Various algorithms have been developed to simulate quantum circuits on classical hardware. Among the most prominent are approaches based on \emph{stabilizer decompositions} and \emph{tensor network contraction}. In this work, we present a unified framework that bridges these two approaches, placing them under a common formalism. Using this, we present two new algorithms to simulate an $n$-qubit circuit $C$: one that runs in $\tilde{O}(T^{\mathsf{tw}(C)})$ time and the other in $\tilde{O}(T^{γ\cdot \mathsf{tw}(C)})$ time, where $\mathsf{tw}(C)$ and $\mathsf{rw}(C)$ refer to the the tree-width and rank-width, respectively, of a tensor network associated to $C$, $T$ is the number of non-Clifford gates in $C$, and $γ\approx 3.42$. The proposed algorithms are simple, only require a linear amount of memory, are trivially parallelizable, and interact nicely with ZX-diagram simplification routines. Furthermore, we introduce the refined complexity measures \emph{focused tree-width} and \emph{focused rank-width}, which are always at least as efficient as their standard equivalent; these can be directly applied within our simulation algorithms, allowing for a more precise upper bound on the run time.

We improve the accuracy of Active Volume resource estimates by explicitly scheduling when Active Volume blocks execute. We present software that uses a greedy strategy to assign each logical qubit a role in each logical cycle (e.g., workspace, stale state storage, and bridge qubits). We empirically derive a novel formula for bridge- and stale-state-qubit overheads and improve the accuracy of runtime estimates, revealing that larger circuits can run on a given computer than previously predicted by analytic models. For a $4\times4$ Fermi-Hubbard simulation test circuit, this yields a $1.76\times$ runtime speedup with a $1.44\times$ reduction in bridge- and stale-state-qubit overheads compared to the model used in arXiv:2501.06165. Moreover, we show that for this test circuit, reaction times are insignificant in runtime estimates for computers with fewer than 600 logical qubits and that the number of reaction layers per logical cycle remains 1 in this regime. Our results pave the way for a full compilation pipeline for the Active Volume architecture and improved analytic resource estimates.

We report recent developments and tests with beams and cosmic rays of the gaseous photomultiplier (GasPM). The GasPM is a photosensor that combines a photocathode with the avalanche-multiplication mechanism of a resistive-plate chamber, offering excellent time resolution and cost-effective scalability. In addition, the GasPM provides precise and efficient Cherenkov-based charged-particle identification if combined with a radiator. Our primary use case aims at an upgrade of the Belle II detector to suppress beam-induced background photons, preferably detected off-collision time, that degrade the performance of the electromagnetic calorimeter. In 2022 we achieved a promising single-photon time-resolution of 25 ps at 3.3 x 10$^6$ gain, using a picosecond-pulse laser and a LaB$_6$ photocathode. However, a 2023 beam test with electrons impinging on a MgF$_2$ window attached to a CsI photocathode showed a worsening to 70 ps. This work aims at addressing the principal causes of the time-resolution degradation. We primarily target ultraviolet-photon emission during excitation and de-excitation of the gas molecules, which leads to a secondary signal that overlaps the primary signal, spoiling time resolution (photon feedback). We design and execute an improved beam test. Along with several GasPM configuration changes, we introduce a new 10 GSPS frequency digitizer to better discriminate primary from secondary signals thus enabling the study of photon feedback. We also conduct a cosmic-ray test using a LaB$_6$ photocathode, which is known to have higher than CsI's resistance to ions drifting backwards onto the photocathode and to air exposure, to probe quantum efficiency in view of an upcoming beam test.

Extracting patient medical conditions from code-switched clinical spoken dialogues is challenging due to rapid turn-taking and highly overlapped speech. We present a robust system evaluated on the DISPLACE-M dataset of real-world Hinglish medical conversations. We propose an End-to-End Neural Diarization with Vector Clustering approach (EEND-VC) to accurately resolve dense and speaker overlaps in Doctor-Patient Conversations (DoPaCo). For transcription, we adapt a Qwen3 ASR model via domain-specific fine-tuning, Devanagari script normalization, and dialogue-level LLM error correction, achieving an 18.59% tcpWER. We benchmark open and proprietary LLMs on medical condition extraction, comparing our text-based cascade system against a multimodal End-to-End (E2E) audio framework. While proprietary E2E models set the performance ceiling, our open cascaded architecture is highly competitive, as it achieved first place out of 25 participants in the DISPLACE-M challenge. All implementations are publicly released.

