Consider a generalised preferential attachment tree with attachment function $f$, that is a random tree, where at each time-step a node connects to an existing node $v$ with probability proportional to $f(\mathrm{deg}(v))$, where $\mathrm{deg}(v)$ denotes the degree of the node in the existing tree. We provide a counter-example to a conjecture of the author asserting that under the assumption $\sum_{j=1}^{\infty} \frac{1}{f(j)^2} < \infty$ there is a persistent hub in the model, that is, a single node that has the maximal degree for all but finitely many time-steps. The counter-example is a minor modification of a related counter-example due to Galganov and Ilienko.

Mergers and interactions can significantly affect the morphological and dynamical properties of galaxies, however the impact of mergers on turbulence at $z > 1$ has not been observationally constrained. In this work we use the interaction strength parameter $Q_P$ to identify likely interacting and isolated galaxies at cosmic noon ($z \sim 1-2$) within the KMOS\textsuperscript{3D} integral field spectroscopy survey, utilising redshifts from the 3D-HST, CANDELS and UVCANDELS surveys. For $186$ galaxies, we measure deconvolved H$α$ kinematics, including velocity dispersion, using a spatially non-parametric approach to account for observational effects in the dynamically diverse range of galaxies. We compare offsets in H$α$ flux, star formation rate (SFR), dust attenuations, and velocity dispersion of likely interacting galaxies to isolated control galaxies matched in mass and lookback time. We find increased H$α$ fluxes and SFRs in the likely interacting sample at the level of $\sim 0.1$ dex, a similar enhancement to studies of local pairs. In contrast, we find no significant increase in the level of velocity dispersion in interacting galaxies compared to their controls. The lack of increase in dispersion may reflect a combination of physical and observational factors, including limits to increasing turbulent motions in an already turbulent medium and spectral resolution limits.

While the geometry of equilibrium states and driven non-equilibrium processes is clearly understood, a geometric description for relaxation towards equilibrium is still lacking. Here, we propose a thermo-geometric measure based on the Rayleigh quotient, reformulating relaxation as a fundamental competition between entropic stiffness and frictional dissipation. Taking a van der Waals gas with two dissipation channels as an example, we explicitly demonstrate its relaxation landscape. Particularly, we find that upon approaching the critical temperature $T_c$, the slow-mode relaxation rate vanishes linearly as $λ_s \propto (T-T_c)/T_c$, indicating critical slowing down. This study completes the thermodynamic geometry framework, providing a general tool for characterizing the relaxation dynamics of complex systems.

We consider the Euler-Poisson system for ions where the electrons are given by a Maxwell-Boltzmann distribution, and we investigate the existence of one-dimensional periodic traveling waves. More precisely, we first establish the existence of a smooth global branch of bifurcation emanating from a constant equilibrium. We then construct a singular traveling wave emerging as the limiting profile at the end of the global curve of bifurcation. Our analysis accommodates a wide class of pressure laws and provides a comprehensive characterization of both smooth and singular traveling waves. A central difficulty in this model arises from the exponential nonlinearity, induced by the nonlocal Poisson-Boltzmann equation, which prevents any explicit representation of the electron field in terms of the ion density. This poses significant obstacles compared to previous studies on related models, where such explicit formulas were crucial for global bifurcation arguments.

The incidence of cybersecurity attacks utilizing social engineering techniques has increased. Such attacks exploit the fact that in every secure system, there is at least one individual with the means to access sensitive information. Since it is easier to deceive a person than it is to bypass the defense mechanisms in place, these types of attacks have gained popularity. This situation is exacerbated by the fact that people are more likely to take risks in their passive form, i.e., risks that arise due to the failure to perform an action. Passive risk has been identified as a significant threat to cybersecurity. To address these threats, there is a need to strengthen individuals' information security awareness (ISA). Therefore, we developed ConGISATA - a continuous gamified ISA training and assessment framework based on embedded mobile sensors; a taxonomy for evaluating mobile users' security awareness served as the basis for the sensors' design. ConGISATA's continuous and gradual training process enables users to learn from their real-life mistakes and adapt their behavior accordingly. ConGISATA aims to transform passive risk situations (as perceived by an individual) into active risk situations, as people tend to underestimate the potential impact of passive risks. Our evaluation of the proposed framework demonstrates its ability to improve individuals' ISA, as assessed by the sensors and in simulations of common attack vectors.

