Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Design of the Transmit Beampattern and Angular Waveform for Colocated MIMO Radar under a Constant Modulus Constraint

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency.
Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study

Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick–hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=−0.973, 95% CI −0.981,−0.962, p<0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.
Programming Robots by Demonstration Using Augmented Reality

The world is living the fourth industrial revolution, marked by the increasing intelligence and automation of manufacturing systems. Nevertheless, there are types of tasks that are too complex or too expensive to be fully automated, it would be more efficient if the machines were able to work with the human, not only by sharing the same workspace but also as useful collaborators. A possible solution to that problem is on human–robot interaction systems, understanding the applications where they can be helpful to implement and what are the challenges they face. This work proposes the development of an industrial prototype of a human–machine interaction system through Augmented Reality, in which the objective is to enable an industrial operator without any programming experience to program a robot. The system itself is divided into two different parts: the tracking system, which records the operator’s hand movement, and the translator system, which writes the program to be sent to the robot that will execute the task. To demonstrate the concept, the user drew geometric figures, and the robot was able to replicate the operator’s path recorded.
Distributed Transmit Power Control for Energy-Efficient Wireless-Powered Secure Communications

In this study, we consider energy-efficient wireless-powered secure communications, in which N sets of transmitter, receiver, and energy harvesting (EH) nodes exist; each EH node is allowed only to harvest energy from the transmitted signals but is not to permitted to decode information. To maximize the sum secrecy energy efficiency (SEE) of the node sets while ensuring minimum EH requirement for each EH node, we propose a distributed transmit power control algorithm using a dual method, where each transmitter adjusts its transmit power iteratively until convergence without sharing information with the other node sets. Through simulations under various environments, we show that the proposed scheme surpasses conventional schemes in terms of the sum SEE and has significantly reduced computation time compared with the optimal scheme, which suggests the effectiveness and applicability of the proposed distributed method.
P300 Brain–Computer Interface-Based Drone Control in Virtual and Augmented Reality

Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain–computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations.
Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction

Arctic sea ice plays a significant role in climate systems, and its prediction is important for coping with global warming. Artificial intelligence (AI) has gained recent attention in various disciplines with the increasing use of big data. In recent years, the use of AI-based sea ice prediction, along with conventional prediction models, has drawn attention. This study proposes a new deep learning (DL)-based Arctic sea ice prediction model with a new perceptual loss function to improve both statistical and visual accuracy. The proposed DL model learned spatiotemporal characteristics of Arctic sea ice for sequence-to-sequence predictions. The convolutional neural network-based perceptual loss function successfully captured unique sea ice patterns, and the widely used loss functions could not use various feature maps. Furthermore, the input variables that are essential to accurately predict Arctic sea ice using various combinations of input variables were identified. The proposed approaches produced statistical outcomes with better accuracy and qualitative agreements with the observed data.
On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Within the last few decades, the need for subject authentication has grown steadily, and biometric recognition technology has been established as a reliable alternative to passwords and tokens, offering automatic decisions. However, as unsupervised processes, biometric systems are vulnerable to presentation attacks targeting the capture devices, where presentation attack instruments (PAI) instead of bona fide characteristics are presented. Due to the capture devices being exposed to the public, any person could potentially execute such attacks. In this work, a fingerprint capture device based on thin film transistor (TFT) technology has been modified to additionally acquire the impedances of the presented fingers. Since the conductance of human skin differs from artificial PAIs, those impedance values were used to train a presentation attack detection (PAD) algorithm. Based on a dataset comprising 42 different PAI species, the results showed remarkable performance in detecting most attack presentations with an APCER = 2.89% in a user-friendly scenario specified by a BPCER = 0.2%. However, additional experiments utilising unknown attacks revealed a weakness towards particular PAI species.
Lunar Surface Fault-Tolerant Soft-Landing Performance and Experiment for a Six-Legged Movable Repetitive Lander

