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Documents authored by Lynch, Nancy


Found 2 Possible Name Variants:

Lynch, Nancy A.

Document
RADON: Repairable Atomic Data Object in Networks

Authors: Kishori M. Konwar, N. Prakash, Nancy A. Lynch, and Muriel Médard

Published in: LIPIcs, Volume 70, 20th International Conference on Principles of Distributed Systems (OPODIS 2016)


Abstract
Erasure codes offer an efficient way to decrease storage and communication costs while implementing atomic memory service in asynchronous distributed storage systems. In this paper, we provide erasure-code-based algorithms having the additional ability to perform background repair of crashed nodes. A repair operation of a node in the crashed state is triggered externally, and is carried out by the concerned node via message exchanges with other active nodes in the system. Upon completion of repair, the node re-enters active state, and resumes participation in ongoing and future read, write, and repair operations. To guarantee liveness and atomicity simultaneously, existing works assume either the presence of nodes with stable storage, or presence of nodes that never crash during the execution. We demand neither of these; instead we consider a natural, yet practical network stability condition N1 that only restricts the number of nodes in the crashed/repair state during broadcast of any message. We present an erasure-code based algorithm RADON_{C} that is always live, and guarantees atomicity as long as condition N1 holds. In situations when the number of concurrent writes is limited, RADON_{C} has significantly improved storage and communication cost over a replication-based algorithm RADON_{R}, which also works under N1. We further show how a slightly stronger network stability condition N2 can be used to construct algorithms that never violate atomicity. The guarantee of atomicity comes at the expense of having an additional phase during the read and write operations.

Cite as

Kishori M. Konwar, N. Prakash, Nancy A. Lynch, and Muriel Médard. RADON: Repairable Atomic Data Object in Networks. In 20th International Conference on Principles of Distributed Systems (OPODIS 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 70, pp. 28:1-28:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)


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@InProceedings{konwar_et_al:LIPIcs.OPODIS.2016.28,
  author =	{Konwar, Kishori M. and Prakash, N. and Lynch, Nancy A. and M\'{e}dard, Muriel},
  title =	{{RADON: Repairable Atomic Data Object in Networks}},
  booktitle =	{20th International Conference on Principles of Distributed Systems (OPODIS 2016)},
  pages =	{28:1--28:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-031-6},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{70},
  editor =	{Fatourou, Panagiota and Jim\'{e}nez, Ernesto and Pedone, Fernando},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2016.28},
  URN =		{urn:nbn:de:0030-drops-70970},
  doi =		{10.4230/LIPIcs.OPODIS.2016.28},
  annote =	{Keywords: Atomicity, repair, fault-tolerance, storage cost, erasure codes}
}

Lynch, Nancy

Document
Fast Lean Erasure-Coded Atomic Memory Object

Authors: Kishori M. Konwar, N. Prakash, Muriel Médard, and Nancy Lynch

Published in: LIPIcs, Volume 153, 23rd International Conference on Principles of Distributed Systems (OPODIS 2019)


Abstract
In this work, we propose FLECKS, an algorithm which implements atomic memory objects in a multi-writer multi-reader (MWMR) setting in asynchronous networks and server failures. FLECKS substantially reduces storage and communication costs over its replication-based counterparts by employing erasure-codes. FLECKS outperforms the previously proposed algorithms in terms of the metrics that to deliver good performance such as storage cost per object, communication cost a high fault-tolerance of clients and servers, guaranteed liveness of operation, and a given number of communication rounds per operation, etc. We provide proofs for liveness and atomicity properties of FLECKS and derive worst-case latency bounds for the operations. We implemented and deployed FLECKS in cloud-based clusters and demonstrate that FLECKS has substantially lower storage and bandwidth costs, and significantly lower latency of operations than the replication-based mechanisms.

Cite as

Kishori M. Konwar, N. Prakash, Muriel Médard, and Nancy Lynch. Fast Lean Erasure-Coded Atomic Memory Object. In 23rd International Conference on Principles of Distributed Systems (OPODIS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 153, pp. 12:1-12:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@InProceedings{konwar_et_al:LIPIcs.OPODIS.2019.12,
  author =	{Konwar, Kishori M. and Prakash, N. and M\'{e}dard, Muriel and Lynch, Nancy},
  title =	{{Fast Lean Erasure-Coded Atomic Memory Object}},
  booktitle =	{23rd International Conference on Principles of Distributed Systems (OPODIS 2019)},
  pages =	{12:1--12:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-133-7},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{153},
  editor =	{Felber, Pascal and Friedman, Roy and Gilbert, Seth and Miller, Avery},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2019.12},
  URN =		{urn:nbn:de:0030-drops-117988},
  doi =		{10.4230/LIPIcs.OPODIS.2019.12},
  annote =	{Keywords: Atomicity, Distributed Storage System, Erasure-codes}
}
Document
Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks

Authors: Yael Hitron, Nancy Lynch, Cameron Musco, and Merav Parter

Published in: LIPIcs, Volume 151, 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)


Abstract
We study input compression in a biologically inspired model of neural computation. We demonstrate that a network consisting of a random projection step (implemented via random synaptic connectivity) followed by a sparsification step (implemented via winner-take-all competition) can reduce well-separated high-dimensional input vectors to well-separated low-dimensional vectors. By augmenting our network with a third module, we can efficiently map each input (along with any small perturbations of the input) to a unique representative neuron, solving a neural clustering problem. Both the size of our network and its processing time, i.e., the time it takes the network to compute the compressed output given a presented input, are independent of the (potentially large) dimension of the input patterns and depend only on the number of distinct inputs that the network must encode and the pairwise relative Hamming distance between these inputs. The first two steps of our construction mirror known biological networks, for example, in the fruit fly olfactory system [Caron et al., 2013; Lin et al., 2014; Dasgupta et al., 2017]. Our analysis helps provide a theoretical understanding of these networks and lay a foundation for how random compression and input memorization may be implemented in biological neural networks. Technically, a contribution in our network design is the implementation of a short-term memory. Our network can be given a desired memory time t_m as an input parameter and satisfies the following with high probability: any pattern presented several times within a time window of t_m rounds will be mapped to a single representative output neuron. However, a pattern not presented for c⋅t_m rounds for some constant c>1 will be "forgotten", and its representative output neuron will be released, to accommodate newly introduced patterns.

Cite as

Yael Hitron, Nancy Lynch, Cameron Musco, and Merav Parter. Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 23:1-23:31, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@InProceedings{hitron_et_al:LIPIcs.ITCS.2020.23,
  author =	{Hitron, Yael and Lynch, Nancy and Musco, Cameron and Parter, Merav},
  title =	{{Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks}},
  booktitle =	{11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
  pages =	{23:1--23:31},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-134-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{151},
  editor =	{Vidick, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.23},
  URN =		{urn:nbn:de:0030-drops-117087},
  doi =		{10.4230/LIPIcs.ITCS.2020.23},
  annote =	{Keywords: biological distributed computing, spiking neural networks, compressed sensing, clustering, random projection, dimensionality reduction, winner-take-all}
}
Document
Brief Announcement
Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit

Authors: Nancy Lynch and Mien Brabeeba Wang

Published in: LIPIcs, Volume 146, 33rd International Symposium on Distributed Computing (DISC 2019)


Abstract
In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems: "First Consecutive Spikes Counting" and "Total Spikes Counting", which model temporal-coding and rate-coding aspects of temporal-to-spatial translation respectively. Assuming an upper bound of T on the length of the temporal input signal, we design two networks that solve two problems, each using O(log T) neurons and terminating in time T+1. We also prove that these bounds are tight.

Cite as

Nancy Lynch and Mien Brabeeba Wang. Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit. In 33rd International Symposium on Distributed Computing (DISC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 146, pp. 48:1-48:3, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)


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@InProceedings{lynch_et_al:LIPIcs.DISC.2019.48,
  author =	{Lynch, Nancy and Wang, Mien Brabeeba},
  title =	{{Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit}},
  booktitle =	{33rd International Symposium on Distributed Computing (DISC 2019)},
  pages =	{48:1--48:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-126-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{146},
  editor =	{Suomela, Jukka},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2019.48},
  URN =		{urn:nbn:de:0030-drops-113551},
  doi =		{10.4230/LIPIcs.DISC.2019.48},
  annote =	{Keywords: Spiking Neural Network, Distributed Algorithm, Biological Networks}
}
Document
On Simple Back-Off in Unreliable Radio Networks

Authors: Seth Gilbert, Nancy Lynch, Calvin Newport, and Dominik Pajak

Published in: LIPIcs, Volume 125, 22nd International Conference on Principles of Distributed Systems (OPODIS 2018)


Abstract
In this paper, we study local and global broadcast in the dual graph model, which describes communication in a radio network with both reliable and unreliable links. Existing work proved that efficient solutions to these problems are impossible in the dual graph model under standard assumptions. In real networks, however, simple back-off strategies tend to perform well for solving these basic communication tasks. We address this apparent paradox by introducing a new set of constraints to the dual graph model that better generalize the slow/fast fading behavior common in real networks. We prove that in the context of these new constraints, simple back-off strategies now provide efficient solutions to local and global broadcast in the dual graph model. We also precisely characterize how this efficiency degrades as the new constraints are reduced down to non-existent, and prove new lower bounds that establish this degradation as near optimal for a large class of natural algorithms. We conclude with an analysis of a more general model where we propose an enhanced back-off algorithm. These results provide theoretical foundations for the practical observation that simple back-off algorithms tend to work well even amid the complicated link dynamics of real radio networks.

