Miracle-dz/UniFAR-FTUs huggingface dataset:
Mean Delay Analysis of MIMO-ZFBF Multiplexing in Random Networks Under LOS/NLOS Path-Loss Model

We analyze the performance of a multiple-input multiple-output multiplexing system in a Poisson bipolar network under line-of-sight/non-line-of-sight (LOS/NLOS) path-loss model and zero-forcing beamforming at receivers. The capacity and outage performance of such a configuration, commonly under the standard path-loss model, have been broadly analyzed; yet little is known about its local transmission delay with considering the traits of LOS/NLOS model. As the effective fading power gain on each data stream is Nakagami-type, and due to the interference correlation across data streams of a link as well as the retransmission attempts, the evaluation of the mean delay is more involved than the capacity/coverage evaluation. Our rigorous analysis provides a lower bound and an approximate upper bound on the mean delay as the functions of density, multiplexing gain, transmission activity, and LOS/NLOS model, which sheds some light on the effect of the LOS component in circumventing the possible divergence of the mean delay. Simulations show the lower bound is very accurate and demonstrate several aspects of multiplexing, path-loss traits, and interference correlation on the mean delay. Exploiting the analysis, we further explore the optimization of effective spatial throughput of the network.
Network Sampling Using k-hop Random Walks for Heterogeneous Network Embedding

Capturing neighborhood information by generating node sequences or node samples is an important prerequisite step for many of the neural network embedding approaches. Majority of the recent studies on neural network embedding exploit random walk as a sampling method, which traverses through adjacent neighbors to generate the node sequences. Traversing through only immediate neighbor may not be suitable particularly for heterogeneous information networks (HIN) where adjacent nodes tend to belong to different types. Therefore, this paper proposes a random walk based sampling approach (RW-k) which generates the node sequences such that adjacent nodes in the sequence are separated by k edges preserving the k-hop proximity characteristics. We exploit the node sequences generated using RW-k sampling for network embedding using skip-gram model. Thereafter, the performance of network embedding is evaluated on future co-authorship prediction task over three heterogeneous bibliographic networks. We compare the efficacy of network embedding using proposed RW-k sampling with recently proposed network embedding models based on random walks namely, Metapath2vec, Node2vec and VERSE. It is evident that the RW-k yields better quality of embedding and out-performs baselines in majority of the cases.
Ultrasound‐assisted Synthesis of Ag‐ZnS/rGO and its Utilization in Photocatalytic Degradation of Tetracycline Under Visible Light Irradiation

Recent improvements based on heterojunction nanocomposites have opened new possibilities in photocatalysis. In this research, an ultrasound‐assisted coprecipitation method was used to fabricate silver, zinc sulfide and reduced graphene oxide (Ag‐ZnS/rGO) nanocomposite, and characterization results indicated that 3% Ag‐ZnS spherical nanoparticles are successfully embedded in rGO matrix. The potential of the Ag‐ZnS/rGO, as a visible light active photocatalyst, was assessed through optimizing degradation of Tetracycline (TC) by response surface methodology. It was found that the photocatalytic degradation of TC increased with an increase in the amount of nanocomposite and irradiation time, whereas it decreased with increasing the initial TC concentration. Under the optimal conditions (10 mg L−1 of TC, 1.25 g L−1 of Ag‐ZnS/rGO, at pH = 7, and irradiation duration 110 min), more than 90% of the TC was degraded. The study of the mechanism of the photocatalytic process disclosed that the synergistic role of surface plasmon resonance (SPR) induced by Ag nanoparticles and p‐type semiconductor feature of rGO leads to ZnS semiconductor stimulation in the visible light region. Eventually, a pseudo‐first order kinetics model was developed based on the proposed mechanism. The obtained results highlight the role of Ag‐ZnS/rGO nanophotocatalyst toward degradation of some antibiotics under visible light.
DELAYED HEADING DATE1 interacts with OsHAP5C/D, delays flowering time and enhances yield in rice

Summary Heading date is an important agronomic trait affecting crop yield. The GRAS protein family is a plant‐specific super family extensively involved in plant growth and signal transduction. However, GRAS proteins are rarely reported have a role in regulating rice heading date. Here, we report a GRAS protein DHD1 (Delayed Heading Date1) delays heading and enhances yield in rice. Biochemical assays showed DHD1 physically interacts with OsHAP5C/D both in vitro and in vivo. DHD1 and OsHAP5C/D located in the nucleus and showed that rhythmic expression. Both DHD1 and OsHAP5C/D affect heading date by regulating expression of Ehd1. We propose that DHD1 interacts with OsHAP5C/D to delay heading date by inhibiting expression of Ehd1.
Load Commutated SCR Based Multilevel Current Source Inverter Fed Induction Motor Drive

