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Left on its own, a quantum state evolves deterministically under the Schr\"odinger Equation, forming superpositions. Upon measurement, however, a stochastic process governed by the Born rule collapses it to a single outcome. This dual evolution of quantum states$-$the core of the Measurement Problem$-$has puzzled physicists and philosophers for nearly a century. Yet, amid the cacophony of competing interpretations, the problem today is not as impenetrable as it once seemed. This paper reviews the current status of the Measurement Problem, distinguishing between what is well understood and what remains unresolved. We examine key theoretical approaches, including decoherence, many-worlds interpretation, objective collapse theories, hidden-variable theories, dualistic approaches, deterministic models, and epistemic interpretations. To make these discussions accessible to a broader audience, we also reference curated online resources that provide high-quality introductions to central concepts., Comment: 52 pages and 8 figures

Inclusive electron scattering cross sections off a hydrogen target at a beam energy of 10.6 GeV have been measured with data collected from the CLAS12 spectrometer at Jefferson Laboratory. These first absolute cross sections from CLAS12 cover a wide kinematic area in invariant mass W of the final state hadrons from the pion threshold up to 2.5 GeV for each bin in virtual photon four-momentum transfer squared $Q^2$ from 2.55 to 10.4~GeV$^2$ owing to the large scattering angle acceptance of the CLAS12 detector. Comparison of the cross sections with the resonant contributions computed from the CLAS results on the nucleon resonance electroexcitation amplitudes has demonstrated a promising opportunity to extend the information on their $Q^2$ evolution up to 10 GeV$^2$. Together these results from CLAS and CLAS12 offer good prospects for probing the nucleon parton distributions at large fractional parton momenta $x$ for $W$ < 2.5 GeV, while covering the range of distances where the transition from the strongly coupled to the perturbative regimes is expected.

The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology labels for a sample of 253,287 galaxies within the DECaLS survey. This dataset has been foundational for numerous deep learning-based galaxy morphology classification studies. However, due to differences in signal-to-noise ratios and resolutions between the DECaLS images and those from BASS and MzLS (collectively referred to as BMz), a neural network trained on DECaLS images cannot be directly applied to BMz images due to distributional mismatch. In this study, we explore an unsupervised domain adaptation (UDA) method that fine-tunes a source domain model trained on DECaLS images with GZD-5 labels to BMz images, aiming to reduce bias in galaxy morphology classification within the BMz survey. Our source domain model, used as a starting point for UDA, achieves performance on the DECaLS galaxies' validation set comparable to the results of related works. For BMz galaxies, the fine-tuned target domain model significantly improves performance compared to the direct application of the source domain model, reaching a level comparable to that of the source domain. We also release a catalogue of detailed morphology classifications for 248,088 galaxies within the BMz survey, accompanied by usage recommendations., Comment: 11 pages, 6 figures, accepted for publication in MNRAS

Autonomous driving needs good roads, but 85% of Brazilian roads have damages that deep learning models may not regard as most semantic segmentation datasets for autonomous driving are high-resolution images of well-maintained urban roads. A representative dataset for emerging countries consists of low-resolution images of poorly maintained roads and includes labels of damage classes; in this scenario, three challenges arise: objects with few pixels, objects with undefined shapes, and highly underrepresented classes. To tackle these challenges, this work proposes the Performance Increment Strategy for Semantic Segmentation (PISSS) as a methodology of 14 training experiments to boost performance. With PISSS, we reached state-of-the-art results of 79.8 and 68.8 mIoU on the Road Traversing Knowledge (RTK) and Technik Autonomer Systeme 500 (TAS500) test sets, respectively. Furthermore, we also offer an analysis of DeepLabV3+ pitfalls for small object segmentation.

