Miracle-dz/UniFAR-FTUs huggingface dataset:
Skin Lesion Segmentation Based on Modification of SegNet Neural Networks

Skin lesion segmentation plays an important role in automatic skin cancer diagnosis. One of the most dangerous types of this cancer is melanoma which requires an early and accurate detection. However, automatic melanoma segmentation on dermoscopic images is a challenging task since images are corrupted by noise like hairs, air bubbles, blood vessel… and with fuzzy boundaries. This paper presents a framework based on deep fully convolutional neural networks to automatically segment skin lesions in dermoscopic images. Particularly, the paper proposes a fully convolutional network (FCN) framework that is based on modification of the SegNet architecture. In particular, we propose to reduce the downsampling and upsampling layers in the original SegNet model, that reduces total learned parameters compared to the original SegNet model. The proposed approach is applied to segment images from ISIC 2017 dataset. Experimental results show the desired performances of the proposed approach in terms of metrics of Dice coefficient and Jaccard indexes.
Hybrid Wireless Positioning and Charging With Switched Field Helmholtz Coils for Wireless Capsule Endoscopy

The state-of-the-art wireless positioning and wireless charging using magnetic coupling designed for wireless capsule endoscopy (WCE) require different magnetic field distributions and system structures. Due to the space and weight constraints of WCE, designing a system that achieves wireless positioning and wireless charging simultaneously becomes an emerging problem. A switched field structure based on the Helmholtz coils is proposed in this article to provide a gradient field distribution for wireless positioning and uniform field distribution for wireless charging by switching the excitation mode of the Helmholtz coils. Focusing on the wireless positioning function, the gradient mode of the Helmholtz coils is able to achieve a millimeter-level positioning accuracy and a degree level orientation accuracy along the $z$ -axis within the effective space. The proposed method provides a feasible way to achieve wireless positioning and charging using the same system structure to meet the requirements of wireless capsule endoscopy and other related biomedical and industrial applications.
Pulsed Eddy Current Testing for Electromagnetic Parameters of a Spherical Conductor

This paper provides a pulsed eddy current testing method for electromagnetic parameters of a ferromagnetic spherical conductor. Firstly, the frequency domain solution of an eddy current field to the conductor is obtained by solving the electromagnetic boundary value problem. And then the time domain expression to the induced voltage across the pickup coil excited by a pulsed current in the drive coil is calculated by inverse Laplace transform based on residue theorem. Secondly, a least squares inverse problem, between the measured and analytical values of induced voltage, is established to determine the conductivity and relative permeability of the steel ball. And the coupling relationship between the two parameters of the steel ball is explored. Thirdly, the amplitude of the excitation current is changed to study the effect on the parameter inversion results caused by detection conditions. Finally, the time domain analytical formula and the parameters inversion method are verified, by comparing the theoretical induced voltage and conductivity inversion results with experimental voltage and conductivity test values by four-point method respectively.
Remote Sensing Data Analysis for the Effects of Three Selected Aerosols on Both Energy Budgets and Health: A Case of South-West Asia

