Use Of Local Information Research Articles

On Generalized Proportional Allocation Policies for Traffic Signal Control

The fast-increasing demand and relatively slow growth of infrastructure capacity are providing a strong motivation for research in real-time urban traffic controls that make the best use of novel sensing in order to increase efficiency and resilience of the transportation system. In our contribution, we focus on a class of dynamic feedback traffic signal control policies that are based on a generalized proportional allocation rule. The proposed traffic signal controls are decentralized (they make use of local information only), scalable (they are independent of the network size and topology), and universal (they do not rely on any information about external inflows or turning ratios). In spite of their fully distributed nature, we prove that such control policies achieve a global objective, maximum throughput, in that they stabilize the urban traffic network whenever possible under the given capacity constraints.The traffic model we consider consists in a network of interconnected vertical queues with deterministic dynamics driven by physical laws (conservation of mass and preservation of non-negativity of the traffic volumes) as well as scheduling constraints (described as a set of phases, each phase consisting in a subset of lanes that can be be given green light simultaneously). This results in a differential inclusion for which we prove existence and, in the special case of orthogonal phases, uniqueness of continuous solutions via a generalization of the reflection principle. Stability is then proved by interpreting the generalized proportional allocation controllers as minimizers of a certain entropy-like function that is then used as a Lyapunov function for the closed-loop system.

Enabling sustainable bulk transfer in environmentally-powered wireless sensor networks

We address the problem of transferring bulk data in environmentally-powered wireless sensor networks where duty cycle compliance is critical for their uninterrupted operation. We propose Pump-and-Nap, a packet train forwarding technique that maximizes throughput while simultaneously enforcing compliance to dynamic duty cycle limitations. A node using Pump-and-Nap operates by pumping a train of packets followed by a napping period where the node forgoes any transmission. Pump-and-Nap employs an adaptive controller to periodically compute the optimal capacity, that is, the maximum number of packets a node can receive and transmit in a train, given its duty cycle constraint. The controller uses prior input-output observations (capacity allocations and their corresponding duty cycle usage) to continuously tune its performance and adapt to wireless link quality variations. Its use of local information makes the controller easily deployable in a distributed fashion. We implemented Pump-and-Nap in TinyOS and evaluated its performance through experiments and testbed simulations. Results show that Pump-and-Nap provides high transfer throughput while it simultaneously tracks the target duty cycle. More importantly, Pump-and-Nap enables sustainable bulk transfer compared to state-of-the-art techniques that greedily maximize throughput at the expense of downtime due to energy depletion.

An Empirical Analysis of Performance Impacts Resulting from Conversion to Franchise Operations

Franchising is an important form of organizational control. Possible benefits of franchising include its ability to reduce agency costs that increase with costly monitoring, and provide incentives for the use of local information by onsite managers. However, these benefits may come at a cost, as franchisees may reduce quality by choosing to free ride. While many studies have investigated the reasons for franchising, few studies have documented the impacts of franchising on unit level operating performance. Using time-series data from a number of lodging properties that were converted to franchisee control from company control, this study documents the performance impacts of franchising. The analysis reveals that conversion results in a modest decline in financial performance and an immediate sharp decline in quality.

Social information from immigrants: multiple immigrant-based sources of information for dispersal decisions in a ciliate.

1. Dispersal is increasingly recognized as being an informed process, based on information organisms obtain about the landscape. While local conditions are often found to drive dispersal decisions, local context is not always a reliable predictor of conditions in neighbouring patches, making the use of local information potentially useless or even maladaptive. In this case, using social information gathered by immigrants might allow adjusting dispersal decisions without paying the costs of prospecting. However, this hypothesis has been largely neglected despite its major importance for ecological and evolutionary processes. 2. We investigated three fundamental questions about immigrant-informed dispersal: Do immigrants convey information that influences dispersal, do organisms use multiple cues from immigrants, and is immigrant-informed dispersal genotype dependent? 33. Using Tetrahymena thermophila ciliates in microcosms, we manipulated the number of immigrants arriving, the density of congeners, the resource quality in neighbouring patches, matrix characteristics and the level of cooperation of individuals in the neighbouring populations. 4. We provide the first experimental evidence that immigrants convey a number of different cues about neighbouring patches and matrix (patch quality, matrix characteristics and cooperation in neighbouring populations) in this relatively simple organism. Furthermore, we demonstrate genotype-dependent immigrant-informed dispersal decisions about patch quality and matrix characteristics. 5. Multiple cues from immigrants and genotype-dependent use of cues have major implications for theoretical metapopulation dynamics and the potential for local adaptation.

