For a fairly general reductive group G/Qp , we explicitly compute the space of locally algebraic vectors in the Breuil–Herzig construction (ρ)ord , for a potentially semistableBorel-valued representationρ ofGal(Q̄p/Qp). The point beingwedealwith thewhole representation, not just its socle—and we go beyond GLn(Qp). In the case of GL2(Qp), this relation is one of the key properties of the p-adic l...

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an algorithm for training SVMs: Sequential Minimal Optimization, or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable. Thus, SMO does not require a numerical QP library. SMO’s computation time is d...

Pothos & Busemeyer (P&B) provide a compelling case that quantum probability (QP) theory is a better match to human judgment than is classical probability (CP) theory. However, any theory (QP, CP, or other) phrased solely at the computational level runs the risk of being underconstrained. One suggestion is to ground QP accounts in mechanism, to leverage a wide range of process-level data.

The Epstein-Barr virus (EBV) BamHI Q promoter (Qp) is the only promoter used for the transcription of Epstein-Barr virus nuclear antigen 1 (EBNA-1) mRNA in cells in the most restricted (type I) latent infection state. However, Qp is inactive in type III latency. With the use of the yeast one-hybrid system, a new cellular gene has been identified that encodes proteins which bind to sequence in Q...

In this paper we propose a novel data hiding procedure called Quantized Projection (QP), that combines elements from quantization (i.e. Quantization Index Modulation, QIM) and spread-spectrum methods. The method is based in quantizing a diversity projection of the host signal, inspired in the statistic used for detection in spread-spectrum algorithms. We carry on a theoretical analysis of QP to...

Computingmaximum a posteriori (MAP) estimation in graphical models is an important inference problem with many applications. We present message-passing algorithms for quadratic programming (QP) formulations of MAP estimation for pairwise Markov random fields. In particular, we use the concaveconvex procedure (CCCP) to obtain a locally optimal algorithm for the non-convex QP formulation. A simil...

A graph G with convex-QP stability number (or simply a convex-QP graph) is a graph for which the stability number is equal to the optimal value of a convex quadratic program, say P (G). There are polynomial-time procedures to recognize convex-QP graphs, except when the graph G is adverse or contains an adverse subgraph (that is, a non complete graph, without isolated vertices, such that the lea...