Hosted-LLM providers have a silent-substitution incentive: advertise a stronger model while serving cheaper replies. Probe-after-return schemes such as SVIP leave a parallel-serve side-channel, since a dishonest provider can route the verifier's probe to the advertised model while serving ordinary users from a substitute. We propose a commit-open protocol that closes this gap. Before any opening request, the provider commits via a Merkle tree to a per-position sparse-autoencoder (SAE) feature-trace sketch of its served output at a published probe layer. A verifier opens random positions, scores them against a public named-circuit probe library calibrated with cross-backend noise, and decides with a fixed-threshold joint-consistency z-score rule. We instantiate the protocol on three backbones -- Qwen3-1.7B, Gemma-2-2B, and a 4.5x scale-up to Gemma-2-9B with a 131k-feature SAE. Of 17 attackers spanning same-family lifts, cross-family substitutes, and rank-<=128 adaptive LoRA, all are rejected at a shared, scale-stable threshold; the same attackers all evade a matched SVIP-style parallel-serve baseline. A white-box end-to-end attack that backpropagates through the frozen SAE encoder does not close the margin, and a feature-forgery attacker that never runs M_hon is bounded in closed form by an intrinsic-dimension argument. Commitment adds <=2.1% to forward-only wall-clock at batch 32.

Knee osteoarthritis (KOA) is a common joint disease that causes pain and mobility issues. While MRI-based deep learning models have demonstrated superior performance in predicting total knee replacement (TKR) and disease progression, their generalizability remains challenging, particularly when applied to imaging data from different sources. In this study, we show that replacing batch normalization with instance normalization, using data augmentation, and applying contrastive loss improves generalization. For training and evaluation, we used MRI data from the Osteoarthritis Initiative (OAI) database, considering sagittal fat-suppressed intermediate-weighted turbo spin-echo (FS-IW-TSE) images as the source domain and sagittal fat-suppressed three-dimensional (3D) dual-echo in steady state (DESS) images as the target domain. The results demonstrated a statistically significant improvement in classification metrics across both domains by replacing batch normalization with instance normalization in the baseline model, generating augmented input views using the Global Intensity Non-linear (GIN) augmentation method, and incorporating a supervised contrastive loss alongside the classification loss to align representations of samples with the same label. The GIN method with contrastive loss performed better than all evaluated single-source domain generalization methods when using 3D instance normalization. Comparing GIN with and without contrastive loss (for both normalization types) showed that adding contrastive loss consistently led to better performance.

Understanding how decision makers balance operational efficiency with environmental and ecological risks is central to vessel navigation. We model vessel speed as a control variable in a constrained optimization framework in which vessel operators balance multiple competing objectives, including transit efficiency, ice related navigational risk, and whale related ecological risk. The underlying risk parameters are estimated using over 14 million Automatic Identification System (AIS) observations from the United States Arctic (2010-2019), together with environmental covariates and spatially explicit whale density estimates. The framework incorporates a nonlinear risk objective, vessel heterogeneity, and regularization to ensure stable and interpretable results. The inferred trade offs reveal distinct decision making patterns across vessel groups and navigational statuses. Vessel types such as Tug Tow and Cargo balance operational speed with environmental and ecological considerations. In contrast, several vessel groups, including Fishing, Passenger, and Unspecified vessels, are strongly influenced by ice related risk, while Pleasure Craft and Tankers exhibit higher sensitivity to whale related risk. Across navigational status categories, similar heterogeneity is observed. The dominant status, under way using engine, displays a clear trade off, whereas other statuses, such as aground and undefined, are strongly shaped by ice related constraints. Statuses including restricted maneuverability and engaged in fishing exhibit higher estimated sensitivity to whale related risk, though with substantial uncertainty. Sensitivity analysis indicates that increasing whale-related risk weighting produces limited changes in model-implied optimal speed, whereas increasing ice-related risk leads to more consistent reductions.