A new scheme for realization of a load commutated multilevel current source inverter (CSI) for induction motor with open-end stator windings is presented in this paper. In this scheme there are two SCR based current source inverters at one end of the stator windings and a capacitor-fed voltage source inverter (VSI) at the other end. The current source inverters are operated in a phase shifted manner for generation of multilevel current waveform in the motor. The VSI is controlled to supply adequate reactive power for keeping the power factor at CSI output terminals sufficiently leading to ensure load commutation of SCRs. The control scheme of the VSI also ensures balancing of the voltage across the capacitor. The proposed topology is experimentally verified on a 1.5hP open-end winding induction motor with the help of a digital signal processor TMS320F28335.
Basal Forebrain Nuclei Display Distinct Projecting Pathways and Functional Circuits to Sensory Primary and Prefrontal Cortices in the Rat

Recent evidence supports that specific projections between different basal forebrain (BF) nuclei and their cortical targets are necessary to modulate cognitive functions in the cortex. We tested the hypothesis of the existence of specific neuronal populations in the BF linking with specific sensory, motor, and prefrontal cortices in rats. Neuronal tracing techniques were performed using retrograde tracers injected in the primary somatosensory (S1), auditory (A1), and visual (V1) cortical areas, in the medial prefrontal cortex (mPFC) as well as in BF nuclei. Results indicate that the vertical and horizontal diagonal band of Broca (VDB/HDB) nuclei target specific sensory cortical areas and maintains reciprocal projections with the prelimbic/infralimbic (PL/IL) area of the mPFC. The basal magnocellular nucleus (B nucleus) has more widespread targets in the sensory-motor cortex and does not project to the PL/IL cortex. Optogenetic stimulation was used to establish if BF neurons modulate whisker responses recorded in S1 and PL/IL cortices. We drove the expression of high levels of channelrhodopsin-2, tagged with a fluorescent protein (ChR2-eYFP) by injection of a virus in HDB or B nuclei. Blue-light pulses were delivered to the BF through a thin optic fiber to stimulate these neurons. Blue-light stimulation directed toward the HDB facilitated whisker responses in S1 cortex through activation of muscarinic receptors. The same optogenetic stimulation of HDB induced an inhibition of whisker responses in mPFC by activation of nicotinic receptors. Blue-light stimulation directed toward the B nucleus had lower effects than HDB stimulation. Our findings pointed the presence of specific neuronal networks between the BF and the cortex that may play different roles in the control of cortical activity.
TZ- VirtIO: Enabling Standardized Inter-Partition Communication in a Trustzone-Assisted Hypervisor

Virtualization technology allows the coexistence and execution of multiple operating systems on top of the same hardware platform. In the embedded systems domain, virtualization has been focused on the isolation of critical requirements like real-time, security and safety from non-critical characteristics. The strict confinement of guest partitions typically provided by virtualization does not suit the modular and inter-cooperative nature of embedded systems. The need for inter-partition communication has been addressed by multiple virtualization solutions, either to enable guest-level device para-virtualization or to ensure increased flexibility regarding cooperative partitions. However, the majority of existing approaches follow an ad hoc approach with limited to none applicability outside their solution's scope. This paper presents TZ-VirtIO, an asynchronous standardized inter-partition communication (IPC) mechanism on top of a TrustZone-assisted dual-OS hypervisor (LTZVisor). The implemented IPC uses the standard VirtIO transport layer. The experiments conducted on a physical platform show a scalable, high-bandwidth and low-overhead solution for both single-core and multi-core architectures.
Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it – the final sequence generator treating the retrieval as additional context. We show on the recent ConvAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations.
IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety

Fires usually occur in homes because of carelessness and changes in environmental conditions. They cause threats to the residential community and may result in human death and property damage. Consequently, house fires must be detected early to prevent these types of threats. The immediate notification of a fire is the most critical issue in domestic fire detection systems. Fire detection systems using wireless sensor networks sometimes do not detect a fire as a consequence of sensor failure. Wireless sensor networks (WSN) consist of tiny, cheap, and low-power sensor devices that have the ability to sense the environment and can provide real-time fire detection with high accuracy. In this paper, we designed and evaluated a wireless sensor network using multiple sensors for early detection of house fires. In addition, we used the Global System for Mobile Communications (GSM) to avoid false alarms. To test the results of our fire detection system, we simulated a fire in a smart home using the Fire Dynamics Simulator and a language program. The simulation results showed that our system is able to detect early fire, even when a sensor is not working, while keeping the energy consumption of the sensors at an acceptable level.
Special Issue: Quality of Service in Wireless Sensor/Actuator Networks and Systems

Wireless communications have long been a part of our daily lives: from remote controls to baby monitors, from cellular to local area networks, connecting us to the Internet and integrating our various gadgets and peripherals. However, industrialists have been very conservative in allowing wireless to enter their factory automation and process control systems. This has been mainly because “wireless” technologies had only been used in very specific and limited industrial scenarios (e.g., to facilitate point-to-point connections) and often granted insufficient reliability, security, or lifetime guarantees. As wireless technology matured (e.g., IEEE 802.11-based and IEEE 802.15.4-based), wireless sensors, actuators, and controllers started penetrating the factory floor, complementing or even replacing their wired counterparts. While these “hybrid” wired/wireless systems are already a commodity, we are at the dawn of a new era, where computers, sensors, and actuators are becoming increasingly embedded and ubiquitous, scaling up systems at unprecedented levels. The number of (embedded, networked) devices will grow dramatically, while the size of individual devices/nodes will necessarily shrink. The so-called “Internet-of-Things” and “Industry 4.0” are very broad umbrellas that fit into this paradigm. Wireless sensor/actuators networks (WSANs) are thus being increasingly used in a panoply of applications, such as industrial automation, process control, ambient assisted living, structural health monitoring, and homeland security. Most of these applications require specific quality-of-service (QoS) guarantees from their underlying communication infrastructures (regardless of their wireless, wired, or hybrid nature). While QoS has been traditionally associated with bit/data rate, network throughput, message delay, and bit/packet error rate, these properties alone do not reflect the overall “quality of service” that needs to be provided for such applications (and for their users). Other (non-functional) properties such as scalability, security, mobility, or energy sustainability must also be considered in the design of such complex cyber-physical systems. Importantly, these properties often conflict. This special issue targeted contributions in wireless sensor/actuator networks and systems (WSANSs) addressing QoS properties (hopefully in combination) such as reliability and robustness, timeliness and real-time, scalability, mobility, security and privacy, and energy efficiency and sustainability. We sought for works that were sufficiently mature, tested, and evaluated through analytical, simulation, or experimental models. Extensions to previously published works were eligible, provided that this fact was clearly stated in the submission and that the new contribution was significant. We were envisaging works covering one or more of the following WSAN topics, with QoS as an overarching aspect:
Capacity Analysis of UAV Communications: Cases of Random Trajectories

This paper analyzes the link capacity between autonomous unmanned aerial vehicles (UAVs) with random three-dimensional (3-D) trajectories. This is distinctively different from existing works typically under the assumption of either two-dimensional (2-D) or deterministic trajectories, and particularly interesting to applications such as surveillance and air combat. The key idea is that we geometrically derive the probability distributions of the UAV-to-UAV (U2U) distances which, by exploiting the Jensen's inequality, can be translated to the closed-form bounds for the capacity between UAVs, and between UAVs and ground stations. Another important aspect is that we extrapolate the idea to dense UAV networks, and analyze the impact of network densification, imperfect channel state information, and interference from ground transmitters on the capacity. Corroborated by simulations, our analysis shows that a U2U link with random 2-D trajectories is superior in terms of capacity due to its short average link distance. It is also revealed that a UAV-to-ground link can incur substantially lower capacity than a U2U link even in the case the 3-D coverage of the UAVs is the same, as the result of its longer average link length.
A Low Computational Cost Model Predictive Controller for Grid Connected Three Phase Four Wire Multilevel Inverter

