Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults

There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age.
Lipid tracking at kilohertz sampling rates on live cell membranes through Interferometric Scattering microscopy

The lateral dynamics of lipids on the cellular membranes are one of the most challenging topics to study in membrane biophysics, needing simultaneously high spatial and temporal resolution. In this study, we have employed Interferometric scattering Microscopy (ISCAT) to explore the dynamics of a biotinylated lipid analogue labelled with streptavidin-coated gold nanoparticles (20 and 40nm in diameter) at 2kHz sampling rate. We developed a statistics-driven analysis pipeline to analyse both ensemble average and single trajectory Mean Squared Displacements from each dataset, and to discern the most likely diffusion mode. We found that the use of larger tags slows down the target motion without affecting the diffusion mode. Moreover, we determined from our statistical analysis that the prevalent diffusion mode of the tracked gold-labelled lipids is compartmentalized diffusion. This model describes the motion of particles diffusing on a corralled surface, with a certain probability of changing compartment. This is compatible with the picket-fence model of membrane structure, already observed by similar studies. Through our analysis, we could determine significant physical parameters, such as average compartment size, dynamic localization uncertainty, and the intra- and inter-compartmental diffusion rates. We then simulated diffusion in an environment compatible with the experimentally-derived parameters and model. The closeness of the results from the analysis of experimental and simulated trajectories validates our analysis and the proposed description of the cell membrane. Finally, we introduce the confinement strength metric to compare diffusivity measurements across techniques and experimental conditions, which we used to successfully compare the present results with other related studies.
Visualizing Metabolic Changes in Probing Human Cancer Cells and Tissues Metabolism Using Vivo 1H or Proton NMR, 13C NMR, 15N NMR and31P NMR Spectroscopy and Self–Organizing Maps under Synchrotron Radiation

In the current study, visualizing metabolic changes in probing human cancer cells and tissues metabolism using vivo 1H or Proton NMR, 13C NMR, 15N NMR and 31P NMR spectroscopy and self– organizing maps under synchrotron radiation was investigated. Cadmium Oxide (CdO) based Nano membranes have received a growing interests to the separation and purification of human cancer cells and tissues [1–48]. Human cancer cells and tissues are most experimentally diagnosed nowadays using vivo 1H or proton NMR, 13C NMR, 15N NMR and 31P NMR spectroscopy and self– organizing maps under synchrotron radiation, a reaction between Cadmium Oxide (CdO) nanoparticles and human cancer cells and tissues, using vivo 1H or proton NMR, 13C NMR, 15N NMR and 31P NMRspectroscopy and self–organizing maps under synchrotron radiation [49–89]. Selective removal of human cancer cells and tissues in a selective Nano membrane reactor enables the human cancer cells and tissues diagnosis using vivo 1Hor proton NMR, 13C NMR, 15N NMR and 31P NMR spectroscopy and self–organizing maps under synchrotron radiation [90–100]. Numerous methods have been proposed to deposit Cadmium Oxide (CdO) Nano films on porous supports. Among them, electroless plating is an autocatalytic electrochemical reaction that offers the flexibility to apply very thin, conformal and continuous coatings to a variety of porous or non–porous supports. In this editorial, Cadmium Oxide (CdO) Nano membranes by different contents of Gold (Au) nanoparticles were prepared by sequential electroless plating technique and were characterized by vivo 1H or proton NMR, 13C NMR , 15N NMR and 31P NMR spectroscopy and self–organizing maps under synchrotron radiation. The Cadmium Oxide (CdO) – Gold (Au) Nano membranes with different contents of Gold (Au) nanoparticles was subjected to different annealing conditions to promote diffusion and distribution of elements to form an alloy matrix. The obtained results revealed that increasing in annealing time enhances the homogeneity of alloy and lead to obtain a Received: November 13, 2017; Accepted: November 20, 2017; Published: November 22, 2017
Adversarial Training Time Attack Against Discriminative and Generative Convolutional Models