We determine the exact outer structure of the Abrikosov vortex in the extreme type-II limit, which occurs when the Ginzburg-Landau parameter $κ$ diverges. In this limit, Ginzburg-Landau theory simplifies, outside a shrinking core, to a closed nonlinear theory for the superfluid velocity subject to an algebraic density constraint. The resulting solution is asymptotically exact everywhere outside the vanishing vortex core, demonstrating that both magnetic field and superconducting density vary on the length scale of the London penetration depth. This establishes that the conventional two-length-scale picture of the vortex does not hold in the $κ\gg 1$ limit.

We present the results of a search for diffuse radio emission in a uniformly selected sample of 30 of the most massive Sunyaev-Zel'dovich selected galaxy clusters at $z > 1$, providing the first statistical constraints on the evolution of cluster-scale diffuse emission beyond this redshift. We also analyse the scaling relations between radio power ($P_{1.4\,\mathrm{GHz}}$) and cluster mass ($M_{\rm 500c}$) in this high-redshift sample. It is well established that radio halos are primarily found in the most massive clusters, where turbulent energy from major mergers can re-accelerate relativistic electrons and amplify magnetic fields on megaparsec scales. Deep MeerKAT 1.28 GHz observations reveal diffuse radio halos in eight clusters (27$\%$), while the remaining 21 (70$\%$) show no emission; one additional cluster (3$\%$) was excluded from the radio analysis due to poor data quality. The halo detection rate in this high-redshift sample is lower than at intermediate redshift, but remains higher than the $\lesssim 10\%$ occurrence generally predicted by theoretical models at $z \gtrsim 1$. The detected radio halos scatter around the best-fitting $P_{1.4\,\mathrm{GHz}}$-$M_{\rm 500c}^{\rm {Unc}}$ relation derived for the MMDCS sample, whereas non-detections populate the lower envelope of the radio power-mass plane, similar to trends seen at lower redshift. No cluster-scale radio relics or mini-halos are identified. Our findings highlight MeerKAT's ability to probe non-thermal processes in the most distant clusters and the need for deeper, lower-frequency surveys to uncover faint diffuse emission and test the persistence of the $P_{1.4\,\mathrm{GHz}}$-$M_{\rm 500}$ relation across cosmic time.

Recent observations have revealed intriguing offsets between the UV and FIR emission in high redshift galaxies. In this study, we use the First Light And Reionisation Epoch Simulations (\textsc{Flares}) to compute the spatial offset of ultraviolet (UV) and far-infrared (FIR) centres for a statistical sample (6890) of massive (M$_{\star}\, \gtrsim10^{9} \,{\rm M_{\odot}}$) high redshift galaxies ($z \in [5,10]$). The galaxies are post-processed with the \textsc{skirt} radiative transfer code, to obtain the full spectral energy distribution and surface brightness profile. We simulate \textit{James Webb Space Telescope (JWST)} Near Infrared Camera (NIRCam; rest-frame 1500 Å, $ \approx 0.031 ''$ resolution) and ALMA rest-frame 158 \um\ ($\approx$ $0.3''$ angular resolution) observations of the galaxies and then calculate the distance between the UV-FIR centres to analyse which physical processes drive the observed UV - FIR spatial offset. We find that $\sim16.23\%$ of galaxies exhibit spatial offsets of $\geq 2.5$ kpc between their UV and FIR emission peaks. We establish that the spatial offsets do not correlate with stellar mass, UV/FIR luminosity, and size. Offsets also do not correlate with AGN feedback or with large-scale environment or merger history. Galaxies with significant offsets preferentially have bluer UV slopes ($-2.5<β<-1.5$), consistent with recent star formation and dust-attenuated cores displacing the observed UV centroid. They show an accelerated star formation history, forming half their $z=5$ stellar mass $\sim$0.1 Gyr earlier than galaxies without offsets. These galaxies are enriched earlier than galaxies without an offset and show enhanced stellar metallicities, indicating a transition to an outward growth at higher redshifts ($z \geq 6$).

