Causality in the Semantics of Esterel: Revisited

We re-examine the challenges concerning causality in the semantics of Esterel and show that they pertain to the known issues in the semantics of Structured Operational Semantics with negative premises. We show that the solutions offered for the semantics of SOS also provide answers to the semantic challenges of Esterel and that they satisfy the intuitive requirements set by the language designers

Event tracking for real-time unaware sensitivity analysis (EventTracker)

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method.EPSR

Rate-Privacy in Wireless Sensor Networks

This paper introduces the concept of rate privacy in the context of wireless sensor networks. Our discussion reveals that the concept indeed is of a great importance for the privacy preservation of such networks. As a result, we propose a buffering scheme to protect the rate from adversaries. Simulation results verify the applicability of our approach

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial Intelligence XXXV, 201

Forecasting benchmarks of long-term stock returns via machine learning

Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners

Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case

Long-term return expectations or predictions play an important role in planning purposes and guidance of long-term investors. Five-year stock returns are less volatile around their geometric mean than returns of higher frequency, such as one-year returns. One would, therefore, expect models using the latter to better reduce the noise and beat the simple historical mean than models based on the former. However, this paper shows that the general tendency is surprisingly the opposite: long-term forecasts over five years have a similar or even better predictive power when compared to the one-year case. We consider a long list of economic predictors and benchmarks relevant for the long-term investor. Our predictive approach consists of adopting and implementing a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. We consistently find that long-term forecasting performs well and recommend drawing more attention to it when designing investment strategies for long-term investors. Furthermore, our preferred predictive model did stand the test of Covid-19 providing a relatively optimistic outlook in March 2020 when uncertainty was all around us with lockdown and facing an unknown new pandemic

Antinociceptive effect of the endemic species Glaucium vitellinum boiss and buhse

Background: Glaucium vitellinum is an endemic species and is extensively exploited as an anti-inflammatory agent in Iranian traditional medicine. Objectives: This study was designed to evaluate the antinociceptive activities of G. vitellinum methanol extract in male mice. Materials and Methods: The formalin and hot-plate methods were used for pain evaluation in mice. Glaucium vitellinum extract (50, 100, 200 and 400 mg/kg body weight IP), saline and morphine (2 mg/kg, IP) were administered 15 minutes prior to the formalin test. The nociceptive responses were divided to two phases; phase I (0 - 15 minutes) and phase II (15 - 60 minutes) were compared to the control and morphine. In the hot-plate test, G. vitellinum extract (80, 160, 200 and 250 mg/kg IP), saline and morphine (5 mg/kg, IP) were administered and, behavioral responses were immediately tested, 15, 30, 45 and 60 minutes after the injection. Comparisons between the groups were carried out using the analysis of variance (ANOVA), and post hoc Tukey's test. Results: All doses of G. vitellinum extract induced anti-nociception activity during the first and second phases of the formalin test. The extract showed a significant (P < 0.05) dose-related inhibition during the first phase compared to the control group. In the second phase of the formalin test, the extract showed analgesic activity comparable to the effect of morphine. In pre-treatment with non-selective opioid receptor antagonist, naloxone could reverse the anti-nociceptive effect of the extract in the formalin test. In the hot-plate method, with the highest dose of 250 mg/kg, the anti-nociceptive activity of the studied extract was comparable to the standard drug, morphine. Conclusions: This study revealed that G. vitellinum extract possessed a significant anti-nociceptive activity in formalin pain models and hot-plate test in mice and might have a potent role against pain. © 2016, School of Pharmacy, Ahvaz Jundishapur University of Medical Sciences

