A Trustworthy Proof Checker

Proof-carrying code (PCC) and other applications in computer security require machine-checkable proofs of properties of machine-language programs. The main advantage of the PCC approach is that the amount of code that must be explicitly trusted is very small: it consists of the logic in which predicates and proofs are expressed, the safety predicate, and the proof checker. We have built a minimal proof checker, and we explain its design principles and the representation issues of the logic, safety predicate, and safety proofs. We show that the trusted computing base (TCB) in such a system can indeed be very small. In our current system the TCB is less than 2,700 lines of code (an order of magnitude smaller even than other PCC systems), which adds to our confidence of its correctness.     

Robust Algorithms for Object Localization

Model-based localization, the task of estimating an object's pose from sensed and corresponding model features, is a fundamental task in machine vision. Exact constant time localization algorithms have been developed for the case where the sensed features and the model features are the same type. Still, it is not uncommon for the sensed features and the model features to be of different types, i.e., sensed data points may correspond to model faces or edges. Previous localization approaches have handled different model and sensed features of different types via sampling and synthesizing virtual features to reduce the problem of matching features of dissimilar types to the problem of matching features of similar types. Unfortunately, these approaches may be suboptimal because they introduce artificial errors. Other localization approaches have reformulated object localization as a nonlinear least squares problem where the error is between the sensed data and model features in image coordinates (the Euclidean image error metric). Unfortunately, all of the previous approaches which minimized the Euclidean image error metric relied on gradient descent methods to find the global minima, and gradient descent methods may suffer from problems of local minima. In this paper, we describe an exact, efficient solution to the nonlinear least squares minimization problem based upon resultants, linear algebra, and numerical techniques. On a SPARC 20, our localization algorithm runs in a few microseconds for rectilinear polygonal models, a few milliseconds for generic polygonal models, and one second for generalized polygonal models (models composed of linear edges and circular arcs).     

Effectiveness of Cognitive-Behavioral Treatment for Panic Disorder Versus Treatment as Usual in a Managed Care Setting.

Eighty clients enrolled in a managed care health plan who identified panic disorder as their primary presenting problem were randomly assigned to treatment by a therapist recently trained in a manual-based empirically supported psychotherapy (M. G. Craske, E. Meadows, & D. H. Barlow, 1994) or a therapist conducting treatment as usual (TAU). Participants in both conditions showed significant change from pre- to posttreatment on a number of measures. Those receiving panic control therapy (PCT) showed greater levels of change than those receiving TAU. Among treatment completers, an average of 42.9% of those in PCT and 18.8% in TAU achieved clinically significant change across measures. The results are discussed with reference to the dissemination of PCT and other evidence-based psychotherapies to clinical practice settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Temporal Coding in Conditioned Inhibition: Analysis of Associative Structure of Inhibition.

Two experiments with rats as subjects were conducted to investigate the associative structure of temporal control of conditioned inhibition through posttraining manipulation of the training excitor-unconditioned stimulus (US) temporal relationship. Experiment 1 found that following simultaneous Pavlovian inhibition training (i.e., A → US/XA-no US) in which a conditioned stimulus (CS A) was established as a delay excitor, maximal inhibition was observed on a summation test when CS X was compounded with a delay transfer CS. Furthermore, posttraining shifts in the A-US temporal relationship from delay to trace resulted in maximal inhibition of a trace transfer CS. Experiment 2 found complementary results to Experiment 1 with an A-US posttraining shift from serial to simultaneous. These results suggest that temporal control of inhibition is mediated by the training excitor-US temporal relationship. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land-Air-Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.     

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.     

Microcavity-integrated graphene photodetector

There is an increasing interest in using graphene (1, 2) for optoelectronic applications. (3-19) However, because graphene is an inherently weak optical absorber (only ≈2.3% absorption), novel concepts need to be developed to increase the absorption and take full advantage of its unique optical properties. We demonstrate that by monolithically integrating graphene with a Fabry-Pérot microcavity, the optical absorption is 26-fold enhanced, reaching values >60%. We present a graphene-based microcavity photodetector with responsivity of 21 mA/W. Our approach can be applied to a variety of other graphene devices, such as electro-absorption modulators, variable optical attenuators, or light emitters, and provides a new route to graphene photonics with the potential for applications in communications, security, sensing and spectroscopy.     

Distinction of the two phases of CuTCNQ by scanning electrochemical microscopy

The two known phases of CuTCNQ and TCNQ (TCNQ = 7,7',8,8'-tetracyanoquinodimethane) have been probed by scanning electrochemical microscopy (SECM) in the feedback mode. The first use of this technique for distinguishing differences in the electronic properties of semiconductor phases exploits the large differences in conductivity that exist between CuTCNQ and the parent TCNQ material and also between the CuTCNQ phases I and II. However, the packing density of the individual CuTCNQ crystals in a film structure also is shown to influence the SECM feedback response. Finally, it is shown that films of pure phase II material or mixtures of the phases can be mapped using feedback mode SECM. The SECM method provides valuable insights for elucidating properties of semiconducting solids that are mounted on insulating substrates.     

Scalable nonlinear dynamical systems for agent steering and crowd simulation

We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent's behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering.

We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments.