Linking Budgeting with Computational Thinking Pedagogy: Program Theory, Performance, and Budgeting

The pedagogy involved with preparing and delivering an analytically based course must contend with a number of important limitations or challenges. The challenges/limitations include needing a context for the use of the analytics being taught; others include where best to embed analytic courses in degree curriculum, determining content and delivery along with a number of additional limitations. A context can be created for these courses by establishing a base of usefulness of the course content and how it relates to other courses and to professional applications. However, one useful approach for a budgeting course is to put the analytics in a context of production and performance. These two significant elements of any problem-solving organization finance and budgeting process are significant features of teaching a course in budgeting. The article presented here is an illustration of a context-based approach along with features of pedagogy based in computational thinking which can be used to operationalize course elements while overcoming other salient limitations for analytic courses. The exemplar of a budgeting course is posed as an example.

Computational modeling of hydrogel cross‐linking based on reaction‐diffusion theory

Alginate-based hydrogel is widely used as bio-ink in 3D bioprinting. For producing the bio-ink and stabilizing the polymer network, the hydrogel shall undergo a gelation process which can be obtained by adding an ionic cross-linker agent, such as Calcium ions for alginate. The diffusion of the crosslinker in the alginate stabilizes the polymeric network thanks to the reaction of Calcium ions with alginate monomers. This work presents a reaction-diffusion computational model of the gelation mechanism in alginate hydrogels. The coupled chemical system is solved using finite element discretizations considering the inhomogeneous evolution of the gelation process in time and space

Quantum measurement and feedback control

In this paper, an overview of research in quantum control is presented. Examples of work in the first theme include numerous applications of semi-definite programming and relaxation methods in quantum information theory. Examples of the second include applications of feedback control in the development of new receiver designs for quantum-optical telecom. The third theme is just beginning to emerge as an active research area, linking fundamental issues in quantum field theory with new challenges for applied and computational mathematics in non-commutative stochastic analysis and optimization

Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed

The motor theory of speech perception holds that we perceive the speech of another in terms of a motor representation of that speech. However, when we have learned to recognize a foreign accent, it seems plausible that recognition of a word rarely involves reconstruction of the speech gestures of the speaker rather than the listener. To better assess the motor theory and this observation, we proceed in three stages. Part 1 places the motor theory of speech perception in a larger framework based on our earlier models of the adaptive formation of mirror neurons for grasping, and for viewing extensions of that mirror system as part of a larger system for neuro-linguistic processing, augmented by the present consideration of recognizing speech in a novel accent. Part 2 then offers a novel computational model of how a listener comes to understand the speech of someone speaking the listener's native language with a foreign accent. The core tenet of the model is that the listener uses hypotheses about the word the speaker is currently uttering to update probabilities linking the sound produced by the speaker to phonemes in the native language repertoire of the listener. This, on average, improves the recognition of later words. This model is neutral regarding the nature of the representations it uses (motor vs. auditory). It serve as a reference point for the discussion in Part 3, which proposes a dual-stream neuro-linguistic architecture to revisits claims for and against the motor theory of speech perception and the relevance of mirror neurons, and extracts some implications for the reframing of the motor theory

Active inference as a computational framework for consciousness

Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018; Hohwy and Seth 2020). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research