Miracle-dz/UniFAR-FTUs huggingface dataset:
Design principles for ultra-dense Wi-Fi deployments

Future indoor networks need to provide broadband services either by increasing bandwidth, using higher order modulations and high spatial reuse. To this end, ultra-dense Wi-Fi networks need to be deployed. This work discusses the challenges of ultra-dense Wi-Fi networks, where inter-AP distances are 5 m or less, and discuss necessary requirements to maximize the high spatial reuse of radio resources through the use of adaptive Clear Channel Assessment (CCA) thresholds and downward facing directional antennas. We also examine the significant impact of uplink transmissions, and present the necessary uplink transmit power control in ultra-dense deployments to avoid the interference coming from hidden nodes in neighboring APs. With the proposed techniques and antennas in place, we show that the limitations placed by high contention and interference in ultra-dense deployments can be overcome to provide over 8 χ gains in downlink throughput when compared with APs with omni-directional and unadjusted CCA thresholds.
Pharmacokinetic simulation for prediction of drug-drug interactions based on agent based modeling

Drug-Drug Interactions (DDI) is a cause of treatment inefficacy and toxicity. Many DDIs are caused by alterations in drug metabolism that related to drug-enzyme actions. Drugs can be substrate, inhibitor or inducer of enzymes which interfere their metabolism. Therefore, this paper proposes a method for simulating the DDI using agent based modeling (ABM). The study consists of three steps: data gathering, drug-enzyme action pairing and ABM generation. The ABM application was developed, allows user to adjust drug concentration, reaction rate parameters, and add others drugs to the system environment. To demonstrate the ABM application, the enzyme reaction of DDI example (simvastatin and amiodarone) was simulated. Simulation results demonstrate that the model could be an effective method to do the DDI simulation without the setting of the real experiment.
Self-Paced AutoEncoder

Autoencoder, which learns latent representations of samples in an unsupervised manner, has great potential in computer vision and signal processing. However, the diversity of samples makes learning a component autoencoder remaining a challenging task. This letter proposes a novel Self-Paced AutoEncoder (SPAE) for unsupervised feature extraction. The motivation behind this letter is to take samples gradually from simple to complex into consideration during training, which is similar to the mechanism of knowledge acquisition for humans. Under the unsupervised learning framework constructed on the autoencoder infrastructure, our SPAE first learns a weak autoencoder via samples with small losses and, then, elevates itself to a relatively strong autoencoder through samples with large losses. Then, the SPAE is generalized to a temporal domain, resulting to temporal SPAE (TSPAE), where the temporal information is explored and exploited to improve the performance. Typically, a TSPAE is capable of compressing temporal sequences into temporal-independent data. Experiments on the image classification and action recognition demonstrate the effectiveness of SPAE and TSPAE.
An improved method for mobility prediction using a Markov model and density estimation

The prediction of an individual's future locations is a significant part of scientific researches. While a variety of solutions have been investigated for the prediction of future locations, predicting departure and arrival times at predicted locations is a task with higher complexity and less attention. While the challenges of combining spatial and temporal information have been stated in various works, the proposed solutions lack accuracy and robustness. This paper proposes a simple yet effective way to predict not only an individual's future location, but also most probable departure and arrival times as well as the most probable route from origin to destination.
Automated detect and avoid: Autonomy and ethics

In the aeronautical, automotive and nautical domain, automation of collision avoidance maneuvering is being addressed. In the automotive domain, the potential occurrence of so-called dilemma situations imposes yet unresolved challenges to the design of the automation. In the aeronautical domain, systems enabling (pilot-supervised) automated selection and execution of collision avoidance maneuvers have been certified. To prevent the need for a collision avoidance maneuver, aircraft have to remain Well Clear. For today's Detect and Avoid systems, the pilot has to choose and initiate the Well Clear maneuver. In this paper, a roadmap for a stepwise increase in automation of Detect and Avoid systems is discussed and the associated challenges are identified. It is argued that, similar to the automotive domain, beyond a certain level of automation, the ethical question under what conditions pilot judgment can be automated must be answered, and that this may well determine the upper limit to the level of automation that is acceptable.
Standalone and grid connected operations of a SynRG based WECS with BESS

