Joint Strategy Research Articles

MAB-based two-tier learning algorithms for joint channel and power allocation in stochastic underwater acoustic communication networks

This study investigates the problem of joint channel and power allocation in stochastic underwater acoustic communication networks. And the multiarmed bandit theory is employed to model this problem which includes unknown variables. This study presents two two-tier learning algorithms, which do not need any prior environment information. In the upper learning, the user plays the predicted best strategy and learns the actual played strategy. In the lower learning, “outdated virtual learning information,” which can be obtained by the information of actual played strategy, is learned. The two-tier actual–virtual learning enormously enriches the learning information and effectively improves the learning ability. And multidimensional learning method is presented to ease the difficulty caused by the coupling of joint strategy. With the evolution of learning time, the emphasis of learning transfers from the channel power sub-strategy dimension to the entire power strategy dimension. Due to the specific learning manner, the algorithms have high tolerance about delay and non-complete information. Simulation results show high performance and adaptability of the proposed learning algorithms.

Retinal vessel segmentation based on Fully Convolutional Neural Networks

The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a multiscale Fully Convolutional Neural Network to cope with the varying width and direction of the vessel structure in the retina. Our proposal uses rotation operations as the basis of a joint strategy for both data augmentation and prediction, which allows us to explore the information learned during training to refine the segmentation. The method was evaluated on three publicly available databases, achieving an average accuracy of 0.9576, 0.9694, and 0.9653, and average area under the ROC curve of 0.9821, 0.9905, and 0.9855 on the DRIVE, STARE, and CHASE_DB1 databases, respectively. It also appears to be robust to the training set and to the inter-rater variability, which shows its potential for real-world applications.

A joint dynamic pricing and production model with asymmetric reference price effect

Reference price plays a significant role in influencing purchase decisions of customers. Due to loss aversion, the asymmetric reference price effect on market demand should be taken into account. This paper develops a joint dynamic pricing and production model with asymmetric reference price effect. In a finite planning horizon, the demand rate is time-varying and depends on price as well as reference price. The decision-making problem with the asymmetric reference price effect turns to be a nonsmooth optimal control problem, which cannot be solved by standard optimal control method. As a special case, we first obtain the joint optimal dynamic pricing and production strategy with symmetric reference price effect by solving the corresponding standard optimal control problem based on Maximum principle. For the case of asymmetric reference price effect, we propose a systematical method on basis of optimality principle to solve the nonsmooth optimal control problem, and obtain the joint strategy. Numerical examples are employed to illustrate the effectiveness of the proposed method. In addition, we assess the sensitivity analysis of system parameters to examine the impacts of asymmetric reference price on optimal pricing and production strategies and total profits.

Stabilizing ultrasmall Au clusters for enhanced photoredox catalysis

Recently, loading ligand-protected gold (Au) clusters as visible light photosensitizers onto various supports for photoredox catalysis has attracted considerable attention. However, the efficient control of long-term photostability of Au clusters on the metal-support interface remains challenging. Herein, we report a simple and efficient method for enhancing the photostability of glutathione-protected Au clusters (Au GSH clusters) loaded on the surface of SiO2 sphere by utilizing multifunctional branched poly-ethylenimine (BPEI) as a surface charge modifying, reducing and stabilizing agent. The sequential coating of thickness controlled TiO2 shells can further significantly improve the photocatalytic efficiency, while such structurally designed core-shell SiO2-Au GSH clusters-BPEI@TiO2 composites maintain high photostability during longtime light illumination conditions. This joint strategy via interfacial modification and composition engineering provides a facile guideline for stabilizing ultrasmall Au clusters and rational design of Au clusters-based composites with improved activity toward targeting applications in photoredox catalysis.

Playing Pollution Games with Thermal Electricity Generators

This paper discusses the economic and environmental implications of a stylised electricity market with transmission grid constraints and shared temporal pollution standards that restrict the joint strategy space of the agents. These are problematic to enforce if individual monitoring is impossible or very expensive. For such situations, we propose a time-dependent (or “open-loop dynamic”), game-theoretic model capable of analysing coupled constraints equilibria, also known as generalised Nash. We compute these equilibria for thermal generators subjected to annual pollution limits and instantaneous grid restrictions for a three-node dc model with a two-period load duration curve. The model illustrates the possibility that well-meaning environmental regulation might harm consumer surplus. It also highlights the cost to efficiency of regulatory attempts to ease the burden of compliance.

Coordination in theory, coordination in practice: the case of the Clusters.

