Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Utility of Multiple Sonar Imaging Bands for Underwater Object Recognition

A new, two-stage approach for performing automatic target recognition (ATR) in multi-band synthetic aperture sonar (SAS) imagery is proposed. The approach consists of a simple detection algorithm, called MondrianB, followed by a more sophisticated classification stage based on convolutional neural networks (CNNs). The approach is extensible in that the same method can be used, without any modifications, for sensor data in the form of an arbitrary number of co-registered image bands (including one). The detection stage does not require any labeled training data. The classification stage relies on ensembles of small, efficient CNNs that collectively provide robust alarm predictions by leveraging unique network architectures and multiple input-data representations. The performance of the proposed ATR approach is verified on a sizable set of data collected at sea by a three-band SAS system. Specifically, the experiments conducted quantity the utility of each imaging band on both the detection and classification stages of a man-made object-recognition task. It is shown that leveraging information from the additional bands improves both detection and classification performance.
Aiming at harmony. Comparing and contrasting International HFrEF Guidelines

Abstract Large randomized controlled trials (RCTs) have led to major changes in the treatment of patients with heart failure and reduced left ventricular ejection fraction (HFrEF) and these advances are included in the recent European Society of Cardiology (ESC) and the American College of Cardiology/American Heart Association/Heart Failure Society of America (ACC/AHA/HFSA) guidelines issued in 2021 and 2022, respectively. According to both guidelines, treatment of patients with HFrEF is based on the administration of four classes of drugs that reduce the primary endpoint of cardiovascular death and HF hospitalizations in RCTs: angiotensin-converting enzyme or angiotensin receptor neprilysin inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter 2 inhibitors. Specific sequences of treatment are not recommended but emphasis is given to reaching treatment with all four drugs as early as possible. Further treatments are considered in selected patients including ivabradine, hydralazine nitrates, digoxin, and the new agent vericiguat. Specific treatments, mostly new, for cardiovascular and non-cardiovascular comorbidities are also given. The aim of this article is to compare the two recent guidelines issued by the ESC and ACC/AHA/HFSA and show the few differences and the many consistent recommendations, now more numerous given the evidence available for many new treatments.
Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer’s Disease Diagnosis

Multi-modal fusion has become an important data analysis technology in Alzheimer’s disease (AD) diagnosis, which is committed to effectively extract and utilize complementary information among different modalities. However, most of the existing fusion methods focus on pursuing common feature representation by transformation, and ignore discriminative structural information among samples. In addition, most fusion methods use high-order feature extraction, such as deep neural network, by which it is difficult to identify biomarkers. In this paper, we propose a novel method named deep multi-modal discriminative and interpretability network (DMDIN), which aligns samples in a discriminative common space and provides a new approach to identify significant brain regions (ROIs) in AD diagnosis. Specifically, we reconstruct each modality with a hierarchical representation through multilayer perceptron (MLP), and take advantage of the shared self-expression coefficients constrained by diagonal blocks to embed the structural information of inter-class and the intra-class. Further, the generalized canonical correlation analysis (GCCA) is adopted as a correlation constraint to generate a discriminative common space, in which samples of the same category gather while samples of different categories stay away. Finally, in order to enhance the interpretability of the deep learning model, we utilize knowledge distillation to reproduce coordinated representations and capture influence of brain regions in AD classification. Experiments show that the proposed method performs better than several state-of-the-art methods in AD diagnosis.
Phase Retrieval from Defocused Images and Its Applications in Lithography

This paper discusses the application of iterative phase retrieval method to phase measurement in optical lithography. A simple algorithm was developed for reconstructing phase information from image intensity distributions measured under different defocus conditions. The phase distribution in the image plane and in the pupil plane are reconstructed from the images obtained using the micro-lithography simulation microscope, MSM100 (Zeiss). Two applications of this method in optical lithography meteorology were investigated. First, the phase transmission of phase-shifting masks was successfully measured both for alternating and for attenuating type masks. The reconstructed phase showed that the uniform background of light leaking from the opaque regions on the mask can affect the phase in the image plane. Second, the possibility of using this method in measuring aberrations of projection optics was examined.
Oblique resonances excited in the near field of a satellite‐borne electric dipole antenna

Near-field patterns of a short electric dipole antenna were measured in the magnetospheric plasma by an instrument aboard the polar orbiting satellite OV1-20S. The most common antenna pattern observed at the fixed operating frequency, 300 kHz, was a dominantly biconic pattern with major axis parallel to the geomagnetic field. The measured cone angles are in reasonable agreement with those predicted by warm plasma theory. There was no discernible systematic asymmetry in these angles such as could be attributed to field-aligned currents in the plasma or to the motion of the satellite through it.
Sternheimer shielding using various approximations

