The integration of non-terrestrial networks (NTN) into 5G new radio (NR) enables a new class of positioning capabilities based on cellular signals transmitted by Low-Earth Orbit (LEO) satellites. In this paper, we investigate joint delay-and-carrier-phase positioning for LEO-based NR-NTN systems and provide a convergence-centric comparison with Global Navigation Satellite Systems (GNSS). We show that the rapid orbital motion of LEO satellites induces strong temporal and geometric diversity across observation epochs, thereby improving the conditioning of multi-epoch carrier-phase models and enabling significantly faster integer-ambiguity convergence. To enable robust carrier-phase tracking under intermittent positioning reference signal (PRS) transmissions, we propose a dual-waveform design that combines wideband PRS for delay estimation with a continuous narrowband carrier for phase tracking. Using a realistic simulation framework incorporating LEO orbit dynamics, we demonstrate that LEO-based joint delay-and-carrier-phase positioning achieves cm-level accuracy with convergence times on the order of a few seconds, whereas GNSS remains limited to meter-level accuracy over comparable short observation windows. These results establish LEO-based cellular positioning as a strong complement and potential alternative to GNSS for high-accuracy positioning, navigation, and timing (PNT) services in future wireless networks.

The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral super-resolution that enhances low-resolution galaxy spectra by a factor of $\sim$10 in resolving power ($R\sim100$ to $R\sim1000$). The model is trained on 1,187 paired JWST/NIRSpec observations from the JADES program, where low-resolution prism spectra are matched with medium-resolution grating spectra (G140M, G235M, G395M) combined into a unified reference covering 1-5 $μ$m. Our three-stage architecture performs an initial super-resolution, infers the redshift from the coarse reconstruction, and then applies a physics-informed residual refinement that uses attention across emission-line tokens to learn inter-line relationships and predict parametric line profiles, alongside a convolutional branch for continuum corrections. Evaluated on a 20% held-out sample, the model achieves noise-limited residuals over most of the spectral range and systematically improves the signal-to-noise ratio of key diagnostic lines including [OII], H$β$, [OIII], and H$α$, often by factors of several. The super-resolved spectra successfully deblend features that are entirely unresolved at prism resolution, such as the [OIII] $λ\lambda4959,5007$ doublet and H$β$. As a proof of concept using JWST data, this approach is readily extensible to the low-resolution grism spectroscopy that will be delivered by Euclid and the Roman Space Telescope, potentially enabling population-level diagnostics across millions of galaxy spectra that would otherwise be inaccessible at grism resolution.

In the ever-evolving battle against malware, binary obfuscation techniques are a formidable barrier to effective analysis by both human security analysts and automated systems. In particular, virtualization or VM-based obfuscation is one of the strongest protection mechanisms that evade automated analysis. Despite widespread use of virtualization, existing automated deobfuscation techniques suffer from three major drawbacks. First, they only work on execution traces, which prevents them from recovering all logic in an obfuscated binary. Second, they depend on dynamic symbolic execution, which is expensive and does not scale in practice. Third, they cannot generate "well-formed" code, which prevents existing binary decompilers from generating human-friendly output. This paper introduces PUSHAN, a novel and generic technique for deobfuscating virtualization-obfuscated binaries while overcoming the limitations of existing techniques. PUSHAN is trace-free and avoids path-constraint accumulation by using VPC-sensitive, constraint-free symbolic emulation to recover a complete CFG of the virtualized function. It is the first approach that also decompiles the protected code into high-quality C pseudocode to enable effective analysis. Crucially, PUSHAN circumvents reliance on path satisfiability, a known NP-hard problem that hampers scalability. We evaluate PUSHAN on more than 1,000 binaries, including targets protected by academic state of the art (Tigress) and commercial-strength obfuscators VMProtect and Themida. PUSHAN successfully deobfuscates these binaries, retrieves their complete CFGs, and decompiles them to C pseudocode. We further demonstrate applicability by analyzing a previously unanalyzed VMProtect-obfuscated malware sample from VirusTotal, where our decompiled output enables LLM-assisted code simplification, reuse, and program understanding.

