A normed vector space is a real or complex vector space in which a norm has been defined. Formally, one says that a normed vector space is a pair (V, ∥ · ∥) where V is a vector space over K and ∥ · ∥ is a norm in V , but then one usually uses the usual abuse of language and refers to V as being the normed space. Sometimes (frequently?) one has to consider more than one norm at the same time; th...

TOPSIS is a multicriteria decision making technique based on the minimization of geometric distances that allows the ordering of compared alternatives in accordance with their distances from the ideal and anti-ideal solutions. The technique, that usually measures distances in the Euclidean norm, implicitly supposes that the contemplated attributes are independent. However, as this rarely occurs...

This paper presents an analytic model to predict average waiting and ridii times in urban transportation systems (such as dial-a-bus and taxicabs), which provide non-transfer door-to-door transportation with a dynamically dispatched fleet of vehicles. Three diierent dispatching algorithms are analyzed with a simple deterministic model, which is then generalized to capture the most relevant stoc...

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distanc...

We investigate the asymptotic properties of the minimum L1-norm estimator of the drift parameter for fractional Ornstein-Uhlenbeck type process satisfying a linear stochastic differential equation driven by a fractional Brownian motion.

We finetune pretrained AlexNet and 16-layer VGGNet models over the yearbook dataset, predicting years in which the input images were taken. We discuss our approach and findings with the best performing model (L1 distance of 5.48 or 5% accuracy over the validation set).

Feature distinctiveness is a measure representing the uniqueness of objects’ features. Previous research found links between noun feature distinctiveness and age of acquisition (i.e. nouns referring to objects with relatively unique features are learned earlier). The present work investigates the links between feature distinctiveness and age of acquisition in verbs. Using high-dimensional vecto...

Different statistical methods for face recognition have been proposed in recent years. They mostly differ in the type of projection and distance measure used. The aim of this paper is to effectively identify a frontal human face with better recognition rate using appearance-based statistical method for Face Recognition. We used Median instead of mean and with different distance measures like ci...

We consider the nonlinear eigenvalue problem −u′′(t) = f(λ, u(t)), u > 0, u(0) = u(1) = 0, where λ > 0 is a parameter. It is known that under some conditions on f(λ, u), the shape of the solutions associated with λ is almost ‘box’ when λ 1. The purpose of this paper is to study precisely the asymptotic shape of the solutions as λ → ∞ from a standpoint of L1-framework. To do this, we establish t...

We are interested by under-determined inverse problems, and more specifically by source localization in magneto and electro-encephalography (M/EEG). Although there is a physical model for the diffusion (or “mixing”) of the sources, the (very) under-determined nature of the problem leads to a difficult inversion. The need for strong and physically relevant priors on the sources is one of the cha...