Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytomegaloviruses Exploit Recycling Rab Proteins in the Sequential Establishment of the Assembly Compartment

Cytomegaloviruses (CMV) reorganize membranous system of the cell in order to develop a virion assembly compartment (VAC). The development starts in the early (E) phase of infection with the reorganization of the endosomal system and the Golgi and proceeds to the late phase until newly formed virions are assembled and released. The events in the E phase involve reorganization of the endosomal recycling compartment (ERC) in a series of cellular alterations that are mostly unknown. In this minireview, we discuss the effect of murine CMV infection on Rab proteins, master regulators of membrane trafficking pathways, which in the cascades with their GEFs and GAPs organize the flow of membranes through the ERC. Immunofluorescence analyzes of murine CMV infected cells suggest perturbations of Rab cascades that operate at the ERC. Analysis of cellular transcriptome in the course of both murine and human CMV infection demonstrates the alteration in expression of cellular genes whose products are known to build Rab cascades. These alterations, however, cannot explain perturbations of the ERC. Cellular proteome data available for human CMV infected cells suggests the potential role of RabGAP downregulation at the end of the E phase. However, the very early onset of the ERC alterations in the course of MCMV infection indicates that CMVs exploit Rab cascades to reorganize the ERC, which represents the earliest step in the sequential establishment of the cVAC.
Bacteria–Host Interactions in Multiple Sclerosis

Multiple sclerosis (MS) is caused by a complex interaction of genetic and environmental factors. Numerous causative factors have been identified that play a role in MS, including exposure to bacteria. Mycobacteria, Chlamydia pneumoniae, Helicobacter pylori, and other bacteria have been proposed as risk factors for MS with different mechanisms of action. Conversely, some pathogens may have a protective effect on its etiology. In terms of acquired immunity, molecular mimicry has been hypothesized as the mechanism by which bacterial structures such as DNA, the cell wall, and intracytoplasmic components can activate autoreactive T cells or produce autoantibodies in certain host genetic backgrounds of susceptible individuals. In innate immunity, Toll-like receptors play an essential role in combating invading bacteria, and their activation leads to the release of cytokines or chemokines that mediate effective adaptive immune responses. These receptors may also be involved in central nervous system autoimmunity, and their contribution depends on the infection site and on the pathogen. We have reviewed the current knowledge of the influence of bacteria on MS development, emphasizing the potential mechanisms of action by which bacteria affect MS initiation and/or progression.
Nonlinear Robust Flight Mode Transition Control for Tail-Sitter Aircraft

In this paper, the nonlinear robust control problem for tail-sitter aircraft in flight mode transitions is addressed. The problem is challenging due to the nonlinearities and uncertainties including parametric uncertainties, unmodeled uncertainties, and external disturbances involved in the vehicle dynamics during the mode transitions. The proposed controller is designed in two steps: first, the dynamic inversion technique is adopted to establish a tracking error model; then, for the tracking error model, a robust controller consisting of a nominal controller and a robust compensator is designed. The nominal controller is designed to obtain the desired tracking performances for the nominal system. The compensator is introduced to restrain the effects of uncertainties. Tracking errors of the closed-loop system are proven to converge into a neighborhood of the origin in a finite time. Simulation results are presented to demonstrate the advantages of the proposed controller, compared with the controller based on the standard loop-shaping method.
Experimental Testbed of Post-OFDM Waveforms Toward Future Wireless Networks

