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The primary focus of this work is to investigate how the most emblematic classical probability density, namely a Gaussian, can be mapped to a valid quantum states. To explore this issue, we consider a Gaussian whose squared variance depends on a parameter $\lambda$. Specifically, depending on the value of $\lambda$, we study what happens in the classical-quantum correspondence as we change the indeterminacy of the classical particle. Furthermore, finding a correspondence between a classical state and a quantum state is not a trivial task. Quantum observables, described by Hermitian operators, do not generally commute, so a precise ordering must be introduced to resolve this ambiguity. In this work, we study two different arbitrary orderings: the first is an arbitrary ordering of the position and momentum observables; the second, which is the main focus of the present work, is an arbitrary ordering of the annihilation and creation operators. In this latter case, we find the interesting result that even a $\delta$-function, which in general has no quantum correspondence, can be mapped into a valid quantum state for a particular ordering, specifically the antinormal one (all creation operators are to the right of all annihilation operators in the product). This means that the Gaussian probability density corresponds to a valid quantum state, regardless of how localized classical particles are in phase space., Comment: 16 Pages, one figure

The advent of digital neutral-atom quantum computers relies on the development of fast and robust protocols for high-fidelity quantum operations. In this work, we introduce a novel scheme for entangling gates using four atomic levels per atom: a ground state qubit and two Rydberg states. A laser field couples the qubit to one of the two Rydberg states, while a microwave field coupling the two Rydberg states enables a resonant dipole-dipole interaction between different atoms. We show that this interaction can mediate controlled-Z gates that are faster and less sensitive to Rydberg decay than state-of-the-art Rydberg gates based on van der Waals interactions. Moreover, we systematically stabilize our protocol against interatomic distance fluctuations and analyze its performance in realistic setups with rubidium or cesium atoms. Our results open up new avenues to the use of dipolar interactions for universal quantum computation with neutral atoms., Comment: 11 pages, 9 figures

Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental framework to assess whether LLMs adhere to ethical and legal standards in the relatively unexplored context of finance. We prompt nine LLMs to impersonate the CEO of a financial institution and test their willingness to misuse customer assets to repay outstanding corporate debt. Beginning with a baseline configuration, we adjust preferences, incentives and constraints, analyzing the impact of each adjustment with logistic regression. Our findings reveal significant heterogeneity in the baseline propensity for unethical behavior of LLMs. Factors such as risk aversion, profit expectations, and regulatory environment consistently influence misalignment in ways predicted by economic theory, although the magnitude of these effects varies across LLMs. This paper highlights both the benefits and limitations of simulation-based, ex post safety testing. While it can inform financial authorities and institutions aiming to ensure LLM safety, there is a clear trade-off between generality and cost.

While guide dogs offer essential mobility assistance, their high cost, limited availability, and care requirements make them inaccessible to most blind or low vision (BLV) individuals. Recent advances in quadruped robots provide a scalable solution for mobility assistance, but many current designs fail to meet real-world needs due to a lack of understanding of handler and guide dog interactions. In this paper, we share lessons learned from developing a human-centered guide dog robot, addressing challenges such as optimal hardware design, robust navigation, and informative scene description for user adoption. By conducting semi-structured interviews and human experiments with BLV individuals, guide-dog handlers, and trainers, we identified key design principles to improve safety, trust, and usability in robotic mobility aids. Our findings lay the building blocks for future development of guide dog robots, ultimately enhancing independence and quality of life for BLV individuals.

Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work, we employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. We apply our technique to two examples: a spin-1/2 model and a spin-1 model, featuring the cluster state and the AKLT state as paradigmatic instances of SPT order, respectively. Using matrix product states, we generate a family of quantum circuits that host a trivial phase and an SPT phase, with a sharp phase transition between them. For the spin-1 case, we implement these circuits on two distinct trapped-ion machines based on qubits and qutrits. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets, highlighting its robustness and effectiveness in quantum data interpretation., Comment: 16 pages, 11 figures

Let $(M,J)$ be a $n$-dimensional complex manifold: a $p$-K\"ahler structure (resp. $p$-symplectic structure) on $M$ is a real, closed $(p,p)$-transverse form $\Omega$ (resp. real, closed $2p$-form whose $(p,p)$-component is transverse). We give obstructions to the existence of such structures on compact complex manifolds. We provide several families of compact complex manifolds which admit both $(n-1)$-symplectic structures and special Hermitian metrics.