Germanium quantum well heterostructures have rapidly emerged as a leading platform for solid-state quantum information processing; however, material quality limits scalability, and higher structural quality, higher purity, as well as zero nuclear spin, are required. Here, we address these problems by employing the heaviest of Ge isotopes, by evaporating high-purity $^{76}$Ge radiation detector material, as utilized in fundamental neutrino particle physics experiments, to fabricate $^{76}$Ge/$^{28}$Si$^{76}$Ge quantum wells for quantum applications and explore the respective challenges. Specifically, we demonstrate improved results on strain-relaxed virtual Si$_{0.2}$Ge$_{0.8}$ substrates, forward graded from Si, with a dislocation density below 3.7$\cdot$10$^{5}$ cm$^{-2}$, explore nuclear spin-free solid-source molecular beam epitaxy, and demonstrate first quantum transport in $^{76}$Ge quantum wells. We demonstrate a record-level quantum well interface width of 0.3 nm by X-ray reflectivity, and quantitatively compare it to atom probe tomography and scanning transmission electron microscopy. The grown layer reveals nuclear-spin-bearing impurity concentrations below 10$^{19}$ cm$^{-3}$ and chemical impurity levels below 10$^{18}$ cm$^{-3}$, except for residual carbon attributed to the graphite crucible of the Ge source, which may reach up to 10$^{19}$ cm$^{-3}$. Low-temperature magneto-transport measurements yield electron mobilities of 6.1$\cdot$10$^4$ cm$^2$V$^{-1}$s$^{-1}$ at 15 mK with a carrier density of 2.2$\cdot$10$^{11}$ cm$^{-2}$, indicating that residual carbon is the dominant scattering mechanism.

We introduce a state-based feedback law that stabilizes quantum states or subspaces associated with extremal values of a continuously monitored observable - a problem motivated by quantum cooling tasks. We then propose an output-based approximation that uses simple filtering of the measurement record to emulate the required feedback signal, thereby avoiding full real-time quantum state estimation, a key bottleneck for implementing and scaling filtering-based feedback control. The performance of the resulting strategy is demonstrated numerically on two test-bed models for feedback cooling.

We describe the large deviations above its typical value of the maximal energy of a spin glass with +/-1 spins. Thanks to the relatively explicit description of the rate function we identify, we then show that the latter is asymptotically quadratic near its minimum if and only if an external magnetic field is present. The proof starts from a Parisi-type formula for the fractional moments of the partition function, which we then leverage to obtain the limit of the Laplace transform of the maximum energy. Using convex-duality arguments, we then rewrite this Laplace transform as a supremum over martingales, and thereby deduce the large-deviation principle with explicit rate function.

For $p,q\ge2$ the $\{p,q\}$-tiling graph is the (finite or infinite) planar graph $T_{p,q}$ where all faces are cycles of length $p$ and all vertices have degree $q$. We give algorithms for the problem of recognizing (induced) subgraphs of these graphs, as follows. - For $1/p+1/q>1/2$, these graphs correspond to regular tilings of the sphere. These graphs are finite, thus recognizing their (induced) subgraphs can be done in constant time. - For $1/p+1/q=1/2$, these graphs correspond to regular tilings of the Euclidean plane. For the Euclidean square grid $T_{4,4}$ Bhatt and Cosmadakis (IPL'87) showed that recognizing subgraphs is NP-hard, even if the input graph is a tree. We show that a simple divide-and conquer algorithm achieves a subexponential running time in all Euclidean tilings, and we observe that there is an almost matching lower bound in $T_{4,4}$ under the Exponential Time Hypothesis via known reductions. - For $1/p+1/q<1/2$, these graphs correspond to regular tilings of the hyperbolic plane. As our main contribution, we show that deciding if an $n$-vertex graph is isomorphic to a subgraph of the tiling $T_{p,q}$ can be done in quasi-polynomial ($n^{O(\log n)}$) time for any fixed $q$. Our results for the hyperbolic case show that it has significantly lower complexity than the Euclidean variant, and it is unlikely to be NP-hard. The Euclidean results also suggest that the problem can be maximally hard even if the graph in question is a tree. Consequently, the known treewidth bounds for subgraphs of hyperbolic tilings do not lead to an efficient algorithm by themselves. Instead, we use convex hulls within the tiling graph, which have several desirable properties in hyperbolic tilings. Our key technical insight is that planar subgraph isomorphism can be computed via a dynamic program that builds a sphere cut decomposition of a solution subgraph's convex hull.

