Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification Guided Deep Convolutional Network for Compressed Sensing

Compressed Sensing (CS) has been successfully applied to image compression in the past few years. However, there are still several challenges that restrict its applications in practice including large memory requirement and unsatisfactory reconstruction performance. To address these challenges, in this paper, we propose a classification guided deep convolutional network for image compressed sensing (CCSNet), which includes a sampling sub-network and a reconstruction sub-network. In the sampling sub-network, multiple convolutional layers are used to sample the original image, which significantly reduces the parameters of the sampling matrix while causes performance degradation moderately compared against existing convolution based sampling methods. In the reconstruction sub-network, a novel two-branch architecture is proposed to improve the adaptability of the model to various textures in natural images. The first branch, named the classification branch, is to classify the sampled measurements of the original image to one of the predefined textural classes. The second branch, named the reconstruction branch, consists of multiple sub-branches, which are responsible for reconstructing the original images belonging to the corresponding textural classes. By jointly utilizing two sub-networks, the entire network can be trained in the form of end-to-end metric with a joint loss function. Experimental results demonstrate that the proposed method provides a significant quality improvement in terms of PSNR compared against state-of-the-art methods.
PrsA2 (CD630_35000) of Clostridioides difficile Is an Active Parvulin-Type PPIase and a Virulence Modulator

Clostridioides difficile is the main cause for nosocomial antibiotic associated diarrhea and has become a major burden for the health care systems of industrial countries. Its main virulence factors, the small GTPase glycosylating toxins TcdA and TcdB, are extensively studied. In contrast, the contribution of other factors to development and progression of C. difficile infection (CDI) are only insufficiently understood. Many bacterial peptidyl-prolyl-cis/trans-isomerases (PPIases) have been described in the context of virulence. Among them are the parvulin-type PrsA-like PPIases of Gram-positive bacteria. On this basis, we identified CD630_35000 as the PrsA2 homolog in C. difficile and conducted its enzymatic and phenotypic characterization in order to assess its involvement during C. difficile infection. For this purpose, wild type CdPrsA2 and mutant variants carrying amino acid exchanges mainly in the PPIase domain were recombinantly produced. Recombinant CdPrsA2 showed PPIase activity toward the substrate peptide Ala-Xaa-Pro-Phe with a preference for positively charged amino acids preceding the proline residue. Mutation of conserved residues in its active site pocket impaired the enzymatic activity. A PrsA2 deficient mutant was generated in the C. difficile 630Δerm background using the ClosTron technology. Inactivation of prsA2 resulted in a reduced germination rate in response to taurocholic acid, and in a slight increase in resistance to the secondary bile acids LCA and DCA. Interestingly, in the absence of PrsA2 colonization of mice by C. difficile 630 was significantly reduced. We concluded that CdPrsA2 is an active PPIase that acts as a virulence modulator by influencing crucial processes like sporulation, germination and bile acid resistance resulting in attenuated mice colonization.
A Hybrid Model for Classification of Biomedical Data Using Feature Filtering and a Convolutional Neural Network

Deep learning is known for its capabilities in analysing large and complex sets of data, without the need of applying noise reduction methods, which is a necessary step for improving the performance of conventional machine learning models. Indeed, the superiority of deep learning over conventional machine learning models resides in their capabilities of analysing large sets of data to learn features directly from the data without the need for manual feature extraction. However, this paper aims to evaluate the hypothesis that by using feature filtering as a preprocessing step prior to feeding the data into the deep learning model, the quality of the data is improved which also leads to a better performing deep learning model. Two complex biomedical datasets which contain a large number of features and sufficient number of patient cases for deep learning were selected for the evaluations. A selection of feature filtering methods were applied to identify the most important features (i.e. top 20% ranked features) at the input level, prior to the data being fed into a deep learning classifier. Once the most important features are selected, these are fed into a deep learning algorithm, and in particular the Convolutional Neural Network, which has been tuned for the particular task. Experiment results demonstrate that applying feature filtering at the input level improves the performance of the deep Convolutional Neural Network, even for the most complex biomedical data such as those utilised in this paper. In particular, for the first dataset, PANCAN, an improvement of 20% was reported in Accuracy, whereas for the second dataset GAMETES Epistasis, an improvement of 10.63% was reported in Accuracy. The results are promising and demonstrate the benefits of filter filtering when deep learning methods are adopted for biomedical classification tasks.
Robust Voltage Tracking Control of Three-Phase Four-Wire Split DC Bus Inverter via Time-Scale Separation Technique

The problem of controller design is investigated for a three-phase four-wire split dc bus inverter with output filters and loads. The proposed time-scale separation technique for controller design gives the possibility to achieve a high accuracy of three-phase voltage generation subject to variations of source voltages or load resistors. The discussed approach to problem of three-phase inverter controller design provides high power quality with small harmonic distortion. The efficiency of the discussed design methodology is highlighted by simulation results.
An  $X$ -Band 48-Way Leaky Waveguide Antenna With High Aperture Efficiency and High Power Capacity

An X-band leaky waveguide antenna (LWA) with high aperture efficiency and high power capacity is investigated in this paper. The antenna is mainly composed of two parts: a sectorial waveguide power divider and an LWA array. First, in the design of the power divider, the magnitude uniformity and phase consistency are ensured by adjusting the channel central angles and introducing phase adjusting structures, respectively. Then, the analysis and design of the LWA array are carried out. New formulas and conditions have been proposed for realizing uniform radiation distribution and maximum aperture efficiency. Five supporting beams are introduced to increase the antenna gain and power capacity. An LWA array as a prototype is fabricated and tested to verify the analysis and simulation. Experimental results agree well with the simulation. The return loss is better than 17 dB at the center frequency 9.7 GHz, the gain is 38.75 dB, and the corresponding aperture efficiency is 80.7%. The power capacity of the LWA array reaches 1.5 GW. These characteristics demonstrate that the proposed LWA array is suitable for radiating high power microwaves. The analysis and design method proposed in this paper is a useful guide for designing the high-aperture efficiency antenna in the future.
Machine Learning Assisted User-scheduling Method for Massive MIMO System

In this paper, we investigate low-complexity statistical channel state information (CSI) based beam-domain multiple access user-scheduling methods for massive multiple-input-multiple-output (MIMO) communications. As the beam-space transmission has been proved to be optimal when only statistical CSI is known at the base-station, the objective of user-scheduling is to maximize the expectation of sum-rate given channel-coupling matrix (CCM). The k-best algorithm is employed in our work to approach the optimal user-scheduling, where the calculation of sum-rate contributes most of the computational complexity. Given the statistical CSI, the probability density function of ordered eigenvalues of Hermitian matrices corresponding the channel matrix are approximated by exponential family distributions. The calculation of sum-rate can therefore be simplified to parameters estimation problems. Then neural networks are designed and machine learning methods are proposed to estimate the sum-rate from CCM. The proposed method has much reduced computational complexity compared with the existing Monte Carlo or deterministic equivalent based sum-rate calculation methods while simulation results demonstrate the near-optimal performance.
Stability Analysis for Switched Uncertain Nonlinear Systems With Dwell Time and Delay in the Active Mode Detection

Stability analysis for switched nonlinear systems with delay in the active mode detection and with polytopic uncertainties is addressed in this letter. General sufficient stability conditions are first established using a hybrid reformulation of the original system. This result is then used to provide new tractable conditions in terms of sum of squares decomposition when polynomial nonlinearities are considered. The proposed stability conditions are illustrated through a numerical example.
Global closed-loop equilibrium properties of a geometric PDAV controller via a coordinate-free linearization

Tracking a desired Pointing Direction and simultaneously obtaining a reference Angular Velocity (PDAV) around the pointing direction constitutes a very involved and compli- cated motion encountered in a variety of robotic, industrial and military applications. In this paper through the utilization of global analysis and simulation techniques, the smooth closed- loop vector fields induced by the geometric PDAV controller from [1], are visualized to gain a deeper understanding of its global stabilization properties. First through the calculation of a coordinate-free form of the closed-loop linearized dynamics, the local stability of each equilibrium of the system is analyzed. The results acquired by means of eigenstructure analysis, are used in predicting the frequency of complex precession/nutation oscillations that arise during PDAV trajectory tracking; an important tool in actuator selection. Finally, by utilizing vari- ational integration schemes, the flow converging to the desired equilibrium and the flow “close” to the stable manifold of the saddle equilibrium of the closed-loop system is visualized and analyzed. Results offer intimate knowledge of the closed-loop vector fields bestowing to the control engineer the ability to anticipate and have an estimate of the evolution of the solutions.
Towards Fingerprint Presentation Attack Detection Based on Convolutional Neural Networks and Short Wave Infrared Imaging

Biometric recognition offers many advantages over traditional authentication methods, but they are also vulnerable to, for instance, presentation attacks. These refer to the presentation of artifacts, such as facial pictures or gummy fingers, to the biometric capture device, with the aim of impersonating another person or to avoid being recognised. As such, they challenge the security of biometric systems and must be prevented. In this paper, we present a new fingerprint presentation attack detection method based on convolutional neural networks and multi-spectral images extracted from the finger in the short wave infrared spectrum. The experimental evaluation, carried out on an initial small database but comprising different materials for the fabrication of the artifacts and including unknown attacks for testing, shows promising results: all samples were correctly classified.
Getting Rid of Night: Thermal Image Classification Based on Feature Fusion

