8658211
A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study
Positron emission tomography (PET) is an important tool for enabling quantification of human brain function. However, quantitative studies using tracer kinetic modeling require the measurement of the tracer time-activity curve in plasma (PTAC) as the model input function. It is widely believed that the insertion of arterial lines and the subsequent collection and processing of the biomedical signal sampled from the arterial blood are not compatible with the practice of clinical PET, as it is invasive and exposes personnel to the risks associated with the handling of patient blood and radiation dose. Therefore, it is of interest to develop practical noninvasive measurement techniques for tracer kinetic modeling with PET. In this paper, a technique is proposed to extract the input function together with the physiological parameters from the brain dynamic images alone. The identifiability of this method is tested rigorously by using Monte Carlo simulation. The results show that the proposed method is able to quantify all the required parameters by using the information obtained from two or more regions of interest (ROIs) with very different dynamics in the PET dynamic images. There is no significant improvement in parameter estimation for the local cerebral metabolic rate of glucose (LCMRGlc) if there are more than three ROIs. The proposed method can provide very reliable estimation of LCMRGlc, which is our primary interest in this study.
30786575
Content-Based Retrieval of Multimedia Information
The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and their applications are briefly presented in this paper.
18173014
A Fast Correlation Attack on the Shrinking Generator
In this paper we demonstrate a fast correlation attack on the shrinking generator with known connections. Our attack is applicable to arbitrary weight feedback polynomial of the generating LFSR and comparisons with other known attacks show that our attack offers good trade-offs between required keystream length, success probability and complexity. Our result confirms Golic's conjecture that the shrinking generator may be vulnerable to fast correlation attacks without exhaustively searching through all possible initial states of some LFSR is correct.
6618252
Learning to Detect Conversation Focus of Threaded Discussions
In this paper we present a novel feature-enriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with feature-oriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.
246827
Handling Biographical Questions with Implicature
Traditional question answering systems adopt the following framework: parsing questions, searching for relevant documents, and identifying/generating answers. However, this framework does not work well for questions with hidden assumptions and implicatures. In this paper, we describe a novel idea, a cascading guidance strategy, which can not only identify potential traps in questions but further guide the answer extraction procedure by recognizing whether there are multiple answers for a question. This is the first attempt to solve implicature problem for complex QA in a cascading fashion using N-gram language models as features. We here investigate questions with implicatures related to biography facts in a web-based QA system, Power-Bio. We compare the performances of Decision Tree, Naive Bayes, SVM (Support Vector Machine), and ME (Maximum Entropy) classification methods. The integration of the cascading guidance strategy can help extract answers for questions with implicatures and produce satisfactory results in our experiments.
14712985
Cryptanalysis of a Knapsack Based Two-Lock Cryptosystem
In this paper we break a knapsack based two-lock cryptosystem proposed at ICICS’03 [7]. The two-lock cryptosystem is a commutative encryption algorithm that is very useful for the construction of the general t-out-of-n oblivious transfers and millionaire protocol. However, our analysis shows that the proposed knapsack based two-lock cryptosystem is extremely insecure. The serious flaw is that the sender in the two-lock cryptosystem can retrieve the secret key of the receiver fairly easily. We have implemented the attack on a Pentium 4 2.5 GHz processor. For the parameters given in [7], it takes only several minutes to break that knapsack based two-lock cryptosystem.
1310259
Mining and Re-ranking for Answering Biographical Queries on the Web
The rapid growth of the Web has made itself a huge and valuable knowledge base. Among them, biographical information is of great interest to society. However, there has not been an efficient and complete approach to automated biography creation by querying the web. This paper describes an automatic web-based question answering system for biographical queries. Ad-hoc improvements on pattern learning approaches are proposed for mining biographical knowledge. Using bootstrapping, our approach learns surface text patterns from the web, and applies the learned patterns to extract relevant information. To reduce human labeling cost, we propose a new IDF-inspired reranking approach and compare it with pattern's precision-based re-ranking approach. A comparative study of the two re-ranking models is conducted. The tested system produces promising results for answering biographical queries.