100,508 research outputs found
Phyllotetranychus Sayed 1938
No full textGenus <i>Phyllotetranychus</i> Sayed, 1938 <p> <b>Type species:</b> <i>Phyllotetranychus aegyptium</i> Sayed, 1938</p> <p> <b>Diagnosis:</b> Full complement of 16 dorsal setae; dorsal setae large, broadly orbicular to ovate, leaf-like and with pseudovenation; setae <i> h 2</i> not flagellate; anterior margin of prodorsum with two pairs of prodorsal projections; palps two-segmented (tibio-tarsus with one eupathidium (<i>ul'ζ</i>) and two setae, femorogenu with one seta (<i>d</i>)); two pairs of pseudanal setae <i> ps 1–2</i> ; ventral, genital and anal plates not sclerotised or developed.</p>Published as part of <i>Mahdavi, Sayed Mosayeb, Latifi, Malihe & Asadi, Mahdieh, 2019, A new species of Phyllotetranychus (Acari: Tenuipalpidae) from Iran, pp. 566-578 in Zootaxa 4565 (4)</i> on page 567, DOI: 10.11646/zootaxa.4565.4.10, <a href="http://zenodo.org/record/2591261">http://zenodo.org/record/2591261</a>
Decisions Under Binary Messaging over Adaptive Networks
No full textWe consider an adaptive network made of interconnected agents engaged in a binary decision task. It is assumed that the agents cannot deliver full-precision messages to their neighbors, but only binary messages. For this scenario, a modified version of the ATC diffusion rule for the agent state evolution is proposed with improved decision performance under adaptive learning scenarios. An approximate analytical characterization of the agents' state is derived, giving insight into the network behavior at steady-state and enabling numerical computation of the decision performance. Computer experiments show that the analytical characterization is accurate for a wide range of the parameters of interest.AS
Detection Under One-Bit Messaging Over Adaptive Networks
No full textThis paper studies the operation of multi-agent networks engaged in binary decision tasks, and derives performance expressions and performance operating curves under challenging conditions with some revealing insights. One of the main challenges in the analysis is that agents are only allowed to exchange one-bit messages, and the information at each agent therefore consists of both continuous and discrete components. Due to this mixed nature, the steady-state distribution of the state of each agent cannot be inferred from direct application of central limit arguments. Instead, the behavior of the continuous component is characterized in integral form by using a log-characteristic function, while the behavior of the discrete component is characterized by means of an asymmetric Bernoulli convolution. By exploiting these results, this paper derives reliable approximate performance expressions for the network nodes that match well with the simulated results for a wide range of system parameters. The results also reveal an important interplay between continuous adaptation under constant step-size learning and the binary nature of the messages exchanged with neighbors.ASL26th European Signal Processing Conference (EUSIPCO), Sep 03-07, 2018, Rome, ITAL
Consistent tomography under partial observations over adaptive networks
No full textThis paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update its online learning algorithm. The solution of this inference task is challenging for two main reasons. First, only the output of the learning algorithm is available to the external observer that must perform the inference based on these indirect measurements. Second, only output measurements from a fraction of the network agents is available, with the total number of agents itself being also unknown. The main focus of this paper is ascertaining under these demanding conditions whether consistent tomography is possible, namely, whether it is possible to reconstruct the interaction profile of the observable portion of the network, with negligible error as the network size increases. We establish a critical achievability result, namely, that for symmetric combination policies and for any given fraction of observable agents, the interacting and non-interacting agent pairs split into two separate clusters as the network size increases. This remarkable property then enables the application of clustering algorithms to identify the interacting agents influencing the observations. We provide a set of numerical experiments that verify the results for finite network sizes and time horizons. The numerical experiments show that the results hold for asymmetric combination policies as well, which is particularly relevant in the context of causation
Estimation and Detection Over Adaptive Networks
No full textIn this chapter, we review the foundations of statistical inference over adaptive networks by considering two canonical problems: distributed estimation and distributed detection. In the former setting, agents cooperate to estimate a model of interest while in the second setting, the agents cooperate to detect a state of nature. We focus on adaptive learning solutions where agents are able to track drifts in the underlying models, and examine performance limits under both estimation and detection formulations. Special attention is paid to the detailed characterization of the steady-state performance. Certain universal laws are highlighted and compared against known laws for estimation and detection in traditional (centralized or decentralized, nonadaptive) inferential systems
