Classical mean and variance normalization (MVN) uses a diagonal transform and a bias vector to normalize the mean and variance of noisy features to reference values. As MVN uses diagonal transform, it ignores correlation between feature dimensions. Although full transform is able to make use of feature correlation, its large amount of parameters may not be estimated reliably from a short observ...

This paper develops econometric tools for studying the jump dependencies between the underlying or latent spot volatilities of two assets from high-frequency observations on a fixed time interval – with a particular interest in the relationship between the individual volatilities of traded assets and the volatilities of aggregate risk factors such as the market volatility. The paper derives an ...

Advances in electronic-neural interfaces have shown great potential for both neuroscience research and medical devices. Much of the work to date has focused on short-range inductive links for power and communication transfer [1]. There is an emerging need for active miniaturized systems that stream neural data in the far field, which would enable the observation of brain activity in unconstrain...

Bootstrap methods have gained wide acceptance and huge popularity in the field of applied statistics. The bootstrap is able to provide accurate answers in cases where other methods are simply not available, or where the usual approximations are invalid. The number of applications in chemistry, however, has been rather limited. One possible cause for this is the overwhelming number of techniques...

The state of mind of a fallible, rational, self-conscious, visual observer is considered with regard to the questions: what distinguishes visual knowledge from other kinds of knowledge, and what is the strongest warrant for belief that such an observer can hold? The answer given to the first question is that visual knowledge is any kind of knowledge that the observer could hold in a two-way, sp...

The fuzzy front-end plays a most crucial part in new product development (NPD), leading to the success of product development and product launch in the market. This study proposes a novel method, TTRI_MP, by combining Crawford and Di Benedetto’s model and Cooper’s model, to strengthen the management of the fuzzy front-end. The proposed method comprises four stages: market exploration and techno...

The fuzzy front-end describes the random and generally vague initial stages of an innovation project. Since digital transformation can be seen as innovation process of an organization, improving the initial stages can be beneficial for the entire process. This literature review takes the unique perspective of the fuzzy front-end within digital transformation. Characteristics of and challenges i...

We consider the task of learning a contextdependent mapping from utterances to denotations. With only denotations at training time, we must search over a combinatorially large space of logical forms, which is even larger with context-dependent utterances. To cope with this challenge, we perform successive projections of the full model onto simpler models that operate over equivalence classes of...

In OFDM systems, in-phase and quadrature (I/Q) imbalances generated in the analog front-end introduce interchannel interference and, consequently, error performance degradation. This paper provides an exact expression involving the twodimensional (2-D) Gaussian Q-function for the error probability of an arbitrary 2-D modulated OFDM signal with I/Q imbalances in frequency-flat Rayleigh fading ch...

We present a modular architecture for recognition and localization of objects in a scene that is motivated from coupling the ventral (“what”) and dorsal (“where”) pathways of human visual processing. Our main target is to demonstrate how online learning can be used to bootstrap the representation from nonspecific cues like stereo depth towards object-specific representations for recognition and...