This report describes the method used to derive fluxes of the trapped proton belt along the GPS orbit (i.e., a Medium-Earth Orbit) during 2000-2010, a period almost covering a solar cycle. This method utilizes a newly developed empirical proton radiation-belt model, with the model output scaled by GPS in-situ measurements, to generate proton fluxes that cover a wide range of energies (50keV- 6MeV) and keep temporal features as well. The new proton radiation-belt model is developed based upon CEPPAD proton measurements from the Polar mission (1996 - 2007). Comparing to the de-facto standard empirical model of AP8, this model is not only based upon a new data set representative of the proton belt during the same period covered by GPS, but can also provide statistical information of flux values such as worst cases and occurrence percentiles instead of solely the mean values. The comparison shows quite different results from the two models and suggests that the commonly accepted error factor of 2 on the AP8 flux output over-simplifies and thus underestimates variations of the proton belt. Output fluxes from this new model along the GPS orbit are further scaled by the ns41 insitu data so as to reflect the dynamic nature of protons in the outer radiation belt at geomagnetically active times. Derived daily proton fluxes along the GPS ns41 orbit, whose data files are delivered along with this report, are depicted to illustrate the trapped proton environment in the Medium-Earth Orbit. Uncertainties on those daily proton fluxes from two sources are evaluated: One is from the new proton-belt model that has error factors < ~3; the other is from the in-situ measurements and the error factors could be ~ 5.

Multiple pairs of bronze pieces were joined along a common seam and then exposed to Nb vapor via sputter deposition during heating at $\sim$715 $^\circ$C to form a diffusion bond between the pieces. Polishing and alignment of the pieces created smooth surfaces normal to the Nb flux with seams perpendicular to the surface (i.e. parallel to the Nb flux). Conversion of Nb to Nb$_3$Sn took place simultaneously with diffusion bonding, resulting in Nb$_3$Sn thin films that coated bronze surfaces and spanned seams with uniform thickness. Characterization of superconducting properties via magneto-optical imaging suggests that supercurrent flows freely across the seam in several examples when cooled to 9 K and shielding or trapping low magnetic field. Modification of the process to coat the pieces with Nb prior to diffusion bonding and Nb$_3$Sn formation resulted in varying degrees of seam coverage by the resultant Nb$_3$Sn films. The pre-coating method did not produce any example with quality comparable to the examples obtained by the hot bronze approach. This work could enable new approaches to joining Nb$_3$Sn materials in magnet conductor and RF cavity applications.

Gaze-based selection in XR requires visual confirmation due to eye-tracking limitations and target ambiguity in 3D contexts. Current designs for wide-FOV displays use world-locked, central overlays, which are not conducive to always-on AR glasses. This paper introduces PeriphAR (per-ree-far), a visualization technique that leverages peripheral vision for feedback during gaze-based selection on a monocular AR display. In a first user study, we isolated text, color, and shape properties of target objects to compare peripheral selection cues. Peripheral vision was more sensitive to color than shape, but this sensitivity rapidly declined at lower contrast. To preserve preattentive processing of color, we developed two strategies to enhance color in users' peripheral vision. In a second user study, our strategy that maximized contrast of the target to the neighboring object with the most similar color was subjectively preferred. As proof of concept, we implemented PeriphAR in an end-to-end system to test performance with real-world object detection.

We develop effective methods for constructing an ensemble of district plans via independent sampling from a reasonable probability distribution on the space of graph partitions. We compare the performance of our algorithms to that of standard Markov Chain based algorithms in the context of grid graphs and state congressional and legislative maps. For the case of perfect population balance between districts, we provide an explicit description of the distribution from which our method samples.

The goal of this work is to investigate the almost pressureless Euler-Poisson (EP) system with repulsive force in the large friction limit. The leading order equations in the limit are shown to be the hyperbolic-elliptic Keller-Segel (KS) system of consumption type. Under suitable assumptions on the initial data, we establish the unique global-in-time solutions to both the EP system and the KS system by establishing the global stability in the large friction limit. In particular, no singularity forms in the asymptotic limit. Moreover, the time asymptotic behavior of the one-dimensional KS flow with vacuum is also discussed.

