10310314
Sensitivity Analysis in Markov Networks
This paper explores the topic of sensitivity analysis in Markov networks, by tackling questions similar to those arising in the context of Bayesian networks: the tuning of parameters to satisfy query constraints, and the bounding of query changes when perturbing network parameters. Even though the distribution induced by a Markov network corresponds to ratios of multi-linear functions, whereas the distribution induced by a Bayesian network corresponds to multi-linear functions, the results we obtain for Markov networks are as effective computationally as those obtained for Bayesian networks. This similarity is due to the fact that conditional probabilities have the same functional form in both Bayesian and Markov networks, which turns out to be the more influential factor. The major difference we found, however, is in how changes in parameter values should be quantified, as such parameters are interpreted differently in Bayesian networks and Markov networks.
17538498
Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDS), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size cvcn given an intractable number of instances. Since ODDS are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.
5672538
When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network probabilities can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytical results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.
15971365
Efficiency of Data Distribution in BitTorrent-Like Systems
BitTorrent (BT) in practice is a very efficient method to share data over a network of clients. In this paper we extend the recent work of Arthur and Panigrahy [1] on modelling the distribution of individual data blocks in BT systems, aiming at a better understanding of why BT can achieve a high degree of parallelism. In particular, we include in our study several new network features that BT systems are using, as well as different local heuristics for routing data blocks in each client. We conduct simulations to figure out to what extent the new network features and routing heuristics would affect the distribution efficiency. Our findings confirm that for the primitive network setting studied in [1], it does require i¾?(blogn) phases for nclients to download bdata blocks. More interestingly, our work suggests that for the more realistic network setting, the heuristics Random and Rarest Block First both allow nclients to download bblocks in b+ O(logn) phases. We believe that the latter bound better reflects the high degree of parallelism of BT observed in reality. It is also worth-mentioning that b+ lognis the smallest possible number of phases needed; it is interesting to see that some simple local routing heuristics have a performance so close to the optimal.
5337517
Throughput Optimized SHA-1 Architecture Using Unfolding Transformation
In this paper, the authors analyze the theoretical delay bound of the SHA-1 algorithm and propose architectures to achieve high throughput hardware implementations which approach this bound. According to the results of FPGA implementations, 3,541 Mbps with a pipeline and 893 Mbps without a pipeline were achieved. Moreover, synthesis results using 0.18mum CMOS technology showed that 10.4 Gbps with a pipeline and 3.1 Gbps without a pipeline can be achieved. These results are much faster than previously published results. The high throughputs are due to the unfolding transformation, which reduces the number of required cycles for one block hash. The authors reduced the required number of cycles to 12 cycles for a 512 bit block and showed that 12 cycles is the optimal in our design
8638055
Function-Based Analysis of an Electronic Commerce Website
Traditionally, studies with the technology acceptance model (TAM) have treated software systems as holistic units. A survey is conducted to understand the model better by dropping from system-wide measurement to function-based measurement. The first part of the survey replicates Gefen and Straub’s (2000) study where only intention is measured at the function level, with consistent results. System-wide perceived ease of use (PEOU) has more effect on an intrinsic task (intention to search for books) than on an extrinsic task (intention to purchase books). In the second part of the survey, all factors are measured at the function level. The result shows that the function-level models are more predictive than the system-wide model. Specifically, each PEOU is a significant predictor for each intention in both models. This study shows the value of using function-based analysis and suggests that function-based analysis is particularly useful and timely for the study of integrated products offering relatively distinct functions.
18001156
A thermally actuated polymer micro robotic gripper for manipulation of biological cells
The development of a novel polymer-based micro robotic gripper that can be actuated in a liquid medium is presented in this paper. The basic structure for the gripper is a trimorph thermal actuator with a platinum metal heater encapsulated by poly-para-xylene C (parylene C) polymer layers. Due to the large difference of thermal expansion coefficients of the different layers, the actuator can be actuated with much large deflection than conventional MEMS actuators. We have developed actuators operating in liquid environment (e.g., DI water) with only 3 V at /spl sim/33 mA input to achieve full deflection (90/spl deg/ change of tip direction). The actuators were also observed to response to 3 Hz square wave input in water with no visible delay. The temperature of the actuators at full deflection was estimated to be about 60/spl deg/C, which is much lower than the typical requirement of >100/spl deg/C to actuate other conventional MEMS actuators. Actuators with dimensions of /spl sim/2 mm/spl times/100/spl mu/m/spl times/0.6/spl mu/m were used to capture Danio rerio follicles (/spl sim/800/spl mu/m average cell diameter) in water. The design, fabrication process, and experimental results for these actuators are presented in this paper.
239370
SIA: secure information aggregation in sensor networks
Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys.One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large sensor networks. In our framework certain nodes in the sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum sensor reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in sensor networks that can handle a malicious aggregator and sensor nodes.