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Quantum acoustics is an emerging platform for hybrid quantum technologies enabling quantum coherent control of mechanical vibrations. High-overtone bulk acoustic resonators (HBARs) represent an attractive mechanical implementation of quantum acoustics due to their potential for exceptionally high mechanical coherence. Here, we demonstrate an implementation of high-coherence HBAR quantum acoustics integrated with a planar superconducting qubit architecture, demonstrating an acoustically-induced-transparency regime of high cooperativity and weak coupling, analogous to the electrically-induced transparency in atomic physics. Demonstrating high-coherence quantum acoustics with planar superconducting devices enables new applications for acoustic resonators in quantum technologies., Comment: 11 pages, 7 figures, 1 table

We investigate perturbations in power-law monomial potentials within inflationary models driven by a single scalar field. By introducing a second term to the original potential, we study how this perturbation influences the slow-roll parameters and analyze the consequent changes in the spectral index, $n_s$, and the tensor-to-scalar ratio, $r$, treating the additional term as a correction to the monomial case. Comparing our numerical results with current cosmological data from the Planck satellite observations on $n_s$, $r$, and the clustering parameter, $\sigma_8$, we place significant constraints on the free parameter of our class of inflationary potentials. We found that the perturbative consistency method we analyze could be an interesting test to explore for more complex inflationary models, looking for features that better match the data that might highlight fundamental physics implications., Comment: 17 pages, 9 figures

In this paper, we conduct sentiment analysis on social media data to study mass opinion about offshore wind energy. We adapt three machine learning models, namely, TextBlob, VADER, and SentiWordNet because different functions are provided by each model. TextBlob provides subjectivity analysis as well as polarity classification. VADER offers cumulative sentiment scores. SentiWordNet considers sentiments with reference to context and performs classification accordingly. Techniques in NLP are harnessed to gather meaning from the textual data in social media. Data visualization tools are suitably deployed to display the overall results. This work is much in line with citizen science and smart governance via involvement of mass opinion to guide decision support. It exemplifies the role of Machine Learning and NLP here.

Several organic cations have been used to passivate perovskite films; however, selecting the optimal cation remains challenging. In this work, we carried out density functional theory calculations to understand the effects induced by 17 different organic cations on the passivation (P-cations) of thin two-dimensional P2MAn-1PbnI3n+1 perovskites films, where n = 1 and 2. We found that the interactions between different types of P-cations and the inorganic slab affect the length and angles of the bonds within the inorganic framework (PbI6-octahedra). In general, the binding mechanism includes the interactions of organic cations with the inorganic framework, which leads to the accumulation of electron density within the halides, indicative of Bronsted--Lowry acid-base interactions. Oxygenated groups facilitate additional H-bond formation through -OH and -COOH groups, promoting the localization of the electron density between layers and improving the energetic stability of the system. Based on the results and analysis, we found that three P-cations might have higher potential for real-life applications, namely 4-fluorophenylethylammonium (FPEA), phenylethylammonium (PEA) and butylammonium (BtA)., Comment: Keywords: Perovskites thin films; Organic cation passivation; MAPbI3-based layers; DFT; 2D materials

Dirac particles have two intrinsic degrees-of-freedom, helicity and chirality. While helicity is conserved in time, chirality is not constant under time evolution for massive particles, yielding the phenomenon of chiral oscillations. So far, chiral oscillations have been mainly described in the framework of single particle relativistic quantum mechanics. In this paper, we present a quantum field theory approach to chiral oscillations in analogy with the one used to describe flavor mixing and oscillations. By taking the expectation value of chiral charges, we obtain the same chiral oscillation formula derived via standard relativistic quantum mechanics. We find that chiral charges are diagonalized by a Bogoliubov transformation: this implies that the vacuum for particles with definite chirality is orthogonal to the one for those with definite energy. In the case of neutrinos, our results can be further extended to include also flavor oscillations., Comment: 7 pages

Metal-halide semiconductors have great potential for real-life photovoltaic applications; however, surface defects induce several challenges to the thermodynamic stability. Here, we employed density functional theory calculations within van der Waals corrections (D3) to investigate the role of monovalent cations (Li, Na, K, Rb and Cs) in the passivation of 2D perovskite ultrathin films.