In this paper, we show that adversarial training time attacks by a few pixel modifications can cause undesirable overfitting in neural networks for both discriminative and generative models. We propose an evolutionary algorithm to search for an optimal pixel attack using a novel cost function inspired by domain adaptation literature to design our training time attack. The proposed cost function explicitly maximizes the generalization gap and domain divergence between clean and corrupted images. Empirical evaluations demonstrate that our adversarial training attack can achieve significantly low testing accuracy (with high training accuracy) on multiple datasets by just perturbing a single pixel in the training images. Even under the use of popular regularization techniques, we identify a significant performance drop compared to clean data training. Our attack is more successful than previous pixel-based training time attacks on state-of-the-art Convolutional Neural Networks (CNNs) architectures, as evidenced by significantly lower testing accuracy. Interestingly, we find that the choice of optimization plays an essential role in robustness against our attack. We empirically observe that Stochastic Gradient Descent (SGD) is resilient to the proposed adversarial training attack, different from adaptive optimization techniques such as the popular Adam optimizer. We identify that such vulnerabilities are caused due to over-reliance on the cross-entropy (CE) loss on highly predictive features. Therefore, we propose a robust loss function that maximizes the mutual information between latent features and input images, in addition to optimizing the CE loss. Finally, we show that the discriminator in Generative Adversarial Networks (GANs) can also be attacked by our proposed training time attack resulting in poor generative performance. Our paper is one of the first works to design attacks for generative models.
Spoofer-to-Target Association in Multi-Spoofer Multi-Target Scenario for Stealthy GPS Spoofing

Global navigation satellite system (GNSS) based navigation is omnipresent in today’s world, providing position, velocity, and time (PVT) information with inexpensive GPS receivers. These receivers are highly vulnerable to intentional interference like GPS spoofing and meaconing. The spoofing of a single GPS receiver using a spoofer setup is widespread, and the concept of spoofing multiple targets with multiple distributed spoofers is also equally adaptable. Traditionally, in distributed spoofers, the multiple spoofers in the surveillance region work independently without knowing other spoofers being installed. Multiple spoofers deployment and its management are optimal for misguiding the multiple GPS receivers in the given surveillance. This paper presents a generalized mathematical model for the multi-spoofer multi-target (MSMT) scenario, spoofer management, and spoofer-to-target association. The received power of spoofed signals is considered as an evaluating parameter for locking the spoofed signals onto the GPS receivers. Three novel centralized networking-based spoofing techniques are proposed to overcome spoofer-to-target association in distributed networking. Firstly, the global nearest neighbor (GNN) based centralized spoofing is proposed. The overall cost of the function is minimized by assigning a unique spoofer-ID to a unique target-ID. In GNN-based centralized spoofing, the overall global cost minimizes, but it does not ensure that every target-to-spoofer assignment is minimum. Secondly, the spoofers of opportunity-based centralized spoofing with the GNN association is proposed to resolve the spoofer-to-target association and to increase the hit ratio. However, it is hard to install more spoofers; therefore, a tunable transmitting power-based centralized spoofing with the GNN association is presented to accomplish efficient spoofer-to-target association and higher hit-ratio. The spoofing efficiency is evaluated using spoofer-to-target association, hit ratio, and position root mean square error (PRMSE). All the proposed algorithms outperform the distributed spoofing. We also observe that the tunable power-based spoofing is an optimal solution in MSMT scenario.
Genetic Algorithm Assisted HIDMS-PSO: A New Hybrid Algorithm for Global Optimisation

In this paper, a new hybrid algorithm, GA-HIDMS-PSO, is introduced by hybridising the state-of-the-art particle swarm optimisation (PSO) variant, the heterogeneous improved dynamic multi-swarm PSO (HIDMS-PSO) with a genetic algorithm (GA). The new hybrid model exploits the heterogeneous features of HIDMS-PSO and the evolutionary characteristics of the GA. In the GA-HIDMS-PSO architecture, HIDMS-PSO acts as the primary search engine, and the GA is employed as the secondary method to assist and slow down the loss of diversity for selected proportions of homogeneous and heterogeneous subpopulations of the HIDMS-PSO algorithm. Both methods run consecutively. As the primary search method, HIDMS-PSO runs for longer periods compared with the GA. The HIDMS-PSO pro-vides the initial solutions for the GA from both homogeneous and heterogeneous subpopulations and final solutions returned from the GA replace prior solutions in the HIDMS-PSO which resumes the search process with potentially more diverse particles to guide the swarm. The GA-HIDMS-PSO algorithm’s performance was tested on the 30 and 50 dimensional CEC’05 and CEC’17 test suites. The results were compared with 24 algorithms, with 12 state-of-the-art PSO variants and 12 other metaheuristics. GA-HIDMS-PSO outperformed all 24 comparison algorithms on both test suites for both 30 and 50 dimensions.
Single-channel EEG-based subject identification using visual stimuli

