Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Collaborative Sparsity Structure via Nonconvex Optimization for Feature Recognition

This paper aims to unveil a collaborative sparsity structure for rigorously describing the universal self-similarity property of mechanical feature information, which is an important task in the field of adaptive feature recognition. The self-similarity pattern among all local feature segments is first highlighted by an elaborately designed partition strategy, and then a row-wise group sparsity penalty is enforced under an appropriate dictionary to effectively capture the latent self-similarity features from noisy observations. Incorporating dictionary learning techniques, a collaborative sparsity learning model (CSLM) is further proposed, and meanwhile solved by a nonconvex optimization solver generated from a block proximal gradient descend framework. Moreover, the convergence property and computational complexity of the developed solver are discussed comprehensively. The advantage of this model is to adaptively achieve a satisfying sparse level to concentrate the underlying feature information and simultaneously enforce that all segments share a same active atom set to retain the desired self-similarity pattern. The proposed CSLM is profoundly evaluated through implementing feature detection for wind turbine gearbox, and it shows superior performances to many state-of-the-art feature recognition techniques.
Neuropsychological assessment of Chilean children with a history of extreme prematurity: An exploratory study

Abstract This study was conducted to explore the neuropsychological abilities of premature Chilean children. Two groups (Premature and Control, 10 children each, age ranging from 5 to 7.11) were established based on weeks of gestation and/or weight at birth. Relevant variables such as age, gender, schooling, and socioeconomic level were matched considering Chile’s particular demographic context. Children were assessed by means of the Evaluación Neuropsicológica Infantil (ENI-2) battery, measuring nine cognitive domains encompassing 23 subscales. In turn, subscales are grouped in two scales: Cognitive Functions and Executive Functions. Since the ENI-2 battery provides norms for Spanish-speaking children, obtained data were inspected both for possible between-group differences and either adjustment or deviance from average range. Results show that premature children perform within typical ranges in all subscales except for Visual attention and Graphic fluency. When comparing both groups, some differences emerged. These differences are most prominent in subscales related to visuoperceptual skills. Interestingly, between-group linguistic performance is very similar. The point is made that early linguistic interventions conducted on premature children seem to positively impact on oral language expression and comprehension. On the contrary, early interventions focused on visuospatial abilities did not seem to attain the same impact. This may be a consequence of visual-information processing problems derived from cortical dorsal stream’s vulnerability, which literature correlates with prematurity.
Relaxed Stability Criteria for Neural Networks with Time-Varying Delay

This paper investigates the stability of neural networks with time-varying delays, and a new relaxed technique in constructing the Lyapunov-Krasovskii functional (LKF) is proposed. All the matrices in this novel LKF are not necessarily required to be positive definite, while some of the matrices could be symmetric instead. A less conservative criterion is obtained in the form of linear matrix inequality (LMI), based on the proposed technique in our constructed LKF and free-matrix-based integral inequality. A numerical example is given to illustrate the less conservatism of the proposed method.
Robust Self-Interference Cancellation for Microstrip Antennas by Means of Phase Reconfigurable Coupler

We propose a robust self-interference cancellation (SIC) technique for microstrip antennas. A microstrip antenna operates as a transmitter located between two identical receiver antennas having orthogonal polarization with respect to each other. The microstrip antennas show out-of-phase electric field distribution in the H-plane. We exploit this characteristic to cancel self-interference (SI) signals through a rat-race circuit, and we achieved more than 50 dB (60 dB) SIC over 180 MHz (120 MHz) at 2.45 GHz. Furthermore, we propose a new phase-reconfigurable rat-race circuit to adjust the SI signals phase response. The proposed technique is robust and resilient to the potential phase offset of SI signals caused by fabrication errors or scatterers nearby the antennas. As a result, the 50 dB (60 dB) SIC bandwidth spreads over 280 MHz (150 MHz). This technique is a good candidate for in-band full-duplex communication systems with dimensions of <inline-formula> <tex-math notation="LaTeX">$\mathbf {1.4\lambda _{0}\times 0.4\lambda _{0}\times 0.05\lambda _{0}}$ </tex-math></inline-formula> and antennas edge-to-edge separation <inline-formula> <tex-math notation="LaTeX">$\mathbf {<\lambda _{0}/10}$ </tex-math></inline-formula>, and is easily applied to other frequency bands.
Inferring disease-associated long non-coding RNAs using genome-wide tissue expression profiles

