Miracle-dz/UniFAR-FTUs huggingface dataset:
Multistage Model Predictive Control based on Data-Driven Distributionally Robust Optimization

In this article, a novel distributionally robust optimization (DRO) based multistage model predictive control (MPC) framework is proposed to hedge against the uncertainty in control problems. Without a priori knowledge of the exact uncertainty distribution, an ambiguity set, constructed based on principal component analysis, incorporates the first-order moment information instead. A linear performance measure is chosen so that the worst-case expected problem can be exactly dualized by adopting the affine decision rule. By considering input and state constraints robustly with respect to a support set, which specifies the domain of the uncertainty, the DRO-based MPC model is developed as a robust optimization problem. The proposed framework is illustrated on a two-mass-spring system and results show the improved control performance compared to traditional control strategies.
Ultra-Wideband Air-to-Ground Channel Measurements and Modeling in Hilly Environment

Unmanned aerial vehicles (UAVs) have aroused great attention for future wireless communications since the drone can be used as an aerial base station and also as a cellular user to perform on-demand tasks. Because the reliable and robust connection between UAV and ground station (GS) plays an essential role in the UAV-based air-to-ground (AG) communications, the accurate radio channel model is needed. In this paper, we conducted AG wideband radio channel measurements in a hilly environment at 6.5 GHz with a bandwidth of 500 MHz, which is one of the high bands for ultra-wideband (UWB) communications in IEEE standards. The large-scale characteristics, including path loss and shadow fading, are represented by the close-in (CI) model. The small-scale characteristics that illustrate the fading and the multipath effect are analyzed with three critical parameters, including the small-scale fading, Rician K-factor and root-mean-square (RMS) delay spread. Afterward, the tapped delay line (TDL) model is built according to the detected multipath in the power delay profile (PDP), and the channel impulse response (CIR) is discussed for wideband channel modeling. The results can be used for the design and analysis of the AG systems.
Aerial Access Nodes and Virtual Wireless Access: A Look into Integration Strategies

One of the significant directions for coping with challenging requirements of 5G and beyond networks is virtual wireless access (VWA). In a VWA framework, physical access nodes form virtual cells tailored to the conditions of the network. Another research area that increases agility of wireless networks is utilizing aerial networks to support its terrestrial counterparts. Despite the recently flourished literature on airborne communications, there are limited studies on integrating aerial nodes into existing network. Therefore, this article investigates efficient integration methods of aerial access nodes into networks with VWA. In particular, we first propose an optimal virtual cell formation method, which is the preferred VWA framework in this study. Second, the VWA framework is combined with airborne communications by utilizing a drone-base-station (drone-BS) as a flexible transmission point. Note that in this case, both the 3D position of the drone-BS and the optimal virtual cell formation need to be obtained. Therefore, several strategies are developed to perform virtual cell formation by considering the flexibility of the drone-BSs with the help of tools from convex optimization and artificial intelligence.
STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting

Traffic flow forecasting is a critical task for urban traffic control and dispatch in the field of transportation, which is characterized by the high nonlinearity and complexity. In this paper, we propose an end-to-end deep learning based dual path framework, i.e., Spatial-Temporal Graph Attention Network (STGAT), for traffic flow forecasting. Specifically, different from previous structure-based approaches, STGAT can be directly generalized to the graph with arbitrary structure. Furthermore, STGAT is capable of handling long temporal sequence by stacking gated temporal convolution layer. The dual path architectures is proposed for taking both potential and existing spatial dependencies into account. By capturing potential spatial dependencies will naturally catch more useful information for forecasting. We design a gated fusion mechanism to combine the outputs from each path. The proposed model can be directly applicable to inductive learning tasks by introducing a graph attention mechanism into spatial-temporal framework, which means our model can be generalized to completely unseen graphs. Moreover, experimental results on two public real-world traffic network datasets, METR-LA and PEMS-BAY, show that our STGAT outperforms the state-of-the-art baselines. Additionally, we demonstrate the proposed model is competent for efficient migration between graphs with different structures.
Convexified contextual optimization for on-the-fly control of smooth systems