This paper details an experimental testbed conceived for studying the capability of new multicarrier waveforms to accommodate 5G requirements. Testbed experiments are done with an implementation of cyclically prefixed orthogonal frequency division multiplexing (CP-OFDM) and its most promising enhancements, i.e., weighted overlap and add based OFDM (WOLA-OFDM) and block-filtered OFDM (BF-OFDM), with configurable universal software radio peripherals (USRPs)-based software defined radio prototype. These experiments are done in a realistic laboratory-like environment, where capabilities of the selected waveforms to accommodate 5G requirements are evaluated while focusing on the optimization of the energy efficiency. On one hand, we provide details and deign guidance to improve energy efficiency and robustness of the studied waveforms through new approaches of digital predistortion (DPD) and peak-to-average power ratio (PAPR) reduction in the presence of RF power amplifier (RF PA). In particular, we focus on the mitigation of in-band and out-of-band non-linear distortions and their effects on power spectrum density (PSD) and bit error rate, respectively. It has been demonstrated that the combination of PAPR reduction and DPD allows the transmitter to significantly improve the spectrum localization without sacrificing the in-band and out-of-band waveform quality, while achieving high power efficiency, thus operating the PA very close to its saturation region, as well. On another hand, we address the impact of the lack of synchronism between transmitters on the performance of the selected waveforms, which is of special relevance for future 5G massive machine type communications applications. Experimental results show that BF-OFDM and WOLA-OFDM would permit the accommodation of 5G requirements when RF PA issues are tackled. In some specific scenarios, ideal spectrum utilization can be realized by these waveforms, using only one tone as guard band while keeping good energy efficiency.
PROTECT Multimodal DB: fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control

This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results.
GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices

This paper addresses the problem of recovering projective camera matrices from collections of fundamental matrices in multiview settings. We make two main contributions. First, given ${n \choose 2}$ fundamental matrices computed for $n$ images, we provide a complete algebraic characterization in the form of conditions that are both necessary and sufficient to enabling the recovery of camera matrices. These conditions are based on arranging the fundamental matrices as blocks in a single matrix, called the $n$-view fundamental matrix, and characterizing this matrix in terms of the signs of its eigenvalues and rank structures. Secondly, we propose a concrete algorithm for projective structure-from-motion that utilizes this characterization. Given a complete or partial collection of measured fundamental matrices, our method seeks camera matrices that minimize a global algebraic error for the measured fundamental matrices. In contrast to existing methods, our optimization, without any initialization, produces a consistent set of fundamental matrices that corresponds to a unique set of cameras (up to a choice of projective frame). Our experiments indicate that our method achieves state of the art performance in both accuracy and running time.
Facet-Based Regularization for Scalable Radio-Interferometric Imaging

Current and future radio telescopes deal with large volumes of data and are expected to generate high resolution gigapixel-size images. The imaging problem in radio interferometry is highly ill-posed and the choice of prior model of the sky is of utmost importance to guarantee a reliable reconstruction. Traditionally, one or more regularization terms (e.g. sparsity and positivity) are applied for the complete image. However, radio sky images can often contain individual source facets in a large empty background. More precisely, we propose to divide radio images into source occupancy regions (facets) and apply relevant regularizing assumptions for each facet. Leveraging a stochastic primal dual algorithm, we show the potential merits of applying facet-based regularization on the radio-interferometric images which results in both computation time and memory requirement savings.
Passivity and Passification of Fuzzy Memristive Inertial Neural Networks on Time Scales

A class of Takagi–Sugeno (T–S) fuzzy memristor-based inertial neural networks (FMINNs) is studied on time scales. The second-order derivative of the state variable in the network denotes the inertial term. At first, one timescale-type FMINNs is formulated on the basis of T–S fuzzy rules. By a variable transformation, the original network is transformed into first-order differential equations. Then, passivity criteria for the FMINNs are presented based on the characteristic function approach, linear matrix inequality techniques, and the calculus of time scales. Furthermore, two classes of control protocols, i.e., memristor- and fuzzy-related control protocols are designed to solve the passification problem for the considered FMINNs. The optimization problem of the passivity performance is also involved. Finally, simulation examples are given to show the effectiveness and validity of the obtained results, and an application is also given in pseudorandom number generation.
Quantized Stabilization for T–S Fuzzy Systems With Hybrid-Triggered Mechanism and Stochastic Cyber-Attacks

