Generating systems from multiple sketched models

Diagrams are often used to model complex systems: in many cases several different types of diagrams are used to model different aspects of the system. These diagrams, perhaps from multiple stakeholders of different specialties, must be combined to achieve a full abstract representation of the system. Many CAD tools offer multi-diagram integration; however, sketch-based diagramming tools are yet to tackle this difficult integration problem. We extend the diagram sketching tool InkKit to combine software engineering sketches of different types. Our extensions support software design processes by providing a sketch-based approach that allows the iterative creation of multiple outputs interacting with one another from the inter-linked models. We demonstrate that InkKit can generate a functional system consisting of a user interface with processes to submit and retrieve data from a database from sketched user interfaces designs and sketched entity relationship diagrams.     

μ-Bases and singularities of rational planar curves

We provide a technique to detect the singularities of rational planar curves and to compute the correct order of each singularity including the infinitely near singularities without resorting to blow ups. Our approach employs the given parametrization of the curve and uses a μ-basis for the parametrization to construct two planar algebraic curves whose intersection points correspond to the parameters of the singularities including infinitely near singularities with proper multiplicity. This approach extends Abhyankar's method of t-resultants from planar polynomial curves to rational planar curves. We also derive the classical result that for a rational planar curve of degree n the sum of all the singularities with proper multiplicity is (n−1)(n−2)/2. Examples are provided to flesh out our results.     

Segmentation of Colour Images with Highlights and Shadows sing Fuzzy-like Reasoning

In this paper, a fuzzy-like technique is presented that resolves several difficult issues related to image segmentation, such as highlights and shadows. Large, relatively continuous, areas within an image are usually easy to segment, and the pixels included within different segments are often determined by using derived edge information. However, in many cases, pixels which lie between segments or in high frequency areas of an image cannot be easily categorised as belonging to any particular segments. Typically, according to the dichromatic reflection model, these pixels may belong to the matte, highlight or shadow area of the closest segment; or, in association with neighbouring pixels, they make up a separate smaller segment. The dichromatic reflection model is applied here to merge highlight and shadow areas with matte areas in an image. By segmenting those pixels into proper regions, the proposed fuzzy-like reasoning approach provides a more human-like segmentation of images.     

Quasiparticle tunneling through rare earth trihydroxide barriers

Symmetric tunneling junctions with 4000-Å-thick Pb electrodes and polycrystalline insulating barriers of Lu(OH)₃, Er(OH)₃, and Ho(OH)₃ have been fabricated. In bulk, these three rare earth trihydroxides are nonmagnetic, antiferromagnetic (T _N<1.1 K), and ferromagnetic (T_c=2.54 K), respectively. Tunneling resistances ranged from 600 to greater than 40,000 with a junction area of 6.25×10^–2 cm². Single-particle tunneling characteristics of these junctions were always broadened relative to the characteristics of Pb-PbO-Pb junctions, although the ratio of the zero-bias tunneling resistance to the normal tunneling resistance in some instances was of the order of 1000. A threefold splitting of the conductance peak at the gap was observed only in junctions with Ho(OH)₃ barriers. The gap peak of junctions with Er(OH)₃ barriers was broadened significantly relative to that of junctions with Lu(OH)₃ barriers. From measurements of the temperature and magnetic field dependences of the tunneling conductance it is argued that the splitting in junctions with Ho(OH)₃ barriers is consistent with the existence of a peak in the electronic density of states at an energy below that of the gap of each of the electrodes. This peak is believed to be the signature of a bound state near the barrier where the pair potential is depressed by virtue of the exchange coupling between the spins of the superconducting electrons and the localized spins of the barrier. Qualitative interpretations of the data support the view that the observed structure in Ho(OH)₃ barrier junctions is neither a consequence of intrinsic gap anisotropy in Pb nor of inelastic magnon-assisted tunnelling.     

Pair-field susceptibility of superconducting Al-Er films

Measurements of the imaginary part of the pair-field susceptibility have been carried out on dirty-limit superconducting Al films doped with Er impurities at temperatures within 20% of the critical temperature T _c. These studies are the first measurements of (, k) as a function of the pair-breaking parameter . Samples exhibiting values of up to 0.1 were studied. At temperatures above T _c the diffusive time-dependent Ginzburg-Landau equation for order-parameter fluctuations was found to be valid in the presence of pair-breaking, with the Ginzburg-Landau time an increasing function of the pair-breaking, in quantitative agreement with theory. The characteristic frequencies of the transverse and longitudinal modes of the order parameter disequilibrium were determined from the measurements of (, k) below T _c by fitting to a functional form which exhibits the essential features of the most detailed theories, in particular those of Orbach and Entin-Wohlman, Dinter, and Schön and Ambegaokar, The propagating charge-imbalance wave was found to be overdamped for large values of , consistent with the theory of the transverse mode in the presence of finite pair-breaking. The peak in the excess current found near the gap voltage appears to be due to a resonance in the longitudinal mode pair-field susceptibility and not the result of single-particle tunneling as previously suggested by imanek and Hayward. The width of this resonance as derived by Schön and Ambegaokar for the gap regime is equal to the reciprocal of the spin-flip scattering time. The other characteristic feature of the pair-field susceptibility is the peak associated with the longitudinal mode, which occurs at a frequency which is a measure of the relaxation of the amplitude of the order parameter. Dinter's theory, in particular, describes the dependence of the width of this peak on the pair-breaking parameter .Support for this research was initially provided by the Department of Energy and later by the NSF under Grant DMR-8006959. The Office of Naval Research provided He gas used in these experiments.     