The cascading launch and cooperative work of lander and rover are the pivotal methods to achieve lunar zero-distance exploration. The separated design results in a heavy system mass that requires more launching costs and a limited exploration area that is restricted to the vicinity of the immovable lander. To solve this problem, we have designed a six-legged movable repetitive lander, called “HexaMRL”, which congenitally integrates the function of both the lander and rover. However, achieving a buffered landing after a failure of the integrated drive units (IDUs) in the harsh lunar environment is a great challenge. In this paper, we systematically analyze the fault-tolerant capacity of all possible landing configurations in which the number of remaining normal legs is more than two and design the landing algorithm to finish a fault-tolerant soft-landing for the stable configuration. A quasi-incentre stability optimization method is further proposed to increase the stability margin during supporting operations after landing. To verify the fault-tolerant landing performance on the moon, a series of experiments, including five-legged, four-legged and three-legged soft-landings with a vertical landing velocity of −1.9 m/s and a payload of 140 kg, are successfully carried out on a 5-DoF lunar gravity ground-testing platform. The HexaMRL with fault-tolerant landing capacity will greatly promote the development of a next-generation lunar prober.
SVG-Loop: Semantic-Visual-Geometric Information-Based Loop Closure Detection

Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level image information and multi-information fusion can improve the robustness of place recognition, a semantic–visual–geometric information-based loop closure detection algorithm (SVG-Loop) is proposed in this paper. In detail, to reduce the interference of dynamic features, a semantic bag-of-words model was firstly constructed by connecting visual features with semantic labels. Secondly, in order to improve detection robustness in different scenes, a semantic landmark vector model was designed by encoding the geometric relationship of the semantic graph. Finally, semantic, visual, and geometric information was integrated by fuse calculation of the two modules. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference.
A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.
Multi-Level Graph Representation for Big Data Arising in Science Mapping (Dagstuhl Seminar 21152)

This report documents the program and the outcomes of Dagstuhl Seminar 21152 “Multi-Level Graph Representation for Big Data Arising in Science Mapping. ” The seminar brought together researchers coming from information visualization, psychology, cognitive science, human-computer interaction, graph drawing, computational geometry, and cartography with interests in the “science of science” to discuss novel graph mining and layout algorithms and their application to the development of science mapping standards and services. Due to the pandemic, this was a “hybrid” event with only 5 in-person participants and 25 by-zoom participants from over ten different countries and at least five different time-zones. There were three overview talks and four special presentations (evening webinars) from different communities represented in the seminar. Abstracts of these talks and presentations are collected in this report. Three working groups formed around open research problems related to the seminar topic and we report about their findings.
Proteome and Nutritional Shifts Observed in Hordein Double-Mutant Barley Lines

Lysine is the most limiting essential amino acid in cereals, and efforts have been made over the decades to improve the nutritional quality of these grains by limiting storage protein accumulation and increasing lysine content, while maintaining desired agronomic traits. The single lys3 mutation in barley has been shown to significantly increase lysine content but also reduces grain size. Herein, the regulatory effect of the lys3 mutation that controls storage protein accumulation as well as a plethora of critically important processes in cereal seeds was investigated in double mutant barley lines. This was enabled through the generation of three hordein double-mutants by inter-crossing three single hordein mutants, that had all been backcrossed three times to the malting barley cultivar Sloop. Proteome abundance measurements were integrated with their phenotype measurements; proteins were mapped to chromosomal locations and to their corresponding functional classes. These models enabled the prediction of previously unknown points of crosstalk that connect the impact of lys3 mutations to other signalling pathways. In combination, these results provide an improved understanding of how the mutation at the lys3 locus remodels cellular functions and impact phenotype that can be used in selective breeding to generate favourable agronomic traits.
Maternal Circulating Exosomal miRNAs as Non-invasive Biomarkers for the Prediction of Fetal Ventricular Septal Defect