We present capivara, a fast and scalable multi-decomposition package designed to study astrophysical properties within distinct structural components of galaxies. Our spectro-decomposition code for analyzing integral field unit (IFU) data enables a more holistic approach, moving beyond conventional radial gradients and the bulge-plus-disk dichotomy. It facilitates comprehensive comparisons of integrated stellar ages and metallicities across various galactic structures. Our classification method naturally identifies outliers and organizes the different pixels based on their dominant spectral features. The algorithm leverages the scalability and GPU acceleration of Torch, outputting both a one-dimensional spectrum and a full data cube for each galaxy component, without relying on Voronoi binning. We demonstrate the capabilities of our approach using a sample of galaxies from the MaNGA survey, processing the resulting data cubes with the starlight spectral fitting code to derive both stellar population and ionized gas properties of the galaxy components. Our method effectively groups regions with similar spectral properties in both the continuum and emission lines. By aggregating the spectra of these regions, we enhance the signal-to-noise ratio of our analysis while significantly speeding up computations by reducing the number of spectra processed simultaneously. capivara will be freely available on GitHub., Comment: Submitted to MNRAS. Comments and suggestions are very welcome

When microspheres are illuminated by tightly focused vortex beams, they can be trapped in a non-equilibrium steady state where they orbit around the optical axis. By using the Mie-Debye theory for optical tweezers, we demonstrate that the orbital period strongly depends on the particle's chirality index. Taking advantage of such sensitivity, we put forth a method to experimentally characterize with high precision the chiroptical response of individual optically trapped particles. The method allows for an enhanced precision at least one order of magnitude larger than that of similar existing enantioselective approaches. It is particularly suited to probe the chiroptical response of individual particles, for which light-chiral matter interactions are typically weak.

Building a practical quantum processor involves integrating millions of physical qubits along with the necessary components for individual qubit manipulation and readout. Arrays of gated silicon spins offer a promising route toward achieving this goal. Optimized radio frequency resonators with high internal quality factor are based on superconducting inductors and enable fast spin readout. All-electrical spin control and gate-dispersive readout remove the need for additional device components and simplify scaling. However, superconducting high-Q tank circuits are susceptible to crosstalk induced ringup from electrical qubit control pulses, which causes fluctuations of the quantum dot potential and is suspected to degrade qubit performance. Here, we report on the coherent and all-electrical control of a hole spin qubit at 1.5K, integrated into a silicon fin field-effect transistor and connected to a niobium nitride nanowire inductor gate-sensor. Our experiments show that qubit control pulses with their broad range of higher harmonics ring up the tank when the control pulse spectrum overlaps with the tank resonance. This can cause a reduction of the readout visibility if the tank ringing amplitude exceeds the excited state splitting of the quantum dot, lifting Pauli spin blockade and thus leading to state preparation and measurement errors. We demonstrate how to circumvent these effects by engineering control pulses around the tank resonances. Importantly, we find that the ringup does not limit the spin coherence time, indicating that efficient high-Q resonators in gate-sensing are compatible with all-electrical spin control., Comment: 12 pages, 4 main and 5 supplementary figures, upcoming presentation at Silicon Quantum Electronics Workshop 2024

Objective: Integrating EHR data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The International Classification of Diseases (ICD) is used to annotate clinical diagnoses; the Human Phenotype Ontology (HPO) to annotate phenotypes. Although these ontologies overlap in biomedical entities described, the extent to which they are interoperable is unknown. We investigate how well aligned these ontologies are and whether such alignments facilitate EHR data integration. Materials and Methods: We conducted an empirical analysis of the coverage of mappings between ICD and HPO. We interpret this mapping coverage as a proxy for how easily clinical data can be integrated with research ontologies such as HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing mappings in the UMLS Metathesaurus. We analyze the proportion of ICD codes mapped to HPO within a real-world EHR dataset. Results and Discussion: Our analysis revealed that only 2.2% of ICD codes have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR data tend to have mappings to HPO; ICD codes that represent rarer medical conditions are seldom mapped. Conclusion: We find that interoperability between ICD and HPO via UMLS is limited. While other mapping sources could be incorporated, there are no established conventions for what resources should be used to complement UMLS.

