A self-timed multipurpose delay sensor for field programmable gate arrays (FPGAs)

This paper presents a novel self-timed multi-purpose sensor especially conceived for Field Programmable Gate Arrays (FPGAs). The aim of the sensor is to measure performance variations during the life-cycle of the device, such as process variability, critical path timing and temperature variations. The proposed topology, through the use of both combinational and sequential FPGA elements, amplifies the time of a signal traversing a delay chain to produce a pulse whose width is the sensor’s measurement. The sensor is fully self-timed, avoiding the need for clock distribution networks and eliminating the limitations imposed by the system clock. One single off- or on-chip time-to-digital converter is able to perform digitization of several sensors in a single operation. These features allow for a simplified approach for designers wanting to intertwine a multi-purpose sensor network with their application logic. Employed as a temperature sensor, it has been measured to have an error of ±0.67 °C, over the range of 20–100 °C, employing 20 logic elements with a 2-point calibration.

Bounded non-deterministic planning for multimedia adaptation

This paper proposes a novel combination of artificial intelligence planning and other techniques for improving decision-making in the context of multi-step multimedia content adaptation. In particular, it describes a method that allows decision-making (selecting the adaptation to perform) in situations where third-party pluggable multimedia conversion modules are involved and the multimedia adaptation planner does not know their exact adaptation capabilities. In this approach, the multimedia adaptation planner module is only responsible for a part of the required decisions; the pluggable modules make additional decisions based on different criteria. We demonstrate that partial decision-making is not only attainable, but also introduces advantages with respect to a system in which these conversion modules are not capable of providing additional decisions. This means that transferring decisions from the multi-step multimedia adaptation planner to the pluggable conversion modules increases the flexibility of the adaptation. Moreover, by allowing conversion modules to be only partially described, the range of problems that these modules can address increases, while significantly decreasing both the description length of the adaptation capabilities and the planning decision time. Finally, we specify the conditions under which knowing the partial adaptation capabilities of a set of conversion modules will be enough to compute a proper adaptation plan.

The role of the social environment in non-suicidal self-injury among LGBTQ youth: A mixed methods study

Non-suicidal self-injury (NSSI), such as cutting and burning, is a widespread social problem among lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ) youth. Extant research indicates that this population is more than twice as likely to engage in NSSI than heterosexual and cisgender (non-transgender) youth. Despite the scope of this social problem, it remains relatively unexamined in the literature. Research on other risk behaviors among LGBTQ youth indicates that experiencing homophobia and transphobia in key social contexts such as families, schools, and peer relationships contributes to health disparities among this group. Consequently, the aims of this study were to examine: (1) the relationship between LGBTQ youth's social environments and their NSSI behavior, and (2) whether/how specific aspects of the social environment contribute to an understanding of NSSI among LGBTQ youth. This study was conducted using an exploratory, sequential mixed methods design with two phases. The first phase of the study involved analysis of transcripts from interviews conducted with 44 LGBTQ youth recruited from a community-based organization. In this phase, five qualitative themes were identified: (1) Violence; (2) Misconceptions, Stigma, and Shame; (3) Negotiating LGBTQ Identity; (4) Invisibility and Isolation; and (5) Peer Relationships. Results from the qualitative phase were used to identify key variables and specify statistical models in the second, quantitative, phase of the study, using secondary data from a survey of 252 LGBTQ youth. The qualitative phase revealed how LGBTQ youth, themselves, described the role of the social environment in their NSSI behavior, while the quantitative phase was used to determine whether the qualitative findings could be used to predict engagement in NSSI among a larger sample of LGBTQ youth. The quantitative analyses found that certain social-environmental factors such as experiencing physical abuse at home, feeling unsafe at school, and greater openness about sexual orientation significantly predicted the likelihood of engaging in NSSI among LGBTQ youth. Furthermore, depression partially mediated the relationships between family physical abuse and NSSI and feeling unsafe at school and NSSI. The qualitative and quantitative results were compared in the interpretation phase to explore areas of convergence and incongruence. Overall, this study's findings indicate that social-environmental factors are salient to understanding NSSI among LGBTQ youth. The particular social contexts in which LGBTQ youth live significantly influence their engagement in this risk behavior. These findings can inform the development of culturally relevant NSSI interventions that address the social realities of LGBTQ youth's lives.

