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Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatiotemporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our model can be easily adapted to multi-type processes. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model. [ABSTRACT FROM AUTHOR]

In a recent paper, it has been shown that, if an N‐port network fulfills the condition of (geometrical) unconditional stability at a given frequency, then its scattering parameters will also necessarily satisfy N easily computable bounds, one per port. In order to complete that picture, this contribution investigates whether a tighter bound can be obtained by combining the N$$ N $$ bounds into just one. The answer is in general negative, except that the 3‐port case does indeed exhibit a peculiar behavior: this can be exploited to reduce the upper bound when the diagonal elements of the scattering matrix are limited in magnitude up to some α$$ \alpha $$, and in particular for α=0$$ \alpha =0 $$ (simultaneous conjugate match). [ABSTRACT FROM AUTHOR]

Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas. [ABSTRACT FROM AUTHOR]

Linear networks (sometimes called chain-type networks) occur frequently in Internet of Things (IoT) applications, where sensors or actuators are deployed along pipelines, roads, railways, mines, and international borders. LoRa, short for Long Range, is an increasingly important technology for the IoT with great potential for linear networking. Despite its potential, limited research has explored LoRa's implementation in such networks. In this paper, we addressed two important issues related to LoRa linear networks. The first is contention, when multiple nodes attempt to access a shared channel. Although originally designed to deal with interference, LoRa's technique of synchronisation with a transmission node permits a novel approach to contention, which we explored. The second issue revolves around routing, where linear networks permit simpler strategies, in contrast to the common routing complexities of mesh networks. We present gossip routing as a very lightweight approach to routing. All our evaluations were carried out using real equipment by developing real networks. We constructed networks of up to three hops in length and up to three nodes in width. We carried out experiments looking at contention and routing. We demonstrate using the novel approach that we could achieve up to 98% throughput. We compared its performance considering collocated scenarios that achieved 84% and 89% throughputby using relay widths of two and three at each hop, respectively. Lastly, we demonstrate the effectiveness of gossip routing by using various transmission probabilities. We noticed high performance up to 98% throughputat T p r o b = 0.90 and T p r o b = 0.80 by employing two and three active relay nodes, respectively. The experimental result showed that, at T p r o b = 0.40, it achieved an average performance of 62.8% and 73.77% by using two and three active relay nodes, respectively. We concluded that LoRa is an excellent technology for Internet of Things applications where sensors and actuators are deployed in an approximately linear fashion. [ABSTRACT FROM AUTHOR]

Characterizing how neural network depth, width, and dataset size jointly impact model quality is a central problem in deep learning theory. We give here a complete solution in the special case of linear networks with output dimension one trained using zero noise Bayesian inference with Gaussian weight priors and mean squared error as a negative log-likelihood. For any training dataset, network depth, and hidden layer widths, we find nonasymptotic expressions for the predictive posterior and Bayesian model evidence in terms of Meijer-G functions, a class of meromorphic special functions of a single complex variable. Through asymptotic expansions of these Meijer-G functions, a rich new picture of the joint role of depth, width, and dataset size emerges. We show that linear networks make provably optimal predictions at infinite depth: the posterior of infinitely deep linear networks with data-agnostic priors is the same as that of shallow networks with evidence-maximizing data-dependent priors. This yields a principled reason to prefer deeper networks when priors are forced to be dataagnostic. Moreover, we show that with data-agnostic priors, Bayesian model evidence in wide linear networks is maximized at infinite depth, elucidating the salutary role of increased depth for model selection. Underpinning our results is an emergent notion of effective depth, given by the number of hidden layers times the number of data points divided by the network width; this determines the structure of the posterior in the large-data limit. [ABSTRACT FROM AUTHOR]

