In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.
[EX A]: Stable Model Counting and Its Application in Probabilistic Logic Programming

[EX Q]: A famous biologically inspired hierarchical model firstly proposed by Riesenhuber and Poggio has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of positionand scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental results, we introduce the Memory and Association Mechanisms into the above biologically inspired model. The main motivations of the work are (a) to mimic the active memory and association mechanism and add the ’top down’ adjustment to the above biologically inspired hierarchical model and (b) to build up an algorithm which can save the space and keep a good recognition performance. More details of the work could be explained as follows: (1) In objects memorizing process: Our proposed model mimics some characteristics of human’s memory mechanism as follows: (a) In our model, one object is memorized by semantic attributes and special image patches (corresponding to episodic memory). The semantic attributes describe each part of the object with clear physical meaning, for example, if eyes and mouths of faces are ’big’ or ’small’ and so on. One special patch is selected if the value of the corresponding semantic feature is far from average one. The patch should be the most prominent part of the object. (b) In our model, different features (semantic attributes and special patches) of one object are stored in distributed places and the common feature of different objects is saved aggregately, which can learn to classify the difference of similar features of different objects. The similarity thresholds to each object can be learnt when new objects are learnt. (2) In object recognition process: In biological process, the associated recognition including familiarity discrimination and recollective matching. In our proposed model, firstly mimicking familiarity discrimination (’knowing’ though the episode), we compare the special patches of candidates with that of saved objects using above mentioned biologically inspired hierarchical model, where the candidates and saved objects have the same prominent semantic features. Then mimicking recollective matching, the comparison results of special patches are combined with semantic feature comparison. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with much less memory requirement.
[EX A]: Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

[EX Q]: In this paper, we describe our autonomous bidding agent, RoxyBot, who emerged victorious in the travel division of the 2006 Trading Agent Competition in a photo finish. At a high level, the design of many successful trading agents can be summarized as follows: (i) price prediction: build a model of market prices; and (ii) optimization: solve for an approximately optimal set of bids, given this model. To predict, RoxyBot builds a stochastic model of market prices by simulating simultaneous ascending auctions. To optimize, RoxyBot relies on the sample average approximation method, a stochastic optimization technique.
[EX A]:
RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel