University of Madeira

About: University of Madeira is a education organization based out in Funchal, Portugal. It is known for research contribution in the topics: Population & Dendrimer. The organization has 1014 authors who have published 2759 publications receiving 59457 citations.

Twelve invertebrate and eight fish species new to the marine fauna of Madeira, and a discussion of the zoogeography of the area

Abstract: The benthic ctenophoreVallicula multiformis, a large undescribed flatworm species of the genusPseudoceros, the prosobranch gastropodTonna maculosa, the opisthobranch gastropodsPlacida cf.dendritica, Caloria elegans, Aeolidiella sanguinea, Janolus cristatus, the decapodBalssia gasti, the sea urchinSchizaster canaliferus and the tunicatesClavelina lepadiformis, Clavelina dellavallei andPycnoclavella taureanensis are recorded from Madeira for the first time. This is the first record of a platyctenid ctenophore in the eastern Atlantic. The teleost fishesPomatoschistus pictus, Vaneaugobius canariensis, Chromogobius sp.,Nerophis ophidion, Hippocampus hippocampus, Acanthocybium solandri, Sphyraena viridensis andSphyraena barracuda are recorded from Madeira for the first time. The presence of the sea-hareAplysia dactylomela at Madeira is confirmed; the species has increased tremendously in abundance in the last four years. The crocodile fishGrammoplites gruveli can occasionally be found in the mantle cavity of cuttlefish (Sepia officinalis) sold at the fish market of Funchal, but does not originate from Madeiran waters. An analysis of 100 new records from the coastal fauna of Madeira shows that, while predominantly of lusitanian, mediterranean and mauritanian affinity, Madeira’s shallow water fauna contains a large component of tropical species.

The state of user experience evaluation practice

Abstract: Despite the growing importance for practice, user experience is often a blurry and doubtful concept both for newcomers and for the industry. Such ambiguity stems from a emergent community of practitioners with diverse backgrounds, to whom user experience encompasses countless interpretations. This paper reports on an online survey deployed to grasp the state of user experience evaluation practice. We learned that evaluations are mainly conducted by HCIs, software engineers or designers and are perceived to strongly impact the user interface, as well as the business logic level. Additionally, informal, low cost methods are widely used and, although most methods rely on paper prototypes, a single artifact is used per evaluation and working prototypes are favored. Moreover, evaluations happen at multiple project phases and various methods are used. Finally, results shows that evaluations are constrained by evaluators' background or occupation. This compels the community to pursue an end-to-end methodology to prevent it.

Neural representation of probabilistic information

Abstract: It has been proposed that populations of neurons process information in terms of probability density functions (PDFs) of analog variables. Such analog variables range, for example, from target luminance and depth on the sensory interface to eye position and joint angles on the motor output side. The requirement that analog variables must be processed leads inevitably to a probabilistic description, while the limited precision and lifetime of the neuronal processing units lead naturally to a population representation of information. We show how a time-dependent probability density ρ(x; t) over variable x, residing in a specified function space of dimension D, may be decoded from the neuronal activities in a population as a linear combination of certain decoding functions φi(x), with coefficients given by the N firing rates ai(t) (generally with D < N). We show how the neuronal encoding process may be described by projecting a set of complementary encoding functions φ'i(x) on the probability density ρ'(x; t), and passing the result through a rectifying nonlinear activation function. We show how both encoders φ'i(x) and decoders φi(x) may be determined by minimizing cost functions that quantify the inaccuracy of the representation. Expressing a given computation in terms of manipulation and transformation of probabilities, we show how this representation leads to a neural circuit that can carry out the required computation within a consistent Bayesian framework, with the synaptic weights being explicitly generated in terms of encoders, decoders, conditional probabilities, and priors.