Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach

© 2019 IEEE. Practical and efficient network slicing often faces real-time dynamics of network resources and uncertain customer demands. This work provides an optimal and fast resource slicing solution under such dynamics by leveraging the latest advances in deep learning. Specifically, we first introduce a novel system model which allows the network provider to effectively allocate its combinatorial resources, i.e., spectrum, computing, and storage, to various classes of users. To allocate resources to users while taking into account the dynamic demands of users and resources constraints of the network provider, we employ a semi-Markov decision process framework. To obtain the optimal resource allocation policy for the network provider without requiring environment parameters, e.g., uncertain service time and resource demands, a Q-learning algorithm is adopted. Although this algorithm can maximize the revenue of the network provider, its convergence to the optimal policy is particularly slow, especially for problems with large state/action spaces. To overcome this challenge, we propose a novel approach using an advanced deep Q-learning technique, called deep dueling that can achieve the optimal policy at few thousand times faster than that of the conventional Q-learning algorithm. Simulation results show that our proposed framework can improve the long-term average return of the network provider up to 40% compared with other current approaches

Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks

© 2018 IEEE. We propose a novel edge computing network architecture that enables edge nodes to cooperate in sharing computing and radio resources to minimize the total energy consumption of mobile users while meeting their delay requirements. To find the optimal task offloading decisions for mobile users, we first formulate the joint task offloading and resource allocation optimization problem as a mixed integer non-linear programming (MINLP). The optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computational intractable problem. To circumvent, we relax the binary decision variables to transform the MINLP to a relaxed optimization problem with real variables. After proving that the relaxed problem is a convex one, we propose two solutions namely ROP and IBBA. ROP is adopted from the interior point method and IBBA is developed from the branch and bound algorithm. Through the numerical results, we show that our proposed approaches allow minimizing the total energy consumption and meet all delay requirements for mobile users

Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter

© 2018 IEEE. For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior-and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods

Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks

© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks

Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks

Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors while optimizing the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop a highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulations, we show that our proposed solution can achieve up to 40% greater performance, i.e., an average throughput of the system, compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV’s energy and charging time on the system performance

Syntactic Markovian Bisimulation for Chemical Reaction Networks

In chemical reaction networks (CRNs) with stochastic semantics based on continuous-time Markov chains (CTMCs), the typically large populations of species cause combinatorially large state spaces. This makes the analysis very difficult in practice and represents the major bottleneck for the applicability of minimization techniques based, for instance, on lumpability. In this paper we present syntactic Markovian bisimulation (SMB), a notion of bisimulation developed in the Larsen-Skou style of probabilistic bisimulation, defined over the structure of a CRN rather than over its underlying CTMC. SMB identifies a lumpable partition of the CTMC state space a priori, in the sense that it is an equivalence relation over species implying that two CTMC states are lumpable when they are invariant with respect to the total population of species within the same equivalence class. We develop an efficient partition-refinement algorithm which computes the largest SMB of a CRN in polynomial time in the number of species and reactions. We also provide an algorithm for obtaining a quotient network from an SMB that induces the lumped CTMC directly, thus avoiding the generation of the state space of the original CRN altogether. In practice, we show that SMB allows significant reductions in a number of models from the literature. Finally, we study SMB with respect to the deterministic semantics of CRNs based on ordinary differential equations (ODEs), where each equation gives the time-course evolution of the concentration of a species. SMB implies forward CRN bisimulation, a recently developed behavioral notion of equivalence for the ODE semantics, in an analogous sense: it yields a smaller ODE system that keeps track of the sums of the solutions for equivalent species.Comment: Extended version (with proofs), of the corresponding paper published at KimFest 2017 (http://kimfest.cs.aau.dk/

Malaria and Fetal Growth Alterations in the 3(rd) Trimester of Pregnancy: A Longitudinal Ultrasound Study.