Electroencephalography (EEG) signals have been recently proposed as a biometrics modality due to some inherent advantages over traditional biometric approaches. In this work, we studied the performance of individual EEG channels for the task of subject identification in the context of EEG-based biometrics using a recently proposed benchmark dataset that contains EEG recordings acquired under various visual and non-visual stimuli using a low-cost consumer-grade EEG device. Results showed that specific EEG electrodes provide consistently higher identification accuracy regardless of the feature and stimuli types used, while features based on the Mel Frequency Cepstral Coefficients (MFCC) provided the highest overall identification accuracy. The detection of consistently well-performing electrodes suggests that a combination of fewer electrodes can potentially provide efficient identification performance, allowing the use of simpler and cheaper EEG devices, thus making EEG biometrics more practical.
Flexible and Ultra-Thin Glass Substrates for RF Applications

Glass has been shown to be a capable core substrate material for high frequency applications. In this paper we examine the capabilities of ultra-thin glass as a flexible material that can be used for high frequency flexible applications. The two stack-ups discussed in this paper are $60\ \mu\mathrm{m}$ in total thickness with a core glass substrate (Schott AF32) of $30\ \mu\mathrm{m}$ thickness. One stack-up uses $15\ \mu\mathrm{m}$ JSR GT-N01 as a buildup dielectric and the other uses $15\ \mu\mathrm{m}$ Taiyo Photo Imageable Dielectric. Since neither of these stack-ups have previously been electrically characterized, this paper characterizes both stack-ups up to 110 GHz using microstrip ring resonators (MRRs) and conductor backed coplanar waveguides (CBCPWs). Based on the characterization results, these stack-ups compare favorably against other stack-ups used for applications in this frequency range. After completing the electrical characterization, the Taiyo PID stack-up is also mechanically characterized for its flexibility using Free Arc Bending. The Free Arc Bending test shows that the ultra-thin glass stack-up is suitable for high frequency bending applications as the tested samples are capable of bending up to a separation of 33% of the sample's total length, displaying the capabilities of this ultra-thin glass substrate as a good candidate for a flexible substrate.
Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones

Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.
Uncertain Disturbance Rejection and Attenuation for Semi-Markov Jump Systems With Application to 2-Degree-Freedom Robot Arm

This paper studies the composite refined anti-disturbance control problem of a 2-degree-of-freedom robot arm system modeled by a semi-Markov jump system with multiple disturbances, which includes harmonic disturbances with unknown frequency and amplitude as well as energy bounded disturbances. Firstly, the semi-Markov jump system model is proposed to construct a novel linear model of the 2-degree-of-freedom robot arm subject to two types of disturbances. Next, in order to estimate the uncertain harmonic disturbance, a novel higher order disturbance observer is introduced to convert the uncertain harmonic disturbance into some parameter uncertainty and then estimate the parameter uncertainty. In addition, a corresponding composite anti-disturbance control scheme is formulated to reject and attenuate the above two types of disturbances, respectively. Furthermore, sufficient conditions that can guarantee that the system is stochastically stable are given. Finally, a simulation study of the obtained model is carried out to illustrate the validity of the composite control design method proposed in this paper.
Detection of Blast Furnace Hearth Lining Erosion by Multi-Information Fusion

The blast furnace hearth refractories are exposed to complex chemical attack and thermal stress erosion, which will lead to the gradual failure of refractories. The campaign life of a blast furnace depends on its remaining hearth refractory lining, so it is very important to understand the remaining thickness of hearth refractory for prolonging the service life of blast furnace. In order to understand the damage of hearth refractory more accurately, a multi-information fusion method for measuring the refractory lining thickness of blast furnace hearth was proposed, which was based on the application advantages of impact echo technology, thermocouple and cooling stave heat flux intensity in blast furnace hearth. The influence of temperature change in different material layers of hearth on wave velocity was considered. In addition, the thickness of carbon brick lining of blast furnace hearth was detected by the method of multi-information fusion. It is found that the multi-information fusion thickness detection method improves the accuracy of thickness measurement compared with the impact echo thickness detection method.
Insights into the Mechanical Properties of SnAgCu Based Solder Materials Including Void Effects: An Atomistic Study