Point processes on linear networks are increasingly being considered to analyse events occurring on particular network-based structures. In this paper, we extend Local Indicators of Spatio-Temporal Association (LISTA) functions to the non-Euclidean space of linear networks, allowing to obtain information on how events relate to nearby events. In particular, we propose the local version of two inhomogeneous second-order statistics for spatio-temporal point processes on linear networks, the K- and the pair correlation functions. We put particular emphasis on the local K-functions, deriving come theoretical results which enable us to show that these LISTA functions are useful for diagnostics of models specified on networks, and can be helpful to assess the goodness-of-fit of different spatio-temporal models fitted to point patterns occurring on linear networks. Our methods do not rely on any particular model assumption on the data, and thus they can be applied for whatever is the underlying model of the process. We finally present a real data analysis of traffic accidents in Medellin (Colombia). [ABSTRACT FROM AUTHOR]

Linear networks (sometimes called chain-type networks) occur frequently in Internet of Things (IoT) applications, where sensors or actuators are deployed along pipelines, roads, railways, mines, and international borders. LoRa, short for Long Range, is an increasingly important technology for the IoT with great potential for linear networking. Despite its potential, limited research has explored LoRa’s implementation in such networks. In this paper, we addressed two important issues related to LoRa linear networks. The first is contention, when multiple nodes attempt to access a shared channel. Although originally designed to deal with interference, LoRa’s technique of synchronisation with a transmission node permits a novel approach to contention, which we explored. The second issue revolves around routing, where linear networks permit simpler strategies, in contrast to the common routing complexities of mesh networks. We present gossip routing as a very lightweight approach to routing. All our evaluations were carried out using real equipment by developing real networks. We constructed networks of up to three hops in length and up to three nodes in width. We carried out experiments looking at contention and routing. We demonstrate using the novel approach that we could achieve up to 98% throughput. We compared its performance considering collocated scenarios that achieved 84% and 89% throughputby using relay widths of two and three at each hop, respectively. Lastly, we demonstrate the effectiveness of gossip routing by using various transmission probabilities. We noticed high performance up to 98% throughputat Tprob = 0.90 and Tprob = 0.80 by employing two and three active relay nodes, respectively. The experimental result showed that, at Tprob = 0.40, it achieved an average performance of 62.8% and 73.77% by using two and three active relay nodes, respectively. We concluded that LoRa is an excellent technology for Internet of Things applications where sensors and actuators are deployed in an approximately linear fashion.

Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas.

The extensively studied fractional-order dynamical networks only contain a uniform fractional derivative, which restricts the scope of fractional-order investigation. One type of Takagi–Sugeno fuzzy second-fractional-order linear networks is established in this article. First, by defining a novel Lyapunov functional, a sufficient criterion is offered to ensure global asymptotic stability of the addressed networks with time delay. Second, a fuzzy state-feedback control scheme is designed for the Takagi–Sugeno fuzzy second-fractional-order delayed linear networks to achieve global stabilization. Third, considering that only a few scholars have worked on adaptive control of fractional-order networks, an adaptive control method is also designed to realize the global stabilization of the Takagi–Sugeno fuzzy second-fractional-order linear networks, which does not contain the sign function and chattering problem can be averted. All results, given as algebraic criteria, are proved directly from the Takagi–Sugeno fuzzy second-fractional-order networks without utilizing the reduced-order approach. Three simulation examples are conducted to show the validity of the theoretical results at last. [ABSTRACT FROM AUTHOR]

The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models. [ABSTRACT FROM AUTHOR]