Dust is associated with numerous hazards to the ecosystem; the dust aerosol affects the community health by polluting the breathable air and its effects on the balance of radiant energy are quite important for scientific research. Aerosols scatter and absorb solar radiation: some aerosols contribute to the heating of the earth and others to the cooling. With those problems, this research presents the behavior of three selected aerosols: dust, black carbon (BC) and sulfate (SO4). The aerosol optical thickness (AOT) extinction behaviors were analyzed in comparison with the cloud amount and the surface skin temperature. In addition, we have quantitatively analyzed the dust’s particulate matter (PM2.5), in the chosen region of interest (RoI). In all cases of this research, in the nearly recent 28 years (from Jan. 1991 to Jul. 2019), the aerosols data source is the second version of the modern-era retrospective analysis for research and applications (MERRA-2). A selection of the focal study region in south-west Asia was due to its location in the world’s dust-belt. Goddard interactive online visualization and analysis infrastructure, GIOVANNI, bridged our research objectives to the data; the Geographical Information System (GIS ArcMap) is one of the software tools that has been utilized to treat the map-based results. In results, among the three selected aerosols, the increase of the dust aerosol always associates with the decrease of the cloud amount in all the seasons of the focal region of interest. Regarding the effects of aerosols on earth’s energy balance, the upwelling longwave flux at the top of atmosphere under clear sky and no aerosol conditions was analyzed with the same longwave flux under normal conditions. On side of health effects in the focal region of interest, all seasons suffer from the heaviest dust’s fine particulate matter (PM2.5). Though there might exist some examples of afforested deserts in Asia, which can be accounted for a good solution to the desert’s dust aerosols, we would recommend further studies about strategies to mitigate the dust hazards on both health and energy budgets.
BRF Negatively Regulates Thermotolerance Defect of fes1a in Arabidopsis

FES1A is a heat shock protein 70 binding protein. Mutation of FES1A leads to a defect in thermotolerance of Arabidopsis; however, independent fes1a mutants exhibit a range in the extent of thermosensitivity. Here, we found that BRF2, a gene adjacent to FES1A and encoding a component of transcription factor IIIB, affects the thermosensitivity of fes1a mutants. Knockout of BRF2 suppressed fes1a thermosensitivity, while overexpression of BRF2 increased thermosensitivity of fes1a. BRF2 in fes1a mutants regulates the transcriptional strength of RNA Polymerase II and accumulation of heat shock proteins and eventually affects the thermotolerance of fes1a. There is a cross-talking between RNA Pol III and Pol II. The cross-talking is initiated by BRF, magnified by the mutation of FES1A, and finally has an effect on thermotolerance.
Speech Enhancement Based on Deep Mixture of Distinguishing Experts

In this work, we propose a new strategy for deep mixture of experts (DMoE) based speech enhancement. DMoE system is difficult to train due to the specific network structure and the necessity of carefully designed pre-training methods to guarantee good performance. We propose using distinguishing deep neural networks (DNNs) as experts, dealing with magnitude spectrogram and log-magnitude spectrogram respectively. The proposed method is compared with the state-of-art DMoE system utilizing hard expectation maximization (HEM) pre-training method. Speech enhancement experiments in 30 (5*6) noise and SNR conditions show the superiority of the proposed method over the baseline method. The average improvements obtained for matched conditions are 0.076 in perceptual evaluation of speech quality (PESQ), 1.824dB in segmental signal to noise ratio (segSNR) and 0.043 in short time objective intelligibility (STOI).
Distinct patterns of thought mediate the link between brain functional connectomes and well-being

Abstract Ongoing thought patterns constitute important aspects of both healthy and abnormal human cognition. However, the neural mechanisms behind these daily experiences and their contribution to well-being remain a matter of debate. Here, using resting-state fMRI and retrospective thought sampling in a large neurotypical cohort (n = 211), we identified two distinct patterns of thought, broadly describing the participants’ current concerns and future plans, that significantly explained variability in the individual functional connectomes. Consistent with the view that ongoing thoughts are an emergent property of multiple neural systems, network-based analysis highlighted the central importance of both unimodal and transmodal cortices in the generation of these experiences. Importantly, while state-dependent current concerns predicted better psychological health, mediating the effect of functional connectomes, trait-level future plans were related to better social health, yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the link between brain physiology and well-being.
Motivating intervention to rehabilitate motor function of the upper limbs via visual feedback of a rehabilitation game*

To establish evidence-based therapy for motor rehabilitation of the upper limbs, we developed a desktop simple exercise machine for upper limbs (D-SEMUL) as a low-cost and high-quality rehabilitation robot. However, the psychological effects of the visual feedback (VF) after the training have not yet been assessed. We investigated the relationship between brain activations and VFs after training in this study. For this purpose, we developed a maze program for the D-SEMUL. Here, the function of the target score (visualized target) was measured as a motivating intervention. We recruited 10 subjects each for a control and an experimental group. Electroencephalogram (EEG) readings from the participants, while they were exposed to the VF after the training, were measured and analyzed to check for the presence of significant differences between two different VFs. Although the results demonstrated no significant differences in the scores among the two groups, the frontal regions of the brain demonstrated a significantly greater degree of activation for VF with target value than without it.
Intrusion Detection in IoT Systems Based on Deep Learning Using Convolutional Neural Network

Internet of Things (IoT) and the fourth Industrial Revolution are key developmental trends of today’s technology. With a variety of devices, environments, and communication protocols, IoT systems are at increased risks of insecurity and vulnerability. Therefore, an effective intrusion detection method, which suits IoT systems, is necessary. This paper proposes a new method of detecting intrusion for IoT systems based on deep learning using a convolutional neural network. The log information of an IoT system such as location, service, address, etc., is extracted into an original feature set. Next the original feature set is improved and encoded into a digital matrix and fed into a convolutional neural network for training and detection. The proposed method is evaluated based on the cross-validation method and has an average accuracy of 98.9%.
Reducing Out-of-Band Radiation in GFDM Systems Using Pulse Shaping Filter Design

Generalized Frequency Division Multiplexing (GFDM) is one of the proposed multicarrier modulation schemes for the fifth generation (5G) communication system. GFDM has lower out-of-band (OOB) radiation compared to the Orthogonal Frequency Division Multiplexing (OFDM) system due to the use of pulse shaping filters. Ideal filters such as the raised cosine (RC) filters are the most common choice to be implemented in GFDM systems. However, ideal filters are non-causal and are not practically realizable. Furthermore, the use of pulse shaping filter in the GFDM system may lead to non-orthogonality and hence, affect the performance of the system. This paper analyzes the performance of the GFDM system with the designed practical pulse shaping filters. The pulse shaping filter is designed using quadratic programming. Numerical simulations show that the designed pulse shaping filter is effective in reducing the OOB radiation of the GFDM system, with about 82.95 dB of OOB radiation suppression.
Trauma resuscitation: can team behaviours in the prearrival period predict resuscitation performance?

Background Optimising team performance is critical in paediatric trauma resuscitation. Previous studies in aviation and surgery link performance to behaviours in the prearrival period. Objective To determine if patterns of human behaviour in the prearrival period of a simulated trauma resuscitation is predictive of resuscitation performance. Design Twelve volunteer trauma teams performed in four simulation scenarios in a paediatric hospital. The scenarios were video recorded, transcribed and analysed in 10-second intervals. Variation in the amount of utterances per team member in the prearrival period was compared with team performance and implicit coordination during the resuscitation. Key results Coders analysed 18 962 s of video. They coded 5204 team member utterances into one of eight communication behaviour categories. Inter-rater reliability was excellent (an average of 83.1% across all four scenarios). The average number of communications occurring during the prearrival period was 18.84 utterances, with a range of 2–42 and a SD of 9.55. The average length of this period was almost 2 minutes (mean =117.30 s, SD=39.20). Lower variance in team member communication during the prearrival better was associated with better implicit coordination (p=0.011) but not team performance (p=0.054) during the resuscitation. Conclusion Patterns of communication in the prearrival trauma resuscitation period predicted implicit coordination and a trend towards significance for team performance which suggests further studies in such patterns are warranted.
Quasi-Zenith Satellite System-based Tour Guide Robot at a Theme Park