Lead-free Sn-Ag-Cu materials otherwise known as SAC materials are widely used in the electronic packaging industry for its nontoxic nature unlike Sn-Pb solder materials. Electronics in the present world is shrinking components sizes more rapidly than ever, which instigates investigation of solder materials properties at the nanoscale. In our previous work, mechanical properties of SAC solder materials were determined through atomistic simulations. Presence of voids in SAC solder joints is not unusual and at the atomic level, the effect of voids can shape mechanical behavior significantly. In this study, molecular dynamics (MD) simulations have been performed to investigate how the stress-strain behavior of SAC305 solder (at nanoscale) changes with the amount of void present in the material. The percentage of void has been varied from 0 to 20%. In the simulations, the inter-atomic interaction is defined using the modified embedded-atom method (MEAM) potential. In addition to voids, the effects of temperature have also been investigated by changing the temperature from 0 to 125° C. From the obtained results, the correlations between the percentages of void in solder and solder mechanical properties like ultimate tensile strength, yield strength, failure strain, and elastic modulus have been determined for different temperatures. The findings of this study might be helpful in designing and manufacturing of nano-electronic devices.
Separation, Purification, Isolation, Identification andBiological Activity of the Aqueous Fruit Extractof “Gorongo”- Solanum macrocarpum L.

The plant was investigated for its chemical constituents and antimicrobial properties. Using gel filtration with Sephadex LH-20 (lipophylic) and thin layer chromatography (TLC) of the fractions with reversed phase, RP-18 plates the aqueous extract (CAE) of the unripe fruit was therefore analysed. The microorganisms used included four Gram positive bacteria and four Gram negative bacteria, and three fungal strains. All the microorganisms used were resistant to the effect of the CAE. The antimicrobial effect was assayed using the disc diffusion antimicrobial selectivity test. The purified saponin from the gel filtration chromatography resulted into five (5) spots with defined outlines (Rf 0.57; 0.65; 0.68; 0.71; 0.75) indicating that the purified saponins consists of a considerable mixture of saponins even in the same part of the plant. The saponins may contribute to the reported antihyperlipidaemic effect attributed to fruit of the plant and supporting the nonmicrobial activity of the fruit of the plant.
A Graph-based Approach for Shepherding Swarms with Limited Sensing Range

Within applications of swarm control, failing to maintain cohesion amongst the agents may lead to mission failure. We study the effect of limited sensing range of swarming agents when guided by a shepherd. A connectivity-aware approach is proposed to enhance the cohesion of the swarm. We combine a graph-based model of the flock with particle swarm optimization (assisted by DBSCAN) to improve the shepherd’s performance in herding sheep (swarm members). The approach is evaluated using multiple initial swarm configurations. Simulation results on swarm sizes of 50 and 100 agents show up to 50% reduction in the task completion time and an average improvement of 25% in the success rate for low density sheep initialization scenarios and competitive results for the higher density scenarios.
Finding the minimum quantity and the optimum placement of on-die temperature sensor in SOC design based on rank analysis

Accurate reading of on-die temperature sensor (OD-TS) has been more and more important in the advanced techniques for dynamic thermal management, on which high performance system-on-chip (SOC) rely. If the large number of OD-TSs are allowed, OD-TS can be placed uniformly to accurately detect the on-die temperature with adequate spatial resolution. However, since OD-TS including its control circuitry requires die area and routing complexity, which is proportional to the number of OD-TS included in SOC, the number of OD-TS is limited by the available area. [1] In this work, the authors will present a systematic methodology to find the minimum number and optimum placement of OD-TS in high performance SOC design based on heterogeneous many core architecture, which is based on analyzing rank and eigen-vector. The given conditions including the power-map for the corresponding scenario and the thermal resistance matrix (TRM) of the corresponding package structure will be used to demonstrate the usefulness of the presented methodology in SOC design. The packages used in the case studies the authors will present are package-on-package (POP) popularly used in smart-phone application. By utilizing the methodology proposed by authors, the die-area and routing area required for TS might be able to be minimized, which is eventually expected to be strong driver of the cost-effective reliable SOC design.