14591723
A Sliding Window Method for Finding Recently Frequent Itemsets over Online Data Streams
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of miming algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.
17498259
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream.
261727
Guaranteed Quality-of-Service Wireless Access to ATM Networks
We study the problem of wireless access to asynchronous transfer modes (ATMs). We consider three classes of ATM sources: constant bit rate (CBR), variable bit rate (VBR), and available bit rate (ABR). We propose a polling scheme with nonpreemptive priority. Under such a scheme, we derive sufficient conditions such that all the CBR sources satisfy their jitter constraints and all the VBR sources satisfy their delay constraints. The remaining bandwidth is used by the ABR sources, for which we adapt a random access scheme proposed by Chen and Lee (1994). For this random access scheme, we derive the throughput-offer load characteristic, and thus the capacity. Based on this, we propose adaptive random access schemes that track the offer load to its optimal value. Our simulations show that our adaptive schemes maintain a high throughput with respect to the whole range of system load.
26277514
Evaluation of an object-caching coprocessor design for object-oriented systems
Object-oriented systems exhibit a very high rate of object creation, but most of the objects are short-lived. As a result, memory management overhead is significant. The paper evaluates an application-specific coprocessor architecture to speed up object creation and memory reclamation in object-oriented systems. The architecture supports a bit-vector approach to dynamic storage allocation and liberation. Newly created objects reside in a cache which is reference counted. The paper presents measurements of the performance of this coprocessor design. Simulation results show that 50% to 70% of objects die before they age out of the cache, greatly reducing the number of references to main memory. Overall, more than 60% of memory traffic is saved by the proposed scheme, and the interval between main-memory garbage collections is extended by more than 60%.<<ETX>>
35558441
Using Interface Refinement to Integrate Formal Verification into the Design Cycle
We present a practical compositional interface refinement methodology which helps to integrate formal verification with the design process. One of the main verification challenges is to keep up with the changes to the specifications as the design evolves, and in particular, the transformations to the interfaces between the components. Interface changes are usually incremental, and therefore, the verification efforts after each change should also be incremental in order to be scalable. This paper presents a compositional interface refinement methodology which addresses all of these issues.
2829100
GAPSCORE: finding gene and protein names one word at a time
MOTIVATION
New high-throughput technologies have accelerated the accumulation of knowledge about genes and proteins. However, much knowledge is still stored as written natural language text. Therefore, we have developed a new method, GAPSCORE, to identify gene and protein names in text. GAPSCORE scores words based on a statistical model of gene names that quantifies their appearance, morphology and context.


RESULTS
We evaluated GAPSCORE against the Yapex data set and achieved an F-score of 82.5% (83.3% recall, 81.5% precision) for partial matches and 57.6% (58.5% recall, 56.7% precision) for exact matches. Since the method is statistical, users can choose score cutoffs that adjust the performance according to their needs.


AVAILABILITY
GAPSCORE is available at http://bionlp.stanford.edu/gapscore/
34576803
Multi-modal gray-level histogram modeling and decomposition
Abstract In this paper, we present a novel multi-modal histogram thresholding method in which no a priori knowledge about the number of clusters to be extracted is needed. The proposed method combines regularization and statistical approaches. By converting the approaching histogram thresholding problem to the mixture Gaussian density modeling problem, threshold values can be estimated precisely according to the parameters belonging to each contiguous cluster. Computational complexity has been greatly reduced since our method does not employ conventional iterative parameter refinement. Instead, an optimal parameter estimation interval was defined before the estimation procedure. This predefined optimal estimation interval reduces time consumption while other histogram decomposition based methods search all feature space to locate an estimation interval for each candidate cluster. Experimental results with both simulated data and real images demonstrate the robustness of our method.