Distributed Probabilistic Polling and Applications to Proportionate Agreement

This paper considers a probabilistic local polling process, examines its properties, and proposes its use in the context of distributed network protocols for achieving consensus. The resulting consensus algorithm is very simple and lightweight, yet it enjoys some desirable properties, such as proportionate agreement (namely, reaching a consensus value of one with probability proportional to the number of ones in the inputs), resilience against dynamic link failures and recoveries, and (weak) self-stabilization. The paper also investigates the maximum influence of small sets and establishes results analogous to those obtained for the problem in the deterministic polling model.     

Laser Ignition of Various Pyrotechnic Mixtures – an Experimental Study

Ignition of several pyrotechnic mixtures by diode‐laser was studied experimentally using a novel combustion chamber. The ignition delay times dependence on laser intensity could be fit by the expression t_ign=aI⁻ⁿ for all compositions, with I being the laser intensity at target and n=1.4–2.1. This is roughly in accordance with thermal ignition theories assuming a semi‐inert solid. Differences in ignition delay times did not depend on fuel alone or oxidizer alone. The temperature of oxidizer decomposition does not correlate with ignition delay time. Furthermore, the steady state combustion temperature, deduced from emission spectra of the composition products are not correlated with ignition delay time. It is proposed that chemical reactions, taking place in the gas‐phase or in the solid‐phase, play a significant role, but are not solely responsible for ignition delay time. The seemingly uncorrelated ignition delay results between pyrotechnics containing either the same fuel or oxidizer hamper the construction of a “unified theory” for laser ignition of pyrotechnic mixtures.     

Neuroprotective Effect of Nerve Growth Factor Loaded in Porous Silicon Nanostructures in an Alzheimer's Disease Model and Potential Delivery to the Brain

Nerve growth factor (NGF) plays a vital role in reducing the loss of cholinergic neurons in Alzheimer's disease (AD). However, its delivery to the brain remains a challenge. Herein, NGF is loaded into degradable oxidized porous silicon (PSiO₂) carriers, which are designed to carry and continuously release the protein over a 1 month period. The released NGF exhibits a substantial neuroprotective effect in differentiated rat pheochromocytoma PC12 cells against amyloid‐beta (Aβ)‐induced cytotoxicity, which is associated with Alzheimer's disease. Next, two potential localized administration routes of the porous carriers into murine brain are investigated: implantation of PSiO₂ chips above the dura mater, and biolistic bombardment of PSiO₂ microparticles through an opening in the skull using a pneumatic gene gun. The PSiO₂‐implanted mice are monitored for a period of 8 weeks and no inflammation or adverse effects are observed. Subsequently, a successful biolistic delivery of these highly porous microparticles into a live‐mouse brain is demonstrated for the first time. The bombarded microparticles are observed to penetrate the brain and reach a depth of 150 µm. These results pave the way for using degradable PSiO₂ carriers as potential localized delivery systems for NGF to the brain.     

Robust design of slow-light tapers in periodic waveguides

This article concerns the design of tapers for coupling power between uniform and slow-light periodic waveguides. New optimization methods are described for designing robust tapers, which not only perform well under nominal conditions, but also over a given set of parameter variations. When the set of parameter variations models the inevitable variations typical in the manufacture or operation of the coupler, a robust design is one that will have a high yield, despite these parameter variations. The ideas of successive refinement, and robust optimization based on multi-scenario optimization with iterative sampling of uncertain parameters, using a fast method for approximately evaluating the reflection coefficient, are introduced. Robust design results are compared to a linear taper, and to optimized tapers that do not take parameter variation into account. Finally, robust performance of the resulting designs is verified using an accurate, but much more expensive, method for evaluating the reflection coefficient.     

A Note on Constant-Round Zero-Knowledge Proofs of Knowledge

In this note, we show the existence of constant-round computational zero-knowledge proofs of knowledge for all $\mathcal {NP}$ . The existence of constant-round zero-knowledge proofs was proven by Goldreich and Kahan (Journal of Cryptology, 1996), and the existence of constant-round zero-knowledge arguments of knowledge was proven by Feige and Shamir (CRYPTO, 1989). However, the existence of constant-round zero-knowledge proofs of knowledge for all $\mathcal {NP}$ is folklore, to the best of our knowledge, since no proof of this fact has been published.     

Neuroendocrine activity and memory-related impairments in posttraumatic stress disorder

This article reviews memory-related impairments in trauma survivors with posttraumatic stress disorder and their possible association to neuroendocrine alterations seen in this disorder. The neuroendocrine profile in PTSD first described in chronically ill combat veterans is characterized by lower basal cortisol levels, higher glucocorticoid receptor number, enhanced sensitivity to exogenous steroids, and increased variation in basal cortisol levels over the diurnal cycle. The generalizability and time course of these neuroendocrine alterations are explored in longitudinal studies and studies in other traumatized populations. These studies suggest that at least some aspects of this neuroendocrine profile can also be seen in other populations, including women, children, and victims of childhood trauma. Additionally, the alterations may be present early in the course of illness, perhaps even in the immediate aftermath of trauma, and may continue to be manifest in elderly trauma survivors. The mechanisms by which these neuroendocrine alterations may influence the formation and processing of traumatic memories are discussed.     

Impossibility Results for Universal Composability in?Public-Key Models and with Fixed Inputs

Universal composability and concurrent general composition consider a setting where secure protocols are run concurrently with each other and with arbitrary other possibly insecure protocols. Protocols that meet the definition of universal composability are guaranteed to remain secure even when run in this strongly adversarial setting. In the case of an honest majority, or where there is a trusted setup phase of some kind (like a common reference string or the key-registration public-key infrastructure of Barak et al.?in FOCS 2004), it has been shown that any functionality can be securely computed in a universally composable way. On the negative side, it has also been shown that in the plain model where there is no trusted setup at all, there are large classes of functionalities which cannot be securely computed in a universally composable way without an honest majority. In this paper, we extend these impossibility results for universal composability. We study a number of public-key models and show for which models the impossibility results of universal composability hold and for which they do not. We also consider a setting where the inputs to the protocols running in the network are fixed before any execution begins. The majority of our results are negative and we show that the known impossibility results for universal composability in the case of no honest majority extend to many other settings.     

Matrix Factorization Techniques for Recommender Systems

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.     

On a confidence gain measure for association rule discovery and scoring

This article presents a new interestingness measure for association rules called confidence gain (CG). Focus is given to extraction of human associations rather than associations between market products. There are two main differences between the two (human and market associations). The first difference is the strong asymmetry of human associations (e.g., the association “shampoo” → “hair” is much stronger than “hair” → “shampoo”), where in market products asymmetry is less intuitive and less evident. The second is the background knowledge humans employ when presented with a stimulus (input phrase). CG calculates the local confidence of a given term compared to its average confidence throughout a given database. CG is found to outperform several association measures since it captures both the asymmetric notion of an association (as in the confidence measure) while adding the comparison to an expected confidence (as in the lift measure). The use of average confidence introduces the “background knowledge” notion into the CG measure. Various experiments have shown that CG and local confidence gain (a low-complexity version of CG) successfully generate association rules when compared to human free associations. The experiments include a large-scale “free sssociation Turing test” where human free associations were compared to associations generated by the CG and other association measures. Rules discovered by CG were found to be significantly better than those discovered by other measures. CG can be used for many purposes, such as personalization, sense disambiguation, query expansion, and improving classification performance of small item sets within large databases. Although CG was found to be useful for Internet data retrieval, results can be easily used over any type of database. Edited by J. Srivastava