Recent research has revealed new and unexpected applications of network control science within biomedicine, pharmacology, and medical therapeutics. These new insights and new applications generated in turn a rediscovery of some old, unresolved algorithmic problems. One of these problems is the Structural Target Control optimization problem, known in previous literature also as Structural Output Controllability problem, which is defined as follows. Given a directed network and a target subset of nodes, the task is to select a small (or the smallest) set of nodes from which the target can be independently controlled, i.e., there exists a set of paths from the selected set of nodes (called driver nodes) to the target nodes such that no two paths intersect at the same distance from their targets. Recently, Structural Target Control optimization problem has been shown to be NP-hard, and several heuristic algorithms were introduced and analyzed, both on randomly generated networks, and on biomedical ones. In this paper, we show that the Structural Target Controllability problem is fixed parameter tractable when parameterized by the number of target nodes. We also prove that the problem is hard to approximate at a factor better than O(log n). Taking into consideration the real case formulations of this problem we identify two more parameters which are naturally constrained by smaller bounds: the maximal length of a controlling path and the size of the set of nodes from which the control can start. With these new parameters we provide an approximation algorithm which is of exponential complexity in the size of the set of nodes from which the control can start and polynomial in all the other parameters. [ABSTRACT FROM AUTHOR]

Consider n identical relocatable sensors , with sensing range r and visibility range 2 r , initially placed at arbitrary positions on a line segment barrier; each sensor can detect the presence of an intruder in its sensing range, and is said to cover the portion of the barrier that intersects with its sensing range. Sensors operate in Look-Compute-Move cycles: in a cycle a sensor determines the positions of sensors in its visibility range, it computes its next position (within its visibility range), and then moves to the calculated position. The barrier coverage problem we consider is for the sensors to independently make decisions and movements so to reach final positions whereby they collectively cover the barrier; in particular we are interested in oblivious (or memoryless) sensors, and focus on the impact that the level of synchrony and the presence/absence of orientation (i.e., global notion of "left-right") have on the solvability of the problem. It is known that without orientation, oblivious sensors can solve the problem if they are fully synchronous (i.e., they operate in synchronous rounds and are all active at every round). In this paper, we prove that orientation is critical to being able to solve the problem if we relax the assumption of full synchronization. We first show that if sensors are unoriented, then barrier coverage is unsolvable even in the semi-synchronous setting. In contrast, if sensors agree on a global orientation, then we give an algorithm for barrier coverage, even in the completely asynchronous setting. Finally, we extend the existing result of Cohen and Peleg and show that convergence to barrier coverage by unoriented sensors in the semi-synchronous model is possible with bounded visibility range 2 r + ρ (for arbitrarily small ρ > 0) and bounded mobility range r. [ABSTRACT FROM AUTHOR]

Spatial statistics is an important field of data science with many applications in very different areas of study such as epidemiology, criminology, seismology, astronomy and econometrics, among others. In particular, spatial statistics has frequently been used to analyze traffic accidents datasets with explanatory and preventive objectives. Traditionally, these studies have employed spatial statistics techniques at some level of areal aggregation, usually related to administrative units. However, last decade has brought an increasing number of works on the spatial incidence and distribution of traffic accidents at the road level by means of the spatial structure known as a linear network. This change seems positive because it could provide deeper and more accurate investigations than previous studies that were based on areal spatial units. The interest in working at the road level renders some technical difficulties due to the high complexity of these structures, specially in terms of manipulation and rectification. The R Shiny app SpNetPrep, which is available online and via an R package named the same way, has the goal of providing certain functionalities that could be useful for a user which is interested in performing an spatial analysis over a road network structure.

Abstract Worldwide, bees have an important role in ecosystem function and the provision of ecosystem services through their role as pollinators. The diversity of bee species in rural landscapes is influenced by the type of landscape features present, and by land-use and management practices. A key challenge is to understand and predict how species vary across the landscape; and the role of functional traits in determining compositional patterns. We systematically sampled wild bees in four types of landscape feature – open farmland, scattered farmland trees, roadside vegetation and streamside vegetation – in rural landscapes in southern Australia. Landscapes were selected to represent wooded or non-wooded combinations of these site types (e.g. roadside vegetation with or without trees), embedded in farmland with different land-uses (e.g. cropping, grazing). The species richness and abundance of bees was greater at sites containing little or no tree cover; and the cumulative richness of species was greater for tree-less sites than for those with trees. In contrast, species evenness was greatest in wooded site types, indicating these were less dominated by abundant generalist species. Open farmland and treeless roadsides had greater functional diversity (based on species traits) than wooded site types. Strong species trait associations were more numerous with open parts of the landscape, reflecting the greater functional diversity of open site types. These results suggest that a suite of the extant bee fauna can exploit large-scale transformation from former extensively wooded ecosystems to open agricultural landscapes. However, not all species are able to exploit modified landscapes and may disappear with further loss of wooded vegetation. Trait-based approaches provide insight into how changes in landscape pattern affect the bee fauna. Failure to adequately cater for multiple functional groups of bees across all landscape features could mean a substantial loss in species that rely on more natural cover, thus affecting ecosystem function. [ABSTRACT FROM AUTHOR]

