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We report the conditional occurrences between three planetary types: super-Earths (m sin i $<$ 10 M$_\oplus$, P $<$ 100 days), warm Jupiters (m sin i $>$ 95 $M_\oplus$, 10 $<$ P $<$ 100 days), and cold Jupiters (m sin i $>$ 95 M$_\oplus$, P $>$ 400 days) for sun-like stars. We find that while the occurrence of cold Jupiters in systems with super-Earths is $22.2\substack{+8.3\\-5.4}$$\%$, compared to $10$$\%$ for the absolute occurrence rate of cold Jupiters, the occurrence of super-Earths in systems with cold Jupiters is $66.0\substack{+18.0\\-16.0}$$\%$, compared to $30$$\%$ for the absolute occurrence rate of super-Earths for sun-like stars. We find that the enhancement of super-Earths in systems with cold Jupiters is evident for sun-like stars, in agreement with several previous studies. We also conduct occurrence studies between warm Jupiters and super-Earths, and between warm Jupiters and cold Jupiters, to consolidate our methods. We conduct an independent observational test to study the effects of cold Jupiters against the inner multiplicity using the well-established giant planet host star metallicity correlation for all transiting planets found to date. The conditional occurrences we find here can be used to constrain the validity of various planetary formation models. The extremely interesting correlations between the super-Earths, cold Jupiters, and warm Jupiters can also be used to understand the formation histories of these planetary types., Comment: 9 pages, 3 tables, 3 figures, published in MNRAS Advance Access

We consider logistic regression including two sets of discrete or categorical covariates that are missing at random (MAR) separately or simultaneously. We examine the asymptotic properties of two multiple imputation (MI) estimators, given in the study of Lee at al. (2023), for the parameters of the logistic regression model with both sets of discrete or categorical covariates that are MAR separately or simultaneously. The proposed estimated asymptotic variances of the two MI estimators address a limitation observed with Rubin's type estimated variances, which lead to underestimate the variances of the two MI estimators (Rubin, 1987). Simulation results demonstrate that our two proposed MI methods outperform the complete-case, semiparametric inverse probability weighting, random forest MI using chained equations, and stochastic approximation of expectation-maximization methods. To illustrate the methodology's practical application, we provide a real data example from a survey conducted in the Feng Chia night market in Taichung City, Taiwan.

Transit observations have become an important technique to probe exoplanets. Therefore, there are many projects carrying on organized observations of transit events, which make a huge amount of light-curve and transit timing data available. We consider this as an excellent opportunity to search for possible orbital decays of exoplanets from this big number of mid-transit times through data-model fitting with both fixed-orbit and orbit-decay models. In order to perform this task, we collect mid-transit-time data from several sources and construct the most complete database up to date. Among 144 hot Jupiters in our study, HAT-P-51b, HAT-P-53b, TrES-5b, WASP-12b are classified as the orbit-decay cases. Thus, in addition to reconfirming WASP-12b as an orbit-decay planet, our results indicate that HAT-P-51b, HAT-P-53b, TrES-5b are potential orbit-decay candidates., Comment: 12 pages, 5 figures, 5 tables, accepted by New Astronomy

Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems. In this paper, we introduce a novel audio-visual SE approach, termed DCUC-Net (deep complex U-Net with conformer network). The proposed DCUC-Net leverages complex domain features and a stack of conformer blocks. The encoder and decoder of DCUC-Net are designed using a complex U-Net-based framework. The audio and visual signals are processed using a complex encoder and a ResNet-18 model, respectively. These processed signals are then fused using the conformer blocks and transformed into enhanced speech waveforms via a complex decoder. The conformer blocks consist of a combination of self-attention mechanisms and convolutional operations, enabling DCUC-Net to effectively capture both global and local audio-visual dependencies. Our experimental results demonstrate the effectiveness of DCUC-Net, as it outperforms the baseline model from the COG-MHEAR AVSE Challenge 2023 by a notable margin of 0.14 in terms of PESQ. Additionally, the proposed DCUC-Net performs comparably to a state-of-the-art model and outperforms all other compared models on the Taiwan Mandarin speech with video (TMSV) dataset.