There is an ongoing need for scalable tools to aid researchers in both retrospective and prospective standardization of discrete entity types -- such as disease names, cell types or chemicals -- that are used in metadata associated with biomedical data. When metadata are not well-structured or precise, the associated data are harder to find and are often burdensome to reuse, analyze or integrate with other datasets due to the upfront curation effort required to make the data usable -- typically through retrospective standardization and cleaning of the (meta)data. With the goal of facilitating the task of standardizing metadata -- either in bulk or in a one-by-one fashion; for example, to support auto-completion of biomedical entities in forms -- we have developed an open-source tool called text2term that maps free-text descriptions of biomedical entities to controlled terms in ontologies. The tool is highly configurable and can be used in multiple ways that cater to different users and expertise levels -- it is available on PyPI and can be used programmatically as any Python package; it can also be used via a command-line interface; or via our hosted, graphical user interface-based Web application (https://text2term.hms.harvard.edu); or by deploying a local instance of our interactive application using Docker.

This paper presents Efficient SMT-Based Context-Bounded Model Checker (ESBMC) v7.6, an extended version based on previous work on ESBMC v7.3 by K. Song et al. The v7.3 introduced a new Clang-based C++ front-end to address the challenges posed by modern C++ programs. Although the new front-end has demonstrated significant potential in previous studies, it remains in the developmental stage and lacks several essential features. ESBMC v7.6 further enhanced this foundation by adding and extending features based on the Clang AST, such as 1) exception handling, 2) extended memory management and memory safety verification, including dangling pointers, duplicate deallocation, memory leaks and rvalue references and 3) new operational models for STL updating the outdated C++ operational models. Our extensive experiments demonstrate that ESBMC v7.6 can handle a significantly broader range of C++ features introduced in recent versions of the C++ standard., Comment: 27 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:2308.05649

Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD $E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks' angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the GPDs $H$ and $E$., Comment: 7 pages, 6 figures

We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D S\'ersic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large datasets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys., Comment: 25 pages, 22 figures, accepted for publication in MNRAS

The strong coupling of plasmonic resonance modes in conductive pyramidal nanoparticles leads to an increase in the density of free charges on the surface. By ensuring plasmonic coupling in the pyramidal nanoparticle lattice, the achieved field intensity is potentiated. At the same time, a strong coupling between resonant modes is guaranteed, which results in the formation of new hybrid modes. In this manuscript, we demonstrated a tunable double anticrossing interaction that results from the interaction between two Localized Surface Plasmon Resonance (LSPR) modes and a Surface Plasmon Polariton (SPP) wave. The tuning is done as a function of the variation of the angle of incidence of the input electric field. From the double anticrossing, an increase in field intensity in a blue-shifted LSPR mode located in the red wavelength region is observed. This demonstrates that at certain angles of incidence, the intensity field obtained is strongly favored, which would be beneficial for applications such as Surface Enhancement Raman Spectroscopy (SERS). Nanoparticle-based lattices have been widely used for biosensor applications. However, one of the major limitations of this type of device is the low tolerance to high concentrations of biomolecules, which significantly affects their performance. According to the studies carried out for this manuscript, it was demonstrated that the implemented geometry allows for the observation of an LSPR mode, which is responsible for the control and synchronization of other perceived resonances. This mode remains almost invariant when subjected to structural variations or changes in the angle of incidence of the electric field. These characteristics eliminate the limitation mentioned above, allowing for sensitivities 10^3 times higher than those achieved in conventional systems based on LSPR used to detect P. brasiliensis antigen., Comment: 21 pages, 3 figures

We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the LSST-era. It incorporates widely used profiles such as the S\'ersic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8,289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, galmoss completed classical S\'ersic profile fitting in about 10 minutes. Benchmark tests show that galmoss achieves computational speeds that are 6 $\times$ faster than those of default implementations., Comment: 12 pages, 8 figures, Accepted for publication in A&C

This paper addresses the task of tracking Lissajous trajectories in the presence of quantized positioning sensors. To do so, theoretical results on tracking of continuous time periodic signals in the presence of output quantization are provided. With these results in hand, the application to Lissajous tracking is explored. The method proposed relies on the internal model principle and dispenses perfect knowledge of the system equations. Numerical results show that an arbitrary small scanning resolution is achievable despite large sensor quantization intervals.