For autonomous service robots used in our daily environment, such as a personal mobility vehicles or delivery robots, localization is one of the most important and fundamental functions. A number of localization techniques, including simultaneous localization and mapping, have been proposed. Although a Global Navigation Satellite System (GNSS) is most commonly used in outdoor environments, its accuracy is around 10 meters and so is inadequate for navigation of an autonomous service robot. Therefore, a GNSS is usually used together with other localization techniques, such as map matching or camera-based localization. In the present study, we adopt the Quasi-Zenith Satellite System (QZSS), which became available in and around Japan on November 2018, for the localization of an autonomous service robot. The QZSS provides high-accuracy position information using electronic reference points and four quasi-zenith satellites, and has a localization error of less than 10 centimeters. In the present paper, we compare the positioning performance of the QZSS and real-time kinematic GPS, and verify the stability and the accuracy of the QZSS in an outdoor environment. In addition, we introduce a tour guide robot system using the QZSS and present the results of a guided tour experiment in a theme park.
Transmission Characteristics of Surface Waves in Defective Absorbing Layers

Magnetic loss materials have been extensively used as surface waves attenuation materials. Transmission characteristics of surface waves in defective absorbing layers were rarely reported. Herein, the transmission attenuation of surface waves on defective flexible magnetic loss-absorbing materials had been studied extensively. By comparison, the transmission performance of the surface wave on materials with defects was obviously deteriorated. The results demonstrated that the slot length was less than half the wavelength of the incident plane wave, the defect did not matter on the monostatic radar cross section. Once the length was larger than half wavelength, the scattering of the surface wave was obvious. Non-specular scattering occurs at the defects, causing an increase in the radar cross section. In order to further reduce the radar cross section caused by non-specular scattering, some effective repair schemes would be needed.
PREPL Deficiency: A Homozygous Splice Site PREPL Mutation in a Patient With Congenital Myasthenic Syndrome and Absence of Ovaries and Hypoplasia of Uterus

Prolyl endopeptidase-like (PREPL) deficiency (MIM 616224) is a very rare congenital disorder characterized by neonatal hypotonia and feeding difficulties, ptosis, neuromuscular symptoms, cognitive impairments, growth hormone deficiency, short stature, and hypergonadotropic hypogonadism. This syndrome is an autosomal recessive disease resulting from mutations in the PREPL gene. Previous reports have associated PREPL deficiency with only one nucleotide substitution, the deletion of four nucleotides, and eight small microdeletions in the PREPL gene In this study, we used whole exome sequencing (WES) to identify a novel homozygous splicing mutation (c.616 + 1G > T) in a 14-year-old Chinese girl with PREPL deficiency. Sequencing of the RT-PCR products from the patient’s blood sample revealed that the c.616 + 1G > T variant disrupted normal splicing in intron 4 leading to an aberrant inclusion of 43 nucleotides in intron, a frameshift, and premature termination codon. Our patient exhibited several of the common phenotypes, including severe neonatal hypotonia, growth impairment and cognitive problems. However, we also observed several unusual phenotypic characteristics: absence of the ovaries, hypoplasia of the uterus, microcephaly and a short neck in patient is alsoobserved. These results provide further evidence for the involvement of PREPL development of the ovaries and uterus. Our findings may provide further insight into the relationship between the genotype and phenotype in collagen-associated diseases and improve the clinical diagnosis of Prolyl endopeptidase-like deficiency.
A Received Signal Strength Based Localization Approach for Multiple Target Nodes via Bayesian Compressive Sensing

In wireless sensor network (WSN), the localization scheme is vital for efficiently utilizing the acquired sensing data from sensor nodes. The received signal strength (RSS) based localization is among one of the most preferable schemes for various applications, due to its ease of implementation without any additional hardware cost. In RSS-based localization scheme, the anchor nodes need to acquire RSS measures of unknown/target nodes and send them to base station where localization of unknown/target node is performed. As the number of unknown/target nodes increases, the control traffic for localization increases, due to RSS measures for each unknown/target node are gathered and send to the base station (BS), which can cause more energy consumption and reduce life time of sensor networks. In this paper, we propose a localization scheme for multiple unknown/target nodes based on Bayesian Compressive Sensing (BCS). The RSS values received from multiple unknown/target nodes are superposed in each anchor node, and further can be constituted as a form of compressed data. In the proposed scheme, each anchor node sends only a compressed RSS measure from multiple unknown/target nodes to the BS and the BS estimates the location of unknown/target nodes using Bayesian Compressive Sensing. The proposed scheme is evaluated through simulation to authenticate its effectiveness in term of accuracy of localization, and reduction of traffic overhead.
Risk Stratification Using Multivariable Fractional Polynomials in Diffuse Large B-Cell Lymphoma