Sternheimer shielding functions . gamma. (r) are derived by means of Sternheimer's procedure for a series of elements (Li, Na, K, Rb, F, Cl, Br, I, Cu, Fe, Ag, and Pr) with varying electronic configurations, with and without valence electrons, which are generally included in molecular-orbital calculations with a limited basis set. Direct and exchange contributions to R / are presented, and the various contributions to . gamma. (r) due to angular and radial excitations are discussed. The discrepancy in R values for halides compared to Sternheimer's values is due mainly to an exchange contribution to R which has been omitted by Sternheimer. Self-consistent calculations of . gamma. (r) have been performed with and without X.cap alpha. exchange interaction. The general tendency of self-consistent . gamma.atsub infinityat ( . infinity.gamma. (r)) results is that direct Coulomb interaction leads to . gamma.atsub infinityat values more positive than those derived from Sternheimer's procedure (noniterative and excluding exchange interaction), while exchange interaction partly balances this effect. The overall effect in the case of iron is that Sternheimer's value is nearly identical to our self-consistent . gamma.atsub infinityat value including exchange contributions. Self-consistency was obtained only formore » neutral atoms and positive ions. However, no such self-consistency was obtained for negative ions. Thus the X/sub b/ approximation is not very appropriate for the present purpose of calculating . gamma. (r) for elements with varying electronic configurations. « less
The Plant Fatty Acyl Reductases

Fatty acyl reductase (FAR) is a crucial enzyme that catalyzes the NADPH-dependent reduction of fatty acyl-CoA or acyl-ACP substrates to primary fatty alcohols, which in turn acts as intermediate metabolites or metabolic end products to participate in the formation of plant extracellular lipid protective barriers (e.g., cuticular wax, sporopollenin, suberin, and taproot wax). FARs are widely present across plant evolution processes and play conserved roles during lipid synthesis. In this review, we provide a comprehensive view of FAR family enzymes, including phylogenetic analysis, conserved structural domains, substrate specificity, subcellular localization, tissue-specific expression patterns, their varied functions in lipid biosynthesis, and the regulation mechanism of FAR activity. Finally, we pose several questions to be addressed, such as the roles of FARs in tryphine, the interactions between transcription factors (TFs) and FARs in various environments, and the identification of post-transcriptional, translational, and post-translational regulators.
Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction

Rapidly and accurately predicting on-site peak ground velocity (PGV) is important for earthquake hazard mitigation. Traditional methods used to predict PGV involve a single physics-based parameter, such as the peak displacement (Pd) or squared velocity integral (IV2) techniques; deep-learning methods involve a single neural network model, such as the convolutional neural network (CNN) or recurrent neural network (RNN) models, to extract feature for estimating the PGV. Here, based on the training dataset from earthquake events that occurred in Japan, we construct a hybrid deep-learning network (HybridNet) for predicting on-site PGV, which consists of CNN and RNN feature extraction blocks. We use physics-based feature time series, waveforms, and a site feature from a single station as the input of HybridNet model. In addition, we concatenate the features from the CNN block, RNN block, and site feature to predict the on-site PGV. We show that concerning the standard deviation of error, the mean absolute error, and the coefficient of determination for PGV prediction, the HybridNet model exhibits better performance on the test dataset than the baseline models. In addition, the potential damage zone (PDZ) can be predicted by interpolating the predicted PGVs at the stations. Based on the predicted PGV of the HybridNet model, we investigate the feasibility of PDZ estimation on five earthquakes ( $M \ge6.5$ ). Also, we find that within a few seconds after the arrival of P-wave, the predicted PDZ is consistent well with the PGV ShakeMap obtained from the U.S. Geological Survey.
Informing Implementation: Perspectives from the Australian University Community Regarding an Animal Assisted Intervention

Simple Summary Animal Assisted Interventions have become increasingly popular in the university setting; however, there is limited research exploring their potential on an Australian university campus and participants’ views prior to implementation. Therefore, this study aimed to explore university staff members’ and students’ interest in participating in an Animal Assisted Intervention and their perspectives on intervention characteristics. Participants indicated their preferred intervention characteristics for the location of the intervention, frequency of participating, and ways of hearing about the intervention. Participants also discussed various considerations that may impact the intervention (such as the accessibility of the location, the impact of workload on participating, the effectiveness of promotion strategies, and factors that may assist therapy animal welfare, such as the therapy animals’ handlers). Gaining an insight into the university community’s views prior to implementation may ensure the intervention is feasible to implement and can be beneficial to both humans and therapy animals. Abstract Animal Assisted Interventions (AAIs) have become increasingly popular in the university setting; however, there is limited research exploring their potential on an Australian university campus and participants’ views prior to implementation. Therefore, this study aimed to explore university staff members’ and students’ interest in participating in an AAI and their perspectives on intervention characteristics. This was a mixed methods study, using an online survey and semi-structured interviews. The survey had 344 responses, and 45 interviews were conducted. A large majority of participants (86%) were interested in participating in an AAI. In the survey, participants indicated their preferred intervention characteristics for the location of the intervention, frequency of participating, and ways of hearing about the intervention. Participants also expressed concerns regarding therapy animal welfare. In interviews, participants discussed various considerations which may impact the intervention (such as the accessibility of the location, the impact of workload on participating, the effectiveness of promotion strategies, and factors that may assist therapy animal welfare, such as the therapy animals’ handlers). Gaining an insight into the university community’s views prior to implementation may ensure the intervention is feasible to implement and can be beneficial to both humans and therapy animals.
Concurrent Decoding of Finger Kinematic and Kinetic Variables based on Motor Unit Discharges