Thermal images are essential to deal with situations in dark environments, as they capture the objects' temperature. While the objects can still be seen in thermal images, the texture is extremely blur or even not observable at all. We propose to extract different features from images that capture various characteristics of the images. As one feature emphasizes one distinguishing aspect differing from the others, we can grasp multiple pieces of evidence from the images and take advantage of each to improve the thermal image classification accuracy. In particular, in additional to corner features usually used in color images, we also extract features from the edges and the shapes of the objects that emphasize the integral image appearance, as well as the temperature characteristics obtained from the image intensity. In this way, even if one feature is not evident in an image, the others can still play a critical role towards the correct classification result. By optimizing the objective function, we maximize the fusion performance of multiple features. By doing so, we can to the largest extent make use of the information exhibited in the thermal images to classify the query image into the correct group. Experiments demonstrate promising thermal image classification result.
Brief Report: Exercise and Blood Pressure in Older Adults—An Updated Look

Background/Objectives Raised blood pressure is a major problem in older adults. Using a random-effects model, a recent meta-analysis reported statistically significant reductions in both resting systolic blood pressure (SBP) and diastolic blood pressure (DBP) as a result of aerobic, resistance, and combined aerobic and resistance exercise in adults ≥65 years. To provide more objective information regarding this nonpharmacologic approach, this study applied more robust methods to this data. Design Meta-analysis of 41 randomized controlled trials representing 96 groups (52 exercise; 44 control). Setting Any location where a randomized controlled trial could be conducted. Participants Adults ≥65 years. Intervention Trials ≥2 weeks that included aerobic, resistance, and/or combined aerobic and resistance exercise as the intervention. Measurements The recently developed inverse heterogeneity model (IVhet) was used to pool findings and the Doi plot was used to examine for small-study effects. Absolute and relative differences between the IVhet and random-effects model were also calculated. Data were reported using the mean difference (exercise minus control) with nonoverlapping 95% confidence intervals considered statistically significant. Results Statistically significant reductions in resting blood pressure were found as a result of aerobic exercise (SBP, -4.7 mmHg, 95% CI, -7.7 to -1.8; DBP, -2.0 mmHg, 95% CI -3.13 to -0.9), SBP but not DBP for resistance training (SBP, -7.0 mmHg, 95% CI, -10.5 to -3.4; DBP, -1.2 mmHg, 95% CI -2.7 to 0.3), and both SBP and DBP for combined aerobic and resistance training (SBP, -5.5 mmHg, 95% CI, -8.3 to -2.7; DBP, -3.7 mmHg, 95% CI -4.8 to -2.7). Conclusions Exclusive of changes in DBP congruent with resistance training, exercise (aerobic, resistance, and combined aerobic and resistance) reduces resting SBP and DBP in older adults. These findings have practical implications when considering exercise for the prevention and treatment of raised blood pressure in older adults.
Fundamental Limits for Joint Relative Position and Orientation Estimation with Generic Antennas

Automated multi-agent robotic systems are a promising technology for extra-terrestrial exploration. Autonomous control of such systems requires position as well as orientation awareness. Having a multi-port antenna system like an antenna array or a multi-mode antenna (MMA) installed on each agent, position and orientation can be estimated when the agents cooperatively communicate via radio signals. The aim of this paper is to derive the fundamental limits for relative, i.e. anchor-free, joint position and orientation estimation. Using wavefield modeling and manifold separation, the inclusion of calibration data from real multi-port antenna systems is possible. The Cramer-Rae bound (CRB) is derived directly on the received signal, thus representing a fundamental limit. We then use the derived bound to analyze the position and orientation estimation capabilities of a multi-agent system employing MMAs.
Malware Economics and its Implication to Anti-Malware Situational Awareness

Malware, like any other software, is developed iteratively and improved in incremental versions over a long period of time. Malware economics requires amortizing the cost of malware development over several attacks. Thus, the malware code persists through many incremental versions of the malware—albeit in a transformed and obfuscated state—while the classic indicators of attack, e.g., domain names, file names, and IP addresses, are parameterized and often change with each new version. Recent breakthroughs in automated malware analysis and code debofuscation make it possible to overcome the challenges imposed by code obfuscation and create new anti-malware tools that use the malware code itself as an immutable indicator in anti-malware defense. The resulting technologies can be used to provide situational awareness of the dynamic threat profile of an organization. A persistent adversary that intends to penetrate a particular organization will send morphed variants of the same malware to a large number of people in an organization. Such an attack campaign may be executed over weeks or months. By correlating malware generated from the same code base, one can detect such persistent campaigns against an organization using the malware blocked by an anti-virus. Results from the field demonstrate that this approach has promise in detecting targeted attacks while the attacks are in progress thus giving the defenders’ enough time to take preventive actions.
Effective Use of Information Systems for Emergency Management: A Representation Theory Perspective