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.

Development of robust behaviour recognition for an at-home biomonitoring robot with assistance of subject localization and enhanced visual tracking.

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.

Exception Reports as a Source of Idiosyncratic Information

Firms commonly use exception reports that describe and justify deviations from guidelines to mitigate poor decisions and employee opportunism. This study examines whether exception reports can also be used to help lower-level employees make better-informed decisions. We propose that exception reports can facilitate the sharing and use of local information that is not systematically collected, shared, and codified into rules, routines, or operating procedures (“idiosyncratic information”). Consistent with our expectation, we find that lower-level employees of a large federal credit union use idiosyncratic information in each other’s exception reports to increase lending, and reduce interest rates and loan charge offs. In additional analyses, we find that this occurs because accountability pressure from superiors makes exception reports a particularly credible source of idiosyncratic information and a common understanding of work practices facilitates the interpretation of exception reports.

Swarmanoid: A Novel Concept for the Study of Heterogeneous Robotic Swarms

Swarm robotics systems are characterized by decentralized control, limited communication between robots, use of local information, and emergence of global behavior. Such systems have shown their potential for flexibility and robustness [1]-[3]. However, existing swarm robotics systems are by and large still limited to displaying simple proof-of-concept behaviors under laboratory conditions. It is our contention that one of the factors holding back swarm robotics research is the almost universal insistence on homogeneous system components. We believe that swarm robotics designers must embrace heterogeneity if they ever want swarm robotics systems to approach the complexity required of real-world systems.

Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.

Profissionais de saúde no processo de vacinação contra hepatite B em duas unidades básicas de Belo Horizonte: uma avaliação qualitativa

According to the Vaccine Coverage Survey, performed in 2007, the immunization coverage against hepatitis B in Belo Horizonte, for infants under one year old, was below the level proposed by the Brazilian National Program of Immunization. This vaccine was used as basis for evaluating the involvement of health professionals in the process of vaccination in two Basic Health Units (UBS, acronym in Portuguese) in the city. This study is qualitative and uses the notions of Social Representations Theory and the method of Structural Analysis of Narrative to carry out the interviews and data analysis. The results show flaws related to controlling and use of the mirror card and the parent orientation, and also the monitoring of vaccination coverage (VC) and use of VC data as input for planning health actions. It was observed that the working process in the UBS is focused on routine tasks, with low creativity of the professionals, which includes representations that maintain strong tendency to value activities focused on the health of individuals to the detriment of public health actions. In conclusion, the vaccination process fault can be overcome with a greater appreciation of everyday actions and with a much better use of local information about vaccination, and some necessary adjustments within the UBS to improve public health actions.

A localized navigation algorithm for Radiation Evasion for nuclear facilities. Part II: Optimizing the “Nearest Exit” Criterion

In this extension from part I (Khasawneh et al., in press), we modify the navigation algorithm which was presented with the objective of optimizing the “Radiation Evasion” Criterion so that navigation would optimize the criterion of “Nearest Exit”. Under this modification, algorithm would yield navigation paths that would guide occupational workers towards Nearest Exit points. Again, under this optimization criterion, algorithm leverages the use of localized information acquired through a well designed and distributed wireless sensor network, as it averts the need for any long-haul communication links or centralized decision and monitoring facility thereby achieving a more reliable performance under dynamic environments.As was done in part I, the proposed algorithm under the “Nearest Exit” Criterion is designed to leverage nearest neighbor information coming in through the sensory network overhead, in computing successful navigational paths from one point to another. For comparison purposes, the proposed algorithm is tested under the two optimization criteria: “Radiation Evasion” and “Nearest Exit”, for different numbers of step look-ahead.We verify the performance of the algorithm by means of simulations, whereby navigational paths are calculated for different radiation fields. We, via simulations, also, verify the performance of the algorithm in comparison with a well-known global navigation algorithm upon which we draw our conclusions.

A localized navigation algorithm for radiation evasion for nuclear facilities: Optimizing the “Radiation Evasion” criterion: Part I

In this paper, we introduce a navigation algorithm having general utility for occupational workers at nuclear facilities and places where radiation poses serious health hazards. This novel algorithm leverages the use of localized information for its operation. Therefore, the need for central processing and decision resources is avoided, since information processing and the ensuing decision-making are done aboard a man-borne device.To acquire the information needed for path planning in radiation avoidance, a well-designed and distributed wireless sensory infrastructure is needed. This will automatically benefit from the most recent trends in technology developments in both sensor networks and wireless communication. When used to navigate based on local radiation information, the algorithm will behave more reliably when accidents happen, since no long-haul communication links are required for information exchange. In essence, the proposed algorithm is designed to leverage nearest neighbor information coming in through the sensory network overhead, to compute successful navigational paths from one point to another.The proposed algorithm is tested under the “Radiation Evasion” criterion. It is also tested for the case when more information, beyond nearest neighbors, is made available; here, we test its operation for different numbers of step look-ahead. We verify algorithm performance by means of simulations, whereby navigational paths are calculated for different radiation fields.

Local empathy provides global minimization of congestion in communication networks

We present a mechanism to avoid congestion in complex networks based on a local knowledge of traffic conditions and the ability of routers to self-coordinate their dynamical behavior. In particular, routers make use of local information about traffic conditions to either reject or accept information packets from their neighbors. We show that when nodes are only aware of their own congestion state they self-organize into a hierarchical configuration that delays remarkably the onset of congestion although leading to a sharp first-order-like congestion transition. We also consider the case when nodes are aware of the congestion state of their neighbors. In this case, we show that empathy between nodes is strongly beneficial to the overall performance of the system and it is possible to achieve larger values for the critical load together with a smooth, second-order-like, transition. Finally, we show how local empathy minimize the impact of congestion as much as global minimization. Therefore, here we present an outstanding example of how local dynamical rules can optimize the system's functioning up to the levels reached using global knowledge.

Face inversion impairs holistic perception: Evidence from gaze-contingent stimulation

Human observers are experts at face recognition, yet a simple 180 degrees rotation of a face photograph decreases recognition performance substantially. A full understanding of this phenomenon-which is believed to be important for clarifying the nature of our expertise in face recognition-is still waiting. According to a long-standing and influential hypothesis, an inverted face cannot be perceived as holistically as an upright face and has to be analyzed local feature by local feature. Here, we tested this holistic perception hypothesis of the face inversion effect by means of a gaze-contingent stimulus presentation. When observers' perception was restricted to one fixated feature at a time by a gaze-contingent window, performance in an individual face matching task was almost unaffected by inversion. However, when a mask covered the fixated feature, preventing the use of local information at high resolution, the decrement of performance with inversion was even larger than in a normal-full view-condition. These observations provide evidence that the face inversion effect is caused by an inability to perceive the individual face as a whole rather than as a collection of specific features and thus support the view that observers' expertise at upright face recognition is due to the ability to perceive an individual face holistically.

A linear‐time algorithm for broadcast domination in a tree

AbstractThe broadcast domination problem is a variant of the classical minimum dominating set problem in which a transmitter of power p at vertex v is capable of dominating (broadcasting to) all vertices within distance p from v. Our goal is to assign a broadcast power f(v) to every vertex v in a graph such that ΣvεVf(v) is minimized, and such that every vertex u with f(u) = 0 is within distance f(v) of some vertex v with f(v)> 0. The problem is solvable in polynomial time on a general graph (Heggernes and Lokshtanov, Disc Math (2006), 3267–3280) and Blair et al. (Congr. Num. (2004), 55–77.) gave an O(n2) algorithm for trees. In this article, we provide an O(n) algorithm for trees. Our algorithm is notable due to the fact that it makes decisions for each vertex v based on “nonlocal” information from vertices far away from v, whereas almost all other linear‐time algorithms for trees only make use of local information. © 2008 Wiley Periodicals, Inc. NETWORKS, 2009

Use of Local Information to Determine the Distance to Voltage Collapse

This paper proposes a simple method of determining the distance to voltage collapse using some local information such as bus voltage magnitude and load current magnitude. First the voltage and current information are carefully processed to establish a voltage stability index (VSI) that varies almost linearly with load. The VSI is then used to estimate the distance to voltage collapse using linear extrapolation. The effects of generator reactive power limits, line tripping, non-uniform increase in load and additional shunt reactive power sources on the VSI are also investigated. The effectiveness of the proposed method of determining the distance to voltage collapse is then tested on the IEEE 30-bus system. The results obtained by the proposed method are also compared with the corresponding actual values found through repetitive power flow simulations and are observed to be in very good agreement.

