Static Link Research Articles

Amplify-and-forward relaying in mobile multi-hop molecular communication via diffusion

In this paper, we investigate amplify-and-forward (AF) relaying scheme for mobile three-dimensional (3D) multi-hop molecular communication via diffusion (MCvD) in the stochastic channel by using different type of molecules (DTM) and same type of molecules (STM) in each hop, respectively. Under the DTM scheme, both the inter-symbol interference (ISI) and the noise of each link are taken into account Compared with the DTM scheme, an additional factor should be considered for the STM scheme, which is the self-interference (SI) effect. Under DTM and STM, we derive the mathematical expressions of optimal detection thresholds at receiver node in mobile two-hop MCvD system by using the maximum-a-posterior (MAP) decision method. Then we extend the analysis to mobile multi-hop MCvD system. Furthermore, the performances of bit error probability and mutual information are evaluated via particle-based simulation of the Brownian motion. The numerical and simulation results reveal that the performance under DTM outperforms that under STM, which indicates the potential of AF relaying scheme under DTM to improve the overall performance of this system. Besides, the comparison results between mobile and static multi-hop link under DTM and STM are given. The obtained results are expected to provide guidance significance for designing mobile multi-hop MCvD system with lower bit error probability and higher mutual information.

On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum.

The external pallidum (globus pallidus pars externa [GPe]) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of dopamine-dependent changes of intrapallidal connectivity on the GPe dynamics. We find that increased self-inhibition of prototypical cells can induce oscillations, whereas increased inhibition of prototypical cells by arkypallidal cells leads to the emergence of a bistable regime. Furthermore, we show that oscillatory input to the GPe, arriving from striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic γ rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled Type I excitable GPe neurons.SIGNIFICANCE STATEMENT Our work provides (1) insight into the theoretical implications of a dichotomous globus pallidus pars externa (GPe) organization, and (2) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Because of the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.

Structure-informed functional connectivity driven by identifiable and state-specific control regions.

Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states.

Load-Optimization in Reconfigurable Networks

Emerging reconfigurable data centers introduce the unprecedented flexibility in how the physical layer can be programmed to adapt to current traffic demands. These reconfigurable topologies are commonly hybrid, consisting of static and reconfigurable links, enabled by e.g. an Optical Circuit Switch (OCS) connected to top-of-rack switches in Clos networks. Even though prior work has showcased the practical benefits of hybrid networks, several crucial performance aspects are not well understood. In this paper, we study the algorithmic problem of how to jointly optimize topology and routing in reconfigurable data centers with a known traffic matrix, in order to optimize a most fundamental metric, maximum link load. We chart the corresponding algorithmic landscape by investigating both un-/splittable flows and (non-)segregated routing policies. We moreover prove that the problem is not submodular for all these routing policies, even in multi-layer trees, where a topological complexity classification of the problem reveals that already trees of depth two are intractable. However, networks that can be abstracted by a single packet switch (e.g., nonblocking Fat-Tree topologies) can be optimized efficiently, and we present optimal polynomialtime algorithms accordingly. We complement our theoretical results with trace-driven simulation studies, where our algorithms can significantly improve the network load in comparison to the state of the art.

Scaling up Frequency-Comb-Based Optical Time Transfer to Long Terrestrial Distances

Frequency-comb-based optical two-way time and frequency transfer (O-TWTFT) can support future ultra-precise clock networks over free-space links. However, demonstrations have thus far only operated at one corner of a complex parameter space, which balances received optical power, timing performance, and update rate. Here, we analyze the performance of O-TWTFT across this parameter space, with a specific focus on extending the link distance at constant launch power and telescope aperture. We perform experiments across a three-node network spanning 28 km of turbulent air, and successfully demonstrate a more than 2000\ifmmode\times\else\texttimes\fi{} reduction in the required received optical power corresponding to a potential 45\ifmmode\times\else\texttimes\fi{} increase in distance. This distance increase does come with an associated reduction in timing precision, with the uncertainty increasing from 60 as to 20 fs at 10 s averaging times. However, this level of precision is still more than adequate for most applications. In addition, it comes with a reduction in sampling rate, which potentially limits this approach to static links. Interestingly, because this system optimization does not require any hardware modifications, O-TWTFT links could be dynamically tuned to support future long-distance optical clock networks over a range of conditions and applications.

Dynamic optimized cooperation in multi‐source multi‐relay wireless networks with random linear network coding

The authors present a strategy that improves the reliability of data transmission from multiple sources towards a destination D via multiple relays over wireless channels. After the broadcast phase, the destination selects the best cooperative direct link sources with the highest instantaneous signal-to-noise ratio , then generates and sends the random linear network coding coefficient matrix to the relays. After receiving the random linear network coding–coded symbols (packets) from the relays, D completes the reception and decoding of all source nodes packets. Monte Carlo simulations are conducted with Galois field (GF) () symbols ( = 2, 4 and 8) and the results are compared with the scenario of static cooperative direct links. An important decrease of symbol error rate (SER) is achieved. Furthermore, the efficiency of this approach is confirmed for the whole range of possible values of path loss exponent on direct links, that is, all types of environment between source nodes and destination D.

Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments

The paper presents a new approach and a related real-time parallel simulation tool for modeling and analyzing a swarm of more than 60 distributed autonomous mobile robots communicating over an unreliable and capacity restricted wireless communication network. It includes a physical simulation of static obstacles, dynamic obstacles with scriptable movement, soil condition, active jammers, static and dynamic link obstacles with configurable damping as well as thermal noise. The simulated ground based mobile robots use CPBP [12] as probabilistic model predictive closed loop controller in combination with gradient free cost functions to evaluate complex unsteady goodness aggregations. The goal of this approach is the development of connection aware swarm behavior for complex missions such as terrain exploration, formations, convoy escorting or creation of a mobile ad hoc network in dynamic disaster areas under realistic environmental conditions. The missions can be combined in any manner and the target extraction of the high-level commands for each agent is done implicitly. To validate the developed behavior, the independent software control kernel is able to be used on real robots as well.

Time-capturing Dynamic Graph Embedding for Temporal Linkage Evolution

Dynamic graph embedding learns representation vectors for vertices and edges in a graph that evolves over time. We aim to capture and embed the evolution of vertices' temporal connectivity. Existing work studies the vertices' dynamic connection changes but neglects the time it takes for edges to evolve, failing to embed temporal linkage information into the evolution of the graph. To capture vertices' temporal linkage evolution, we model dynamic graphs as a sequence of snapshot graphs, appending the respective timespans of edges (ToE). We co-train a linear regressor to embed ToE while inferring a common latent space for all snapshot graphs by a matrix-factorization-based model to embed vertices' dynamic connection changes. Vertices' temporal linkage evolution is captured as their moving trajectories within the common latent representation space. Our embedding algorithm converges quickly with our proposed training methods, which is very time efficient and scalable. Extensive evaluations on several datasets show that our model can achieve significant performance improvements, i.e. 22.98% on average across all datasets, over the state-of-the-art baselines in the tasks of vertex classification, static and time-aware link prediction, and ToE prediction.

Dynamic tail dependence on China's carbon market and EU carbon market

<abstract> <p>This study explores the dynamic relationship between the European carbon emission price (EUA) and the Shenzhen carbon emission price (SZA) in the time and frequency domain. Since they represent major carbon emission rights prices in the markets, they show a close correlation and tail correlation between them. Given the current global implementation to reduce carbon economy and China's implementation of a dual-carbon policy, it is of great value to explore the dynamic relationship between the two major carbon markets. Firstly, this paper uses a wavelet method to decompose the returned sequence into different frequency components to certify the dependent construction under different time scales. Secondly, this paper uses a wide range of static and time-varying link functions to describe the tail-dependent. The empirical results show that under different time scales, the dependence construction between EUA and SZA has significant time variation. The results of this study have important policy implications for understanding the transmission of carbon prices between different markets, as well as for investors and policy makers.</p> </abstract>

Efficient non-segregated routing for reconfigurable demand-aware networks

More and more networks are becoming reconfigurable: not just the routing can be programmed, but the physical layer itself as well. Various technologies enable this programmability, ranging from optical circuit switches to beamformed wireless connections and free-space optical interconnects. Existing reconfigurable network topologies are typically hybrid in nature, consisting of static and a reconfigurable links. However, even though the static and reconfigurable links form a joint structure, routing policies are artificially segregated and hence do not fully exploit the network resources: the state of the art is to route large elephant flows on direct reconfigurable links, whereas the remaining traffic is left to the static network topology. Recent work showed that such artificial segregation is inefficient, but did not provide the tools to actually leverage the benefits on non-segregated routing.In this paper, we provide several algorithms which take advantage of non-segregated routing, by jointly optimizing topology and routing. We compare our algorithms to segregated routing policies and also evaluate their performance in workload-driven simulations, based on real-world traffic traces. We find that our algorithms do not only outperform segregated routing policies, in various settings, but also come close to the optimal solution, computed by a integer linear program formulation, also presented in this paper. Finally, we also provide insights into the complexity of the underlying combinatorial optimization problem, by deriving approximation hardness results.

