Search

Defects in materials are ubiquitous and one of their adverse effects in $\alpha$-Al$_2$O$_3$ is the initiation of corrosion. While this process starts near the surface, the defects involved and their electronic structure need to be elucidated with high accuracy. Since point defects are confined to a small spatial region, defect embedding theory allows the definition of an active space, comprising of the defect electronic states, that is coupled to the environment of the host material. The active space Hamiltonian is of small rank, enabling access to its electronic properties using a high-level or even exact quantum theory. In this paper we use these techniques and first-principles simulations to compute the structural and electronic properties of near-surface vacancies for the (0001) surface of $\alpha$-Al$_2$O$_3$, and investigate the influence of defects and hydration on the initiation and propagation of corrosion. We report the defect electronic structure for strongly localized ground and excited states of the surface O vacancy and compare results obtained using full configuration interaction and a variational quantum eigensolver on a quantum computer. Error mitigation techniques are explored and shown to reduce the error due to the hardware noise to the point where the quantum result agrees with the exact solution within chemical accuracy.

Many of the existing TTS systems cannot accurately synthesize text containing a variety of numerical formats, resulting in reduced intelligibility of the synthesized speech. This research aims to develop a numerical format classifier that can classify six types of numeric contexts. Experiments were carried out using the proposed context-based feature extraction technique, which is focused on extracting keywords, punctuation marks, and symbols as the features of the numbers. Support Vector Machine, K-Nearest Neighbors Linear Discriminant Analysis, and Decision Tree were used as classifiers. We have used the 10-fold cross-validation technique to determine the classification accuracy in terms of recall and precision. It can be found that the proposed solution is better than the existing feature extraction technique with improvement to the classification accuracy by 30% to 37%. The use of the number format classification can increase the intelligibility of the TTS systems., Comment: 21 pages, 6 tables, 1 figure

Hallucinations, the tendency to produce irrelevant/incorrect responses, are prevalent concerns in generative AI-based tools like ChatGPT. Although hallucinations in ChatGPT are studied for textual responses, it is unknown how ChatGPT hallucinates for technical texts that contain both textual and technical terms. We surveyed 47 software engineers and produced a benchmark of 412 Q&A pairs from the bug reports of two OSS projects. We find that a RAG-based ChatGPT (i.e., ChatGPT tuned with the benchmark issue reports) is 36.4% correct when producing answers to the questions, due to two reasons 1) limitations to understand complex technical contents in code snippets like stack traces, and 2) limitations to integrate contexts denoted in the technical terms and texts. We present CHIME (ChatGPT Inaccuracy Mitigation Engine) whose underlying principle is that if we can preprocess the technical reports better and guide the query validation process in ChatGPT, we can address the observed limitations. CHIME uses context-free grammar (CFG) to parse stack traces in technical reports. CHIME then verifies and fixes ChatGPT responses by applying metamorphic testing and query transformation. In our benchmark, CHIME shows 30.3% more correction over ChatGPT responses. In a user study, we find that the improved responses with CHIME are considered more useful than those generated from ChatGPT without CHIME.

Despite the significance of coronal mass ejections (CMEs) in space weather, a comprehensive understanding of their interior morphology remains a scientific challenge, particularly with the advent of many state-of-the-art solar missions such as Parker Solar Probe (Parker) and Solar Orbiter (SO). In this study, we present an analysis of a complex CME as observed by the Wide-Field Imager for Solar PRobe (WISPR) heliospheric imager during Parker's seventh solar encounter. The CME morphology does not fully conform with the general three-part density structure, exhibiting a front and core not significantly bright, with a highly structured overall configuration. In particular, its morphology reveals non-concentric nested rings, which we argue are a signature of the embedded helical magnetic flux rope (MFR) of the CME. For that, we analyze the morphological and kinematical properties of the nested density structures and demonstrate that they outline the projection of the three-dimensional structure of the flux rope as it crosses the lines of sight of the WISPR imager, thereby revealing the magnetic field geometry. Comparison of observations from various viewpoints suggests that the CME substructures can be discerned owing to the ideal viewing perspective, close proximity, and spatial resolution of the observing instrument., Comment: 21 pages, 11 figures

