Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential regulatory role of miRNA and mRNA link to metabolism affected by chronic intermittent hypoxia

Aim: Intermittent hypoxia (IH) is the prominent feature of obstructive sleep apnea (OSA) pathophysiology, which is an in dependent risk factor of cardiovascular complications. The effects of IH on adipocyte metabolism were explored by high-throughput sequencing technology. Methods: Plasma was collected from OSA patients and control group to perform mRNA sequencing. 3T3-L1 cells were differentiated into adipocytes then subjected to a 5%–21% O2 hypoxic environment (IH) for 24 h. High-throughput sequencing method was used to determine differential mRNA and miRNA patterns in fat cells exposed to IH. We then performed Gene Ontology (GO) analysis, identified relevant KEGG pathways and miRNA-target-pathways. Results: Sequencing data showed that OSA affected the expression of 343 mRNAs in the plasma. At the same time, we found that IH affected the expression of 3034 mRNAs in the adipocytes. In addition, 68 differentially expressed mRNAs were overlapped in plasma from OSA patient and IH-induced adipocyte model. We observe that 68 differential genes could be connected to 49 reciprocally expressed miRNAs. We showed that IH significantly reduced the expression of miR-182-5p and miR-30c-2-3p. KEGG predicted that the function of expressed miR-182-5p and miR-30c-2-3p was enriched to AKT signaling pathway. Notably, IH activated PI3K/AKT pathway in fat cells. Conclusion: Our results demonstrated that IH might induce adipocyte metabolism by regulating miR-182-5p and miR-30c-2-3p.
A Case for Line-Of-Sight Blockage Detection as a Primitive in Millimeter-Wave Networks

Millimeter-wave is the core technology for current wireless standards to enable multi-Gbps throughput and ultra-low latency connectivity. However, designing reliable mmWave networks that provide high quality of service is challenging because of the extreme sparsity of the mmWave channel and frequent unavailability of the Line-Of-Sight (LOS) path. To this end, we propose a primitive to detect the state of the LOS path across the entire mmWave frequency band in afrequency-agnostic manner. The primitive leverages two key observations: under open LOS, the path is correlated between any two mmWave frequencies; and under the blockage, such correlation disappears. Such a primitive allows a mmWave picocell to achieve the latency of handover below sub-millisecond consistently, change its PHY, MAC, and Network layer policies at run-time adaptively, and ensure the link doesn't suffer from outage proactively. We have validated this primitive on three distinct mmWave frequency bands: 24 GHz; 77 GHz; and 122 GHz, and in the future, we propose to extend the validation to several mmWave frequencies, up to 300 GHz. We also discuss the system-level challenges pertaining to design and implementation of such a primitive for a practical, flexible mmWave networking stack.
Multimodal contrastive learning for unsupervised video representation learning

In this paper, we propose a multimodal unsupervised video learning algorithm designed to incorporate information from any number of modalities present in the data. We cooperatively train a network corresponding to each modality: at each stage of training, one of these networks is selected to be trained using the output of the other networks. To verify our algorithm, we train a model using RGB, optical flow, and audio. We then evaluate the effectiveness of our unsupervised learning model by performing action classification and nearest neighbor retrieval on a supervised dataset. We compare this triple modality model to contrastive learning models using one or two modalities, and find us-ing all three modalities in tandem provides a 1.5% improvement in UCF101 classification accuracy, a 1.4% improvement in R@1 retrieval recall, a 3.5% improvement in R@5 retrieval recall, and a 2.4% improvement in R@10 retrieval recall as compared to using only RGB and optical flow, demonstrating the merit of utilizing as many modalities as possible in a cooperative learning model.
Improving Phonetic Transcriptions of Children's Speech by Pronunciation Modelling with Constrained CTC-Decoding

