Partial Fairness in Secure Two-Party Computation

A protocol for secure computation is fair if either both parties learn the output or else neither party does. A seminal result of Cleve (STOC ’86) is that, in general, complete fairness is impossible to achieve in two-party computation. In light of this, various techniques for obtaining partial fairness have been suggested in the literature. We propose a definition of partial fairness within the standard real-/ideal-world paradigm. We also show broad feasibility results with respect to our definition: partial fairness is possible for any (randomized) functionality f:X×Y→Z ¹×Z ² at least one of whose domains or ranges is polynomial in size. Our protocols are always private, and when one of the domains has polynomial size our protocols also achieve the usual notion of security with abort. We work in the standard communication model (in particular, we do not assume simultaneous channels) and, in contrast to some prior work, rely only on standard cryptographic assumptions (e.g., enhanced trapdoor permutations).     

Limit state design framework for geosynthetic-reinforced soil structures

Conventional design of geosynthetic-reinforced soil structures is divided into two categories, walls and slopes, based on the batter of its facing system. Internal stability, characterized as sufficient reinforcement anchoring and strength, is performed using earth pressure-based design criteria for reinforced walls while reinforced slopes are founded on limit equilibrium (LE) slope stability analyses. LE analyses are also used to assess the global or compound stability of both types of structures, accounting for the geometry of the reinforced, retained and foundation soils. The application of LE-based methods typically results in determination of a slip surface corresponding to the lowest attained Safety Factor (SF), known as the Factor of Safety (Fs); however, it yields little information about reinforcement loading or connection load. In this study, use of the analyzed spatial distribution of SF known as a Safety Map, is modified to attain a prescribed constant Fs at any location in the reinforced soil mass. This modified framework, implemented through an iterative, top-down procedure of LE slope stability analyses originating from the crest of a reinforced structure and exiting at progressively lower elevations on the facing, enables the determination of a Tension Map that illustrates the required distribution of reinforcement tension to attain a prescribed limit state of equilibrium. This tension map is directly constrained by a pullout capacity envelope at both the rear and front of each reinforcement layer, providing a unified, LE-based approach towards assessing an optimal selection of mutually dependent strength and layout of the reinforcement. To illustrate the utility of the Limit State framework, a series of instructive examples are presented. The results demonstrate the effects of facing elements, closely-spaced reinforcements, secondary reinforcement layers, and is compared to conventional design approaches.     

Molecular circuits for associative learning in single-celled organisms

We demonstrate how a single-celled organism could undertake associative learning. Although to date only one previous study has found experimental evidence for such learning, there is no reason in principle why it should not occur. We propose a gene regulatory network that is capable of associative learning between any pre-specified set of chemical signals, in a Hebbian manner, within a single cell. A mathematical model is developed, and simulations show a clear learned response. A preliminary design for implementing this model using plasmids within Escherichia coli is presented, along with an alternative approach, based on double-phosphorylated protein kinases.     

Learning equilibrium as a generalization of learning to optimize

We argue that learning equilibrium is an appropriate generalization to multi-agent systems of the concept of learning to optimize in single-agent setting. We further define and discuss the concept of weak learning equilibrium.     

Evo-devo of Child Growth: The Role of Weaning in the Transition from Infancy to Childhood

Homo sapiens are unique in having a life history phase of childhood, which follows infancy, as defined by breastfeeding. This review uses evolutionary life history theory in understanding child growth in a broad evolutionary perspective, using the data and theory of evolutionary predictive adaptive growth-related strategies for transition from infancy to childhood. We have previously shown that a delayed infancy–childhood transition has a lifelong impact on stature. Feeding practices during infancy are fundamental elements of nutrition as they program for future growth and body composition. A relationship between the duration of breastfeeding and the nature of weaning has been suggested as a possible cause for later obesity and growth patterns. This review highlights the role that breast milk feeding and variations in the weaning age have on transition to childhood, growth, and body composition.     

A Generic Integrated Line Detection Algorithm and Its Object–Process Specification

A generic integrated line detection algorithm (GILDA) is presented and demonstrated. GILDA is based on the generic graphics recognition approach, which abstracts the graphics recognition as a stepwise recovery of the multiple components of the graphic objects and is specified by the object–process methodology. We define 12 classes of lines which appear in engineering drawings and use them to construct a class inheritance hierarchy. The hierarchy highly abstracts the line features that are relevant to the line detection process. Based on the “Hypothesis and Test” paradigm, lines are detected by a stepwise extension to both ends of a selected first key component. In each extension cycle, one new component which best meets the current line's shape and style constraints is appended to the line. Different line classes are detected by controlling the line attribute values. As we show in the experiments, the algorithm demonstrates high performance on clear synthetic drawings as well as on noisy, complex, real-world drawings.