Noise tolerance of attractor and feedforward memory models

In short-term memory networks, transient stimuli are represented by patterns of neural activity that persist long after stimulus offset. Here, we compare the performance of two prominent classes of memory networks, feedback-based attractor networks and feedforward networks, in conveying information about the amplitude of a briefly presented stimulus in the presence of gaussian noise. Using Fisher information as a metric of memory performance, we find that the optimal form of network architecture depends strongly on assumptions about the forms of nonlinearities in the network. For purely linear networks, we find that feedforward networks outperform attractor networks because noise is continually removed from feedforward networks when signals exit the network; as a result, feedforward networks can amplify signals they receive faster than noise accumulates over time. By contrast, attractor networks must operate in a signal-attenuating regime to avoid the buildup of noise. However, if the amplification of signals is limited by a finite dynamic range of neuronal responses or if noise is reset at the time of signal arrival, as suggested by recent experiments, we find that attractor networks can outperform feedforward ones. Under a simple model in which neurons have a finite dynamic range, we find that the optimal attractor networks are forgetful if there is no mechanism for noise reduction with signal arrival but nonforgetful (perfect integrators) in the presence of a strong reset mechanism. Furthermore, we find that the maximal Fisher information for the feedforward and attractor networks exhibits power law decay as a function of time and scales linearly with the number of neurons. These results highlight prominent factors that lead to trade-offs in the memory performance of networks with different architectures and constraints, and suggest conditions under which attractor or feedforward networks may be best suited to storing information about previous stimuli.     

Density dependence of the elastic constants of hcp hydrogen and deuterium

The elastic constants of hcp H₂ and D₂ are calculated for densities up to 10 cm³/mole (20 kbar at T = 4.2 K). An Isotropic pair potential is utilized in the computations and cubic anharmonic corrections are incorporated into the lattice dynamics. The results are compared with sound velocity, specific heat, and Brillouin and neutron scattering measurements.     

Learning from examples with unspecified attribute values

A challenging problem within machine learning is how to make good inferences from data sets in which pieces of information are missing. While it is valuable to have algorithms that perform well for specific domains, to gain a fundamental understanding of the problem, one needs a “theory” about how to learn with incomplete data. The important contribution of such a theory is not so much the specific algorithmic results, but rather that it provides good ways of thinking about the problem formally. In this paper we introduce the unspecified attribute value (UAV) learning model as a first step towards a theoretical framework for studying the problem of learning from incomplete data in the exact learning framework.In the UAV learning model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is “?” (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect.We show that any class of functions learnable in Angluin’s exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model.     

The Value of Temptation

There is an implicit assumption in electronic commerce that induces the buyers to believe that their deals will be handled appropriately. However, after a seller has already committed to a buyer, he may be tempted by several requests though he will not be able to supply them all. We analyze markets in which a finite set of automated buyers interacts repeatedly with a finite set of automated sellers. These sellers can satisfy one buyer at a time, and they can be tempted to break a commitment they already have. We have found the perfect equilibria that exist in markets with a finite horizon, and with an unrestricted horizon. A significant result stemming from our study reveals that sellers are almost always tempted to breach their commitments. However, we also show that if markets' designers implement an external mechanism that restricts the automated buyers actions, then sellers will keep their commitments.     

Robophysical study of excavation in confined environments

Soil-dwelling social insects build complex nests. Nest excavation is performed by multiple animals simultaneously and is governed by local interactions of the workers with other nest-mates and their surroundings. To investigate collective confined excavation challenges, we built groups of robotic excavators capable of performing hours of autonomous tunnel excavation in a model cohesive granular medium. Excavator behavior was governed by a simple set of rules triggered by interactions with the surrounding environment and other robots. The rate of tunnel growth and energetic costs of excavation were measured for groups of different numbers operating in wide and narrow tunnels. To extend the results to systems with large numbers of robots, we developed a cellular automata model. Experiments and simulations showed that in sufficiently wide tunnels an increase in the size of the excavating group increased the excavation rates without a significant increase in the energy consumption per robot. A decrease in the tunnel width resulted in a decrease in the excavation rates and increase in the energetic costs of excavation. We attribute this effect to the emergence of multiple time-consuming interactions (clogs) among excavating robots in the confined spaces. Although in all situations clogs were resolvable, clog resolution took longer in the systems with larger number of robots and narrower tunnels. We expect that our robotic system can be used to investigate the behavior of social insects in confined spaces as well as inspire more sophisticated search-and-rescue robotics.