The atomised nature of the humanitarian system has led to frequent and repeated attempts to coordinate humanitarian activity. Since 2005, some of the best resourced, and arguably most successful, coordination mechanisms have been the humanitarian Clusters, groups of UN (United Nations) and non-UN actors that engage in sectoral coordination of humanitarian response (such as the provision of healthcare and water) at the global and country level. Nevertheless, it is not clear exactly what 'coordination' means in the context of a Cluster. Formal guidance suggests that they should be aiming to create a single, joint strategy to guide the activities of members. Actual experience of the Clusters, however, indicates that looser forms of coordination are more effective. This finding resonates with organisational theory, and with the experience of emergency management professionals beyond the international humanitarian sector. To capitalise fully on the success of the Clusters, policymakers may need to rethink their attitudes to, and expectations of, coordination.

Learning to Play Efficient Coarse Correlated Equilibria

The majority of the distributed learning literature focuses on convergence to Nash equilibria. Coarse correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific coarse correlated equilibria. In this paper, we provide one such algorithm, which guarantees that the agents’ collective joint strategy will constitute an efficient coarse correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

Learning algorithms for joint resource block and power allocation in underlay D2D networks

We investigate the problem of resource block (RB) and power allocation jointly and in a distributed manner using game theoretic learning solutions, in an underlay device-to-device network where device pairs communicate directly with each other by reusing the spectrum allocated to the cellular users. We formulate the joint RB and power allocation as multi-agent learning problems with discrete strategy sets; and suggest partially distributed and fully distributed learning algorithms to determine the RB and power level to be used by each device pair. The partially distributed algorithms, viz., Fictitious Play and its variant Fading Memory Joint Strategy Fictitious Play with Inertia, achieve Nash Equilibrium (NE) of the sum-rate maximization game in a static wireless environment. The completely distributed and uncoupled Stochastic Learning Algorithm converges to pure strategy NE of the interference mitigation game in a time-varying radio environment. We provide proofs for the existence of NE and convergence of the learning algorithms to the NE. Performance of the proposed schemes are evaluated in log-normal, Rayleigh and Nakagami fading environments and compared with an existing hybrid scheme and a centralized scheme. The simulation results show that the partially distributed schemes give the same performance as the centralized scheme, and the fully distributed scheme gives similar performance as the hybrid scheme but with much reduced signaling and computation overhead.

Выбор совместной стратегии расхождения судов изменением параметров движения при их внешнем управлении

Выбор совместной стратегии расхождения судов изменением параметров движения при их внешнем управлении

Joint dynamic pricing and preservation technology investment for an integrated supply chain with reference price effects

In this paper, we propose a system of joint dynamic pricing and preservation technology investment decisions for deterioration items in an integrated supply chain management environment involving a manufacturer and a retailer, a controllable deterioration rate, and price-dependent demand. Because the purchasing decisions of consumers usually involve psychologically encoded prices based upon past shopping experiences, the effects of initial reference prices are also incorporated into the proposed model. An optimal dynamic pricing and preservation technology investment model is then established to determine joint strategy, maximizing the discounted total profit over an infinite time horizon from the perspectives of the retailer and integrated supply chain. We also characterize the properties of the optimal pricing and preservation technology investment decisions, and conduct numerical studies to investigate the impact of initial reference price and various system parameters on the optimal strategies and discounted total profit for the retailer/integrated supply chain. Finally, we offer concluding remarks and suggestions for future studies.

A Joint Strategy To Evaluate the Microscopic Origin of the Second-Harmonic-Generation Response in Nonpolar ABCO3F Compounds.

In this paper, a joint strategy was proposed to investigate the microscopic origin of the second-harmonic-generation (SHG) response in nonpolar ABCO3F compounds. The SHG coefficients of ABCO3F were evaluated using finite-field and sum-over-states methods. The tendency of the obtained SHG tensors is in good agreement with the powder SHG response. The atomic contribution was investigated using variation of the atomic charges and bandwidth of occupied atomic states. The results show that oxygen states play a key role in determining the SHG response, and the neighboring divalent cations exert a indirect influence via covalent interaction. The bidentate bonding pattern is beneficial to obtaining a largely enhanced SHG response.

Reformed Residual Network With Sparse Feedbacks for 3D Reconstruction From a Single Image

Deep neural networks are difficult to train due to the large number of unknown parameters. To increase the trainable performance, we present a novel network model with moderate depth for three-dimensional reconstruction. The proposed network model, called SFResNet, only has eight layers, and sparse feedbacks were added in the middle and last layers, which is mainly used to add the constraints and improve the stability of the network model. In addition, a joint strategy is proposed to reduce the artificial Mosaic trace at the seam of the patches; hence, SFResNet can also evaluate an input image of any size. Visually pleasing output results can be produced with a reconstructed shape and normal surface. The experimental results show the effectiveness of the proposed method.

