where P(xi) is the probability of the ith element in P(x), Q(xi) is the probability of the ith element in the probability distribution Q(x) of the event fitted by the theory. The larger value of KL divergence p
The Vector Form Intrinsic Finite Element method and several other form-finding methods for general networksLi, QingpengBorgart, AndrewWu, YueProceedings of IASS Annual Symposia
Next, for each vector, we need to find a new vector that will NEVER be parallel to the vectors in Vnormalized. I'll be tricky here, to insure we will always succeed. The idea will be to find the element with the smallest absolute value in each vector, and modify that element. Tha...
The first vector element specifies the leading edge of the first bin. The last element specifies the trailing edge of the last bin. The trailing edge is only included for the last bin." So add on a number that's greater than the maximum element in your data. Inf is a good choi...
in the form of “i: j” (which means the ith element of the first protein is aligned with the jth element of the second protein), or “i–j:k–l” (which means the ith to the jth element of the first protein are aligned with the kth to the lth element of the second protein)....
Live streaming services enable the audience to interact with one another and the streamer over live content. The surging popularity of live streaming platforms has created a competitive environment. To retain existing viewers and attract newcomers, strea
The above process (2.6) can be easily extended to the oversampled case, in whichL>m+n+1and the matrixCabove is of sizeL\times (m+n+2). In this case the matrix in (2.6) has at least as many rows as columns, and does not necessarily have a null vector. Then the task is to ...
(represents URA) for 2-D or 3-D positioning, respectively arraySize = ; % Normalized spacing between the antenna elements with respect to % wavelength elementSpacing = ; % Antenna switching pattern, must be a 1xM row vector and M must be in the % range [2, 74/slotDuration+1] ...
After feature generation, linear regression is performed to yield the model prediction (each model is the scalar product of the SISSO-identified descriptor with the vector of fitted coefficients, via linear regression) and the models are ranked based on their prediction performance. Optimal subspaces ...
The desired bound then follows from noting that , which holds true because each element in C has a unique representation as a product of two primes. Finally, the case when h is odd follows the fact that a set is also a set for every . More generally, in the case when for some sui...