The evolving industry applications of neutral point clamped multilevel inverters have motivated the researchers to improve its controllability and performance. Finite control set model predictive controller (FCSMPC) dominates over the conventional controllers because of its unconditional stability and ease in digital implementation. However, computational burden is an issue which needs to be addressed in FCSMPC for higher level inverters. This paper reports two novel methods which employ three-dimensional natural coordinate space vector geometry of grid connected neutral point clamped three phase three level four wire multilevel inverters to lower the computation burden in FCSMPC. One of the methods is dedicated for controlling only the grid currents. In this method, The coordinates of optimal switching state is obtained directly without evaluating any objective function. Whereas, in the other method, the grid currents and neutral point voltage, both are controlled simultneously. This method evaluates eight objective functions to get the optimal switching state. MATLAB simulation results are provided to compare the performance of the proposed controller with the conventional FCSMPC.
Robust Adaptive Fault-Tolerant Control of Nonlinear Systems With Assignable Tracking Performance Under Time-Varying Control Coefficients

This paper presents a robust adaptive fault-tolerant control method for a class of strict-feedback nonlinear systems with nonparametric uncertainties and unknown time-varying control coefficients as well as actuation faults. By employing a time-varying scaling function and an integrable function into control design, an adaptive Nussbaum gain-based fault-tolerant control approach is established, which displays the following salient features compared with the existing results: 1) the incorporation of time-varying scaling function renders that the tracking error converges to an adjustable compact set within an arbitrarily prescribed time at a decay rate no less than a pre-specified value, and 2) the utilization of the integrable function can handle the nonparametric uncertainties and actuation faults, which ensures that the nonlinear system is stable over <inline-formula> <tex-math notation="LaTeX">$[0,\infty$ </tex-math></inline-formula>) and the tracking error converges to zero as <inline-formula> <tex-math notation="LaTeX">$t\to \infty $ </tex-math></inline-formula>. Furthermore, it is proved that the developed control law can guarantee that all signals of the closed-loop systems are globally uniformly ultimately bounded. Simulation results validate the benefits and effectiveness of the proposed scheme.
Moving Object Segmentation in Jittery Videos by Stabilizing Trajectories Modeled in Kendall's Shape Space

Moving Object Segmentation is a challenging task for jittery/wobbly videos. For jittery videos, the non-smooth camera motion makes discrimination between foreground objects and background layers hard to solve. While most recent works for moving video object segmentation fail in this scenario, our method generates an accurate segmentation of a single moving object. The proposed method performs a sparse segmentation, where frame-wise labels are assigned only to trajectory coordinates, followed by the pixel-wise labeling of frames. The sparse segmentation involving stabilization and clustering of trajectories in a 3-stage iterative process. At the 1st stage, the trajectories are clustered using pairwise Procrustes distance as a cue for creating an affinity matrix. The 2nd stage performs a block-wise Procrustes analysis of the trajectories and estimates Frechet means (in Kendall's shape space) of the clusters. The Frechet means represent the average trajectories of the motion clusters. An optimization function has been formulated to stabilize the Frechet means, yielding stabilized trajectories at the 3rd stage. The accuracy of the motion clusters are iteratively refined, producing distinct groups of stabilized trajectories. Next, the labels obtained from the sparse segmentation are propagated for pixel-wise labeling of the frames, using a GraphCut based energy formulation. Use of Procrustes analysis and energy minimization in Kendall's shape space for moving object segmentation in jittery videos, is the novelty of this work. Second contribution comes from experiments performed on a dataset formed of 20 real-world natural jittery videos, with manually annotated ground truth. Experiments are done with controlled levels of artificial jitter on videos of SegTrack2 dataset. Qualitative and quantitative results indicate the superiority of the proposed method.
An Overview and a Benchmark of Active Learning for One-Class Classification

Active learning stands for methods which increase classification quality by means of user feedback. An important subcategory is active learning for one-class classifiers, i.e., for imbalanced class distributions. While various methods in this category exist, selecting one for a given application scenario is difficult. This is because existing methods rely on different assumptions, have different objectives, and often are tailored to a specific use case. All this calls for a comprehensive comparison, the topic of this article. This article starts with a categorization of the various methods. We then propose ways to evaluate active learning results. Next, we run extensive experiments to compare existing methods, for a broad variety of scenarios. One result is that the practicality and the performance of an active learning method strongly depend on its category and on the assumptions behind it. Another observation is that there only is a small subset of our experiments where existing approaches outperform random baselines. Finally, we show that a well-laid-out categorization and a rigorous specification of assumptions can facilitate the selection of a good method for one-class classification.