Substantial amounts of air-shower simulations are needed to derive the instrument response for analyzing Imaging Air Cherenkov Telescope (IACT) data. This process is both computationally intensive and requires repetition under varying observation conditions, due to detector aging, changes in the atmosphere, or the instrument hardware. Generative models offer an efficient alternative, significantly accelerating simulations while compactly storing extensive simulation libraries, and providing a differentiable surrogate model of the instrument. However, their applicability has so far been limited in gamma-ray astronomy, particularly for modeling hadronic showers that dominate the background and exhibit significant intrinsic fluctuations that are challenging to model. In this study, we present the first application of score-based diffusion models to generate monoscopic $γ$-ray and proton shower images with nearly 2,000 pixels and benchmark the performance against Wasserstein GANs using H.E.S.S. simulations. We examine quality using both low-level parameters and well-established shower-shape observables, and assess analysis readiness via state-of-the-art $γ$-hadron separation. While GAN-based approaches can reproduce $γ$-ray showers with high fidelity, they fail to generate proton events of comparable quality, leading to a measurable degradation in analysis performance. In contrast, score-based diffusion modles achieve significantly superior quality for $γ$-ray and proton showers, accurately reproducing high-level correlations and generating events that are statistically indistinguishable from simulations at the analysis level. These results establish diffusion-based models as the first analysis-ready surrogate model of a single IACT, opening new prospects for fast instrument response generation, detector optimization, and connected downstream tasks.

Miniaturized and reconfigurable interfaces between confined optical modes within integrated photonic chips and structured light propagating in free space would serve as a cornerstone for fundamental optical science and modern photonic technology. In this work, we exploit the anisotropic nonlinear susceptibility tensors associated with thin-film lithium niobate to construct nanophotonic chip-space interfaces capable of flexibly generating and multi-dimensionally engineering structured light via injections of photons to on-chip waveguides. By harnessing the nonlinear Čerenkov radiation in integrated nonlinear microring resonators, we successfully tailor the spatial profile, polarization state, emission wavelength, topological charge and temporal wave packet of structured optical vortices, exhibiting reconfigurabilities and tuning ranges far beyond the state-of-the-art. To further showcase the capabilities of our platform, we use a single pump to generate tunable optical skyrmions via the spin-orbit coupling and multi-state integrated vortex microcombs in the visible range via synergistic $χ^{(2)}$ and $χ^{(3)}$ nonlinear optical processes. Our work bridges the research fields of structured light and integrated nonlinear optics, providing unprecedented opportunities for spatiotemporal light generation and on-chip multidimensional nonlinear optics.

In recent years, large language models (LLMs) have advanced rapidly, substantially enhancing their code understanding and generation capabilities and giving rise to powerful code assistants. However, in practical repository development, excessively long-horizon conversational context may overwhelm models, causing the loss of critical information and degraded performance, thereby limiting the utility of code assistants. Existing context management methods proposed to mitigate this context dilemma primarily target general-purpose conversations, while repository-oriented solutions remain largely unexplored, which is largely due to the lack of reliable evaluation benchmarks. To bridge this gap, we present LoCoEval, the first long-horizon conversational context management benchmark tailored to repository-oriented development scenarios. Adhering to three key principles, LoCoEval is constructed via an LLM-driven pipeline that generates realistic and diverse repository-oriented conversations, capturing key interaction patterns such as iterative requirements, noisy input, and retrospective questions. We evaluate 7 baselines, including 4 representative context management methods, using 3 advanced backbone LLMs on LoCoEval. The results reveal substantial challenges faced by standalone LLMs and existing approaches, especially memory systems, in repository-oriented conversational scenarios. To address these limitations, we further propose an improved method integrating conversational and repository information into a unified memory, which outperforms all baselines (*Oracle* excluded) and demonstrates robustness. Additionally, we investigated the impact of various factors on method performance, providing actionable insights for future research.