Contribution to the volume "In Memory of Steven Weinberg" to appear in Nuclear Physics B., Comment: Contribution to the volume "In Memory of Steven Weinberg" to appear in Nuclear Physics B

The optical and radiochemical purification of the scintillating liquid, which will fill the central detector of the JUNO experiment, plays a crucial role in achieving its scientific goals. Given its gigantic mass and dimensions and an unprecedented target value of about 3% @ 1 MeV in energy resolution, JUNO has set severe requirements on the parameters of its scintillator, such as attenuation length (Lat>20 m at 430 nm), transparency, light yield, and content of radioactive contaminants (238U,232Th<10-15 g/g). To accomplish these needs, the scintillator will be processed using several purification methods, including distillation in partial vacuum and gas stripping, which are performed in two large scale plants installed at the JUNO site. In this paper, layout, operating principles, and technical aspects which have driven the design and construction of the distil- lation and gas stripping plants are reviewed. The distillation is effective in enhancing the optical properties and removing heavy radio-impurities (238U,232Th, 40K), while the stripping process exploits pure water steam and high-purity nitrogen to extract gaseous contaminants (222Rn, 39Ar, 85Kr, O2) from the scintillator. The plant operating parameters have been tuned during the recent com- missioning phase at the JUNO site and several QA/QC measurements and tests have been performed to evaluate the performances of the plants. Some preliminary results on the efficiency of these purification processes will be shown., Comment: 11 pages, 7 figures

In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino properties or astrophysical neutrinos. Thanks to its safety properties (such as low toxicity and high flash point) and easy scalability, linear alkyl benzene is the most common solvent used to produce liquid scintillators for large mass experiments. The knowledge of the refractive index is a pivotal point to understand the detector response, as this quantity (and its wavelength dependence) affects the Cherenkov radiation and photon propagation in the medium. In this paper, we report the measurement of the refractive index of the JUNO liquid scintillator between 260-1064 nm performed with two different methods (an ellipsometer and a refractometer), with a sub percent level precision. In addition, we used an interferometer to measure the group velocity in the JUNO liquid scintillator and verify the expected value derived from the refractive index measurements., Comment: 6 pages, 9 figures

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

A major challenge in performing quantum error correction (QEC) is implementing reliable measurements and conditional feed-forward operations. In quantum computing platforms supporting unconditional qubit resets, or a constant supply of fresh qubits, alternative schemes which do not require measurements are possible. In such schemes, the error correction is realized via crafted coherent quantum feedback. We propose implementations of small measurement-free QEC schemes, which are fault-tolerant to circuit-level noise. These implementations are guided by several heuristics to achieve fault-tolerance: redundant syndrome information is extracted, and additional single-shot flag qubits are used. By carefully designing the circuit, the additional overhead of these measurement-free schemes is moderate compared to their conventional measurement-and-feed-forward counterparts. We highlight how this alternative approach paves the way towards implementing resource-efficient measurement-free QEC on neutral-atom arrays., Comment: 16 pages, 10 figures

The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.

We study nonthermal production of heavy dark matter from the dynamics of the background scalar field during a first-order phase transition, predominantly from bubble collisions. In scenarios where bubble walls achieve runaway behavior and get boosted to very high energies, we find that it is possible to produce dark matter with mass several orders of magnitude above the symmetry breaking scale or the highest temperature ever reached by the thermal plasma. We also demonstrate that the existing formalism for calculating particle production from bubble dynamics in a first-order phase transition is not gauge invariant, and can lead to spurious results. While a rigorous and complete resolution of this problem is still lacking, we provide a practical prescription for the computation that avoids unphysical contributions and should provide reliable order-of-magnitude estimates of this effect. Furthermore, we point out the importance of three-body decays of the background field excitations into scalars and gauge bosons, which provide the dominant contributions at energy scales above the scale of symmetry breaking. Using our improved results, we find that scalar, fermion, and vector dark matter are all viable across a large range of mass scales, from O(10) TeV to a few orders of magnitude below the Planck scale, and the corresponding phase transitions can be probed with current and future gravitational wave experiments., Comment: 41 pages, 3 figures

While there is no replacement for the learned expertise, devotion, and social benefits of a guide dog, there are cases in which a robot navigation assistant could be helpful for individuals with blindness or low vision (BLV). This study investigated the potential for an industrial agile robot to perform guided navigation tasks. We developed two interface prototypes that allowed for spatial information between a human-robot pair: a voice-based app and a flexible, responsive handle. The participants (n=21) completed simple navigation tasks and a post-study survey about the prototype functionality and their trust in the robot. All participants successfully completed the navigation tasks and demonstrated the interface prototypes were able to pass spatial information between the human and the robot. Future work will include expanding the voice-based app to allow the robot to communicate obstacles to the handler and adding haptic signals to the handle design., Comment: This is an extended version of the late-breaking report presented as "Rayna Hata and Stacy A. Doore. Assistive Agile Robot for Non-visual Navigation. IEEE International Conference on Intelligent Robots and Systems, Detroit, MI. October 1-5, 2023"

Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation efficiency compared to traditional mobility aids. However, only a relatively small proportion of BLV individuals work with dog guides due to their limited availability and associated maintenance responsibilities. There is considerable recent interest in addressing this challenge by developing legged guide dog robots. This study was designed to determine critical aspects of the handler-guide dog interaction and better understand handler needs to inform guide dog robot development. We conducted semi-structured interviews and observation sessions with 23 dog guide handlers and 5 trainers. Thematic analysis revealed critical limitations in guide dog work, desired personalization in handler-guide dog interaction, and important perspectives on future guide dog robots. Grounded on these findings, we discuss pivotal design insights for guide dog robots aimed for adoption within the BLV community.