The use of synthetic data to deidentify data and to improve predictive models is well-attested to. The augmentation of datasets using synthetically generated data is an alluring proposition: in the best case, it generates realistic data \textit{in silico} at a fraction of the cost of authentic data which may be found \textit{in vivo} or \textit{in vitro}. This poses novel epistemic challenges. We contend that synthetic data augmentation is best understood as a novel way of accounting for prior knowledge. In this manuscript, we propose a definition of synthetic distributions and analyze how synthetic data augmentation interplays with standard accounts of maximum likelihood and Bayesian estimation. We observe that the marginal Fisher information contributed by synthetic data processes is subject to fundamental bounds, and enumerate obstacles to the use of synthetic data augmentation to aid in inferential tasks. We then articulate a Bayesian formulation of the way that synthetic data augmentation can be coherently understood, but argue that naive approaches to the specification of the prior are epistemically unjustifiable. This suggests that enhanced scrutiny must be placed on identifying justifiable priors to warrant the use and inclusion of data drawn from specific synthetic distributions. While our analysis shows the challenges and limitations of using synthetic data augmentation to improve upon traditional statistical model reasoning, it does suggest that augmentation is the principal approach analysts using outcome reasoning (i.e. using train/test splits to justify the analysis) can constrain an otherwise high-dimensional model space, providing an alternative to trying to encode the constraints into the potentially complex architecture of the algorithm.

Exponential quintessence models motivated by string compactifications naturally involve both a dilatonic scalar and its axionic partner evolving on a curved field space, while spatial curvature enlarges the cosmological phase space and may affect late-time dynamics. We perform a systematic analysis of the minimal two-field exponential system in a curved FLRW background including radiation and matter components, combining a complete dynamical systems classification with analytical approximations and numerical integration. In the scalar-dominated limit, non-geodesic trajectories can sustain accelerated expansion on steep potentials, and in curved universes a scaling-curvature fixed point can in principle soften the requirements for acceleration. However, we show that these mechanisms arise in distinct invariant manifolds and cannot be simultaneously realized in the presence of a background fluid: no non-geodesic scaling fixed point exists within an open region of parameter space. As a consequence, in the observationally viable thawing regime the axion does not track the background fluid and spatial curvature becomes dynamically subdominant, leading to an effectively single-field evolution. We further identify a degeneracy between curvature effects and scalar-field dynamics that limits their independent impact on late-time cosmology. Confronting the model with current cosmological background data (Planck 2018 distance priors, Pantheon+, BAO, and cosmic chronometers), we obtain an upper bound $λ\lesssim 0.75$ (95 percent CL) on the potential slope. Our results demonstrate that even in the minimal multifield setup with spatial curvature, sustained late-time acceleration requires a sufficiently flat potential, so that the tension between cosmic acceleration and quantum gravity expectations persists within this framework.

In this work we contrast the behaviour of two spherically symmetric matter models in a class of spherically symmetric spacetimes which feature a weak null singularity. This class in particular contains spherically symmetric perturbations of subextremal Reissner-Nordström under the Einstein--Maxwell--scalar field system, a system for which a $C^2$ formulation of the strong cosmic censorship conjecture was proved by Luk-Oh, arXiv:1702.05715 and Dafermos, arXiv:1201.1797. Firstly, we consider the Cauchy problem of spherically symmetric dust falling into the weak null singularity (WNS) where the initial dust velocity is normal to a smooth spacelike curve with certain properties. We prove that the flow of the dust velocity does not experience any shell-crossing before or at the singularity, the velocity vector remains timelike, and that the dust energy density remains bounded as matter approaches the singularity. Secondly, we consider the characteristic initial value problem for stiff perfect fluid falling into the WNS. By relating the stiff fluid velocity and energy density to a scalar field satisfying the homogeneous linear wave equation, we prove that this energy density becomes infinite as we approach the weak null singularity. Furthermore, we show that the ingoing component of the stiff fluid velocity blows up while the outgoing component approaches zero at the singularity. Therefore the velocity vector approaches an ingoing null vector tangent to the singular hypersurface.

