Inference. Many of the Existing Site’s customers will be drawn to the traffic generators in the new center, and allocate some of their limited eating out dollars to whatever restaurant choices are available when they are there, since it is convenient. All or a substantial portion of the 8% drop in sales can reasonably be attributed to the new center. Multi-State Disclosure Document Control No. 040114 Exhibit E to Procedures for Resolving Disputes Relating to the Development of New Restaurants Example 4: Same as Example 3, except that a new EPL Restaurant opens at the new Power Center 6 months after the last anchor tenant opens. Existing Site’s sales then decline further, to a 12% overall decline vs. before the Power Center opened, as follows: Existing Site’s Avg. Monthly Sales: Amount Cumulative % Change 12 months prior to New Power Center opening $ 83,333 — First 6 months New Power Center open $ 76,666 (8 %) Next 12 months New Restaurant Open $ 73,333 (12 %) Inference: It would appear that the majority of the decline is due to the existence of the new Power Center (8%), and that only about 4% is due to the New Restaurant (= 12%- 8%).
Inference. Again, while not conclusive by itself, this data suggests that the Existing Site has experienced a twelve percent (12%) decrease vs. expectation during the Post Period due to localized factors specific to the Existing Site trade area, and not due to DMA-wide variables. Multi-State Disclosure Document Control No. 040114 Exhibit E to Procedures for Resolving Disputes Relating to the Development of New Restaurants Development Agreement #618533 Northeast Houston, Texas STEP II: If there appears to be an impact on the Existing Site that is due to factors within its trade area, rather than due to broader, DMA-wide trends (Example 2 above), identify all significant factors that may have contributed to this impact in addition to the New Restaurant. These could include but may not be limited to the following:
Inference. Inference is done in two stages. Firstly, the prior parameters for CPIM and for each of the colour palettes are estimated from training data. Secondly, using these priors and given a test image, approximate Bayesian posterior beliefs over the class of each pixel and the colour distribution for each class are computed. The inference procedures for these stages are summarised below, with mathematical derivations, which are straightforward but lengthy, relegated to the supplementary material.xyPIM Prior The PIM prior parameters, →π(z) , are estimated directly from the fully labelled images by counting the number of times that each pixel belongs to each class in the train- ing data. Left–right symmetry is enforced by flipping and averaging the estimated priors.Fig. 4b–e shows the resulting distribution over classes at each pixel in the image.Discrete Palette Prior The Dirichlet prior for the discrete background palette (see Fig. 1b) is learned from all pixels in the non-skin images of the Compaq database. The discretekIkdistribution for each image, →π(c), can be integrated out analytically to get a Pólya distribution over the number of times that the colours in each histogram bin appear in each image. We estimate a regularised maximum likelihood setting of →α(c) for this distribution using themethod of Minka . The regularisation simply adds a small initial count to each histogrambin so as to avoid numerical singularities associated with zero counts. The mean of the Dirichlet prior over the background palette is visualised in Fig. 5a.Due to a lack of labelled training data for clothing pixels, the prior distribution estimated for the background palette is also used as the prior for the clothing palette.Continuous Palette Prior The prior parameters for continuous colour distributions are also estimated using a regularised maximum likelihood procedure. In this case the data likelihood is a bit more complex than for discrete distributions. First, we compute the maximum like- lihood estimate for the normal distribution over colours in each image in the training data. Next, the maximum likelihood fit of the palette prior parameters is computed by numeri-cal optimisation. The prior parameters (→η, →τ, →α and →β ) were introduced in Fig. 2. Each parameter vector has 3 entries for the 3 colour components. Since colour distributions are axis-aligned, we optimise one entry from each of these vectors (i.e., 4 parameters) at a time.The skin palette prior is estimated f...
Inference. The model proposed with (6), (7) and (8), defines a joint likelihood over the rest pose, the rigidity configuration, all temporal poses, the observed points and their selection vari- ables, and prediction noise σ:t t 0(9)→no . Rk(→nv ) ≥ cos(θmax),¯ Y t ¯Y t t t t kovwhere →nt is the surface normal of observation o, →n0 is the surface normal of the template at vertex v, Rt is the rotation component of Tt , and θmax is an arbitrary threshold.p(T) p(T |T, C)t∈T o∈Otp(yo | ko, T , σ ) , (14)k Volume Observations. We introduce a compatibility test specific to volumetric fitting, by assuming that the distance of inner surface points to the shape’s surface remains ap- proximately constant under deformation. Let us define the distance between an inner shape point x and the shape’s sur- face by:d(x, ∂Ω) = min d(x, p). (10)p∈∂ΩIn our observation model, this hypothesis can be lever- aged by the following compatibility test: a volumetric ob- servation o can be associated to a template point s only ifd(x0, ∂Ω0) = d(yt , ∂Ωt). (11)It can be shown that this likelihood can be maximized usingan Expectation Maximization algorithm [2, 12, 4], yielding maximum a posteriori estimates of the pose parameters T¯ , T and prediction noise σ. This results in an algorithm iter- ating between two steps.Intuitively, The E-Step computes all observation clus- ter assignment probabilities over K, based on the distance to the predicted template positions under the currently esti- mated poses. Compatibility rules are applied at this stage. Probabilities over inter-cluster rigid links C are also esti- mated based on the current deformation energy of the poses. The M-Step updates the rest pose T¯ , all poses T, and pre- diction noise σ, using the assignment and rigid link prob- abilities to weigh individual observation contributions to each cluster transform estimate.s oTo account for small deviations to this assumption, which might occur under e.g. slight compression or dilation of the perceived shape, we relax the equality constraint up to a precision ϵ, where ϵ accounts for the distance-to-surface inconsistency caused by the discrete sampling of the tem- plate. Using the triangular inequality, it can be shown that this error is bounded by the maximum cell radius over the set of the template’s CVT cells. This leads to the following compatibility test:o s od(yt , ∂Ωt) − ϵ ≤ d(x0, ∂Ω0) ≤ d(yt , ∂Ωt) + ϵ (12)
Inference. XNOR-Popcount-based binary convolution algorithms bring around 58x speedups to full- precision float convolutions on a 64-bit CPU . Our STQ-A nets prune out entire channels directly which can be removed from the model, while expander representations of binary convolution layers can be converted to filters through a fast dense convolution algorithm provided in . The compressed forms of our final networks only contain binary channels, thus allowing use of fast binary convolution algorithms.×In general, for a PFR p, the speedup for a given convolution layer gained through our STQ networks would be S1 S2 where S1 is the speedup through fast binary operations – 58x for XNOR-Nets – and S2 is the speedup gained through having fewer filters, which is
Inference. Compared to an alternative solution in which the name of a particular issuer and subject are cited in a certificate (e.g. if “John Doe” were issued a certificate from the “Department of Corrections”), no similar inference may be drawn with an epass as the Government of Canada issues certificates.