Numerical modeling of thermal dust polarization from aligned grains in the envelope of evolved stars with updated POLARIS

Magnetic fields are thought to influence the formation and evolution of evolved star envelopes. Thermal dust polarization from magnetically aligned grains is potentially a powerful tool for probing magnetic fields and dust properties in these circumstellar environments. In this paper, we present numerical modeling of thermal dust polarization from the envelope of IK Tau using the magnetically enhanced radiative torque (MRAT) alignment theory implemented in our updated POLARIS code. Due to the strong stellar radiation field, the minimum size required for RAT alignment of silicate grains is $\sim 0.005 - 0.05\,\rm\mu m$. Additionally, ordinary paramagnetic grains can achieve perfect alignment by MRAT in the inner regions of $r < 500\,\rm au$ due to stronger magnetic fields of $B\sim 10$ mG - 1G, producing thermal dust polarization degree of $\sim 10\,\%$. The polarization degree can be enhanced to $\sim 20-40\%$ for grains with embedded iron inclusions. We also find that the magnetic field geometry affects the alignment size and the resulting polarization degree due to the projection effect in the plane-of-sky. We also study the spectrum of polarized thermal dust emission and find the increased polarization degree toward $\lambda > 50\,\rm\mu m$ due to the alignment of small grains by MRAT. Furthermore, we investigate the impact of rotational disruption by RATs (RAT-D) and find the RAT-D effect cause a decrease in the dust polarization fraction. Finally, we compare our numerical results with available polarization data observed by SOFIA/HAWC+ for constraining dust properties, suggesting grains are unlikely to have embedded iron clusters and might have slightly elongated shapes. Our modeling results suggest further observational studies at far-infrared/sub-millimeter wavelengths to understand the properties of magnetic fields and dust in AGB envelopes.Comment: 27 pages, 23 figures, 1 table, to be submitte

Retrieval of interatomic separations of molecules from laser-induced high-order harmonic spectra

We illustrate an iterative method for retrieving the internuclear separations of N$_2$, O$_2$ and CO$_2$ molecules using the high-order harmonics generated from these molecules by intense infrared laser pulses. We show that accurate results can be retrieved with a small set of harmonics and with one or few alignment angles of the molecules. For linear molecules the internuclear separations can also be retrieved from harmonics generated using isotropically distributed molecules. By extracting the transition dipole moment from the high-order harmonic spectra, we further demonstrated that it is preferable to retrieve the interatomic separation iteratively by fitting the extracted dipole moment. Our results show that time-resolved chemical imaging of molecules using infrared laser pulses with femtosecond temporal resolutions is possible.Comment: 14 pages, 9 figure

A roadmap for the design of four-terminal spin valves and the extraction of spin diffusion length

Graphene is a promising substrate for future spintronics devices owing to its remarkable electronic mobility and low spin-orbit coupling. Hanle precession in spin valve devices is commonly used to evaluate the spin diffusion and spin lifetime properties. In this work, we demonstrate that this method is no longer accurate when the distance between inner and outer electrodes is smaller than six times the spin diffusion length, leading to errors as large as 50% for the calculations of the spin figures of merit of graphene. We suggest simple but efficient approaches to circumvent this limitation by addressing a revised version of the Hanle fit function. Complementarily, we provide clear guidelines for the design of four-terminal spin valves able to yield flawless estimations of the spin lifetime and the spin diffusion coefficient.Comment: 7 pages, 5 figure

A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Studying Magnetic Fields and Dust in M17 Using Polarized Thermal Dust Emission Observed by SOFIA/HAWC

We report on the highest spatial resolution measurement to date of magnetic fields (B-fields) in M17 using thermal dust polarization measurements taken by SOFIA/HAWC+ centered at a wavelength of 154 μm. Using the Davis–Chandrasekhar–Fermi method, in which the polarization angle dispersion calculated using the structure function technique is the quantity directly observed by SOFIA/HAWC+, we found the presence of strong B-fields of 980 ± 230 and 1665 ± 885 μG in the lower-density M17-N and higher-density M17-S regions, respectively. The B-field morphology in M17-N possibly mimics the fields in gravitationally collapsing molecular cores, while in M17-S the fields run perpendicular to the density structure. M17-S also displays a pillar feature and an asymmetric large-scale hourglass-shaped field. We use the mean B-field strengths to determine Alfvénic Mach numbers for both regions, finding that B-fields dominate over turbulence. We calculate the mass-to-flux ratio, λ, finding λ = 0.07 for M17-N and 0.28 for M17-S. These subcritical λ values are consistent with the lack of massive stars formed in M17. To study dust physics, we analyze the relationship between dust polarization fraction, p, emission intensity, I, gas column density, N(H2), polarization angle dispersion function, S, and dust temperature, T d. p decreases with intensity as I −α with α = 0.51. p tends to first increase with T d, but then decreases at higher T d. The latter feature, seen in M17-N at high T d when N(H2) and S decrease, is evidence of the radiative torque disruption effect

Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training

Federated Recommendation (FedRec) systems have emerged as a solution to safeguard users' data in response to growing regulatory concerns. However, one of the major challenges in these systems lies in the communication costs that arise from the need to transmit neural network models between user devices and a central server. Prior approaches to these challenges often lead to issues such as computational overheads, model specificity constraints, and compatibility issues with secure aggregation protocols. In response, we propose a novel framework, called Correlated Low-rank Structure (CoLR), which leverages the concept of adjusting lightweight trainable parameters while keeping most parameters frozen. Our approach substantially reduces communication overheads without introducing additional computational burdens. Critically, our framework remains fully compatible with secure aggregation protocols, including the robust use of Homomorphic Encryption. The approach resulted in a reduction of up to 93.75% in payload size, with only an approximate 8% decrease in recommendation performance across datasets. Code for reproducing our experiments can be found at https://github.com/NNHieu/CoLR-FedRec.Comment: 12 pages, 6 figures, 4 table

A Cosine Similarity-based Method for Out-of-Distribution Detection

The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods.Comment: Accepted paper at ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability. 10 pages (4 main + appendix