We introduce a new class of quantum channels, the Generalized Multilevel Amplitude Damping (GMAD) channels, to model noise and decoherence effects in a qudit coupled to a thermal environment. The degradation of energetic resources under GMADs is investigated by evaluating work functionals and ergotropic capacitances, with particular attention to the coherent and incoherent contributions to ergotropy, for which we introduce new quantifiers. Our analysis sheds light on how to optimally prepare a qudit in a thermal environment in order to preserve its value from the perspective of work extraction, and reveals several counterintuitive phenomena: the ergotropic capacitance of a GMAD channel is not monotonic in the temperature of the environment; moreover, iterating the map can lead to crossings between ergotropic functionals at different temperatures, indicating the presence of a Markovian Mpemba effect.

We theoretically investigate the Aharonov-Bohm effect in a thick-walled Weyl semimetal (WSM) cylinder subject to an external axial magnetic field. By employing a low-energy effective Hamiltonian, we analytically solve the eigenvalue problem for both infinite and finite-length cylindrical geometries. We apply infinite-mass boundary conditions at the radial walls and MIT bag boundary conditions at the cylinder caps to properly account for intra-node confinement and inter-valley scattering, respectively. Our numerical results demonstrate that the spatial separation of the Weyl nodes acts as an internal chiral gauge field. This geometric field intrinsically breaks time-reversal (TR) symmetry, lifting the chiral degeneracy even at zero external flux. This symmetry breaking manifests as spontaneous persistent currents and the unfolding of conductance channels. Furthermore, longitudinal confinement induces propagation-direction-dependent energy splitting, altering the partial density of states and causing spatio-chiral current imbalances.

The DECam Rogue Earths and Mars Survey (DREAMS), a NOIRLab survey program, has been conducting a three-year survey covering a 5\,deg$^2$ area in the Galactic bulge since 2025 June. Its primary science goal is to detect low-mass free-floating planets through microlensing, while its minute-level cadence also enables the detection and characterization of rapid phenomena on timescales of minutes to hours such as stellar flares and pulsating stars. Here, we present the data reduction and calibration of the DREAMS observations obtained in 2025 and introduce the first DREAMS data release (DR1). DR1 includes 1,856 $z$-band observations and 325 $r$-band observations for 59,372,789 stars. The DREAMS DR1 catalog contains at least twice as many stars as any previous catalog covering the same 5\,deg$^2$ area. We present DREAMS light curves for a known blue large-amplitude pulsator and a known transiting system to demonstrate the survey's capabilities. We also perform a pilot search for short-duration variables over about 0.4% of the DR1 sample, identifying one new short microlensing event, two stellar flares, and 24 new short variables. This suggests that DREAMS DR1 may contain hundreds of stellar flares and thousands of previously unknown short variables.

We present the first high-resolution near-infrared spectroscopy of the nucleus of the nearby, well-known Seyfert galaxy NGC 4151 (the "Eye of Sauron"). Past studies of this source have revealed that it exhibits a variable absorption feature associated with the He I $λ$10,830 AA emission line, potentially indicative of obscuration events affecting the central engine. Here, we take advantage of the IRTF/iSHELL and TNG/GIANO-B spectrographs to observe this feature with unprecedented spectral resolution ($λ/Δλ> 50,000$), being able to study in detail the structure of the absorption trough and its variations over a time span of $\sim$700 days. In order to infer a connection between the He I absorption variability and that of the X-ray ionising continuum, we also analyse the publicly available data collected by the Swift-XRT instrument over the same period of time, unveiling a potential driving mechanism in the changes of the outflow ionisation state due to the X-ray flux variations. We also derive outflow physical parameters - $\dot{M}_{\rm out} \lesssim 10^{-2}$ M$_\odot$ yr$^{-1}$, $r_{\rm out} \sim 3$ pc, $v_{\rm max} \sim 1000$ km s$^{-1}$ - that are in line with those of comparable ionised winds found in similar targets, where the outflow is not powerful enough to trigger a significant AGN feedback ($\dot{E}_{\rm kin}/L_{\rm bol} \sim 0.001$%). Such findings point at a scenario in which a dusty and clumpy outflow that obscures NGC 4151 up to galactic scales responds to changes in the ionising X-ray flux, similarly to what happens in quasars with broad absorption lines and Seyferts with multiphase outflows such as NGC 5548.

