Approximate computing is a technique for bridging the growing imbalance between computational power and computational needs by trading small amounts of accuracy for large amounts of performance or energy. Current approximate computing techniques are configured to choose how to approximate based either on training inputs that are representative of the “real” input or on occasional runtime checks...

The credit value-at-risk model underpinning the Basel II Internal Ratings-Based approach assumes that idiosyncratic risk has been diversified away fully in the portfolio, so that economic capital depends only on systematic risk contributions. We develop a simple methodology for approximating the effect of undiversified idiosyncratic risk on VaR. The supervisory review process (Pillar 2) of the ...

Managing and querying information with varying temporal granularities is an important problem in databases. Although there is a substantial body of work on temporal granularities for the relational data model [11], a comprehensive framework is lacking for the object-oriented paradigm. To the best of our knowledge, a formal treatment of temporal queries with multiple granularities has not been c...

The recursive prediction and filtering formulas of the Kalman filter are difficult to implement in nonlinear state space models. For Gaussian linear state space models, or for models with qualitative state variables, the recursive formulas of the filter require the updating of a finite number of summary statistics. However, in the general framework a function has to be updated, which makes the ...

This paper addresses changes of spatial granularity in route directions in relation to information needs in multi-modal traveling. We outline a model of variability in granularity and apply this model to empirical data. Results reveal that human as well as computer generated route directions provide onedimensional route elements in hierarchically structured ways that reflect the salient structu...

In this paper, we propose a new algorithm, which utilizes the enhancement layer prediction to further improve the coding efficiency of current Fine Granularity Scalability (FGS) defined in MPEG-4. The proposed algorithm adaptively uses (1) the previously reconstructed enhancement layer macroblock after motion compensation along with (2) current reconstructed base layer macroblock and (3) the co...

Background: SCOP is a manual classification of protein domain structures. CATH is a classification of protein domain structures created through a combination of manual and automatic methods. The Dali Domain Classification (henceforth Dali) is an automatically generated classification of protein domain structures. EVEREST is an automatically generated classification of protein domains that uses ...

label switching technology is a flexible and high performance method for forwarding layer three packets that are mapped to layer two flows. each label switch router, lsr, needs a specific label for every flow, where the number of labels depends on the mapping and granularity of flows. data and control driven methods are the most significant and popular methods in label mapping policy. the above...

With respect to the two-sided matching problem, where the agents’ preferences are in the format of multi-granularity uncertain linguistic terms, a matching method is proposed. Some basic concepts and definitions on uncertain linguistic term and 2-tuple are firstly given. Based on this, the two-sided matching problem with multi-granularity uncertain linguistic terms is described. Then, multi-gra...

PURPOSE The aim of this study is to report a patient with multipe evanescent white dot syndrome (MEWDS) presenting with classic foveal granularity and pathology localized to the outer retina. METHODS Case study methodology was used in the current study. RESULTS A 34-year-old Caucasian female presented with photopsias and blurry vision in her left eye. Examination, particularly the foveal gr...