Norrish type I reactions selectively cleave carbon-carbon bonds directly adjacent to carbonyl groups. Despite their broad use in combination with aromatic carbonyls for additive manufacturing and dental UV curing applications, the nature of the photochemically active state and its population mechanism remain insufficiently understood. Detailed mechanistic insight requires mapping of the photoexcited population flow involving internal conversion and intersystem crossing. We present a time-domain study of gas phase acetophenone as a prototypical aromatic carbonyl combining soft X-ray time-resolved near-edge X-ray absorption fine structure (TR-NEXAFS) spectroscopy at the oxygen K-edge with ab initio multiple spawning (AIMS) simulations. Exploiting the specific sensitivity of TR-NEXAFS spectroscopy to states with $nπ^*$ character, we observe population transfer from the initially excited $^1ππ^*$ state to the $^1nπ^*$ state with a time constant of $(0.13 \pm 0.02)$ ps after an initial induction period of $(0.12 \pm 0.02)$ ps without population transfer, in quantitative agreement with the AIMS simulations. The population in the $^1nπ^*$ state subsequently decays via intersystem crossing, likely mediated by a $^3ππ^*$ state, within $(3.17 \pm 0.66)$ ps to a long-lived $^3nπ^*$ state, which is presumed to be active towards Norrish type I chemistry.

We present a geometric approach to designing distributed unknown input observers (DUIOs) for linear time-invariant systems, where measurements are distributed across nodes and each node is influenced by \emph{unknown inputs} through distinct channels. The proposed distributed estimation scheme consists of a network of observers, each tasked with reconstructing the entire system state despite having access only to local input-output signals that are individually insufficient for full state observation. Unlike existing methods that impose stringent rank conditions on the input and output matrices at each node, our approach leverages the $(C,A)$-invariant (conditioned invariant) subspace at each node from a geometric perspective. This enables the design of DUIOs in both continuous- and discrete-time settings under relaxed conditions, for which we establish sufficiency and necessity. The effectiveness of our methodology is demonstrated through extensive simulations, including a practical case study on a power grid system.

Multiplex tissue imaging (MTI) enables high- dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo- rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mech- anistic, stochastic simulators of TME evolution; yet their high- dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced- order, simulation-driven framework for therapeutic strategy design using ABM-derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low-dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we learn a probabilistic Markov State Model (MSM) that captures metastable states and the transitions between them. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on dominant governing parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) for treatment scheduling. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements.

We present the first atmospheric observation and characterisation of the aligned, 1468 K hot Jupiter, NGTS-2b, with one JWST NIRSpec/G395H transit. These observations complete the GO 3838 observing campaign of the BOWIE-ALIGN program, which aims to investigate the link between hot Jupiter atmospheric composition and formation history through the atmospheric analysis of planets orbiting F stars that are aligned and misaligned with the host stellar spin axis. The 2.84-5.18 micron spectrum shows weak absorption features attributed to H$_2$O and CO$_2$ absorption, which our free chemistry retrievals fit with posteriors that converge on high mean molecular weight solutions attained through significant H$_2$O mixing ratios. By comparing our results to interior modelling, we show that some of these solutions exceed the 43.5x solar upper limit we obtained from our interior structure models. Such solutions are likely due to cloud-metallicity degeneracies and insufficient wavelength coverage to resolve them. We show that, in the case of our observations, the likelihood distribution of H$_2$O abundances is flat and uninformative, such that our retrievals are biased by the prior. Additionally, our statistically favoured atmospheric solution contains absorption from SO. The chemical abundances retrieved with this model are likely not astrophysically feasible and we demonstrate that the presence of SO is driven by only two data points. Our equilibrium chemistry retrievals hint at a subsolar C/O ratio and supersolar metallicity; however, we find wide posterior distributions that extend to solar values.