A note on the periodic orbits of a self excited rigid body

The aim of the present paper is to study the periodic orbits of a perturbed self excited rigid body with a fixed point. For studying these periodic orbits we shall use averaging theory of first order.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Juncker’s Mission to the New Energy Team: Avoiding the hard choices. CEPS Commentary, 24 September 2014

In his mission letter to Arias Cañete, Jean-Claude Juncker asked the designated Commissioner for Climate Action and Energy to focus on further developing EU policy for renewables in order to “be a world leader in this sector” and on promoting the EU Emissions Trading System “to ensure that we reach our climate goals in a cost-effective way”. Furthermore, he would like Alenka Bratušek, the designated Vice-President for Energy Union, to focus on “completing the internal energy market” and on “increasing competition”. In assessing the feasibility and desirability of this remit, this commentary finds these objectives to be very ambitious but more importantly, partially conflicting, given the state of play in EU energy markets.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

The effect of age, gender and attitudes on self-regulation in driving

Self-regulation in driving has primarily been studied as a precursor to driving cessation in older people, who minimise driving risk and compensate for physical and cognitive decline by avoiding driving in challenging circumstances, e.g. poor weather conditions, in the dark and at busy times. This research explores whether other demographic groups of drivers adopt self-regulatory behaviours and examines the effects of affective and instrumental attitudes on self-regulation across the lifespan. Quantitative data were collected from 395 drivers. Women were significantly more likely than men to engage in self-regulation, and to be negatively influenced by their emotions (affective attitude). A quadratic effect of age on self-regulation was determined such that younger and older drivers reported higher scores for self-regulation than middle-years' drivers. However, this effect was affected by experience such that when experience was controlled for, self-regulation increased with age. Nevertheless, anxious driving style and negative affective attitude were independent predictors of self-regulation behaviours. Results suggest that self-regulation behaviours are present across the driving lifespan and may occur as a result of driving anxiety or low confidence rather than as an effect of ageing.

Minimizing nasty surprises with better informed decision-making in self-adaptive systems

Designers of self-adaptive systems often formulate adaptive design decisions, making unrealistic or myopic assumptions about the system's requirements and environment. The decisions taken during this formulation are crucial for satisfying requirements. In environments which are characterized by uncertainty and dynamism, deviation from these assumptions is the norm and may trigger 'surprises'. Our method allows designers to make explicit links between the possible emergence of surprises, risks and design trade-offs. The method can be used to explore the design decisions for self-adaptive systems and choose among decisions that better fulfil (or rather partially fulfil) non-functional requirements and address their trade-offs. The analysis can also provide designers with valuable input for refining the adaptation decisions to balance, for example, resilience (i.e. Satisfiability of non-functional requirements and their trade-offs) and stability (i.e. Minimizing the frequency of adaptation). The objective is to provide designers of self adaptive systems with a basis for multi-dimensional what-if analysis to revise and improve the understanding of the environment and its effect on non-functional requirements and thereafter decision-making. We have applied the method to a wireless sensor network for flood prediction. The application shows that the method gives rise to questions that were not explicitly asked before at design-time and assists designers in the process of risk-aware, what-if and trade-off analysis.

Leader-member exchange and contextual performance:the mediating influence of organization-based self-esteem

This study examined the mediating influence of organization-based self-esteem (OBSE) on the relationship between leader-member exchange (LMX) and contextual performance. Respondents were Indian employees and their immediate supervisors. Results of structural equation modeling (SEM) that compared the fit of a fully mediated model to a partially mediated model revealed support for the hypothesized fully mediated model. Specifically, the SEM results showed the relationship between LMX and the contextual performance facets of interpersonal facilitation and job dedication to be indirect, through OBSE. Support for the hypothesized mediating influence of OBSE highlights the multiple motivational underpinnings of contextual performance

New Binary Extremal Self-Dual Codes of Lengths 50 and 52

* This work was partially supported by the Bulgarian National Science Fund under Contract No. MM – 503/1995.

On Binary Self-Dual Codes of Length 62 with an Automorphism of Order 7

Николай Янков - Класифицирани са с точност до еквивалетност всички оптимални двоични самодуални [62, 31, 12] кодове, които притежават автоморфизъм от ред 7 с 8 независими цикъла при разлагане на независими цикли. Използвайки метода за конструиране на самодуални кодове, притежаващи автоморфизъм от нечетен прост ред е доказано, че съществуват точно 8 нееквивалентни такива кода. Три от получените кодове имат тегловна функция, каквато досега не бе известно да съществува.

On Self-Similar Extremal Processes

We study the limit behaviour of the sequence of extremal processes under a regularity condition on the norming sequence ζn and asymptotic negligibility of the max-increments of Yn.

Spiral Walk Exposed

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

In this paper we develop set of novel Markov Chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. The novel diffusion bridge proposal derived from the variational approximation allows the use of a flexible blocking strategy that further improves mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample applications the algorithm is accurate except in the presence of large observation errors and low to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient. © 2011 Springer-Verlag.