Passive network tomography for erroneous networks: A network coding approach

Passive network tomography uses end-to-end observations of network communication to characterize the network, for instance to estimate the network topology and to localize random or adversarial glitches. Under the setting of linear network coding this work provides a comprehensive study of passive network tomography in the presence of network (random or adversarial) glitches. To be concrete, this work is developed along two directions: 1. Tomographic upper and lower bounds (i.e., the most adverse conditions in each problem setting under which network tomography is possible, and corresponding schemes (computationally efficient, if possible) that achieve this performance) are presented for random linear network coding (RLNC). We consider RLNC designed with common randomness, i.e., the receiver knows the random code-books all nodes. (To justify this, we show an upper bound for the problem of topology estimation in networks using RLNC without common randomness.) In this setting we present the first set of algorithms that characterize the network topology exactly. Our algorithm for topology estimation with random network errors has time complexity that is polynomial in network parameters. For the problem of network error localization given the topology information, we present the first computationally tractable algorithm to localize random errors, and prove it is computationally intractable to localize adversarial errors. 2. New network coding schemes are designed that improve the tomographic performance of RLNC while maintaining the desirable low-complexity, throughput-optimal, distributed linear network coding properties of RLNC. In particular, we design network codes based on Reed-Solomon codes so that a maximal number of adversarial errors can be localized in a computationally efficient manner even without the information of network topology.Comment: 40 pages, under submission for IEEE Trans. on Information Theor

Interfacing the Network: An Embedded Approach to Network Instrument Creation

This paper discusses the design, construction, and development of a multi-site collaborative instrument, The Loop, developed by the JacksOn4 collective during 2009-10 and formally presented in Oslo at the arts.on.wires and NIME conferences in 2011. The development of this instrument is primarily a reaction to historical network performance that either attempts to present traditional acoustic practice in a distributed format or utilises the network as a conduit to shuttle acoustic and performance data amongst participant nodes. In both scenarios the network is an integral and indispensible part of the performance, however, the network is not perceived as an instrument, per se. The Loop is an attempt to create a single, distributed hybrid instrument retaining traditionally acoustic interfaces and resonant bodies that are mediated by the network. The embedding of the network into the body of the instrument raises many practical and theoretical discussions, which are explored in this paper through a reflection upon the notion of the distributed instrument and the way in which its design impacts the behaviour of the participants (performers and audiences); the mediation of musical expression across networks; the bi-directional relationship between instrument and design; as well as how the instrument assists in the realisation of the creators’ compositional and artistic goals

Vascular network segmentation: an unsupervised approach

Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We consider a partition of the volume obtained by a watershed algorithm based on the distance from the nearest vessel. Each territory is characterized by its volume and the local vascular density. The volume and density maps are first regularized by minimizing the total variation. Then, a new approach is proposed to segment the volume from the two previous restored images based on hypothesis testing. Results are presented on 3D micro-tomographic images of the brain micro-vascular network

Regional surname affinity: a spatial network approach

OBJECTIVE We investigate surname affinities among areas of modern‐day China, by constructing a spatial network, and making community detection. It reports a geographical genealogy of the Chinese population that is result of population origins, historical migrations, and societal evolutions. MATERIALS AND METHODS We acquire data from the census records supplied by China's National Citizen Identity Information System, including the surname and regional information of 1.28 billion registered Chinese citizens. We propose a multilayer minimum spanning tree (MMST) to construct a spatial network based on the matrix of isonymic distances, which is often used to characterize the dissimilarity of surname structure among areas. We use the fast unfolding algorithm to detect network communities. RESULTS We obtain a 10‐layer MMST network of 362 prefecture nodes and 3,610 edges derived from the matrix of the Euclidean distances among these areas. These prefectures are divided into eight groups in the spatial network via community detection. We measure the partition by comparing the inter‐distances and intra‐distances of the communities and obtain meaningful regional ethnicity classification. DISCUSSION The visualization of the resulting communities on the map indicates that the prefectures in the same community are usually geographically adjacent. The formation of this partition is influenced by geographical factors, historic migrations, trade and economic factors, as well as isolation of culture and language. The MMST algorithm proves to be effective in geo‐genealogy and ethnicity classification for it retains essential information about surname affinity and highlights the geographical consanguinity of the population.National Natural Science Foundation of China, Grant/Award Numbers: 61773069, 71731002; National Social Science Foundation of China, Grant/Award Number: 14BSH024; Foundation of China of China Scholarships Council, Grant/Award Numbers: 201606045048, 201706040188, 201706040015; DOE, Grant/Award Number: DE-AC07-05Id14517; DTRA, Grant/Award Number: HDTRA1-14-1-0017; NSF, Grant/Award Numbers: CHE-1213217, CMMI-1125290, PHY-1505000 (61773069 - National Natural Science Foundation of China; 71731002 - National Natural Science Foundation of China; 14BSH024 - National Social Science Foundation of China; 201606045048 - Foundation of China of China Scholarships Council; 201706040188 - Foundation of China of China Scholarships Council; 201706040015 - Foundation of China of China Scholarships Council; DE-AC07-05Id14517 - DOE; HDTRA1-14-1-0017 - DTRA; CHE-1213217 - NSF; CMMI-1125290 - NSF; PHY-1505000 - NSF)Published versio

A modular T-mode design approach for analog neural network hardware implementations

A modular transconductance-mode (T-mode) design approach is presented for analog hardware implementations of neural networks. This design approach is used to build a modular bidirectional associative memory network. The authors show that the size of the whole system can be increased by interconnecting more modular chips. It is also shown that by changing the interconnection strategy different neural network systems can be implemented, such as a Hopfield network, a winner-take-all network, a simplified ART1 network, or a constrained optimization network. Experimentally measured results from CMOS 2-μm double-metal, double-polysilicon prototypes (MOSIS) are presented