Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer.

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein-Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters' emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.

Using Gaze for Behavioural Biometrics.

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.

Characterization of Five Psychrotolerant Alcanivorax spp. Strains Isolated from Antarctica.

Five psychrotolerant Alcanivorax spp. strains were isolated from Antarctic coastal waters. Strains were screened for molecular and physiological properties and analyzed regarding their growth capacity. Partial 16S rDNA, alk-B1, and P450 gene sequencing was performed. Biolog EcoPlates and the API 20E test were used to evaluate metabolic and biochemical profiles. Bacterial growth in sodium acetate was determined at 4, 15, 20, and 25 °C to evaluate the optimal temperature. Furthermore, the ability of each strain to grow in a hydrocarbon mixture at 4 and 25 °C was assayed. Biosurfactant production tests (drop-collapse and oil spreading) and emulsification activity tests (E24) were also performed. Concerning results of partial gene sequencing (16S rDNA, alk-B1, and P450), a high similarity of the isolates with the same genes isolated from other Alcanivorax spp. strains was observed. The metabolic profiles obtained by Biolog assays showed no significant differences in the isolates compared to the Alcanivorax borkumensis wild type. The results of biodegradative tests showed their capability to grow at different temperatures. All strains showed biosurfactant production and emulsification activity. Our findings underline the importance to proceed in the isolation and characterization of Antarctic hydrocarbon-degrading bacterial strains since their biotechnological and environmental applications could be useful even for pollution remediation in polar areas.