Geostatistics is a branch of statistics concerned with stochastic processes over continuous domains, with Gaussian processes (GPs) providing a flexible and principled modelling framework. However, the high computational cost of simulating or computing likelihoods with GPs limits their scalability to large datasets. This paper introduces the piecewise continuous Gaussian process (PCGP), a new process that retains the rich probabilistic structure of traditional GPs while offering substantial computational efficiency. As will be shown and discussed, existing scalable approaches that define stochastic processes on continuous domains -- such as the nearest-neighbour GP (NNGP) and the radial-neighbour GP (RNGP) -- rely on conditional independence structures that effectively constrain the measurable space on which the processes are defined, which may induce undesirable probabilistic behaviour and compromise their practical applicability, particularly in complex latent GP models. The PCGP mitigates these limitations and provides a theoretically grounded and computationally efficient alternative, as demonstrated through numerical illustrations.

Nonlocal games provide application-level benchmarks for quantum hardware whose classical performance bounds are information-theoretic, holding against all classical strategies regardless of computational resources. We implement a 14-vertex graph coloring game, the smallest graph exhibiting a quantum-classical separation for this game type, on four trapped-ion quantum processors across three institutions. One system achieved a win rate that surpasses the classical bound with statistical significance, marking the first violation of a classical bound in a graph coloring nonlocal game on quantum hardware. The remaining systems achieved win rates comparable to the best superconducting processors evaluated on the same game, further illustrating the potential of nonlocal games as cross-architecture quantum benchmarks.

We study error-correcting codes in the space $\mathcal{S}_{n,q}$ of length-$n$ multisets over a $q$-ary alphabet under the deletion metric, motivated by permutation channels in which ordering is completely lost and errors act only on symbol multiplicities. We develop two complementary directions. First, we present polynomial Sidon-type constructions over finite fields, in both projective and affine forms, yielding multiset $t$-deletion-correcting codes in the regime $t<q$ with redundancy $t+O(1)$, independent of the blocklength $n$. Second, we develop a geometric analysis of deletion balls in $\mathcal{S}_{n,q}$. Using difference-vector representations together with a diagonal reduction of the relevant generating functions, we derive exact generating-function expressions for individual deletion-ball sizes, exact formulas for the number of ordered pairs of multisets at a fixed distance $m$, and consequently for the average ball size. We prove that radius-$r$ deletion balls are minimized at extreme multisets and maximized at the most balanced multisets, giving a formal global characterization of extremal centers in $\mathcal{S}_{n,q}$. We further relate the maximal-ball value to the ideal difference set $S_{q-1}(r,r)$ through boundary truncation, obtaining explicit closed forms for $q=2$ and $q=3$. These geometric results lead to volume-based bounds on code size, including sphere-packing upper bounds, a boundary-aware analysis of code--anticode arguments, and Gilbert--Varshamov-type lower bounds governed by exact average ball sizes. For fixed $q$ and $t$, the resulting average-ball lower bound matches the interior-difference-set scale asymptotically.

We present a calculus that models a simple sort of process interaction. Our calculus consists of a collection of terms together with a rewrite relation, parameterised by an arbitrary multicategory whose morphisms we understand as non-interactive processes. We show that our calculus is confluent and terminating, and that terms modulo the induced convertibility relation form a virtual double category. We relate our calculus to the free cornering of a monoidal category, which is a double-categorical model of process interaction that is similar in spirit to the calculus presented herein. Precisely, we construct a functor from the virtual double category given by our calculus into the underlying virtual double category of the free cornering of the free monoidal category on the multicategory of non-interacting processes. If we think of the terms of our calculus as programs and the rewriting system as an operational semantics for these programs, this functor gives a sound denotational semantics for our calculus in terms of the free cornering.

This paper presents a global, coordinate-free formulation of the Fokker-Planck equation on Riemannian manifolds. In the Stratonovich formulation, the infinitesimal generator is expressed intrinsically through Lie derivatives, and its adjoint is derived via the divergence theorem, yielding a concise geometric form of the Fokker-Planck equation. In the Ito formulation, a diffusion tensor field is introduced to generalize the Euclidean diffusion matrix, and a tensor-field-based analysis establishes an intrinsic double-divergence representation of the Fokker-Planck equation. The proposed framework provides a globally valid and geometrically consistent interpretation of diffusion and probability transport on Riemannian manifolds, supported by compact and intuitive proofs.

