High level languages on any machine through VAL

High level languages have gradually become the norm over the last 20 years, but recently the cost of the processor has been so reduced as to make the software the dominant cost of any system design. For microprocessors, this presents problems of compiler cost, due to the rapid obsolescence of instructions sets, and of compiler inefficiency, as microprocessor instruction sets were not suitable for high level languages until very recently. By designing a compiler that can produce code for several microprocessors with minimal rework, and can produce efficient object code by virtue of skeleton optimization, the advantages of high level languages can be available to all.     

A column‐generation‐based approach to fleet design problems mixing owned and hired vehicles

We look at the problem of choosing a fleet of vehicles to carry out delivery tasks across a long time horizon. The delivery quantities may vary significantly from day to day, and from season to season, and the underlying routing problem may have rich constraints, for example, time windows, multiple compartments, multiple commodities, and compatibility constraints. We consider the option of hiring extra vehicles from external carriers in order to efficiently carry out the day‐to‐day operations while containing the costs of owning the fleet. The goal is to design a fleet so as to minimize the sum of costs of routing the fleet every day of the horizon, the acquisition costs, the maintenance costs, and the costs of hiring external vehicles. In the literature, there is no previous work on fleet design for a long time horizon, which also considers the hiring options. We propose to tackle the problem using column generation and develop three different heuristics. The methods proposed are tested and compared on a set of real‐world problems. It is also shown how introducing the possibility of hiring helps reducing the overall cost and the number of idle vehicles over the planning horizon.     

A Vector-based Representation for Image Warping

A method for image analysis, representation and re-synthesis is introduced. Unlike other schemes it is not pixel based but rather represents a picture as vector data, from which an altered version of the original image can be rendered. Representing an image as vector data allows performing operations such as zooming, retouching or colourising, avoiding common problems associated with pixel image manipulation. This paper brings together methods from the areas of computer vision, image compositing and image based rendering to prove that this type of image representation is a step towards accurate and efficient image manipulation.     

Metal Dusting of Alumina-Forming Creep-Resistant Austenitic Stainless Steels

Three developmental alumina-forming austenitic stainless steels were exposed to metal dusting conditions at 650?°C in a gas of 50%CO–49%H₂–1%H₂O (a _C: 36.7 and $ p_{{{\text{O}}_{2} }} $ : 2.83?×?10^?26?atm) under thermal cycling conditions. Metal wastage measurement showed initially slow kinetics followed by a fast weight loss. This observation is attributed to the formation of protective chromia/alumina oxide scales in the early stage of the reaction, followed by local oxide failure/spallation during cyclic reaction. Metal dusting initiated from these local defects, and pitting-type attack was observed after 131 cycles of reaction. After 352 cycles, severe dusting had developed, forming heavy and distinctive “tentacles” of superficial coke. This carbon deposit was composed of fine carbon filaments. Examination by TEM of the coke-metal reaction front showed direct surface metal disintegration, indicating that the dusting follows the classical mechanism for austenitic materials. Etching with aqua regia revealed a carburised zone formed in the alloy underneath the coke layer. Analysis by TEM of this zone revealed the formation of ultra-fine, needle-shaped chromium carbide precipitates within a chromium depleted austenite matrix.     

Reinterpreting literature sorption data considering both absorption into organic carbon and adsorption onto black carbon

We hypothesized that the sorption of polycyclic aromatic hydrocarbons (PAHs) to natural sediments and soils should consider both absorption into a biogenic/diagenetic organic carbon (OC) fraction and adsorption onto a combustion-derived, black carbon (BC) fraction. Here, two sets of literature data were reevaluated to illustrate that an OC absorbent and a BC adsorbenttogether can (1) account for sediment--pore-waterdistribution coefficients observed in the field that are greater than predicted by a simple f(OC)K(OC) partitioning model and (2) explain a group of nonlinear phenanthrene isotherms observed in the laboratory with a single value for the BC-normalized distribution coefficient (log K(BC) = 6.1 i 0.04) and a Freundlich exponent (n approximately 0.6 if log K(OC) = 4.0) that is strongly dependent on the K(OC) value selected.     

Finding approximate matches in large lexicons

Approximate string matching is used for spelling correction and personal name matching. In this paper we show how to use string matching techniques in conjunction with lexicon indexes to find approximate matches in a large lexicon. We test several lexicon indexing techniques, including n-grams and permuted lexicons, and several string matching techniques, including string similarity measures and phonetic coding. We propose methods for combining these techniques, and show experimentally that these combinations yield good retrieval effectiveness while keeping index size and retrieval time low. Our experiments also suggest that, in contrast to previous claims, phonetic codings are markedly inferior to string distance measures, which are demonstrated to be suitable for both spelling correction and personal name matching.     

The development and application of a multiple wavelength illumination technique for the vision-based process monitoring of aero-structure riveting

Airframe riveting is a critical process that requires high levels of process monitoring and quality assurance due to the very high risk associated with the failure of such joints. This paper describes the development of the enabling technology developed for a machine vision-based process monitoring system. One of the key factors affecting the performance of a machine vision system is the quality of the lighting. In the application described in this paper the available lighting was severely limited by the confined space in which the system had to operate. The problem was also compounded by the reflective nature of the objects to be examined. The initial images obtained were not suitable for further processing due to the presence of significant shadows and specular reflections. A novel solution to this problem based on multiple wavelength illumination and signal processing is presented along with results from experimental trials of the approach.     