Many materials exhibit various types of phase transitions at different temperatures, with many also demonstrating polymorphism. Doping materials can significantly alter their conductivity. In light of this, we have investigated the electrical conductivity of $LaCoO_3$, specifically its temperature dependence when doped with Yttrium (Y). The crystal structure of Lanthanum Yttrium Cobalt oxide $(La_{1-x}Y_x Co O_3)$ adopts a perovskite form, characterized by the general stoichiometry $ABX_3$, where A and B are cations, and X is an anion. This material undergoes a magnetic phase transition between $50-100$ K, a structural phase transition between $100-300$ K, and an insulator-to-metal transition at $500$ K. At room temperature, $LaCoO_3$ exhibits polaron-type hopping conduction. Our aim was to understand the electrical conductivity at $300$ K and how it varies with temperature when $La^{3+}$ is replaced by $Y^{3+}$. The electrical properties of the perovskite crystal are consistent with small polaron hopping conduction, which theoretically follows Mott's variable range hopping model, where conductivity obeys an exponential law, and resistivity follows an inverse exponential pattern. In this work, we compare the experimental resistivity graph with the theoretical inverse of the conductivity graph, showing that our experimental results align with the polaron hopping conduction model within a certain range. Additionally, the experiment confirms polymorphism in various cases. We observed that increasing the concentration of $Y^{3+}$ enhances the metallic properties of $La_{1-x} Y_x Co O_3$, and we found a significant correlation between conductivity and symmetry. Furthermore, the study highlights the material's phase transitions and polymorphic behavior., Comment: 10 pages, 7 figures

Restricted Boltzmann machines are used for probabilistic learning and are capable of capturing complex dependencies in data. They are employed for diverse purposes such as dimensionality reduction, feature learning and can be used for representing and analyzing physical systems with minimal data. In this paper, we investigate a complex, strongly correlated magnetic spin system with multiple metastable states (magnetic artificial spin ice) using a restricted Boltzmann machine. Magnetic artificial spin ice is of interest because degeneracies can be specified leading to complex states that support unusual collective dynamics. We investigate two distinct geometries exhibiting different low-temperature orderings to evaluate the machine's performance and adaptability in capturing diverse magnetic behaviors. Data sets constructed with spin configurations importance-sampled from the partition function of square and pinwheel artificial spin ice Hamiltonians at different temperatures are used to extract features of distributions using a restricted Boltzmann machine. Results indicate that the restricted Boltzmann machine algorithm is sensitive to features that define the artificial spin ice configuration space and is able to reproduce the thermodynamic quantities of the system away from criticality - a feature useful for faster sample generation. Additionally, we demonstrate how the restricted Boltzmann machine can distinguish between different artificial spin ice geometries in data even when structural defects are present.

We present the identification and physical analysis of a possible magnetic island feature seen in white-light images observed by the Wide-field Imager for Solar Probe (WISPR) on board the Parker Solar Probe (Parker). The island is imaged by WISPR during Parker's second solar encounter on 2019 April 06, when Parker was ~38 solar radii from the Sun center. We report that the average velocity and acceleration of the feature are approximately 334 km s and -0.64 m s-2. The kinematics of the island feature, coupled with its direction of propagation, indicate that the island is likely entrained in the slow solar wind. The island is elliptical in shape with a density deficit in its center, suggesting the presence of a magnetic guide field. We argue that this feature is consistent with the formation of this island via reconnection in the current sheet of the streamer. The feature's aspect ratio (calculated as the ratio of its minor to major axis) evolves from an elliptical to a more circular shape that approximately doubles during its propagation through WISPR's field of view. The island is not distinct in other white-light observations from the Solar and Heliospheric Observatory (SOHO) and the Solar Terrestrial Relations Observatory (STEREO) coronagraphs, suggesting that this is a comparatively faint heliospheric feature and that viewing perspective and WISPR's enhanced sensitivity are key to observing the magnetic island.

We present a method, referred to as Deep Harmonic Finesse (DHF), for separation of non-stationary quasi-periodic signals when limited data is available. The problem frequently arises in wearable systems in which, a combination of quasi-periodic physiological phenomena give rise to the sensed signal, and excessive data collection is prohibitive. Our approach utilizes prior knowledge of time-frequency patterns in the signals to mask and in-paint spectrograms. This is achieved through an application-inspired deep harmonic neural network coupled with an integrated pattern alignment component. The network's structure embeds the implicit harmonic priors within the time-frequency domain, while the pattern-alignment method transforms the sensed signal, ensuring a strong alignment with the network. The effectiveness of the algorithm is demonstrated in the context of non-invasive fetal monitoring using both synthesized and in vivo data. When applied to the synthesized data, our method exhibits significant improvements in signal-to-distortion ratio (26% on average) and mean squared error (80% on average), compared to the best competing method. When applied to in vivo data captured in pregnant animal studies, our method improves the correlation error between estimated fetal blood oxygen saturation and the ground truth by 80.5% compared to the state of the art., Comment: Presented at the 61st ACM/IEEE Design Automation Conference (DAC '24)