Fully-adaptive algorithms for long-lived renaming

Long-lived renaming allows processes to repeatedly get distinct names from a small name space and release these names. This paper presents two long-lived renaming algorithms in which the name a process gets is bounded above by the number of processes currently occupying a name or performing one of these operations. The first algorithm is asynchronous, uses LL/SC objects, and has step complexity that is linear in the number of processes, c, currently getting or releasing a name. The second is synchronous, uses registers and counters, and has step complexity that is polylogarithmic in c. Both tolerate any number of process crashes.     

Automated discovery of local search heuristics for satisfiability testing

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.     

Optimal neuronal tuning for finite stimulus spaces

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.     

On Kalman filtering over fading wireless channels with controlled transmission powers

We study stochastic stability of centralized Kalman filtering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.     

Recycle of valuable products from oily cold rolling mill sludge

Oily cold rolling mill （CRM） sludge contains lots of iron and alloying elements along with plenty of hazardous organic components, which makes it as an attractive secondary source and an environmental contaminant at the same time. The compound methods of ＂vacuum distillation ＋ oxidizing roasting＂ and ＂vacuum distillation ＋ hydrogen reduction＂ were employed for the recycle of oily cold rolling mill sludge. First, the sludge was dynamically vacuum distilled in a rotating furnace at 50 r/rain and 600℃ for 3 h, which removed almost hazardous organic components, obtaining 89.2wt% ferrous resultant. Then, high purity ferric oxide powders （99.2wt%） and reduced iron powders （98.9wt%） were obtained when the distillation residues were oxidized and reduced, respectively. The distillation oil can be used for fuel or chemical feedstock, and the distillation gases can be collected and reused as a fuel.     

Assessing Positive and Negative Changes in the Aftermath of Adversity: Psychometric Evaluation of the Changes in Outlook Questionnaire.

The Changes in Outlook Questionnaire (CiOQ; S. Joseph, R. Williams, & W. Yule, 1993) is a 26-item self-report measure that was designed to assess positive and negative changes in the aftermath of adversity. This article had 3 aims: 1st, to investigate the factor structure of the CiOQ; 2nd, to test for internal consistency reliability and convergent and discriminant validity; and, 3rd, to investigate the association between positive and negative changes in outlook, posttraumatic stress, and psychological distress. Three studies are reported. Study 1 provides evidence that positive and negative changes are statistically separable and that the 2-factor model is a better fit than the 1-factor model. Studies 2 and 3 provide evidence for internal consistency reliability, convergent and discriminant validity of the CiOQ, and its associations with posttraumatic stress and psychological distress. In conclusion, the CiOQ has much promise for research on responses to stressful and traumatic events. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Microwave remote sensing of physically buried objects in the Negev Desert: implications for environmental research

We report remote detections of physically buried specularly reflecting objects using microwave radar at two sites: Ashalim and Tseelim in the northern region of the Negev Desert, Israel. These detections provide confirmation that microwave subsurface remote sensing is a genuine phenomenon. At Ashalim, a scatterometer operating in the P-band (441 MHz, 68 cm) was mounted on a cherry picker truck at a height of 8 m and used to detect two triangular aluminum mesh reflectors (forming a 1-m square area reflector) buried down to a depth of 8 cm in dry sand. At Tseelim, the same scatterometer was mounted on an airplane flying at an altitude of 70 m and used to detect 1-m square aluminum reflectors (each one submerged at a different location along the airplane flight path) buried down to a depth of 20 cm. The experimental results compare favorably with a theoretical model that incorporates radar absorption effects arising in the sandy subsurface layer and radar interference effects arising from phase differences between reflections from the surface and buried reflector. The theoretical modeling also predicts the detection of a subsurface reflector down to a depth of about 4.4 m. This experiment and the associated modeling approach is the first of a series of planned experiments, which we outline for the detection and the theoretical evaluation of buried reflectors using remote microwave and VHF radar. We identify potential subject areas for environmental research.     

Applying Plan Recognition Algorithms To Program Understanding

Program understanding is often viewed as the task of extracting plans and design goals from program source. As such, it is natural to try to apply standard AI plan recognition techniques to the program understanding problem. Yet program understanding researchers have quietly, but consistently, avoided the use of these plan recognition algorithms. This paper shows that treating program understanding as plan recognition is too simplistic and that traditional AI search algorithms for plan recognition are not suitable, as is, for program understanding. In particular, we show (1) that the program understanding task differs significantly from the typical general plan recognition task along several key dimensions, (2) that the program understanding task has particular properties that make it particularly amenable to constraint satisfaction techniques, and (3) that augmenting AI plan recognition algorithms with these techniques can lead to effective solutions for the program understanding problem.     

Observer matrix gain optimization for stochastic continuous time nonlinear systems

It is shown that for a class of stationary stochastic nonlinear systems (satisfying a global Lipschitz condition) the high-gain observer with a constant gain matrix may guarantee an upper bound for the averaged quadratic error of state estimation. The nonlinearity is assumed to be a priory known. The main contribution of this paper consists in designing of a numerical procedure for the optimal gain matrix minimizing this upper bound. The convergence analysis of this procedure is presented as well as an example illustrating its finite steps workability: it is shown that within a neighborhood of the optimal matrix gain the others provide lower estimation performance.