Decision Learning and Adaptation over Multi-Task Networks
No full textThis paper studies the operation of multi-agent networks engaged in multi-task decision problems under the paradigm of simultaneous learning and adaptation. Two scenarios are considered:one in which a decision must be taken among multiple states of nature that are known but can vary over time and space, and another in which there exists a known 'normal' state of nature and the task is to detect unpredictable and unknown deviations from it. In both cases the network learns from the past and adapts to changes in real time in a multi-task scenario with different clusters of agents addressing different decision problems. The system design takes care of challenging situations with clusters of complicated structure, and the performance assessment is conducted by computer simulations. A theoretical analysis is developed to obtain a statistical characterization of the agents' status at steady-state, under the simplifying assumption that clustering is made without errors. This provides approximate bounds for the steady-state decision performance of the agents. Insights are provided for deriving accurate performance prediction by exploiting the derived theoretical results
Decision-making algorithms for learning and adaptation with application to COVID-19 data
No full textThis work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for the latter. Exploiting classical tools from quickest detection, we propose a tailored version of Page's test, referred to as BLLR (barrier log-likelihood ratio) test, and demonstrate its applicability to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak
Graph Learning Over Partially Observed Diffusion Networks: Role of Degree Concentration
No full textThis work examines the problem of learning the topology of a network from the samples of a diffusion process evolving at the network nodes, under the restriction that a limited fraction thereof is probed (partial observability). We provide the following main contributions. Given an estimator of the combination matrix (i.e., the matrix that quantifies the pairwise interaction between nodes), we introduce the notion of identifiability gap, a minimum separation between the entries of the estimated matrix that is critical to enable discrimination between connected and unconnected node pairs. Then we focus on the popular Granger estimator. First, we prove that this matrix estimator, followed by a universal clustering algorithm inspired by the k-means algorithm, learns faithfully the probed subgraph as the network size increases. This result is proved for the case where the network topology is obtained through an Erdős-Rényi random graph under statistical concentration of the node degrees, and the combination matrix is symmetric with nonzero entries bounded in terms of the reciprocal of the maximal and minimal degree. The analysis explores different connectivity regimes, including the dense regime where the probed nodes are influenced by many connections coming from the latent (hidden) part of the graph. Second, we answer a sample complexity question and establish that the number of samples for the Granger estimator scales almost quadratically with the expected graph degree. We also propose three other estimators that are proved to achieve faithful graph learning, and compare them to the Granger estimator, gaining nontrivial insights especially for the case of directed graphs
Graph Learning under Partial Observability
No full textMany optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There is a large body of literature examining the effect of the graph structure on the performance of decentralized processing strategies. In this article, we examine the inverse problem and consider the reverse question: How much information does observing the behavior at the nodes of a graph convey about the underlying topology? For large-scale networks, the difficulty in addressing such inverse problems is compounded by the fact that usually only a limited fraction of the nodes can be probed, giving rise to a second important question: Despite the presence of unobserved nodes, can partial observations still be sufficient to discover the graph linking the probed nodes? The article surveys recent advances on this challenging learning problem and related questions
Local Tomography of Large Networks under the Low-Observability Regime
No full textThis article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, where only a small fraction of the agents can be feasibly observed. The goal is to infer the underlying subnetwork of interactions and we refer to this problem as local tomography. In order to study the large-scale setting, we adopt a proper stochastic formulation where the unobserved part of the network is modeled as an Erdős-Rényi random graph, while the observable subnetwork is left arbitrary. The main result of this work is to establish that, under this setting, local tomography is actually possible with high probability, provided that certain conditions on the network model are met (such as stability and symmetry of the network combination matrix). Remarkably, such conclusion is established under the low-observability regime, where the cardinality of the observable subnetwork is fixed, while the size of the overall network scales to infinity
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