Marine heatwaves (MHWs) threaten marine ecosystems and significantly impact weather patterns. In the Arabian Sea, summer MHWs are of particular concern due to their potential impacts on the Indian summer monsoon, a lifeline for nearly a billion people. However, the drivers of these MHWs and their influence on atmospheric circulation and monsoon rainfall remain poorly understood. Using satellite observations, reanalysis datasets, and numerical model experiments, we investigate the key drivers of MHW events and assess their impacts. When SST warming trends are retained, the eastern and northern Arabian Sea emerge as MHW hotspots, showing rapid increases during 1982-2023, largely due to anthropogenic warming. On detrending the SSTs to remove the influence of anthropogenic warming on individual MHWs, we find that most MHWs are short-lived (lasting <= 20 days) and are initiated by enhanced surface shortwave radiation and reduced latent heat loss associated with the suppressed convection phase of the Boreal Summer Intraseasonal Oscillations (BSISOs). Interannual SST anomalies, including ENSO and Indian Ocean Dipole (IOD), further modulate the year-to-year MHW variability. Conversely, the warm SSTs during MHWs exert strong atmospheric feedbacks. MHWs in the eastern Arabian Sea drive cyclonic winds, intensify moisture convergence and increase the risk of extreme precipitation along the southwest coast of India. In the northern Arabian Sea, MHW-induced cyclones trigger intense rainfall over northwestern India and Pakistan, contributing to extreme events like the 2022 Pakistan floods. These findings improve our capacity to predict Arabian Sea MHWs and assess their risks, offering significant socio-economic and ecological benefits.

We introduce a generalized low-density parity-check decoding framework for quantum Tanner codes utilizing soft-output guessing random additive noise decoding (SOGRAND). By soft-output decoding entire component codes, we mitigate trapping sets and cycles, resulting in improved convergence. SOGRAND, combined with ordered statistic decoding (OSD) post-processing, outperforms the standard belief propagation plus OSD baseline by up to three orders of magnitude in logical error rate, providing a way forward for scalable decoding of the emerging class of Tanner-code-based quantum codes.

While diffusion in crystalline solids is quantitatively understood through defect-mediated atomic hops, no comparable quantitative framework exists for glasses. In these systems, the origin of large diffusion activation energies remains puzzling, despite local rearrangements involving low barriers. Using molecular dynamics simulations of metallic glasses, we decompose diffusion into random-walk and correlation contributions and find that back-and-forth correlated motion, not local rearrangement barriers, dominates the activation energy, resolving how low-barrier rearrangements yield large macroscopic activation energies. These correlations arise from asymmetry between forward and reverse barriers, a generic feature of disordered energy landscapes. We find that the correlation-driven mechanism is active beyond metallic glass alloys, including SiO2 and a single-component Lennard-Jones glass. The latter demonstrates that the correlation originates from structural disorder rather than chemical complexity. The framework also explains accelerated surface diffusion, where reduced activation energies arise primarily from weaker correlations rather than changes in local rearrangement barriers. Our results establish a direct, quantitative link between atomic-scale dynamics and macroscopic transport, providing a predictive basis for kinetics in disordered materials.

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the HPHT environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, we employ GPU-accelerated ReaxFF simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release conditions. We further investigate how the initial nanodiamond morphology influences the resulting transformation products. Evolution of nanostructure, allotrope, carbon hybridization, and ring statistics are tracked. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp2 layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into full sp2 structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Lonsdaleite emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained MLP regressors on over 10^5 node-hours of simulations. The model reliably predicts the number of graphitized layers from T-P trajectories with R^2 exceeding 0.90. Collectively, morphological control combined with optimized quench-decompression conditions promote the selective synthesis of nanocarbon allotropes.

The object of study in this paper is the expected $2$-Wasserstein distance between the empirical measures of several point processes and their respective limit. For this, the main tool developed is a smoothing procedure in Euclidean spaces using the heat equation with Neumann boundary conditions. It is applied to the spectrum of Random Normal Matrices with \textit{reasonable} assumptions, as well as to several families of Homogeneous Point Processes such as the infinite Ginibre ensemble, the Bessel ensemble, and the zero set of the planar Gaussian Analytic Function.