This paper explores the application of artificial intelligence techniques in audio and voice processing, focusing on the integration of wake words and speaker recognition for secure access in embedded systems. With the growing prevalence of voice-activated devices such as Amazon Alexa, ensuring secure and user-specific interactions has become paramount. Our study aims to enhance the security framework of these systems by leveraging wake words for initial activation and speaker recognition to validate user permissions. By incorporating these AI-driven methodologies, we propose a robust solution that restricts system usage to authorized individuals, thereby mitigating unauthorized access risks. This research delves into the algorithms and technologies underpinning wake word detection and speaker recognition, evaluates their effectiveness in real-world applications, and discusses the potential for their implementation in various embedded systems, emphasizing security and user convenience. The findings underscore the feasibility and advantages of employing these AI techniques to create secure, user-friendly voice-activated systems.

Failure mode and effects analysis (FMEA) is a critical tool for mitigating potential failures, particular during ramp-up phases of new products. However, its effectiveness is often limited by the missing reasoning capabilities of the FMEA tools, which are usually tabular structured. Meanwhile, large language models (LLMs) offer novel prospects for fine-tuning on custom datasets for reasoning within FMEA contexts. However, LLMs face challenges in tasks that require factual knowledge, a gap that retrieval-augmented generation (RAG) approaches aim to fill. RAG retrieves information from a non-parametric data store and uses a language model to generate responses. Building on this idea, we propose to advance the non-parametric data store with a knowledge graph (KG). By enhancing the RAG framework with a KG, our objective is to leverage analytical and semantic question-answering capabilities on FMEA data. This paper contributes by presenting a new ontology for FMEA observations, an algorithm for creating vector embeddings from the FMEA KG, and a KG enhanced RAG framework. Our approach is validated through a human study and we measure the performance of the context retrieval recall and precision.

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection. Created for the RSNA 2023 Abdominal Trauma Detection competition, the dataset encourages the development of advanced machine learning models for detecting abdominal injuries on CT scans. The dataset encompasses detection and classification of traumatic injuries across multiple organs, including the liver, spleen, kidneys, bowel, and mesentery. Annotations were created by expert radiologists from the American Society of Emergency Radiology (ASER) and Society of Abdominal Radiology (SAR). The dataset is annotated at multiple levels, including the presence of injuries in three solid organs with injury grading, image-level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs. With the release of this dataset, we hope to facilitate research and development in machine learning and abdominal trauma that can lead to improved patient care and outcomes., Comment: 40 pages, 2 figures, 3 tables

We demonstrate the existence of photon traps within the framework of nonlinear electrodynamics. The trapping mechanism is based on the fact that, for null background fields, the optical metric reduces to the Kerr-Schild form, which plays a prominent role in the context of black hole physics. We then construct explicit examples where photons are confined in a region of spacetime, such that a distant observer cannot interact with them. Finally, we argue that the trapping scheme is quite universal, being entirely compatible with causality, energy conditions, and hyperbolicity., Comment: 13 pages, 5 figures

In cavity magnomechanical systems, magnetic excitations couple simultaneously with mechanical vibrations and microwaves, incorporating the tunability of magnetism and the long lifetimes of mechanical modes. Applications of such systems, such as thermometry and sensing, require precise measurement of the mechanical degree-of-freedom. In this paper, we propose a scheme for realizing backaction evading measurements of the mechanical vibrations in cavity magnomechanics. Our proposal involves driving the microwave cavity with two tones separated by twice the phonon frequency and with amplitudes satisfying a balance relation. We show that the minimum added imprecision noise is obtained for drives centered around the lower frequency magnon-microwave polaritons, which can beat the standard quantum limit at modest drive amplitudes. Our scheme is a simple and flexible way of engineering backaction evasion measurements that can be further generalized to other multimode systems., Comment: Revised version. 9 Pages, 8 Figures plus supplementary information (11 pages, 2 figures)