A reliable and functional neural interface is necessary to control individual finger movements of assistive robotic hands. Non-invasive surface electromyogram (sEMG) can be used to predict fingertip forces and joint kinematics continuously. However, concurrent prediction of kinematic and dynamic variables in a continuous manner remains a challenge. The purpose of this study was to develop a neural decoding algorithm capable of concurrent prediction of fingertip forces and finger dynamic movements. High-density electromyogram (HD-EMG) signal was collected during finger flexion tasks using either the index or middle finger: isometric, dynamic, and combined tasks. Based on the data obtained from the two first tasks, motor unit (MU) firing activities associated with individual fingers and tasks were derived using a blind source separation method. MUs assigned to the same tasks and fingers were pooled together to form MU pools. Twenty MUs were then refined using EMG data of a combined trial. The refined MUs were applied to a testing dataset of the combined task, and were divided into five groups based on the similarity of firing patterns, and the populational discharge frequency was determined for each group. Using the summated firing frequencies obtained from five groups of MUs in a multivariate linear regression model, fingertip forces and joint angles were derived concurrently. The decoding performance was compared to the conventional EMG amplitude-based approach. In both joint angles and fingertip forces, MU-based approach outperformed the EMG amplitude approach with a smaller prediction error (Force: 5.36±0.47 vs 6.89±0.39 %MVC, Joint Angle: 5.0±0.27° vs 12.76±0.40°) and a higher correlation (Force: 0.87±0.05 vs 0.73±0.1, Joint Angle: 0.92±0.05 vs 0.45±0.05) between the predicted and recorded motor output. The outcomes provide a functional and accurate neural interface for continuous control of assistive robotic hands.
Genome-Wide Identification of the CER Gene Family and Significant Features in Climate Adaptation of Castanea mollissima

The plant cuticle is the outermost layer of the aerial organs and an important barrier against biotic and abiotic stresses. The climate varies greatly between the north and south of China, with large differences in temperature and humidity, but Chinese chestnut is found in both regions. This study investigated the relationship between the wax layer of chestnut leaves and environmental adaptation. Firstly, semi-thin sections were used to verify that there is a significant difference in the thickness of the epicuticular wax layer between wild chestnut leaves in northwest and southeast China. Secondly, a whole-genome selective sweep was used to resequence wild chestnut samples from two typical regional populations, and significant genetic divergence was identified between the two populations in the CmCER1-1, CmCER1-5 and CmCER3 genes. Thirty-four CER genes were identified in the whole chestnut genome, and a series of predictive analyses were performed on the identified CmCER genes. The expression patterns of CmCER genes were classified into three trends—upregulation, upregulation followed by downregulation and continuous downregulation—when chestnut seedlings were treated with drought stress. Analysis of cultivars from two resource beds in Beijing and Liyang showed that the wax layer of the northern variety was thicker than that of the southern variety. For the Y-2 (Castanea mollissima genome sequencing material) cultivar, there were significant differences in the expression of CmCER1-1, CmCER1-5 and CmCER3 between the southern variety and the northern one-year-grafted variety. Therefore, this study suggests that the CER family genes play a role in environmental adaptations in chestnut, laying the foundation for further exploration of CmCER genes. It also demonstrates the importance of studying the adaptation of Chinese chestnut wax biosynthesis to the southern and northern environments.
Nearly Optimal Allocation of Experimental Units Using Observed Covariate Values

In many of the experimental situations to which the analysis-of-covariance model applies, the values of the covariates are known prior to the actual experiment so that they can be used in allocating the experimental units to the treatments. Due to the large number of possible allocations, the computation of an allocation that is D-optimal for inferences on the treatment means will generally not be practical. Good allocations can be constructed by a multistage procedure that allocates one or more units at each stage. These allocations can be made even better by applying an iterative algorithm that induces small changes in the design at each iteration. The techniques were applied to an example taken from the chemical industry and produced highly efficient allocations. Similar design techniques can be used in experimental situations where the experimental units become available and must be allocated in stages.
Application of QBA to Assess the Emotional State of Horses during the Loading Phase of Transport