We consider the problem of data-driven, on-the-fly, constrained control of unknown nonlinear dynamics from a single finite-horizon trajectory. We are motivated by applications with severe limits on data availability and require online controller synthesis. Using a one-step receding horizon control framework, we pose the data-driven optimal control problem as a contextual optimization problem. Specifically, we seek to iteratively minimize a smooth (black-box) objective function over iteration-dependent constraints. We propose C2Opt, a data-driven, convex-optimization-based approach to solve this problem. We utilize correct-by-construction function approximation bounds on the objective function to guide the decision process. Our bounds are synthesized from data and smoothness assumptions, and accommodates incorporation of side information like convexity, monotonicity, and loose bounds on the function. We then use tractable convex optimization problems to solve for the iterates, given the context and past data. We utilize C2Opt to successfully drive a unicycle to a target destination under control constraints, using only the data collected from a single finite-horizon trajectory.
An Empirical Study on the Factors of Virtual Simulation Teaching Effect

Analyzing the factors of virtual simulation teaching effect is helpful to find better teaching method. This paper selects the students in Heyuan Polytechnic as the research sample, combined with the relevant literature research, aiming at the factors that affect virtual simulation teaching effect, and uses the structural equation model to make empirical analysis. The result shows that: usefulness, feedback and reflection, operation and application all have positive effects on participation. Students’ participation and exploration will affect teaching effect, and have significant positive impact on improving students’ outcome of knowledge mastery, operation skill and communication skill. suggestions were put forward to improve the effect of virtual simulation teaching base on research result.
Regression-Based Uplink Interference Identification and SINR Prediction for 5G Ultra-Dense Network

Ultra-dense network (UDN) is recognized as a key technology for the fifth generation (5G) network to deliver high capacity. Due to cell densification, UDN suffers from heavy intercell interference (ICI). To mitigate ICI, an accurate interference modeling is essential. Compared to distributed mechanism, centralized interference management is more adept at addressing server ICI problem, especially in dense deployments. Hence, we adapt centralized radio access network (C-RAN) architecture to obtain global channel state data. In this paper, a novel regressionbased uplink interference identification and signal-to-interference ratio (SINR) prediction algorithm is proposed for C-RAN based 5G UDN. By leveraging big data technology with in-depth analysis of the cause of ICI, this algorithm can precisely model the interference relationship and thus achieve very accurate SINR prediction. In addition, the proposed algorithm is highly efficient in computing, as demonstrated by complexity analysis.
Biological control of common blight of bean (Phaseolus vulgaris) causedby Xanthomonas axonopodis pv. phaseoli by using the bacteriumRahnella aquatilis

The aim of this study was to evaluate the bacterium Rahnella aquatilis (Ra) for protection of bean plants against common blight disease caused by Xanthomonas axonopodis pv. phaseoli (Xap). Xap isolates were isolated from a naturally blighted leaves of bean plants grown in Assiut governorate. The blight symptoms were produced by all three isolates, but the isolates differed in their degree of the pathogenicity. Xap1 was the most virulence one against bean plants. The effect of Ra against common blight of bean plant was tested. In vitro studies, Ra exhibited inhibitor effect against the pathogen. Under greenhouse and field conditions, beanvariety “Giza 6” treated by Ra resulted in marked disease suppression. Ahigh decrease of the disease was correlated with a reduction of the bacterial multiplication. In physiological studies, bean plants treated by Ra exhibited higher phenolic compounds contents and higher activity of peroxidase (PO) enzyme than untreated plants. In conclusion, application of Ra was effective and could be recommended for controlling the bean common blight disease.
A Low-Complexity Algorithm for Achieving Secrecy Capacity in MIMO Wiretap Channels