Lower Leg Joint Strategy in the Sagittal Plane during Single-leg Landing

Lower Leg Joint Strategy in the Sagittal Plane during Single-leg Landing

The Green Leap Forward? Precarious Labour in a Greening China

Building upon research from the forthcoming book One Road, Many Dreams, (Drache, Kingsmith, Qi) this paper examines the tensions between China’s increasingly comprehensive environmental provisions and its labour policies in the context of China’s shifting expenditures from production to services. In accordance with its commitments to Paris Climate Accord, China’s trade agreements and policy initiatives under the framework of the One Belt, One Road (OBOR) Initiative have taken a more flexible approach and showed greater willingness to prioritize substantive environmental provisions. However, recent attempts by China to bring its heavy industries in line with these new environmental regulations and to initiate a sustained effort to move towards a greener economy has resulted in the laying off of thousands of heavy industry workers, (Guo, 2017). In an attempt to address this problem China has initiated a transition towards an economy with a larger service industry, which the government claims, will “help facilitate a joint strategy of stringent pollution control combined with job growth,” (PRC, 2017). The paper discusses President Xi’s vision of “the ecological civilization” in relation to benchmark environment and labour provisions to highlight the ways China maintains an inflexible position on labour rights, while also positioning itself as a leader against environmental degradation. It concludes with the increasing strain between precarious work and a “greening China” by surveying how current labour policies are plagued by a series of problems that will negatively affect China’s greener ambitions lest in considering new models of “employment configuration” (Swider, 2015).

Potential Game-Based Non-Myopic Sensor Network Planning for Multi-Target Tracking

This paper presents a potential game-based method for non-myopic planning of mobile sensor networks in the context of target tracking. The planning objective is to select the sequence of sensing points over more than one future time steps to maximize information about the target states. This multi-step lookahead scheme is studied to overcome getting trapped at local information maximum when there are gaps in sensing coverage due to constraints on the sensor platform mobility or limitations in sensing capabilities. However, the long-term planning becomes computationally intractable as the length of planing horizon increases. This work develops a gametheoretic approach to address the computational challenges. The main contributions of this paper are twofold: (a) to formulate a non-myopic planning problem for tracking multiple targets into a potential game, the size of which linearly increases as the number of planning steps (b) to design a learning algorithm exploiting the joint strategy fictitious play and dynamic programming, which overcomes the gaps in sensing coverage. Numerical examples of multi-target tracking demonstrate that the proposed method gives better estimation performance than myopic planning and is computationally tractable.

Weeds: reserves of parasitic phytonematode species populations

Purpose. Analysis of domestic and foreign literature on the role of alternative weed hosts of phytonematode families Heteroderidae and Meloidogynidae. Results. The nematodes of the Heteroderidae family can be hosted by 350 species of weeds from 40 families and 155 genera. Most of these weeds belong to five families: Chenopodiaceae (37 species from three genera), Solanaceae (44 species from 6 genera), Poaceae (47 species from 24 genera), Brassicaceae (67 species from 29 genera) and Fabaceae (85 species out of 25 genera) and have a total of 280 species of 87 genera. Weeds belonging to the families Lathurus (13 species), Vicia (15 species), Chenopodium (14 species) and Solanum (30 species) generally comprise 72 nematode host species. All other genera have fewer than 10 species of weed hosts. Nematodes of the Meloidogynidae family can be hosted by 994 weed species from 118 families and 487 genera. The greatest number of weeds that host parasitic nematodes belong to five families: Fabaceae (308 species), Asteraceae (267 species), Poaceae (8 species), Solanaceae (155 species) and Amaranthaceae (89) and among the families Solanum (61 species), Amaranthus (40 species), Trifolium (36 species), Nicotiana (30 species), Chenopodium and Euphorbia (each 28 species). Conclusions . In order to develop a joint strategy of effective protection of crops from weeds and parasitic nematode species, the biological and ecological characteristics of these harmful organisms should be thoroughly studied and the factors that would reduce their negative impact on plants and prevent crop losses to establish should be found.

Adaptive Crowd Segmentation Based on Coherent Motion Detection

In order to obtain effective crowd relationships for crowd behavior analysis, an adaptive crowd segmentation method based on coherent motion detection is proposed. This method can improve the accuracy of segmentation results and be adaptively applied to various collective scenes that have different distributions at different scales. Firstly, an orientation clustering algorithm and a spatial joint strategy are proposed to preliminarily profile all agents into several partitions with different motion orientations. Then, the Natural Nearest Neighbor algorithm is introduced to construct the adaptive crowd motion networks combining with the profiling results, which can describe the neighborhood relationships of agents with stronger coherence. Finally, the improved Coherent Neighbor Invariance optimized by fusing motion information of neighbors is proposed to segment crowds with coherent motions from the crowd motion networks. The experiment results on videos depicting real-world crowd scenes indicate that the proposed method is effective and adaptive to various scenes.