Elevation of CD56brightCD16- lymphocytes in MDR pulmonary tuberculosis

Background: Protective immune responses induced in the majority of people infected with Mycobacterium tuberculosis enable them to control TB infection. Objective: The aim of this study was to investigate CD56 and CD16 positive peripheral blood mononuclear cells (PBMCs) and leukocyte subsets from multi-drug resistant pulmonary tuberculosis (MDR-TB), and compare them with nonresistant (NR) TB patients and healthy controls. Methods: 13 MDR-tuberculosis patients, 20 NR-TB individuals and 40 healthy subjects were included. Peripheral blood mononuclear cells were double stained with fluorochrome conjugated antibodies against CD56 and CD16 cell surface markers. The phenotype of positive cells was then analyzed by flow cytometry and the percent- ages of CD56+ CD16+, CD56- CD16+, CD56dimCD16+/-, and CD56brightCD16+/- subsets were calculated. Results: There was a significant decline in the percentage of CD56+CD16+ lymphocytes in both MDR and NR-TB patients compared with healthy controls. We also observed lower proportions of CD56dim/brightCD16+ in addition to higher percentages of CD56dim/brightCD16- subsets in all TB patients (p�0.05). In MDR- TB, our findings demonstrated a distinct phenotypic feature with increased levels of CD56brightCD16- in comparison with both NR-TB and healthy subjects. Conclusion: Considering the function of CD56/CD16 expressing cells in TB, we suggest that pheno- typic characteristics of PBMCs in MDR-TB may correlate with their status of drug re- sistance and probably with their high mortality rates

Antinociceptive effect of the endemic species Nepeta depauperata Benth

Background: Nepeta depauperata Benth is an endemic species and is extensively exploited as an anti-inflammatory agent in Iranian traditional medicine. Objectives: This study was designed to evaluate the antinociceptive activity of methanol extract of N. depauperata in male mice. Materials and Methods: The anti-nociceptive activities of the extract were investigated by the formalin test and Hot plate test respectively. Comparisons between the groups were carried out using one-way analysis of variance (ANOVA), and post Hoc Tukey test. Results: N. depauperata extract showed anti-nociceptive effect. Doses of 50, 100 and 200 mg/kg reduced the paw flexing time in formalin test from the control (P < 0.05 in both phases). The doses of 25, 50, 100 and 200 mg/kg; 100 and 200 mg/kg reduced the pawlicking time in first and second phases of the formalin test from the control, respectively (P < 0.05). The observed effect was not reversed by naloxone. In Hot plate test, doses of 160 and 250 mg/kg significantly reduced the nociception in comparisons to control (P < 0.05). All doses of the studied extract also showed antinociceptive activity. Conclusions: This study revealed that the methanol extract of N.depauperata may minimize both the acute and chronic forms of nociception and may have potent role against inflammation. © 2016, School of Pharmacy, Ahvaz Jundishapur University of Medical Sciences

An Integrated Model of Patient and Staff Satisfaction Using Queuing Theory.

This paper investigates the connection between patient satisfaction, waiting time, staff satisfaction, and service time. It uses a variety of models to enable improvement against experiential and operational health service goals. Patient satisfaction levels are estimated using a model based on waiting (waiting times). Staff satisfaction levels are estimated using a model based on the time spent with patients (service time). An integrated model of patient and staff satisfaction, the effective satisfaction level model, is then proposed (using queuing theory). This links patient satisfaction, waiting time, staff satisfaction, and service time, connecting two important concepts, namely, experience and efficiency in care delivery and leading to a more holistic approach in designing and managing health services. The proposed model will enable healthcare systems analysts to objectively and directly relate elements of service quality to capacity planning. Moreover, as an instrument used jointly by healthcare commissioners and providers, it affords the prospect of better resource allocation.The authors acknowledge the North West London Hospitals NHS Trust for supporting this research. We also give special thanks to the following persons for their various contributions to this work: Justin Gore for being a cosupervisor of the project; Professors Lorraine De Sousa of Brunel University and Janet Smart of Oxford University for their many constructive critiques of the initial work. Finally, the research was in part supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England (CLAHRC EoE) at Cambridge and Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.This is the final published version of the article, originally published in IEEE Journal of Translational Engineering in Health and Medicine, 3, 2015, DOI: 10.1109/JTEHM.2015.240043