcoin and Ethereum is often framed as a purely cryptographic problem. In practice, it also presents significant economic and infrastructural challenges: in globally replicated networks, increases in transaction size and verification cost are multiplied across all participating nodes. Existing post-quantum signature schemes, including lattice-based constructions such as CRYSTALS-Dilithium and stateless hash-based schemes such as SPHINCS+, introduce substantial increases in signature size. At blockchain scale, these increases translate into higher storage, bandwidth, and validation requirements, potentially requiring multiple generations of hardware improvement to become operationally routine. Historical experience suggests that even moderate increases in data footprint can be contentious, as illustrated by the Bitcoin block size debates (2015--2017). We propose a hash-based commit--reveal construction that replaces a single signature-bearing transaction with two lightweight transactions, each containing a fixed-size (32-byte) hash output derived from well-established primitives such as SHA-256, BLAKE, or Keccak. This approach achieves post-quantum security under standard hash assumptions while increasing the effective transaction footprint by only approximately 1.5$\times$ to 2$\times$ per authorization event. These results indicate that practical post-quantum migration may benefit from rethinking transaction semantics rather than directly adopting larger signature schemes, and that viable designs for decentralized systems must account for system-wide cost amplification.

We produce an infinite family of $2$-complexes that are intrinsically linked when embedded into four dimensions. In particular, we show that any embedding into $\mathbb{R}^4$ of the suspension of a graph containing $K_6$ as a minor contains a non-trivially linked 1 and 2-cycle.

Motivated by the advance of dynamical quantum phase transitions (DQPTs), we analyze the zeros of the complex-time survival (Loschmidt) amplitude in finite quantum systems and develop a general framework for their approximation based on the stability of zeros of holomorphic functions. We show that the large-scale properties of the distribution of zeros are governed by the envelope of the energy distribution of the initial state and can be constructed from chains of periodic zeros associated with its dominant contributions. In this picture, zeros reach the real-time axis when two or more eigenstates become equally populated at the maximum of the envelope, providing a finite-size precursor of DQPTs. We apply the method to quenched ground states in the Ising model with tunable interaction range and demonstrate close agreement between the approximate and exact distributions of zeros. We prove that the approximate construction becomes exact for BCS ground-state quenches in two-band models. To describe short-time dynamics, we introduce a minimal Gaussian model with a nearly equidistant spectrum. Slow dephasing continuously deforms the initial zero pattern into the asymptotic two-level structure, explaining anomalous DQPTs as a delayed approach of zeros to the real-time axis. Our results identify the energy envelope as the key ingredient shaping dynamical critical behavior and provide a universal interpretation of the whole zero distribution of the complex-time survival amplitude.

According to Quantum Darwinism, system-environment interactions both einselect particular system properties and encode them redundantly in many independent subsets of the environment, called fragments. This redundancy implies that an observer can recover the einselected information by accessing just one such fragment. However, the protocol by which such reconstruction should occur is often left unspecified. Considering a system $Γ$ interacting with a multipartite environment $Ξ$, we investigate whether, and under what conditions, the einselected state of $Γ$ can be recovered from environmental fragments using the Petz recovery map. We show that the fidelity between the system's initial state and the state reconstructed via Petz recovery develops a plateau as a function of the fragment size. Our results are supported by both analytical arguments and numerical simulations of large but tractable models.

As car cabins evolve with the integration of diverse sensors, traditional car cabins are transforming into smart environments. This shift raises important questions about how privacy is understood and managed in such spaces. In this work, we investigate privacy perceptions from the perspectives of both vehicle owners (i.e., people who purchase and own cars) and non-owners (i.e., people who temporarily use cars, such as family members, friends, or renters). Through semi-structured interviews with eighteen participants, we identified key factors that influence these groups' views on privacy. Our findings reveal factors that commonly influence privacy preferences for both owners and non-owners, as well as factors that have a stronger impact on one group over the other. Drawing on these insights, we discuss design implications for future designs to better support and balance the diverse privacy needs of multiple stakeholders in smart car cabins.

We study posterior contraction rates for mixing measures in homoscedastic location-scale mixture models with infinitely many components. While posterior convergence at the level of densities is well understood, ensuring convergence of the latent mixing measure is more challenging and has remained an open problem in settings where both location and scale parameters are unknown. We address this by deriving novel lower-bounds that connect the $L^1$ distance between mixture densities to discrepancies, based on the Wasserstein distances and the operator norm, between the underlying mixing measures and scale matrices. Our approach combines the dual formulation of the $W_1$ distance with functional-analytic approximation techniques. This leads to general inequalities, whose strength is determined (i) by the smoothness of the mixture kernel via the rate of decay of its characteristic function, and (ii) by a key lower-bound on the $L^1$ metric involving the operator norm discrepancy between scale parameters. Moreover, a novel PDE inversion condition yields a sharper inequality for important ordinary-smooth cases. We specialize these bounds to popular mixtures based on multivariate Gaussian, Cauchy, and Laplace kernels. As a consequence, we obtain first-of-their-kind contraction rates in the context of Dirichlet process mixtures with an unknown scale parameter shared across components. As a byproduct of our inequalities, we can distinguish the convergence behavior of the location mixing measure from that of the scale parameter across a range of kernel choices, leading to nuanced insights into their respective rates.