MOTIVATION Long non-coding RNAs (lncRNAs) are important regulators in wide variety of biological processes, which are linked to many diseases. Compared to protein-coding genes (PCGs), the association between diseases and lncRNAs is still not well studied. Thus, inferring disease-associated lncRNAs on a genome-wide scale has become imperative. RESULTS In this study, we propose a machine learning-based method, DislncRF, which infers disease-associated lncRNAs on a genome-wide scale based on tissue expression profiles. DislncRF uses random forest models trained on expression profiles of known disease-associated PCGs across human tissues to extract general patterns between expression profiles and diseases. These models are then applied to score associations between lncRNAs and diseases. DislncRF was benchmarked against a gold standard dataset and compared to other methods. The results show that DislncRF yields promising performance and outperforms the existing methods. The utility of DislncRF is further substantiated on two diseases in which we find that top scoring candidates are supported by literature or independent datasets. AVAILABILITY AND IMPLEMENTATION https://github.com/xypan1232/DislncRF. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
A Low-Cost Dual-Mode Frequency Multiplier Based on a Novel Five-Port Ring Coupler

This letter presents a low cost dual-mode frequency multiplier that achieves a frequency doubler and tripler simultaneously with high performance. A five-port ring coupler is employed for the dual-mode frequency multiplier application. The operating principle of the dual-mode frequency multiplier is described and analyzed. Based on it, a 10–20/30 GHz multiplier is designed and fabricated. Measured results demonstrate that the conversion loss of the doubler and tripler is 10–12.5 and 20.4–23.6 dB, respectively, within the input frequency range from 9 to 10.8 GHz.
Parallel Multi-Island Genetic Algotirth for Sorting Unsigned Genomes by Reversals

Sorting unsigned permutations by reversals is an NP-hard optimization problem with applications in computational molecular biology. Several approximation and metaheuristic algorithms were proposed, among them, in a previous work, a competitive genetic algorithm and its parallel version using island models were proposed. In this paper, focusing on improving accuracy, new island models are proposed by diversifying the distribution of genetic material between islands through static and dynamic communication topologies. In static topologies, communication between islands is predefined and maintained during the computation, while in dynamic topologies the communication is continuously modified. The proposed island models use parallelism in a global and a local level, in which respectively, the exchange of individuals between islands and the fitness computation occurs. Results from the experiments performed with randomly generated synthetic permutations show that parallel island models using both dynamic and static communication topologies outperform parallel approaches found in the literature in terms of run-time as well as accuracy.
A Simple Robust Stabilization Control Law of Underactuated Surface Vessels

In this paper, a new time-varying static feedback control law is proposed solving the stabilization control problem of underactuated surface vessels. The control idea comes from the driving experience of human. By employing Lyapunov's direct method and Barbalat's lemma, it is proved strictly that the proposed controller globally asymptotically stabilizes the underactuated surface vessel to desired constant location and orientation, and forces its velocities to zero. The resulting controller is just a very simple linear function of forward/lateral position and orientation errors, without any modeling parameter. Compared with the previous work, the controller is more practical in real-word applications because of the unneeded velocity sensors and the robust property with respect to model parameters. Effectiveness of the proposed controller is demonstrated by numerical simulation.
Arithmetic Operations on Triangular Fuzzy Numbers via Credibility Measures: An Inverse Distribution Approach

With extensive applications of fuzzy numbers, many methods for fuzzy arithmetics especially the basic operations have been developed based on Zadeh’s extension principle. Among these methods, the interval arithmetic approach and the standard approximation method are the most important and commonly used exact and approximate methods, respectively. In this paper, regarding the continuous and strictly monotone functions of triangular fuzzy numbers, we propose an inverse distribution approach to deriving the exact or well approximate membership functions for arithmetic results by embedding the credibility measure of fuzzy sets into fuzzy arithmetics. Besides, some non-complicated and complicated examples are given to illustrate the performance of the new approach, together with a detailed comparison with the interval arithmetic approach and the standard approximate method. Furthermore, the inverse distribution approach is also applied to the fuzzy system reliability calculation based on fault tree compared with serval current related researches.
Propensity-matched comparison of vascular closure devices after transcatheter aortic valve replacement using MANTA versus ProGlide.