Hydrogen-based zero-emission ships are a key element in the decarbonization of the maritime sector. To strengthen these their economic competitiveness, it is key to drive their costs to a minimum. Current literature mainly focuses on fuel consumption minimization, but there is a lack of explicit consideration of costs arising from cell degradation and optimization-based approaches that leverage information on future load trajectories. This work aims at minimizing the operational cost of fuel cell-battery hybrid shipboard power systems, accounting for hydrogen consumption and cell degradation as the main cost drivers. A degradation-aware predictive energy management strategy utilizing data-driven load forecasting is designed and showcased at the example of a virtually retrofitted harbor tug. This work shows that the real onboard measurements of the vessel can be utilized to make accurate load predictions over 15min. Results indicate that the degradation-aware, predictive control simultaneously reduces the hydrogen consumption by up to 5.8% and the cell degradation by up to 36.4% with an aged fuel cell system when compared to a filter-based benchmark applied to real operating data of the harbor tug. With an increased prediction horizon of 1h, further significant reductions of 3.8% and 14.0% could be shown.

As a current trend in Artificial Intelligence (AI), large foundation models are increasingly employed as the core of AI services. However, even after training, serving such models at scale remains a challenging task due to their heavy resource footprints, particularly in terms of GPU memory. While recent works revealed unique characteristics of systems serving foundation models that distinguish them from traditional distributed computing systems, there is still a lack of fundamental understanding of the underlying system management problems. This work aims at addressing this gap by extracting a novel problem of "server chain composition" via block placement and cache allocation for serving chainstructured jobs with large memory footprints, which models a fundamental problem in serving large foundation models through pipeline parallelism. After showing the NP-hardness of the optimal solution, the focus is turned to developing scalable algorithms with guaranteed performance under state-of-the-art load balancing. Application of the proposed solution to a distributed large language model (LLM) serving system shows significant reduction of response times compared to state-of-the-art solutions.

Near-infrared (NIR) emitters operating in the 650-900 nm range are highly attractive for imaging and sensing in turbid media; however, cadmium-free InP-based quantum dots (QDs) often suffer from limited brightness due to nonradiative pathways and inefficient photon outcoupling. In particular, heterostructured InP QDs can exhibit band alignments that induce partial spatial separation of charge carriers, leading to reduced electron-hole wavefunction overlap. This modifies intrinsic recombination dynamics and enhances the sensitivity of their emission to the surrounding photonic environment. Here, we investigate silver toroidal plasmonic nanoantenna dimers (Ag TPNDs) through finite-difference-time-domain (FDTD) simulations as a geometry-tunable platform for enhancing NIR emission of heterostructured InP-based QDs. The coupled toroidal geometry supports strongly confined bonding modes that generate intense nanogap hotspots, while its resonance can be systematically tuned through the toroid aspect ratio. By spectrally aligning the antenna response with QD emission bands (675-845 nm), we achieve large Purcell enhancements together with high quantum efficiencies, demonstrating efficient conversion of enhanced decay rates into radiative emission. We further show that nanometer-scale variations in emitter-antenna separation strongly modulate the radiative rates and spectral response. These results establish toroidal plasmonic nanodimers as a topology-driven platform for controlling emission in NIR quantum emitters and for advancing NIR nanophotonic applications.

Public-private graph, where a public network is visible to everyone and every user is also associated with its own small private graph accessed by itself only, widely exists in real-world applications of social networks and financial networks. Most existing work on community search, finding a query-dependent community containing a given query, only studies on a public graph, neglecting the privacy issues in public-private networks. However, considering both the public and private attributes of users enables community search to be more accurate, comprehensive, and personalized to discover hidden patterns. In this paper, we study a novel problem of attributed community search in public-private graphs (ACS-PP), aiming to find a connected k-core community that shares the most keywords with the query node. This problem uncovers structurally cohesive communities, such as interest-based user groups or core teams in collaborative networks. To optimize search efficiency, we propose an integrated scheme of constructing a public global graph index and a private personalized graph index. For the private index, we developed a compact structure of the PP-FP-tree index. The PP-FP-tree is constructed based on the public and private neighbors of the query node in the public-private graph, serving as an efficient index to mine frequent node sets that share the most common attributes with the query node. Extensive experiments on real public-private graph datasets validate both the efficiency and quality of our proposed PP-FP search algorithm against existing competitors. The case study on public-private collaboration networks provides insights into the discovery of public-private communities.

In the present work we investigate the phenomenological implications of a vanishing effective Majorana neutrino mass within a $3+1$ neutrino framework adding a eV-scale sterile neutrino beside three active neutrino states in light of latest cosmology driven bounds on sum of neutrino masses ($\sum_{i}m_i$). We explore the parameter space where the destructive interference between active and sterile states leads to vanishing amplitude, $M_{ee}$, of neutrinoless double beta ($0νββ$) decay. The allowed parameter space has been identified and predictions have been obtained taking into account the latest Planck and DESI+CMB bound on $\sum_{i}m_i$. We find that these bounds restrict the sterile mixing angle $θ_{14}$ and the lightest active neutrino mass. Furthermore, we incorporate the refined precision data from JUNO experiment regarding solar oscillation parameters ($θ_{12}, Δm_{21}^2$). We find that the sterile neutrino parameters like $θ_{14}$ may not be sensitive to the JUNO precision measurements as the constraint imposed by precise $θ_{12}$ is washed out by new cancellations driven through additional CP violating phases leading to vanishing $|M_{ee}|$.

Understanding the thermodynamics of radiation and the quantum-mechanical interactions between light and matter is important both for theoretical purposes and for technological advances, such as determining the limits of key processes like light-to-usable-energy conversion efficiencies. In this report, we discuss the physics of these two aspects, considering spontaneous emission as a pathway, and highlight the limitations of such descriptions in assessing energy-harvesting efficiency. In view of these limitations, we adopt a simplified approach to evaluate the free energy of radiation, providing a framework to assess various aspects of light-to-usable-energy conversion efficiencies. Our approach allows a theoretical estimate of the thermodynamic maximum limit for light-to-usable-energy conversion, which is approximately 74%. We validate this free energy estimate by modeling and accurately reproducing the Shockley-Queisser limit (~ 33%), which imposes a practical constraint on solar-to-usable-energy conversion efficiency. Beyond free-energy considerations, our model incorporates various processes, such as spontaneous emission, nonradiative thermal losses, and photon upconversion, allowing us to evaluate their roles. The model further suggests that, under certain conditions, the maximum conversion efficiency can reach approximately 48%, for example with multijunction solar cells or via photon upconversion. These findings further suggest that the true thermodynamic limit for light-to-usable-energy conversion may be much higher (approximately 74%). However, accurately estimating this limit requires a more complete understanding of the thermodynamics of light, light-matter interactions, and the connection between them.

We study the problem of designing \emph{sublinear spectral clustering oracles} for well-clusterable graphs. Such an oracle is an algorithm that, given query access to the adjacency list of a graph $G$, first constructs a compact data structure $\mathcal{D}$ that captures the clustering structure of $G$. Once built, $\mathcal{D}$ enables sublinear time responses to \textsc{WhichCluster}$(G,x)$ queries for any vertex $x$. A major limitation of existing oracles is that constructing $\mathcal{D}$ requires $Ω(\sqrt{n})$ memory, which becomes a bottleneck for massive graphs and memory-limited settings. In this paper, we break this barrier and establish a memory-time trade-off for sublinear spectral clustering oracles. Specifically, for well-clusterable graphs, we present oracles that construct $\mathcal{D}$ using much smaller than $O(\sqrt{n})$ memory (e.g., $O(n^{0.01})$) while still answering membership queries in sublinear time. We also characterize the trade-off frontier between memory usage $S$ and query time $T$, showing, for example, that $S\cdot T=\widetilde{O}(n)$ for clusterable graphs with a logarithmic conductance gap, and we show that this trade-off is nearly optimal (up to logarithmic factors) for a natural class of approaches. Finally, to complement our theory, we validate the performance of our oracles through experiments on synthetic networks.