This paper examines quantized stabilization for Takagi–Sugeno (T–S) fuzzy systems with a hybrid-triggered mechanism and stochastic cyber-attacks. A hybrid-triggered scheme, which is described by a Bernoulli variable, is adopted to mitigate the burden of the network. By taking the effect of the hybrid-triggered scheme and stochastic cyber-attacks into consideration, a mathematical model for a closed-loop control system with quantization is constructed. Theorems for main results are developed to guarantee the asymptotical stability of networked control systems by using Lyapunov stability theory and linear matrix inequality techniques. Based on the derived sufficient conditions in theorems, the controller gains are presented in an explicit form. Finally, two practical examples demonstrate the feasibility of designed algorithm.
Protection Against Reconstruction and Its Applications in Private Federated Learning

In large-scale statistical learning, data collection and model fitting are moving increasingly toward peripheral devices---phones, watches, fitness trackers---away from centralized data collection. Concomitant with this rise in decentralized data are increasing challenges of maintaining privacy while allowing enough information to fit accurate, useful statistical models. This motivates local notions of privacy---most significantly, local differential privacy, which provides strong protections against sensitive data disclosures---where data is obfuscated before a statistician or learner can even observe it, providing strong protections to individuals' data. Yet local privacy as traditionally employed may prove too stringent for practical use, especially in modern high-dimensional statistical and machine learning problems. Consequently, we revisit the types of disclosures and adversaries against which we provide protections, considering adversaries with limited prior information and ensuring that with high probability, ensuring they cannot reconstruct an individual's data within useful tolerances. By reconceptualizing these protections, we allow more useful data release---large privacy parameters in local differential privacy---and we design new (minimax) optimal locally differentially private mechanisms for statistical learning problems for \emph{all} privacy levels. We thus present practicable approaches to large-scale locally private model training that were previously impossible, showing theoretically and empirically that we can fit large-scale image classification and language models with little degradation in utility.
Internal Digital Platforms and Generative Mechanisms of Digital Innovation

As digitalization puts increasing competitive pressures on companies to innovate in the digital space, corporate information system (IS) landscapes are increasingly serving as platforms that facilitate the development of digital value propositions on top of existing data and functionality. Beyond the boundaries of a single firm, the enabling role of multi-sided digital platforms for innovation is a well-researched phenomenon. Internal digital platforms, on the other hand, have received less attention in the IS community. Their part in the quest for digital innovation remains unclear. In a critical realist research approach, this paper investigates the digitalization journey of the LEGO Group and identifies three generative mechanisms – modular upgradability, economies of substitution, and reproduction –, through which an internal digital platform enables speed and efficiency of digital innovation within a company. Based on Systems Theory, a holistic conceptualization of an internal digital platform is developed that unifies traditional enterprise systems with modern digital technologies. Consequently, the analysis elaborates how the interactions among these heterogeneous components shape digital innovation in a company and develops the generative mechanisms that together form a system-theoretic model. Furthermore, the findings reveal the strategic choice of granularity that companies are facing during design of individual platform components. Therefore, the study does not only contribute to IS theory, but also provides relevant implications for practitioners.
Understanding the Staircase Modulation Strategy and its Application in Asymmetric Cascaded H-Bridge Multilevel Inverter

The staircase modulation strategy is the best choice for Asymmetric Cascaded H-bridge Multilevel Inverter (ACMI) because it makes the output voltage to present a high number of levels and makes one of the ACMI modules to operate at low frequency. However, the staircase modulation is usually employed without giving its deserved attention. This paper presents a clear understanding of the staircase modulation strategy and its applications in an ACMI. The principle of operation is carefully described, also indicating how to command the ACMI transistors. The output voltage of the two superior modules is evaluated for different value of iterations. Special attention is given to the power distribution among the ACMI modules when the staircase modulation is applied. It is demonstrated how the power is processed in each module considering an ACMI with three modules. Experimental results clarify and show the efficacy of the concepts presented in this paper.
Investigation of Emotion Exchange Motifs in Bot/Human Interactions During Riot Events