Language sample analysis (LSA) is a powerful tool for both therapeutic applications and research of child speech and language development. Nevertheless, it is not routinely used, due to the high cost of manual transcription and analysis. Assistance by automatic speech recognition for children has the potential to enable a wide-spread use of LSA. However, the development of modern speech recognition systems heavily relies on large scale datasets. Therefore, it faces the same obstacle of high cost for transcription as LSA itself. In this paper, we study how cheaply transcribed child speech, i.e., limited to an ortho-graphic transcription, can be improved on a phonetic level by leveraging a CTC based automatic speech recognition model, trained on a small phonetically transcribed dataset. We constrain the CTC decoding by modeling variation of the pronunciation given the orthographic transcription using weighted ﬁnite state automata. Our experiments show that the transcription is improved in terms of phone error rate by relative 14% when applying our method. Additionally, we show how the improved transcript can in turn be leveraged to improve the training of a new model.
The Effects of Framing Persuasive Messages about Vaping The Effects of Framing Persuasive Messages about Vaping

The effectiveness of persuasive messages is largely dependent on multiple underlying features of the message such as message framing, behavioral context, and value alignment. I implemented the current study as a 2 X 2 (Message Valence by Message Language) within-subjects design that evaluated effective persuasive messages by manipulating message valence into gain-framed or loss-framed statements in combination with manipulating message language into dogmatic language or suggestive language. I presented a counterbalanced sequence of message blocks with ratings for message persuasiveness, freedom threats, and counterarguments at the end of each message block to serve as measurements. My results revealed a significant interaction between message valence and message language on message persuasiveness and freedom threats. There was also a significant main effect of message language on message persuasiveness, freedom threats, and counterarguments. I concluded that individual receptivity to effective persuasive messages is overall dependent on whether the message instills the perception that one’s freedom is threatened as well as whether the message aligns with one’s self-concept.
Extending the animosity model in times of the COVID-19 pandemic: A cross-national validation of the health animosity scale

: The concept of consumer animosity encompasses factors that influence consumers’ negative attitudes and feelings toward other countries, which decrease consumers' buying behavior concerning foreign products. However, the impact of health concerns has been widely neglected in the literature on consumer animosity. To fill this void, this paper extends the established animosity models by introducing the new concept of health animosity and by developing a health animosity scale. The paper empirically validates the new scale in a multi-national study in the U.S., Germany, Brazil, and India. The scale measures the respondents’ health animosity toward China, as many consumers worldwide blame China for the COVID-19 (coronavirus disease) pandemic and criticize the government’s misbehavior during the outbreak. The analyses confirm suitable convergent and discriminant validity of the newly developed scale for most of the countries. The paper extends the understanding on consumer animosity, as not only incidents due to single nations' concrete purposes (e.g., military, economic or political actions) but also natural catastrophes and the reactions of single nations can cause feelings of animosity. The research implications outline directions for future studies, which could investigate the consequences of health animosity.
A Qualitative Analysis of the Challenges and Threats facing Cooperative Extension at the County Level

The Cooperative Extension Service faced many challenges and threats over its history. Persistent internal challenges affecting Extension are communication with stakeholders, high employee turnover, and a marketing deficit. Common external threats included reduced funding, increases in non-traditional audiences, and inadequate facilities. Guided by a needs assessment framework, this study sought to identify the challenges and threats facing the [State] Cooperative Extension Service at the county level. Using a basic qualitative research design, the final reports from the 2016 county program reviews were used as the primary source of data. Results were consistent with the literature and showed persistent challenges facing Extension were staff limitations, marketing and communication ineffectiveness, limited program coverage, inadequate volunteer recruitment, and lack of funding. Threats found were unresponsiveness to changing audiences and inadequate facilities. It is recommended that Extension’s strategic plan focus on priority areas while emphasizing the urgency needed to close identified gaps in marketing and communication, staff development and retention, and program quality, delivery, and coverage. Steps are needed to improve its performance by efficiently allocating resources to serve a diverse clientele, strengthen agents’ programmatic areas, and build strong partnerships with relevant organizations.
Stripe-based connected components labelling

A fast stripe-based connected component labelling algorithm is proposed for binary image labelling. Stripe extraction strategy is used to transform the pixel-connected issues, which most of the previously proposed algorithms focused on, into stripe-connected issues. The stripe-union strategy treats the combination of the neighbouring stripes as the mergence of rooted trees. Finally, comparisons are performed with other famous fast labelling algorithms. The proposed algorithm has shown better performance than the state-of-the-art algorithm in real images test, while the auxiliary memory is not required at all compared with all competitors.
A Joint Particle Filter for Quaternion-Valued $\alpha$-Stable Signals via the Characteristic Function

The filtering paradigm is revisited through the perspective of characteristic functions. This results in the derivation of a novel particle filtering technique for sequential estimation/tracking of quaternion-valued $\alpha$-stable random signals. Importantly, the derived particle filter incorporates an efficient information fusion format and collaborative/distributed estimation framework to accommodate the push toward use of sensor networks. The distributed setting provides for the distribution of computational complexity among agents of a sensor network, while allowing each agent to retain an estimate of the state. Furthermore, the quaternion-valued structure allows for the derivation of a rigorous algorithm that is advantageous when dealing with signals of a multidimensional nature commonly encountered in sensor arrays.
Relationship between Precipitation Core Behavior in Cumulonimbus Clouds and Surface Rainfall Intensity on 18 August 2011 in the Kanto Region, Japan

This paper analyzes the behavior of the precipitation cores (PCOs) in three cumulonimbus clouds that caused localized heavy rainfall on 18 August 2011 in the Kanto region, Japan, and their relationship with temporal variations in surface rainfall intensity. The 3D structure of the cumulonimbus clouds was observed at 2-min intervals using a research X-band dual-polarization radar. A PCO was defined as a 3D contiguous region that contained one local maximum of horizontal radar reflectivity Z h , and the PCOs were automatically detected using adaptive thresholds. Subjective tracking procedures using the PCO dataset, which was based on observations made every 2 min, identified 15 PCOs during the total lifespan of the three cumulonimbus clouds. The PCOs generally descended toward the ground after their appearance aloft. The average appearance height of the PCOs of 5.25 km above sea level (ASL) was slightly above the ambient 0°C level (5.1 km ASL). The duration of each PCO was roughly proportional to its appearance height. Of the 12 temporal peaks at maximum surface rainfall intensity (> 10 mm h −1 ) recorded from the three cumu lonimbus clouds, 10 were associated with the descent of PCOs. In each cumulonimbus cloud, the first PCO was detected 10–12 min before rainfall heavier than 10 mm h −1 was recorded. These results indicate that the behavior of PCOs is closely related to the onset of strong surface rainfall and subsequent fluctuations in surface rainfall intensity.
Depth Recognition of Hard Inclusions in Tissue Phantoms for Robotic Palpation

Medical palpation is an effective diagnosis method in which physicians use tactile sensation to diagnose a patient’s pathology. Robotic palpation is a novel technique that leverages robots to assist medical diagnosis. The problem of tactile information loss in Robot-assisted Minimally Invasive Surgery (RMIS) has limited the development of the robot-assisted surgical system. Meanwhile, surgeons are difficult to acquire some key information about lesions only via visual feedback, such as tumor depth. To address the issue, we propose a tactile perception algorithm on the basis of the CNN-LSTM network, which achieves the depth recognition of hard inclusions in tissue phantoms. The method realizes the classification of twelve depths of hard inclusions. In addition, due to using hypergeometric distribution encoding, the proposed method can exploit the ordinal information of the labels to significantly improve the recognition accuracy rate. The experimental results on 720 real tactile data show that the average recognition rate is 96.45%. Compared with other state-of-the-art methods, the recognition accuracy rate of the proposed algorithm is the highest.
GAN - Based Medical Image Registration for Augmented Reality Applications

Recently, with the development of AR/VR technology, boosting virtual information to the real world has been applied to various fields to increase convenience. In particular, in the medical field, different types of image information, such as X-ray, CT, and MRI data are used in surgery with AR devices for medical diagnosis and analysis of the cause of a patient's disease. This paper proposes an approach by which to explore a patient's posture and joints to match X-ray image information in an AR environment using deep learning networks such as GAN along with pose estimation. Thereby, we employ the CP-VTON+ virtual try-on network architecture to map chest X-ray information to the patient's body. Finally, we compare the try-on results of the chest X-ray image of the patient's body using the proposed method and CP-VTON+. The mean SSIM value of the proposed method is 0.0272 higher than that of CP-VTON+, and the mean PSNR value is 5.49 higher than that of CP-VTON+. The proposed method is more appropriate for application to AR devices for medical diagnosis and analysis due to the characteristics of medical images, as even minor misdiagnoses can lead to fatalities.
Efficient Computation of Instantons for Multi-Dimensional Turbulent Flows with Large Scale Forcing

Extreme events play a crucial role in fluid turbulence. Inspired by methods from field theory, these extreme events, their evolution and probability can be computed with help of the instanton formalism as minimizers of a suitable action functional. Due to the high number of degrees of freedom in multi-dimensional fluid flows, traditional global minimization techniques quickly become prohibitive in their memory requirements. We outline a novel method for finding the minimizing trajectory in a wide class of problems that typically occurs in turbulence setups, where the underlying dynamical system is a non-gradient, non-linear partial differential equation, and the forcing is restricted to a limited length scale. We demonstrate the efficiency of the algorithm in terms of performance and memory by computing high resolution instanton field configurations corresponding to viscous shocks for 1D and 2D compressible flows.
Robust State Estimation in the Presence of Stealthy Cyberattacks

State estimation is an integral component of autonomous Cyber-Physical Systems (CPSs) which require an estimate of the system state for their navigation and control logic. As the sensors used for state estimation may be prone to cyberattacks, they are typically accompanied by an on-board fault detection system which attempts to detect cyberattacks injected through the sensors. However, stealthy cyberattacks can remain undetected by the fault detection system and could cause catastrophic failure of the CPS. In this paper, we characterize the vulnerability of state estimation to such attacks and propose a cyberattack-resilient state estimation algorithm. The proposed algorithm is designed in conjunction with the fault detection system, and is able to mitigate the impact of stealthy cyberattacks and achieve robust state estimation performance across various system specifications and attack scenarios. We demonstrate this fact through theoretical analysis as well as using numerical simulations.
The rise and decline of cold fusion

We are all ecologists now, so when Martin Fleischmann and Stanley Pons announced on 23 March 1989 that at Utah, they had caused deuterium ions to fuse giving out heat using electrolysis in a simple cell at room temperature – cold fusion – we all wanted to believe it. At first we were a bit sceptical, but then came more information – they had measured excess heat and observed neutrons, gammas, and tritium. And next day there was independent confirmation from Steve Jones of nearly Brigham Young University. Other confirmations followed quickly. The early days of April were the high point when perhaps 500 million people had heard of cold fusion, Fleischmann and Pons, and had dreams of sea water yielding limitless amounts of heavy water that could provide energy without pollution.
Access Control and Deployment Design for Multi-UAV Assisted Wireless Networks

In this letter, we investigate the ground user (GU) access control and unmanned aerial vehicle base station (UAV-BS) deployment design in a multiple UAV-BSs assisted wireless network. The objective of each GU is to maximize its own throughput, and the objective of each UAV-BS is to maximize the minimum throughput of GUs served by it. Firstly, we optimize the GU access control by game theory. The access control game is proved to be an exact potential game, and we use the best response algorithm to solve the Nash equilibrium (NE). Then, we optimize the UAV-BS deployment design through convex optimization theory. The simulation results demonstrate that the proposed approach outperforms other five baselines.
VHERS: A Novel Variational Mode Decomposition and Hilbert Transform-Based EEG Rhythm Separation for Automatic ADHD Detection