Simple Summary The traditional approach to animal welfare has been primarily based on assessing negative factors. However, over the past few years, due to changes in societal beliefs and values and improved scientific understanding of animals, researchers are being asked to develop tools to identify positive animal-based measures and, more in general, to evaluate animals’ emotional states. Transportation, mainly the loading phase, is a crucial welfare concern in horses and the development, enhancement, and implementation of positive animal-based indicators during transport are of critical importance. This study aimed at investigating the accuracy of Qualitative Behaviour Assessment (QBA), using both free choice profiling and fixed list methodologies, in horses during the loading phase of transport. A total of 13 stakeholders were asked to assess different sets of videos of horses being loaded for road transport using both their own descriptors and a list of descriptors. Our results showed that giving first the possibility to observer to use their own descriptor is useful to the development and implementation of a fixed list with terms that perfectly fit with the specific conditions we want to evaluate. This would allow the observers to better understand and quantify each given descriptor and therefore obtain a good QBA accuracy level. Furthermore, a specific descriptors list was developed to be used in pre-transport loading situations. Abstract To identify feasible indicators to evaluate animals’ emotional states as a parameter to assess animal welfare, the present study aimed at investigating the accuracy of free choice profiling (FCP) and fixed list (FL) approach of Qualitative Behaviour Assessment (QBA) in horses during the loading phase of transport. A total of 13 stakeholders were trained to score 2 different sets of videos of mixed breed horses loaded for road transport, using both FCP and FL, in 2 sessions. Generalized Procustes Analysis (GPA) consensus profile explained a higher percentage of variation (80.8%) than the mean of 1000 randomized profiles (41.2 ± 1.6%; p = 0.001) for the FCP method, showing an excellent inter-observer agreement. GPA identified two main factors, explaining 65.1% and 3.7% of the total variation. Factor 1 ranging from ‘anxious/ to ‘calm/relaxed’, described the valence of the horses’ emotional states. Factor 2, ranging from ‘bright’ to ‘assessing/withdrawn’, described the arousal. As for FL, Principal Component Analysis (PCA) first and second components (PC1 and PC2, respectively), explaining on average 59.8% and 12.6% of the data variability, had significant agreement between observers. PC1 ranges from relaxed/confident to anxious/frightened, while PC2 from alert/inquisitive to calm. Our study highlighted the need for the use of descriptors specifically selected, throughout a prior FCP process for the situation we want to evaluate to get a good QBA accuracy level.
Over-the-Air Antenna Array Calibration for mmWave Hybrid Beamforming Systems Based on Monte Carlo Markov Chain Method

The antenna phase calibration is critical for the millimeter wave (mmWave) hybrid beamforming communication system. Especially, with the increasing number of antennas being deployed in future massive MIMO systems, it is unrealistic to calibrate each antenna path in a microwave anechoic chamber. This work proposes a phase calibration based on over-the-air (OTA) scheme for mmWave hybrid beamforming systems, where the calibration process is performed automatically by the online signal transmission. The challenge for the OTA-based phase calibration is the imperfect channel estimation due to the phase error. To solve the challenge, we simplify the channel estimation process by integrating the beam alignment and phase calibration process. Consequently, the phase calibration is formatted to an MMSE-criterion based optimization problem solved by the Monte Carlo Markov Chain (MCMC) with probabilistic conversion improved Gibbs sampling method. Furthermore, an alternating iteration algorithm is developed to calibrate phase deviation for both the transmitter and receiver. We also extend our method to solve the joint amplitude and phase calibration problem. According to the simulation result, the calibration results outperform existing methods when the signal-to-noise ratio is less than 15 dB. Besides, the proposed method exhibits a high beamforming gain during the calibration process, which paves the way for future massive device deployment in a practical system.
Understanding barriers to mental healthcare access by integrating stigma theory and the theory of planned behavior

ABSTRACT The inability to access needed mental healthcare can be a challenge for individuals situated in various social environments and can be understood and predicted via multiple theoretical perspectives. This paper explores access to mental healthcare through the lenses of stigma theory and the Theory of Planned Behavior (TPB) by presenting central tenants of the theories and a brief review of empirical evidence in support of the individual theories. The combined TPB and stigma theory model merges self-stigma with the TPB construct of attitude and public stigma within the construct of subjective norms. A case example illustrates the application of the combined model in understanding barriers to mental health treatment. Discussion of the case study within the combined theoretical framework also acknowledges how culture within the social environment could have impacted mental health outcomes. Thus, it is necessary to intervene in areas of attitude, subjective norms, perceived behavioral control and stigma to shape the social environment through policy, practice and research and to improve mental health.