Supervised Associative Learning in Spiking Neural Network

In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations

Culinary tourism and post-pandemic travel: Ecosystem responses to an external shock

Purpose: The COVID-19 (SARS-CoV-2) global pandemic forced hospitality and tourism service providers to respond by pivoting business models in line with governmental restrictions to curb the spread of the virus. This paper explores the online responsiveness of tourism-affiliated culinary service providers to a major external disruption. Methods: This study uses ecosystem resilience and Internet marketing theories to analyze 139 web homepages of culinary tourism service providers promoted by the official tourism website of Jamaica, to measure of Jamaica to measure online responsiveness to the COVID-19 pandemic. Results: Findings show that web page responses vary between the official tourism web page and the restaurants promoted on its site. Responses also vary across restaurant affiliation clusters and across location clusters. Further, resilient web page responses are more commonly associated with hotel restaurants and eponymous restaurants. Implications: Culinary service providers promoted by the official tourism marketing arm of a destination should consistently practice resilient online marketing response to external shocks. This study provides a novel analysis of online responsiveness to COVID-19 and contributes a summary framework for resilient response by culinary ecosystem providers preparing for post-pandemic travel

PCA and K-Means decipher genome

In this paper, we aim to give a tutorial for undergraduate students studying statistical methods and/or bioinformatics. The students will learn how data visualization can help in genomic sequence analysis. Students start with a fragment of genetic text of a bacterial genome and analyze its structure. By means of principal component analysis they ``discover'' that the information in the genome is encoded by non-overlapping triplets. Next, they learn how to find gene positions. This exercise on PCA and K-Means clustering enables active study of the basic bioinformatics notions. Appendix 1 contains program listings that go along with this exercise. Appendix 2 includes 2D PCA plots of triplet usage in moving frame for a series of bacterial genomes from GC-poor to GC-rich ones. Animated 3D PCA plots are attached as separate gif files. Topology (cluster structure) and geometry (mutual positions of clusters) of these plots depends clearly on GC-content.Comment: 18 pages, with program listings for MatLab, PCA analysis of genomes and additional animated 3D PCA plot

Cladding strategies for building-integrated photovoltaics

Photovoltaic cladding on the surfaces of commercial buildings has the potential for considerable reductions in carbon emissions due to embedded renewable power generation displacing conventional power utilization. In this paper, a model is described for the optimization of photovoltaic cladding densities on commercial building surfaces. The model uses a modified form of the ‘fill factor’ method for photovoltaic power supply coupled to new regression-based procedures for power demand estimation. An optimization is included based on a defined ‘mean index of satisfaction’ for matched power supply and demand (i.e., zero power exportation to the grid). The mean index of satisfaction directly translates to the reduction in carbon emission that might be expected over conventional power use. On clear days throughout the year, reductions of conventional power use of at least 60% can be achieved with an optimum cladding pattern targeted to lighting and small power load demands

Novel designs for Penning ion traps

We present a number of alternative designs for Penning ion traps suitable for quantum information processing (QIP) applications with atomic ions. The first trap design is a simple array of long straight wires which allows easy optical access. A prototype of this trap has been built to trap Ca+ and a simple electronic detection scheme has been employed to demonstrate the operation of the trap. Another trap design consists of a conducting plate with a hole in it situated above a continuous conducting plane. The final trap design is based on an array of pad electrodes. Although this trap design lacks the open geometry of the traps described above, the pad design may prove useful in a hybrid scheme in which information processing and qubit storage take place in different types of trap. The behaviour of the pad traps is simulated numerically and techniques for moving ions rapidly between traps are discussed. Future experiments with these various designs are discussed. All of the designs lend themselves to the construction of multiple trap arrays, as required for scalable ion trap QIP.Comment: 11 pages, 10 figure

Framing Professional Learning Analytics as Reframing Oneself

Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically

Robust formation of morphogen gradients

We discuss the formation of graded morphogen profiles in a cell layer by nonlinear transport phenomena, important for patterning developing organisms. We focus on a process termed transcytosis, where morphogen transport results from binding of ligands to receptors on the cell surface, incorporation into the cell and subsequent externalization. Starting from a microscopic model, we derive effective transport equations. We show that, in contrast to morphogen transport by extracellular diffusion, transcytosis leads to robust ligand profiles which are insensitive to the rate of ligand production

Synchronized dynamics of cortical neurons with time-delay feedback

The dynamics of three mutually coupled cortical neurons with time delays in the coupling are explored numerically and analytically. The neurons are coupled in a line, with the middle neuron sending a somewhat stronger projection to the outer neurons than the feedback it receives, to model for instance the relay of a signal from primary to higher cortical areas. For a given coupling architecture, the delays introduce correlations in the time series at the time-scale of the delay. It was found that the middle neuron leads the outer ones by the delay time, while the outer neurons are synchronized with zero lag times. Synchronization is found to be highly dependent on the synaptic time constant, with faster synapses increasing both the degree of synchronization and the firing rate. Analysis shows that presynaptic input during the interspike interval stabilizes the synchronous state, even for arbitrarily weak coupling, and independent of the initial phase. The finding may be of significance to synchronization of large groups of cells in the cortex that are spatially distanced from each other.Comment: 21 pages, 11 figure