The post-natal stages are almost linearly aligned and in the correct biological chronological order, suggesting that expression vectors aggregated from many independent experiments contain biologically representative data.
Therefore, I define a vector of aggregate asset holdings ({mathbf {n}}=sum _{i=1}^I {mathbf {n}}_{i}), which is ({mathbf {n}}' =(0 ;;1;;1;; cdots ;;1)) because the risk-free asset is in zero net supply.
Averaged aggregate is a big homogeneous particle with direction of polarization M A computed as a vector sum of vectors of polarization of all nanoparticles in the aggregate M A computed as an average of magnitudes of all nanoparticles divided by number of nanoparticles in the aggregate n: M A = ∑ i = 1 n M i n. (5).
The averaged aggregate is a big homogeneous particle with its direction of magnetization vectors MA which is computed as a vector sum of the magnetization vectors of all nanoparticles in the aggregate MA and computed as an average of the sizes of all nanoparticles divided by the number of nanoparticles in the aggregate n.
On the other hand, the Market Vectors LatAm Aggregate Bond fund underperformed its benchmark by 386 basis points, or 3.86 percentage points.
In order to formalize the difference between the traditional input output approach and Eq. (1), Eq. (1) can be rewritten in matrix form: Z_{text{REIM}} = AX + Y (2 where A is the input output matrix and Y is a vector of aggregated final demand that also includes the impact of the exogenous variables noted in (1); the time index is omitted to simplify and all variables change in time.
RT quantiles were then calculated for each aggregate vector of RTs and appended to the vector of response probabilities before being submitted to the cost function.
Vector of Locally Aggregated Descriptor (VLAD) is a very popular feature coding method in image classification and image retrieval.
At the video-level, Vector of Locally Aggregated Descriptors (VLAD) is firstly adopted to encode spatial representation, and then multiple-layer Long Short-Term Memory (LSTM) networks are introduced to represent temporal information.
Moreover, a novel soft Vector of Locally Aggregated Descriptors (soft-VLAD) is developed to further represent the extracted features, combining the advantage of Gaussian Mixture Models (GMMs) and VLAD by encoding data according to their overall probability distribution and the corresponding difference with respect to clustered centers.
Vector of locally aggregated descriptors.