We survey recent results on graphs and their Laplacians related to the behavior of the graph at large. In particular, we focus on Liouville theorems, recurrence and characterizations of Dirichlet forms via boundary terms.

Suppose $G$ is a $k$-uniform hypergraph on $n$ vertices such that every $(k-1)$-subset $S$ of $V(G)$ belongs to at least $δn$ edges, where $δ> 1/2$. Let $Ψ(G)$ denote the number of tight Hamilton cycles in $G$, that is, cyclic orderings of $V(G)$ in which every $k$ consecutive vertices form an edge. We prove that $\logΨ(G)\ge kh(G)-n\log{n\choose k-1}+n\log n-n\log e-o(n)$, where $h(G)$ is the hypergraph entropy of $G$, defined via perfect fractional matchings. This bound is tight, for example, for all (nearly) regular hypergraphs, in particular for the binomial random hypergraph. It also implies a conjecture by Ferber, Hardiman and Mond, stating that $Ψ(G)\ge (δ-o(1))^n n!$.

In this paper, we identify the smallest set of control input nodes and an associated output feedback law that achieves complete disturbance decoupling for a class of coupled oscillator networks. The focus is specifically on systems linearized around a stable phase-locked synchronized state. The proposed theoretical framework is applied to the linearized swing dynamics of power grids operating near synchronization. In this context, the disturbance decoupling problem corresponds to isolating subsets of nodes from exogenous disturbances by means of batteries that can both add or withdraw active power. Numerical simulations carried out on the IEEE New England 39-bus system show that the proposed methodology not only yields a minimal actuator placement ensuring effective disturbance rejection, but also preserves the internal stability of the closed-loop system.

Non-van der Waals two-dimensional (2D) materials derived from strongly bonded non-layered crystals have recently emerged as a novel and rising platform for nanoscale research. While uncovering and tuning their (opto-)electronic, catalytic, and magnetic properties has been the focus of intense research, the impact of spin-orbit coupling (SOC) onto their electronic structure has not yet been explored in detail. Studying these effects is, however, particularly relevant due to their surface cation termination and the presence of heavy elements in several representative compounds. Here, we investigate the effect of SOC onto the electronic structure of 2D AgBiO3, NaBiO3, and SbTlO3. While the first two systems show negligible band renormalization upon inclusion of relativistic effects around the band gap, SbTlO3 showcases a large SOC induced splitting (229meV) for the lowest conduction bands associated with a band inversion. Substitution of Tl with Pb forming SbPbO3 brings the band-inverted feature to the Fermi level. Analysis of topological invariants and investigation of edge states of zig-zag and armchair ribbons within the 200meV gap confirms the topological nature of the band splitting. Our work thus establishes a foundation for the systematic study of robust non-van der Waals 2D topological insulators.

To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implementation details for the iterative process, including the regularized optimization algorithm and the geometric search cross-validation tuning algorithm. Three distinct penalty methods, Lasso, Ridge, and Elastic-net regularization, were meticulously considered. Meanwhile, the proposed Theta-regularized Kriging models were tested on nine common numerical functions and two practical engineering examples. The results demonstrate that, compared with other penalized Kriging models, the proposed model performs better in terms of accuracy and stability.

Inadequate dietary micronutrient intake is a significant risk factor for deficiency and remains a major global health challenge. Nutrition programmes and interventions are most effective when targeted to populations at greatest risk. Household Consumption and Expenditure Surveys (HCES) are a widely available source of dietary data; however, they are often not powered for estimation below the first administrative level, limiting their utility for geographically targeted interventions. To address this, we applied Bayesian Small Area Estimation (SAE) methods to estimate the prevalence of apparent inadequate intake at the second administrative level. Three approaches were considered: a cluster level Beta binomial model and two area level models (mean smoothing and joint smoothing). Models were evaluated using a Rwanda HCES survey that supports inference at this scale. All models were implemented in a fully Bayesian framework to propagate uncertainty. Simulation results in Rwanda showed that the cluster level Beta binomial model achieved the strongest performance, while the area level joint smoothing model was the most reliable alternative among models accounting for survey design. Based on these results, models were applied to Senegal and Nigeria. In Senegal, second administrative level estimates captured meaningful subnational variation, reduced uncertainty relative to direct estimates, and remained consistent with first administrative level benchmarks. In Nigeria, despite smaller sample sizes and survey design constraints, modelled estimates reduced extreme uncertainty and showed good agreement with first administrative level estimates. This study demonstrates that Bayesian SAE methods can be applied to HCES data to generate reliable fine scale estimates of inadequate micronutrient intake, supporting localised nutrition interventions.