In this paper, we analyze a data-set consisting of 4.5 million tweets related to four highly emotional events and investigate the interaction patterns that emerge when social bots communicate with human users. In particular, we propose an emotion-annotated n-layer multiplex network model to study the involvement of bots in the exchange of emotional messages. To this end, we study four events: a) the riots that happened during the 2017 G20 summit in Hamburg, b) the 2017 Charlottesville riot, c) the 2017 Catalonia independence referendum riot, and d) the riots that happened after the Philadelphia Eagles won the 2018 Superbowl. In our analysis, we found that: 1) when identifying significant triadic patterns (motifs) in the the respective communication network, a number of star-like subgraphs emerge as representative and significant communication patterns, 2) bots tend to send messages of a higher emotional intensity, as compared to humans, 3) though bots predominantly send retweets, they also compose original messages that may potentially harm the reputation of the person they are targeted at, and 4) in contrast to previous findings, we found that during riot events up to 87.18 % of the involved bots actively engage in a direct communication with human users.
Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.
Odometry Based on Auto-Calibrating Inertial Measurement Unit Attached to the Feet

Location of pedestrian in indoor environment remains an open problem. A cheap and reliable sensor in this context is the inertial measurement units (IMU). However, due to the bias of both the accelerometer and the gyroscope, integrating directly the inertial measurements leads to tremendous drift, as the state of the system (position, orientation, velocity, bias) is not fully observable. In this paper, we consider the specific case of a foot-mounted IMU. We exploit specific prior knowledge (foot landing at zero velocity) in order to make the full state of the IMU observable. These information are gathered in a graphical model (a factor graph), and are exploited to build a maximum-likelihood estimator. The technical difficulty is to handle the size of the graph such that it is tractable in a limited time window, that we do by relying on the pre-integration technique. In that existing framework, our contributions are to reformulate the pre-integration method using quaternions while giving a simpler algebraic formulation, and to apply this method for estimating the human foot-pose during walking. We validate these concepts on long-range trajectories capture with human subject and compare the results with ground-truth measurements and previous results of the state of the art.
Physicochemical characterization and antioxidant activity of Palestinian honey samples

Abstract Physicochemical characteristics, main minerals, and antioxidant activity were determined for Palestinian honey samples belonging to different floral and geographical origins. The features of the analyzed samples were within the established international standards for honey quality control. One clear exception was the hydroxymethylfurfural (HMF) of the Ziziphus sample purchased from the Jericho region, which is the lowest city in the word characterized by a hot desert climate. The observed HMF value was 81.86 ± 2.64 mg/kg being two folds the maximum allowed in honey samples (40 mg/kg). As a second objective of the present work, the parameters were divided into two groups with different discriminatory power. The assessed physicochemical parameters, and the antioxidant activities, specific to the botanical origin discrimination, were used to run the first PCA. A strong correlation could be seen between the bioactive compounds and the antioxidant activities despite the geographical origin of the samples. Thyme and Ziziphus samples were the best samples, while citrus sample presented the lowest activity. Regarding the geographical discrimination, Ash and mineral contents in addition to the electrical conductivity were used. The output PCA conserved high represent ability of the data in the two‐first components being 82.72% and 9.60%. A little discrimination between the samples produced in the north and those produced in the south of the country, but it was not perfect. The intervention of the botanical variability could be the reason.
Transformation of mechanism of sales and services promotion in digital environment

Digitalization of economy will not solve the problem of gaining consumers’ trust. Consumers are getting more independent in their choice; they learn to escape watching advertisements. That is why reputation of an organization, together with price, is becoming a powerful stimulus of consumer choice. In order to overcome informational overload of digital environment and create reputational assets, it is necessary to change the strategies of marketing communications. The content of messages should address basic values of consumers, therefore the emphasis should be shifted to promoting social ideas. High importance of an idea means high involvement of consumers and that allows overcoming informational overload of digital environment. Correlation of expenses and profits from realizing a social idea and also the value of an idea for the society and an individuum determine four types of promoting strategies. Interconnection of an organization business and the problem being solved enables not only forming reputation but also performs commercialization of a social idea in the form of sales of a material product. Promoting a social idea requires network structure of building the process of communication. The network model of communication takes into account multiplicity of subjects participating in the communication and also ‘self-sufficiency’ which is intrinsic to the idea as benefit suggested for exchanging.