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We investigate the observable characteristics of the extended atmospheres of AGB stars across a wide range of radio and (sub-)mm wavelengths using state-of-the-art 1D dynamical atmosphere and wind models over one pulsation period. We also study the relationships between the observable features and model properties. We further study practical distance ranges for observable sources assuming the capabilities of current and upcoming observatories. We present time-variable, frequency-dependent profiles of pulsating AGB stars' atmospheres, illustrating observable features in resolved and unresolved observations, including disc brightness temperature, photosphere radius, and resolved and unresolved spectral indices. Notably, temporal variations in disc brightness temperature closely mirror the temperature variability of the stellar atmosphere. We find that while the photospheric radius decreases due to gas dilution in the layers between consecutive shocks, the increase in the observed stellar radius reflects shock propagation through the atmosphere during the expansion phase, providing a direct measurement method for the shock velocity. Furthermore, our models indicate that enhanced gas temperatures after the passage of a strong shock might be observable in the high-frequency ALMA bands as a decrease in the brightness temperature with increasing frequency. We demonstrate that synthetic observations based on state-of-the-art dynamical atmosphere and wind models are necessary for proper interpretations of current (ALMA and VLA) and future (SKA and ngVLA) observations and that multi-wavelength observations of AGB stars are crucial for empirical studies of their extended atmospheres., Comment: Accepted for publication in ApJ. 14 pages, 7 figures, and 2 tables in the main text. Appendix including figures for additional models and extended frequency ranges

The mathematical perspective for creating trochoids [through a solid rule that is based on the pure rolling a circle along a straight line or another circle (centered trochoid)] is violated and changed it to a novel vision which is based on the combination of rolling and sliding motions of a circle along a straight line or another circle! In this new vision we have not to define a trochoid as a path that is swept by an attached point to a pure rolling circle along a straight line or another circle. Instead, a trochoid can be defined as a path that is swept by a definite point on the circumference of a rolling and sliding circle along a straight line or another circle! In this article we present two different methods for implement a definite combination of sliding and rolling motions for a circle along another one in order to make a simple experimental simulation to create centered trochoids and co-centered ellipses. In our novel mathematical vision not only, the physical concepts are playing basic role but also one can deduce the parametric equations of trochoids and ellipses on the bases of rolling and sliding motions. In this perspective, an ellipse can be visualized as a closed plane curve that can be generated through a definite combination of rolling and sliding motions due to two co-polarized rotational motions with different commensurable angular frequencies! Centered trochoids and ellipses can be implemented with the help of Virtual Rotating Circles Technique (VRCT) by an innovative device that we have named it Mechanical Oscilloscope (MO) and other trochoids by a Virtual Sliding Simulator (VSS). Although the functions of our devices are independent from other electronic equipment, but we can create trochoids on the bases of functional operation of (VSS) and (MO) with the help of computer., Comment: This paper is subjected in the field of mathematical physics and includes 28 pages, 7 figures and 13 pictures. Through the study of this article an interesting reader would be able to deduce parametric equations of centered trochoids and co- centered ellipse on the basis combination of rolling and sliding motions of a circle along another one

The transport of energy through convection is important during many stages of stellar evolution, and is best studied in our Sun or giant evolved stars. Features that are attributed to convection are found on the surface of massive red supergiant stars. Also for lower mass evolved stars, indications of convection are found, but convective timescales and sizes remain poorly constrained. Models indicate that convective motions are crucial for the production of strong winds that return the products of stellar nucleosynthesis into the interstellar medium. Here we report a series of reconstructed interferometric images of the surface of the evolved giant star R Doradus. The images reveal a stellar disc with prominent small scale features that provide the structure and motions of convection on the stellar surface. We find that the dominant structure size of the features on the stellar disc is $0.72\pm0.05$ astronomical units (au). We measure the velocity of the surface motions to vary between $-18$ and $+20$ km s$^{-1}$, which means the convective timescale is approximately one month. This indicates a possible difference between the convection properties of low-mass and high-mass evolved stars., Comment: This version of the article has been accepted for publication in Nature, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at http://dx.doi.org/10.1038/s41586-024-07836-9

This study analyzes the Ames Housing Dataset using CatBoost and LightGBM models to explore feature importance and causal relationships in housing price prediction. We examine the correlation between SHAP values and EconML predictions, achieving high accuracy in price forecasting. Our analysis reveals a moderate Spearman rank correlation of 0.48 between SHAP-based feature importance and causally significant features, highlighting the complexity of aligning predictive modeling with causal understanding in housing market analysis. Through extensive causal analysis, including heterogeneity exploration and policy tree interpretation, we provide insights into how specific features like porches impact housing prices across various scenarios. This work underscores the need for integrated approaches that combine predictive power with causal insights in real estate valuation, offering valuable guidance for stakeholders in the industry.