For a set $A$ let ${\mathbf {SC}_A}$ be the poset of simplicial complexes whose vertices are in $A$. For a function $f : A \rightarrow B$ there are functors $ f^{! !}, f^{**}, f^{ii}: {\mathbf {SC}_A} \rightarrow {\mathbf {SC}_B}, \quad f^{!*}, f^{i*} : {\mathbf {SC}_B} \rightarrow {\mathbf {SC}_A}, $ forming a five sequence of adjoints $f^{ !!} \dashv f^{* !} \dashv f^{* *} \dashv f^{*i} \dashv f^{ii}$. We investigate in detail these functors, and use this to give three categorical structures on simplicial complexes on finite sets such that the Stanley-Reisner correspondence to commutative monomial rings gives dualities.

Quantum computing is gaining attention as a new approach for solving complex problems in many scientific fields. In atmospheric and oceanic sciences, it may help reduce computational costs of simulating large and nonlinear systems. However, research into the use of quantum computers in this area is still in its earlier stage, and suitable applications have not been established yet. This study explores the use of quantum computing for calculating the collision-coalescence process of cloud droplets, which dominates the size growth of liquid particles in the cloud microphysics. Inspired by the quantum algorithms developed in the field of financial engineering, we propose a new algorithm based on a master equation that describes the time evolution of the droplet mass distribution. Our algorithm uses the quantum amplitudes to encode the probability distribution of droplet mass and calculates the expected number of droplets via the quantum amplitude estimation. Our resource analysis shows that the number of T gates scales as $O(N^2)$, where $N$ is the number of bins of the mass distributions. This is an essential improvement over the classical methods that scale only exponentially with $N$. This efficiency improvement is achieved by using quantum arithmetic in the superposition and by encoding the transition histories instead of the full distributions at each time step. Our results suggest that the collision-coalescence process is one of the promising targets of quantum computing in the field of atmospheric science.

In this paper we study the singular set in the parabolic obstacle problem for general obstacles $φ\in C^{2,1}$. We prove that the singular set has parabolic Hausdorff dimension at most $n-1$. Prior to our result, this was only known when $Δφ\equiv -1$. Our approach combines a truncated parabolic frequency formula and monotonicity estimates with an iterative argument showing that the frequency is saturated for all values of the truncation parameter between $2$ and $3$.

We provide a sharp result that guarantees that a distributional solution satisfying the Prodi-Serrin condition is regular in the spatial variables. The solution does not need to belong to the (local) Leray-Hopf class.

The DarkSide-20k experiment, currently under construction at LNGS, will employ a next-generation dual-phase liquid-argon Time Projection Chamber (TPC) with SiPM-based Photon Detector Units and low-background materials to achieve the ambitious goal of operating with an instrumental background close to zero. Proto-0 is a small-scale dual-phase argon TPC operated at INFN Naples, designed to validate the integration of DarkSide-20k key technologies in a realistic detector environment and to study charge extraction and electroluminescence signal formation. In this short paper we report on the early operation of Proto-0 and its single-phase commissioning. We focus on the measurement of the scintillation light yield using external and internal calibration sources.

Methane is the simplest hydrocarbon, yet it exhibits an extraordinarily complicated series of crystal phases. Notably, the non-plastic phases have large unit cells with nearly, but not quite cubic symmetry. Furthermore, although non-polar molecules interact very weakly, their reorganisation across phase transitions is very sluggish. Here, we demonstrate that these complex structures can be understood as simple packing of near-spherical supermolecular clusters of methane molecules: the departure from cubic symmetry arising from the non-spherical nature of the molecules. We use molecular dynamics based on density functional theory calculations to simulate the finite-temperature crystal structures of methane, finding that the complex Phase A is based around a 13-molecule regular icosahedron, with 8 additional molecules forming the 21-molecule unit cell. Similarly, Phase B is based on a body-centred cubic bcc packing of 17-molecule Z16 polyhedra, with the remaining 12 molecules per cell in tetrahedral interstices. We demonstrate that the favored intermolecular separation depends sensitively on molecular orientation, leading to hindered rotation and suppressed entropy. The structures are determined by a trade-off between efficient packing and entropy.