We consider a secure transmission including a transmitter, a receiver and an eavesdropper, each being equipped with multiple antennas. The aim is to develop a low-complexity and scalable method to find a globally optimal solution to the problem of secrecy rate maximization under a total power constraint at the transmitter. In principle, the original formulation of the problem is nonconvex. However, it can be equivalently translated into finding a saddle point of a minimax convex-concave program. An existing approach finds the saddle point using the Newton method, whose computational cost increases quickly with the number of transmit antennas, making it unsuitable for large scale antenna systems. To this end, we propose an iterative algorithm based on alternating optimization, which is guaranteed to converge to a saddle point, and thus achieves a globally optimal solution to the considered problem. In particular, each subproblem of the proposed iterative method admits a closed-form solution. We analytically show that the iteration cost of our proposed method is much cheaper than that of the known solution. As a result, numerical results demonstrate that the proposed method remarkably outperforms the existing one in terms of the overall run time.
Cramer-Rao Bound Approach and Error Performance for Spatial Modulation over Beckmann Fading Channels

Spatial modulation (SM) is a promising low-complexity alternative to the state-of-art multiple-input multiple-output (MIMO) schemes due to its unique transmission approach. In this work, the impact of channel imperfections on the performance of SM-MIMO systems are investigated over generalized Beckmann fading channels, which introduce a versatile multipath fading model that includes several distributions such as Ricean, Nakagami-m and Beaulieu-Xie, as special cases. Specifically, an optimum maximum likelihood detection (MLD) method is proposed and the average pairwise error probability (APEP) is calculated by analytical derivations. Cramer-Rao bound approach is utilized for further improvement on decreasing the deteriorating effects that rise from estimation errors. The obtained results, confirmed by computer simulations, provide a comprehensive perspective to the performance of the SM-MIMO wireless communication systems.
Development, regeneration, and parasitic interactions between the lichens Fulgensia bracteata and Toninia caeruleonigricans

A possible source of Trebouxia algae for lichens are the thalli of lichens that are attacked by another mycobiont that captures the algae for its own symbiotic use. Such a process is described for Fulgensia bracteata living in parasitic association with Toninia caeruleonigricans. The development of the thalli of Toninia and the parasitic attack of Fulgensia are analysed in detail. The infection of Toninia is achieved by ascospores and can be demonstrated by the presence of secondary lichen substances typical for Fulgensia, which occur in infected thalli of Toninia. Fulgensia also overgrows other lichens, but these thalli are not invaded. The special abilities of Toninia and various methods for distribution and regeneration compensate for the parasitism and the association remains stable. The ecology of the small developmental stages of Toninia and Fulgensia shows important differences. Key words: Fulgensia, Toninia, Trebouxia development, parasitism, ecology.
Lyapunov-Like Conditions for Tight Exit Probability Bounds through Comparison Theorems for SDEs

Computing upper bounds on exit probabilities—the probability that a system reaches certain "bad" sets—may assist decision-making in control of stochastic systems. Existing analytical bounds for systems described by stochastic differential equations are quite loose, especially for low-probability events, which limits their applicability in practical situations. In this paper we analyze why existing bounds are loose, and conclude that it is a fundamental issue with the underlying techniques based on martingale inequalities. As an alternative, we give comparison results for stochastic differential equations that via a Lyapunov-like function allow exit probabilities of an n-dimensional system to be upper-bounded by an exit probability of a one-dimensional Ornstein-Uhlenbeck process. Even though no closed-form expression is known for the latter, it depends on three or four parameters and can be a priori tabulated for applications. We extend these ideas to the controlled setting and state a stochastic analogue of control barrier functions. The bounds are illustrated on numerical examples and are shown to be much tighter than those based on martingale inequalities.
Impact of tadpoles and mosquito larvae on ephemeral pond structure and processes