What is the role of IS in scholarship on Emergency Management (EM)? To respond to this question, this literature review moves through two steps. First, we present an overview of current theories that dominate IS scholarship in EM. We discuss some of their limitations, and why we should consider redirecting current IS scholarship through a theoretical framework that prioritizes the analysis of systems-in-use. Second, we propose to overcome the limitations of the current theoretical frameworks by adopting a Representation Theory (RT) approach (Burton-Jones and Grange 2012). The concept of Effective Use is pivotal for RT, but until now it has not been applied to chaotic environments. Thus, we intermingle RT with dominant Information Systems theories for EM to inform a reconceptualization of Effective Use for EM systems. In particular, we extend the original model of Effective Use by: (1) integrating a new construct, situational awareness, as a successor of faithful representation, and (2) adding a new element of use, dynamic environment, to the current list of users, systems, and tasks.
Instability Analysis and Mitigation of Oscillation in Wound Rotor Brushless Doubly-Fed Machine

This study analyzes the cause of low-frequency oscillations in a brushless doubly-fed machine (BDFM) under the V/F scalar control strategy, where the control winding is fed by a voltage-source inverter in the frequency range of 5– 20 Hz. The dynamic reactive power harmonic current compensation method is used for mitigating these oscillations. The pole distribution of a 30-kW BDFM is calculated using MATLAB. The inherent mechanism responsible for the instability of the BDFM is clarified, and the factors causing the excited instability are analyzed. Finally, the effectiveness of the dynamic reactive power harmonic current compensation method is verified through a motor power generation experiment and a driver system.
Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network

Air pollution has a serious impact on our daily life, and how to quickly and easily measure the air pollution level without any expensive equipment is a quite challenging task. This paper proposes an air pollution estimation method using deep hybrid convolutional neural network from a single image, e.g., captured by a smartphone. The captured image is input to the main network, a very deep network, which solves the side effects of increased depth (degradation issues) by skip connection. This can improve network performance by simply increasing the depth of the network. Dark channel map is computed and fed into a secondary network to enrich the features with implicit representation. We have collected 1575 images of different scenes with different values of PM2.5 to train the network in the end-to-end fusion mode. Experimental results on synthetic dataset and real captured dataset demonstrate that our method achieves excellent performance on classification of air pollution levels from a single captured image.
Modified Local Ternary Pattern Based Face Recognition Using SVM

Face recognition (FR) has drawn considerable interest and attention in the area of pattern recognition. FR is still a challenging task in real time applications even though they are a number of face recognition algorithms which are available and work in various constrained environment. The paper proposes a FR algorithm using Modified Local Ternary Pattern (MLTP) with multi class Support Vector Machine (SVM) classifier. The MLTP features of the face images are classified by an Error-Correcting Output Code (ECOC) multiclass model with SVM. The proposed method is tested on six standard face databases. The experimental results have been demonstrated that the performance of MLTP with SVM can achieve higher recognition accuracy compared to the conventional methods.
Effect of Playful Physical Interactive Tiles on Cognition

We perform randomized controlled trial in order to investigate the effect of a modular interactive playware, the MOTO tiles, on cognitive abilities. Twenty university students were enrolled and equally assigned to an intervention group and a control group. The intervention was a 30 minutes training program consisting of 4 MOTO games. Training outcomes were measured by 4 cognitive tests implemented on Android tablet. Results of statistical analyses reveal that the intervention group got more improvement in reaction time of the tests Visual Search, Choice Reaction Time, and Sternberg Scanning.
About Pathologic Linear Circuits With Inconsistent Initial Conditions

This paper presents a procedure for the regularization of the inconsistent initial conditions in linear electrical circuits, which contain loops consisting of independent and/or controlled voltage sources and capacitors, or cut-sets of independent and/or controlled current sources and inductors. Modeling of switched circuits by idealized models of switches have often led to discontinuities of circuit variables at the time instances of switching, phenomena called as inconsistent initial conditions. In these cases, Dirac impulses and their various order derivatives can occur in the expressions of the capacitor currents and inductor voltages, which are proportional to the derivatives of the discontinuous variables (charges/voltages, and fluxes/currents respectively). Because of these impulses, the inconsistent initial conditions at $t_{0-}$ are converted into consistent initial conditions at $t_{0+}$. In this paper we present a simple method for the analysis of complex switched circuits that solves this problem.