A family of NP-complete data aggregation problems

Summary We consider a family of general aggregation problems and prove each of its members to be NP-complete in the strong sense. These problems require that we partition a set of objects into aggregates. The goal is to minimize the expected cost of satisfying an anticipated collection of requests for subsets of the objects, where the cost of satisfying a request includes both the number and the sizes of the aggregates which must be retrieved. The aggregation problems are viewed as very basic versions of important database optimization problems, including: the partitioning of data items into record types, the clustering of records into physical blocks of storage, and the partitioning of a database into granules to support locking. The NP-completeness results demonstrate that such optimization problems are intractable, even when simplified to the extreme. The fact that the problems are NP-complete in the strong sense also rules out pseudopolynomial time solutions, unless P = NP.     

Prioritizing Information for the Discovery of Phenomena

We consider the problem of prioritizing a collection of discrete pieces of information, or transactions. The goal is to rank the transactions in such a way that the user can best pursue a subset of the transactions in hopes of discovering those which were generated by an interesting source. The problem is shown to differ from traditional classification in several fundamental ways. Ranking algorithms are divided into classes, depending on the amount of information they may utilize. We demonstrate that while ranking by the least constrained algorithm class is consistent with classification, such is not the case for a more constrained class of algorithms. We demonstrate also that while optimal ranking by the former class is easy, optimal ranking by the latter class is NP-hard. Finally, we present detectors which solve optimally restricted versions of the ranking problem, including symmetric anomaly detection.     

Advanced methods for displays and remote control of robots

An in-depth evaluation of the usability and situation awareness performance of different displays and destination controls of robots are presented. In two experiments we evaluate the way information is presented to the operator and assess different means for controlling the robot. Our study compares three types of displays: a “blocks” display, a HUD (head-up display), and a radar display, and two types of controls: touch screen and hand gestures. The HUD demonstrated better performance when compared to the blocks display and was perceived to have greater usability compared to the radar display. The HUD was also found to be more useful when the operation of the robot was more difficult, i.e., when using the hand-gesture method. The experiments also pointed to the importance of using a wide viewing angle to minimize distortion and for easier coping with the difficulties of locating objects in the field of view margins. The touch screen was found to be superior in terms of both objective performance and its perceived usability. No differences were found between the displays and the controllers in terms of situation awareness. This research sheds light on the preferred display type and controlling method for operating robots from a distance, making it easier to cope with the challenges of operating such systems.     

An experimental study of factors associated with driver frustration and overtaking intentions

This study examined directly the impact of various factors associated with driving on ‘A-class’ roads in the United Kingdom (specifically length of platoon, proportion of heavy goods vehicles (HGVs), speed and opportunities for overtaking) on self-reported frustration and overtaking intentions. The impact of situational variables (being under time pressure, and time behind a slower moving platoon) were also examined, as was the association between frustration and self-reported overtaking intentions. 183 members of the public from the areas around Perth and Inverness, Scotland took part in the study. Participants viewed simulated ‘driver’s viewpoint’ clips representing all the combinations of the experimental variables (except time pressure, which was a between-groups variable, and time behind platoon, which was examined separately in four specific clips). After each clip, participants responded on a paper questionnaire as to the level of frustration they would feel for a given clip, and the likelihood that at some point during the clip they would have attempted an overtake manoeuvre. The findings show that the links between traffic variables such as speed and platoon length, and behaviourally-relevant variables such as frustration and overtaking intentions, are not simple. Although there are broad and predictable effects of speed and platoon length (lower speeds and longer platoons leading to greater frustration) these are mediated by other variables, and it is not always the case that more frustration leads to more intention to overtake. Analysis of driver attitudes identified three clusters (low, medium and high risk drivers) and suggests that higher risk drivers’ levels of frustration are more affected by situational changes than those of lower risk drivers.     

Influence of vasopressin and amiloride on shunt pathways of frog skin

Studies were done to determine the influence of vasopressin and amiloride on the sodium backflux of nonedge-damaged frog skin. As before, the bidirectional fluxes of sodium appeared to be limited primarily to the active transport pathways of control skins. When the skins were treated with vasopressin, the sodium backflux was increased selectively (over that of sucrose) and more so than was expected for the active transportpathway alone. In control skins, amiloride had no effect on the flux of the shunt pathway. However, with vasopressin-treated skins, amiloride at 10(-6) M decreased the sodium backflux of the shunt pathway to levels observed previously for the control skins. These observations are compatible with the idea that the shunt pathway may in part be cellular and in part extracellular. When the changes in open-circuit voltage and shunt resistance caused by vasopressin and amiloride (as determined from the I-V relationships) were examined, the data were also found to be compatible with the idea that vasopressin exerted an influence on the shunt pathway of the frog skin, and this effect of vasopressin could be reversed by amiloride.     

A Human-Robot Collaborative Reinforcement Learning Algorithm

This paper presents a new reinforcement learning algorithm that enables collaborative learning between a robot and a human. The algorithm which is based on the Q(λ) approach expedites the learning process by taking advantage of human intelligence and expertise. The algorithm denoted as CQ(λ) provides the robot with self awareness to adaptively switch its collaboration level from autonomous (self performing, the robot decides which actions to take, according to its learning function) to semi-autonomous (a human advisor guides the robot and the robot combines this knowledge into its learning function). This awareness is represented by a self test of its learning performance. The approach of variable autonomy is demonstrated and evaluated using a fixed-arm robot for finding the optimal shaking policy to empty the contents of a plastic bag. A comparison between the CQ(λ) and the traditional Q(λ)-reinforcement learning algorithm, resulted in faster convergence for the CQ(λ) collaborative reinforcement learning algorithm.     

Catching lightning for alternative energy

The article reviews the current literature related to lightning and makes a case for using lightning as an alternative source of energy. Objections to using lightning as an alternative source of energy are listed. Current literature is reviewed and articles are suggested as useful for building a tower, or using rockets or lasers to target a strike, or for quantifying a lightning strike.     

An alternating optimization approach for mixed discrete non-linear programming

This article contributes to the development of the field of alternating optimization (AO) and general mixed discrete non-linear programming (MDNLP) by introducing a new decomposition algorithm (AO-MDNLP) based on the augmented Lagrangian multipliers method. In the proposed algorithm, an iterative solution strategy is proposed by transforming the constrained MDNLP problem into two unconstrained components or units; one solving for the discrete variables, and another for the continuous ones. Each unit focuses on minimizing a different set of variables while the other type is frozen. During optimizing each unit, the penalty parameters and multipliers are consecutively updated until the solution moves towards the feasible region. The two units take turns in evolving independently for a small number of cycles. The validity, robustness and effectiveness of the proposed algorithm are exemplified through some well known benchmark mixed discrete optimization problems.