We consider a linear stochastic fluid network under Markov modulation, with a focus on the probability that the joint storage level attains a value in a rare set at a given point in time. The main objective is to develop efficient importance sampling algorithms with provable performance guarantees. For linear stochastic fluid networks without modulation, we prove that the number of runs needed (so as to obtain an estimate with a given precision) increases polynomially (whereas the probability under consideration decays essentially exponentially); for networks operating in the slow modulation regime, our algorithm is asymptotically efficient. Our techniques are in the tradition of the rare-event simulation procedures that were developed for the sample-mean of i.i.d. one-dimensional light-tailed random variables, and intensively use the idea of exponential twisting. In passing, we also point out how to set up a recursion to evaluate the (transient and stationary) moments of the joint storage level in Markov-modulated linear stochastic fluid networks. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the effectiveness of cooperative broadcast in high-density linear networks is studied. In the considered protocol, a single source sends messages continuously causing interference among messages and the goal is to reach to the entire network via cooperating relays. Sufficient conditions for successful broadcast are derived under two different transmission schemes: the unidirectional scheme and the bidirectional scheme. Our analysis shows that the broadcast behavior depends on the pathloss exponent γ and the type of transmission scheme even though the channel between two nodes are affected by small-scale fading. Unlike the noncooperative multihop broadcast, the cooperative broadcast propagates the message to the entire network successfully when γ ≤ 1 provided that network parameters satisfy certain conditions. Furthermore, for γ > 1, cooperative scheme becomes optimal under the bidirectional transmission when the information rate is below a threshold; however if the transmissions are unidirectional, noncooperative scheme becomes optimal. When compared with 2D networks, we demonstrate that the advantages of cooperative broadcast in linear networks are limited. [ABSTRACT FROM AUTHOR]

Spatial statistics is an important field of data science with many applications in very different areas of study such as epidemiology, criminology, seismology, astronomy and econometrics, among others. In particular, spatial statistics has frequently been used to analyze traffic accidents datasets with explanatory and preventive objectives. Traditionally, these studies have employed spatial statistics techniques at some level of areal aggregation, usually related to administrative units. However, last decade has brought an increasing number of works on the spatial incidence and distribution of traffic accidents at the road level by means of the spatial structure known as a linear network. This change seems positive because it could provide deeper and more accurate investigations than previous studies that were based on areal spatial units. The interest in working at the road level renders some technical difficulties due to the high complexity of these structures, specially in terms of manipulation and rectification. The R Shiny app SpNetPrep, which is available online and via an R package named the same way, has the goal of providing certain functionalities that could be useful for a user which is interested in performing an spatial analysis over a road network structure. [ABSTRACT FROM AUTHOR]

End-to-end reliability for Wireless Sensor Network communications is usually provided by upper stack layers. Furthermore, most of the studies have been related to star, mesh, and tree topologies. However, they rarely consider the requirements of the multi-hop linear wireless sensor networks, with thousands of nodes, which are universally used for monitoring applications. Therefore, they are characterized by long delays and high energy consumption. In this paper, we propose an energy efficient link level routing algorithm that provides end-to-end reliability into multi-hop wireless sensor networks with a linear structure. The algorithm uses implicit acknowledgement to provide reliability and connectivity with energy efficiency, low latency, and fault tolerance in linear wireless sensor networks. The proposal is validated through tests with real hardware. The energy consumption and the delay are also mathematically modeled and analyzed. The test results show that our algorithm decreases the energy consumption and minimizes the delays when compared with other proposals that also apply the explicit knowledge technique and routing protocols with explicit confirmations, maintaining the same characteristics in terms of reliability and connectivity.

Computational analysis of the structure of intra-cellular molecular interaction networks can suggest novel therapeutic approaches for systemic diseases like cancer. Recent research in the area of network science has shown that network control theory can be a powerful tool in the understanding and manipulation of such bio-medical networks. In 2011, Liu et al. developed a polynomial time algorithm computing the size of the minimal set of nodes controlling a linear network. In 2014, Gao et al. generalized the problem for target control, minimizing the set of nodes controlling a target within a linear network. The authors developed a Greedy approximation algorithm while leaving open the complexity of the optimization problem. We prove here that the target controllability problem is NP-hard in all practical setups, i.e., when the control power of any individual input is bounded by some constant. We also show that the algorithm provided by Gao et al. fails to provide a valid solution in some special cases, and an additional validation step is required. We fix and improve their algorithm using several heuristics, obtaining in the end an up to 10-fold decrease in running time and also a decrease in the size of solutions. [ABSTRACT FROM AUTHOR]

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called levels division graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared with its best rival in the literature, dynamic source routing (DSR). Compared with DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption. [ABSTRACT FROM PUBLISHER]

We consider the stability of the longest-queue-first (LQF) scheduling policy in wireless networks with multihop traffic under the one-hop interference model. Although it is well known that the back-pressure algorithm achieves the maximal stability, its computational complexity is prohibitively high. In this paper, we consider LQF, a low-complexity scheduling algorithm, which has been shown to have near-optimal throughput performance in many networks with single-hop traffic flows. We are interested in the performance of LQF for multihop traffic flows. In this scenario, the coupling between queues due to multihop traffic flows makes the local-pooling-factor analysis difficult to perform. Using the fluid-limit techniques, we show that LQF achieves the maximal stability for linear networks with multihop traffic and a single destination on the boundary of the network under the one-hop interference model. [ABSTRACT FROM AUTHOR]

This paper presents a new and systematic procedure to design sub-optimal hierarchical feedback controllers for the leader–follower consensus problem in homogeneous multi-agent systems. First, the given multi-agent system is treated as a two-layer hierarchical system where the agents perform local actions in the lower layer and interact with others in the upper layer to achieve some global goals. Then the consensus controller design is formulated as a hierarchical state feedback control problem. Employing LQR approach with an appropriately selected performance index, an optimal hierarchical state feedback controller is derived which includes two terms namely local and global terms. Consequently, by removing the local term, the remaining global term is proved to make the multi-agent system consensus which results in a sub-optimal hierarchical consensus controller. Finally, some numerical examples are introduced to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with the description of general linear passive circuit features by using convolution equations. For this purpose, the variables of linear networks are transformed to the logarithmic scale, which is widely used in the case of frequency variable. On the other hand, it is an unconventional method for complex frequency and time variables. The introduced relations are suitable for complex calculations in a direct and simple way. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a novel network decomposition method that can detect faults of linear analog integrated circuit (IC) in network. The nodal admittance matrix (NAM) of linear analog IC is a function of its internal component values, which can be used for fault detection. However, it is difficult to obtain the NAM of linear analog IC in network. We propose a network decomposition based method to calculate the NAM of the IC under test in network. The IC under test is fault free, if its NAM lies inside the tolerance limit. Otherwise, it is faulty. The effectiveness of the proposed method is validated through benchmark circuits. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a framework for analysis of plant-wide processes from a network perspective. Using the concept of dissipativity, the conditions for plant-wide input–output stability and performance are developed, based on the dissipativity of individual subsystems and the topology of the network of the plant-wide process. Dynamic supply rates, expressed as quadratic differential forms, are proposed not only to render dissipativity based analysis less conservative but also allow the dynamic plant-wide performance criteria to be specified in terms of desired closed loop supply rates. The links between the plant-wide supply rate, finite gain in an extended input–output space and weighted norm are explored in this paper. These results lay a foundation for a supply rate-centric approach to plant-wide distributed control. [Copyright &y& Elsevier]