This work advocates an effective way of energy management in a microgrid consisting of a synchronous reluctance generator (SynRG) based wind energy conversion system (WECS) connected to a DC bus, which voltage is maintained by a battery energy storage system (BESS). The system is capable of feeding a fairly constant value of power to the grid apart from supplying the local loads. Apart from maintaining the DC link voltage, the BESS compensates for the fluctuations in the wind power by channelizing the power in and out of the BESS as per the power generation and power demand. The SynRG is operated in direct torque control (DTC) mode corresponding to maximum power point (MPP) of the WECS by adjusting the control inputs to the machine side converter. The battery in BESS, is integrated to the DC bus using a bidirectional converter (BDC), which helps in charging and discharging of the battery. A voltage source converter (VSC) connected on the grid side evacuates the power from the common DC link and feeds it to the three-phase grid. This VSC is controlled in such a way as to maintain the required power factor on the grid side as per the reference values of the direct and quadrature axes components of currents specified. The proposed system is designed, modeled and simulated in Simulink/ MATLAB environment.
A novel role of metal response element binding transcription factor 2 at the Hox gene cluster in the regulation of H3K27me3 by polycomb repressive complex 2

Polycomb repressive complex 2 (PRC2) is known to play an important role in the regulation of early embryonic development, differentiation, and cellular proliferation by introducing methyl groups onto lysine 27 of histone H3 (H3K27me3). PRC2 is tightly associated with silencing of Hox gene clusters and their sequential activation, leading to normal development and differentiation. To investigate epigenetic changes induced by PRC2 during differentiation, deposition of PRC2 components and levels of H3K27me3 were extensively examined using mouse F9 cells as a model system. Contrary to positive correlation between PRC2 deposition and H3K27me3 level, down-regulation of PRC2 components by shRNA and inhibition of EZH1/2 resulted in unexpected elevation of H3K27me3 level at the Hox gene cluster despite its global decrease. We found that metal response element binding transcriptional factor 2 (MTF2), one of sub-stoichiometric components of PRC2, was stably bound to Hox genes. Its binding capability was dependent on other core PRC2 components. A high level of H3K27me3 at Hox genes in Suz12-knock out cells was reversed by knockdown of Mtf2.This shows that MTF2 is necessary to consolidate PRC2-mediated histone methylation. Taken together, our results indicate that expression of Hox gene clusters during differentiation is strictly modulated by the activity of PRC2 secured by MTF2.
Modeling and design of solid state smart transformer for microgrid

While the techno-economic feasibility of solid state transformer (SST) is constantly under question, some of the leaders in power sector have recognized the potential of the technology and have started efforts of commercialization. One of the important advantages of SST is its ability to integrate to low voltage dc grids and vehicle-to-grid (V2G) technology. One of the key area of research in this field is to develop an accurate model for the SST to demonstrate the control capabilities and perform the dynamic analysis of the system. Although detailed models have been proposed for individual components of SST, only lower order averaged models are used for modelling the composite system. In this article, an accurate composite model of SST that incorporates high switching frequency is developed. The model is controlled using a conventional voltage control technique and a with a newly proposed power control technique. The control methods are then used accordingly to develop applications to integrate electric vehicles with its battery charging station in a dc microgrid.
Efficient digital implementation of space vector modulation for three-level NPC converters used in wind energy conversion systems

Wind Energy converter systems require a sophisticated control structure to perform accurate and efficient power conversion. The converter modulation is always a challenging topic. This Paper presents an efficient timing study of Space Vector Modulation (SVM) for Three Level Neutral Point Clamped Converters (3L-NPC). Various patterns have been proposed with different segments numbers and dwell time distribution. By increasing the number of segments, a fast Digital Signal Processors (DSP) is required to ensure the efficient execution of the SVM algorithm. In this paper, a systematic method to select the SVM switching time in relation to the DSP sampling time is presented. Moreover, a new technique for constructing the switching patterns is introduced. The proposed calculations are verified analytically and through simulating a detailed multilevel converter. The results demonstrate the critical relation between pattern selection, SVM switching frequency, and the DSP sampling frequency.
State Dependent Riccati Equation Based Rotor-Side Converter Control for Doubly Fed Wind Generator

This paper proposes a rotor-side converter (RSC) control of doubly fed induction generator (DFIG) through state-dependent Riccati equation (SDRE) approach to enhance the low voltage ride through (LVRT) ability. In order to guarantee the stability of the DFIG-based wind farm under external disturbances, a robust input-to-state stability (ISS)-based controller is presented in cooperation with the SDRE approach. The robust ISS controller is proved to be able to stabilize the wind generation system, while the system dynamics are not guaranteed to be of high performance. By parameterizing the robust ISS controller, the SDRE approach is used to provide guidance for the selection of ISS control parameters. Due to design flexibility, the SDRE control has the ability to enhance dynamic performances of wind generation system through optimizing the state-dependent coefficients. Time-domain simulations are carried out to verify the effectiveness of the proposed control approach. Compared with the conventional PI controller, exact linearization-based nonlinear controller, and robust ISS controller, the proposed SDRE-based RSC control has the ability to optimize the dynamics of the wind generation system during and after faults, and has better LVRT performance.
A Novel Autonomous Celestial Navigation Method Using Solar Oscillation Time Delay Measurement