Clinical guidelines underscore the importance of regularly monitoring and surveilling arteriovenous fistula (AVF) access in hemodialysis patients to promptly detect any dysfunction. Although phono-angiography/sound analysis overcomes the limitations of standardized AVF stenosis diagnosis tool, prior studies have depended on conventional feature extraction methods, restricting their applicability in diverse contexts. In contrast, representation learning captures fundamental underlying factors that can be readily transferred across different contexts. We propose an approach based on deep denoising autoencoders (DAEs) that perform dimensionality reduction and reconstruction tasks using the waveform obtained through one-level discrete wavelet transform, utilizing representation learning. Our results demonstrate that the latent representation generated by the DAE surpasses expectations with an accuracy of 0.93. The incorporation of noise-mixing and the utilization of a noise-to-clean scheme effectively enhance the discriminative capabilities of the latent representation. Moreover, when employed to identify patient-specific characteristics, the latent representation exhibited performance by surpassing an accuracy of 0.92. Appropriate light-weighted methods can restore the detection performance of the excessively reduced dimensionality version and enable operation on less computational devices. Our findings suggest that representation learning is a more feasible approach for extracting auscultation features in AVF, leading to improved generalization and applicability across multiple tasks. The manipulation of latent representations holds immense potential for future advancements. Further investigations in this area are promising and warrant continued exploration.

Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LBP and sciatica patients. Standard operative assessments, traditional Chinese medicine body constitution assessments, planned surgical approach, and vowel pronunciation recordings were collected and stored in different modalities. Our work provides insights into leveraging modality combinations, multimodals, and fusion strategies. The interpretability of models and correlations between modalities were also inspected. Based on the recruited 105 patients, we found that combining standard operative assessments, body constitution assessments, and planned surgical approach achieved the best performance in 0.81 accuracy. Our approach is effective and can be widely applied in general practice due to simplicity and effective.

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise., Comment: published in IEEE Journal of Translational Engineering in Health & Medicine

We present the transit timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26~b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST), Transiting Exoplanet Survey Satellite (TESS), and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 $\pm$ 0.05 minutes, which could result from the presence of an additional $0.02 M_{Jup}$ planet at the 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains $2.4^{+2.9}_{-1.6}$% of H$_2$O with a derived temperature of $590^{+60}_{-50}$ K., Comment: 34 pages, accepted by AJ

We present and analyze 58 transit light curves of TrES-3b and 98 transit light curves of Qatar-1b observed by Transiting Exoplanet Survey Satellite (TESS), plus two transit light curves of Qatar-1b observed by us using a ground-based 1.23\,m telescope. These light curves are combined with the best-quality light curves taken from the Exoplanet Transit Database (ETD) and literature. The precisely determined mid-transit times from these light curves enable us to obtain the refined orbital ephemerides with improved precision for both hot Jupiters. From the timing analysis, we find an indication for the presence of transit timing variations (TTVs) in both systems. Since the observed TTVs are unlikely to be short-term and periodic, the possibility of additional planets in the orbits close to TrES-3b and Qatar-1b are ruled out. Possible causes of long-term TTVs such as orbital decay, apsidal precession, the Applegate mechanism and line-of-sight acceleration are also examined. However, none of these possibilities are found to explain the observed TTV of TrES-3b. In contrast to this, the line-of-sight acceleration appears to be a plausible explanation for the observed TTV of Qatar-1b. In order to confirm these findings, further high-precision transit and RV observations of both systems would be worthwhile., Comment: 38 pages, 16 figures, 8 tables, accepted for publication in AJ

With a focus on off-the-shelf components, Twinkle is the first in a series of cost competitive small satellites managed and financed by Blue Skies Space Ltd. The satellite is based on a high-heritage Airbus platform that will carry a 0.45 m telescope and a spectrometer which will provide simultaneous wavelength coverage from 0.5-4.5 $\rm{\mu m}$. The spacecraft prime is Airbus Stevenage while the telescope is being developed by Airbus Toulouse and the spectrometer by ABB Canada. Scheduled to begin scientific operations in 2025, Twinkle will sit in a thermally-stable, sun-synchronous, low-Earth orbit. The mission has a designed operation lifetime of at least seven years and, during the first three years of operation, will conduct two large-scale survey programmes: one focused on Solar System objects and the other dedicated to extrasolar targets. Here we present an overview of the architecture of the mission, refinements in the design approach, and some of the key science themes of the extrasolar survey., Comment: Presented at SPIE Astronomical Telescopes & Instrumentation 2022

