Newly-developed methods from the theory of intrinsically disordered proteins can be applied to the flexible glycan structures that coat cellular surfaces and provide rich channels for biological information transmission. Extension of a mechanistic ‘arm-in-sleeve’ model via a nonrigid molecule symmetry analysis leads to expectation of empirical observation of punctuated ‘spectral’ classification...

Explanations in cognitive science rely predominantly on computational modeling. Though the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current paper offers a systematic account of computational explanation in cognitive science in a largely mechanistic fra...

The next step for the paradigm of embodied music cognition (EMC) is to move from building theories of (empirical) data toward building systematic explanations of organisms’ capacities to process music. I argue that for the mirror neuron (MN) hypothesis to be effective when applied to music, these neurons’ capacities should be proven to be nontrivially and causally connected with musical organis...

A steady-state model, based on a combination of empirical and mechanistic relationships, is developed to predict respiratory water loss from terrestrial vertebrates. Model parameters are evaluated from published data for the banner-tail kangaroo rat (Dipodomys spectabilis). A threedimensional representation of model behavior is presented, emphasizing the interaction of organismal and environmen...

Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances. In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs. We are interested in finding w...

The purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails. In such situations usual empirical averages may fail to provide reliable estimates and empirical risk minimization may provide large excess risk. However, some robust mean estimators proposed in the literature may be used to replace empiri...

In this note we study lower bounds on the empirical minimization algorithm. To explain the basic set up of this algorithm, let (Ω, μ) be a probability space and set X to be a random variable taking values in Ω, distributed according to μ. We are interested in the function learning (noiseless) problem, in which one observes n independent random variables X1, . . . , Xn distributed according to μ...

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchmentscale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models ...

How do the actions of individual genes contribute to the complex morphologies of animals and plants? How widespread are these genes taxonomically? How many genes are involved in the morphological differences observed between species, and can we identify them? To what extent can empirical data and theory be reconciled? We provide an overview of some recent attempts to answer these questions, ans...

Photosynthesis is a process that indicates the productivity of crops. The estimation this variable can be achieved through methods based on mathematical models. Mathematical models are usually classified as empirical, mechanistic, and hybrid. To mathematically model photosynthesis, it essential to know: input/output variables their units; modeling used its classification (empirical, or hybrid);...