Cite as

Seth Gilbert, Nancy Lynch, Calvin Newport, and Dominik Pajak. On Simple Back-Off in Unreliable Radio Networks. In 22nd International Conference on Principles of Distributed Systems (OPODIS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 125, pp. 27:1-27:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)


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@InProceedings{gilbert_et_al:LIPIcs.OPODIS.2018.27,
  author =	{Gilbert, Seth and Lynch, Nancy and Newport, Calvin and Pajak, Dominik},
  title =	{{On Simple Back-Off in Unreliable Radio Networks}},
  booktitle =	{22nd International Conference on Principles of Distributed Systems (OPODIS 2018)},
  pages =	{27:1--27:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-098-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{125},
  editor =	{Cao, Jiannong and Ellen, Faith and Rodrigues, Luis and Ferreira, Bernardo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2018.27},
  URN =		{urn:nbn:de:0030-drops-100877},
  doi =		{10.4230/LIPIcs.OPODIS.2018.27},
  annote =	{Keywords: radio networks, broadcast, unreliable links, distributed algorithm, robustness}
}
Document
Brief Announcement
Brief Announcement: On Simple Back-Off in Unreliable Radio Networks

Authors: Seth Gilbert, Nancy Lynch, Calvin Newport, and Dominik Pajak

Published in: LIPIcs, Volume 121, 32nd International Symposium on Distributed Computing (DISC 2018)


Abstract
In this paper, we study local broadcast in the dual graph model, which describes communication in a radio network with both reliable and unreliable links. Existing work proved that efficient solutions to these problems are impossible in the dual graph model under standard assumptions. In real networks, however, simple back-off strategies tend to perform well for solving these basic communication tasks. We address this apparent paradox by introducing a new set of constraints to the dual graph model that better generalize the slow/fast fading behavior common in real networks. We prove that in the context of these new constraints, simple back-off strategies now provide efficient solutions to local broadcast in the dual graph model. These results provide theoretical foundations for the practical observation that simple back-off algorithms tend to work well even amid the complicated link dynamics of real radio networks.

Cite as

Seth Gilbert, Nancy Lynch, Calvin Newport, and Dominik Pajak. Brief Announcement: On Simple Back-Off in Unreliable Radio Networks. In 32nd International Symposium on Distributed Computing (DISC 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 121, pp. 48:1-48:3, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{gilbert_et_al:LIPIcs.DISC.2018.48,
  author =	{Gilbert, Seth and Lynch, Nancy and Newport, Calvin and Pajak, Dominik},
  title =	{{Brief Announcement: On Simple Back-Off in Unreliable Radio Networks}},
  booktitle =	{32nd International Symposium on Distributed Computing (DISC 2018)},
  pages =	{48:1--48:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-092-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{121},
  editor =	{Schmid, Ulrich and Widder, Josef},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2018.48},
  URN =		{urn:nbn:de:0030-drops-98373},
  doi =		{10.4230/LIPIcs.DISC.2018.48},
  annote =	{Keywords: radio networks, broadcast, unreliable links, distributed algorithm, robustness}
}
Document
Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks

Authors: Nancy Lynch, Cameron Musco, and Merav Parter

Published in: LIPIcs, Volume 67, 8th Innovations in Theoretical Computer Science Conference (ITCS 2017)