We propose a generalized Debye-Huckel model from Poisson-Fermi theory to predict the mean activity coefficient of electrolytes in water-methanol mixtures with arbitrary percentage of methanol from 0 to 100%. The model applies to any number of ionic species and accounts for both short and long ion-ion, ion-water, ion-methanol, and water-methanol interactions, the size effect of all particles, and the dielectric effect of mixed-solvent solutions. We also present a numerical algorithm with mathematical and physical details for using the model to fit or predict experimental data. The model has only 3 empirical parameters to fit the experimental data of NaF, NaCl, and NaBr, for example, in pure-water solutions. It then uses another 3 parameters to predict the activities of these salts in mixed-solvent solutions for any percentage of methanol. Values of these parameters show mathematical or physical meaning of ionic activities under variable mixing condition and salt concentration. The algorithm can automatically determine optimal values for the 3 fitting parameters without any manual adjustments., Comment: 3 figures

Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the ability to speak. Although various techniques have been proposed to this end, electropalatography (EPG), which is a monitoring technique that records contact between the tongue and hard palate during speech, has not been adequately explored. Herein, we propose a novel multimodal EPG-to-speech (EPG2S) system that utilizes EPG and speech signals for speech generation and enhancement. Different fusion strategies based on multiple combinations of EPG and noisy speech signals are examined, and the viability of the proposed method is investigated. Experimental results indicate that EPG2S achieves desirable speech generation outcomes based solely on EPG signals. Further, the addition of noisy speech signals is observed to improve quality and intelligibility. Additionally, EPG2S is observed to achieve high-quality speech enhancement based solely on audio signals, with the addition of EPG signals further improving the performance. The late fusion strategy is deemed to be the most effective approach for simultaneous speech generation and enhancement., Comment: Accepted By IEEE Signal Processing Letter

The transit timing variation (TTV) and transmission spectroscopy analyses of the planet HAT-P-37b, which is a hot Jupiter orbiting an G-type star, were performed. Nine new transit light curves are obtained and analysed together with 21 published light curves from the literature. The updated physical parameters of HAT-P-37b are presented. The TTV analyses show a possibility that the system has an additional planet which induced the TTVs amplitude signal of 1.74 $\pm$ 0.17 minutes. If the body is located near the 1:2 mean motion resonance orbit, the sinusoidal TTV signal could be caused by the gravitational interaction of a sub-Earth mass planet with mass of 0.06 $M_\oplus$. From the analysis of an upper mass limit for the second planet, the Saturn mass planet with orbital period less than 6 days is excluded. The broad-band transmission spectra of HAT-P-37b favours a cloudy atmospheric model with an outlier spectrum in $B$-filter., Comment: 22 pages, accepted by AJ

Objective: Improving geographical access remains a key issue in determining the sufficiency of regional medical resources during health policy design. However, patient choices can be the result of the complex interactivity of various factors. The aim of this study is to propose a deep neural network approach to model the complex decision of patient choice in travel distance to access care, which is an important indicator for policymaking in allocating resources. Method: We used the 4-year nationwide insurance data of Taiwan and accumulated the possible features discussed in earlier literature. This study proposes the use of a convolutional neural network (CNN)-based framework to make predictions. The model performance was tested against other machine learning methods. The proposed framework was further interpreted using Integrated Gradients (IG) to analyze the feature weights. Results: We successfully demonstrated the effectiveness of using a CNN-based framework to predict the travel distance of patients, achieving an accuracy of 0.968, AUC of 0.969, sensitivity of 0.960, and specificity of 0.989. The CNN-based framework outperformed all other methods. In this research, the IG weights are potentially explainable; however, the relationship does not correspond to known indicators in public health, similar to common consensus. Conclusions: Our results demonstrate the feasibility of the deep learning-based travel distance prediction model. It has the potential to guide policymaking in resource allocation., Comment: accepted by IEEE Journal of Translational Engineering in Health and Medicine

In order to gain possible hints for planet formation from the current data of known extra-solar planets, the period-ratios and mass-ratios of adjacent planet pairs in multi-planet systems are determined. A moderate period-ratio-mass-ratio correlation is found to have a correlation coefficient r=0.5779 with 99% confidence interval (0.464, 0.672). In contrast, for non-adjacent planet pairs, the correlation coefficient is r=0.2820 with 99% confidence interval (0.133, 0.419). Our results reveal the imprint of planet-planet interactions of the adjacent planet pairs in a certain fraction of the multi-planet systems during the stage of planet formation., Comment: 10 pages, 3 figures, 1 table, accepted by New Astronomy

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks., Comment: 48 pages, 8 figures. Accepted by PLoS One