Commonly used proof strategies by automated reasoners organise proof search either by ordering-based saturation or by reducing goals to subgoals. In this paper, we combine these two approaches and advocate a SAT-based method with symmetry breaking for connection calculi in first-order logic, with the purpose of further pushing the automation in first-order classical logic proofs. In contrast to classical ways of reducing first-order logic to propositional logic, our method encodes the structure of the proof search itself. We present three distinct SAT encodings for connection calculi, analyse their theoretical properties, and discuss the effect of using SAT/SMT solvers on these encodings. We implemented our work in the new solver upCoP and showcase its practical feasibility.

Symbiotic Binaries contain a white dwarf accreting material from a red giant star through a wind. We present the results of a search for outbursts from Symbiotic Binaries using photometric data obtained using the GOTO all-sky survey taken from 2023 onwards. After identifying ten candidate outbursting systems, we used ATLAS photometry to characterise their photometric behaviour before 2023, leaving five systems which showed photometric behaviour consistent with an outburst. The ATLAS data showed how important the photometric history of an object is in determining whether a photometric feature is a likely outburst event. The outburst from LMC N67 is the first reported Z And-type outburst from a Symbiotic binary in the LMC. OGLE SMC-LPV-4044 and HK Sco show previously unreported outbursts. QW Sge and V4141 Sgr show outbursts starting in 2024, which have already been reported and are ongoing. By better identifying and characterising Z And-type outbursts from many systems, it will be possible to better understand the physics of these events, which are still not fully understood.

The Laser Interferometer Space Antenna (LISA) will open a new observational window in the millihertz gravitational-wave band, enabling the detection of tens of thousands of compact stellar remnant binaries across the Milky Way. Most of LISA's sources will be double white dwarf (WDWD) systems, while neutron star-white dwarf (NSWD) binaries and higher-mass systems will be orders of magnitude rarer but of significant astrophysical interest. Disentangling these populations is challenging due to the strong overlap in their gravitational-wave features. In this work, we investigate the use of machine-learning techniques to classify LISA-detectable binaries based solely on LISA observables. Using mock catalogues of Galactic binaries constructed from population-synthesis studies, we evaluate a range of machine-learning classifiers. We find that ensemble-based methods-particularly gradient-boosting algorithms such as XGBoost-deliver the best performance on our highly imbalanced dataset. WDWD systems are identified with a recall of $\sim 99\%$, reflecting their dominant presence, and high-mass binaries are also classified with high recall ($\ge 85\%$). In contrast, NSWD systems remain the most challenging population to distinguish: their features overlap strongly with those of WDWD binaries, making them particularly prone to misclassification. Despite this, XGBoost correctly identifies 85.6% of NSWD systems in our simulated LISA detections, outperforming simple statistical approaches based on kernel density estimation. We further demonstrate that machine-learning classification can effectively support the interpretation of LISA data, enabling the identification of eccentric binaries and extremely rare subclasses.

Our aim is to establish whether geometric observables, such as length, area or volume of a physical object, viewed by different observers Poisson commute or not. To illustrate this, we compute the Poisson bracket of two lengths associated to a rigid rod and measured by two different geodesic (inertial) observers, one of which is at rest while the other is moving with respect to the rod. Our calculation shows that geometric observables measured by different observers generically do not Poisson commute, not even in Minkowski spacetime. This non-trivial result provides interesting insights into questions related to the presence of a fundamental scale in the context of quantum gravity.

This review presents a thermodynamic perspective on quantum coupled transport processes in nanoscale systems. Our analysis is formulated within the framework of entropy production rate, the central quantity governing non-equilibrium processes and expressed through conjugate force-flux pairs. Although thermodynamic laws are universal across classical and quantum domains, the discussion is developed within a microscopic open quantum system framework, focusing on quantum dots (QDs) coupled to electronic reservoirs. We first examine elementary single transport processes and highlight their strong thermodynamic constraints in the near-equilibrium regime. This motivates the study of coupled transport, where multiple force-flux pairs coexist and interact, leading to richer thermodynamic behaviour. Using entropy production as the guiding principle, we analyse coupled energy and particle transport in a minimal two-terminal single-QD setup and show how conventional thermoelectric phenomena, including Seebeck and Peltier effects as well as thermoelectric heat engines and refrigerators, naturally emerge as thermodynamic cross-effects. We then extend the framework to a three-terminal coupled quantum dot (CQD) geometry, which provides a versatile platform for studying coupled transport and reduces, under suitable constraints, to the well-known Sánchez-Büttiker configuration. Beyond standard cross-effects, we discuss the phenomenon of inverse currents in coupled transport (ICC), where a current flows against mutually parallel thermodynamic forces without violating the second law. We show that ICC requires breaking the symmetry between energy and particle transport and identify the conditions for its realization in coupled quantum-dot systems with attractive interdot interactions.