Abstract
We initiate a line of investigation into biological neural networks from an algorithmic perspective. We develop a simplified but biologically plausible model for distributed computation in stochastic spiking neural networks and study tradeoffs between computation time and network complexity in this model. Our aim is to abstract real neural networks in a way that, while not capturing all interesting features, preserves high-level behavior and allows us to make biologically relevant conclusions. In this paper, we focus on the important 'winner-take-all' (WTA) problem, which is analogous to a neural leader election unit: a network consisting of $n$ input neurons and n corresponding output neurons must converge to a state in which a single output corresponding to a firing input (the 'winner') fires, while all other outputs remain silent. Neural circuits for WTA rely on inhibitory neurons, which suppress the activity of competing outputs and drive the network towards a converged state with a single firing winner. We attempt to understand how the number of inhibitors used affects network convergence time. We show that it is possible to significantly outperform naive WTA constructions through a more refined use of inhibition, solving the problem in O(\theta) rounds in expectation with just O(\log^{1/\theta} n) inhibitors for any \theta. An alternative construction gives convergence in O(\log^{1/\theta} n) rounds with O(\theta) inhibitors. We complement these upper bounds with our main technical contribution, a nearly matching lower bound for networks using \ge \log \log n inhibitors. Our lower bound uses familiar indistinguishability and locality arguments from distributed computing theory applied to the neural setting. It lets us derive a number of interesting conclusions about the structure of any network solving WTA with good probability, and the use of randomness and inhibition within such a network.

Cite as

Nancy Lynch, Cameron Musco, and Merav Parter. Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 67, pp. 15:1-15:44, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)


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@InProceedings{lynch_et_al:LIPIcs.ITCS.2017.15,
  author =	{Lynch, Nancy and Musco, Cameron and Parter, Merav},
  title =	{{Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks}},
  booktitle =	{8th Innovations in Theoretical Computer Science Conference (ITCS 2017)},
  pages =	{15:1--15:44},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-029-3},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{67},
  editor =	{Papadimitriou, Christos H.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2017.15},
  URN =		{urn:nbn:de:0030-drops-81952},
  doi =		{10.4230/LIPIcs.ITCS.2017.15},
  annote =	{Keywords: biological distributed algorithms, neural networks, distributed lower bounds, winner-take-all networks}
}
Document
An Efficient Communication Abstraction for Dense Wireless Networks

Authors: Magnús M. Halldórsson, Fabian Kuhn, Nancy Lynch, and Calvin Newport

Published in: LIPIcs, Volume 91, 31st International Symposium on Distributed Computing (DISC 2017)


Abstract
In this paper we study the problem of developing efficient distributed algorithms for dense wireless networks. For many problems in this setting, fast solutions must leverage the reality that radio signals fade with distance, which can be exploited to enable concurrent communication among multiple sender/receiver pairs. To simplify the development of these algorithms we describe a new communication abstraction called FadingMAC which exposes the benefits of this concurrent communication, but also hides the details of the underlying low-level radio signal behavior. This approach splits efforts between those who develop useful algorithms that run on the abstraction, and those who implement the abstraction in concrete low-level wireless models, or on real hardware. After defining FadingMAC, we describe and analyze an efficient implementation of the abstraction in a standard low-level SINR-style network model. We then describe solutions to the following problems that run on the abstraction: max, min, sum, and mean computed over input values; process renaming; consensus and leader election; and optimal packet scheduling. Combining our abstraction implementation with these applications that run on the abstraction, we obtain near-optimal solutions to these problems in our low-level SINR model - significantly advancing the known results for distributed algorithms in this setting. Of equal importance to these concrete bounds, however, is the general idea advanced by this paper: as wireless networks become more dense, both theoreticians and practitioners must explore new communication abstractions that can help tame this density.

Cite as

Magnús M. Halldórsson, Fabian Kuhn, Nancy Lynch, and Calvin Newport. An Efficient Communication Abstraction for Dense Wireless Networks. In 31st International Symposium on Distributed Computing (DISC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 91, pp. 25:1-25:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)


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@InProceedings{halldorsson_et_al:LIPIcs.DISC.2017.25,
  author =	{Halld\'{o}rsson, Magn\'{u}s M. and Kuhn, Fabian and Lynch, Nancy and Newport, Calvin},
  title =	{{An Efficient Communication Abstraction for Dense Wireless Networks}},
  booktitle =	{31st International Symposium on Distributed Computing (DISC 2017)},
  pages =	{25:1--25:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-053-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{91},
  editor =	{Richa, Andr\'{e}a},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2017.25},
  URN =		{urn:nbn:de:0030-drops-79898},
  doi =		{10.4230/LIPIcs.DISC.2017.25},
  annote =	{Keywords: wireless networks, abstractions, SINR, signal fading}
}
Document
Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks

Authors: Nancy Lynch, Cameron Musco, and Merav Parter

Published in: LIPIcs, Volume 91, 31st International Symposium on Distributed Computing (DISC 2017)