Plant stress phenotyping traditionally relies on expert assessments and specialized models, limiting scalability in agriculture. Recent advances in multimodal large language models (LLMs) offer potential solutions to this challenge. We present AgEval, a benchmark comprising 12 diverse plant stress phenotyping tasks, to evaluate these models' capabilities. Our study assesses zero-shot and few-shot in-context learning performance of state-of-the-art models, including Claude, GPT, Gemini, and LLaVA. Results show significant performance improvements with few-shot learning, with F1 scores increasing from 46.24% to 73.37% in 8-shot identification for the best-performing model. Few-shot examples from other classes in the dataset have negligible or negative impacts, although having the exact category example helps to increase performance by 15.38%. We also quantify the consistency of model performance across different classes within each task, finding that the coefficient of variance (CV) ranges from 26.02% to 58.03% across models, implying that subject matter expertise is needed - of 'difficult' classes - to achieve reliability in performance. AgEval establishes baseline metrics for multimodal LLMs in agricultural applications, offering insights into their promise for enhancing plant stress phenotyping at scale. Benchmark and code can be accessed at: https://anonymous.4open.science/r/AgEval/

As the dependence on computer systems expands across various domains, focusing on personal, industrial, and large-scale applications, there arises a compelling need to enhance their reliability to sustain business operations seamlessly and ensure optimal user satisfaction. System logs generated by these devices serve as valuable repositories of historical trends and past failures. The use of machine learning techniques for failure prediction has become commonplace, enabling the extraction of insights from past data to anticipate future behavior patterns. Recently, large language models have demonstrated remarkable capabilities in tasks including summarization, reasoning, and event prediction. Therefore, in this paper, we endeavor to investigate the potential of large language models in predicting system failures, leveraging insights learned from past failure behavior to inform reasoning and decision-making processes effectively. Our approach involves leveraging data from the Intel Computing Improvement Program (ICIP) system crash logs to identify significant events and develop CrashEventLLM. This model, built upon a large language model framework, serves as our foundation for crash event prediction. Specifically, our model utilizes historical data to forecast future crash events, informed by expert annotations. Additionally, it goes beyond mere prediction, offering insights into potential causes for each crash event. This work provides the preliminary insights into prompt-based large language models for the log-based event prediction task., Comment: Accepted in ICITCOM'24. Copyrights will be with IEEE

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model's language capability. Additionally, GPT-4o's vision and speech capabilities are tested through image classification and object recognition tasks, as well as accent classification. The multimodal evaluation assesses the model's performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities. This paper highlights the need for more comprehensive benchmarks and robust evaluation frameworks, encompassing qualitative assessments involving human judgment as well as error analysis. Future work should focus on expanding datasets, investigating prompt-based assessment, and enhancing few-shot learning techniques to test the model's practical applicability and performance in real-world scenarios.

In recent years, handheld and portable terahertz instruments have been in rapid development for various applications ranging from non-destructive testing to biomedical imaging and sensing. For instance, we have deployed our Portable Handheld Spectral Reflection (PHASR) Scanners for in vivo full-spectroscopic imaging of skin burns in large animal models in operating room settings. In this paper, we debut the polarimetric version of the PHASR Scanner, and describe a generalized calibration technique to map the spatial and spectral dependence of the Jones matrix of an imaging scanner across its field of view. Our design is based on placement of two orthogonal photoconductive antenna (PCA) detectors separated by a polarizing beam splitter in the PHASR Scanner housing. We show that at least three independent measurements of a well-characterized polarimetric calibration target are sufficient to determine the polarization state of the incident beam at the sample location, as well as to extract the Jones propagation matrix from the sample location to the detectors. We have tested the accuracy of our scanner by validating polarimetric measurements obtained from a birefringent crystal rotated to various angles, as compared to the theoretically predicted response of the sample. This new version of our PHASR scanner can be used for high-speed imaging and investigation of heterogeneity of polarization-sensitive samples in the field., Comment: 13 pages, 6 figures