Conventionally, the observed isolation of hot Jupiters, marked by a paucity of nearby low-mass planetary companions, has been interpreted as evidence of high-eccentricity tidal migration for these close-in gas giants. This loneliness is in stark contrast with the compact configurations commonly observed for warm Jupiters, indicating a curious dichotomy in dynamical origins between these two classes of short-period giants. In this study, however, we adopt a unified quiescent framework for both giant populations wherein they emerge from the protoplanetary disk as the sole Jovian planet within a compact multi-super-Earth system. We use long-term numerical simulations to show that post-disk dynamical evolution will naturally result in an observed preferential isolation for hot Jupiters relative to warm Jupiters. Specifically, their companions achieve significantly larger period ratios and mutual inclinations, rendering them more difficult to detect - especially via the transit method. Additionally, we find that this paradigm is consistent with the enigmatic population of longer-period hot Jupiters hosting interior companions on coplanar orbits. Another prediction of this model, best tested through high-precision Doppler campaigns, is the existence of a population of close-in (P<50 days) but mutually inclined outer companions to hot Jupiters.

Matrix string theory (arXiv:hep-th/9703030, arXiv:hep-th/9701025, arXiv:hep-th/9710009) is a conjectured duality between two-dimensional maximally supersymmetric $U(N)$ Yang-Mills theory and type-IIA string theory in ten-dimensional Minkowski spacetime. The IR description of this gauge theory is governed by the symmetric product orbifold $(\mathbb{R}^8)^N/S_N$ CFT. The leading irrelevant deformation from this IR fixed point is the Dijkgraaf-Verlinde-Verlinde operator, which comes with an unknown Wilson coefficient. We determine this coefficient using non-renormalization arguments from the UV gauge theory. The result is consistent with the matrix string theory conjecture and gives a first-principles check of the relation between $g_{\rm YM}$ and the string coupling. We also comment on the prospects for fixing further Wilson coefficients using similar methods.

Future gravitational wave (GW) standard siren catalogues will probe the late-time expansion history of the Universe across redshift ranges largely inaccessible to traditional electromagnetic observations. To determine how effectively this background distance information can distinguish between viable cosmological models, we introduce a model-independent reconstruction framework utilizing Gaussian Process Regression (GPR). Analyzing mock LISA and Einstein Telescope (ET) catalogues across six fiducial cosmological backgrounds-$Λ$CDM, CPL, CPL+$Λ$, interacting dark matter, interacting dark energy and axion inspired early dark energy. We reconstruct the comoving distance and its derivatives. Crucially, we propagated the full GP covariance, including derivative cross-covariances, to robustly evaluate the Hubble parameter $H(z)$ and other diagnostics such as $q(z)$, $\mathcal{O}_{m}(z)$ $w_{\rm total}(z)$ and $κ(z)$. While our analysis demonstrates that GW bright standard sirens faithfully recover fiducial expansion histories, applying pointwise marginal Hellinger distance reveals that background measurements alone do not provide decisive statistical separation among models. Instead, derivative sensitive diagnostics pinpoint specific redshift windows (e.g., $z\simeq1.6-1.8$ for ET and $z\simeq2.6-2.9$ for LISA) where future catalogues will maximize their discriminatory power. As machine learning methodologies become increasingly integral to astrophysics and cosmology, this Bayesian GPR pipeline offers a principled, nonparametric approach to precisely identifying where the most valuable cosmological information lies.

Resistance in standard conductors decreases with increasing cross-section. Yet, in low-dimensional superconductors and superfluids residual resistance arises from topological fluctuations of the order parameter manifesting as phase slips in one-dimensional (1D) and vortices in two-dimensional (2D) systems. How resistance and dissipation evolve as geometry interpolates between these regimes remains an open question. This evolution is masked in solid-state experiments by disorder, impurities, and geometric imperfections, and poses theoretical challenges due to competing dissipative processes and pronounced finite-size effects. Here, we use a defect-free unitary Fermi gas in a digitally programmable transport geometry to isolate geometric effects on superfluid dissipation and discover a paradox: in the crossover from 1D to 2D, the resistance reaches a minimum. There, widening a channel increases its resistance. Narrower, quasi-1D channels show dissipation described by Langer-Ambegaokar-McCumber-Halperin theory of phase slips. In this regime, varying the channel width yields the predicted exponential scaling of the activation factor over more than ten orders of magnitude. Wider, quasi-2D channels show dissipation consistent with a finite-size vortex model. The minimal dissipation in the dimensional crossover reflects a transition in the dominant dissipative mechanism, with both phase slips and vortices simultaneously suppressed. Our measurements suggest a route to minimizing dissipation in superconducting devices and provide a benchmark for theoretical efforts aimed at describing the dimensional crossover.

Sub-Neptunes are among the most common type of close-in planets found in our galaxy, yet their bulk composition remains largely uncertain; H-rich envelopes overlaying rocky cores, volatile-rich planets, and carbon-rich interiors all remain viable configurations for members of this population. Atmospheric characterization has been proposed as a means of distinguishing between these scenarios, but growing evidence suggests that sub-Neptunes may host molten atmosphere-mantle interfaces which could alter the composition of their atmosphere. We use the PICASO 1D climate model, coupled to interior-structure and magma-atmosphere chemistry frameworks to quantify how clouds alter the atmospheric and interior structure of sub-Neptunes. For temperate sub-Neptunes like TOI-270 d, we find that clouds can lead to $\ge{1000}$ K heating at depth (${\sim}10^{4}$ bar) and $\sim{600}$ K cooling at shallow pressures ($\sim$1 bar). This heating is very sensitive to the cloud sedimentation efficiency and, to a lesser extent, to metallicity. Most sub-Neptunes in our sample should have a molten atmosphere-mantle interface, except TOI-1231 b and GJ 1214 b. For these two planets, cloudy models have a molten interface whereas clear models can allow a solid boundary. Clouds can heat the atmosphere-mantle interfaces by a temperature difference between $\sim{1400}-2600$ K for most sub-Neptunes in our sample. Such cloud-driven heating can substantially change the composition of the interface with abundances of O$_2$, SiH$_4$, and SiO showing a $\ge{36}$\% increase between cloudy and clear models of TOI-270 d. We discuss the implications of our results for the thermal evolution and measurements of intrinsic heat flux for this population.

We study spin and entanglement dynamics in spin-1/2 XXZ chains under periodic monitoring and show that this system exhibits two measurement-induced phase transitions: a steady-state entanglement phase transition similar to those in monitored quantum circuits and a ballistic-to-diffusive transition in transient dynamics. Specifically, we discover that at low monitoring rate, an initial configuration containing a domain wall $|\uparrow\uparrow\uparrow\ldots \downarrow\downarrow\downarrow\ldots\rangle$ spreads ballistically while, at large monitoring rates, the domain melting is diffusive. Extensive numerical simulations, supported by theoretical arguments, indicate that the ballistic-diffusive transition is intimately interlinked with the entanglement phase transition. In contrast to the entanglement phase transitions, which require exponentially complex postselection, the ballistic-diffusive transition can be observed without postselection and constitutes an experimentally accessible manifestation of the many-body Zeno effect.

The rapid evolution of large language model based multiagent systems has transformed digital communication, with platforms like MoltBook emerging as essential agent native environments for observing autonomous social behaviors. While existing literature has documented the structural topology of these networks, there remains a critical gap in understanding the semantic content and emotional undercurrents of agent discourse. In this study, we propose a multi-dimensional analytical framework, utilizing human AI collaboration leveraging the Hermes agent powered by the Minimax 2.7 LLM to facilitate data collection and preliminary analysis. Our methodology synthesizes Social Network Analysis with sentiment analysis and thematic visualization to decode inter-agent interactions. We argue that benchmarking agent social dynamics against human social networks is inherently limited; thus, this study focuses exclusively on the intrinsic mechanics of agent-native communication. By integrating structural network metrics with qualitative diagnostics, we provide a holistic view of interaction quality within the MoltBook ecosystem. This collaborative approach not only addresses the need for semantic depth in agent network analysis but also offers valuable insights into the emergent dynamics of decentralized autonomous digital networks.