Causal discovery and inference from observational data is an essential problem in statistics posing both modeling and computational challenges. These are typically addressed by imposing strict assumptions on the joint distribution such as linearity. We consider the problem of the Bayesian estimation of the effects of hypothetical interventions in the Gaussian Process Network (GPN) model, a flexible causal framework which allows describing the causal relationships nonparametrically. We detail how to perform causal inference on GPNs by simulating the effect of an intervention across the whole network and propagating the effect of the intervention on downstream variables. We further derive a simpler computational approximation by estimating the intervention distribution as a function of local variables only, modeling the conditional distributions via additive Gaussian processes. We extend both frameworks beyond the case of a known causal graph, incorporating uncertainty about the causal structure via Markov chain Monte Carlo methods. Simulation studies show that our approach is able to identify the effects of hypothetical interventions with non-Gaussian, non-linear observational data and accurately reflect the posterior uncertainty of the causal estimates. Finally we compare the results of our GPN-based causal inference approach to existing methods on a dataset of $A.~thaliana$ gene expressions.

We introduce a variational Monte Carlo algorithm for approximating finite-temperature quantum many-body systems, based on the minimization of a modified free energy. We employ a variety of trial states -- both tensor networks as well as neural networks -- as variational ans\"atze for our numerical optimization. We benchmark and compare different constructions in the above classes, both for one- and two-dimensional problems, with systems made of up to \(N=100\) spins. Despite excellent results in one dimension, our results suggest that the numerical ans\"atze employed have certain expressive limitations for tackling more challenging two-dimensional systems., Comment: 7+9 pages, 3+4 figures

Traditional animal testing for toxicity is expensive, time consuming, ethically questioned, sometimes inaccurate because of the necessity to extrapolate from animal to human, and in most cases not formally validated according to modern standards. This is driving regulatory bodies and companies in backing alternative methods focusing on in silico and in vitro approaches. These are complex to implement and validate, and their wide adoption is not yet established despite legal directives providing an imperative. It is difficult to link a cell level response to effects on a whole organism, but the advances in high-throughput toxicogenomics towards elucidating the mechanism of action of substances are gradually reducing this gap and fostering the adoption of Next Generation Safety Assessment approaches. Quantitative in vitro to in vivo extrapolation (QIVIVE) methods hold the promise to reveal how to use in vitro -omics data to predict the potential for in vivo toxicity. They could improve lead compounds prioritisation, reduce time and costs, also in numbers of animal lives, and help with the complexity of extrapolating between species. We provide a description of QIVIVE state of the art, including how the problems of dosing and timing are being approached, how in silico simulation can take into account the variability of individuals, and how multiple techniques can be integrated to face complex tasks like the prediction of long term toxicity, including a close look into the open problems and challenges ahead., Comment: We authors have decided to withdraw the preprint from arXiv at this time. This decision stems from our desire to revisit and refine certain specific points in our work

Nowadays, people can retrieve and share digital information in an increasingly easy and fast fashion through the well-known digital platforms, including sensitive data, inappropriate or illegal content, and, in general, information that might serve as probative evidence in court. Consequently, to assess forensics issues, we need to figure out how to trace back to the posting chain of a digital evidence (e.g., a picture, an audio) throughout the involved platforms -- this is what Digital (also Forensics) Ballistics basically deals with. With the entry of Machine Learning as a tool of the trade in many research areas, the need for vast amounts of data has been dramatically increasing over the last few years. However, collecting or simply find the "right" datasets that properly enables data-driven research studies can turn out to be not trivial in some cases, if not extremely challenging, especially when it comes with highly specialized tasks, such as creating datasets analyzed to detect the source media platform of a given digital media. In this paper we propose an automated approach by means of a digital tool that we created on purpose. The tool is capable of automatically uploading an entire image dataset to the desired digital platform and then downloading all the uploaded pictures, thus shortening the overall time required to output the final dataset to be analyzed., Comment: 6 pages, 6 figures, presented at 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

Deep Audio Analyzer is an open source speech framework that aims to simplify the research and the development process of neural speech processing pipelines, allowing users to conceive, compare and share results in a fast and reproducible way. This paper describes the core architecture designed to support several tasks of common interest in the audio forensics field, showing possibility of creating new tasks thus customizing the framework. By means of Deep Audio Analyzer, forensics examiners (i.e. from Law Enforcement Agencies) and researchers will be able to visualize audio features, easily evaluate performances on pretrained models, to create, export and share new audio analysis workflows by combining deep neural network models with few clicks. One of the advantages of this tool is to speed up research and practical experimentation, in the field of audio forensics analysis thus also improving experimental reproducibility by exporting and sharing pipelines. All features are developed in modules accessible by the user through a Graphic User Interface. Index Terms: Speech Processing, Deep Learning Audio, Deep Learning Audio Pipeline creation, Audio Forensics.