We present a complete classification of spherical knotoids with up to six crossings and conjecture that our classification up to seven crossings is complete. Our work extends the tradition of knot tabulation to the setting of knotoids introduced by Turaev. We describe the methods used to enumerate diagrams, simplify them, and distinguish equivalence classes using a collection of invariants including the Kauffman bracket, the Arrow polynomial, the Affine index polynomial, the Mock Alexander polynomial, and the Yamada polynomial of the closure. We also investigate the chirality and rotational symmetries of these knotoids. Applications to protein entanglement illustrate the importance of such classifications.

Integrated superconducting spectrometers are developing to the point that they are enabling integral field units, providing large area spectral mapping capabilities for astronomy in the sub-millimeter band. However, these integral field units are only worthwhile if they have a high efficiency, but to date the efficiency of on-chip filterbanks has been quite poor. Here we demonstrate a filterbank with high efficiency by using directional filters. Using a cryogenic thermal load and a noise measurement in combination with a continuous-wave terahertz source to obtain the spectral response of the filters, we are able to accurately measure the filterbank efficiency, accounting for all quasi-optical elements within our setup. We experimentally obtain an average peak coupling efficiency to the detectors of 75% in a filterbank that sparsely samples between 125 GHz to 220 GHz using filters with a mean loaded quality factor of 19.6. Our results demonstrate that a filterbank with a high efficiency is achievable using directional filters, giving a clear route towards efficient integral field units.

Prosodic differences in autism are well-documented, but cross-linguistic evidence remains limited. This study investigates prosody in autism across a multilingual corpus of Finnish, French, and Slovak speakers. 88 acoustic features from over 5,000 inter-pausal units were extracted, and data were reduced via Principal Component Analysis (PCA) and analyzed using Linear Mixed-Effects Models (LMMs). Cross-linguistically, autistic speakers exhibited increased general intensity variability and a clearer, less breathy voice quality (higher Harmonics-to-Noise Ratio and alpha ratio), alongside reduced temporal intensity dynamics and lower central f0. Monolingual analyses revealed language-specific nuances: Slovak results aligned with cross-linguistic f0 patterns but diverged on voice quality, while Finnish results mirrored the broader voice quality findings. These results emphasize including voice quality and intensity dynamics in the study of possible language-independent markers of autism, alongside traditional pitch measures. The findings challenge deficiency-based models, suggesting instead a complex, acoustically distinct prosodic profile across languages.

We study binary mergers of dark matter cores in the Bose-Einstein condensate (BECDM) model. We include two scenarios: scalar self-interaction and the presence of a gravitationally coupled ideal gas. Using 3D simulations of the Gross-Pitaevskii-Poisson and Schrödinger-Poisson-Euler systems, we analyze the properties of the resulting remnants. We find that the final core-mass ratio reaches a stable average value after the merger. Repulsive self-interaction increases the mass of the final solitonic core, while attractive interaction enhances mass loss. In mergers involving an ideal gas, namely of fermion-boson stars, a stable solitonic core always forms in the bosonic component, even when the gas dominates, whereas the gas itself does not form a compact core. We explain these results using energy scalings and find that self-interaction, equilibrium cores follow $E \propto -M^3$, which leads to an almost universal merger fraction. Self-interaction changes this scaling, because repulsive $g$ moves the system toward a milder $E \propto -M^2$ scaling and increases mass retention, while attractive $g$ strengthens binding and favors mass ejection. In the case of interaction with an ideal gas, this component only modifies the gravitational background and does not change the intrinsic scaling of the bosonic part. These results show that the merger outcome is not universal but controlled by the interaction strength, while solitonic BECDM cores remain robust across diverse environments including gas.