Memristive devices have revolutionized non-volatile memory and neuromorphic computing, yet the geometry of their hysteresis loops -- in particular, the occurrence and robustness of multiple self-crossings -- remains poorly understood. Here we introduce a topological and algebraic framework that treats the number of transverse self-intersections of a memristor hysteresis loop as a robust integer-valued invariant. Drawing on differential topology, singularity theory, and cusp catastrophe, we employ discriminants and resultants to stratify the six-dimensional parameter space. This approach partitions the parameter space into structurally stable regions separated by explicitly computable catastrophe surfaces. We demonstrate that the crossing number remains strictly invariant under continuous deformations and changes only at self-tangencies or cusp singularities, thereby providing a complete classification of all multi-lobed hysteresis behaviors. These insights bridge device physics with modern singularity theory and suggest a clear roadmap for exploiting higher-order memory effects in next-generation electronics and brain-inspired hardware.

We discuss a spectral property for the virial operator of the 2D Zakharov-Kuznetsov (ZK) equation. This is a crucial ingredient to establish blow-up or asymptotic stability of solitary waves in higher-dimensional problems. This model in 3D setting was originally introduced by Zakharov and Kuznetsov in plasma physics, and is also a higher-dimensional generalization of the well-known Korteweg-de Vries (KdV) equation. The problem of stability of solitary waves in ZK equation or stable blow-up in modified ZK (or KdV-type) equation is an important physical question, for which virial operators and their spectral properties are the essential elements of the analysis. In this paper we investigate this problem analytically and reduce it to verifying numerically only some signs of inner products and certain eigenvalues.

Silicon photomultipliers (SiPMs) have become the preferred photodetectors in next-generation neutrino experiments, yet no unified closed-form analytical expression free of truncation and numerical convolution has been established for their full charge response spectrum, which must simultaneously capture correlated cross-talk and afterpulsing effects absent in conventional photomultiplier tubes (PMTs). We present a unified closed-form model for the SiPM charge response within the characteristic-function framework, treating pedestal noise, single-electron-response (SER) charge, internal optical cross-talk, and afterpulsing on equal footing. The characteristic-function representation factorises the full charge spectrum into three independent physical components: pedestal, single-electron response (SER), and avalanche count statistics. Prompt internal optical cross-talk is modelled as a Galton-Watson branching process with Poisson offspring; building on the Generalised Poisson count statistics identified by Vinogradov, we derive a Lambert $W$ closed form for the total-progeny PGF via Lagrange-Bürmann inversion, providing the analytical handle needed for efficient event-level reconstruction. Afterpulsing is modelled as a per-avalanche geometric chain, derived as the maximum-entropy Poisson-Gamma mixture: the exponential prior-maximum-entropy for a positive continuous yield with fixed mean-marginalised over a Poisson count yields the geometric per-avalanche distribution, whose $N$-avalanche total is Negative Binomial. This naturally encompasses the Poisson afterpulsing limit and recursive afterpulse chains while preserving analytical closure. The resulting eight-parameter expression is further applied to derive an explicit per-channel charge-time likelihood for event-level energy reconstruction without numerical convolution at inference time.

In many operational contexts, solutions to NP-hard combinatorial optimization problems, modeled by means of Mixed-Integer Linear Programming (MILP), may become infeasible due to unpredictable disruptions. Typically, reoptimizing by solving the MILP formulation on the perturbed instance is not possible as new solutions must be obtained in a very short computing time, while simple repairing heuristics may result in low-quality solutions. To bridge this gap, we propose a learning-to-reoptimize framework, and apply it to the Lot Sizing Problem (LSP) under machine breakdown disruptions. We design a fix-and-optimize strategy aided by a Graph Neural Network (GNN) that efficiently computes a new solution within the neighborhood of a repaired solution. By representing the instance, the original solution and the disruption as a feature graph, we train a GNN to predict the likelihood that specific binary variables require to be modified. These predictions guide the selection of a small subset of variables to be reoptimized by an MILP solver, while the other variables are hard-fixed. Numerical experiments on a large dataset demonstrate that our approach handles effectively different problem sizes, and that it significantly outperforms a baseline alternative approach, yielding larger cost reductions within the same limited time budget.