Nuclear star clusters (NSCs) are dense star clusters located at the centre of galaxies spanning a wide range of masses and morphologies. Analysing NSC occupation statistics in different environments provides an invaluable window into investigating early conditions of high-density star formation and mass assembly in clusters and group galaxies. We use HST/ACS deep imaging to obtain a catalogue of dwarf galaxies in two galaxy clusters in the Shapley Supercluster: the central cluster Abell 3558 and the northern Abell 1736a. The Shapley region is an ideal laboratory to study nucleation as it stands as the highest mass concentration in the nearby Universe. We investigate the NSC occurrence in quiescent dwarf galaxies as faint as $M_{I} = -10$ mag and compare it with all other environments where nucleation data is available. We use galaxy cluster/group halo mass as a proxy for the environment and employ a Bayesian logistic regression framework to model the nucleation fraction ($f_{n}$) as a function of galaxy luminosity and environment. We find a notably high $f_n$ in Abell 3558: at $M_{I} \approx -13.1$ mag, half the galaxies in the cluster host NSCs. This is higher than in the Virgo and Fornax clusters but comparable to the Coma Cluster. On the other hand, the $f_n$ in Abell 1736a is relatively lower, comparable to groups in the Local Volume. We find that the probability of nucleation varies with galaxy luminosity remarkably similarly in galaxy clusters. These results reinforce previous findings of the important role of the environment in NSC formation/growth., Comment: 20 pages, 10 figures, accepted for publication in the MNRAS

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighbor permutation entropy, an innovative extension of the permutation entropy tailored for unstructured data, irrespective of their spatial or temporal configuration and dimensionality. Our approach builds upon nearest neighbor graphs to establish neighborhood relations and uses random walks to extract ordinal patterns and their distribution, thereby defining the k-nearest neighbor permutation entropy. This tool not only adeptly identifies variations in patterns of unstructured data, but also does so with a precision that significantly surpasses conventional measures such as spatial autocorrelation. Additionally, it provides a natural approach for incorporating amplitude information and time gaps when analyzing time series or images, thus significantly enhancing its noise resilience and predictive capabilities compared to the usual permutation entropy. Our research substantially expands the applicability of ordinal methods to more general data types, opening promising research avenues for extending the permutation entropy toolkit for unstructured data., Comment: 14 two-column pages, 6 figures

This research examines the nonadiabatic dynamics of cyclobutanone after excitation into the n-3s Rydberg S2 state. It stems from our contribution to the Special Topic of the Journal of Chemical Physics to test the predictive capability of computational chemistry against unseen experimental data. Decoherence-corrected fewest-switches surface hopping (DC-FSSH) was used to simulate nonadiabatic dynamics with full and approximated nonadiabatic couplings. Several simulation sets were computed with different electronic structure methods, including a multiconfigurational wavefunction (MCSCF) specially built to describe dissociative channels, multireference semiempirical approach, time-dependent density functional theory, algebraic diagrammatic construction, and coupled cluster. MCSCF dynamics predicts a slow deactivation of the S2 state (10 ps), followed by an ultrafast population transfer from S1 to S0 (<100 fs). CO elimination (C3 channel) dominates C2H4 formation (C2 channel). These findings radically differ from the other methods, which predicted S2 lifetimes 10 to 250 times shorter and C2 channel predominance. These results suggest that routine electronic structure methods may hold low predictive power for the outcome of nonadiabatic dynamics., Comment: The main manuscript contains 28 pages with 8 figures. The supplementary material contains 14 pages with 12 figures. In total, the merged pdf document has 42 pages with 20 figures

State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions, due to e.g. weather or time of day, with dramatic consequences on the visual localization accuracy. In this paper, we improve this retrieval step and tailor it to the final localization task. Among the several changes we advocate for, we propose to synthesize variants of the training set images, obtained from generative text-to-image models, in order to automatically expand the training set towards a number of nameable variations that particularly hurt visual localization. After expanding the training set, we propose a training approach that leverages the specificities and the underlying geometry of this mix of real and synthetic images. We experimentally show that those changes translate into large improvements for the most challenging visual localization datasets. Project page: https://europe.naverlabs.com/ret4loc, Comment: Accepted at ICLR 2024. Project Page: https://europe.naverlabs.com/ret4loc