Structural Features and Ligand Selectivity for 10 Intermediates in the Activation Process of β2-Adrenergic Receptor.

It has already been suggested by researchers that there should be multiple intermediate states in the activation process for G-protein-coupled receptors (GPCRs). However, the intermediate states are very short-lived and hardly captured by the experiments, leading to very limited understanding of their structural features and drug efficacies. In this work, a novel joint strategy of targeted molecular dynamics simulation, conventional molecular dynamics simulation, and virtual screening is developed to address the problems. The results from 10 intermediate conformations obtained from the work reveal that the ligand pocket is very unstable and fluctuates between the inactive state and the active one in the case of ligand-free, in particular for ECL2 as a gate-keeper of the ligand-binding. The ligand-binding site could be stable in the active state with a small volume and a completely closed ECL2, only when the G-protein-binding region is fully activated. In addition, the activations of the ligand-binding pocket and G-protein-binding site are relatively independent and exhibit a loose allosteric coupling, which contributes to the existence of multiple intermediate conformations. Interestingly, the screening performance of the agonists does not increase on increasing the overall activity of the intermediate state, but is dependent on the activated extent of the ligand pocket. The receptor is prone to bind the agonist when closing ECL2 and reducing the ligand-binding pocket volume, whereas it is more favorable for binding the antagonist when opening ECL2 and increasing the pocket volume. These observations added to previous studies could help us better understand the activation mechanism of GPCRs and provide valuable information for drug design.

Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

To meet the exponential increasing high data rate demand of mobile users, heterogeneous ultra-dense networks (UDN) is widely seen as an essential technology to provide high-rate transmissions to nearby mobile users. However, the dense and random deployment of small base stations (SBSs) overlaid by macro base stations and their uncoordinated operation lead to important questions about the power consumption and aggressive frequency reuse of heterogeneous UDN. For the problem of huge power consumption and spectrum resource tension in heterogeneous UDN, a joint strategy of SBSs sleep and spectrum allocation is proposed. By using stochastic geometry, the coverage probabilities of base stations and the average ergodic rates of mobile users are derived in each tier and the whole network. In addition, we formulate the coverage probability maximization and power consumption minimization problems, and determine the optimal operating regimes for SBSs, and as well as spectrum allocation. The numerical results show that the SBSs sleep and spectrum allocation can reduce the power consumption and interference of the whole network.

Sustainability-oriented higher education networks: Characteristics and achievements in the context of the UN DESD

Higher education institutions (HEIs) are, as a sector, an important actor in society and their role in sustainability transition has been acknowledged. However, their commitments to sustainability expressed through the adoption of various declarations are often not effective unless combined with other ‘internal’ and ‘external’ strategies (concerned with activities within and outside the institution, respectively). Networking is an example of the latter: universities enter into coalitions in order to discuss progress in sustainability implementation, exchange experience and formulate joint policies. The presented research explores the field of sustainability-oriented higher education networks, analyses their key areas of interest, inter-network relationships, links with the policy field, and (conditions for) cooperation in joint programs and strategy development. Based on information gained through desk-top research to map the field from 14 HE networks' web pages, a survey was designed and distributed to selected university networks, with 15 responses received (30% response rate). Qualitative analysis of the data concentrated on the elements of social capital: the diversity of the networks, heterogeneity of the membership, size, regional scope and communication formats. The networks' activity areas range from those supporting sustainability initiatives within individual HEIs (education, research, campus operations, outreach) and networking initiatives directed beyond individual institutional change (contribution to policy debate at national, regional and global level, professional development and student network initiatives). The achievements of the analysed networks show a considerable contribution to the UN Decade of Education for Sustainable Development (DESD) in all their reported outcomes. Dialogue with key actors held after the exploratory mapping and the survey stages resulted in further delineation of the benefits and goals of cooperation: achievement of academia's cumulative impact on the sustainable development of our societies. This study highlights the significance of the autonomous character of HEIs based on data from 25 HE networks in total, and how they not only follow, but also stimulate the policy environment, consciously contributing to shared (sustainability) goals. Networking thus may be an important mechanism for systemic change in higher education, and the nature and role of social capital embedded in higher education networks should be the subject of further exploration.