We prove a mixed-characteristic analogue of Kunz's theorem in terms of perfectoid towers: a Noetherian local ring of residue characteristic $p$ is regular if and only if it admits a flat map to a Noetherian ring that extends to a perfectoid tower. This result is deduced from another mixed-characteristic analogue due to O. Gabber and J. Lurie. We also characterize regularity for perfectoid towers via vanishing of single higher $\mathrm{Tor}$-module of the residue field with a perfectoid algebra.

Modern weather stations in Germany record daily temperatures every 10 minutes, whereas measurements from historical reference periods are often only available at much coarser temporal resolutions, typically hourly. This discrepancy must be accounted for when comparing historical and current daily temperature patterns. Motivated by this problem, we develop two-sample inference procedures for functional data under sampling schemes where one sample is densely observed while the other is relatively sparse. Building on recent ideas from transfer learning for functional data, we derive estimators of the difference of the mean functions that attain optimal convergence rates in the supremum norm. We further establish a functional central limit theorem in the space of continuous functions and develop multiplier bootstrap methods for constructing uniform confidence bands. Extensions to functional time series are also discussed. Applying the proposed methodology to daily temperature curves from German weather stations, analyzed separately by month, reveals that climate change has altered not only average temperatures but also intraday temperature patterns. In particular, for stations such as Berlin, warming from morning to early afternoon exceeds the daily average increase, whereas evening and nighttime temperatures exhibit comparatively smaller increases.

In this paper, we carry out a numerical study of misaligned accretion disks around spinning supermassive black holes. Here, we conduct a parameter survey covering a range of initial disk misalignment angles ($\mathcal{T}_\mathrm{init}=15^\circ, 45^\circ, 75^\circ$) with either the Magnetically Arrested Disk (MAD) or Standard And Normal Evolution (SANE) magnetic field configurations, using the general-relativistic magnetohydrodynamic (GRMHD) code KHARMA. We find that models in the MAD state can align with the black hole up to $\sim10 \, r_g$, even in extremely misaligned models ($\mathcal{T}_\mathrm{init}=75^\circ$), which has not been seen before. Models without a dynamically important magnetic field remain misaligned up to the black hole, with a maximum disk tilt at $\sim10 \, r_g$, the peak's radial distance from the black hole increases with increasing disk misalignment. However, the maximum disk tilt does not have a linear relationship with the initial disk misalignment, and appears to have a maximum value of $\sim50^\circ$. We also show misaligned disk simulations produced in KHARMA are consistent with other GRMHD codes, for a variety of problems.

Modern clinical trials and cohort studies gather low-cost data on all participants but may have limited resources to assess expensive exposures such as biomarkers or genomic data. When interest lies in associations involving expensive exposures, two-phase designs provide a cost-effective framework by using information available on all participants to guide the targeted selection of a subset for additional measurements. We extend this framework to studies with ordinal outcomes, a common yet previously unexplored setting. We propose three outcome-informed phase 2 sampling designs -- outcome-dependent sampling (ODS), covariate-stratified ODS, and residual-dependent sampling -- that leverage phase 1 data to enrich phase 2 selection with informative subjects. We then develop analysis methods for valid and efficient estimation/inference, including conditional likelihood methods with ascertainment-corrected maximum likelihood estimation, multiple imputation, and a full likelihood method using sieve maximum likelihood estimation. Across a range of scenarios, simulation studies show that the proposed methods substantially improve efficiency over simple random sampling with standard maximum likelihood estimation. We further demonstrate their practical utility by examining the association between interleukin-6 and a four-level clinical status outcome -- discharged, hospitalized but not in the ICU, hospitalized in the ICU, and death -- 14 days after randomization into the Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis trial.

We characterize positivity preserving maps $T: B(\mathcal{H})_h \otimes \mathbb{R}[x_1, \dots, x_n] \to B(\mathcal{H})_h \otimes \mathbb{R}[x_1, \dots, x_n]$ on $\mathbb{R}^n$ and on compact sets $K \subseteq \mathbb{R}^n$. This also characterizes local operator moment sequences and general operator moment sequences via positivity preserving maps.