Abstract
We study distributed algorithms implemented in a simplified biologically inspired model for stochastic spiking neural networks. We focus on tradeoffs between computation time and network complexity, along with the role of noise and randomness in efficient neural computation. It is widely accepted that neural spike responses, and neural computation in general, is inherently stochastic. In recent work, we explored how this stochasticity could be leveraged to solve the 'winner-take-all' leader election task. Here, we focus on using randomness in neural algorithms for similarity testing and compression. In the most basic setting, given two n-length patterns of firing neurons, we wish to distinguish if the patterns are equal or epsilon-far from equal. Randomization allows us to solve this task with a very compact network, using O((sqrt(n) log n)/epsilon) auxiliary neurons, which is sublinear in the input size. At the heart of our solution is the design of a t-round neural random access memory, or indexing network, which we call a neuro-RAM. This module can be implemented with O(n/t) auxiliary neurons and is useful in many applications beyond similarity testing - e.g., we discuss its application to compression via random projection. Using a VC dimension-based argument, we show that the tradeoff between runtime and network size in our neuro-RAM is nearly optimal. To the best of our knowledge, we are the first to apply these techniques to stochastic spiking networks. Our result has several implications - since our neuro-RAM can be implemented with deterministic threshold gates, it demonstrates that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem. It also establishes a separation between our networks, which spike with a sigmoidal probability function, and well-studied deterministic sigmoidal networks, whose gates output real number values, and which can implement a neuro-RAM much more efficiently.

Cite as

Nancy Lynch, Cameron Musco, and Merav Parter. Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks. In 31st International Symposium on Distributed Computing (DISC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 91, pp. 33:1-33:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)


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@InProceedings{lynch_et_al:LIPIcs.DISC.2017.33,
  author =	{Lynch, Nancy and Musco, Cameron and Parter, Merav},
  title =	{{Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks}},
  booktitle =	{31st International Symposium on Distributed Computing (DISC 2017)},
  pages =	{33:1--33:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-053-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{91},
  editor =	{Richa, Andr\'{e}a},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2017.33},
  URN =		{urn:nbn:de:0030-drops-79863},
  doi =		{10.4230/LIPIcs.DISC.2017.33},
  annote =	{Keywords: spiking neural networks, biological distributed algorithms, circuit design}
}
Document
Modeling Computational Security in Long-Lived Systems

Authors: Ran Canetti, Ling Cheung, Dilsun Kaynar, Nancy Lynch, and Olivier Pereira

Published in: Dagstuhl Seminar Proceedings, Volume 8491, Theoretical Foundations of Practical Information Security (2009)


Abstract
For many cryptographic protocols, security relies on the assumption that adversarial entities have limited computational power. This type of security degrades progressively over the lifetime of a protocol. However, some cryptographic services, such as timestamping services or digital archives, are emph{long-lived} in nature; they are expected to be secure and operational for a very long time (ie super-polynomial). In such cases, security cannot be guaranteed in the traditional sense: a computationally secure protocol may become insecure if the attacker has a super-polynomial number of interactions with the protocol. This paper proposes a new paradigm for the analysis of long-lived security protocols. We allow entities to be active for a potentially unbounded amount of real time, provided they perform only a polynomial amount of work emph{per unit of real time}. Moreover, the space used by these entities is allocated dynamically and must be polynomially bounded. We propose a new notion of emph{long-term implementation}, which is an adaptation of computational indistinguishability to the long-lived setting. We show that long-term implementation is preserved under polynomial parallel composition and exponential sequential composition. We illustrate the use of this new paradigm by analyzing some security properties of the long-lived timestamping protocol of Haber and Kamat.

Cite as

Ran Canetti, Ling Cheung, Dilsun Kaynar, Nancy Lynch, and Olivier Pereira. Modeling Computational Security in Long-Lived Systems. In Theoretical Foundations of Practical Information Security. Dagstuhl Seminar Proceedings, Volume 8491, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2009)


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@InProceedings{canetti_et_al:DagSemProc.08491.3,
  author =	{Canetti, Ran and Cheung, Ling and Kaynar, Dilsun and Lynch, Nancy and Pereira, Olivier},
  title =	{{Modeling Computational Security in Long-Lived Systems}},
  booktitle =	{Theoretical Foundations of Practical Information Security},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8491},
  editor =	{Ran Canetti and Shafi Goldwasser and G\"{u}nter M\"{u}ller and Rainer Steinwandt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08491.3},
  URN =		{urn:nbn:de:0030-drops-18908},
  doi =		{10.4230/DagSemProc.08491.3},
  annote =	{Keywords: Long lived security; universally composable security;}
}
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