The risk stratification of diffuse large B-cell lymphoma (DLBCL) is crucial. The International Prognostic Index, the most commonly used and the traditional risk stratification system, is composed of fixed and artificially dichotomized attributes. We aimed to develop a novel prognostic model that allows the incorporation of up-to-date attributes comprehensively without information loss. We analyzed 204 patients with primary DLBCL who were uniformly treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) from 2007 to 2012 at Asan Medical Center. Using the multivariable fractional polynomial (MFP) method and bootstrap resampling, we selected the variables of significance and the best fitted functional form in fractional polynomials. Age, serum β2-microglobulin, serum lactate dehydrogenase, and BCL2 expression were selected as significant variables in predicting overall survival (OS), while age was excluded in predicting 2-years event-free survival. The prognostic score calculated by the MFP model effectively classifies patients into four risk groups with 5-years OS of 89.91% (low risk), 81.21% (low-intermediate risk), 66.40% (high-intermediate risk), and 37.89% (high risk). We suggest a new prognostic model that is simple and flexible. By using the MFP method, we can incorporate various clinicopathologic factors into a risk stratification system without arbitrary dichotomization.
Transcranial Direct Current Stimulation of the Medial Prefrontal Cortex Has No Specific Effect on Self-referential Processes

The processing of self-referential information can be influenced by transcranial magnetic stimulation (TMS). The present randomized controlled study investigated whether similar effects can be elicited through the application of transcranial direct current stimulation (tDCS) regarding the “self-serving bias” (SSB) and the “mnemic neglect effect” (MNE). Seventy-five healthy males (Mage = 25; SD = 4.3) were investigated in a between-groups design with random assignment by applying anodal, cathodal, or sham tDCS to the medial prefrontal cortex (mPFC). After stimulation, the participants judged if 80 personality traits (40 positive, 40 negative) were self-descriptive or not. Finally, the participants had to recall the previously presented adjectives. All three stimulation groups showed the expected SSB and MNE. Still, and contrary to our hypotheses, tDCS revealed neither a significant interaction effect between groups and valence concerning the number of chosen self-referential traits (F(2,72) = 1.36, p = 0.26, ηG2 = 0.02) nor an interaction effect between groups, valence, and self-reference concerning the percentage of recalled words (F(2,71) = 0.69, p = 0.50, ηG2 = 0.01). However, a post hoc inspection of effect sizes revealed that less negative traits were indicated as self-referential in the anodal compared to the cathodal group (ES: −0.59; CI: −1.16 to −0.03). Moreover, the participants showed—regardless of self-reference and type of stimulation—a better recall with tDCS in comparison to sham stimulation. Our results indicate that tDCS of the mPFC in healthy young men has no influence on the SSB and the MNE. However, tDCS seems to improve memory performance.
Design and Control of Parallel Gripper with Linear and Curved Trajectory Consisting of Only Revolute Pairs

A parallel gripper that can grasp an object notjust between the fingers, using the linear trajectory, but also in front of the fingers, using the curved trajectory, is proposed. With an aim to make it dust-and-waterproof, the link structure is designed to combine two mechanisms consisting of only revolute pairs, called the Chebyshev’s lambda mechanism and double parallel link. The former constrains the fingertips on the trajectory and the latter always keeps the gripping face of the fingertip vertical. As a trigger of grasping motion, it is also possible to detect contact between the fingertip and grasping target on the basis of the actuators’ backdrive, without any sensors on the fingertip. Kinematic analysis, control methods, and experiments with a fabricated gripper are also presented.