Teaching Java backwards

Teaching programming concepts in a more object-oriented way is a growing trend in Computer Science education. This paper takes the idea of abstraction-first teaching a step further, by using Bloom’s Taxonomy to design a course to present factual content early, followed by higher-level cognitive skills. In the course described here, factual content was covered rapidly, then reinforced by assignments, laboratory sessions and tutorials, aimed at building higher cognitive skills. The resulting course was successful in relatively rapidly bringing a class doing a “bridging” diploma up to the level required for dealing with a second-year course. As compared with previous runs of the diploma where much more time was spent in preparation for advanced courses, the class using the new approach performed better, suggesting that the approach used is worthy of further study.     

Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data

Continuing, severe outbreaks of mountain pine beetle (Dendroctonus ponderosae) across western North America have resulted in widespread mortality of lodgepole pine (Pinus contorta). Multiple studies have used high spatial resolution satellite data to map areas of beetle kill; these studies have largely focused on mapping red canopy cover associated with recent tree mortality and have not examined mapping gray canopy cover that occurs after red needles have dropped. The work presented here examines the use of newly available GeoEye-1 data for mapping both red and gray canopy area in southeastern Wyoming lodgepole pine forest. A 0.5 m spatial resolution, pan-sharpened GeoEye-1 image was used to classify areas of green, red, and gray canopy cover. Reference data were collected at twelve 500 m² field plots. Shadow-normalized green, red, and gray canopy area from classified GeoEye-1 data closely agreed with field-estimated green, red, and gray canopy area. Mean absolute error in canopy cover for the twelve sample plots was 8.3% for the green class, 5.4% for the red class, and 7.2% for the gray class. When all twelve plots were aggregated, remotely sensed estimates of green, red, and gray cover were within 1.7% of the field-estimated cover. Our results demonstrate that high spatial resolution spaceborne multispectral data are a promising tool for mapping canopy mortality caused by mountain pine beetle outbreaks.     

Query indexing with containment-encoded intervals for efficient stream processing

Many continual range queries can be issued against data streams. To efficiently evaluate continual queries against a stream, a main memory-based query index with a small storage cost and a fast search time is needed, especially if the stream is rapid. In this paper, we study a CEI-based query index that meets both criteria for efficient processing of continual interval queries. This new query index is an indirect indexing approach. It centres around a set of predefined virtual containment-encoded intervals, or CEIs. The CEIs are used to first decompose query intervals and then perform efficient search operations. The CEIs are defined and labeled such that containment relationships among them are encoded in their IDs. The containment encoding makes decomposition and search operations efficient; from the encoding of the smallest CEI containing a data point, the encodings of other containing CEIs can be easily derived. Closed-form formulae for the bounds of the average index storage cost are derived. Simulations are conducted to evaluate the effectiveness of the CEI-based query index and to compare it with alternative approaches. The results show that the CEI-based query index significantly outperforms existing approaches in terms of both storage cost and search time. Kun-Lung Wu received the B.S. degree in electrical engineering from the National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in computer science from the University of Illinois at Urbana–Champaign. He is with the IBM Thomas J. Watson Research Center, currently a member of the Software Tools and Techniques Group. His current research interests include data streams, continual queries, mobile computing, Internet technologies and applications, database systems and distributed and parallel computing. He has published extensively and holds various patents in these areas. Dr. Wu is a Senior Member of the IEEE Computer Society and a member of the ACM. He was an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering, 2000–2004. He was the general chair for the 3rd International Workshop on e-Commerce and Web-Based Information Systems (WECWIS 2001). He has served as an organising and program committee member on various conferences. He has received various IBM awards, including IBM Corporate Environmental Affair Excellence Award, Research Division Award and Invention Achievement Awards. He received a best paper award from IEEE EEE 2004. He is an IBM Master Inventor. Shyh-Kwei Chen received the B.S. degree in computer science and information engineering from National Taiwan University, Taipei, Taiwan, in 1983, the M.S. degree in computer science from the University of Minnesota, Minneapolis, in 1987, and the Ph.D. degree in computer science from University of Illinois at Urbana–Champaign, in 1994. Dr. Chen has been with the IBM Thomas J. Watson Research Center, Yorktown Heights, New York since October 1994, where he is currently a research staff member. His current research interests include XML, electronic commerce, business performance management, data engineering and compilers. He is a member of the ACM, the IEEE and the IEEE Computer Society. Philip S. Yu received the B.S. degree in electrical engineering from National Taiwan University, the M.S. and Ph.D. degrees in electrical engineering from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and is currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing and performance modelling. Dr. Yu has published more than 400 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents. Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is an associate editor of ACM Transactions on Internet Technology. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor and advisory board member of IEEE Transactions on Knowledge and Data Engineering and also a guest coeditor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he was the program cochair of the 11th International Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, and the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, and the program chair of the 2nd International Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases and the 2nd International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair of the 14th International Conference on Data Engineering and the general cochair of the 2nd IEEE International Conference on Data Mining. He has received several IBM honours, including two IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, two Research Division Awards and the 81st Plateau of Invention Achievement Awards. He received an Outstanding Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor and was recognised as one of the IBM's 10 top leading inventors in 1999.