The X-ray spectra of neutron stars with moderate magnetic fields ($B\sim 10^{12}$ G) in high-mass X-ray binaries (HMXBs) at low X-ray luminosities ($L_\mathrm{X}\lesssim 10^{35}$ erg/s) are characterized by a double humped shape. This shape has been explained either as the radiation from a two-temperature magnetized atmosphere, where thermal radiation dominates at soft X-rays below about 10 keV, and cyclotron radiation with an imprinted cyclotron line dominates at high energies, or by the complex redistribution of primary X-rays in a structured atmosphere. The theoretical explanations of the double humped structure predict the spectra to depend on the magnetic field. We aim to connect the model predictions with observations. We analyzed archival NuSTAR observations of four HMXBs consisting of a neutron star and a Be star (BeXRBs), with known magnetic fields at luminosities low enough to show the characteristic double-hump spectrum. We modeled these spectra empirically and derived a relation between the energy of the intersection of the two humps and the magnetic field strength. In a second step, we tested whether this correlation is supported by fitting synthetic spectra simulated with the physically self-consistent polcap model. We find a linear correlation between the magnetic field strength and the intersection energy for the real BeXRB NuSTAR spectra and polcap-based simulated NuSTAR spectra alike. The effect of the magnetic field on spectral formation results in an observable correlation between the field strength and spectral shape. This derived positive correlation between intersection energy and magnetic field strength also allowed us to roughly estimate the magnetic field strength. Additional observations of XRBs and dedicated modeling efforts will be necessary to determine whether this approach is valid beyond the B-field range that was tested in this work.

Computational power can be measured by assigning an algebraic structure to a computational device. Here, we convert a small patch of Conway's Game of Life into a transformation semigroup. The conversion captures not only time evolution but also interactive operations. In this way, the cellular automaton becomes directly programmable. Once this measurement is made, we apply hierarchical decompositions to the resulting algebraic object as a way of understanding it. These decompositions are based on a macro/micro-state division inspired by statistical mechanics. However, cellular automata have a large number of global states. Therefore, we focus on partitioning the state space and creating morphic images approximations that can serve as macro-level descriptions. The methods developed here are not limited to cellular automata; they apply more generally to discrete dynamical systems.

In this paper, we investigate induced and nonlinear fiber topological pressure for random dynamical systems. We define a non-averaged induced fiber pressure via spanning and separated sets, characterize it as the pseudo-inverse of the classical fiber topological pressure studied previously, and establish the corresponding variational principle. We also define the nonlinear fiber pressure and prove the associated variational principles. Finally, we extend the combined theory to the higher-dimensional setting.

We demonstrate unconventional photon blockade in a symmetric Kerr dimer driven with equal-amplitude fields at a $90^\circ$ phase difference. The minimum inter-cavity coupling is $J_{\min} = γ/4$ at a Kerr nonlinearity $U \ll γ$ achievable in standard photonic molecules. The quadrature-driven site emits strongly antibunched light with a smooth, oscillation-free second-order correlator directly resolvable with standard detectors. The scheme operates under continuous-wave and pulsed excitation, and fabrication disorder can be fully compensated by re-tuning the drive phase, removing the need for post-fabrication cavity trimming.