Attention deficit hyperactivity disorder (ADHD) is an isogenous pattern of hyperactivity, impulsivity, and inattention, resulting in disorders like anxiety, disability in learning, and depression. The electroencephalogram (EEG) signals are a valuable source for early detection of ADHD. However, EEG’s nonlinear and nonstationary nature makes its direct analysis very difficult. Different rhythms of EEG offer a robust solution for the automatic detection of ADHD. Therefore, a novel variational mode and Hilbert transform (HT)-based EEG rhythm separation (VHERS) is developed. The instantaneous frequency envelops (IFE) and instantaneous amplitude (IA) are extracted using variational mode decomposition (VMD) and HT. The delta, theta, alpha, beta, and gamma rhythms are constructed from the corresponding IFE and IA. Different entropy-based features are evaluated from the rhythms, selected using statistical analyses [mean and standard deviation (STD)], and classified using multiple techniques. The proposed VHERS has obtained the highest performance of 100% sensitivity (SNST), 99.95% accuracy (ACCY), a specificity (SPFC) of 99.89%, Cohen’s Kappa (KAPPA) of 99.9%, the precision (PRCS) of 99.91%, $F$ -1 score of 0.999, Mathews correlation coefficient (MCC) of 99.9%, and area under the curve of 99.95%, respectively, using a sigmoid kernel of an extreme learning machine (ELM) classifier. The performance shows that the delta rhythm has provided more insights into ADHD and NC EEG signals. The degraded performance for gamma rhythm is due to the overlapping nature of the ADHD and NC EEG features. The proposed VHERS model can help experts to detect ADHD in real-time scenarios.
A Nonlinear Predictive Control Strategy for Landing on an Asteroid

Autonomous operation of spacecraft near small bodies requires advanced Guidance, Navigation, and Control (GNC) capabilities to ensure safety and to enable scientific and economic activity. This paper proposes a Nonlinear Model Predictive Control (NMPC) strategy in the context of an autonomous descent and landing mission to the surface of the asteroid Eros 433 as part of a complete GNC architecture. The GNC architecture integrates the NMPC based translational controller with an attitude control system, reference trajectory generator, feature point-based state estimator, and gravity disturbance compensator. Simulation results using only output feedback from a high-fidelity simulator of Eros 433 that includes a gravity model, nonadditive measurement noise, and impulsive thrust model demonstrates the effectiveness of the proposed strategy.
D-CenterNet: An Anchor-Free Detector With Knowledge Distillation for Industrial Defect Detection

Lightweight anchor-free detectors are currently gaining more and more popularity in the field of industrial defect detection. In general, it is difficult to achieve competitive performance compared to deep anchor-based detector. Knowledge distillation is an effective way to solve this problem. In this article, we propose a novel anchor-free method based on knowledge distillation, called D-CenterNet. In our method, we employ an optimization approach using an adaptive label encoding strategy to better regress the centroid information of extreme aspect ratio (EAR) defects and improve detection accuracy. Backbone is improved for industrial defects with large aspect ratios and complex backgrounds, and strip pooling (SP) module is introduced to extract image features more efficiently. By redefining the distilled knowledge as the interrelationship between the output information, we train the student model using the pseudo-label generated by the teacher model, and then improve the model using the real label data to further enhance the performance of the student model without increasing model complexity. The experimental results on Aliyun Tianchi fabric dataset demonstrate that D-CenterNet achieves 75.5% mean average precision (mAP) and increases the mAP of the student model (60.1%, before distillation) to 63.8% (after distillation) without increasing the complexity of the model. Extensive experimental results validate the superiority of our method and demonstrate that our D-CenterNet achieves state-of-the-art (SOTA) performance in both accuracy and real-time performance.