Detecting interaction effects (IEs) in meta-regression is challenging, especially when few studies are available and many plausible interactions are considered. In many meta-analyses, interpretability is essential, which limits the use of complex machine learning methods. Tree-based approaches offer a potentially useful compromise, but their role in meta-regression with random effects is not yet well understood. This paper examines how traditional linear and tree-based methods can support variable selection for IEs in random effects meta-regression. We compare test-based and information-criterion-based linear selection procedures with meta-CART approaches. These include fixed effect and random effects trees and their stability-selected ensemble variants. All methods are evaluated using a real-world meta-analytic dataset and a plasmode simulation study. The data-generating process assumes linear IEs and is complemented by settings with nonlinear interactions. Our results show that under strictly linear interactions, linear selection methods perform as expected and achieve superior performance for IE detection. Tree-based methods are more conservative when the number of studies is small, but become competitive as sample size increases, particularly the stability-selected variants. When IEs deviate from strict linearity, even in simple ways, the performance of linear methods deteriorates, whereas tree-based approaches, especially stability-selected fixed effect trees, provide a more robust alternative. Overall, stability-selected random effects trees are useful complementary tools for IE detection in applied meta-regression, particularly for metric covariates. They are well suited for pre-selection and sensitivity analyses, and selection frequency patterns in tree ensembles can help reveal structural patterns in the data.

We present a cross-linguistic study of speech in autistic and non-autistic children speaking Finnish, French, and Slovak. We combine supervised classification with within-language and cross-corpus transfer experiments to evaluate classification performance within and across languages and to probe which acoustic cues are language-specific versus language-general. Using a large set of acoustic-prosodic features, we implement speaker-level classification benchmarks as an analytical tool rather than to seek state-of-the-art performance. Within-language models, evaluated with speaker-level cross-validation, yielded heterogeneous results. The Finnish model performed best (Accuracy 0.84, F1 0.88), followed by Slovak (Accuracy 0.63, F1 0.68) and French (Accuracy 0.68, F1 0.56). We then tested cross-language generalization. A model trained on all pooled corpora reached an overall Accuracy of 0.61 and F1 0.68. Leave-one-corpus-out experiments, which test transfer to an unseen language, showed moderate success when testing on Slovak (F1 0.70) and Finnish (F1 0.78), but poor transfer to French (F1 0.42). Feature-importance analyses across languages highlighted partially shared, but not fully language-invariant, acoustic markers of autism. These findings suggest that some autism-related speech cues generalize across typologically distinct languages, but robust cross-linguistic classifiers will likely require language-aware modeling and more homogeneous recording conditions.

Attestation means providing evidence that a remote target system is worthy of trust for some sensitive interaction. Although attestation is already used in network access control, security management, and trusted execution environments, it mainly concerns only a few system components. A clever adversary might manipulate these shallow attestations to mislead the relying party. Reliable attestations require layering. We construct attestations whose layers report evidence about successive components of the target system. Reliability also requires structuring the target system so only a limited set of components matters. We show how to structure an example system for reliable attestations despite a well-defined, relatively strong adversary. It is based on widely available hardware, such as Trusted Platform Modules, and software, such as Linux with SELinux. We isolate our principles in a few maxims that guide system development. We provide a cogent analysis of our mechanisms against our adversary model, as well as an empirical appraisal of the resulting system. We also identify two improvements to the mechanisms so attestation can succeed against strengthened adversaries. The performance burden of our attestation is negligible, circa 1.3 percent. After our first example, we vary our application level, and then also its underlying hardware anchor to use confidential computing with AMD's SEV-SNP. The same maxims help us achieve trustworthy attestations.

Symmetry-protected topological (SPT) phases extend the Landau paradigm of quantum matter by admitting distinct symmetry-preserving phases that lack any local order parameter. Demonstrating these phases at scale on programmable quantum processors is a key milestone in using quantum hardware to probe emergent many-body phenomena, yet it is impeded by the circuit depth normally required to capture non-trivial entanglement. Here we use a tensor network based approximate quantum compiling (AQC) protocol to construct shallow quantum circuits (18-39 CNOT depth), which prepare 100-site ground states of the spin-1/2 bond-alternating Heisenberg chain in both SPT phases, to 97.9-99.0% fidelity. Upon executing the circuits on IBM quantum hardware, the resulting states exhibit all defining signatures of SPT order including non-local string order for strings of up to length 20, characteristic degeneracies in the entanglement spectrum and clear evidence of symmetry-protected edge modes. The simultaneous observation of these independent diagnostics establishes current quantum computers as versatile platforms for large-scale studies of symmetry-protected quantum matter. More broadly, our results establish a practical foundation for probing non-equilibrium quench dynamics of such systems in regimes that challenge classical computational methods.