Across a broad range of qubits, a pervasive trade-off becomes obvious: increased coherence seems to be only possible at the cost of qubit speed. This is consistent with the notion that protecting a qubit from its noisy surroundings also limits the control over it. Indeed, from ions to atoms, to superconductors and spins, the leading qubits share a similar Q-factor - the product of speed and coherence time - even though the speed and coherence of various qubits can differ by up to 8 orders of magnitude. This is the qubit speed-coherence dilemma: qubits are either coherent but slow or fast but short-lived. Here, we demonstrate a qubit for which we can triple the speed while simultaneously quadrupling the Hahn-echo coherence time when tuning a local electric field. In this way, the qubit speed and coherence scale together without compromise on either quantity, boosting the Q-factor by over an order of magnitude. Our qubit is a hole spin in a Ge/Si core/shell nanowire providing strong 1D confinement, resulting in the direct Rashba spin-orbit interaction. Due to Heavy-hole light-hole mixing a maximum of the spin-orbit strength is reached at finite electrical field. At the local maximum, charge fluctuations are decoupled from the qubit and coherence is enhanced, yet the drive speed becomes maximal. Our proof-of-concept experiment shows that a properly engineered qubit can be made faster and simultaneously more coherent, removing an important roadblock. Further, it demonstrates that through all-electrical control a qubit can be sped up, without coupling more strongly to the electrical noise environment. As charge fluctuators are unavoidable in semiconductors and all-electrical control is highly scalable, our results improve the prospects for quantum computing in Si and Ge., Comment: Main: 6 pages with 3 display items plus 2 pages for references and methods Supplementary: 12 pages with 7 display items

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real 'needle in the haystack' problem, which, until now, has resisted complete automation due to device variability and fabrication imperfections. In this study, we present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. We demonstrate this automation, achieved without human intervention, in a Ge/Si core/shell nanowire device. Our approach integrates deep learning, Bayesian optimization, and computer vision techniques. We expect this automation algorithm to apply to a wide range of semiconductor qubit devices, allowing for statistical studies of qubit quality metrics. As a demonstration of the potential of full automation, we characterise how the Rabi frequency and g-factor depend on barrier gate voltages for one of the qubits found by the algorithm. Twenty years after the initial demonstrations of spin qubit operation, this significant advancement is poised to finally catalyze the operation of large, previously unexplored quantum circuits.

Pattern discovery plays a central role in both descriptive and predictive tasks across multiple domains. Actionable patterns must meet rigorous statistical significance criteria and, in the presence of target variables, further uphold discriminative power. Our work addresses the underexplored area of guiding pattern discovery by integrating statistical significance and discriminative power criteria into state-of-the-art algorithms while preserving pattern quality. We also address how pattern quality thresholds, imposed by some algorithms, can be rectified to accommodate these additional criteria. To test the proposed methodology, we select the triclustering task as the guiding pattern discovery case and extend well-known greedy and multi-objective optimization triclustering algorithms, $\delta$-Trimax and TriGen, that use various pattern quality criteria, such as Mean Squared Residual (MSR), Least Squared Lines (LSL), and Multi Slope Measure (MSL). Results from three case studies show the role of the proposed methodology in discovering patterns with pronounced improvements of discriminative power and statistical significance without quality deterioration, highlighting its importance in supervisedly guiding the search. Although the proposed methodology is motivated over multivariate time series data, it can be straightforwardly extended to pattern discovery tasks involving multivariate, N-way (N>3), transactional, and sequential data structures. Availability: The code is freely available at https://github.com/JupitersMight/MOF_Triclustering under the MIT license.

We demonstrate a new method for determining the radius of micron-sized particles trapped by a vortex laser beam. The technique is based on measuring the rotation experienced by the center of mass of a microsphere that is laterally displaced by a Stokes drag force to an off-axis equilibrium position. The rotation results from an optical torque pointing along the direction opposite to the vortex beam angular momentum. We fit the rotation angle data for different Laguerre-Gaussian modes taking the radius as a fitting parameter in the Mie-Debye theory of optical tweezers. We also discuss how micron-sized beads can be used as probes for optical aberrations introduced by the experimental setup., Comment: 11 pages, 5 figures