Role of information and communication networks in malaria survival

BackgroundQuite often symptoms of malaria go unrecognized or untreated. According to the Multilateral Initiative on Malaria, 70% of the malaria cases that are treated at home are mismanaged. Up to 82% of all malaria episodes in sub-Saharan Africa are treated outside the formal health sector. Fast and appropriate diagnosis and treatment of malaria is extremely important in reducing morbidity and mortality.MethodData from 70 different countries is pooled together to construct a panel dataset of health and socio-economic variables for a time span of (1960–2004). The generalized two-stage least squares and panel data models are used to investigate the impact of information and communication network (ICN) variables on malaria death probability. The intensity of ICN is represented by the number of telephone main lines per 1,000 people and the number of television sets per 1,000 people.ResultsThe major finding is that the intensity of ICN is associated with reduced probability of deaths of people that are clinically identified as malaria infected. The results are robust for both indicators i.e. interpersonal and mass communication networks and for all model specifications examined.ConclusionThe results suggest that information and communication networks can substantially scale up the effectiveness of the existing resources for malaria prevention. Resources spent in preventing malaria are far less than needed. Expanded information and communication networks will widen the avenues for community based "participatory development", that encourages the use of local information, knowledge and decision making. Timely information, immediate care and collective knowledge based treatment can be extremely important in reducing child mortality and achieving the millennium development goal.

Protein secondary structure and stability determined by combining exoproteolysis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

In the post-genomic era, several projects focused on the massive experimental resolution of the three-dimensional structures of all the proteins of different organisms have been initiated. Simultaneously, significant progress has been made in the ab initio prediction of protein three-dimensional structure. One of the keys to the success of such a prediction is the use of local information (i.e. secondary structure). Here we describe a new limited proteolysis methodology, based on the use of unspecific exoproteases coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), to map quickly secondary structure elements of a protein from both ends, the N- and C-termini. We show that the proteolytic patterns (mass spectra series) obtained can be interpreted in the light of the conformation and local stability of the analyzed proteins, a direct correlation being observed between the predicted and the experimentally derived protein secondary structure. Further, this methodology can be easily applied to check rapidly the folding state of a protein and characterize mutational effects on protein conformation and stability. Moreover, given global stability information, this methodology allows one to locate the protein regions of increased or decreased conformational stability. All of this can be done with a small fraction of the amount of protein required by most of the other methods for conformational analysis. Thus limited exoproteolysis, together with MALDI-TOF MS, can be a useful tool to achieve quickly the elucidation of protein structure and stability.

Decentralization in Financial Institutions: Theory and Evidence from China

Decentralization facilitates more efficient use of local information but also can lead to agency problems and commitment failures. This paper develops simple models of a two-tier hierarchy (the center vs. the local) to study the optimal division of lending authority between central and local bank managers. Using a unique data set collected by the authors from a survey of rural financial institutions in China, we empirically assess the determinants of decentralization. We find that concerns over agency problems (collusion and government influence) and especially commitment failures (excessive refinancing) explain the recent trend toward centralization of lending authority. We conclude that the inability of financial systems to exploit local information in weak institutional environments may substantially limit the efficiency of financial intermediation despite financial market liberalization.

Social Security Systems in Low‐Income Countries: Concepts, Constraints and the Need for Cooperation

This paper describes existing social security systems in developing countries and analyses their institutional strengths and weaknesses. To this end, it identifies four main providers of social security, namely the State, the market, member‐based organizations, and private households. Given the strengths and weaknesses of each system, the paper explores the possibilities of a public‐private partnership which should be based on the use of local information on the one hand while profiting from possibilities of risk sharing and risk pooling on the other.