Pregnancy associated malaria is associated with decreased birth weight, but in-utero evaluation of fetal growth alterations is rarely performed. The objective of this study was to investigate malaria induced changes in fetal growth during the 3(rd) trimester using trans-abdominal ultrasound. An observational study of 876 pregnant women (398 primi- and secundigravidae and 478 multigravidae) was conducted in Tanzania. Fetal growth was monitored with ultrasound and screening for malaria was performed regularly. Birth weight and fetal weight were converted to z-scores, and fetal growth evaluated as fetal weight gain from the 26th week of pregnancy. Malaria infection only affected birth weight and fetal growth among primi- and secundigravid women. Forty-eight of the 398 primi- and secundigravid women had malaria during pregnancy causing a reduction in the newborns z-score of -0.50 (95% CI: -0.86, -0.13, P = 0.008, multiple linear regression). Fifty-eight percent (28/48) of the primi- and secundigravidae had malaria in the first half of pregnancy, but an effect on fetal growth was observed in the 3(rd) trimester with an OR of 4.89 for the fetal growth rate belonging to the lowest 25% in the population (95%CI: 2.03-11.79, P<0.001, multiple logistic regression). At an individual level, among the primi- and secundigravidae, 27% experienced alterations of fetal growth immediately after exposure but only for a short interval, 27% only late in pregnancy, 16.2% persistently from exposure until the end of pregnancy, and 29.7% had no alterations of fetal growth. The effect of malaria infections was observed during the 3(rd) trimester, despite infections occurring much earlier in pregnancy, and different mechanisms might operate leading to different patterns of growth alterations. This study highlights the need for protection against malaria throughout pregnancy and the recognition that observed changes in fetal growth might be a consequence of an infection much earlier in pregnancy.\u

Keratin 8/18 Regulation of Cell Stiffness-Extracellular Matrix Interplay through Modulation of Rho-Mediated Actin Cytoskeleton Dynamics

Cell mechanical activity generated from the interplay between the extracellular matrix (ECM) and the actin cytoskeleton is essential for the regulation of cell adhesion, spreading and migration during normal and cancer development. Keratins are the intermediate filament (IF) proteins of epithelial cells, expressed as pairs in a lineage/differentiation manner. Hepatic epithelial cell IFs are made solely of keratins 8/18 (K8/K18), hallmarks of all simple epithelia. Notably, our recent work on these epithelial cells has revealed a key regulatory function for K8/K18 IFs in adhesion/migration, through modulation of integrin interactions with ECM, actin adaptors and signaling molecules at focal adhesions. Here, using K8-knockdown rat H4 hepatoma cells and their K8/K18-containing counterparts seeded on fibronectin-coated substrata of different rigidities, we show that the K8/K18 IF-lacking cells lose their ability to spread and exhibit an altered actin fiber organization, upon seeding on a low-rigidity substratum. We also demonstrate a concomitant reduction in local cell stiffness at focal adhesions generated by fibronectin-coated microbeads attached to the dorsal cell surface. In addition, we find that this K8/K18 IF modulation of cell stiffness and actin fiber organization occurs through RhoA-ROCK signaling. Together, the results uncover a K8/K18 IF contribution to the cell stiffness-ECM rigidity interplay through a modulation of Rho-dependent actin organization and dynamics in simple epithelial cells

Identification of a New Rhoptry Neck Complex RON9/RON10 in the Apicomplexa Parasite Toxoplasma gondii