Abstract: Autoencoders are unsupervised machine learning circuits, with typically one hidden layer, whose learning goal is to minimize an average distortion measure between inputs and outputs. Linear autoencoders correspond to the special case where only linear transformations between visible and hidden variables are used. While linear autoencoders can be defined over any field, only real-valued linear autoencoders have been studied so far. Here we study complex-valued linear autoencoders where the components of the training vectors and adjustable matrices are defined over the complex field with the norm. We provide simpler and more general proofs that unify the real-valued and complex-valued cases, showing that in both cases the landscape of the error function is invariant under certain groups of transformations. The landscape has no local minima, a family of global minima associated with Principal Component Analysis, and many families of saddle points associated with orthogonal projections onto sub-space spanned by sub-optimal subsets of eigenvectors of the covariance matrix. The theory yields several iterative, convergent, learning algorithms, a clear understanding of the generalization properties of the trained autoencoders, and can equally be applied to the hetero-associative case when external targets are provided. Partial results on deep architecture as well as the differential geometry of autoencoders are also presented. The general framework described here is useful to classify autoencoders and identify general properties that ought to be investigated for each class, illuminating some of the connections between autoencoders, unsupervised learning, clustering, Hebbian learning, and information theory. [Copyright &y& Elsevier]

Maritime traditions that extend along coastlines are more vulnerable to disruption and disappearance than areal trading networks. The paper describes two cases from Africa, the likely early movement of Bantu speakers down the coast of West Africa and the Swahili trading diaspora that reached southern Mozambique by at least the seventh century. Both of these have disappeared from the ethnographic and historical record but can be recovered through archaeology and linguistics. A parallel is made with the trade route that linked the coastal region of Peru and Ecuador with Western Mexico and may have been active from as early as 4,000 bp until the Spanish conquest. The hypothesis is that areal networks, such as those in island Southeast Asia and the Pacific, which are driven by colonisation and bidirectional exchange, are more likely to persist because they are more resilient due to the number of broken 'links' they can withstand. Linear expansions may be driven by a quest for trade and resources but are usually not necessary to survival. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address the biological sequence alignment problem, which is one of the most commonly used steps in several bioinformatics applications. We employ the Divisible Load Theory (DLT) paradigm that is suitable for handling large-scale processing on network-based systems to achieve a high degree of parallelism. Using the DLT paradigm, we propose a strategy in which we carefully partition the computation work load among the processors in the system so as to minimize the overall computation time of determining the maximum similarity between the DNA/protein sequences. We consider handling such a computational problem on networked computing platforms connected as a linear daisy chain. We derive the individual load quantum to be assigned to the processors according to computation and communication link speeds along the chain. We consider two cases of sequence alignment where post-processes, i.e., trace-back processes that are required to determine an optimal alignment, may or may not be done at individual processors in the system. We derive some critical conditions to determine if our strategies are able to yield an optimal processing time. We apply three different heuristic strategies proposed in the literature to generate sub-optimal solutions for processing time when the above conditions cannot be satisfied. To testify the proposed schemes, we use real-life DNA samples of house mouse mitochondrion and the DNA of human mitochondrion obtained from the public database GenBank [GenBank, http://www.ncbi.nlm.nih.gov] in our simulation experiments. By this study, we conclusively demonstrate the applicability and potential of the DLT paradigm to such biological sequence related computational problems. [Copyright &y& Elsevier]

