TY - JOUR
AB - Cooperative behavior, where one individual incurs a cost to help another, is a wide spread phenomenon. Here we study direct reciprocity in the context of the alternating Prisoner's Dilemma. We consider all strategies that can be implemented by one and two-state automata. We calculate the payoff matrix of all pairwise encounters in the presence of noise. We explore deterministic selection dynamics with and without mutation. Using different error rates and payoff values, we observe convergence to a small number of distinct equilibria. Two of them are uncooperative strict Nash equilibria representing always-defect (ALLD) and Grim. The third equilibrium is mixed and represents a cooperative alliance of several strategies, dominated by a strategy which we call Forgiver. Forgiver cooperates whenever the opponent has cooperated; it defects once when the opponent has defected, but subsequently Forgiver attempts to re-establish cooperation even if the opponent has defected again. Forgiver is not an evolutionarily stable strategy, but the alliance, which it rules, is asymptotically stable. For a wide range of parameter values the most commonly observed outcome is convergence to the mixed equilibrium, dominated by Forgiver. Our results show that although forgiving might incur a short-term loss it can lead to a long-term gain. Forgiveness facilitates stable cooperation in the presence of exploitation and noise.
AU - Zagorsky, Benjamin
AU - Reiter, Johannes
AU - Chatterjee, Krishnendu
AU - Nowak, Martin
ID - 2247
IS - 12
JF - PLoS One
TI - Forgiver triumphs in alternating prisoner's dilemma
VL - 8
ER -
TY - JOUR
AB - Linked (Open) Data - bibliographic data on the Semantic Web. Report of the Working Group on Linked Data to the plenary assembly of the Austrian Library Network (translation of the title). Linked Data stands for a certain approach to publishing data on the Web. The underlying idea is to harmonise heterogeneous data sources of different origin in order to improve their accessibility and interoperability, effectively making them queryable as a big distributed database. This report summarises relevant developments in Europe as well as the Linked Data Working Group‘s strategic and technical considerations regarding the publishing of the Austrian Library Network’s (OBV’s) bibliographic datasets. It concludes with the mutual agreement that the implementation of Linked Data principles within the OBV can only be taken into consideration accompanied by a discussion about the provision of the datasets under a free license.
AU - Danowski, Patrick
AU - Goldfarb, Doron
AU - Schaffner, Verena
AU - Seidler, Wolfram
ID - 2256
IS - 3/4
JF - VÖB Mitteilungen
TI - Linked (Open) Data - Bibliographische Daten im Semantic Web
VL - 66
ER -
TY - CONF
AB - In a digital signature scheme with message recovery, rather than transmitting the message m and its signature σ, a single enhanced signature τ is transmitted. The verifier is able to recover m from τ and at the same time verify its authenticity. The two most important parameters of such a scheme are its security and overhead |τ| − |m|. A simple argument shows that for any scheme with “n bits security” |τ| − |m| ≥ n, i.e., the overhead is lower bounded by the security parameter n. Currently, the best known constructions in the random oracle model are far from this lower bound requiring an overhead of n + logq h , where q h is the number of queries to the random oracle. In this paper we give a construction which basically matches the n bit lower bound. We propose a simple digital signature scheme with n + o(logq h ) bits overhead, where q h denotes the number of random oracle queries.
Our construction works in two steps. First, we propose a signature scheme with message recovery having optimal overhead in a new ideal model, the random invertible function model. Second, we show that a four-round Feistel network with random oracles as round functions is tightly “public-indifferentiable” from a random invertible function. At the core of our indifferentiability proof is an almost tight upper bound for the expected number of edges of the densest “small” subgraph of a random Cayley graph, which may be of independent interest.
AU - Kiltz, Eike
AU - Pietrzak, Krzysztof Z
AU - Szegedy, Mario
ID - 2258
TI - Digital signatures with minimal overhead from indifferentiable random invertible functions
VL - 8042
ER -
TY - CONF
AB - The learning with rounding (LWR) problem, introduced by Banerjee, Peikert and Rosen at EUROCRYPT ’12, is a variant of learning with errors (LWE), where one replaces random errors with deterministic rounding. The LWR problem was shown to be as hard as LWE for a setting of parameters where the modulus and modulus-to-error ratio are super-polynomial. In this work we resolve the main open problem and give a new reduction that works for a larger range of parameters, allowing for a polynomial modulus and modulus-to-error ratio. In particular, a smaller modulus gives us greater efficiency, and a smaller modulus-to-error ratio gives us greater security, which now follows from the worst-case hardness of GapSVP with polynomial (rather than super-polynomial) approximation factors.
As a tool in the reduction, we show that there is a “lossy mode” for the LWR problem, in which LWR samples only reveal partial information about the secret. This property gives us several interesting new applications, including a proof that LWR remains secure with weakly random secrets of sufficient min-entropy, and very simple constructions of deterministic encryption, lossy trapdoor functions and reusable extractors.
Our approach is inspired by a technique of Goldwasser et al. from ICS ’10, which implicitly showed the existence of a “lossy mode” for LWE. By refining this technique, we also improve on the parameters of that work to only requiring a polynomial (instead of super-polynomial) modulus and modulus-to-error ratio.
AU - Alwen, Joel F
AU - Krenn, Stephan
AU - Pietrzak, Krzysztof Z
AU - Wichs, Daniel
ID - 2259
IS - 1
TI - Learning with rounding, revisited: New reduction properties and applications
VL - 8042
ER -
TY - JOUR
AB - Maternal exposure to infection occurring mid-gestation produces a three-fold increase in the risk of schizophrenia in the offspring. The critical initiating factor appears to be the maternal immune activation (MIA) that follows infection. This process can be induced in rodents by exposure of pregnant dams to the viral mimic Poly I:C, which triggers an immune response that results in structural, functional, behavioral, and electrophysiological phenotypes in the adult offspring that model those seen in schizophrenia. We used this model to explore the role of synchronization in brain neural networks, a process thought to be dysfunctional in schizophrenia and previously associated with positive, negative, and cognitive symptoms of schizophrenia. Exposure of pregnant dams to Poly I:C on GD15 produced an impairment in long-range neural synchrony in adult offspring between two regions implicated in schizophrenia pathology; the hippocampus and the medial prefrontal cortex (mPFC). This reduction in synchrony was ameliorated by acute doses of the antipsychotic clozapine. MIA animals have previously been shown to have impaired pre-pulse inhibition (PPI), a gold-standard measure of schizophrenia-like deficits in animal models. Our data showed that deficits in synchrony were positively correlated with the impairments in PPI. Subsequent analysis of LFP activity during the PPI response also showed that reduced coupling between the mPFC and the hippocampus following processing of the pre-pulse was associated with reduced PPI. The ability of the MIA intervention to model neurodevelopmental aspects of schizophrenia pathology provides a useful platform from which to investigate the ontogeny of aberrant synchronous processes. Further, the way in which the model expresses translatable deficits such as aberrant synchrony and reduced PPI will allow researchers to explore novel intervention strategies targeted to these changes.
AU - Dickerson, Desiree
AU - Bilkey, David
ID - 476
IS - DEC
JF - Frontiers in Behavioral Neuroscience
TI - Aberrant neural synchrony in the maternal immune activation model: Using translatable measures to explore targeted interventions
VL - 7
ER -