Solar oscillations cause significant variations in sunlight spectral wavelength and intensity over short time periods. These oscillations have been studied in detail over the years, both theoretically and using actual observations. Through detecting the sunlight spectral central wavelengths and intensity and recording the moment when solar oscillation occurs, the time delay between the sunlight coming from the Sun directly and the sunlight reflected by a celestial body such as the satellite of a planet or asteroid can be obtained. Because the solar oscillation time delay is determined by the relative positions of the spacecraft, the reflective celestial body, and the Sun, it can be adopted as the navigation measurement to provide the spacecraft's position information. In this paper, a novel celestial navigation method using the solar oscillation time delay measurement is proposed. The implicit measurement model of time delay is built, and the implicit unscented Kalman filter is applied. Simulation results indicate that the position error and velocity error of the proposed method for the transfer orbit are about 3.55 km and 0.077 m/s, respectively, and those for the surrounding orbit are about 1.76 km and 1.57 m/s, respectively. The impact of four factors on the navigation performance is also investigated.
2D proactive uplink resource allocation algorithm for event based MTC applications

We propose a two dimension (2D) proactive uplink resource allocation (2D-PURA) algorithm that aims to reduce the delay/latency in event-based machine-type communications (MTC) applications. Specifically, when an event of interest occurs at a device, it tends to spread to the neighboring devices. Consequently, when a device has data to send to the base station (BS), its neighbors later are highly likely to transmit. Thus, we propose to cluster devices in the neighborhood around the event, also referred to as the disturbance region, into rings based on the distance from the original event. To reduce the uplink latency, we then proactively allocate resources for these rings. To evaluate the proposed algorithm, we analytically derive the mean uplink delay, the proportion of resource conservation due to successful allocations, and the proportion of uplink resource wastage due to unsuccessful allocations for 2D-PURA algorithm. Numerical results demonstrate that the proposed method can save over 16.5 and 27 percent of mean uplink delay, compared with the 1D algorithm and the standard method, respectively.
Expression Empowered ResiDen Network for Facial Action Unit Detection

The paper explores the topic of Facial Action Unit (FAU) detection in the wild. In particular, we are interested in answering the following questions: (1) How useful are residual connections across dense blocks for face analysis? (2) How useful is the information from a network trained for categorical Facial Expression Recognition (FER) for the task of FAU detection? The proposed network (ResiDen) exploits dense blocks along with residual connections and uses auxiliary information from a FER network. The experiments are performed on the EmotionNet and DISFA datasets. The experiments show the usefulness of facial expression information for AU detection. The proposed network achieves state-of-the-art results on the two datasets. Analysis of the results for cross dataset protocol shows the effectiveness of the network.
Crowdsourcing with Arbitrary Adversaries

Most existing works on crowdsourcing assume that the workers follow the Dawid-Skene model, or the one-coin model as its special case, where every worker makes mistakes independently of other workers and with the same error probability for every task. We study a significant extension of this restricted model. We allow almost half of the workers to deviate from the one-coin model and for those workers, their probabilities of making an error to be task-dependent and to be arbitrarily correlated. In other words, we allow for arbitrary adversaries, for which not only error probabilities can be high, but which can also perfectly collude. In this adversarial scenario, we design an efficient algorithm to consistently estimate the workers’ error probabilities.
Heteroscedastic Regression and Active Learning for Modeling Affordances in Humanoids

We investigate the learning of object and tool affordances in the iCub robot. We adopt the setup proposed in a previous experiment, using a Bayesian network (BN) in place of a least square support vector machine (LSSVM). The collected data, consisting of continuous and discrete variables, are used for learning the structure of the BN. Hence, the model is leveraged to: 1) identify a regression function for the prediction of the effects of actions on objects, calculated as the mean of the observed values; and 2) provide information on the reliability of the predicted values through the estimation of the variance for subsets of local observations. The information on the input-dependent variance is used to guide the learning algorithm in order to improve the performance of the robot, and hence to reduce the variance from the predicted values. The replacement of the LSSVM with the BN model provides a general probabilistic framework for dependencies among random variables; we perform conditional probability queries that enable the robot to choose the actions to perform on objects and select the most appropriate tool to obtain desired effects. The capability to make inference enables the robot to gather a better understanding of the world.
Impact of Antenna Correlation on Full-Duplex Two-Way Massive MIMO Relaying Systems