We propose a Poisson-Bikerman (PBik) formula for calculating the differential capacitance (DC) of electrical double layers (EDLs) in aqueous electrolytes or ionic liquids. The PBik theory is a generalization of the classical Poisson-Boltzmann theory to include different steric energies of different-sized ions and water similar to different electrical energies for different-charged ions. Water and ions with interstitial voids in this molecular mean field theory have their physical volumes as they do in molecular dynamics simulations. The PBik formula derived from Fermi distributions of ions and water in arbitrary shape and volume reduces to the Bikerman-Freise formula derived from the lattice model of equal-sized ions. The DC curves predicted by the Gouy-Chapman formula are U-shaped (for point-like ions with zero volume and very dilute solutions). The curves change from U shape to camel shape (Bactrian) and then to bell shape (for finite size ions) as the volume fraction of ions and water changes from zero to medium value then to large value. The transition is characterized by critical and inflection voltages in terms of the particle volume fraction. These voltages determine steric and electrical energies that describe the space/charge competition and saturation properties of ions and water packed in the condensed layer of EDLs under high field conditions. Steric energy is as important as electrical energy in these conditions. PBik computes symmetric DC curves from delicately balanced steric interactions of asymmetric-size ions and water like the experimental data of KPF6 in aqueous solution. It computes asymmetric curves and captures delicately balanced steric or electrical interactions of ions having different volumes or charges in ionic liquids., Comment: 25 pages, 9 figures

We investigate whether certain extra-solar multi-planet systems simultaneously follow the scaling and spacing rules of the angular-momentum-deficit model. The masses and semi-major axes of exoplanets in ten multi-planet systems are considered. It is found that GJ 667C, HD 215152, HD 40307, and Kepler-79 systems are currently close to configurations of the angular-momentum-deficit model. In a gas-poor scenario, GJ 3293, HD 141399, and HD 34445 systems are those which had a configuration of the angular-momentum-deficit model in the past and get scattered away due to post gaseous effects. In addition, no matter in gas-free or gas-poor scenario, 55 Cnc, GJ 876, and WASP-47 systems do not follow the angular-momentum-deficit model. Therefore, our results reveal important formation histories of these multi-planet systems., Comment: 22 pages, 6 figures, published in Astrophysics and Space Science

The photometric light curves of BRITE satellites were examined through a machine learning technique to investigate whether there are possible exoplanets moving around nearby bright stars. Focusing on different transit periods, several convolutional neural networks were constructed to search for transit candidates. The convolutional neural networks were trained with synthetic transit signals combined with BRITE light curves until the accuracy rate was higher than 99.7 $\%$. Our method could efficiently lead to a small number of possible transit candidates. Among these ten candidates, two of them, HD37465, and HD186882 systems, were followed up through future observations with a higher priority. The codes of convolutional neural networks employed in this study are publicly available at http://www.phys.nthu.edu.tw/$\sim$jiang/BRITE2020YehJiangCNN.tar.gz., Comment: 23 pages, 4 tables, 8 figures, published by PASP

Considering the importance of investigating the transit timing variations (TTVs) of transiting exoplanets, we present a follow-up study of HAT-P-12b. We include six new light curves observed between 2011 and 2015 from three different observatories, in association with 25 light curves taken from the published literature. The sample of the data used, thus covers a time span of about 10.2 years with a large coverage of epochs (1160) for the transiting events of the exoplanet HAT-P-12b. The light curves are used to determine the orbital parameters and conduct an investigation of possible transit timing variations. The new linear ephemeris shows a large value of reduced chi-square = 7.93, and the sinusoidal fitting using the prominent frequency coming from a periodogram shows a reduced chi-square around 4. Based on these values and the corresponding O-C diagrams, we suspect the presence of a possible non-sinusoidal TTV in this planetary system. Finally, we find that a scenario with an additional non-transiting exoplanet could explain this TTV with an even smaller reduced chi-square value of around 2., Comment: 19 pages, 7 tables, 6 figures, accepted by RAA

Motivated by the unsettled conclusion on whether there are any transit timing variations (TTVs) for the exoplanet Qatar-1b, 10 new transit light curves are presented and the TTV analysis with a baseline of 1400 epochs are performed. Because the linear model provides a good fitting with reduced chi-square = 2.59 and the false-alarm probabilities of possible TTV frequencies are as large as 35 %, our results are consistent with a null-TTV model. Nevertheless, a new ephemeris with the reference time T_0 = 2455647.63360\pm 0.00008 (BJD) and the period P= 1.4200236\pm 0.0000001 (day) is obtained. In addition, the updated orbital semi-major axis and planetary radius in unit of stellar radius are being provided, and the lower limit of modified stellar tidal quality factor is also determined., Comment: typo corrected, 24 pages, 5 tables, 5 figures, accepted by AJ