Fluctuation theorems show how coarse graining transforms microscopic symmetry into observable irreversibility. Here we ask whether an analogous symmetrybased diagnostic can be constructed for financial markets. At the microscopic level, each transaction pairs a buyer and a seller, whereas trading decisions are typically made from coarse-grained price histories. Using symmetric takeprofit and stop-loss rules, we compare the holding-time distributions of long and short trading ensembles generated from the same price series. Across equityindices, individual stocks and cryptocurrencies, the log-ratio of the two distributions shows a robust crossover. It remains nearly constant at short durations but becomes linear in the tail, implying an exponential directional asymmetry. The tail slope defines an effective market temperature, an operational measure of fluctuation intensity on the chosen observation scale. A Bachelier first-passage benchmark captures the exponential tails but not the asymmetry, because long and short positions share the same leading decay rate. By contrast, short-time correlations between overlapping positions provide a minimal mechanism for the asymmetry by generating direction-dependent subleading relaxation spectra in a coarse-grained Markov description. Together, these results establish a fluctuation-theorem-like diagnostic of irreversibility in financial markets and, more broadly, in complex systems accessible only through coarse-grained observables.

Dynamic networks consist of interconnected dynamical systems. The subsystems can be viewed as transformations of input signals into output signals, where signals flow from one system into another through interconnections. The signal flows represent directions of information flow, thus a dynamic network can be visualised by a directed graph. In contrast, natural and physical laws only impose relations between systems variables, while variables are shared among systems via interconnections. Sharing is independent of direction, and therefore a dynamic network originating from physics can be visualised by an undirected graph. Typically, dynamic networks are considered to have either directed or undirected interconnections. For both situations, network models, analytic tools, and identification algorithms have been developed. However, dynamic networks can also have both directed and undirected interconnections, for example, in physical networks equipped with digital controllers. In this work, we present mixed linear dynamic networks that contain both undirected and directed interconnections, where the nature of the interconnecting dynamics needs to be incorporated into the modelling framework, identifiability analysis, and identification procedure. For these mixed networks, we derive dynamic network models; formulate conditions for consistent identification of all dynamics in the network; and develop a tractable identification algorithm that delivers consistent estimates.

Light's intrinsic carrier frequency of hundreds of terahertz theoretically enables information processing at terahertz clock rates. In optical quantum computing, continuous-variable quantum teleportation is the fundamental building block for deterministic logic operations. This protocol transfers unknown quantum states between nodes using quantum entanglement and real-time feedforward of measurement outcomes. However, electrical feedforward bottlenecks currently restrict operational bandwidths to approximately 100 megahertz, preventing the exploitation of light's ultimate speed. Here we show 1-terahertz-bandwidth all-optical quantum teleportation, completely bypassing this electronic limitation. By transferring Bell measurement outcomes optically, we successfully teleported vacuum states across the terahertz band and real-time random coherent wavepackets with a 42-picosecond temporal width. Evaluating the intrinsic state transfer quality, we achieved teleportation fidelities of $\mathcal{F}=0.784$ for the broadband vacuum states and $\mathcal{F}=0.770$ for the dynamic coherent wavepackets. Both results strictly surpass the classical limit of $\mathcal{F}=0.5$, demonstrating genuine quantum teleportation at ultrafast speeds. Our results establish that optical quantum processing speeds are constrained solely by the nonlinear medium's 1-picosecond-scale response, rather than classical electrical interfaces. This methodology provides a cornerstone for terahertz-clock quantum computers capable of overcoming Moore's law, and paves the way for a high-capacity, telecom-compatible quantum internet.

Training GUI agents with traditional centralized methods faces significant cost and scalability challenges. Federated learning (FL) offers a promising solution, yet its potential is hindered by the lack of benchmarks that capture real-world, cross-platform heterogeneity. To bridge this gap, we introduce FedGUI, the first comprehensive benchmark for developing and evaluating federated GUI agents across mobile, web, and desktop platforms. FedGUI provides a suite of six curated datasets to systematically study four crucial types of heterogeneity: cross-platform, cross-device, cross-OS, and cross-source. Extensive experiments reveal several key insights: First, we show that cross-platform collaboration improves performance, extending prior mobile-only federated learning to diverse GUI environments; Second, we demonstrate the presence of distinct heterogeneity dimensions and identify platform and OS as the most influential factors. FedGUI provides a vital foundation for the community to build more scalable and privacy-preserving GUI agents for real-world deployment. Our code and data are publicly available at https://github.com/wwh0411/FedGUI..