This paper investigates the impact of antenna correlation on full duplex massive multiple-input multiple-output (MIMO) two-way relaying systems. To be general, all nodes in the system are equipped with multiple antennas and arbitrary antenna correlation is considered. Deterministic equivalent of the achievable sum-rate is first given, which enables the derivation of the asymptotic sum-rate when the number of relay antennas is large. The impact of antenna correlation on the asymptotic sum-rate is then thoroughly analyzed. The results reveal that with a large number of relay antennas, the asymptotic sum-rate is independent of the antenna correlation at the relay’s transmitter side, however it is Schur-concave with respect to the eigenvalue vectors of the involved antenna correlation matrices at both the users’ transceiver sides and the relay’s receiver side. In other words, the antenna correlations at both the users’ transceiver sides and the relay’s receiver side have detrimental effects on the sum-rate. Moreover, the antenna correlation at the users is more significant to the asymptotic sum-rate than that at the relay. The analysis is general and can be easily reduced to consider half-duplex and/or one-way massive MIMO relaying systems. Numerical results are finally provided to validate the analysis. It is also shown that a larger number of user antennas may not always lead to a higher sum-rate, and thus proper selection of the number of user antennas is necessary to maximize the sum-rate.
Transfer Learning for User Action Identication in Mobile Apps via Encrypted Trafc Analysis

Recent academic studies have demonstrated the possibility of inferring user actions performed in mobile apps by analyzing the resulting encrypted network traffic. Due to the multitude of app versions, mobile operating systems, and device models (collectively referred to in this paper as configurations) previous approaches are not applicable to real life settings. In this work, we ex-tend the ability of these approaches to generalize across different configurations. We treat the different configurations as a case for transfer learning, and adapt the co-training method to sup-port the transfer learning process. Our approach leverages a small number of labeled instances of encrypted traffic from a source configuration, in order to construct a classifier capable of identi-fying a users actions in a different (target) configuration which is completely unlabeled. Experi-ments on real datasets collected from different applications on Android devices show that the proposed method achieves F1 measures over 0.8 for most of the considered user actions.
An Antimicrobial Peptide Induces FIG1-Dependent Cell Death During Cell Cycle Arrest in Yeast

Although most antibiotics act on cells that are actively dividing and non-dividing cells such as in microbe sporulation or cancer stem cells represent a new paradigm for the control of disease. In addition to their relevance to health, such antibiotics may promote our understanding of the relationship between the cell cycle and cell death. No antibiotic specifically acting on microbial cells arrested in their cell cycle has been identified until the present time. In this study we used an antimicrobial peptide derived from α-pheromone, IP-1, targeted against MATa Saccharomyces cerevisiae cells in order to assess its dependence on cell cycle arrest to kill cells. Analysis by flow cytometry and fluorescence microscopy of various null mutations of genes involved in biological processes activated by the pheromone pathway (the mitogen-activated protein kinase pathway, cell cycle arrest, cell proliferation, autophagy, calcium influx) showed that IP-1 requires arrest in G0/G1 in order to kill yeast cells. Isolating cells in different cell cycle phases by elutriation provided further evidence that entry into cell cycle arrest, and not into G1 phase, is necessary if our peptide is to kill yeast cells. We also describe a variant of IP-1 that does not activate the pheromone pathway and consequently does not kill yeast cells that express the pheromone’s receptor; the use of this variant peptide in combination with different cell cycle inhibitors that induce cell cycle arrest independently of the pheromone pathway confirmed that it is cell cycle arrest that is required for the cell death induced by this peptide in yeast. We show that the cell death induced by IP-1 differs from that induced by α-pheromone and depends on FIG1 in a way independent of the cell cycle arrest induced by the pheromone. Thus, IP-1 is the first molecule described that specifically kills microbial cells during cell cycle arrest, a subject of interest beyond the process of mating in yeast cells. The experimental system described in this study should be useful in the study of the mechanisms at play in the communication between cell cycle arrest and cell death on other organisms, hence promoting the development of new antibiotics.
The development of the evaluation methodology of the methodological quality of scientific publications about clinical trials and requirements for the information system for its automation

The article is dedicated to the development of evaluation methodology and the requirements for the information system for the automation of the methodological quality assessment of scientific publications about clinical research. It contains the description of the relationship between clinical guidelines and the documents regulating the organization of medical care in Russia, the functional model of the process of updating the clinical guidelines based on methodological quality assessment of scientific publications, main criteria for the search and selection of such publications, the algorithm of methodological quality assessment of scientific publications depending on the types of research and the key requirements for the software and hardware for the automatization of the process.