Fusion of disconnected mesh components with branching shapes

Branching structure is a common feature of many natural objects. Given some mesh components with a branching shape missing, this paper presents a novel approach to fuse the mesh components: connectivity graph of the branching shape is constructed to seamlessly connect the boundaries of given components; then, natural geometry is imposed on the connectivity graph exploiting the information of given boundaries. We present a method to construct a branching connectivity graph to connect arbitrary number of given boundaries. Also, a method to generate natural geometry of the connectivity graph that smoothly fuses the boundaries of mesh components is exploited. Some examples are given to demonstrate that our new scheme can be used in a couple of applications, such as fast tree trunk modeling, mesh composition and shell generation.     

Data‐Driven Bending Elasticity Design by Shell Thickness

We present a method to design the deformation behavior of 3D printed models by an interactive tool, where the variation of bending elasticity at different regions of a model is realized by a change in shell thickness. Given a soft material to be used in 3D printing, we propose an experimental setup to acquire the bending behavior of this material on tubes with different diameters and thicknesses. The relationship between shell thickness and bending elasticity is stored in an echo state network using the acquired dataset. With the help of the network, an interactive design tool is developed to generate non‐uniformly hollowed models to achieve desired bending behaviors. The effectiveness of this method is verified on models fabricated by different 3D printers by studying whether their physical deformation can match the designed target shape.     

Satellite observations of circulation features associated with a bowhead whale feeding ‘hotspot’ near Barrow, Alaska

Satellite images, along with oceanographic, meteorological, and whale aerial survey data, are used to illustrate aspects of ocean circulation associated with a bowhead whale feeding ‘hotspot’ near Barrow, Alaska. In response to weak winds, a strong front forms near the shelf-break along the southern edge of Barrow Canyon when the Alaska Coastal Current flows adjacent to the southern flank of Barrow Canyon or intrudes onto the western Beaufort shelf. This front is of particular local interest because it is indicative of aggregation and retention of zooplankton on the western Beaufort shelf and, as a result, is a locus for bowhead whales pausing to feed during their westward fall migration. Groups (4 or more individuals) of bowhead whales are primarily seen on the western Beaufort shelf following wind conditions that promote the formation of this front.     

Combining Local and Global Model Checking

The verification process of reactive systems in local model checking [1,7] and in explicit state model checking is[13,15] on-the-fly. Therefore only those states of a system have to be traversed that are necessary to prove a property. In addition, if the property does not hold, than often only a small subset of the state space has to be traversed to produce a counterexample. Global model checking [6,23] and, in particular, symbolic model checking [4,22] can utilize compact representations of the state space, e.g. BDDs [3], to handle much larger designs than what is possible with local and explicit model checking. We present a new model checking algorithm for LTL that combines both approaches. In essence, it is a generalization of the tableau construction of [1] that enables the use of BDDs but still is on-the-fly.     

Characterization of virus replication

New viruses spread faster than ever and current signature based detection do not protect against these unknown viruses. Behavior based detection is the currently preferred defense against unknown viruses. The drawback of behavior based detection is the ability only to detect specific classes of viruses or have successful detection under certain conditions plus false positives. This paper presents a characterization of virus replication which is the only virus characteristic guaranteed to be consistently present in all viruses. Two detection models based on virus replication are developed, one using operation sequence matching and the other using frequency measures. Regression analysis was generated for both models. A safe list is used to minimize false positives. In our testing using operation sequence matching, over 250 viruses were detected with 43 subsequences. There were minimal false negatives. The replication sequence of just one virus detected 130 viruses, 45% of all tested viruses. Our testing using frequency measures detected all test viruses with no false negatives. The paper shows that virus replication can be identified and used to detect known and unknown viruses.     

Bounded Model Checking Using Satisfiability Solving

The phrase model checking refers to algorithms for exploring the state space of a transition system to determine if it obeys a specification of its intended behavior. These algorithms can perform exhaustive verification in a highly automatic manner, and, thus, have attracted much interest in industry. Model checking programs are now being commercially marketed. However, model checking has been held back by the state explosion problem, which is the problem that the number of states in a system grows exponentially in the number of system components. Much research has been devoted to ameliorating this problem.In this tutorial, we first give a brief overview of the history of model checking to date, and then focus on recent techniques that combine model checking with satisfiability solving. These techniques, known as bounded model checking, do a very fast exploration of the state space, and for some types of problems seem to offer large performance improvements over previous approaches. We review experiments with bounded model checking on both public domain and industrial designs, and propose a methodology for applying the technique in industry for invariance checking. We then summarize the pros and cons of this new technology and discuss future research efforts to extend its capabilities.     

Capitalising Knowledge: Corporate Knowledge Management Investments

Knowledge is increasingly recognized as the key resource of business. Digital networks provide access to vast amounts of data and information but knowledge management tools and systems are required to translate this in a meaningful way. Knowledge management initiatives are unlikely to be successful unless they are integrated with business strategy, and related to the development of the core capabilities of the organisation. Sharing the discovery and synthesis of intellectual activity involves the creation of knowledge communities of practice. There is much evidence of commercially inspired corporate initiatives in this direction across all industrial sectors: leveraging existing intellectual capital; sharing best practice across multiple locations; data mining to build customer relationships; and creating knowledge networks to allow state–of–the–art solutions in professional services.     

The development of multiple reaction monitoring assays for liver-derived plasma proteins

There is wide interpatient variability in toxicity to chemotherapeutic drugs and a lack of routine clinical tests for prospectively identifying patients at risk of developing toxicity from chemotherapy. An empirically driven MS strategy has been developed to monitor liver-derived plasma proteins as potential biomarkers of early toxicity. Multiple reaction monitoring (MRM) has been used to assess 46 candidate peptides from 18 liver-derived proteins. Following an iterative process of assay design, optimisation and assessment we selected 29 MRM assays (median CV 4.6%, range 1.2-11.6%) and monitored changes in levels of plasma proteins from a small number of colorectal cancer (CRC) patients undergoing chemotherapy. We demonstrated MRM assay robustness, and show that patients undergo minor elevation in plasma proteins when profiled on Day 3 of the chemotherapeutic regime. The MRM assays were in general agreement with 2-D DIGE-based quantitation from the same patient samples. The data supports the application of MRM-based methods as facile, highly reproducible, medium-throughput techniques that warrant expanded investigation for clinical utility in identifying patients at risk of developing chemotoxicity.     

Kosha: A Peer-to-Peer Enhancement for the Network File System

The storage needs of modern scientific applications are growing exponentially, and designing economical storage solutions for such applications – especially in Grid environments – is an important research topic. This work presents Kosha, a system that aims to harvest redundant storage space on cluster nodes and user desktops to provide a reliable, shared file system that acts as a large distributed storage. Kosha utilizes peer-to-peer (p2p) mechanisms to enhance the widely-used Network File System (NFS). P2P storage systems provide location transparency, mobility transparency, load balancing, and file replication – features that are not available in NFS. On the other hand, NFS provides hierarchical file organization, directory listings, and file permissions, which are missing from p2p storage systems. By blending the strengths of NFS and p2p storage systems, Kosha provides a low overhead storage solution. Our experiments show that compared to unmodified NFS, Kosha introduces a 3.3% fixed overhead and 4.5% additional overhead as nodes are increased from two to sixteen. For larger number of nodes, the additional overhead increases slowly. Kosha achieves load balancing in distributed directories, and guarantees or better file availability.*This work was supported in part by an NSF CAREER award (ACI-0238379).Troy A. Johnson was supported by a U.S. Department of Education GAANN doctoral fellowship.