Latitudinal gradient in the respiration quotient and the implications for ocean oxygen availability

Climate-driven depletion of ocean oxygen strongly impacts the global cycles of carbon and nutrients as well as the survival of many animal species. One of the main uncertainties in predicting changes to marine oxygen levels is the regulation of the biological respiration demand associated with the biological pump. Derived from the Redfield ratio, the molar ratio of oxygen to organic carbon consumed during respiration (i.e., the respiration quotient, [Formula: see text]) is consistently assumed constant but rarely, if ever, measured. Using a prognostic Earth system model, we show that a 0.1 increase in the respiration quotient from 1.0 leads to a 2.3% decline in global oxygen, a large expansion of low-oxygen zones, additional water column denitrification of 38 Tg N/y, and the loss of fixed nitrogen and carbon production in the ocean. We then present direct chemical measurements of [Formula: see text] using a Pacific Ocean meridional transect crossing all major surface biome types. The observed [Formula: see text] has a positive correlation with temperature, and regional mean values differ significantly from Redfield proportions. Finally, an independent global inverse model analysis constrained with nutrients, oxygen, and carbon concentrations supports a positive temperature dependence of [Formula: see text] in exported organic matter. We provide evidence against the common assumption of a static biological link between the respiration of organic carbon and the consumption of oxygen. Furthermore, the model simulations suggest that a changing respiration quotient will impact multiple biogeochemical cycles and that future warming can lead to more intense deoxygenation than previously anticipated.

Modeling and Control of a Mini Hybrid Hydro Matrix / Wind in Micro Grid Applications

The performance of controlled hybrid renewable hydro matrix wheels/ wind in micro grid application during the variations of water speed and load is described and investigated. This hybrid contained three wheels and three wind turbines modeled in MATLAB Simulink. This presented hybrid model consists of a water wheel and a generator in the main channel, wind turbine drives an induction generator, battery, variable static load, DC link, and control unit. Appropriate controllers are used to maintain the DC-link voltage constant at its desired value with variations of load, water and wind speed. The obtained simulated result shows that the studied hybrid system with the proposed controller and the storage system have a better performance of load voltage and current waveform compared with the case of not using an energy storage unit and the case of without a controller under the water, wind speed and load variations.

Epidemic Spreading in Temporal and Adaptive Networks with Static Backbone

Activity-driven networks (ADNs) are a powerful paradigm to study epidemic spreading in temporal networks, where the dynamics of the disease and the evolution of the links share a common time-scale. One of the key assumptions of ADNs is the lack of preferential connections among individuals. This assumption hinders the application of ADNs to several realistic scenarios where some contacts persist in time, rather than intermittently activate. Here, we examine an improved modeling framework that superimposes an ADN to a static backbone network, toward the integration of persistent contacts with time-varying connections. To demonstrate the interplay between the ADN and the static backbone, we investigate the effect of behavioral changes on the disease dynamics. In this framework, each individual may adapt his/her activity as a function of the health status, thereby adjusting the relative weight of time-varying versus static links. To illustrate the approach, we consider two classes of backbone networks, Erdös-Rényi and random regular, and two disease models, SIS and SIR. A general mean-field theory is established for predicting the epidemic threshold, and numerical simulations are conducted to shed light on the role of network parameters on the epidemic spreading and estimate the epidemic size.

Exploitation of Elliptical Polarization for Improved Orthogonal Static Links

Orthogonal static or fixed links with polarization multiplexing can be formed by using dual-polarized antennas with a low cross-coupling from one polarization to the other. This is limited by the level of achievable polarization purity of two orthogonal dual-polarized antennas. Any practical antenna is inherently elliptically polarized (EP), resulting in either circular or linear polarization (LP) impurity. This article exploits such impurity where simple semi-directional EP antennas are designed to have minimal cross-coupling. The results show that a dual polar static link can reach the capacity limit using EP, which is not reached with LP. Channel measurements in both free-space and indoor environments were carried out from 2.2 to 2.4 GHz using a derived cross-polar ratio to quantify the impact of multipath scattering on static links.

Enabling Dynamic Communication for Runtime Circuit Relocation

Runtime circuit relocation has been proposed for mitigating the effect of permanent damages in reconfigurable hardware, such as field-programmable gate arrays (FPGAs), with potentials to improve reliability and reduce or eliminate system downtime. However, a major obstacle to the adoption of circuit relocation is the presence of static communication links between the circuits. Existing solutions to this are either computationally expensive or counterintuitive to system reliability. This article proposes a dynamic communication mechanism that is able to circumvent the static links. The clock buffers in a typical FPGA use independent wires and, thus, do not constitute static routing. These are repurposed as network links to provide dynamic communication for relocatable circuits, with a demonstrator based on a four-node star network showing a bandwidth of 428.58 Mb/s for a 32-bit payload at an overhead of only 144 slices on a seven-series FPGA.