Competition between organisms can influence both the abundance of species and the function of ecosystems. Here, I report the results of a field-based aquatic microcosm experiment, where the timing of arrival and abundance of two herbivores, tadpoles (Limnodynastes tasmaniensis) and mosquito larvae (Ochlerotatus notoscriptus), were manipulated to determine their impact on invertebrate community structure and ecosystem processes. Although successful establishment decreased with experimental time, there was no evidence that interactions between tadpoles and mosquitoes decreased the other species' subsequent survival. However, there were negative effects of tadpole addition on other invertebrates, with decreases in the abundance of zooplankton (Moina australiensis) and dipterans (Ephydridae and Chironomus oppositus). The addition of both tadpoles and mosquito larvae also increased ecosystem productivity. The negative effect of tadpoles on invertebrate abundance may result from competition for food or space, while complementary tadpole and mosquito effects on ecosystem processes are likely to result from changes in the dominant pond state. Tadpoles and mosquito larvae might influence the development of the dominant pond state through preferential consumption of edible phytoplankton and bacteria, reducing the competitive pressure on relatively inedible metaphyton, which forms dominating mats.
Interannual variability of the South Georgia marine ecosystem : biological and physical sources of variation in the abundance of krill

Interannual variability is a characteristic feature of the Southern Ocean ecosystem, yet the relative roles of biological and physical processes in generating these fluctuations are unknown. There is now extensive evidence that there are years when there is a very low abundance of Antarctic krill (Euphausia superba) in the South Georgia area, and that this variation affects much of the ecosystem, with the most obvious impacts on survival and breeding success of some of the major predators on krill. The open nature of the South Georgia ecosystem means this variability has large-scale relevance, but even though there are unique time series of data available, information on some key processes is limited. Fluctuations in year-class success in parts, or all, of the krill population across the Scotia Sea can generate large changes in the available biomass. The ocean transport pathways maintain the large-scale ecosystem structure by moving krill over large distances to areas where they are available to predator colonies. This large-scale physical system shows strong spatial and temporal coherence in the patterns of the interannual and subdecadal variability. This physical variability affects both the population dynamics of krill and the transport pathways, emphasizing that both the causes and the consequences of events at South Georgia are part of much larger-scale processes.
Lightweight Carrier Sensing in LoRa: Implementation and Performance Evaluation

In LoRa, leading communication technology for the Internet of Things (IoT), the so-called Class A devices are proposed for applications that require low energy consumption. However, the MAC layer of Class A devices is based on pure ALOHA, which performs poorly when the network includes a large number of devices. In this paper, we propose a Lightweight Carrier Sensing (LSC) mechanism for LoRa end devices, which does not include back-off and, thus, results in negligible overhead on end devices. LCS is based on the Channel Activity Detection (CAD) procedure already implemented at the hardware level in all modems based on the standard LoRa chipset. We first theoretically analyze the benefits of LCS on a simplified LoRa network to understand its benefits over pure ALOHA. We present the design and implementation of the proposed LCS and provide measurement results to demonstrate its feasibility in real-world LoRa networks. We have also implemented LCS in a detailed custom-build LoRa simulator to study LCS impact on network energy consumption and scalability in large-scale LoRa networks. Our results show that not only LCS supports more end devices, but also results in significantly lower energy consumption compared to ALOHA, thus efficiently improving network scalability without additional complexity or overhead.
Epipolar Transformer for Multi-view Human Pose Estimation

A common way to localize 3D human joints in a synchronized and calibrated multi-view setup is a two-step process: (1) apply a 2D detector separately on each view to localize joints in 2D, (2) robust triangulation on 2D detections from each view to acquire the 3D joint locations. However, in step 1, the 2D detector is constrained to solve challenging cases which could be better resolved in 3D, such as occlusions and oblique viewing angles, purely in 2D without leveraging any 3D information. Therefore, we propose the differentiable "epipolar transformer", which empowers the 2D detector to leverage 3D-aware features to improve 2D pose estimation. The intuition is: given a 2D location p in the reference view, we would like to first find its corresponding point p' in the source view, then combine the features at p' with the features at p, thus leading to a more 3D-aware feature at p. Inspired by stereo matching, the epipolar transformer leverages epipolar constraints and feature matching to approximate the features at p′. The key advantages of the epipolar transformer are: (1) it has minimal learnable parameters, (2) itcanbeeasilypluggedintoexistingnetworks,moreover (3) it is interpretable, i.e., we can analyze the location p' to understand whether matching over the epipolar line was successful. Experiments on Human3.6M [9] show that our approach has consistent improvements over the baselines. Specifically, in the condition where no external data is used, our Human3.6M model trained with ResNet-50 and image size 256 x 256 outperforms state-of-the-art by a large margin and achieves MPJPE 26.9 mm. Code is available1. This is the workshop version ofour CVPR 2020 paper [8]
Optimizing induced mutation technique for the improvement of agronomic traits in pigeon pea [cajanus cajan (l.) millsp.] landraces

Creation of variability through induced mutation is cardinal in genotype selection. However, the stability of the mutants in the subsequent generations is of utmost importance for improvement and commercialization. This current research was aimed at evaluating the performance of pigeon pea landraces following amiprophos methyl (APM) treatment and thereafter assesses the stability of the M 1 mutants. Thirty seeds each of two varieties of pigeon pea (brown “Fiofio”, white “Fiofio”) were soaked in 0, 4, 6 and 8 ppm amiprophos methyl (APM) for 48 hours. After the first growing season (2010/2011), mutant seeds were harvested and replanted in the next growing season (2011/2012). The result from the parental generation showed that the brown “Fiofio” variety grew taller (270.44cm), produced more branches plant -1 (17.31), had broader leaf area plant -1 (83.37cm 2 ) and increased petiole length plant -1 (6.83cm) while the white “Fiofio” produced more leaves plant -1 (~310). Additionally, the germination percentage (~98 days) increased including the days to 50% flowering (183 days) for the brown variety while days to 50% maturity increased for white variety (~198 days). Plants raised from the M 1 mutant seeds of white “Fiofio” variety produced more pods plant -1 (~157 pods) and had increase seed yield plant -1 (777 seeds) with increased weight of 100-seeds (17.32g). However, plants raised from brown “Fiofio” produced more number of leaves plant -1 (~2716), had taller plants (230.6cm) with increased days to 50% flowering and maturity (184; 176 days, respectively). It was observed that there was stability of traits that were hitherto positively affected by 4 ppm APM treatment such as increased number of leaves (2568), number of flowers (494), number of pods (192) and seed yield plant -1 (946) . Explicitly, there were obvious positive significant effects of APM treatment on pigeon pea, especially at 4 ppm. The improved traits were further stabilized in the M 1 generation. Most of the genetic estimates of morphological traits were high and wide, especially the broad sense heritabilities. PCVs were higher than GCVs encouraging phenotypic selection of traits. The implication is that selection can be made on these traits for further trials.
A Novel Music Emotion Recognition Model for Scratch-generated Music

In recent years, Scratch has been a popular programming platform for young children. To help children express emotions for projects, Scratch provides children with a music module to create desirable background music. However, in Scratch, there is not a tool helping recognize the emotion of music. Besides, as Scratch-generated music differs from regular music, existing music emotion recognition models perform poor in Scratch-generated music. To overcome it, in this paper, we propose a novel music emotion recognition model for Scratch-generated music. First, we build a Scratch-generated dataset by the main melody extraction algorithm. Then, for each music, we extract their underlying features and input them to the CNN module. After that, the features learned by CNN are input to RNN to get the final classification results. In our model, the CNN module can learn the important features of music while RNN can learn the sequential features. The experimental results show that the proposed model performs better than traditional music emotion recognition models.