We evaluate different Neural Radiance Fields (NeRFs) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in three scenarios with increasing complexity and compare the results with the point cloud obtained using LiDAR as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 minutes of training on the GPU, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4% in the average F1 score. This optimization process significantly enhances the speed and efficiency of 3D reconstruction using NeRFs. Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process., Comment: Published in 'Plant Phenomics'

To ensure safe, secure, and efficient advanced air mobility (AAM) operations, an AAM surveillance network is needed to detect and track AAM traffic. Additionally, a cloud-based surveillance data collection, monitoring, and distribution center is needed, where AAM operators and service suppliers, law enforcement agencies, correctional facilities and municipalities can subscribe to for receiving relevant AAM traffic data to plan and monitor AAM operations. In this work, we develop an optimization model to design a surveillance sensor network for AAM that minimizes total sensor cost while providing full coverage in the desired region of operation, considering terrain types of that region, terrain-based sensor detection probabilities, and meeting the minimum detection probability requirement. Moreover, we present a framework for low altitude surveillance information clearinghouse (LASIC), connected to the optimized AAM surveillance network for receiving live surveillance feed. Additionally, we conduct a cost-benefit analysis of the AAM surveillance network and LASIC to justify investment in it. We examine six potential types of AAM sensors and homogeneous and heterogeneous network types. Our analysis reveals the sensor types that are the most profitable options for detecting cooperative and non-cooperative aircraft. According to the findings, heterogeneous networks are more cost-effective than homogeneous sensor networks. Based on the sensitivity analysis, changes in parameters such as subscription fees, number of subscribers, sensor detection probabilities, and minimum required detection probability significantly impact the surveillance network design and cost benefit analysis.

Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained significant attention in the recent years is DNN fault localization. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique, named deepmufl, applicable to a wide range of DNN models. We have implemented deepmufl and have evaluated its effectiveness using 109 bugs obtained from StackOverflow. Our results show that deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position, outperforming state-of-the-art static and dynamic DNN fault localization systems that are also designed to target the class of bugs supported by deepmufl. Moreover, we observed that we can halve the fault localization time for a pre-trained model using mutation selection, yet losing only 7.55% of the bugs localized in top-1 position., Comment: 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023)

This research paper investigates the effectiveness of simple linear models versus complex machine learning techniques in breast cancer diagnosis, emphasizing the importance of interpretability and computational efficiency in the medical domain. We focus on Logistic Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM) and optimize their performance using the UCI Machine Learning Repository dataset. Our findings demonstrate that the simpler linear model, LR, outperforms the more complex DT and SVM techniques, with a test score mean of 97.28%, a standard deviation of 1.62%, and a computation time of 35.56 ms. In comparison, DT achieved a test score mean of 93.73%, and SVM had a test score mean of 96.44%. The superior performance of LR can be attributed to its simplicity and interpretability, which provide a clear understanding of the relationship between input features and the outcome. This is particularly valuable in the medical domain, where interpretability is crucial for decision-making. Moreover, the computational efficiency of LR offers advantages in terms of scalability and real-world applicability. The results of this study highlight the power of simplicity in the context of breast cancer diagnosis and suggest that simpler linear models like LR can be more effective, interpretable, and computationally efficient than their complex counterparts, making them a more suitable choice for medical applications., Comment: 15 pages, 6 figures, conference or journal

Speckle patterns observed in coherent optical imaging reflect important characteristic information of the scattering object. To capture speckle patterns, angular resolved or oblique illumination geometries are usually employed in combination with Rayleigh statistical models. We present a portable and handheld 2-channel polarization-sensitive imaging instrument to directly resolve terahertz (THz) speckle fields in a collocated telecentric back-scattering geometry. The polarization state of the THz light is measured using two orthogonal photoconductive antennas and can be presented in the form of the Stokes vectors of the THz beam upon interaction with the sample. We report on the validation of the method in surface scattering from gold-coated sandpapers, demonstrating a strong dependence of the polarization state on the surface roughness and the frequency of the broadband THz illumination. We also demonstrate non-Rayleigh first-order and second-order statistical parameters, such as degree of polarization uniformity (DOPU) and phase difference, for quantifying the randomness of polarization. This technique provides a fast method for broadband THz polarimetric measurement in the field and has the potential for detecting light depolarization in applications ranging from biomedical imaging to non-destructive testing.