Objective: This study aimed to identify maternal circulating exosomal miRNAs as potential non-invasive biomarkers for the early detection of fetal ventricular septal defects (VSDs). Methods: In total, 182 pregnant women, comprising 91 VSD cases and 91 matched controls, were included in this study. Exosomes were isolated; dysregulated exosomal miRNAs were profiled using next-generation sequencing. Differential abundance of miRNAs was verified using quantitative real-time polymerase chain reaction (qRT-PCR). Diagnostic accuracy was evaluated by constructing receiver operating characteristic (ROC) curves. Results: In total, 77 serum exosomal miRNAs were found to be differentially expressed in the VSD group compared to their expression in the control group. Among these, five downregulated exosomal miRNAs were validated using qRT-PCR. hsa-miR-146a-5p was identified to be capable of distinguishing VSD cases from controls (area under the ROC curve [AUC]: 0.997; p < 1.00E-05). Conclusion: Circulating exosomal miRNAs, particularly hsa-miR-146a-5p, may be predictive biomarkers for the non-invasive prenatal diagnosis of fetal VSDs.
Decision Models and Technology Can Help Psychiatry Develop Biomarkers

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.
Disease-Associated Gut Microbiota Reduces the Profile of Secondary Bile Acids in Pediatric Nonalcoholic Fatty Liver Disease

Children with nonalcoholic fatty liver disease (NAFLD) display an altered gut microbiota compared with healthy children. However, little is known about the fecal bile acid profiles and their association with gut microbiota dysbiosis in pediatric NAFLD. A total of 68 children were enrolled in this study, including 32 NAFLD patients and 36 healthy children. Fecal samples were collected and analyzed by metagenomic sequencing to determine the changes in the gut microbiota of children with NAFLD, and an ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system was used to quantify the concentrations of primary and secondary bile acids. The associations between the gut microbiota and concentrations of primary and secondary bile acids in the fecal samples were then analyzed. We found that children with NAFLD exhibited reduced levels of secondary bile acids and alterations in bile acid biotransforming-related bacteria in the feces. Notably, the decrease in Eubacterium and Ruminococcaceae bacteria, which express bile salt hydrolase and 7α-dehydroxylase, was significantly positively correlated with the level of fecal lithocholic acid (LCA). However, the level of fecal LCA was negatively associated with the abundance of the potential pathogen Escherichia coli that was enriched in children with NAFLD. Pediatric NAFLD is characterized by an altered profile of gut microbiota and fecal bile acids. This study demonstrates that the disease-associated gut microbiota is linked with decreased concentrations of secondary bile acids in the feces. The disease-associated gut microbiota likely inhibits the conversion of primary to secondary bile acids.
Microfluidic Preparation of Janus Microparticles With Temperature and pH Triggered Degradation Properties

Based on the phase separation phenomenon in micro-droplets, polymer-lipid Janus particles were prepared on a microfluidic flow focusing chip. Phase separation of droplets was caused by solvent volatilization and Janus morphology was formed under the action of interfacial tension. Because phase change from solid to liquid of the lipid hemisphere could be triggered by physiological temperature, the lipid hemisphere could be used for rapid release of drugs. While the polymer we selected was pH sensitive that the polymer hemisphere could degrade under acidic conditions, making it possible to release drugs in a specific pH environment, such as tumor tissues. Janus particles with different structures were obtained by changing the experimental conditions. To widen the application range of the particles, fatty alcohol and fatty acid-based phase change materials were also employed to prepare the particles, such as 1-tetradecanol, 1-hexadecanol and lauric acid. The melting points of these substances are higher than the physiological temperature, which can be applied in fever triggered drug release or in thermotherapy. The introduction of poly (lactic-co-glycolic acid) enabled the formation of multicompartment particles with three distinct materials. With different degradation properties of each compartment, the particles generated in this work may find applications in programmed and sequential drug release triggered by multiple stimuli.