Abstract: The paper aims at methodological studies on selection of optimal neural network that performs modeling of chemical reactivity of a given group of chemical compounds. The problem (prediction of biological activity in enzymatic reaction catalyzed by ethylbenzene dehydrogenase) is taken as a case study for assessment of various types of neural networks. The main goal of the study is to select the best type of the network, optimal dimension of the layers, proper parameters of the network as well as the optimal form of data representation for purpose of neural-based modeling of complex empirical data. Various approaches (linear networks, regression and classification multiple layer perceptrons, generalized regression neural networks) are compared and tested. [Copyright &y& Elsevier]

Recently, the merits of cooperative communication in the physical layer have been explored. However, the impact of cooperative communication on the design of the higher layers has not been well-understood yet. Cooperative routing in wireless networks has gained much interest due to its ability to exploit the broadcast nature of the wireless medium in designing power efficient routing algorithms. Most of the existing cooperation based routing algorithms are implemented by finding a shortest path route first and then improving the route using cooperative communication. As such, these routing algorithms do not fully exploit the merits of cooperative communications, since the optimal cooperative route might not be similar to the shortest path route. In this paper, we propose a cooperation-based routing algorithm, namely, the minimum power cooperative routing (MPCR) algorithm, which makes full use of the cooperative communications while constructing the minimum-power route. The MPCR algorithm constructs the minimum-power route, which guarantees certain throughput, as a cascade of the minimum-power single-relay building blocks from the source to the destination. Thus, any distributed shortest path algorithm can be utilized to find the optimal cooperative route with polynomial complexity. Using analysis, we show that the MPCR algorithm can achieve power saving of 65.61% in regular linear networks and 29.8% in regular grid networks compared to the existing cooperation-based routing algorithms, where the cooperative routes are constructed based on the shortest-path routes. From simulation results, MPCR algorithm can have 37.64% power saving in random networks compared to those cooperation-based routing algorithms. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, the equivalence between various divisible load-scheduling policies and continuous time Markov chains is demonstrated. This provides a basic unification of both data parallel divisible load scheduling and Markov chain models for the first time in 16 years of research. Such equivalence is demonstrated for divisible scheduling on linear daisy chains and single and two level tree networks. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problem of scheduling multiple divisible loads on heterogeneous linear daisy chain networks. Our objective is to design a load distribution strategy such that the total processing time of a set of loads is minimized. We assume that the set of loads are resident in one of the farthest end processors, which has a scheduler that will distribute the load to the other processors in the network. When distributing a load from the set, the distribution pattern of the previous load has to be taken into consideration to ensure that no processors are left idle and there are no collisions in the communication links. We design single and multi-installments strategies to achieve the above objective. We derive certain important conditions to determine whether an optimum solution exists. We propose two heuristic strategies when an optimum solution is unattainable. Using all the above strategies, we conduct four different simulation experiments to track the performance of strategies under several real-life situations. We conducted four different simulation experiments based on the two heuristic strategies to identify the best combination suitable for our multiple-loads distribution strategy. We also run simulations for a homogeneous system to quantify the performance under 3 different policies, that is, when the loads are (a) unsorted, (b) sorted with smallest load first (SLF) and (c) sorted with largest load first (LLF). A detailed analysis of the simulation results is presented and based on these, recommendations are made for the choice of strategies. Finally, we compare the performance of a single-load distribution strategy against the multiple-loads distribution strategy designed in this paper to quantify the exact performance gain that can be achieved. Illustrative examples are also provided for ease of understanding. [Copyright &y& Elsevier]