Performance evaluation of neural network assisted motion detection schemes implemented within indoor optical camera based communications.

This paper investigates the performance of the neural network (NN) assisted motion detection (MD) over an indoor optical camera communication (OCC) link. The proposed study is based on the performance evaluation of various NN training algorithms, which provide efficient and reliable MD functionality along with vision, illumination, data communications and sensing in indoor OCC. To evaluate the proposed scheme, we have carried out an experimental investigation of a static indoor downlink OCC link employing a mobile phone front camera as the receiver and an 8 × 8 red, green and blue light-emitting diodes array as the transmitter. In addition to data transmission, MD is achieved using a camera to observe user's finger movement in the form of centroids via the OCC link. The captured motion is applied to the NN and is evaluated for a number of MD schemes. The results show that, resilient backpropagation based NN offers the fastest convergence with a minimum error of 10-5 within the processing time window of 0.67 s and a success probability of 100 % for MD compared to other algorithms. We demonstrate that, the proposed system with motion offers a bit error rate which is below the forward error correction limit of 3.8 × 10-3, over a transmission distance of 1.17 m.

PSO-ANE: Adaptive Network Embedding With Particle Swarm Optimization

Network embedding plays an important role in various network applications, such as node classification and link prediction. Lots of structure-based network embedding methods have been proposed. Yet, they suffer from unsteady embedding performances due to the parameter sensitivity. How to extract valuable attribute information in networks with less parameter influence is still a challenge. In this paper, we propose a novel algorithm, named adaptive network embedding with particle swarm optimization (PSO-ANE), which is based on the second-order dynamic random walk and PSO method, for learning network representations. A second-order dynamic random walk is designed to search a suitable strategy for each node based on the structure-based transition probability, the centrality-based transition probability, and the static link weights. To reduce the parameter dependence, PSO is adopted for key-parameter optimization to get global steady network embedding. The experiments validate that the proposed method outperforms the existing state-of-the-art techniques on multilabel classification, multiclass classification, and link prediction tasks.

Measurements of Beamswitching Gains and Fade Dynamics for 28 GHz Indoor Static Links in the Presence of Pedestrian Traffic

We measured channel dynamics for fixed wireless indoor channels at 28 GHz. We characterize pedestrian-induced fades in open indoor spaces and the effectiveness of beamswitching to mitigate them. We find that fades of the dominant propagation path may be as deep as 9 dB at the 90th percentile and that alternative signal paths are typically not strong enough to provide significant beamswitching gains.

Comparison of Routing Algorithms With Static and Dynamic Link Cost in Software Defined Networking (SDN)

In this paper, we compared the existing routing algorithms in the context of Software Defined Networking (SDN), where a logically centralized controller acquires the global view of the network, selects the paths using a routing algorithm, and installs the determined routing rules to the switches. We divided existing routing algorithms (RA) into three categories: RA with static link cost (RA-SLC), RA with dynamic link cost (RA-DLC), and RA with dynamic link cost and minimum interference (RA-DLCMI). We then implemented various routing algorithms from each category using RYU SDN controller and compared their performances on Mininet emulator. For the network state information (NSI) needed for the routing algorithms, we first consider the idealistic case where the accurate NSI is available, as assumed in the literature. However, since this is not possible in practice, we also considered the practical case where the controller periodically collects the NSI with some inaccuracy. In our experiments, we observed that while RA-DLC and RA-DLCMI give similar performance, RA-SLC falls behind of RA-DLC and RA-DLCMI in the number of accepted flows and total throughput. We also showed that the performance of every algorithm is adversely affected from the inaccuracy of NSI, calling for further research on developing effective NSI collection methods.

Proactive Chat in a Discovery Service: What Users are Asking

This study investigates questions users asked via both a proactive chat widget and a static “Ask Us” link in an academic library’s discovery service. Chat transcripts were coded according to four question categories: Reference, Borrowing, Technology Help, and Directional. Chat transcripts were also coded into more specific question types within each of the categories. Results showed that a high proportion of library users asked reference questions in the discovery service, regardless of whether they used a proactive invitation or a static link. Results also showed that library users were less likely to ask borrowing, technology help, and directional questions via the discovery service. Within the Reference category, a higher proportion of users tended to ask for help finding known items when they clicked on the “Ask Us” link. Conversely, a higher proportion of the proactive chat questions involved searching on a topic. These findings support the inclusion of proactive chat in search interfaces and provide valuable information about knowledge and skills that chat staff should possess.