Apicomplexan parasites secrete and inject into the host cell the content of specialized secretory organelles called rhoptries, which take part into critical processes such as host cell invasion and modulation of the host cell immune response. The rhoptries are structurally and functionally divided into two compartments. The apical duct contains rhoptry neck (RON) proteins that are conserved in Apicomplexa and are involved in formation of the moving junction (MJ) driving parasite invasion. The posterior bulb contains rhoptry proteins (ROPs) unique to an individual genus and, once injected in the host cell act as effector proteins to co-opt host processes and modulate parasite growth and virulence. We describe here two new RON proteins of Toxoplasma gondii, RON9 and RON10, which form a high molecular mass complex. In contrast to the other RONs described to date, this complex was not detected at the MJ during invasion and therefore was not associated to the MJ complex RON2/4/5/8. Disruptions of either RON9 or RON10 gene leads to the retention of the partner in the ER followed by subsequent degradation, suggesting that the RON9/RON10 complex formation is required for proper sorting to the rhoptries. Finally, we show that the absence of RON9/RON10 has no significant impact on the morphology of rhoptry, on the invasion and growth in fibroblasts in vitro or on virulence in vivo. The conservation of RON9 and RON10 in Coccidia and Cryptosporidia suggests a specific relation with development in intestinal epithelial cells

Oral treatment with a zinc complex of acetylsalicylic acid prevents diabetic cardiomyopathy in a rat model of type-2 diabetes: activation of the Akt pathway.

BACKGROUND: Type-2 diabetics have an increased risk of cardiomyopathy, and heart failure is a major cause of death among these patients. Growing evidence indicates that proinflammatory cytokines may induce the development of insulin resistance, and that anti-inflammatory medications may reverse this process. We investigated the effects of the oral administration of zinc and acetylsalicylic acid, in the form of bis(aspirinato)zinc(II)-complex Zn(ASA)2, on different aspects of cardiac damage in Zucker diabetic fatty (ZDF) rats, an experimental model of type-2 diabetic cardiomyopathy. METHODS: Nondiabetic control (ZL) and ZDF rats were treated orally with vehicle or Zn(ASA)2 for 24 days. At the age of 29-30 weeks, the electrical activities, left-ventricular functional parameters and left-ventricular wall thicknesses were assessed. Nitrotyrosine immunohistochemistry, TUNEL-assay, and hematoxylin-eosin staining were performed. The protein expression of the insulin-receptor and PI3K/AKT pathway were quantified by Western blot. RESULTS: Zn(ASA)2-treatment significantly decreased plasma glucose concentration in ZDF rats (39.0 +/- 3.6 vs 49.4 +/- 2.8 mM, P < 0.05) while serum insulin-levels were similar among the groups. Data from cardiac catheterization showed that Zn(ASA)2 normalized the increased left-ventricular diastolic stiffness (end-diastolic pressure-volume relationship: 0.064 +/- 0.008 vs 0.084 +/- 0.014 mmHg/microl; end-diastolic pressure: 6.5 +/- 0.6 vs 7.9 +/- 0.7 mmHg, P < 0.05). Furthermore, ECG-recordings revealed a restoration of prolonged QT-intervals (63 +/- 3 vs 83 +/- 4 ms, P < 0.05) with Zn(ASA)2. Left-ventricular wall thickness, assessed by echocardiography, did not differ among the groups. However histological examination revealed an increase in the cardiomyocytes' transverse cross-section area in ZDF compared to the ZL rats, which was significantly decreased after Zn(ASA)2-treatment. Additionally, a significant fibrotic remodeling was observed in the diabetic rats compared to ZL rats, and Zn(ASA)2-administered ZDF rats showed a similar collagen content as ZL animals. In diabetic hearts Zn(ASA)2 significantly decreased DNA-fragmentation, and nitro-oxidative stress, and up-regulated myocardial phosphorylated-AKT/AKT protein expression. Zn(ASA)2 reduced cardiomyocyte death in a cellular model of oxidative stress. Zn(ASA)2 had no effects on altered myocardial CD36, GLUT-4, and PI3K protein expression. CONCLUSIONS: We demonstrated that treatment of type-2 diabetic rats with Zn(ASA)2 reduced plasma glucose-levels and prevented diabetic cardiomyopathy. The increased myocardial AKT activation could, in part, help to explain the cardioprotective effects of Zn(ASA)2. The oral administration of Zn(ASA)2 may have therapeutic potential, aiming to prevent/treat cardiac complications in type-2 diabetic patients