A spectral collocation operator with the summation-by-parts property was introduced by Chan to develop entropy-stable discontinuous Galerkin (DG) semi-discretizations (https://doi.org/10.1016/j.jcp.2018.02.033). The present work shows that semi-discretizations based on this collocation operator produce solutions that are equivalent to solutions of a DG semi-discretization using the same underlying quadrature. The equivalence holds regardless of the number of degrees of freedom in the collocation scheme and when the quadrature is not strictly positive. Extraneous degrees of freedom in the collocation scheme are associated with the nullspace of the operator and remain zero throughout an unsteady simulation. If necessary, nullspace consistency can be recovered by introducing projection-based numerical dissipation that targets only the extraneous modes. The equivalence between collocation and DG solutions is verified for the constant-coefficient advection equation and Burgers' equation on triangular meshes. The numerical results show that equivalence breaks down for entropy-stable semi-discretizations of Burgers' equation based on a skew-symmetric splitting, but that equivalence can be recovered by projecting the collocation scheme's residual onto the relevant polynomial space. In addition to investigating equivalence, the results demonstrate that the collocation operator produces semi-discretizations with favorable spectral radii compared with a commonly used summation-by-parts operator construction.

Transport-induced self-sustained oscillations in electromechanical systems convert a static electrochemical bias into robust, autonomous oscillatory motion in the absence of any external periodic drive. However, an exact description of such self-oscillations remains challenging in nanoscale electromechanical devices featuring a simultaneously large bosonic Hilbert space, strong interactions, and structured fermionic leads. We formulate a tensor-network framework that combines a binary representation of the vibrational mode with mesoscopic reservoir embeddings that enable controlled access to the self-oscillatory steady states and relevant transport observables without explicit real-time propagation. We demonstrate the emergence of mechanical self-oscillations across a broad set of operating conditions, in which strong electromechanical backaction, nonadiabatic oscillator dynamics, and energy-dependent electronic tunneling processes compete. Furthermore, we observe that for both slow and fast vibrating mechanical modes, suppressed vibrational occupation fluctuations in the self-oscillation window along the electromechanical coupling strength sweep is preceded by a peak in the occupation fluctuations. Collectively, we explore how both intrinsic system properties and environmental parameters govern such autonomous oscillations over a broad range of operating conditions. The generality of our framework will enable the method to be employed straightforwardly to more complicated or experimentally relevant scenarios.

The shock-to-detonation transition in energetic materials is governed by coupled processes spanning Angstroms to millimeters and femtoseconds to microseconds, where traditional multiscale models fail due to the lack of scale separation. We address this grand challenge by directly bridging large-scale molecular dynamics (MD) simulations with continuum finite-element (FE) models using MISTnetX, a convolutional deep neural network. Trained on MD simulations of shock propagation through complex microstructures, MISTnetX captures shock-microstructure interactions, hotspot formation, and the transition to deflagration, supplying critical sub-grid information to FE simulations of mechanics, shocks, thermal transport, and chemistry. Applied to a synthetic but realistic nanostructured plastic-bonded RDX composite, MISTnetX enables parameter-free prediction of the full run-to-detonation transition.

We probe the cosmic expansion scenario within the framework of $f(R, L_{m})$ gravity by employing a well-motivated functional form of $f(R, L_{m}) = \frac{R}{2} + L_{m}^λ$. Specifically, we introduce three novel cosmological models formulated in terms of the redshift-dependent Hubble parameter $H(z)$, offering deeper insights into the underlying cosmic dynamics. The models are further utilized to investigate the expansion history of the universe and the evolution of several cosmological parameters. By using the Bayesian methods based on the $χ^{2}$-minimization technique, the median values of the model parameters are determined for the cosmic chronometer (CC) and joint (CC+Pantheon) datasets. A comprehensive study of the deceleration parameter, energy density, pressure and the equation of state parameter is carried out to understand the universe's evolution. Additionally, the validity of the energy conditions and the behavior of the statefinder diagnostic are thoroughly examined. Finally, the thermodynamic viability of the models is confirmed through the evolution of temperature and entropy density, and the estimated age of the universe further exemplifies good agreement with late-time astronomical observations.