Transforming Monitoring Structures with Resilient Encoders. Application to Repeated Games

An important feature of a dynamic game is its monitoring structure namely, what the players effectively see from the played actions. We consider games with arbitrary monitoring structures. One of the purposes of this paper is to know to what extent an encoder, who perfectly observes the played actions and sends a complementary public signal to the players, can establish perfect monitoring for all the players. To reach this goal, the main technical problem to be solved at the encoder is to design a source encoder which compresses the action profile in the most concise manner possible. A special feature of this encoder is that the multi-dimensional signal (namely, the action profiles) to be encoded is assumed to comprise a component whose probability distribution is not known to the encoder and the decoder has a side information (the private signals received by the players when the encoder is off). This new framework appears to be both of game-theoretical and information-theoretical interest. In particular, it is useful for designing certain types of encoders that are resilient to single deviations and provide an equilibrium utility region in the proposed setting; it provides a new type of constraints to compress an information source (i.e., a random variable). Regarding the first aspect, we apply the derived result to the repeated prisoner's dilemma.Comment: Springer, Dynamic Games and Applications, 201

Strong causality from weak via continuous monitoring

Repeated unbiased measurements cause a continual application of the weak causality principle, leading to an apparent arrow of time for continuously-monitored quantum systems

Human activity recognition making use of long short-term memory techniques

The optimisation and validation of a classifiers performance when applied to real world problems is not always effectively shown. In much of the literature describing the application of artificial neural network architectures to Human Activity Recognition (HAR) problems, postural transitions are grouped together and treated as a singular class. This paper proposes, investigates and validates the development of an optimised artificial neural network based on Long-Short Term Memory techniques (LSTM), with repeated cross validation used to validate the performance of the classifier. The results of the optimised LSTM classifier are comparable or better to that of previous research making use of the same dataset, achieving 95% accuracy under repeated 10-fold cross validation using grouped postural transitions. The work in this paper also achieves 94% accuracy under repeated 10-fold cross validation whilst treating each common postural transition as a separate class (and thus providing more context to each activity)

Combination of different methods for direct control of Vicia hirsuta in winter wheat

Combinations of three different direct methods for controlling Vicia hirsuta (kainite application, flame weeding and harrowing) were investigated in field experiments. They were based on different strategies at early growth stages of V. hirsuta and standardised harrowing at late growth stages. The highest efficacy of kainite application and flame weeding was achieved at the one leaf stage of V. hirsuta. Winter wheat regeneration from damage caused by both kainite and thermal control was satisfactory when treatments were applied at early growth stages (GS 23). Vicia hirsuta plants that survived kainite application or flame weeding were successfully controlled by repeated harrowing at later crop growth stages; crop growth was not affected. Seed production of V. hirsuta declined with increasing harrowing in all treatments; however the strongest and most reliable reduction was achieved when flame weeding had been previously applied. All combinations of direct measures reduced winter wheat grain-yield losses and enhanced thousand-grain weight more efficiently than the use of a single method only. The highest wheat-grain yield was gained after repeated harrowing (3 times) both with and without kainite application

Detecting variable responses in time-series using repeated measures ANOVA: Application to physiologic challenges.

We present an approach to analyzing physiologic timetrends recorded during a stimulus by comparing means at each time point using repeated measures analysis of variance (RMANOVA). The approach allows temporal patterns to be examined without an a priori model of expected timing or pattern of response. The approach was originally applied to signals recorded from functional magnetic resonance imaging (fMRI) volumes-of-interest (VOI) during a physiologic challenge, but we have used the same technique to analyze continuous recordings of other physiological signals such as heart rate, breathing rate, and pulse oximetry. For fMRI, the method serves as a complement to whole-brain voxel-based analyses, and is useful for detecting complex responses within pre-determined brain regions, or as a post-hoc analysis of regions of interest identified by whole-brain assessments. We illustrate an implementation of the technique in the statistical software packages R and SAS. VOI timetrends are extracted from conventionally preprocessed fMRI images. A timetrend of average signal intensity across the VOI during the scanning period is calculated for each subject. The values are scaled relative to baseline periods, and time points are binned. In SAS, the procedure PROC MIXED implements the RMANOVA in a single step. In R, we present one option for implementing RMANOVA with the mixed model function "lme". Model diagnostics, and predicted means and differences are best performed with additional libraries and commands in R; we present one example. The ensuing results allow determination of significant overall effects, and time-point specific within- and between-group responses relative to baseline. We illustrate the technique using fMRI data from two groups of subjects who underwent a respiratory challenge. RMANOVA allows insight into the timing of responses and response differences between groups, and so is suited to physiologic testing paradigms eliciting complex response patterns

Low force electrical switching using gold coated vertically aligned multi-walled carbon nanotubes surfaces

Gold coated vertically aligned multi-walled carbon-nanotubes (Au/MWCNT) surfaces are investigated to determine the electrical contact performance under low force conditions with repeated load cycling. The multi-walled CNT's are synthesized on silicon planar and sputter coated with a gold film. These planar surfaces are mounted on the tip of a PZT actuator and mated with a coated Au hemispherical probe. The load is typical of MEMs devices, with a 4V supply, 1 and 10mA current, and applied force of 1mN. The contact resistance (Rc) is monitored with the repeated loading cycles (over 1000 and a million cycle) to determine reliability and durability testing. The surfaces are compared with a reference Au-Au contact under the same experimental conditions. This study shows the potential for the application of CNT surfaces as an interface in low force electrical contact applications