AIMS Vascular and bleeding complications increase morbidity and mortality in transcatheter aortic valve replacement (TAVR). However, data regarding the efficacy and safety of the MANTA percutaneous vascular closure device (VCD) are scarce. The present study sought to compare VARC-2 complications between collagen plug-based closure using "MANTA" and suture-based closure using "ProGlide" to evaluate the efficacy of the novel MANTA VCD. METHODS AND RESULTS We performed a retrospective, propensity score-matched study to compare vascular and bleeding complications in 325 consecutive patients who underwent TAVR using MANTA and ProGlide. The 1:1 propensity score matching resulted in 111 matched pairs. For MANTA- versus ProGlide-treated patients, all-cause mortality (0% vs. 4%, p=0.02), vascular complications (14% vs. 21%, p=0.21), and bleeding complications (18% vs. 33%, p=0.01) were observed. Access-site vascular injury was significantly less frequent in patients who received MANTA versus ProGlide (8% vs. 17%, p=0.04). MANTA resulted in a significantly lower haemoglobin decrease (16.4 vs. 20.0 g/l, p=0.04) and shorter hospital stay after TAVR (3.3 vs. 5.8 days, p=0.02). It was also associated with fewer bleeding complications (OR 0.44, 95% CI: 0.23-0.83; p=0.01). Moreover, significant decreases of all endpoints were not seen across the procedure date tertiles in the MANTA group. CONCLUSIONS MANTA resulted in a significantly lower complication rate, especially for bleeding, than did ProGlide, despite the operators' inexperience in the use of MANTA.
Taguchi optimization of Surface Plasmon Resonance-Kretschmann biosensor using FDTD

This paper reports on the optimization of full-width-at-half-maximum (FWHM) of the surface plasmon resonance (SPR) curve from a graphene-based SPR biosensor. The biosensor was designed in the Kretschmann configuration which is commonly known as the most effective technique for plasmon excitation using Lumerical's finite-difference-time-domain (FDTD) analysis. The performance of the SPR biosensor can be monitored by analysing the FWHM of the SPR curves where a smaller value is desired due to a narrower resonance peak which generally corresponds to a higher transmission power resulting in a more sensitive bio-detection. To optimise the biosensor's FWHM, Taguchi's L9 orthogonal array method was utilized involving four control factors at three level values using the objective function of Smaller-the-Better (STB) signal-to-noise ratio (SNR) characteristic. The results show that the FWHM improved to 1.1261°with best settings of A3B2C3D1. The Taguchi-optimized biosensor achieved 36.98% increase in sensitivity for urea detection.
Invariant Models for Causal Transfer Learning

Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the usual covariate shift assumption and assume that it holds true for a subset of predictor variables: the conditional distribution of the target variable given this subset of predictors is invariant over all tasks. We show how this assumption can be motivated from ideas in the field of causality. We focus on the problem of Domain Generalization, in which no examples from the test task are observed. We prove that in an adversarial setting using this subset for prediction is optimal in Domain Generalization; we further provide examples, in which the tasks are sufficiently diverse and the estimator therefore outperforms pooling the data, even on average. If examples from the test task are available, we also provide a method to transfer knowledge from the training tasks and exploit all available features for prediction. However, we provide no guarantees for this method. We introduce a practical method which allows for automatic inference of the above subset and provide corresponding code. We present results on synthetic data sets and a gene deletion data set.
Analysis on partial discharges variability in mineral oil under long-term AC voltage

The paper presents results of measurements and analysis on partial discharge (PD) behaviour in mineral oil under long-term AC voltage. The conventional electrical method is applied for apparent charge measurements. Surface PDs are generated by model source immersed in brand new and 35-year used mineral oil respectively. The high voltage (HV) electrode is 1cm thick brass plate with a diameter of 3 cm, while the grounded (GND) electrode is 1cm thick steel plate with a diameter of 12cm. There is also a square solid dielectric barrier made of 8mm thick pressboard oil impregnated paper and the size of 15cm × 15cm placed between the both electrodes. The PD source is powered continuously by the AC voltage with its relative level of 1.3 of the inception voltage (U) of the PD source within 168 hours. Signals generated by the continuously occurred PDs within 168 hours are registered. Various qualitative as well as quantitative indicators describing the PD variability in time are assigned and compared to different oil conditions. Furthermore some long-term characteristics of the applied PD model source in mineral oils of different condition are also presented. Finally statistical tools are applied for the results analysis and presentation. From among all assigned indicators there are discriminated those that depend as well as not depend on PD long-term duration. Based on the regression models analysis there are discovered trends that potentially allow to apply the results for modeling of the PD variability in time. Subsequently such approach may potentially support the development and extend the abilities of the diagnostic tools and maintenance policy in electrical power industry.
Switch-Type Modulators and PAs for Efficient Transmitters in the 5G Wireless Infrastructure

This paper discusses envelope-tracking solutions for broadband mm-wave transmitters as required in future 5G base stations. The unique feature is to use switch-type circuits based on GaN devices in the modulators. Starting from state-of-the-art results in the 2 GHz band, the potential of advanced solutions for 30 GHz transmitters with bandwidths beyond 1 GHz is discussed. Both discrete-level class-G and class-S modulators are considered.
Soft and Declarative Fishing of Information in Big Data Lake

In recent years, many fields that experience a sudden proliferation of data, which increases the volume of data that must be processed and the variety of formats the data is stored in have been identified. This causes pressure on existing compute infrastructures and data analysis methods, as more and more data are considered as a useful source of information for making critical decisions in particular fields. Among these fields exist several areas related to human life, e.g., various branches of medicine, where the uncertainty of data complicates the data analysis, and where the inclusion of fuzzy expert knowledge in data processing brings many advantages. In this paper, we show how fuzzy techniques can be incorporated in big data analytics carried out with the declarative U-SQL language over a big data lake located on the cloud. We define the concept of big data lake together with the Extract, Process, and Store process performed while schematizing and processing data from the Data Lake, and while storing results of the processing. Our solution, developed as a Fuzzy Search Library for Data Lake, introduces the possibility of massively parallel, declarative querying of big data lake with simple and complex fuzzy search criteria, using fuzzy linguistic terms in various data transformations, and fuzzy grouping. Presented ideas are exemplified by a distributed analysis of large volumes of biomedical data on Microsoft Azure cloud. Results of performed tests confirm that the presented solution is highly scalable on the Cloud and is a successful step toward soft and declarative processing of data on a large scale. The solution presented in this paper directly addresses three characteristics of big data, i.e., volume, variety, and velocity, and indirectly addresses, veracity and value.
Randomized Consensus-Based Distributed Kalman Filtering Over Wireless Sensor Networks

This article is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes, which interfere the global consensus, are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. Information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of sensors. In addition, the computational burden can be distributed for a sensor, which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step; thus, the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Examples and simulations are provided to illustrate the theoretical results.
Cloud-Assisted Key Distribution in Batch for Secure Real-Time Mobile Services

Establishing promising and efficient key distribution is the cornerstone of security when applying cryptographic methods to guard the privacy of smart devices in the Internet of Things (IoT). However, when using conventional key distribution methods in real-time mobile services, it is usually deficient to build multiple channels with strong security simultaneously on a single data server. Therefore, we focus our research on collecting or monitoring real-time and remote data, which is commonly observed in the health care field of the IoT. In this research, there are difficulties to apply the conventional key distribution approaches during interactions when considering both security and efficiency. Moreover, when preserving personal privacy, anonymity is also important in key distribution processes. We design a system of real-time mobile services and the related security requirements in this paper, and apply a novel and interesting Identity-Based Key Encapsulation Mechanism (IBKEM) to instantiate our system and achieve anonymous key distribution with only one-pass communication for mobile clients in batch. Our instantiated system demonstrates both strong security and practice.
Serotonin Mediates Depression of Aggression After Acute and Chronic Social Defeat Stress in a Model Insect

In all animals, losers of a conflict against a conspecific exhibit reduced aggressiveness, often coupled with depression-like symptoms, particularly after multiple defeats. While serotonin (5HT) is involved, discovering its natural role in aggression and depression has proven elusive. We show how 5HT influences aggression in male crickets, before, and after single and multiple defeats using serotonergic drugs, at dosages that had no obvious deleterious effect on general motility: the 5HT synthesis inhibitor alpha-methyltryptophan (AMTP), the 5HT2 receptor blocker ketanserin, methiothepin which blocks 5HT receptor subtypes other than 5HT2, 5HT's precursor 5-hydroxytryptophan (5HTP) and re-uptake inhibitor fluoxetine. Contrasting reports for other invertebrates, none of the drugs influenced aggression at the first encounter. However, the recovery of aggression after single defeat, which normally requires 3 h in crickets, was severely affected. Losers that received ketanserin or AMTP regained their aggressiveness sooner, whereas those that received fluoxetine, 5HTP, or methiothepin failed to recover within 3 h. Furthermore, compared to controls, which show long term aggressive depression 24 h after 6 defeats at 1 h intervals, crickets that received AMTP or ketanserin regained their full aggressiveness and were thus more resilient to chronic defeat stress. In contrast, 5HTP and fluoxetine treated crickets showed long term aggressive depression 24 h after only 2 defeats, and were thus more susceptible to defeat stress. We conclude that 5HT acts after social defeat via a 5HT2 like receptor to maintain depressed aggressiveness after defeat, and to promote the susceptibility to and establishment of long-term depression after chronic social defeat. It is known that the decision to flee and establishment of loser depression in crickets is controlled by nitric oxide (NO), whereas dopamine (DA), but not octopamine (OA) is necessary for recovery after defeat. Here we show that blocking NO synthesis, just like ketanserin, affords resilience to multiple defeat stress, whereas blocking DA receptors, but not OA receptors, increases susceptibility, just like fluoxetine. We discuss the possible interplay between 5HT, NO, DA, and OA in controlling aggression after defeat, as well as similarities and differences to findings in mammals and other invertebrate model systems.