We propose a physical mechanism for fine-tuning the phase of a domain wall around the equilibrium directions involving the application of an external field and a spin polarized current in opposite directions. In this way, the position of the domain wall remains unchanged, but its phase can be slightly modified around the initial equilibrium positions, applying the external field/current smoothly. We point out a few applications to which this fine tuning may lead., Comment: Written in portuguese. Submited to Revista Brasileira de Ensino de Fisica on March 15 2024

The development of quantum acoustics has enabled the cooling of mechanical objects to their quantum ground state, generation of mechanical Fock-states, and Schrodinger cat states. Such demonstrations have made mechanical resonators attractive candidates for quantum information processing, metrology, and macroscopic tests of quantum mechanics. Here, we experimentally demonstrate a direct quantum-acoustic equivalent of a single-atom laser. A single superconducting qubit coupled to a high-overtone bulk acoustic resonator is used to drive the onset of phonon lasing. We observe the absence of a sharp lower lasing threshold and characteristic upper lasing threshold, unique predictions of single-atom lasing. Lasing of an object with a 25 ug mass represents a new regime of laser physics. It provides a possible tool for generating large amplitude coherent states in circuit quantum acoustodynamics, which is important for fundamental and quantum information applications., Comment: 19 pages, 13 Figures

While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA); a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. STREAMLINE is designed to tackle biomedical binary classification tasks while adhering to best practices and accommodating complexity, scalability, reproducibility, customization, and model interpretation. Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was observed with the addition of each subsequent feature set to predict any OSA, with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA data suggests that CF features provide additional value in predicting moderate/severe OSA, but neither CF nor IO features meaningfully improved the prediction of any OSA beyond established demographics, comorbidity and symptom characteristics., Comment: 23 pages, 7 figures, 1 table, 1 supplemental information document (77 pages), and 7 ancillary files

Quantum magnonics, an emerging field focusing on the study of magnons for quantum applications, requires precise measurement methods capable of resolving single magnons. Existing techniques introduce additional dissipation channels and are not apt for magnets in free space. Brillouin light scattering (BLS) is a well-established technique for probing the magnetization known for its high sensitivity and temporal resolution. The coupling between magnons and photons is controlled by a laser input, so it can be switched off when a measurement is not needed. In this article, we theoretically investigate the efficacy of BLS for quantum tomography of magnons. We model a finite optomagnonic waveguide, including the optical noise added by the dielectric, to calculate the signal-to-noise ratio (SNR). We find that the SNR is typically low due to a small magneto-optical coupling; nevertheless, it can be significantly enhanced by injecting squeezed vacuum into the waveguide. We reconstruct the density matrix of the magnons from the statistics of the output photons using a maximum likelihood estimate. The classical component of a magnon state, defined as the regions of positive Wigner function, can be reconstructed with a high accuracy while the non-classical component necessitates either a higher SNR or a larger dataset. The latter requires more compact data structures and advanced algorithms for post-processing. The SNR is limited partially by the input laser power that can be increased by designing the optomagnonic cavity with a heat sink.

We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold standard method, Dual-Energy X-ray Absorptiometry (DXA), utilizing 1273 healthy adults spanning various ages, sexes, and BFP levels. The results demonstrate that ShapedNet outperforms in 19.5% state of the art computer vision-based approaches for body fat estimation, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and Mean Absolute Error (MAE) of 1.42. The study evaluates both gender-based and Gender-neutral approaches, with the latter showcasing superior performance. The method estimates BFP with 95% confidence within an error margin of 4.01% to 5.81%. This research advances multi-task learning and body composition assessment theory through ShapedNet., Comment: Preprinted version in October 2023. The paper is under consideration at Pattern Recognition Letters