The problem of distributing and processing a divisible load in a heterogeneous linear network of processors with arbitrary processors release times is considered. A divisible load is very large in size and has computationally intensive CPU requirements. Further, it has the property that the load can be partitioned arbitrarily into any number of portions and can be scheduled onto processors independently for computation. The load is assumed to arrive at one of the farthest end processors, referred to as boundary processors, for processing. The processors in the network are assumed to have nonzero release times, i.e., the time instants from which the processors are available for processing the divisible load. Our objective is to design a load distribution strategy by taking into account the release times of the processors in such a way that the entire processing time of the load is a minimum. We consider two generic cases in which all processors have identical release times and when all processors have arbitrary release times. We adopt both the single and multi-installment strategies proposed in the divisible load scheduling literature in our design of load distribution strategies, wherever necessary, to achieve a minimum processing time. Finally, when optimal strategies cannot be realized, we propose two heuristic strategies, one for the identical case, and the other for nonidentical release times case, respectively. Several conditions are derived to determine whether or not optimal load distribution exists and illustrative examples are provided for the ease of understanding. [ABSTRACT FROM AUTHOR]

In this paper, we study a general linear networked system that contains a tunable memory subsystem; that is, it is decoupled from an optical field for state transportation during the storage process, while it couples to the field during the writing or reading process. The input is given by a single photon state or a coherent state in a pulsed light field. We then completely and explicitly characterize the condition required on the pulse shape achieving the perfect state transfer from the light field to the memory subsystem. The key idea to obtain this result is the use of zero-dynamics principle, which in our case means that, for perfect state transfer, the output field during the writing process must be a vacuum. A useful interpretation of the result in terms of the transfer function is also given. Moreover, a four-node network composed of atomic ensembles is studied as an example, demonstrating how the input field state is transferred to the memory subsystem and what the input pulse shape to be engineered for perfect memory looks like.

We introduce Local Indicators of Spatio-Temporal Association (LISTA) functions on linear networks and use them to build a statistical test for local second-order structure. This allows to identify differences in the spatio-temporal clustering behaviour of two point patterns, a point pattern of interest and a background one, both occurring on the same linear network. We assess the performance of the testing procedure for local second-order structure through simulation studies under a variety of scenarios that also account for different generating point processes. We show that the proposed local test is able to correctly identify the spatio-temporal difference in the local second-order structure of two point patterns occurring on the same linear network, finding that the power of the test increases as the number of points and the clustered structure of the point pattern of interest increase. We finally use the proposed methodology for the analysis of two traffic-related problems. [ABSTRACT FROM AUTHOR]

The paper proposes an induction based approach, that employs automata as inductive invariants, for verifying safety and liveness properties of linear networks of arbitrary size. The proposed method has been shown to be complete for verifying safety properties. Automated methods that check for correcteness of such families of networks are proposed. These methods are based on generating an invariant automaton from the correctness property which is also specified by an automaton. These methods have been implemented and successfully tested on some examples. [ABSTRACT FROM AUTHOR]

A mathematical model for dispersive, multiconductor transmission lines is introduced that makes possible the utilization of the Pade approximation via the Lanczos (PVL) process to the analysis of linear networks that contain transmission line systems. The mathematical model is based on the use of Chebyshev polynomials for the representation of the spatial variation of the transmission-line voltages and currents. A simple collocation procedure is used to obtain a matrix representation of the transmission line equations with matrix coefficients that are first order polynomials in the Laplace-transform variable s and in which terminal transmission-line voltages and currents appear explicitly. Thus, the model is compatible with both the PVL algorithm and the modified nodal analysis formalism. Results from the numerical simulation of both digital interconnect-type and microwave circuits are presented to demonstrate the validity and discuss the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm is presented for the transshipment problem that is an adaption of the method used by Jones, Saigal, and Schneider for solving single-commodity, spatial-equilibrium problems. The approach uses a variable-dimension strategy in which a sequence of subproblems is formed by solving the problem ‘one-node-at-a-time’. The algorithm is tested on uncapacitated transportation problems. Although the computational results are not directly comparable to other methods (since the algorithm is implemented in C under UNIX), the results show that the method is very effective and may be competitive with the best available algorithms for linear network problems. [ABSTRACT FROM AUTHOR]