As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources. To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.

The question of a possible quantum critical point lying inside of a superconducting phase is central for understanding unconventional superconductivity. In various unconventional superconductors, non-Fermi-liquid/'strange-metal' $T^{n}$ variations, with $n<2$, of the electrical resistivity have been identified as the signature of magnetic quantum criticality. However, a difficulty is to prove experimentally that a non-Fermi-liquid/'strange-metal' law identified at temperatures above the superconducting temperature is the signature of an intrinsic zero-temperature quantum critical regime. In the heavy-fermion paramagnet UTe$_2$, unconventional superconductivity develops in the vicinity of a metamagnetic quantum phase transition induced by a magnetic field, and the quantum critical magnetic properties are suspected to play a role for the superconducting mechanism. In this work, we present a comparative analysis of electrical resistivity data collected on two UTe$_2$ samples of different qualities, in magnetic fields tilted by angles $θ\simeq35-40$~$^\circ$ from $\mathbf{b}$ to $\mathbf{c}$. Fits to the data have been performed either with a Fermi-liquid function $ρ=ρ_0+AT^{2}$ or with a non-Fermi-liquid/'strange-metal' function $ρ=ρ_0+A_nT^n$. Near to a superconducting phase induced beyond 40~T, non-physical residual resistivities $ρ_0<0$ are extracted from the $T^n$ fits, revealing that a 'hidden' Fermi-liquid $T^2$ regime may be ultimately recovered at low temperature. The results obtained here highlight the importance to investigate high-quality samples with low residual resistivity to confirm - or not - the presence of a suspected 'hidden' quantum critical behavior masked by superconductivity.

Levitated mesoscopic particles hold the promise of revolutionizing gravity sensing by using quantum effects. However, conventional quantum gravimeters based on such systems fail to harness the intrinsic large-mass advantage of the particles, because their commonly utilized auxiliary quantum systems counteract the role of mass as a resource. To overcome this limitation, we propose a quantum gravimetry by directly using the mechanical qubit (QM) formed by a levitated particle as the gravity sensor. Without resorting to the auxiliary quantum system, our scheme enables a straightforward readout of the particle's motion under gravitational influence. The obtained sensitivity behaves as a $m^{-1/2}$-scaling with the mass $m$. We also generalize our scheme to the \textit{mechanical cat qubit} as the gravity sensor. The sensitivity further scales as $N^{-1/2}$ with the mean phonon number $N$. In the experimentally realizable parameter regime, a sensitivity on the order of $0.1~ \text{\textmu}\text{Gal}/\sqrt{\text{Hz}}$ can be achieved, which outperforms the traditional schemes by two orders of magnitude. Reaching the \textit{double standard quantum limits} with $m$ and $N$ simultaneously, our scheme provides a feasible route toward compact high-sensitivity quantum gravimetry.

We investigate expansive solutions of the $N$-body problem in $\mathbb{R}^d$ ($d\ge2$) driven by homogeneous Newtonian potentials of degree $-α$. We establish the existence of half-entire expansive motions with prescribed initial configuration and asymptotic direction for a wide range of homogeneity exponents $α$. Our approach is variational and relies on the minimization of a suitably renormalized Lagrangian action, allowing us to treat in a unified framework the hyperbolic, parabolic, and hyperbolic-parabolic regimes in the sense of Chazy's classification. Beyond existence, we derive refined asymptotic expansions for all classes of expansive solutions, identifying higher-order correction terms and improving previously known growth estimates, including the classical Newtonian case $α=1$. In particular, for hyperbolic-parabolic solutions, we provide a detailed description of the interplay between linear escape of cluster centers and internal parabolic dynamics, extending the cluster scattering picture to general homogeneous potentials. Finally, we interpret these solutions within the geometric framework of the Jacobi-Maupertuis metric and the weak KAM theory. In this perspective, expansive motions correspond to geodesic rays and calibrating curves for the associated Hamilton-Jacobi equation, yielding a dynamical characterization of the boundary at infinity and a refined description of global viscosity solutions.