15193012
Unsupervised link discovery in multi-relational data via rarity analysis
A significant portion of knowledge discovery and data mining research focuses on finding patterns of interest in data. Once a pattern is found, it can be used to recognize satisfying instances. The new area of link discovery requires a complementary approach, since patterns of interest might not yet be known or might have too few examples to be learnable. We present an unsupervised link discovery method aimed at discovering unusual, interestingly linked entities in multi-relational datasets. Various notions of rarity are introduced to measure the "interestingness" of sets of paths and entities. These measurements have been implemented and applied to a real-world bibliographic dataset where they give very promising results.
746230
Designing the Market Game for a Trading Agent Competition
The authors discuss the design and operation of a trading agent competition, focusing on the game structure and some of the key technical issues in running and playing the game. They also describe the competition's genesis, its technical infrastructure, and its organization. The article by A. Greenwald and P. Stone (2001), describes the competition from a participant's perspective and describes the strategies of some of the top-placing agents. A visualization of the competition and a description of the preliminary and final rounds of the TAC are available in IC Online (http://computer.org/internet/tac.htm).
31903182
The Fourier slice theorem for range data reconstruction
Abstract This paper proposes a new approach to resolve the ambiguity problem in multistriping laser triangulation systems [1] . The application of this new method to circularly symmetrical objects can achieve time and space complexity of order one. No optical shutter is required in this new method. The new solution is based on the Fourier slice theorem, which forms the basis of X-ray CT (computed tomography) reconstruction. This paper demonstrates how this theorem can also be used to resolve multistripe laser ambiguity effectively.
8641285
A data compression technique for sensor networks with dynamic bandwidth allocation
In this paper, we have presented a new data compression technique, designed for historical information compression in sensor networks. Our method employs the LVQ learning process to construct the codebook and the codebook's updates are compressed to save bandwidth for sensor data transmission. In addition, we have addressed the dynamic bandwidth allocation problem in sensor networks. Our DBA algorithm can dynamically adjust the communication bandwidth of different sensors in order to balance data compression qualities at different sensors.
22001959
A Pinwheel Scheduler for Three Distinct Numbers with a Tight Schedulability Bound
Abstract. Given a multiset of positive integers 
 $A=\{a_1,a_2,\ldots,a_n\}$ , the pinwheel problem is to find an infinite sequence over 
 $\{1,2,\ldots,n\}$ such that there is at least one symbol i within any subsequence of length ai. The density of A is defined as 
 $\rho(A)=\sum^n_{i=1} (1/a_i)$ . In this paper we limit ourselves to instances composed of three distinct integers. The best scheduler [5] published previously can schedule all instances with a density of less than 0.77. A new and fast scheduling scheme based on spectrum partitioning is presented in this paper which improves the 0.77 result to a new 
 $\frac{5}{6}\approx 0.83$ density threshold. This scheduler has achieved the tight schedulability bound of this problem.
18158634
Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
This paper describes a GA for job shop scheduling problems. Using the Giffler and Thompson algorithm, we created two new operators, THX crossover and mutation, which better transmit temporal relationships in the schedule. The approach produced excellent results on standard benchmark job shop scheduling problems. We further tested many models and scales of parallel GAs in the context of job shop scheduling problems. In our experiments, the hybrid model consisting of coarse-grain GAs connected in a fine-grain-GA-style topology performed best, appearing to integrate successfully the advantages of coarse-grain and fine-grain GAs.
51877153
ATPC: adaptive transmission power control for wireless sensor networks
Extensive empirical studies presented in this paper confirm that the quality of radio communication between low power sensor devices varies significantly with time and environment. This phenomenon indicates that the previous topology control solutions, which use static transmission power, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This paper presents ATPC, a lightweight algorithm of Adaptive Transmission Power Control for wireless sensor networks. In ATPC, each node builds a model for each of its neighbors, describing the correlation between transmission power and link quality. With this model, we employ a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. The intellectual contribution of this work lies in a novel pairwise transmission power control, which is significantly different from existing node-level or network-level power control methods. Also different from most existing simulation work, the ATPC design is guided by extensive field experiments of link quality dynamics at various locations and over a long period of time. The results from the real-world experiments demonstrate that 1) with pairwise adjustment, ATPC achieves more energy savings with a finer tuning capability and 2) with online control, ATPC is robust even with environmental changes over time.