Organic light-emitting transistors (OLETs) are multifunctional optoelectronic devices that combine in a single structure the advantages of organic light emitting diodes (OLEDs) and organic field-effect transistors (OFETs). However, low charge mobility and high threshold voltage are critical hurdles to practical OLETs implementation. This work reports on the improvements obtained by using polyurethane films as dielectric layer material in place of the standard poly(methylmethacrylate) (PMMA) in OLET devices. It was found that polyurethane drastically reduces the number of traps in the device thereby improving electrical and optoelectronic device parameters. In addition, a model was developed to rationalize an anomalous behavior at the pinch-off voltage. Our findings represent a step forward to overcome the limiting factors of OLETs that prevent their use in commercial electronics by providing a simple route for low-bias device operation., Comment: 25 pages, 5 figures, 1 table

In gate-based dispersive sensing, the response of a resonator attached to a quantum dot gate is detected by a reflected radio-frequency signal. This enables fast readout of spin qubits and tune up of arrays of quantum dots, but comes at the expense of increased susceptibility to crosstalk, as the resonator can amplify spurious signals and induce fluctuations in the quantum dot potential. We attach tank circuits with superconducting NbN inductors and internal quality factors $Q_{\mathrm{i}}$>1000 to the interdot barrier gate of silicon double quantum dot devices. Measuring the interdot transition in transport, we quantify radio-frequency crosstalk that results in a ring-up of the resonator when neighbouring plunger gates are driven with frequency components matching the resonator frequency. This effect complicates qubit operation and scales with the loaded quality factor of the resonator, the mutual capacitance between device gate electrodes, and with the inverse of the parasitic capacitance to ground. Setting qubit frequencies below the resonator frequency is expected to substantially suppress this type of crosstalk., Comment: 7 pages, 4 figures, supplementary information

This paper introduces ESBMC v7.3, the latest Efficient SMT-Based Context-Bounded Model Checker version, which now incorporates a new clang-based C++ front-end. While the previous CPROVER-based front-end served well for handling C++03 programs, it encountered challenges keeping up with the evolving C++ language. As new language and library features were added in each C++ version, the limitations of the old front-end became apparent, leading to difficult-to-maintain code. Consequently, modern C++ programs were challenging to verify. To overcome this obstacle, we redeveloped the front-end, opting for a more robust approach using clang. The new front-end efficiently traverses the Abstract Syntax Tree (AST) in-memory using clang APIs and transforms each AST node into ESBMC's Intermediate Representation. Through extensive experimentation, our results demonstrate that ESBMC v7.3 with the new front-end significantly reduces parse and conversion errors, enabling successful verification of a wide range of C++ programs, thereby outperforming previous ESBMC versions.

We say that a pair of nonnegative integer sequences $(\{a_k\}_{k\geq 0},\{b_k\}_{k\geq 0})$ is Whitney-realizable if there exists a poset $P$ for which (the absolute values) of the Whitney numbers of the first and second kind are given by the numbers $a_k$ and $b_k$ respectively. The pair is said to be Whitney-dualizable if, in addition, there exists another poset $Q$ for which their Whitney numbers of the first and second kind are instead given by $b_k$ and $a_k$ respectively. In this case, we say that $P$ and $Q$ are Whitney duals. We use results on Whitney duality, recently developed by the first two authors, to exhibit a family of sequences which allows for multiple realizations and Whitney-dual realizations. More precisely, we study edge labelings for the families of posets of pointed partitions $\Pi_n^{\bullet}$ and weighted partitions $\Pi_n^{w}$ which are associated to the operads $\mathcal{P}erm$ and $\mathcal{C}om^2$ respectively. The first author and Wachs proved that these two families of posets share the same pair of Whitney numbers. We find EW-labelings for $\Pi_n^{\bullet}$ and $\Pi_n^{w}$ and use them to show that they also share multiple nonisomorphic Whitney dual posets. In addition to EW-labelings, we also find two new EL-labelings for $\Pi_n^\bullet$ answering a question of Chapoton and Vallette. Using these EL-labelings of $\Pi_n^\bullet$, and an EL-labeling of $\Pi_n^w$ introduced by the first author and Wachs, we give combinatorial descriptions of bases for the operads $\mathcal{P}re\mathcal{L}ie, \mathcal{P}erm,$ and $\mathcal{C}om^2$. We also show that the bases for $\mathcal{P}erm$ and $\mathcal{C}om^2$ are PBW bases., Comment: 37 pages, 20 figures

Ceballos and Pons introduced the $s$-weak order on $s$-decreasing trees, for any weak composition $s$. They proved that it has a lattice structure and further conjectured that it can be realized as the $1$-skeleton of a polyhedral subdivision of a polytope. We answer their conjecture in the case where $s$ is a strict composition by providing three geometric realizations of the $s$-permutahedron. The first one is the dual graph of a triangulation of a flow polytope of high dimension. The second one, obtained using the Cayley trick, is the dual graph of a fine mixed subdivision of a sum of hypercubes that has the conjectured dimension. The third one, obtained using tropical geometry, is the $1$-skeleton of a polyhedral complex for which we can provide explicit coordinates of the vertices and whose support is a permutahedron as conjectured., Comment: 39 pages, 14 figures

Tensor data analysis allows researchers to uncover novel patterns and relationships that cannot be obtained from matrix data alone. The information inferred from the patterns provides valuable insights into disease progression, bioproduction processes, weather fluctuations, and group dynamics. However, spurious and redundant patterns hamper this process. This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations, extending well-established principles for assessing the statistical significance of patterns in matrix data. A comprehensive discussion on binomial testing for false positive discoveries is entailed at the light of: variable dependencies, temporal dependencies and misalignments, and \textit{p}-value corrections under the Benjamini-Hochberg procedure. Results gathered from the application of state-of-the-art triclustering algorithms over distinct real-world case studies in biochemical and biotechnological domains confer validity to the proposed statistical frame while revealing vulnerabilities of some triclustering searches. The proposed assessment can be incorporated into existing triclustering algorithms to mitigate false positive/spurious discoveries and further prune the search space, reducing their computational complexity. Availability: The code is freely available at https://github.com/JupitersMight/TriSig under the MIT license.

We present a spatially resolved stellar population analysis of 61 jellyfish galaxies and 47 control galaxies observed with ESO/MUSE attempting to understand the general trends of the stellar populations as a function of the stripping intensity and mass. This is the public sample from the GASP programme, with $0.01 < z < 0.15$ and $8.9 <\log(M_{\star}/M_{\odot}) < 12.0$. We apply the spectral population synthesis code FADO to fit self-consistently both the stellar and nebular contributions to the spectra of the sources. We present 2D morphological maps for mean stellar ages, metallicities, gas-phase oxygen abundances, and star formation rates for the galaxies with Integrated Nested Laplace Approximation (INLA), which is efficient in reconstructing spatial data of extended sources. We find that "extreme stripping" and "stripping" galaxies are typically younger than the other types. Regarding stellar and nebular metallicities, the "stripping" and "control passive" galaxies are the most metal-poor. Based on the phase space for jellyfish cluster members we find trends in ages, metallicities, and abundances with different regions of the diagram. We also compute radial profiles for the same quantities. We find that both the stripping and the stellar masses seem to influence the profiles, and we see differences between various groups and distinct mass bins. The radial profiles for different mass bins present relations already shown in the literature for undisturbed galaxies, i.e., profiles of ages and metallicities tend to increase with mass. However, beyond $\sim0.75$ effective radius, the ages of the most massive galaxies become similar to or lower than the ages of the lower mass ones., Comment: Accepted by MNRAS. 71 pages (13 of main text and 58 of supplementay material). 123 figures

Context: When selecting a classifier to use for a supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, i.e. contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would save those designing an analysis pipeline from the computational expense of a full cosmology forecast. Aims: This study tests the assumption that classification metrics are an appropriate proxy for cosmology metrics. Methods: We emulate photometric SN Ia cosmology samples with controlled contamination rates of individual contaminant classes and evaluate each of them under a set of classification metrics. We then derive cosmological parameter constraints from all samples under two common analysis approaches and quantify the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. Results: We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are insensitive to the latter. Conclusions: We therefore discourage exclusive reliance on classification-based metrics for cosmological analysis design decisions, e.g. classifier choice, and instead recommend optimizing using a metric of cosmological parameter constraining power., Comment: 9 pages, 6 figures; submitted to A&A