We report the results of calculations of inclusive soft hadron production in proton-lead collisions at the LHC in the framework of modified quark-gluon string model (QGSM) extended to $pA$ interactions. Our consideration involves the nuclear modification of previously proposed transverse momentum dependent (TMD, or unintegrated) gluon density in a proton, which provides a self-consistent simultaneous description of numerous HERA and LHC data on $pp$, $ep$ and $γp$ processes. Such nuclear modification is based on well established property of geometrical scaling from nucleons to nuclei. Focusing on the region of small $x$ and low scales, we obtain predictions for transverse momentum spectra of pions and kaons at $p_T \leq 1$~GeV. Our results are compared with recent data reported by the CMS, ATLAS and ALICE Collaborations at $\sqrt s = 5.02$~TeV. We find that the developed approach provides a better description of low-$p_T$ data than the predictions made by other groups.

Estimation of the mean and covariance functions is a fundamental problem in functional data analysis, particularly for discretely observed functional data. In this work, we study a regularization-based framework for estimating the mean and the covariance functions within a reproducing kernel Hilbert space (RKHS) setting. Our approach utilizes a spectral regularization technique under Hölder-type source conditions, allowing for a broad class of regularization schemes and accommodating a wide range of smoothness assumptions on the target functions. Unlike previous works in the literature, the proposed work does not require the target functions to belong to the underlying RKHS. Convergence rates for the proposed estimators are derived, and optimality is established by obtaining matching minimax lower bounds.

In this paper we consider the twice-renormalized, complex-valued modified KdV (mKdV) on the one-dimensional torus introduced by Chapouto. Our main result is the construction of an invariant measure supported at low-regularity. This work complements the work of Kenig et al., which constructed invariant measures supported in higher-regularity spaces for the non-renormalized mKdV. Due to the low-regularity of the support of the measure, we are forced to work in Fourier-Lebesgue spaces. The fact that we consider the complex-valued mKdV makes the problem more complicated than the real-valued case, which was previously considered.

This paper presents novel theoretical results to guarantee multi-agent set invariance using Matrix Control Barrier Functions in sampled-data systems. More specifically, the paper presents conditions under which heterogeneous control-affine agents applying zero-order-hold control inputs can compute control inputs to render safe sets defined by matrix inequalities forward invariant. It then introduces methods to guarantee set invariance while accounting for the presence of adversarial agents seeking to drive the system state to unsafe sets. Finally, the paper presents theoretical extensions of these set invariance results to systems having high relative degree with respect to the matrix-valued safe set function.

The main contributions of this paper are twofold: First, we present an in-depth analysis of the impact of frame rate reductions on the visual quality of the video and the encoding as well as decoding energy. Second, we propose a lightweight frame rate selection method for energy- and quality-aware encoding. Concerning the first contribution, this paper performs extensive encoding and decoding measurements, followed by an investigation of the impact of temporal downsampling on the energy demand of encoding and decoding at different frame rates. Furthermore, we determine the objective visual quality of the downsampled videos. As a result of this investigation, we identify content- and quantization-setting-dependent energy-aware frame rates, i.e., the temporal downsampling factors that lead to Pareto-optimality in terms of energy and quality. We demonstrate that significant energy savings are achieved while maintaining constant visual quality. Subsequently, a subjective experiment is conducted to verify this observation regarding perceptual quality using mean opinion scores. As the second contribution, we propose an energy-aware frame rate selection method that extracts spatio-temporal features from the video sequences. Based on these features, the proposed method employs a feature-based supervised machine learning approach to predict energy-aware frame rates for a given quantization parameter and video sequence, aiming to reduce energy consumption during encoding and decoding. The experimental results demonstrate that the proposed method offers significant energy savings, with an average of 17.46% and 17.60% of encoding and decoding energy demand reduction, respectively, alongside 3.38% average bitrate savings at a constant quality.