Background: Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS). The main cause of the MS is yet to be revealed, but the most probable theory is based on the molecular mimicry that concludes some infections in the activation of T cells against brain auto-antigens that initiate the disease cascade.Objectives: The Purpose of this research is the...

It is critical to build an effective prediction model to improve the accuracy of financial distress prediction. Some existing literatures have demonstrated that single classifier has limitations and combination of multiple prediction methods has advantages in financial distress prediction. In this paper, we extend the research of multiple predictions to integrate with rough set and Dempster–Sha...

This paper consists of an overview on universal prediction from an information-theoretic perspective. Special attention is given to the notion of probability assignment under the self-information loss function, which is directly related to the theory of universal data compression. Both the probabilistic setting and the deterministic setting of the universal prediction problem are described with...

Many estuaries in the world are ungauged. The International Association of Hydrological Sciences completed its science decade on Prediction in Ungauged Basins (PUB) in 2012 (Hrachowitz et al., 2013). Prediction on the basis of limited data is a challenge in hydrology, but not less so in estuaries, where data on fundamental processes are often lacking. In this paper, relatively simple, but scien...

Accurate return value prediction is a key tool for enabling effective speculative method-level parallelism, which will be a standard feature in the next generation of chip-multiprocessor architectures. In this paper we give some information theoretic measures that indicate intrinsic predictability of method return values. This is in stark contrast to the current ad-hoc heuristic measures impose...

Abstract: The theme of motor representation was initiated by Merleau-Ponty (1962). Grush (2004) and Pezzulo (2008, 2011) attempt to explain it by highlighting the role of internal models in simulation of motor effectors. Simulation represents facts of external states without accurately directing the motor system to its goal-state. The present paper explains motor representation in an alternativ...

Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; tha...

We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin bounds known for both standard multi-class and general structured prediction...

Following the recent survey by the same author of Szegö’s theorem and orthogonal polynomials on the unit circle (OPUC) in the scalar case, we survey the corresponding multivariate prediction theory and matrix OPUC (MOPUC). AMS 2000 subject classifications: Primary 60G10; secondary 60G25.

While philosophers of science and epistemologists are well acquainted with Bayesian methods of belief updating, there is a new Bayesian revolution sweeping neuroscience and perceptual psychology. First proposed by Helmholtz, predictive coding is the view that the human brain is fundamentally a hypothesis generator. Though predictive coding has most prominently offered a theory of perception – t...